data analysis sample in quantitative research

Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

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These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations.

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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74 Comments

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Based on the given learning points, this is inferential analysis, thus, use ‘t-tests, ANOVA, correlation and regression analysis’

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Handbook of Research Methods in Health Social Sciences pp 955–969 Cite as

Data Analysis in Quantitative Research

• Yong Moon Jung 2  
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Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility. Conducting quantitative data analysis requires a prerequisite understanding of the statistical knowledge and skills. It also requires rigor in the choice of appropriate analysis model and the interpretation of the analysis outcomes. Basically, the choice of appropriate analysis techniques is determined by the type of research question and the nature of the data. In addition, different analysis techniques require different assumptions of data. This chapter provides introductory guides for readers to assist them with their informed decision-making in choosing the correct analysis models. To this end, it begins with discussion of the levels of measure: nominal, ordinal, and scale. Some commonly used analysis techniques in univariate, bivariate, and multivariate data analysis are presented for practical examples. Example analysis outcomes are produced by the use of SPSS (Statistical Package for Social Sciences).

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Jung, Y.M. (2019). Data Analysis in Quantitative Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_109

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What Are The Primary Methods Used In Quantitative Data Analysis For Research?

What are the primary methods used in quantitative data analysis for research?

Quantitative data analysis in research primarily employs statistical and computational techniques to interpret numerical data. This includes methods like cross-tabulation, which draws inferences between datasets in a tabular format, and MaxDiff Analysis, aimed at understanding respondent preferences by identifying the most and least preferred options. Descriptive statistics summarize data through measures like percentages or means, while inferential statistics predict characteristics for a larger population based on summarized data.

Examples of these methods in action include using cross-tabulation to analyze consumer behavior across different demographics or employing descriptive statistics to calculate the average sales revenue of a product. The choice of method depends on the research question and the nature of the data.

How does quantitative data analysis differ from qualitative analysis in research?

Quantitative data analysis differs from qualitative analysis primarily in its focus on numerical data and statistical methods to answer questions of "how many" and "how much". It seeks to quantify variables and generalize results from a sample to a population. In contrast, qualitative analysis focuses on non-numerical data, aiming to understand concepts, thoughts, or experiences through methods such as interviews or observations. Quantitative analysis uses metrics and numerical figures, while qualitative analysis explores the depth and complexity of data without quantifying.

For instance, while quantitative analysis might calculate the percentage of people who prefer a certain product, qualitative analysis might explore why people prefer that product through open-ended survey responses or interviews.

What are the four main types of quantitative research, and how do they differ?

The four main types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to describe characteristics of a population or phenomenon. Correlational research investigates the relationship between two or more variables without implying causation. Causal-Comparative/Quasi-Experimental research looks at cause-and-effect relationships between variables when controlled experiments are not feasible. Experimental Research, the most rigorous form, manipulates one variable to determine its effect on another, allowing for control over the research environment.

• Descriptive research might involve surveying a population to gather data on current trends.
• Correlational research could analyze the relationship between study habits and academic performance.
• Causal-Comparative studies may explore the impact of a new teaching method on student learning outcomes.
• Experimental research often involves controlled trials to test the efficacy of a new drug.

How do researchers choose the appropriate quantitative analysis method for their study?

Choosing the appropriate quantitative analysis method involves considering the research question, the nature of the data, and the research design. Descriptive statistics are suitable for summarizing data, while inferential statistics are used for making predictions about a population from a sample. Cross-tabulation is effective for exploring relationships between categorical variables, and MaxDiff Analysis is useful for preference ranking. The choice also depends on the type of quantitative research being conducted, whether it's descriptive, correlational, causal-comparative, or experimental.

Researchers must also consider the data's scale of measurement and the assumptions underlying different statistical tests to ensure the validity of their findings.

What challenges do researchers face in quantitative data analysis?

Researchers face several challenges in quantitative data analysis, including data quality issues, such as missing or inaccurate data, and the complexity of statistical methods. Ensuring the representativeness of the sample and dealing with confounding variables that may affect the results are also significant challenges. Additionally, interpreting the results correctly and avoiding misinterpretation or overgeneralization of data is crucial.

Addressing these challenges requires careful planning, rigorous methodology, and a deep understanding of statistical principles.

How has technology impacted quantitative data analysis in research?

Technology has significantly impacted quantitative data analysis by enabling more sophisticated statistical analysis, automating data collection and processing, and facilitating the visualization of complex data. Software tools and platforms allow researchers to handle large datasets and perform complex analyses more efficiently. AI and machine learning algorithms have also enhanced the ability to identify patterns and predict outcomes in large datasets.

Technological advancements have made quantitative data analysis more accessible and powerful, expanding the possibilities for research across various fields.

How can data management platforms enhance efficiency in quantitative research?

Data management platforms play a crucial role in enhancing efficiency in quantitative research by streamlining data discovery, centralization, and documentation. These platforms automate the process of finding and organizing data, which significantly reduces the time researchers spend on data preparation. By providing a centralized repository for all incoming data and metadata, researchers can easily access and analyze the data they need without navigating through disparate sources.

For example, a data management platform can automate the documentation of datasets, ensuring that researchers have up-to-date metadata for their analysis, which is essential for accurate and reliable research outcomes.

What is the significance of AI in automating data discovery and documentation for research?

AI plays a transformative role in automating data discovery and documentation, significantly benefiting quantitative research. AI-powered tools can automatically categorize, tag, and document data, making it easier for researchers to find relevant datasets for their analysis. This automation not only saves time but also enhances the accuracy of data documentation, reducing the risk of errors that could compromise research integrity.

AI-driven data management platforms can also provide predictive insights, suggesting relevant datasets based on the research context, which streamlines the research process and fosters more informed decision-making.

How do no-code integrations in data platforms facilitate quantitative research?

No-code integrations in data platforms facilitate quantitative research by enabling researchers to connect various data sources and tools without the need for complex coding. This democratizes data analysis, allowing researchers with limited programming skills to perform sophisticated analyses. By simplifying the integration process, researchers can quickly combine datasets, apply statistical models, and visualize results, accelerating the research cycle.

• For instance, a researcher can integrate survey data with sales figures to analyze consumer behavior without writing a single line of code.
• No-code integrations also allow for seamless updates and modifications to the research setup, adapting to evolving research needs.

What role does collaboration play in enhancing the outcomes of quantitative research?

Collaboration is pivotal in enhancing the outcomes of quantitative research, as it brings together diverse expertise and perspectives. Data management platforms that support collaboration, such as through integrated communication tools, enable researchers to share insights, discuss findings, and refine methodologies in real-time. This collaborative environment fosters a more comprehensive analysis, as researchers can pool their knowledge and skills to tackle complex research questions more effectively.

Moreover, collaboration facilitated by these platforms can lead to more innovative approaches to data analysis, leveraging collective intelligence to push the boundaries of what is possible in quantitative research.

How does the integration of communication tools in data platforms streamline research workflows?

The integration of communication tools in data platforms streamlines research workflows by enabling seamless interaction among team members. This integration allows researchers to discuss data, share insights, and make decisions without leaving the data environment. It reduces the need for external communication tools, minimizing disruptions and ensuring that all discussions are contextualized within the relevant data.

Such streamlined communication enhances efficiency, as decisions can be made quickly and implemented directly within the research workflow, ensuring that projects move forward smoothly and cohesively.

In what ways do data management platforms support data governance in quantitative research?

Data management platforms support data governance in quantitative research by providing tools and features that ensure data quality, security, and compliance. These platforms offer centralized control over data access, enabling researchers to define who can view or modify data. They also automate documentation and metadata management, ensuring that data usage is transparent and traceable.

By facilitating data governance, these platforms help maintain the integrity and reliability of research data, which is essential for producing valid and credible research outcomes.

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Quantitative Data Analysis: Types, Analysis & Examples

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Analysis of Quantitative data enables you to transform raw data points, typically organised in spreadsheets, into actionable insights. Refer to the article to know more!

Analysis of Quantitative Data : Data, data everywhere — it’s impossible to escape it in today’s digitally connected world. With business and personal activities leaving digital footprints, vast amounts of quantitative data are being generated every second of every day. While data on its own may seem impersonal and cold, in the right hands it can be transformed into valuable insights that drive meaningful decision-making. In this article, we will discuss analysis of quantitative data types and examples!

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Table of Contents

What is the Quantitative Analysis Method?

Quantitative Analysis refers to a mathematical approach that gathers and evaluates measurable and verifiable data. This method is utilized to assess performance and various aspects of a business or research. It involves the use of mathematical and statistical techniques to analyze data. Quantitative methods emphasize objective measurements, focusing on statistical, analytical, or numerical analysis of data. It collects data and studies it to derive insights or conclusions.

In a business context, it helps in evaluating the performance and efficiency of operations. Quantitative analysis can be applied across various domains, including finance, research, and chemistry, where data can be converted into numbers for analysis.

Also Read: Analysis vs. Analytics: How Are They Different?

What is the Best Analysis for Quantitative Data?

The “best” analysis for quantitative data largely depends on the specific research objectives, the nature of the data collected, the research questions posed, and the context in which the analysis is conducted. Quantitative data analysis encompasses a wide range of techniques, each suited for different purposes. Here are some commonly employed methods, along with scenarios where they might be considered most appropriate:

1) Descriptive Statistics:

• When to Use: To summarize and describe the basic features of the dataset, providing simple summaries about the sample and measures of central tendency and variability.
• Example: Calculating means, medians, standard deviations, and ranges to describe a dataset.

2) Inferential Statistics:

• When to Use: When you want to make predictions or inferences about a population based on a sample, testing hypotheses, or determining relationships between variables.
• Example: Conducting t-tests to compare means between two groups or performing regression analysis to understand the relationship between an independent variable and a dependent variable.

3) Correlation and Regression Analysis:

• When to Use: To examine relationships between variables, determining the strength and direction of associations, or predicting one variable based on another.
• Example: Assessing the correlation between customer satisfaction scores and sales revenue or predicting house prices based on variables like location, size, and amenities.

4) Factor Analysis:

• When to Use: When dealing with a large set of variables and aiming to identify underlying relationships or latent factors that explain patterns of correlations within the data.
• Example: Exploring underlying constructs influencing employee engagement using survey responses across multiple indicators.

5) Time Series Analysis:

• When to Use: When analyzing data points collected or recorded at successive time intervals to identify patterns, trends, seasonality, or forecast future values.
• Example: Analyzing monthly sales data over several years to detect seasonal trends or forecasting stock prices based on historical data patterns.

6) Cluster Analysis:

• When to Use: To segment a dataset into distinct groups or clusters based on similarities, enabling pattern recognition, customer segmentation, or data reduction.
• Example: Segmenting customers into distinct groups based on purchasing behavior, demographic factors, or preferences.

The “best” analysis for quantitative data is not one-size-fits-all but rather depends on the research objectives, hypotheses, data characteristics, and contextual factors. Often, a combination of analytical techniques may be employed to derive comprehensive insights and address multifaceted research questions effectively. Therefore, selecting the appropriate analysis requires careful consideration of the research goals, methodological rigor, and interpretative relevance to ensure valid, reliable, and actionable outcomes.

Analysis of Quantitative Data in Quantitative Research

Analyzing quantitative data in quantitative research involves a systematic process of examining numerical information to uncover patterns, relationships, and insights that address specific research questions or objectives. Here’s a structured overview of the analysis process:

1) Data Preparation:

• Data Cleaning: Identify and address errors, inconsistencies, missing values, and outliers in the dataset to ensure its integrity and reliability.
• Variable Transformation: Convert variables into appropriate formats or scales, if necessary, for analysis (e.g., normalization, standardization).

2) Descriptive Statistics:

• Central Tendency: Calculate measures like mean, median, and mode to describe the central position of the data.
• Variability: Assess the spread or dispersion of data using measures such as range, variance, standard deviation, and interquartile range.
• Frequency Distribution: Create tables, histograms, or bar charts to display the distribution of values for categorical or discrete variables.

3) Exploratory Data Analysis (EDA):

• Data Visualization: Generate graphical representations like scatter plots, box plots, histograms, or heatmaps to visualize relationships, distributions, and patterns in the data.
• Correlation Analysis: Examine the strength and direction of relationships between variables using correlation coefficients.

4) Inferential Statistics:

• Hypothesis Testing: Formulate null and alternative hypotheses based on research questions, selecting appropriate statistical tests (e.g., t-tests, ANOVA, chi-square tests) to assess differences, associations, or effects.
• Confidence Intervals: Estimate population parameters using sample statistics and determine the range within which the true parameter is likely to fall.

5) Regression Analysis:

• Linear Regression: Identify and quantify relationships between an outcome variable and one or more predictor variables, assessing the strength, direction, and significance of associations.
• Multiple Regression: Evaluate the combined effect of multiple independent variables on a dependent variable, controlling for confounding factors.

6) Factor Analysis and Structural Equation Modeling:

• Factor Analysis: Identify underlying dimensions or constructs that explain patterns of correlations among observed variables, reducing data complexity.
• Structural Equation Modeling (SEM): Examine complex relationships between observed and latent variables, assessing direct and indirect effects within a hypothesized model.

7) Time Series Analysis and Forecasting:

• Trend Analysis: Analyze patterns, trends, and seasonality in time-ordered data to understand historical patterns and predict future values.
• Forecasting Models: Develop predictive models (e.g., ARIMA, exponential smoothing) to anticipate future trends, demand, or outcomes based on historical data patterns.

8) Interpretation and Reporting:

• Interpret Results: Translate statistical findings into meaningful insights, discussing implications, limitations, and conclusions in the context of the research objectives.
• Documentation: Document the analysis process, methodologies, assumptions, and findings systematically for transparency, reproducibility, and peer review.

Also Read: Learning Path to Become a Data Analyst in 2024

Analysis of Quantitative Data Examples

Analyzing quantitative data involves various statistical methods and techniques to derive meaningful insights from numerical data. Here are some examples illustrating the analysis of quantitative data across different contexts:

How to Write Data Analysis in Quantitative Research Proposal?

Writing the data analysis section in a quantitative research proposal requires careful planning and organization to convey a clear, concise, and methodologically sound approach to analyzing the collected data. Here’s a step-by-step guide on how to write the data analysis section effectively:

Step 1: Begin with an Introduction

• Contextualize : Briefly reintroduce the research objectives, questions, and the significance of the study.
• Purpose Statement : Clearly state the purpose of the data analysis section, outlining what readers can expect in this part of the proposal.

Step 2: Describe Data Collection Methods

• Detail Collection Techniques : Provide a concise overview of the methods used for data collection (e.g., surveys, experiments, observations).
• Instrumentation : Mention any tools, instruments, or software employed for data gathering and its relevance.

Step 3 : Discuss Data Cleaning Procedures

• Data Cleaning : Describe the procedures for cleaning and pre-processing the data.
• Handling Outliers & Missing Data : Explain how outliers, missing values, and other inconsistencies will be managed to ensure data quality.

Step 4 : Present Analytical Techniques

• Descriptive Statistics : Outline the descriptive statistics that will be calculated to summarize the data (e.g., mean, median, mode, standard deviation).
• Inferential Statistics : Specify the inferential statistical tests or models planned for deeper analysis (e.g., t-tests, ANOVA, regression).

Step 5: State Hypotheses & Testing Procedures

• Hypothesis Formulation : Clearly state the null and alternative hypotheses based on the research questions or objectives.
• Testing Strategy : Detail the procedures for hypothesis testing, including the chosen significance level (e.g., α = 0.05) and statistical criteria.

Step 6 : Provide a Sample Analysis Plan

• Step-by-Step Plan : Offer a sample plan detailing the sequence of steps involved in the data analysis process.
• Software & Tools : Mention any specific statistical software or tools that will be utilized for analysis.

Step 7 : Address Validity & Reliability

• Validity : Discuss how you will ensure the validity of the data analysis methods and results.
• Reliability : Explain measures taken to enhance the reliability and replicability of the study findings.

Step 8 : Discuss Ethical Considerations

• Ethical Compliance : Address ethical considerations related to data privacy, confidentiality, and informed consent.
• Compliance with Guidelines : Ensure that your data analysis methods align with ethical guidelines and institutional policies.

Step 9 : Acknowledge Limitations

• Limitations : Acknowledge potential limitations in the data analysis methods or data set.
• Mitigation Strategies : Offer strategies or alternative approaches to mitigate identified limitations.

Step 10 : Conclude the Section

• Summary : Summarize the key points discussed in the data analysis section.
• Transition : Provide a smooth transition to subsequent sections of the research proposal, such as the conclusion or references.

Step 11 : Proofread & Revise

• Review : Carefully review the data analysis section for clarity, coherence, and consistency.
• Feedback : Seek feedback from peers, advisors, or mentors to refine your approach and ensure methodological rigor.

What are the 4 Types of Quantitative Analysis?

Quantitative analysis encompasses various methods to evaluate and interpret numerical data. While the specific categorization can vary based on context, here are four broad types of quantitative analysis commonly recognized:

• Descriptive Analysis: This involves summarizing and presenting data to describe its main features, such as mean, median, mode, standard deviation, and range. Descriptive statistics provide a straightforward overview of the dataset’s characteristics.
• Inferential Analysis: This type of analysis uses sample data to make predictions or inferences about a larger population. Techniques like hypothesis testing, regression analysis, and confidence intervals fall under this category. The goal is to draw conclusions that extend beyond the immediate data collected.
• Time-Series Analysis: In this method, data points are collected, recorded, and analyzed over successive time intervals. Time-series analysis helps identify patterns, trends, and seasonal variations within the data. It’s particularly useful in forecasting future values based on historical trends.
• Causal or Experimental Research: This involves establishing a cause-and-effect relationship between variables. Through experimental designs, researchers manipulate one variable to observe the effect on another variable while controlling for external factors. Randomized controlled trials are a common method within this type of quantitative analysis.

Each type of quantitative analysis serves specific purposes and is applied based on the nature of the data and the research objectives.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Steps to Effective Quantitative Data Analysis 

Quantitative data analysis need not be daunting; it’s a systematic process that anyone can master. To harness actionable insights from your company’s data, follow these structured steps:

Step 1 : Gather Data Strategically

Initiating the analysis journey requires a foundation of relevant data. Employ quantitative research methods to accumulate numerical insights from diverse channels such as:

• Interviews or Focus Groups: Engage directly with stakeholders or customers to gather specific numerical feedback.
• Digital Analytics: Utilize tools like Google Analytics to extract metrics related to website traffic, user behavior, and conversions.
• Observational Tools: Leverage heatmaps, click-through rates, or session recordings to capture user interactions and preferences.
• Structured Questionnaires: Deploy surveys or feedback mechanisms that employ close-ended questions for precise responses.

Ensure that your data collection methods align with your research objectives, focusing on granularity and accuracy.

Step 2 : Refine and Cleanse Your Data

Raw data often comes with imperfections. Scrutinize your dataset to identify and rectify:

• Errors and Inconsistencies: Address any inaccuracies or discrepancies that could mislead your analysis.
• Duplicates: Eliminate repeated data points that can skew results.
• Outliers: Identify and assess outliers, determining whether they should be adjusted or excluded based on contextual relevance.

Cleaning your dataset ensures that subsequent analyses are based on reliable and consistent information, enhancing the credibility of your findings.

Step 3 : Delve into Analysis with Precision

With a refined dataset at your disposal, transition into the analytical phase. Employ both descriptive and inferential analysis techniques:

• Descriptive Analysis: Summarize key attributes of your dataset, computing metrics like averages, distributions, and frequencies.
• Inferential Analysis: Leverage statistical methodologies to derive insights, explore relationships between variables, or formulate predictions.

The objective is not just number crunching but deriving actionable insights. Interpret your findings to discern underlying patterns, correlations, or trends that inform strategic decision-making. For instance, if data indicates a notable relationship between user engagement metrics and specific website features, consider optimizing those features for enhanced user experience.

Step 4 : Visual Representation and Communication

Transforming your analytical outcomes into comprehensible narratives is crucial for organizational alignment and decision-making. Leverage visualization tools and techniques to:

• Craft Engaging Visuals: Develop charts, graphs, or dashboards that encapsulate key findings and insights.
• Highlight Insights: Use visual elements to emphasize critical data points, trends, or comparative metrics effectively.
• Facilitate Stakeholder Engagement: Share your visual representations with relevant stakeholders, ensuring clarity and fostering informed discussions.

Tools like Tableau, Power BI, or specialized platforms like Hotjar can simplify the visualization process, enabling seamless representation and dissemination of your quantitative insights.

Also Read: Top 10 Must Use AI Tools for Data Analysis [2024 Edition]

Statistical Analysis in Quantitative Research

Statistical analysis is a cornerstone of quantitative research, providing the tools and techniques to interpret numerical data systematically. By applying statistical methods, researchers can identify patterns, relationships, and trends within datasets, enabling evidence-based conclusions and informed decision-making. Here’s an overview of the key aspects and methodologies involved in statistical analysis within quantitative research:

• Mean, Median, Mode: Measures of central tendency that summarize the average, middle, and most frequent values in a dataset, respectively.
• Standard Deviation, Variance: Indicators of data dispersion or variability around the mean.
• Frequency Distributions: Tabular or graphical representations that display the distribution of data values or categories.
• Hypothesis Testing: Formal methodologies to test hypotheses or assumptions about population parameters using sample data. Common tests include t-tests, chi-square tests, ANOVA, and regression analysis.
• Confidence Intervals: Estimation techniques that provide a range of values within which a population parameter is likely to lie, based on sample data.
• Correlation and Regression Analysis: Techniques to explore relationships between variables, determining the strength and direction of associations. Regression analysis further enables prediction and modeling based on observed data patterns.

3) Probability Distributions:

• Normal Distribution: A bell-shaped distribution often observed in naturally occurring phenomena, forming the basis for many statistical tests.
• Binomial, Poisson, and Exponential Distributions: Specific probability distributions applicable to discrete or continuous random variables, depending on the nature of the research data.

4) Multivariate Analysis:

• Factor Analysis: A technique to identify underlying relationships between observed variables, often used in survey research or data reduction scenarios.
• Cluster Analysis: Methodologies that group similar objects or individuals based on predefined criteria, enabling segmentation or pattern recognition within datasets.
• Multivariate Regression: Extending regression analysis to multiple independent variables, assessing their collective impact on a dependent variable.

5) Data Modeling and Forecasting:

• Time Series Analysis: Analyzing data points collected or recorded at specific time intervals to identify patterns, trends, or seasonality.
• Predictive Analytics: Leveraging statistical models and machine learning algorithms to forecast future trends, outcomes, or behaviors based on historical data.

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Analysis of Quantitative Data FAQs

What is quantitative data analysis.

Quantitative data analysis involves the systematic process of collecting, cleaning, interpreting, and presenting numerical data to identify patterns, trends, and relationships through statistical methods and mathematical calculations.

What are the main steps involved in quantitative data analysis?

The primary steps include data collection, data cleaning, statistical analysis (descriptive and inferential), interpretation of results, and visualization of findings using graphs or charts.

What is the difference between descriptive and inferential analysis?

Descriptive analysis summarizes and describes the main aspects of the dataset (e.g., mean, median, mode), while inferential analysis draws conclusions or predictions about a population based on a sample, using statistical tests and models.

How do I handle outliers in my quantitative data?

Outliers can be managed by identifying them through statistical methods, understanding their nature (error or valid data), and deciding whether to remove them, transform them, or conduct separate analyses to understand their impact.

Which statistical tests should I use for my quantitative research?

The choice of statistical tests depends on your research design, data type, and research questions. Common tests include t-tests, ANOVA, regression analysis, chi-square tests, and correlation analysis, among others.

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Part I: Sampling, Data Collection, & Analysis in Quantitative Research

In this module, we will focus on how quantitative research collects and analyzes data, as well as methods for obtaining sample population.

• Levels of Measurement
• Reliability and Validity
• Population and Samples
• Common Data Collection Methods
• Data Analysis
• Statistical Significance versus Clinical Significance

Objectives:

• Describe levels of measurement
• Describe reliability and validity as applied to critical appraisal of research
• Differentiate methods of obtaining samples for population generalizability
• Describe common data collection methods in quantitative research
• Describe various data analysis methods in quantitative research
• Differentiate statistical significance versus clinical significance

Levels of measurement

Once researchers have collected their data (we will talk about data collection later in this module), they need methods to organize the data before they even start to think about statistical analyses. Statistical operations depend on a variable’s level of measurement. Think about this similarly to shuffling all of your bills in some type of organization before you pay them. With levels of measurement, we are precisely recording variables in a method to help organize them.

There are four levels of measurement:

Nominal:  The data can only be categorized

Ordinal:  The data can be categorized and ranked

Interval:   The data can be categorized, ranked, and evenly spaced

Ratio:   The data can be categorized, ranked, even spaced, and has a natural zero

Going from lowest to highest, the 4 levels of measurement are cumulative. This means that they each take on the properties of lower levels and add new properties.

• A variable is nominal  if the values could be interchanged (e.g. 1 = male, 2 = female OR 1 = female, 2 = male).
• A variable is ordinal  if there is a quantitative ordering of values AND if there are a small number of values (e.g. excellent, good, fair, poor).
• A variable is usually considered interval  if it is measured with a composite scale or test.
• A variable is ratio level if it makes sense to say that one value is twice as much as another (e.g. 100 mg is twice as much as 50 mg) (Polit & Beck, 2021).

Reliability and Validity as Applied to Critical Appraisal of Research

Reliability measures the ability of a measure to consistently measure the same way. Validity measures what it is supposed to  measure. Do we have the need for both in research? Yes! If a variable is measured inaccurately, the data is useless. Let’s talk about why.

For example, let’s set out to measure blood glucose for our study. The validity  is how well the measure can determine the blood glucose. If we used a blood pressure cuff to measure blood glucose, this would not be a valid measure. If we used a blood glucose meter, it would be a more valid measure. It does not stop there, however. What about the meter itself? Has it been calibrated? Are the correct sticks for the meter available? Are they expired? Does the meter have fresh batteries? Are the patient’s hands clean?

Reliability  wants to know: Is the blood glucose meter measuring the same way, every time?

Validity   is asking, “Does the meter measure what it is supposed to measure?” Construct validity: Does the test measure the concept that it’s intended to measure? Content validity: Is the test fully representative of what it aims to measure? Face validity: Does the content of the test appear to be suitable to its aims?

Leibold, 2020

Obtaining Samples for Population Generalizability

In quantitative research, a population is the entire group that the researcher wants to draw conclusions about.

A sample is the specific group that the researcher will actually collect data from. A sample is always a much smaller group of people than the total size of the population. For example, if we wanted to investigate heart failure, there would be no possible way to measure every single human with heart failure. Therefore, researchers will attempt to select a sample of that large population which would most likely reflect (AKA: be a representative sample) the larger population of those with heart failure. Remember, in quantitative research, the results should be generalizable to the population studied.

A researcher will specify population characteristics through eligibility criteria. This means that they consider which characteristics to include ( inclusion criteria ) and which characteristics to exclude ( exclusion criteria ).

For example, if we were studying chemotherapy in breast cancer subjects, we might specify:

• Inclusion Criteria: Postmenopausal women between the ages of 45 and 75 who have been diagnosed with Stage II breast cancer.
• Exclusion Criteria: Abnormal renal function tests since we are studying a combination of drugs that may be nephrotoxic. Renal function tests are to be performed to evaluate renal function and the threshold values that would disqualify the prospective subject is serum creatinine above 1.9 mg/dl.

Sampling Designs:

There are two broad classes of sampling in quantitative research: Probability and nonprobability sampling.

Probability sampling : As the name implies, probability sampling means that each eligible individual has a random chance (same probability) of being selected to participate in the study.

There are three types of probability sampling:

Simple random sampling :  Every eligible participant is randomly selected (e.g. drawing from a hat).

Stratified random sampling : Eligible population is first divided into two or more strata (categories) from which randomization occurs (e.g. pollution levels selected from restaurants, bars with ordinances of state laws, and bars with no ordinances).

Systematic sampling : Involves the selection of every __ th eligible participant from a list (e.g. every 9 th  person).

Nonprobability sampling : In nonprobability sampling, eligible participants are selected using a subjective (non-random) method.

There are four types of nonprobability sampling:

Convenience sampling : Participants are selected for inclusion in the sample because they are the easiest for the researcher to access. This can be due to geographical proximity, availability at a given time, or willingness to participate in the research.

Quota sampling : Participants are from a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, the researcher could divide a population by the state they live in, income or education level, or sex. The population is divided into groups (also called strata) and samples are taken from each group to meet a quota.

Consecutive sampling : A sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Consecutive sampling is defined as a nonprobability technique where samples are picked at the ease of a researcher more like convenience sampling, only with a slight variation. Here, the researcher selects a sample or group of people, conducts research over a period, collects results, and then moves on to another sample.

Purposive sampling : A group of non-probability sampling techniques in which units are selected because they have characteristics that the researcher needs in their sample. In other words, units are selected “on purpose” in purposive sampling.

Common Data Collection Methods in Quantitative Research

There are various methods that researchers use to collect data for their studies. For nurse researchers, existing records are an important data source. Researchers need to decide if they will collect new data or use existing data. There is also a wealth of clinical data that can be used for non-research purposed to help answer clinical questions.

Let’s look at some general data collection methods and data sources in quantitative research.

Existing data  could include medical records, school records, corporate diaries, letters, meeting minutes, and photographs. These are easy to obtain do not require participation from those being studied.

Collecting new data:

Let’s go over a few methods in which researcher can collect new data. These usually requires participation from those being studied.

Self-reports can be obtained via interviews or questionnaires . Closed-ended questions can be asked (“Within the past 6 months, were you ever a member of a fitness gym?” Yes/No) or open-ended questions such as “Why did you decide to join a fitness gym?” Important to remember (this sometimes throws students off) is that conducting interviews and questionnaires does not mean it is qualitative in nature! Do not let that throw you off in assessing whether a published article is quantitative or qualitative. The nature of the questions, however, may help to determine the type of research (quantitative or qualitative), as qualitative questions deal with ascertaining a very organic collection of people’s experiences in open-ended questions. 

Advantages of questionnaires (compared to interviews):

• Questionnaires are less costly and are advantageous for geographically dispersed samples.
• Questionnaires offer the possibility of anonymity, which may be crucial in obtaining information about certain opinions or traits.

Advances of interviews (compared to questionnaires):

• Higher response rates
• Some people cannot fill out a questionnaire.
• Opportunities to clarify questions or to determine comprehension
• Opportunity to collect supplementary data through observation

Psychosocial scales are often utilized within questionnaires or interviews. These can help to obtain attitudes, perceptions, and psychological traits. 

Likert Scales :

• Consist of several declarative statements ( items ) expressing viewpoints
• Responses are on an agree/disagree continuum (usually five or seven response options).
• Responses to items are summed to compute a total scale score.

Visual Analog Scale:

• Used to measure subjective experiences (e.g., pain, nausea)
• Measurements are on a straight line measuring 100 mm.
• End points labeled as extreme limits of sensation

Observational Methods include the observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research .

When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation. When individuals are observed in their natural environment, this is known as naturalistic observation.  In participant observation, the researcher immerses himself or herself in the environment and becomes a member of the group being observed.

Biophysiologic Measures are defined as ‘those physiological and physical variables that require specialized technical instruments and equipment for their measurement’. Biophysiological measures are the most common instruments for collecting data in medical science studies. To collect valid and reliable data, it is critical to apply these measures appropriately.

• In vivo  refers to when research or work is done with or within an entire, living organism. Examples can include studies in animal models or human clinical trials.
• In vitro is used to describe work that’s performed outside of a living organism. This usually involves isolated tissues, organs, or cells.

Let’s watch a video about Sampling and Data Collection that I made a couple of years ago.

This guide explains what quantitative data analysis is and why it’s important, and gives you a four-step process to conduct a quantitative data analysis, so you know exactly what’s happening in your business and what your users need .

Collect quantitative customer data with Hotjar

Use Hotjar’s tools to gather the customer insights you need to make quantitative data analysis a breeze.

What is quantitative data analysis? 

Quantitative data analysis is the process of analyzing and interpreting numerical data. It helps you make sense of information by identifying patterns, trends, and relationships between variables through mathematical calculations and statistical tests. 

With quantitative data analysis, you turn spreadsheets of individual data points into meaningful insights to drive informed decisions. Columns of numbers from an experiment or survey transform into useful insights—like which marketing campaign asset your average customer prefers or which website factors are most closely connected to your bounce rate. 

Without analytics, data is just noise. Analyzing data helps you make decisions which are informed and free from bias.

What quantitative data analysis is not

But as powerful as quantitative data analysis is, it’s not without its limitations. It only gives you the what, not the why . For example, it can tell you how many website visitors or conversions you have on an average day, but it can’t tell you why users visited your site or made a purchase.

For the why behind user behavior, you need qualitative data analysis , a process for making sense of qualitative research like open-ended survey responses, interview clips, or behavioral observations. By analyzing non-numerical data, you gain useful contextual insights to shape your strategy, product, and messaging. 

Quantitative data analysis vs. qualitative data analysis 

Let’s take an even deeper dive into the differences between quantitative data analysis and qualitative data analysis to explore what they do and when you need them.

The bottom line: quantitative data analysis and qualitative data analysis are complementary processes. They work hand-in-hand to tell you what’s happening in your business and why.  

💡 Pro tip: easily toggle between quantitative and qualitative data analysis with Hotjar Funnels . 

The Funnels tool helps you visualize quantitative metrics like drop-off and conversion rates in your sales or conversion funnel to understand when and where users leave your website. You can break down your data even further to compare conversion performance by user segment.

Spot a potential issue? A single click takes you to relevant session recordings , where you see user behaviors like mouse movements, scrolls, and clicks. With this qualitative data to provide context, you'll better understand what you need to optimize to streamline the user experience (UX) and increase conversions .

Hotjar Funnels lets you quickly explore the story behind the quantitative data

4 benefits of quantitative data analysis

There’s a reason product, web design, and marketing teams take time to analyze metrics: the process pays off big time. 

Four major benefits of quantitative data analysis include:

1. Make confident decisions 

With quantitative data analysis, you know you’ve got data-driven insights to back up your decisions . For example, if you launch a concept testing survey to gauge user reactions to a new logo design, and 92% of users rate it ‘very good’—you'll feel certain when you give the designer the green light. 

Since you’re relying less on intuition and more on facts, you reduce the risks of making the wrong decision. (You’ll also find it way easier to get buy-in from team members and stakeholders for your next proposed project. 🙌)

2. Reduce costs

By crunching the numbers, you can spot opportunities to reduce spend . For example, if an ad campaign has lower-than-average click-through rates , you might decide to cut your losses and invest your budget elsewhere. 

Or, by analyzing ecommerce metrics , like website traffic by source, you may find you’re getting very little return on investment from a certain social media channel—and scale back spending in that area.

3. Personalize the user experience

Quantitative data analysis helps you map the customer journey , so you get a better sense of customers’ demographics, what page elements they interact with on your site, and where they drop off or convert . 

These insights let you better personalize your website, product, or communication, so you can segment ads, emails, and website content for specific user personas or target groups.

4. Improve user satisfaction and delight

Quantitative data analysis lets you see where your website or product is doing well—and where it falls short for your users . For example, you might see stellar results from KPIs like time on page, but conversion rates for that page are low. 

These quantitative insights encourage you to dive deeper into qualitative data to see why that’s happening—looking for moments of confusion or frustration on session recordings, for example—so you can make adjustments and optimize your conversions by improving customer satisfaction and delight.

💡Pro tip: use Net Promoter Score® (NPS) surveys to capture quantifiable customer satisfaction data that’s easy for you to analyze and interpret. 

With an NPS tool like Hotjar, you can create an on-page survey to ask users how likely they are to recommend you to others on a scale from 0 to 10. (And for added context, you can ask follow-up questions about why customers selected the rating they did—rich qualitative data is always a bonus!)

Hotjar graphs your quantitative NPS data to show changes over time

4 steps to effective quantitative data analysis 

Quantitative data analysis sounds way more intimidating than it actually is. Here’s how to make sense of your company’s numbers in just four steps:

1. Collect data

Before you can actually start the analysis process, you need data to analyze. This involves conducting quantitative research and collecting numerical data from various sources, including: 

Interviews or focus groups 

Website analytics

Observations, from tools like heatmaps or session recordings

Questionnaires, like surveys or on-page feedback widgets

Just ensure the questions you ask in your surveys are close-ended questions—providing respondents with select choices to choose from instead of open-ended questions that allow for free responses.

Hotjar’s pricing plans survey template provides close-ended questions

 2. Clean data

Once you’ve collected your data, it’s time to clean it up. Look through your results to find errors, duplicates, and omissions. Keep an eye out for outliers, too. Outliers are data points that differ significantly from the rest of the set—and they can skew your results if you don’t remove them.

By taking the time to clean your data set, you ensure your data is accurate, consistent, and relevant before it’s time to analyze. 

3. Analyze and interpret data

At this point, your data’s all cleaned up and ready for the main event. This step involves crunching the numbers to find patterns and trends via mathematical and statistical methods. 

Two main branches of quantitative data analysis exist: 

Descriptive analysis : methods to summarize or describe attributes of your data set. For example, you may calculate key stats like distribution and frequency, or mean, median, and mode.

Inferential analysis : methods that let you draw conclusions from statistics—like analyzing the relationship between variables or making predictions. These methods include t-tests, cross-tabulation, and factor analysis. (For more detailed explanations and how-tos, head to our guide on quantitative data analysis methods.)

Then, interpret your data to determine the best course of action. What does the data suggest you do ? For example, if your analysis shows a strong correlation between email open rate and time sent, you may explore optimal send times for each user segment.

4. Visualize and share data

Once you’ve analyzed and interpreted your data, create easy-to-read, engaging data visualizations—like charts, graphs, and tables—to present your results to team members and stakeholders. Data visualizations highlight similarities and differences between data sets and show the relationships between variables.

Software can do this part for you. For example, the Hotjar Dashboard shows all of your key metrics in one place—and automatically creates bar graphs to show how your top pages’ performance compares. And with just one click, you can navigate to the Trends tool to analyze product metrics for different segments on a single chart. 

Hotjar Trends lets you compare metrics across segments

Discover rich user insights with quantitative data analysis

Conducting quantitative data analysis takes a little bit of time and know-how, but it’s much more manageable than you might think. 

By choosing the right methods and following clear steps, you gain insights into product performance and customer experience —and you’ll be well on your way to making better decisions and creating more customer satisfaction and loyalty.

FAQs about quantitative data analysis

What is quantitative data analysis.

Quantitative data analysis is the process of making sense of numerical data through mathematical calculations and statistical tests. It helps you identify patterns, relationships, and trends to make better decisions.

How is quantitative data analysis different from qualitative data analysis?

Quantitative and qualitative data analysis are both essential processes for making sense of quantitative and qualitative research .

Quantitative data analysis helps you summarize and interpret numerical results from close-ended questions to understand what is happening. Qualitative data analysis helps you summarize and interpret non-numerical results, like opinions or behavior, to understand why the numbers look like they do.

 If you want to make strong data-driven decisions, you need both.

What are some benefits of quantitative data analysis?

Quantitative data analysis turns numbers into rich insights. Some benefits of this process include: 

Making more confident decisions

Identifying ways to cut costs

Personalizing the user experience

Improving customer satisfaction

What methods can I use to analyze quantitative data?

Quantitative data analysis has two branches: descriptive statistics and inferential statistics. 

Descriptive statistics provide a snapshot of the data’s features by calculating measures like mean, median, and mode. 

Inferential statistics , as the name implies, involves making inferences about what the data means. Dozens of methods exist for this branch of quantitative data analysis, but three commonly used techniques are: 

Cross tabulation

Factor analysis

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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• Published: 11 April 2024

Quantitative text analysis

• Kristoffer L. Nielbo   ORCID: orcid.org/0000-0002-5116-5070 1 ,
• Folgert Karsdorp 2 ,
• Melvin Wevers   ORCID: orcid.org/0000-0001-8177-4582 3 ,
• Alie Lassche   ORCID: orcid.org/0000-0002-7607-0174 4 ,
• Rebekah B. Baglini   ORCID: orcid.org/0000-0002-2836-5867 5 ,
• Mike Kestemont 6 &
• Nina Tahmasebi   ORCID: orcid.org/0000-0003-1688-1845 7  

Nature Reviews Methods Primers volume  4 , Article number:  25 ( 2024 ) Cite this article

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• Interdisciplinary studies

Text analysis has undergone substantial evolution since its inception, moving from manual qualitative assessments to sophisticated quantitative and computational methods. Beginning in the late twentieth century, a surge in the utilization of computational techniques reshaped the landscape of text analysis, catalysed by advances in computational power and database technologies. Researchers in various fields, from history to medicine, are now using quantitative methodologies, particularly machine learning, to extract insights from massive textual data sets. This transformation can be described in three discernible methodological stages: feature-based models, representation learning models and generative models. Although sequential, these stages are complementary, each addressing analytical challenges in the text analysis. The progression from feature-based models that require manual feature engineering to contemporary generative models, such as GPT-4 and Llama2, signifies a change in the workflow, scale and computational infrastructure of the quantitative text analysis. This Primer presents a detailed introduction of some of these developments, offering insights into the methods, principles and applications pertinent to researchers embarking on the quantitative text analysis, especially within the field of machine learning.

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Introduction

Qualitative analysis of textual data has a long research history. However, a fundamental shift occurred in the late twentieth century when researchers began investigating the potential of computational methods for text analysis and interpretation 1 . Today, researchers in diverse fields, such as history, medicine and chemistry, commonly use the quantification of large textual data sets to uncover patterns and trends, producing insights and knowledge that can aid in decision-making and offer novel ways of viewing historical events and current realities. Quantitative text analysis (QTA) encompasses a range of computational methods that convert textual data or natural language into structured formats before subjecting them to statistical, mathematical and numerical analysis. With the increasing availability of digital text from numerous sources, such as books, scientific articles, social media posts and online forums, these methods are becoming increasingly valuable, facilitated by advances in computational technology.

Given the widespread application of QTA across disciplines, it is essential to understand the evolution of the field. As a relatively consolidated field, QTA embodies numerous methods for extracting and structuring information in textual data. It gained momentum in the late 1990s as a subset of the broader domain of data mining, catalysed by advances in database technologies, software accessibility and computational capabilities 2 , 3 . However, it is essential to recognize that the evolution of QTA extends beyond computer science and statistics. It has heavily incorporated techniques and algorithms derived from  corpus linguistics 4 , computer linguistics 5 and information retrieval 6 . Today, QTA is largely driven by  machine learning , a crucial component of  data science , artificial intelligence (AI) and natural language processing (NLP).

Methods of QTA are often referred to as techniques that are innately linked with specific tasks (Table  1 ). For example, the sentiment analysis aims to determine the emotional tone of a text 7 , whereas entity and concept extraction seek to identify and categorize elements in a text, such as names, locations or key themes 8 , 9 . Text classification refers to the task of sorting texts into groups with predefined labels 10 — for example, sorting news articles into semantic categories such as politics, sports or entertainment. In contrast to machine-learning tasks that use supervised learning , text clustering, which uses  unsupervised learning , involves finding naturally occurring groups in unlabelled texts 11 . A significant subset of tasks primarily aim to simplify and structure natural language. For example, representation learning includes tasks that automatically convert texts into numerical representations, which can then be used for other tasks 12 . The lines separating these techniques can be blurred and often vary depending on the research context. For example, topic modelling, a type of statistical modelling used for concept extraction, serves simultaneously as a clustering and representation learning technique 13 , 14 , 15 .

QTA, similar to machine learning, learns from observation of existing data rather than by manipulating variables as in scientific experiments 16 . In QTA, experiments encompass the design and implementation of empirical tests to explore and evaluate the performance of models, algorithms and techniques in relation to specific tasks and applications. In practice, this involves a series of steps. First, text data are collected from real-world sources such as newspaper articles, patient records or social media posts. Then, a specific type of machine-learning model is selected and designed. The model could be a tree-based decision model, a clustering technique or more complex encoder–decoder models for tasks such as translation. Subsequently, the selected model is trained on the collected data, learning to make categorizations or predictions based on the data. The performance of the model is evaluated using predominantly intrinsic performance metrics (such as accuracy for a classification task) and, to a lesser degree, extrinsic metrics that measure how the output of the model impacts a broader task or system.

Three distinct methodological stages can be observed in the evolution of QTA: feature-based models, representation learning models and generative models (Fig.  1 ). Feature-based models use efficient machine-learning techniques, collectively referred to as shallow learning, which are ideal for tabular data but require manual feature engineering. They include models based on  bag-of-words models , decision trees and support vector machines and were some of the first methods applied in QTA. Representation learning models use deep learning techniques that automatically learn useful features from text. These models include architectures such as the highly influential  transformer architecture 17 and techniques such as masked language modelling, as used in language representation models such as Bidirectional Encoder Representations from Transformers (BERT) 18 . BERT makes use of the transformer architecture, as do most other large language models after the introduction of the architecture 17 . This shift towards automatic learning representations marked an important advance in natural language understanding. Generative models, trained using autoregressive techniques, represent the latest frontier. These models, such as generative pre-trained transformer GPT-3 (ref. 19 ), GPT-4 and Llama2 (ref. 20 ), can generate coherent and contextually appropriate responses and are powerful tools for natural language generation. Feature-based models preceded representation learning, which in turn preceded generative models.

a , Feature-based models in which data undergo preprocessing to generate features for model training and prediction. b , Representation learning models that can be trained from scratch using raw data or leverage pre-trained models fine-tuned with specific data. c , Generative models in which a prompt guides the generative deep learning model, potentially augmented by external data, to produce a result.

Although these models are temporally ordered, they do not replace each other. Instead, each offers unique methodological features and is suitable for different tasks. The progress from small models with limited computing capacity to today’s large models with billions of parameters encapsulates the transformation in the scale and complexity of the QTA.

The evolution of these models reflects the advancement of machine-learning infrastructure, particularly in the emergence and development of tooling frameworks. These frameworks, exemplified by platforms such as scikit-learn 21 and Hugging Face 22 , have served as essential infrastructure for democratizing and simplifying the implementation of increasingly sophisticated models. They offer user-friendly interfaces that mask the complexities of the algorithms, thereby empowering researchers to harness advanced methodologies with minimal prerequisite knowledge and coding expertise. The advent of high-level generative models such as GPT-3 (ref. 19 ), GPT-4 and Llama2 (ref. 20 ) marks milestones in the progression. Renowned for their unprecedented language understanding and generation capabilities, these models have the potential to redefine access to the sophisticated text analysis by operating on natural language prompts, effectively bypassing the traditional need for coding. It is important to emphasize that these stages represent an abstraction that points to fundamental changes to the workflow and underlying infrastructure of QTA.

This Primer offers an accessible introduction to QTA methods, principles and applications within feature-based models, representation learning and generative models. The focus is on how to extract and structure textual data using machine learning to enable quantitative analysis. The Primer is particularly suitable for researchers new to the field with a pragmatic interest in these techniques. By focusing on machine-learning methodologies, a comprehensive overview of several key workflows currently in use is presented. The focus consciously excludes traditional count-based and rule-based methods, such as keyword and collocation analysis. This decision is guided by the current dominance of machine learning in QTA, in terms of both performance and scalability. However, it is worth noting that machine-learning methods can encompass traditional approaches where relevant, adding to their versatility and broad applicability. The experiments in QTA are presented, including problem formulation, data collection, model selection and evaluation techniques. The results and real-world applications of these methodologies are discussed, underscoring the importance of reproducibility and robust data management practices. The inherent limitations and potential optimizations within the field are addressed, charting the evolution from basic feature-based approaches to advanced generative models. The article concludes with a forward-looking discussion on the ethical implications, practical considerations and methodological advances shaping the future of QTA. Regarding tools and software, references to specific libraries and packages are omitted as they are relatively easy to identify given a specific task. Generally, the use of programming languages that are well suited for QTA is recommended, such as Python, R and Julia, but it is also acknowledged that graphical platforms for data analysis provide similar functionalities and may be better suited for certain disciplines.

Experimentation

In QTA, the term experiment assumes a distinct character. Rather than mirroring the controlled conditions commonly associated with randomized controlled trials, it denotes a structured procedure that aims to validate, refine and compare models and findings. QTA experiments provide a platform for testing ideas, establishing hypotheses and paving the way for advancement. At the heart of these experiments lies a model — a mathematical and computational embodiment of discernible patterns drawn from data. A model can be considered a learned function that captures the intricate relationship between textual features and their intended outcomes, allowing for informed decisions on unseen data. For example, in the sentiment analysis, a model learns the association between specific words or phrases and the emotions they convey, later using this knowledge to assess the sentiment of new texts.

The following section delineates the required steps for a QTA experiment. This step-by-step description encompasses everything from problem definition and data collection to the nuances of model selection, training and validation. It is important to distinguish between two approaches in QTA: training or fine-tuning a model, and applying a (pre-trained) model (Fig.  1 ). In the first approach, a model is trained or fine-tuned to solve a QTA task. In the second approach, a pre-trained model is used to solve a QTA task. Finally, it is important to recognize that experimentation, much like other scientific pursuits, is inherently iterative. This cyclic process ensures that the devised models are not just accurate but also versatile enough to be applicable in real-world scenarios.

Problem formulation

Problem formulation is a crucial first step in QTA, laying the foundation for subsequent analysis and experimentation. This process involves several key considerations, which, when clearly defined beforehand, contributes to the clarity and focus of the experiment. First, every QTA project begins with the identification of a research question. The subsequent step is to determine the scope of the analysis, which involves defining the boundaries of the study, such as the time period, the type of texts to be analysed or the geographical or demographic considerations.

An integral part of this process is to identify the nature of the analytical task. This involves deciding whether the study is a classification task, for example, in which data are categorized into predefined classes; a clustering task, in which data are grouped based on similarities without predefined categories; or another type of analysis. The choice of task has significant implications for both the design of the study and the selection of appropriate data and analytical techniques. For instance, a classification task such as sentiment analysis requires clearly defined categories and suitable labelled data, whereas a clustering task might be used in the exploratory data analysis to uncover underlying patterns in the data.

After selecting data to support the analysis, an important next step is deciding on the level of analysis. QTA can be conducted at various levels, such as the document-level, paragraph-level, sentence-level or even word-level. The choice largely depends on the research question, as well as the nature of the data set.

Classification

A common application of a classification task in QTA is the sentiment analysis. For instance, in analysing social media comments, a binary classification might be employed in which comments are labelled as positive or negative. This straightforward example showcases the formulation of a problem in which the objective is clear-cut classification based on predefined sentiment labels. In this case, the level of analysis might be at the sentence level, focusing on the sentiment expressed in each individual comment.

From this sentence-level information, it is possible to extrapolate to general degrees of sentiment. This is often done when companies want to survey their products or when political parties want to analyse their support, for example, to determine how many people are positive or negative towards the party 23 . Finally, from changing degrees of sentiment, one can extract the most salient aspects that form this sentiment: recurring positive or negative sentiments towards price or quality, or different political issues.

Modelling of themes

The modelling of themes involves the identification of prevalent topics, for example, in a collection of news articles. Unlike the emotion classification task, here the researcher is interested in uncovering underlying themes or topics, rather than classifying texts into predefined categories. This problem formulation requires an approach that can discern and categorize emergent topics from the textual data, possibly at the document level, to capture broader thematic elements. This can be done without using any predefined hypotheses 24 , or by steering topic models towards certain seed topics (such as a given scientific paper or book) 25 . Using such topic detection tools, it can be determined how prevalent topics are in different time periods or across genre to determine significance or impact of both topics and authors.

Modelling of temporal change

Consider a study aiming to track the evolution of literary themes over time. In this scenario, the problem formulation would involve not only the selection of texts and features but also a temporal dimension, in which changes in themes are analysed across different time periods. This type of analysis might involve examining patterns and trends in literary themes, requiring a longitudinal approach to text analysis, for example, in the case of scientific themes or reports about important events 26 or themes as proxy for meaning change 27 . Often, when longitudinal analysis is considered, additional challenges are involved, such as statistical properties relating to increasing or decreasing quantity or quality of data that can influence results, see, for example, refs. 28 , 29 , 30 , 31 .

In similar fashion, temporal analysis of changing data happens in a multitude of disciplines from linguistics, as in computational detection of words that experience change in meaning 32 , to conceptual change in history 33 , poetry 34 , medicine 35 , political science 36 , 37 and to the study of ethnical biases and racism 38 , 39 , 40 .

The GIGO principle, meaning ‘garbage in, garbage out’, is ever present in QTA because without high-quality data even the most sophisticated models can falter, rendering analyses inaccurate or misleading. To ensure robustness in, for example, social media data, its inherently informal and dynamic nature must be acknowledged, often characterized by non-standard grammar, slang and evolving language use. Robustness here refers to the ability of the data to provide reliable, consistent analysis, despite these irregularities. This requires implementing specialized preprocessing techniques that can handle such linguistic variability without losing contextual meaning. For example, rather than discarding non-standard expressions or internet-specific abbreviations, these elements should be carefully processed to preserve their significant role in conveying sentiment and meaning. Additionally, ensuring representativeness and diversity in the data set is crucial; collecting data across different demographics, topics and time frames can mitigate biases and provide a more comprehensive view of the discourse if this is needed. Finally, it is important to pay attention to errors, anomalies and irregularities in the data, such as optical character recognition errors and missing values, and in some cases take steps to remediate these in preprocessing. More generally, it is crucial to emphasize that the quality of a given data set depends on the research question. Grammatically well-formed sentences may be high-quality data for training a linguistic parser; social media could never be studied as people on social media rarely abide by the rules of morphology and syntax. This underscores the vital role of data not just as input but also as an essential component that dictates the success and validity of the analytical endeavour.

Data acquisition

Depending on the research objective, data sets can vary widely in their characteristics. For the emotion classifier, a data set could consist of many social media comments. If the task is to train or fine-tune a model, each comment should be annotated with its corresponding sentiment label (labels). If the researcher wants to apply a pre-trained model, then only a subset of the data must be annotated to test the generalizability of the model. Labels can be annotated manually or automatically, for instance, by user-generated ratings, such as product reviews or social media posts, for example. Training data should have sufficient coverage of the phenomenon under investigation to capture its linguistic characteristics. For the emotion classifier, a mix of comments are needed, ranging from brief quips to lengthy rants, offering diverse emotional perspectives. Adhering to the principle that there are no data like more data, the breadth and depth of such a data set significantly enhance the accuracy of the model. Traditionally, data collection was arduous, but today QTA researchers can collect data from the web and archives using dedicated software libraries or an  application programming interface . For analogue data, optical character recognition and handwritten text recognition offer efficient conversion to machine-readable formats 41 . Similarly, for auditory language data, automatic speech recognition has emerged as an invaluable tool 42 .

Data preprocessing

In feature-based QTA, manual data preprocessing is one of the most crucial and time-consuming stages. Studies suggest that researchers can spend up to 80% of their project time refining and managing their data 43 . A typical preprocessing workflow for feature-based techniques requires data cleaning and text normalization. Standard procedures include transforming all characters to lower case for uniformity, eliminating punctuation marks and removing high-frequency functional words such as ‘and’, ‘the’ or ‘is’. However, it is essential to recognize that these preprocessing strategies should be closely aligned with the specific research question at hand. For example, in the sentiment analysis, retaining emotive terms and expressions is crucial, whereas in syntactic parsing, the focus might be on the structural elements of language, requiring a different approach to what constitutes ‘noise’ in the data. More nuanced challenges arise in ensuring the integrity of a data set. For instance, issues with character encoding require attention to maintain language and platform interoperability, which means resorting to universally accepted encoding formats such as UTF-8. Other normalization steps, such as  stemming or lemmatization , involve reducing words to their root forms to reduce lexical variation. Although these are standard practices, their application might vary depending on the research objective. For example, in a study focusing on linguistic diversity, aggressive stemming may erase important stylistic or dialectal markers. Many open-source software libraries exist nowadays that can help automate such processes for various languages. The impact of these steps on research results underscores the necessity of a structured and well-documented approach to preprocessing, including detailed reporting of all preprocessing steps and software used, to ensure that analyses are both reliable and reproducible. The practice of documenting preprocessing is crucial, yet often overlooked, reinforcing its importance for the integrity of research.

With representation learning and generative techniques, QTA has moved towards end-to-end models that take raw text input such as social media comments and directly produces the final desired output such as emotion classification, handling all intermediate steps without manual intervention 44 . However, removal of non-textual artefacts such as HTML codes and unwanted textual elements such as pornographic material can still require substantial work to prepare data to train an end-to-end model.

Annotation and labelling

Training and validating a (pre-trained) model requires annotating the textual data set. These data sets come in two primary flavours: pre-existing collections with established labels and newly curated sets awaiting annotation. Although pre-existing data sets offer a head-start, owing to their readymade labels, they must be validated to ensure alignment with research objectives. By contrast, crafting a data set from scratch confers flexibility to tailor the data to precise research needs, but it also ushers in the intricate task of collecting and annotating data. Annotation is a meticulous endeavour that demands rigorous consistency and reliability. To ensure inter-annotator agreement (IAA) 45 , for example, annotations from multiple annotators are compared using metrics such as  Fleiss’ kappa ( κ ) to assess consistency. A high IAA score not only indicates annotation consistency but also lends confidence in the reliability of the data set. There is no universally accepted manner to interpret κ statistics, although κ  ≥ 0. 61 is generally considered to indicate ‘substantial agreement’ 46 .

Various tools and platforms support the annotation process. Specialized software for research teams provides controlled environments for annotation tasks. Crowdsourcing is another approach, in which tasks are distributed among a large group of people. This can be done through non-monetized campaigns, focusing on volunteer participation or gamification strategies to encourage user engagement in annotation tasks 47 . Monetized platforms, such as Amazon Mechanical Turk, represent a different facet of crowdsourcing in which microtasks are outsourced for financial compensation. It is important to emphasize that, although these platforms offer a convenient way to gather large-scale annotations, they raise ethical concerns regarding worker exploitation and fair compensation. Critical studies, such as those of Paolacci, Chandler and Ipeirotis 48 and Bergvall-Kåreborn and Howcroft 49 , highlight the need for awareness and responsible use of such platforms in research contexts.

Provenance and ethical considerations

Data provenance is of utmost importance in QTA. Whenever feasible, preference should be given to open and well-documented data sets that comply with the principles of FAIR (findable, accessible, interoperable and reusable) 50 . However, the endeavour to harness data, especially online, requires both legal and ethical considerations. For instance, the General Data Protection Regulation delineates the rights of European data subjects and sets stringent data collection and usage criteria. Unstructured data can complicate standard techniques for data depersonalization (for example, data masking, swapping and pseudonymization). Where these techniques fail, differential privacy may be a viable alternative to ensure that the probability of any specific output of the model does not depend on the information of any individual in the data set 51 .

Recognition of encoded biases is equally important. Data sets can inadvertently perpetuate cultural biases towards attributes such as gender and race, resulting in sampling bias. Such bias compromises research integrity and can lead to models that reinforce existing inequalities. Gender, for instance, can have subtle effects that are not easily detected in textual data 52 . A popular approach to rectifying biases is  data augmentation , which can be used to increase the diversity of a data set without collecting new data 53 . This is achieved by applying transformations to existing textual data, creating new and diverse examples. The main goal of data augmentation is to improve model generalization by exposing it to a broader range of data variations.

Model selection and design

Model selection and design set the boundaries for efficiency, accuracy and generalizability of any QTA experiment. Choosing the right model architecture depends on several considerations and will typically require experimentation to compare the performance of multiple models. Although the methodological trajectory of QTA provides a roadmap, specific requirements of the task, coupled with available data volume, often guide the final choice. Although some tasks require that the model be trained from scratch owing to, for instance, transparency and security requirements, it has become common to use pre-trained models that provide text representations originating from training on massive data sets. Pre-trained models can be fine-tuned for a specific task, for example, emotion classification. Training feature-based models may be optimal for smaller data sets, focusing on straightforward interpretability. By contrast, the complexities of expansive textual data often require representation learning or generative models. In QTA, achieving peak performance is a trade-off among model interpretability, computational efficiency and predictive power. As the sophistication of a model grows, hyperparameter tuning, regularization and loss function require meticulous consideration. These decisions ensure that a model is not only accurate but also customized for research-specific requirements.

Training and evaluation

During the training phase, models learn patterns from the data to predict or classify textual input. Evaluation is the assessment phase that determines how the trained model performs on unseen data. Evaluation serves multiple purposes, but first and foremost, it is used to assess how well the model performs on a specific task using metrics such as accuracy, precision and recall. For example, knowing how accurately the emotion classifier identifies emotions is crucial for any research application. Evaluation of this model also allows researchers to assess whether it is biased towards common emotions and whether it generalizes across different types of text sources. When an emotion classifier is trained on social media posts, a common practice, its effectiveness can be evaluated on different data types, such as patient journals or historical newspapers, to determine its performance across varied contexts. Evaluation enables us to compare multiple models to select the most relevant for the research problem. Additional evaluation involves hyperparameter tuning, resource allocation, benchmarking and model fairness audits.

Overfitting is often a challenge in model training, which can occur when a model is excessively tailored to the peculiarities of the training data and becomes so specialized that its generalizability is compromised. Such a model performs accurately on the specific data set but underperforms on unseen examples. Overfitting can be counteracted by dividing the data into three distinct subsets: the training set, the validation set and the test set. The training set is the primary data set from which the model learns patterns, adjusts its weights and fine-tunes itself based on the labelled examples provided. The validation set is used to monitor and assess the performance of the model during training. It acts as a checkpoint, guides hyperparameter tuning and ensures that the model is not veering off track. The test set is the final held-out set on which the performance of the model is evaluated. The test set is akin to a final examination, assessing how well the model generalizes to unseen data. If a pre-trained model is used, only the data sets used to fine-tune the model are necessary to evaluate the model.

The effectiveness of any trained model is gauged not just by how well it fits the training data but also by its performance on unseen samples. Evaluation metrics provide objective measures to assess performance on validation and test sets as well as unseen examples. The evaluation process is fundamental to QTA experiments, as demonstrated in the text classification research 10 . Several evaluation metrics are used to measure performance. The most prominent are accuracy (the proportion of all predictions that are correct), precision (the proportion of positive predictions that are actually correct) and recall (the proportion of actual positives that were correctly identified). The F1 score amalgamates precision and recall and emerges as a balanced metric, especially when class distributions are skewed. An effective evaluation typically uses various complementary metrics.

In QTA, a before-and-after dynamic often emerges, encapsulating the transformation from raw data to insightful conclusions 54 . This paradigm is especially important in QTA, in which the raw textual data can be used to distil concrete answers to research questions. In the preceding section, the preliminary before phase, the process of setting up an experiment in QTA, is explored with emphasis on the importance of model training and thorough evaluation to ensure robustness. For the after phase, the focus pivots to the critical step of applying the trained model to new, unseen data, aiming to answer the research questions that guide exploration.

Research questions in QTA are often sophisticated and complex, encompassing a range of inquiries either directly related to the text being analysed or to the external phenomena the text reflects. The link between the output of QTA models and the research question is often vague and under-specified. When dealing with a complex research question, for example, the processes that govern the changing attitudes towards different migrant groups, the outcome of any one QTA model is often insufficient. Even several models might not provide a complete answer to the research question. Consequently, challenges surface during the transition from before to after, from setting up and training to applying and validating. One primary obstacle is the validation difficulty posed by the uniqueness and unseen nature of the new data.

Validating QTA models on new, unseen data introduces a layer of complexity that highlights the need for robust validation strategies, to ensure stability, generalizability and replicability of results. Although the effectiveness of a model might have been calibrated in a controlled setup, its performance can oscillate when exposed to the multifaceted layers of new real-world data. Ensuring consistent model performance is crucial to deriving meaningful conclusions aligned with the research question. This dual approach of applying the model and subsequently evaluating its performance in fresh terrains is central to the after phase of QTA. In addition to validating the models, the results that stem from the models need to be validated with respect to the research question. The results need to be representative for the data as a whole; they need to be stable such that the answer does not change if different choices are made in the before phase; and they need to provide an answer to the research question at hand.

This section provides a road map for navigating the application of QTA models to new data and a chart of methodologies for evaluating the outcomes in line with the research question (questions). The goal is to help researchers cross the bridge between the theoretical foundations of QTA and its practical implementation, illuminating the steps that support the successful application and assessment of QTA models. The ensuing discussion covers validation strategies that cater to the challenges brought forth by new data, paving the way towards more insightful analysis.

Application to new data

After the training and evaluation phases have been completed, the next step is applying the trained model to new, unseen data (Fig.  2 ). The goal is to ensure that the application aligns with the research questions and aids in extracting meaningful insights. However, applying the model to new data is not without challenges.

Although the illustration demonstrates a feature-based modelling approach, the fundamental principle remains consistent across different methodologies, be it feature-based, representation learning or generative. A critical consideration is ensuring the consistency in content and preprocessing between the training data and any new data subjected to inference.

Before application of the model, it is crucial to preprocess the new data similar to the training data. This involves routine tasks such as tokenization and lemmatization, but also demands vigilance for anomalies such as divergent text encoding formats or missing values. In such cases, additional preprocessing steps might be required and should be documented carefully to ensure reproducibility.

Another potential hurdle is the discrepancy in data distributions between the training data and new data, often referred to as domain shift. If not addressed, domain shifts may hinder the efficacy of the model. Even thematically, new data may unearth categories or motifs that were absent during training, thus challenging the interpretative effectiveness of the model. In such scenarios, transfer learning or domain adaptation techniques are invaluable tools for adjusting the model so that it aligns better with the characteristics of the new data. In transfer learning, a pre-trained model provides general language understanding and is fine-tuned with a small data set for a specific task (for example, fine-tuning a large language model such as GPT or BERT for emotion classification) 55 , 56 . Domain adaptation techniques similarly adjust a model from a source domain to a target domain; for example, an emotion classifier trained on customer reviews can be adapted to rate social media comments.

Given the iterative nature of QTA, applying a model is not necessarily an end point; it may simply be a precursor to additional refinement and analysis. Therefore, the adaptability of the validation strategies is paramount. As nuances in the new data are uncovered, validation strategies may need refinement or re-adaptation to ensure the predictions of the model remain accurate and insightful, ensuring that the answers to the research questions are precise and meaningful. Through careful application and handling of the new data, coupled with adaptable validation strategies, researchers can significantly enhance the value of their analysis in answering the research question.

Evaluation metrics

QTA models are often initially developed and validated on well-defined data sets, ensuring their reliability in controlled settings. This controlled environment allows researchers to set aside a held-out test set to gauge the performance of a model, simulating how it will fare on new data. The real world, however, is considerably more complex than any single data set can capture. The challenge is how to transition from a controlled setting to novel data sets.

One primary challenge is the mismatch between the test set and real-world texts. Even with the most comprehensive test sets, capturing the linguistic variation, topic nuance and contextual subtlety present in new data sets is not a trivial task, and researchers should not be overconfident regarding the universal applicability of a model 57 . The situation does not become less complicated when relying on pre-trained or off-the-shelf models. The original training data and its characteristics might not be transparent or known with such models. Without appropriate documentation, predicting the behaviour of a model on new data may become a speculative endeavour 58 .

The following sections summarize strategies for evaluating models on new data.

Model confidence scores

In QTA, models often generate confidence or probability scores alongside predictions, indicating the confidence of the model in its accuracy. However, high scores do not guarantee correctness and can be misleading. Calibrating the model refines these scores to align better with true label likelihoods 59 . This is especially crucial in high-stakes QTA applications such as legal or financial text analysis 60 . Calibration techniques adjust the original probability estimates, enhancing model reliability and the trustworthiness of predictions, thereby addressing potential discrepancies between the expressed confidence of the model and its actual performance.

Precision at k

Precision at k (P@ k ) is useful for tasks with rankable predictions, such as determining document relevance. P@ k measures the proportion of relevant items among the top- k ranked items, providing a tractable way to gauge the performance of a model on unseen data by focusing on a manageable subset, especially when manual evaluation of the entire data set is infeasible. Although primarily used in information retrieval and recommender system , its principles apply to QTA, in which assessing the effectiveness of a model in retrieving or categorizing relevant texts is crucial.

External feedback mechanisms

Soliciting feedback from domain experts is invaluable in evaluating models on unseen data. Domain experts can provide qualitative insights into the output of the model, identifying strengths and potential missteps. For example, in topic modelling, domain experts can assess the coherence and relevance of the generated topics. This iterative feedback helps refine the model, ensuring its robustness and relevance when applied to new, unseen data, thereby bridging the gap between model development and practical application.

Software and tools

When analysing and evaluating QTA models on unseen data, researchers often turn to specialized tools designed to increase model transparency and explain model predictions. Among these tools, LIME (Local Interpretable Model-agnostic Explanations) 61 and SHAP (SHapley Additive exPlanations) 62 have gained traction for their ability to provide insights into model behaviour per instance, which is crucial when transitioning to new data domains.

LIME focuses on the predictions of machine-learning models by creating locally faithful explanations. It operates by perturbing the input data and observing how the predictions change, making it a useful tool to understand model behaviour on unseen data. Using LIME, researchers can approximate complex models with simpler, interpretable models locally around the prediction point. By doing so, they can gain insight into how different input features contribute to the prediction of the model, which can be instrumental in understanding how a model might generalize to new, unseen data.

SHAP, by contrast, provides a unified measure of feature importance across different data types, including text. It uses game theoretic principles to attribute the output of machine-learning models to their input features. This method allows for a more precise understanding of how different words or phrases in text data influence the output of the model, thereby offering a clearer picture of the behaviour of the model on new data domains. The SHAP library provides examples of how to explain predictions from text analysis models applied to various NLP tasks including sentiment analysis, text generation and translation.

Both LIME and SHAP offer visual tools to help researchers interpret the predictions of the model, making it easier to identify potential issues when transitioning to unseen data domains. For instance, visualizations allow researchers to identify words or phrases that heavily influence the decisions of the model, which can be invaluable in understanding and adjusting the model for new text data.

Interpretation

Interpretability is paramount in QTA as it facilitates the translation of complex model outcomes into actionable insights relevant to the research questions. The nature and complexity of the research question can significantly mould the interpretation process by requiring various information signals to be extracted from the text, see, for example, ref.  63 . For example, in predicting election outcomes based on sentiments expressed in social media 64 , it is essential to account for both endorsements of parties as expressed in the text and a count of individuals (that is, statistical signals) to avoid the results being skewed because some individuals make a high number of posts. It is also important to note whether voters of some political parties are under-represented in the data.

The complexity amplifies when delving into understanding why people vote (or do not vote) for particular parties and what arguments sway their decisions. Such research questions demand a more comprehensive analysis, often necessitating the amalgamation of insights from multiple models, for example, argument mining, aspect-based sentiment analysis and topic models. There is a discernible gap between the numerical or categorical outputs of QTA models — such as classification values, proportions of different stances or vectors representing individual words — and the nuanced understanding required to fully address the research question. This understanding is achieved either using qualitative human analysis or applying additional QTA methods and extracts a diverse set of important arguments in support of different stances, or provides qualitative summaries of a large set of different comments. Because it is not only a matter of ‘what’ results are found using QTA, but the value that can be attributed to those results.

When interpreting the results of a computational model applied to textual data for a specific research question, it is important to consider the completeness of the answer (assess whether the output of the model sufficiently addresses the research question or whether there are aspects left unexplored), the necessity of additional models (determine whether the insights from more models are needed to fully answer the research question), the independence or co-dependence of results (in cases in which multiple models are used, ascertain whether their results are independent or co-dependent and adjust for any overlap in insights accordingly), clarify how the results are used to support an answer (such as the required occurrence of a phenomenon in the text to accept a concept, or how well a derived topic is understood and represented) and the effect of methodology (evaluate the impact of the chosen method or preprocessing on the results, ensuring the reproducibility and robustness of the findings against changes in preprocessing or methods).

Using these considerations alongside techniques such as LIME and SHAP enhances the evaluation of the application of the model. For instance, in a scenario in which a QTA model is used to analyse customer reviews, LIME and SHAP could provide nuanced insights on a peer-review basis and across all reviews, respectively. Such insights are pivotal in assessing the alignment of the model with the domain-relevant information necessary to address the research questions and in making any adjustments needed to enhance its relevance and performance. Moreover, these techniques and considerations catalyse a dialogue between model and domain experts, enabling a more nuanced evaluation that extends beyond mere quantitative metrics towards a qualitative understanding of the application of the model.

Applications

The applicability of QTA can be found in its ability to address research questions across various disciplines. Although these questions are varied and tasks exist that do not fit naturally into categories, they can be grouped into four primary tasks: extracting, categorizing, predicting and generating. Each task is important in advancing understanding of large textual data sets, either by examining phenomena specific to a text or by using texts as a proxy for phenomena outside the text.

Extracting information

In the context of QTA, information extraction goes beyond mere data retrieval; it also involves identifying and assessing patterns, structures and entities within extensive textual data sets. At its core are techniques such as frequency analysis, in which words or sets of words are counted and their occurrences plotted over periods to reveal trends or shifts in usage and syntactical analysis, which targets specific structures such as nouns, verbs and intricate patterns such as passive voice constructions. Named entity recognition pinpoints entities such as persons, organizations and locations using syntactic information and lexicons of entities.

These methodologies have proven useful in various academic domains. For example, humanities scholars have applied QTA to track the evolution of literary themes 65 . Word embedding has been used to shed light on broader sociocultural shifts such as the conceptual change of ‘racism’, or detecting moments of linguistic change in American foreign relations 40 , 66 . In a historical context, researchers have used diachronic word embeddings to scrutinize the role of abolitionist newspapers in influencing public opinion about the abolition of slavery, revealing pathways of lexical semantic influence, distinguishing leaders from followers and identifying others who stood out based on the semantic changes that swept through this period 67 . Topic modelling and topic linkage (the extent to which two topics tend to co-appear) have been applied to user comments and submissions from the ‘subreddit’ group r/TheRedPill to study how people interact with ideology 68 . In the medical domain 69 , QTA tools have been used to study narrative structures in personal birth stories. The authors utilized a topic model based on latent Dirichlet allocation (LDA) to not only represent the sequence of events in every story but also detect outlier stories using the probability of transitioning between topics.

Historically, the focus was predominantly on feature-based models that relied on manual feature engineering. Such methods were transparent but rigid, constraining the richness of the textual data. Put differently, given the labour-intensive selection of features and the need to keep them interpretable, the complexity of a text was reduced to a limited set of features. However, the advent of representation learning has catalysed a significant paradigm shift. It enables more nuanced extraction, considers contextual variations and allows for sophisticated trend analysis. Studies using these advanced techniques have been successful in, for example, analysing how gender stereotypes and attitudes towards ethnic minorities in the USA evolved during the twentieth and twenty-first centuries 38 and tracking the emergence of ideas in the domains of politics, law and business through contextual embeddings combined with statistical modelling 70 (Box  1 ).

Box 1 Using text mining to model prescient ideas

Vicinanza et al. 70 focused on the predictive power of linguistic markers within the domains of politics, law and business, positing that certain shifts in language can serve as early indicators of deeper cognitive changes. They identified two primary attributes of prescient ideas: their capacity to challenge existing contextual assumptions, and their ability to foreshadow the future evolution of a domain. To quantify this, they utilized Bidirectional Encoder Representations from Transformers, a type 2 language model, to calculate a metric termed contextual novelty to gauge the predictability of an utterance within the prevailing discourse.

Their study presents compelling evidence that prescient ideas are more likely to emerge from the periphery of a domain than from its core. This suggests that prescience is not solely an individual trait but also significantly influenced by contextual factors. Thus, the researchers extended the notion of prescience to include the environments in which innovative ideas are nurtured, adding another layer to our understanding of how novel concepts evolve and gain acceptance.

Categorizing content

It remains an indispensable task in QTA to categorize content, especially when dealing with large data sets. The challenge is not only logistical but also methodological, demanding sophisticated techniques to ensure precision and utility. Text classification algorithms, supervised or unsupervised, continue to have a central role in labelling and organizing content. They serve crucial functions beyond academic settings; for instance, digital libraries use these algorithms to manage and make accessible their expansive article collections. These classification systems also contribute significantly to the systematic review of the literature, enabling more focused and effective investigations of, for example, medical systematic reviews 71 . In addition, unsupervised techniques such as topic modelling have proven invaluable in uncovering latent subject matter within data sets 72 (Box  2 ). This utility extends to multiple scenarios, from reducing redundancies in large document sets to facilitating the analysis of open-ended survey responses 73 , 74 .

Earlier approaches to categorization relied heavily on feature-based models that used manually crafted features for organization. This traditional paradigm has been disrupted by advances in representation learning, deep neural networks and word embeddings, which has introduced a new age of dynamic unsupervised and semi-supervised techniques for content categorization. GPT models represent another leap forward in text classification tasks, outpacing existing benchmarks across various applications. From the sentiment analysis to text labelling and psychological construct detection, generative models have demonstrated a superior capability for context understanding, including the ability to parse complex linguistic cues such as sarcasm and mixed emotions 75 , 76 , 77 . Although the validity of these models is a matter of debate, they offer explanations for their reasoning, which adds a layer of interpretability.

Box 2 Exploring molecular data with topic modelling

Schneider et al. 72 introduced a novel application of topic modelling to the field of medicinal chemistry. The authors adopt a probabilistic topic modelling approach to organize large molecular data sets into chemical topics, enabling the investigation of relationships between these topics. They demonstrate the effectiveness of the quantitative text analysis method in identifying and retrieving chemical series from molecular sets. The authors are able to reproduce concepts assigned by humans in the identification and retrieval of chemical series from sets of molecules. Using topic modelling, the authors are able to show chemical topics intuitively with data visualization and efficiently extend the method to a large data set (ChEMBL22) containing 1.6 million molecules.

Predicting outcomes

QTA is not limited to understanding or classifying text but extends its reach into predictive analytics, which is an invaluable tool across many disciplines and industries. In the financial realm, sentiment analysis tools are applied to news articles and social media data to anticipate stock market fluctuations 78 . Similarly, political analysts use sentiment analysis techniques to make election forecasts, using diverse data sources ranging from Twitter (now X) feeds to party manifestos 79 . Authorship attribution offers another intriguing facet, in which predictive abilities of the QTA are harnessed to identify potential authors of anonymous or pseudonymous works 80 . A notable instance was the unmasking of J.K. Rowling as the author behind the pseudonym Robert Galbraith 81 . Health care has also tapped into predictive strengths of the QTA: machine-learning models that integrate natural language and binary features from patient records have been shown to have potential as early warning systems to prevent unnecessary mechanical restraint of psychiatric inpatients 82 (Box  3 ).

In the era of feature-based models, predictions often hinged on linear or tree-based structures using manually engineered features. Representation learning introduced embeddings and sequential models that improved prediction capabilities. These learned representations enrich predictive tasks, enhancing accuracy and reliability while decreasing interpretability.

Box 3 Predicting mechanical restraint: assessing the contribution of textual data

Danielsen et al. 82 set out to assess the potential of electronic health text data to predict incidents of mechanical restraint of psychiatric patients. Mechanical restraint is used during inpatient treatments to avert potential self-harm or harm to others. The research team used feature-based supervised machine learning to train a predictive model on clinical notes and health records from the Central Denmark Region, specifically focusing on the first hour of admission data. Of 5,050 patients and 8,869 admissions, 100 patients were subjected to mechanical restraint between 1 h and 3 days after admission. Impressively, a random forest algorithm could predict mechanical restraint with considerable precision, showing an area under the curve of 0.87. Nine of the ten most influential predictors stemmed directly from clinical notes, that is, unstructured textual data. The results show the potential of textual data for the creation of an early detection system that could pave the way for interventions that minimize the use of mechanical restraint. It is important to emphasize that the model was limited by a narrow scope of data from the Central Denmark Region, and by the fact that only initial mechanical restraint episodes were considered (in other words, recurrent incidents were not included in the study).

Generating content

Although the initial QTA methodologies were not centred on content generation, the rise of generative models has been transformative. Models such as GPT-4 and Llama2 (ref. 20 ) have brought forth previously unimagined capabilities, expanding the potential of QTA to create content, including coherent and contextually accurate paragraphs to complete articles. Writers and content creators are now using tools based on models such as GPT-4 to augment their writing processes by offering suggestions or even drafting entire sections of texts. In education, such models aid in developing customized content for students, ensuring adaptive learning 83 . The capacity to create synthetic data also heralds new possibilities. Consider the domain of historical research, in which generative models can simulate textual content, offering speculative yet data-driven accounts of alternate histories or events that might have been; for example, relying on generative models to create computational software agents that simulate human behaviour 84 . However, the risks associated with text-generating models are exemplified by a study in which GPT-3 was used for storytelling. The generated stories were found to exhibit many known gender stereotypes, even when prompts did not contain explicit gender cues or stereotype-related content 85 .

Reproducibility and data deposition

Given the rapidly evolving nature of the models, methods and practices in QTA, reproducibility is essential for validating the results and creating a foundation upon which other researchers can build. Sharing code and trained models in well-documented repositories are important to enable reproducible experiments. However, sharing and depositing raw data can be challenging, owing to the inherent limitations of unstructured data and regulations related to proprietary and sensitive data.

Code and model sharing

In QTA research, using open source code has become the norm and the need to share models and code to foster innovation and collaboration has been widely accepted. QTA is interdisciplinary by nature, and by making code and models public, the field has avoided unnecessary silos and enabled collaboration between otherwise disparate disciplines. A further benefit of open source software is the flexibility and transparency that comes from freely accessing and modifying software to meet specific research needs. Accessibility enables an iterative feedback loop, as researchers can validate, critique and build on the existing work. Software libraries, such as scikit-learn, that have been drivers for adopting machine learning in QTA are testimony to the importance of open source software 21 .

Sharing models is not without challenges. QTA is evolving rapidly, and models may use specific versions of software and hardware configurations that no longer work or that yield different results with other versions or configurations. This variability can complicate the accessibility and reproducibility of research results. The breakthroughs of generative AI in particular have introduced new proprietary challenges to model sharing as data owners and sources raise objections to the use of models that have been trained on their data. This challenge is complicated, but fundamentally it mirrors the disputes about intellectual property rights and proprietary code in software engineering. Although QTA as a field benefits from open source software, individual research institutions may have commercial interests or intellectual property rights related to their software.

On the software side, there is currently a preference for scripting languages, especially Python, that enable rapid development, provide access to a wide selection of software libraries and have a large user community. QTA is converging towards code and model sharing through open source platforms such as GitHub and GitLab with an appropriate open source software license such as the MIT license . Models often come with additional disclaimers or use-based restrictions to promote responsible use of AI, such as in the RAIL licenses . Pre-trained models are also regularly shared on dedicated machine-learning platforms such as Hugging Face 22 to enable efficient fine-tuning and deployment. It is important to emphasize that although these platforms support open science, these services are provided by companies with commercial interests. Open science platforms such as Zenodo and OSF can also be used to share code and models for the purpose of reproducibility.

Popular containerization software has been widely adopted in the machine-learning community and has spread to QTA. Containerization, that is, packaging all parts of a QTA application — including code and other dependencies — into a single standalone unit ensures that model and code run consistently across various computing environments. It offers a powerful solution to challenges such as reproducibility, specifically variability in software and hardware configurations.

Data management and storage

Advances in QTA in recent years are mainly because of the availability of vast amounts of text data and the rise of deep learning techniques. However, the dependency on large unstructured data sets, many of which are proprietary or sensitive, poses unique data management challenges. Pre-trained models irrespective of their use (for example, representation learning or generative) require extensive training on large data sets. When these data sets are proprietary or sensitive, they cannot be readily available, which limits the ability of researchers to reproduce results and develop competitive models. Furthermore, models trained on proprietary data sets often lack transparency regarding their collection and curation processes, which can hide potential biases in the data. Finally, there can be data privacy issues related to training or using models that are trained on sensitive data. Individuals whose data are included may not have given their explicit consent for their information to be used in research, which can pose ethical and legal challenges.

It is a widely adopted practice in QTA to share data and metadata with an appropriate license whenever possible. Data can be deposited in open science platforms such as Zenodo, but specialized machine-learning platforms are also used for this purpose. However, it should be noted that QTA data are rarely unique, unlike experimental data collected through random controlled trials. In many cases, access to appropriate metadata and documentation would enable the data to be reconstructed. In almost all cases, it is therefore strongly recommended that researchers share metadata and documentation for data, as well as code and models, using a standardized document or framework, a so-called datasheet. Although QTA is not committed to one set of principles for (meta)data management, European research institutions are increasingly adopting the FAIR principles 50 .

Documentation

Although good documentation is vital in all fields of software development and research, the reliance of QTA on code, models and large data sets makes documentation particularly crucial for reproducibility. Popular resources for structuring projects include project templating tools and documentation generators such as Cookiecutter and Sphinx . Models are often documented with model cards that provide a detailed overview of the development, capabilities and biases of the model to promote transparency and accountability 86 . Similarly, datasheets or data cards can be used to promote transparency for data used in QTA 87 . Finally, it is considered good practice to provide logs for models that document parameters, metrics and events for QTA experiments, especially during training and fine-tuning. Although not strictly required, logs are also important for documenting the iterative process of model refinement. There are several platforms that support the creation and visualization of training logs ( Weights & Biases and MLflow ).

Limitations and optimizations

The application of QTA requires scrutiny of its inherent limitations and potentials. This section discusses these aspects and elucidates the challenges and opportunities for further refinement.

Limitations in QTA

Defining research questions.

In QTA, the framing of research questions is often determined by the capabilities and limitations of the available text analysis tools, rather than by intellectual inquiry or scientific curiosity. This leads to task-driven limitations, in which inquiry is confined to areas where the tools are most effective. For example, relying solely on bag-of-words models might skew research towards easily quantifiable aspects, distorting the intellectual landscape. Operationalizing broad and nuanced research questions into specific tasks may strip them of their depth, forcing them to conform to the constraints of existing analytical models 88 .

Challenges in interpretation

The representation of language of underlying phenomena is often ambiguous or indirect, requiring careful interpretation. Misinterpretations can arise, leading to challenges related to historical, social and cultural context of a text, in which nuanced meanings that change across time, class and cultures are misunderstood 89 . Overlooking other modalities such as visual or auditory information can lead to a partial understanding of the subject matter and limit the full scope of insights. This can to some extent be remedied by the use of grounded models (such as GPT-4), but it remains a challenge for the community to solve long term.

Determining reliability and validation

The reliability and stability of the conclusions drawn from the QTA require rigorous validation, which is often neglected in practice. Multiple models, possibly on different types of data, should be compared to ensure that conclusions are not artefacts of a particular method or of a different use of the method. Furthermore, cultural phenomena should be evolved to avoid misguided insights. Building a robust framework that allows testing and comparison enhances the integrity and applicability of QTA in various contexts 90 .

Connecting analysis to cultural insights

Connecting text analysis to larger cultural claims necessitates foundational theoretical frameworks, including recognizing linguistic patterns, sociolinguistic variables and theories of cultural evolution that may explain changes. Translating textual patterns into meaningful cultural observations requires understanding how much (or how little) culture is expressed in text so that findings can be generalized beyond isolated observations. A theoretical foundation is vital to translate textual patterns into culturally relevant insights, making QTA a more effective tool for broader cultural analysis.

Balancing factors in machine learning

Balancing factors is critical in aligning machine-learning techniques with research objectives. This includes the trade-off between quality and control. Quality refers to rigorous, robust and valid findings, and control refers to the ability to manage specific variables for clear insights. It is also vital to ensure a balance between quantity and quality in data source to lead to more reliable conclusions. Balance is also needed between correctness and accuracy, in which the former ensures consistent application of rules, and the latter captures the true nature of the text.

From features-based to generative models

QTA has undergone a profound evolution, transitioning from feature-based approaches to representation learning and finally to generative models. This progression demonstrates growing complexity in our understanding of language, reflecting the maturity in the field of QTA. Each stage has its characteristics, strengths and limitations.

In the early stages, feature-based models were both promising and limiting. The simplicity of their design, relying on explicit feature engineering, allowed for the targeted analysis. However, this simplicity limited their ability to grasp complex, high-level patterns in language. For example, the use of bag-of-words models in the sentiment analysis showcased direct applicability, but also revealed limitations in understanding contextual nuances. The task-driven limitations of these models sometimes overshadowed genuine intellectual inquiry. Using a fixed (often modern) list of words with corresponding emotional valences may limit our ability to fully comprehend the complexity of emotional stances in, for example, historical literature. Despite these drawbacks, the ability to customize features provided researchers with a direct and specific understanding of language phenomena that could be informed by specialized domain knowledge 91 .

With the emergence of representation learning, a shift occurred within the field of QTA. These models offered the ability to capture higher-level abstractions, forging a richer understanding of language. Their scalability to handle large data sets and uncover complex relationships became a significant strength. However, this complexity introduced new challenges, such as a loss of specificity in analysis and difficulties in translating broad research questions into specific tasks. Techniques such as Word2Vec enabled the capture of semantic relationships but made it difficult to pinpoint specific linguistic features. Contextualized models, in turn, allow for more specificity, but are typically pre-trained on huge data sets (not available for scrutiny) and then applied to a research question without any discussion of how well the model fits the data at hand. In addition, these contextualized models inundate with information. Instead of providing one representation for a word (similar to Word2Vec does), they provide one representation for each occurrence of the word. Each of these representations is one order of magnitude larger than vectors typical for Word2Vec (768–1,600 dimensions compared with 50–200) and comes in several varieties, one for each of the layers of the mode, typically 12.

The introduction of generative models represents the latest stage of this evolution, providing even greater complexity and potential. Innovative in their design, generative models provide opportunities to address more complex and open-ended research questions. They fuel the generation of new ideas and offer avenues for novel approaches. However, these models are not without their challenges. Their high complexity can make interpretation and validation demanding, and if not properly managed, biases and ethical dilemmas will emerge. The use of generative models in creating synthetic text must be handled with care to avoid reinforcing stereotypes or generating misleading information. In addition, if the enormous amounts of synthetically generated text are used to further train the models, this will lead to a spiral of decaying quality as eventually a majority of the training data will have been generated by machines (the models often fail to distinguish synthetic text from genuine human-created text) 92 . However, it will also allow researchers to draw insights from a machine that is learning on data it has generated itself.

The evolution from feature-based to representation learning to generative models reflects increasing maturity in the field of QTA. As models become more complex, the need for careful consideration, ethical oversight and methodological innovation intensifies. The challenge now lies in ensuring that these methodologies align with intellectual and scientific goals, rather than being constrained by their inherent limitations. This growing complexity mirrors the increasing demands of this information-driven society, requiring interdisciplinary collaboration and responsible innovation. Generative models require a nuanced understanding of the complex interplay between language, culture, time and society, and a clear recognition of constraints of the QTA. Researchers must align their tools with intellectual goals and embrace active efforts to address the challenges through optimization strategies. The evolution in QTA emphasizes not only technological advances but also the necessity of aligning the ever-changing landscape of computational methodologies with research questions. By focusing on these areas and embracing the accompanying challenges, the field can build robust, reliable conclusions and move towards more nuanced applications of the text analysis. This progress marks a significant step towards an enriched exploration of textual data, widening the scope for understanding multifaceted relationships. The road ahead calls for a further integration of theory and practice. It is essential that evolution of QTA ensures that technological advancement serves both intellectual curiosity and ethical responsibility, resonating with the multifaceted dynamics of language, culture, time and society 93 .

Balancing size and quality

In QTA, the relationship between data quantity and data quality is often misconceived. Although large data sets serve as the basis for training expansive language models, they are not always required when seeking answers to nuanced research questions. The wide-ranging scope of large data sets can offer comprehensive insights into broad trends and general phenomena. However, this often comes at the cost of a detailed understanding of context-specific occurrences. An issue such as  frequency bias exemplifies this drawback. Using diverse sampling strategies, such as stratified sampling to ensure representation across different social groups and bootstrapping methods to correct for selection bias, can offer a more balanced, contextualized viewpoint. Also, relying on methods such as burst or change-point detection can help to pinpoint moments of interest in data sets with a temporal dimension. Triangulating these methods across multiple smaller data sets can enhance reliability and depth of the analysis.

The design of machine-learning models should account for both the frequency and the significance of individual data points. In other words, the models should be capable of learning not just from repetitive occurrences but also from singular, yet critical, events. This enables the machine to understand rare but important phenomena such as revolutions, seminal publications or watershed individual actions, which would typically be overlooked in a conventional data-driven approach. The capacity to learn from such anomalies can enhance the interpretative depth of the model, enabling them to offer more nuanced insights.

Although textual data have been the mainstay for computational analyses, it is not the only type of data that matters, especially when the research questions involve cultural and societal nuances. Diverse data types including images, audio recordings and even physical artefacts should be integrated into the research to provide a more rounded analysis. Additionally, sourcing data from varied geographical and sociocultural contexts can bring multiple perspectives into the frame, thus offering a multifaceted understanding that textual data from English sources alone cannot capture.

Ethical, practical and efficient models

The evolving landscape of machine learning, specifically with respect to model design and utility, reflects a growing emphasis on efficiency and interpretive value. One notable shift is towards smaller, more energy-efficient models. This transition is motivated by both environmental sustainability and economic pragmatism. With computational costs soaring and the environmental toll becoming untenable, the demand for smaller models that maintain or even exceed the quality of larger models is escalating 94 .

Addressing the data sources used to train models is equally critical, particularly when considering models that will serve research or policy purposes. The provenance and context of data dictate its interpretive value, requiring models to be designed with a hierarchical evaluation of data sources. Such an approach could improve the understanding of a model of the importance of each data type given a specific context, thereby improving the quality and reliability of its analysis. Additionally, it is important to acknowledge the potential ethical and legal challenges within this process, including the exploitation of workers during the data collection and model development.

Transparency remains another pressing issue as these models become integral to research processes. Future iterations should feature a declaration of content that enumerates not only the origin of the data but also its sociocultural and temporal context, preprocessing steps, any known biases, along with the analytical limitations of the model. This becomes especially important for generative models, which may produce misleading or even harmful content if the original data sources are not properly disclosed and understood. Important steps have already been taken with the construction of model cards and data sheets 95 .

Finally, an emergent concern is the risk of feedback loops compromising the quality of machine-learning models. If a model is trained on its own output, errors and biases risk being amplified over time. This necessitates constant vigilance as it poses a threat to the long-term reliability and integrity of AI models. The creation of a gold-standard version of the Internet, not polluted by AI-generated data, is also important 96 .

Refining the methodology and ethos

The rapid advances in QTA, particularly the rise of generative models, have opened up a discourse that transcends mere technological prowess. Although earlier feature-based models require domain expertise and extensive human input before they could be used, generative models can already generate convincing output based on relatively short prompts. This shift raises crucial questions about the interplay between machine capability and human expertise. The notion that advanced algorithms might eventually replace researchers is a common misplaced apprehension. These algorithms and models should be conceived as tools to enhance human scholarship by automating mundane tasks, spawning new research questions and even offering novel pathways for data analysis that might be too complex or time-consuming for human cognition.

This paradigm shift towards augmentative technologies introduces a nuanced problem-solving framework that accommodates the complexities intrinsic to studying human culture and behaviour. The approach of problem decomposition, a cornerstone in computer science, also proves invaluable here, converting overarching research queries into discrete, operationalizable components. These elements can then be addressed individually through specialized algorithms or models, whose results can subsequently be synthesized into a comprehensive answer. As we integrate increasingly advanced tuning methods into generative models — such as prompt engineering, retrieval augmented generation and parameter-efficient fine-tuning — it is important to remember that these models are tools, not replacements. They are most effective when employed as part of a broader research toolkit, in which their strengths can complement traditional scholarly methods.

Consequently, model selection becomes pivotal and should be intricately aligned with the nature of the research inquiry. Unsupervised learning algorithms such as clustering are well suited to exploratory research aimed at pattern identification. Conversely, confirmatory questions, which seek to validate theories or test hypotheses, are better addressed through supervised learning models such as regression.

The importance of a well-crafted interpretation stage cannot be overstated. This is where the separate analytical threads are woven into a comprehensive narrative that explains how the individual findings conjoin to form a cohesive answer to the original research query. However, the lack of standardization across methodologies is a persistent challenge. This absence hinders the reliable comparison of research outcomes across various studies. To remedy this, a shift towards establishing guidelines or best practices is advocated. These need not be rigid frameworks but could be adapted to fit specific research contexts, thereby ensuring methodological rigor alongside innovative freedom.

Reflecting on the capabilities and limitations of current generative models in QTA research is crucial. Beyond recognizing their utility, the blind spots — questions they cannot answer and challenges they have yet to overcome — need to be addressed 97 , 98 . There is a growing need to tailor these models to account for nuances such as frequency bias and to include various perspectives, possibly through more diverse data sets or a polyvocal approach.

In summary, a multipronged approach that synergizes transparent and informed data selection, ethical and critical perspectives on model building and selection, and an explicit and reproducible result interpretation offers a robust framework for tackling intricate research questions. By adopting such a nuanced strategy, we make strides not just in technological capability but also in the rigor, validity and credibility of QTA as a research tool.

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Acknowledgements

K.L.N. was supported by grants from the Velux Foundation (grant title: FabulaNET) and the Carlsberg Foundation (grant number: CF23-1583). N.T. was supported by the research programme Change is Key! supported by Riksbankens Jubileumsfond (grant number: M21-0021).

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Kristoffer L. Nielbo

Meertens Institute, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands

Folgert Karsdorp

Department of History, University of Amsterdam, Amsterdam, The Netherlands

Melvin Wevers

Institute of History, Leiden University, Leiden, The Netherlands

Alie Lassche

Department of Linguistics, Aarhus University, Aarhus, Denmark

Rebekah B. Baglini

Department of Literature, University of Antwerp, Antwerp, Belgium

Mike Kestemont

Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Gothenburg, Sweden

Nina Tahmasebi

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Introduction (K.L.N. and F.K.); Experimentation (K.L.N., F.K., M.K. and R.B.B.); Results (F.K., M.K., R.B.B. and N.T.); Applications (K.L.N., M.W. and A.L.); Reproducibility and data deposition (K.L.N. and A.L.); Limitations and optimizations (M.W. and N.T.); Outlook (M.W. and N.T.); overview of the Primer (K.L.N.).

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Correspondence to Kristoffer L. Nielbo .

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A set of rules, protocols and tools for building software and applications, which programs can query to obtain data.

A model that represents text as a numerical vector based on word frequency or presence. Each text corresponds to a predefined vocabulary dictionary, with the vector.

Intersection of linguistics, computer science and artificial intelligence that is concerned with computational aspects of human language. It involves the development of algorithms and models that enable computers to understand, interpret and generate human language.

The branch of linguistics that studies language as expressed in corpora (samples of real-world text) and uses computational methods to analyse large collections of textual data.

A technique used to increase the size and diversity of language data sets to train machine-learning models.

The application of statistical, analytical and computational techniques to extract insights and knowledge from data.

( κ ). A statistical measure used to assess the reliability of agreement between multiple raters when assigning categorical ratings to a number of items.

A phenomenon in which elements that are over-represented in a data set receive disproportionate attention or influence in the analysis.

A field of study focused on the science of searching for information within documents and retrieving relevant documents from large databases.

A text normalization technique used in natural language processing in which words are reduced to their base or dictionary form.

In quantitative text analysis, machine learning refers to the application of algorithms and statistical models to enable computers to identify patterns, trends and relationships in textual data without being explicitly programmed. It involves training these models on large data sets to learn and infer from the structure and nuances of language.

A field of artificial intelligence using computational methods for analysing and generating natural language and speech.

A type of information filtering system that seeks to predict user preferences and recommend items (such as books, movies and products) that are likely to be of interest to the user.

A set of techniques in machine learning in which the system learns to automatically identify and extract useful features or representations from raw data.

A text normalization technique used in natural language processing, in which words are reduced to their base or root form.

A machine-learning approach in which models are trained on labelled data, such that each training text is paired with an output label. The model learns to predict the output from the input data, with the aim of generalizing the training set to unseen data.

A deep learning model that handles sequential data, such as text, using mechanisms called attention and self-attention, allowing it to weigh the importance of different parts of the input data. In the quantitative text analysis, transformers are used for tasks such as sentiment analysis, text classification and language translation, offering superior performance in understanding context and nuances in large data sets.

A type of machine learning in which models are trained on data without output labels. The goal is to discover underlying patterns, groupings or structures within the data, often through clustering or dimensionality reduction techniques.

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Nielbo, K.L., Karsdorp, F., Wevers, M. et al. Quantitative text analysis. Nat Rev Methods Primers 4 , 25 (2024). https://doi.org/10.1038/s43586-024-00302-w

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10 Quantitative Data Analysis Software for Every Data Scientist

Are you curious about digging into data but not sure where to start? Don’t worry; we’ve got you covered! As a data scientist, you know that having the right tools can make all the difference in the world. When it comes to analyzing quantitative data, having the right quantitative data analysis software can help you extract insights faster and more efficiently. 

From spotting trends to making smart decisions, quantitative analysis helps us unlock the secrets hidden within our data and chart a course for success.

In this blog post, we’ll introduce you to 10 quantitative data analysis software that every data scientist should know about.

What is Quantitative Data Analysis?

Quantitative data analysis refers to the process of systematically examining numerical data to uncover patterns, trends, relationships, and insights. 

Unlike analyzing qualitative data, which deals with non-numeric data like text or images, quantitative research focuses on data that can be quantified, measured, and analyzed using statistical techniques.

What is Quantitative Data Analysis Software?

Quantitative data analysis software refers to specialized computer programs or tools designed to assist researchers, analysts, and professionals in analyzing numerical data. 

These software applications are tailored to handle quantitative data, which consists of measurable quantities, counts, or numerical values. Quantitative data analysis software provides a range of features and functionalities to manage, analyze, visualize, and interpret numerical data effectively.

Key features commonly found in quantitative data analysis software include:

• Data Import and Management: Capability to import data from various sources such as spreadsheets, databases, text files, or online repositories. 
• Descriptive Statistics: Tools for computing basic descriptive statistics such as measures of central tendency (e.g., mean, median, mode) and measures of dispersion (e.g., standard deviation, variance).
• Data Visualization: Functionality to create visual representations of data through charts, graphs, histograms, scatter plots, or heatmaps. 
• Statistical Analysis: Support for conducting a wide range of statistical tests and analyses to explore relationships, test hypotheses, make predictions, or infer population characteristics from sample data.
• Advanced Analytics: Advanced analytical techniques for more complex data exploration and modeling, such as cluster analysis, principal component analysis (PCA), time series analysis, survival analysis, and structural equation modeling (SEM).
• Automation and Reproducibility: Features for automating analysis workflows, scripting repetitive tasks, and ensuring the reproducibility of results. 
• Reporting and Collaboration: Tools for generating customizable reports, summaries, or presentations to communicate analysis results effectively to stakeholders.

Benefits of Quantitative Data Analysis

Quantitative data analysis offers numerous benefits across various fields and disciplines. Here are some of the key advantages:

Making Confident Decisions

Quantitative data analysis provides solid, evidence-based insights that support decision-making. By relying on data rather than intuition, you can reduce the risk of making incorrect decisions. This not only increases confidence in your choices but also fosters buy-in from stakeholders and team members.

Cost Reduction

Analyzing quantitative data helps identify areas where costs can be reduced or optimized. For instance, if certain marketing campaigns yield lower-than-average results, reallocating resources to more effective channels can lead to cost savings and improved ROI.

Personalizing User Experience

Quantitative analysis allows for the mapping of customer journeys and the identification of preferences and behaviors. By understanding these patterns, businesses can tailor their offerings, content, and communication to specific user segments, leading to enhanced user satisfaction and engagement.

Improving User Satisfaction and Delight

Quantitative data analysis highlights areas of success and areas for improvement in products or services. For instance, if a webpage shows high engagement but low conversion rates, further investigation can uncover user pain points or friction in the conversion process. Addressing these issues can lead to improved user satisfaction and increased conversion rates.

Best 10 Quantitative Data Analysis Software

1. questionpro.

Known for its robust survey and research capabilities, QuestionPro is a versatile platform that offers powerful data analysis tools tailored for market research, customer feedback, and academic studies. With features like advanced survey logic, data segmentation, and customizable reports, QuestionPro empowers users to derive actionable insights from their quantitative data.

Features of QuestionPro

• Customizable Surveys
• Advanced Question Types:
• Survey Logic and Branching
• Data Segmentation
• Real-Time Reporting
• Mobile Optimization
• Integration Options
• Multi-Language Support
• Data Export
• User-friendly interface.
• Extensive question types.
• Seamless data export capabilities.
• Limited free version.

Pricing : 

Starts at $99 per month per user.

2. SPSS (Statistical Package for the Social Sciences

SPSS is a venerable software package widely used in the social sciences for statistical analysis. Its intuitive interface and comprehensive range of statistical techniques make it a favorite among researchers and analysts for hypothesis testing, regression analysis, and data visualization tasks.

• Advanced statistical analysis capabilities.
• Data management and manipulation tools.
• Customizable graphs and charts.
• Syntax-based programming for automation.
• Extensive statistical procedures.
• Flexible data handling.
• Integration with other statistical software package
• High cost for the full version.
• Steep learning curve for beginners.

Pricing: 

• Starts at $99 per month.

3. Google Analytics

Primarily used for web analytics, Google Analytics provides invaluable insights into website traffic, user behavior, and conversion metrics. By tracking key performance indicators such as page views, bounce rates, and traffic sources, Google Analytics helps businesses optimize their online presence and maximize their digital marketing efforts.

• Real-time tracking of website visitors.
• Conversion tracking and goal setting.
• Customizable reports and dashboards.
• Integration with Google Ads and other Google products.
• Free version available.
• Easy to set up and use.
• Comprehensive insights into website performance.
• Limited customization options in the free version.
• Free for basic features.

Hotjar is a powerful tool for understanding user behavior on websites and digital platforms. Hotjar enables businesses to visualize how users interact with their websites, identify pain points, and optimize the user experience for better conversion rates and customer satisfaction through features like heatmaps, session recordings, and on-site surveys.

• Heatmaps to visualize user clicks, taps, and scrolling behavior.
• Session recordings for in-depth user interaction analysis.
• Feedback polls and surveys.
• Funnel and form analysis.
• Easy to install and set up.
• Comprehensive insights into user behavior.
• Affordable pricing plans.
• Limited customization options for surveys.

Starts at $39 per month.

While not a dedicated data analysis software, Python is a versatile programming language widely used for data analysis, machine learning, and scientific computing. With libraries such as NumPy, pandas, and matplotlib, Python provides a comprehensive ecosystem for data manipulation, visualization, and statistical analysis, making it a favorite among data scientists and analysts.

• The rich ecosystem of data analysis libraries.
• Flexible and scalable for large datasets.
• Integration with other tools and platforms.
• Open-source with a supportive community.
• Free and open-source.
• High performance and scalability.
• Great for automation and customization.
• Requires programming knowledge.
• It is Free for the beginners.

6. SAS (Statistical Analysis System)

SAS is a comprehensive software suite renowned for its advanced analytics, business intelligence, and data management capabilities. With a wide range of statistical techniques, predictive modeling tools, and data visualization options, SAS is trusted by organizations across industries for complex data analysis tasks and decision support.

• Wide range of statistical procedures.
• Data integration and cleansing tools.
• Advanced analytics and machine learning capabilities.
• Scalable for enterprise-level data analysis.
• Powerful statistical modeling capabilities.
• Excellent support for large datasets.
• Trusted by industries for decades.
• Expensive licensing fees.
• Steep learning curve.
• Contact sales for pricing details.

Despite its simplicity compared to specialized data analysis software, Excel remains popular for basic quantitative analysis and data visualization. With features like pivot tables, functions, and charting tools, Excel provides a familiar and accessible platform for users to perform tasks such as data cleaning, summarization, and exploratory analysis.

• Formulas and functions for calculations.
• Pivot tables and charts for data visualization.
• Data sorting and filtering capabilities.
• Integration with other Microsoft Office applications.
• Widely available and familiar interface.
• Affordable for basic analysis tasks.
• Versatile for various data formats.
• Limited statistical functions compared to specialized software.
• Not suitable for handling large datasets.
• Included in Microsoft 365 subscription plans, starts at $6.99 per month.

8. IBM SPSS Statistics

Building on the foundation of SPSS, IBM SPSS Statistics offers enhanced features and capabilities for advanced statistical analysis and predictive modeling. With modules for data preparation, regression analysis, and survival analysis, IBM SPSS Statistics is well-suited for researchers and analysts tackling complex data analysis challenges.

• Advanced statistical procedures.
• Data preparation and transformation tools.
• Automated model building and deployment.
• Integration with other IBM products.
• Extensive statistical capabilities.
• User-friendly interface for beginners.
• Enterprise-grade security and scalability.
• Limited support for open-source integration.

Minitab is a specialized software package designed for quality improvement and statistical analysis in manufacturing, engineering, and healthcare industries. With tools for experiment design, statistical process control, and reliability analysis, Minitab empowers users to optimize processes, reduce defects, and improve product quality.

• Basic and advanced statistical analysis.
• Graphical analysis tools for data visualization.
• Statistical methods improvement.
• DOE (Design of Experiments) capabilities.
• Streamlined interface for statistical analysis.
• Comprehensive quality improvement tools.
• Excellent customer support.
• Limited flexibility for customization.

Pricing:  

• Starts at $29 per month.

JMP is a dynamic data visualization and statistical analysis tool developed by SAS Institute. Known for its interactive graphics and exploratory data analysis capabilities, JMP enables users to uncover patterns, trends, and relationships in their data, facilitating deeper insights and informed decision-making.

• Interactive data visualization.
• Statistical modeling and analysis.
• Predictive analytics and machine learning.
• Integration with SAS and other data sources.
• Intuitive interface for exploratory data analysis.
• Dynamic graphics for better insights.
• Integration with SAS for advanced analytics.
• Limited scripting capabilities.
• Less customizable compared to other SAS products.

QuestionPro is Your Right Quantitative Data Analysis Software?

QuestionPro offers a range of features specifically designed for quantitative data analysis, making it a suitable choice for various research, survey, and data-driven decision-making needs. Here’s why it might be the right fit for you:

Comprehensive Survey Capabilities

QuestionPro provides extensive tools for creating surveys with quantitative questions, allowing you to gather structured data from respondents. Whether you need Likert scale questions, multiple-choice questions, or numerical input fields, QuestionPro offers the flexibility to design surveys tailored to your research objectives.

Real-Time Data Analysis 

With QuestionPro’s real-time data collection and analysis features, you can access and analyze survey responses as soon as they are submitted. This enables you to quickly identify trends, patterns, and insights without delay, facilitating agile decision-making based on up-to-date information.

Advanced Statistical Analysis

QuestionPro includes advanced statistical analysis tools that allow you to perform in-depth quantitative analysis of survey data. Whether you need to calculate means, medians, standard deviations, correlations, or conduct regression analysis, QuestionPro offers the functionality to derive meaningful insights from your data.

Data Visualization

Visualizing quantitative data is crucial for understanding trends and communicating findings effectively. QuestionPro offers a variety of visualization options, including charts, graphs, and dashboards, to help you visually represent your survey data and make it easier to interpret and share with stakeholders.

Segmentation and Filtering 

QuestionPro enables you to segment and filter survey data based on various criteria, such as demographics, responses to specific questions, or custom variables. This segmentation capability allows you to analyze different subgroups within your dataset separately, gaining deeper insights into specific audience segments or patterns.

Cost-Effective Solutions

QuestionPro offers pricing plans tailored to different user needs and budgets, including options for individuals, businesses, and enterprise-level organizations. Whether conducting a one-time survey or needing ongoing access to advanced features, QuestionPro provides cost-effective solutions to meet your requirements.

Choosing the right quantitative data analysis software depends on your specific needs, budget, and level of expertise. Whether you’re a researcher, marketer, or business analyst, these top 10 software options offer diverse features and capabilities to help you unlock valuable insights from your data.

If you’re looking for a comprehensive, user-friendly, and cost-effective solution for quantitative data analysis, QuestionPro could be the right choice for your research, survey, or data-driven decision-making needs. With its powerful features, intuitive interface, and flexible pricing options, QuestionPro empowers users to derive valuable insights from their survey data efficiently and effectively.

So go ahead, explore QuestionPro, and empower yourself to unlock valuable insights from your data!

LEARN MORE         FREE TRIAL

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Original research article, distribution of branched glycerol dialkyl glycerol tetraether (brgdgt) lipids from soils and sediments from the same watershed are distinct regionally (central chile) but not globally.

• 1 Department of Geology and Environmental Science, University of Pittsburgh, Pittsburgh, PA, United States
• 2 Departamento de Química Ambiental, Universidad Católica de la Santísima Concepción, Concepción, Chile
• 3 Centro de Investigación en Biodiversidad y Ambientes Sustentables (CIBAS), Concepción, Chile
• 4 Departamento de Sistemas Acuáticos, Facultad de Ciencias Ambientales y Centro EULA-Chile, Universidad de Concepción, Concepción, Chile
• 5 Ecogestión Ambiental Ltda., Chiguayante, Chile

Quantitative reconstructions of past continental climates are vital for understanding contemporary and past climate change. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are unique bacterial lipids that have been proposed as universal paleothermometers due to their correlation with temperature in modern settings. Thus, brGDGTs may serve as a crucial paleotemperature proxy for understanding past climate variations and improving regional climate projections, especially in critical but under constrained regions. That said, complications can arise in their application due to varying source contributions (e.g., soils vs. peats vs. lacustrine). As such, this study investigates brGDGT distributions in Chilean lake surface sediments and corresponding watershed soils to determine the source of brGDGTs to lake sediments. Global datasets of brGDGTs in lake sediments and soils were additionally compiled for comparison. Distinct brGDGT distributions in Chilean lakes and soils indicate minimal bias from soil inputs to the lacustrine sediments as well as in situ lacustrine production of brGDGTs, which supports the use of brGDGTs in lake sediments as reliable paleotemperature proxies in the region. The ΣIIIa/ΣIIa ratio, initially promising as a brGDGT source indicator in marine settings, shows global complexities in lacustrine settings, challenging the establishment of universal thresholds for source apportionment. That said, we show that the ratio can be successfully applied in Chilean lake surface sediments. Direct comparisons with watershed soils and further research are crucial for discerning brGDGT sources in lake sediments and improving paleotemperature reconstructions on regional and global scales moving forward. Overall, this study contributes valuable insights into brGDGT variability, essential for accurate paleoreconstructions.

1 Introduction

Quantitative reconstructions of past continental climates are crucial for understanding climate change and informing climate models. Branched glycerol dialkyl glycerol tetraethers (brGDGTs), cell membrane-spanning lipids unique to bacteria, have been suggested as a universal continental paleothermometer as they exhibit strong correlations with environmental variables, especially temperature (e.g., Sinninghe Damsté et al., 2000 ; Weijers et al., 2006 ; Chen et al., 2018 ; 2022 ; Halamka et al., 2023 ). The responsiveness of these lipids to changing conditions suggests they can serve as sensitive indicators of past climate variations, allowing for quantitative reconstructions of temperature changes. In particular, brGDGTs preserved in lake sediments offer high-resolution records of past temperature changes ( Castañeda and Schouten, 2011 ; Schouten et al., 2013 ). Initially, it was thought that these compounds were derived from watershed soils and transported to lakes via erosion and runoff but in situ production in lakes is now evident ( Tierney and Russell, 2009 ; Tierney et al., 2012 ; Wang et al., 2012 ; Buckles et al., 2014a ; Buckles et al., 2014b ; Loomis et al., 2014 ; Peterse et al., 2014 ; Weber et al., 2015 ; Hu et al., 2016 ; Qian et al., 2019 ; Yao et al., 2020 ; Wu et al., 2021 ; Zhang et al., 2021 ; Zhao et al., 2021 ; Raberg et al., 2022 ).

Differences in how brGDGTs respond to temperature in lakes, compared to soils and peats, have led to the development of lake-specific temperature calibration models ( Tierney et al., 2010 ; Zink et al., 2010 ; Pearson et al., 2011 ; Sun et al., 2011 ; Loomis et al., 2012 ; Wang et al., 2016 ; 2021 ; Dang et al., 2018 ; Russell et al., 2018 ; Martínez-Sosa et al., 2021 ; Raberg et al., 2021 ; Lei et al., 2023 ; O’Beirne et al., 2023 ; Zhao et al., 2023 ). These calibrations aim to account for the unique responses of brGDGTs within lacustrine environments. That said, the lack of a robust lacustrine end-member brGDGT signal means that the relative contributions of lake and soil sources to lacustrine sedimentary lipid pools remain uncertain (e.g., Tierney et al., 2012 ; Buckles et al., 2014a ; Wang et al., 2023 ). Consequently, the potential for calibration biases due to different sources of brGDGTs poses a significant challenge for the application of brGDGT-based paleothermometry. Indeed, it has long been known that soil-based calibrations do not accurately reconstruct temperature from lake sediments ( Blaga et al., 2010 ; Tierney et al., 2010 ; Sun et al., 2011 ; Loomis et al., 2012 ). Thus, there is a need to understand the relative contributions of in situ lacustrine production and soil input, as well as how varying source contributions may impact the use of brGDGTs as temperature proxies in lakes.

The ΣIIIa/ΣIIa ratio was initially proposed to distinguish the origins of brGDGTs in marine sediments. In a global analysis, 90% of soils had a ΣIIIa/ΣIIa ratio below 0.59, while 90% of marine sediments had a ratio exceeding 0.92 ( Xiao et al., 2016 ). This contrast highlights the potential for identifying the origins of brGDGTs in aquatic environments. The ΣIIIa/ΣIIa ratio was first applied to Lake St. Front sediments and watershed soils ( Martin et al., 2019 ) where it was found to be a reliable indicator for tracking the varying abundances of soil-sourced brGDGTs using the ratio cutoff values established for marine sediments ( Xiao et al., 2016 ). The ratio has since been applied in Lake Höglwörth, Southern Germany, although without additional comparison of surrounding watershed soils ( Acharya et al., 2023 ). This ratio clearly offers promise but needs to be further tested to assess its reliability before it is widely applied to lake sediments as a brGDGT source indicator.

In this regard, we analyzed the distributions of brGDGTs in 15 lake surface sediments and corresponding watershed soils from central-south Chile—a region with limited availability of historical climate observations where proxies and paleoclimate records thus become crucial in understanding past climate variability. We also compare the validity of the established marine thresholds of the ΣIIIa/ΣIIa ratio when applied to 1) the Chilean samples; 2) samples from four previously published studies from China and the Eastern Canadian Arctic and 3) samples in a global compilation of 692 lake surface sediment samples and 773 soil samples.

2 Materials and methods

2.1 study location and sample collection.

Fifteen paired (30 total) lake surface sediment (0–1 cm) and corresponding watershed soil (0–5 cm) samples were collected in January 2017, 2018, and 2019 from central-south Chile, spanning a latitudinal range from 38° to 44°S ( Figure 1 ). Coordinates for sampling sites are available in Supplementary Table S1 .

Figure 1 . Map of sample locations in Chile. (A) West coast of South America with the country of Chile highlighted in black. (B) Zoom in on sample locations. The letters A–O correspond to panels in Figure 2 .

2.2 Sample preparation and instrumental analysis

Lake surface sediments and soils (with prior sieving, 2 mm mesh, of soils) were freeze-dried, homogenized, and extracted to obtain extractable lipids. To obtain the Total Lipid Extract (TLE) samples were extracted either via Automated Solvent Extractor (Dionex ASE 350) at the University of Pittsburgh or via Microwave Assisted Extractor (Milestone Ethos Easy) at the Universidad Católica de la Santísima Concepción. The TLE were then separated by Solid Phase Extraction using aminopropyl columns as described in Russell and Werne (2007) , and the neutral fraction further separated by alumina column chromatography, following the procedures outlined in Powers et al. (2004) . Polar fractions were filtered via 0.45 mm PTFE filters prior to instrumental analysis.

The analysis of brGDGTs involved high-performance liquid chromatography-atmospheric pressure chemical ionization-mass spectrometry (HPLC-APCI-MS), as detailed in Hopmans et al. (2016) . In brief, a Thermo Ultimate 3000 series LC with a silica pre-column and two HILIC silica columns (BEH HILIC, 2.1 × 150 mm x 1.7 µm; Waters) in series, maintained at 30°C, was coupled to a Thermo TSQ triple quadrupole MS with an APCI source. The positive ion APCI settings included sheath gas (N2) at 20 AU, auxiliary gas (N2) at 2 AU, ion transfer tube temperature at 275°C, and vaporizer temperature at 375°C. Mass scanning ranged from 700 to 1300 m/z at a scan rate of 500 Da/s, with a Q1 resolution of 0.7 full width at half maximum.

BrGDGTs were identified by comparing their relative retention times and mass spectra with published reference values (e.g., De Jonge et al., 2013 ; Hopmans et al., 2016 ). The areas corresponding to individual brGDGTs were integrated from the total ion chromatogram (TIC) using Xcalibur software with Genesis integration. Peak areas were integrated with a minimum signal-to-noise ratio (S/N) cutoff of 3:1 to ensure data integrity.

Analysis of n-alkanes was described in Contreras et al. (2023) .

2.3 Ratio calculations

The fractional abundance (fA) of each of the brGDGTs was calculated according to Eq. 1 and are available in Supplementary Table S1 .

where x = the integrated peak area of an individual brGDGT.

The ΣIIIa/ΣIIa ( Xiao et al., 2016 ) was calculated using the fAs of brGDGT IIIa and IIa isomers (Eq. 2 ).

The Methylation of 5-Methyl Branched Tetraethers (MBT’ 5ME ) ratio was calculated (Eq. 3 ) using the fAs of the corresponding brGDGTs ( De Jonge et al., 2014 ).

2.4 Published datasets of brGDGTs in lake surface sediments and soils

Data from four studies ( Yao et al., 2020 ; Wu et al., 2021 ; Raberg et al., 2022 ; Wang et al., 2023 ) was compiled, focusing on paired lake surface sediment and corresponding watershed soil samples. In all four studies, it was observed that brGDGTs in lake surface sediments predominantly originated from lacustrine sources. Yao et al. (2020) studied lake surface sediments and soils in northeastern China. Wu et al. (2021) focused on Lake Yangzonghai and its surrounding watershed soils in southwestern China and found that the distribution of lacustrine brGDGTs correlated significantly with bottom water dissolved oxygen (DO) concentration, which is in turn linked to water depth. Raberg et al. (2022) examined lakes in the Eastern Canadian Arctic. Wang et al. (2023) investigated paired lake surface sediments and soils across China.

Global lake surface sediment brGDGT data was compiled from several previously published studies and includes 65 samples from Russell et al. (2018) , 35 samples from Dang et al. (2018) , 36 samples from Weber et al. (2018) , one sample from Miller et al. (2018) , one sample from Qian et al. (2019) , one sample from Ning et al. (2019) , one sample from Cao et al. (2020) , two samples from Dugerdil et al. (2021) , 43 samples from Raberg et al. (2021) , 157 samples from Martínez-Sosa et al. (2021) , 107 samples from Kou et al. (2022) , 102 samples from Lei et al. (2023) , 91 samples from Zhao et al. (2023) , and 50 samples from O’Beirne et al. (2023) .

Global soil brGDGT data was downloaded as Supplementary Material from Véquaud et al. (2022) . The dataset includes 128 samples from De Jonge et al. (2014) , 76 samples from Dearing et al. (2020) , 27 samples from Xiao et al. (2015) , 26 samples from Yang et al. (2015) , 44 samples from Lei et al. (2016) , 148 samples from Wang et al. (2016) , 27 samples from Ding et al. (2015) , 11 samples from Huguet et al. (2019) , 52 samples from Véquaud et al. (2021a) , and 49 samples from Véquaud et al. (2021b) .

2.5 Data analysis

Data analysis was completed using the free and open-source software R (v. 4.3.1; R Core Team, 2023) and RStudio (v. 2023.9.1.494; Posit team, 2023). Principal Component Analysis (PCA) was applied to the fAs of brGDGTs to uncover any underlying structure or patterns in how brGDGTs were distributed between lake surface sediments and watershed soils. PCA was completed using the stats package (v. 4.3.1; R Core Team, 2023) and plotted using ggplot2 (v. 3.4.3; Wickham, 2016 ). Data was scaled and centered before running the PCA. Additional statistical analyses were completed using the ggstatsplot package (v. 0.12.0: Patil, 2021 ).

3 Results and discussion

3.1 contrasting brgdgt distributions in lake surface sediments and watershed soils.

In both lake surface sediments and watershed soils five of the fifteen commonly reported brGDGTs (IIc’, IIIb, IIIb’, IIIc, and IIIc’) were below detection ( Figure 2 ), which is not uncommon in lake systems ( Lei et al., 2023 ; O’Beirne et al., 2023 ). The distributions of the ten detected brGDGTs in lake surface sediments and corresponding watershed soils display distinctly different distributions ( Figure 2 ). The most striking difference among the distributions of brGDGTs between lake surface sediments and their watershed soils is the predominance of brGDGTs IIIa and IIIa’ in lake surface sediments and brGDGT Ia in soils. This distinction is further emphasized in the PCA on combined lake and soil samples, where soils cluster predominantly in quadrant I, aligned with brGDGT Ia, while lake surface sediments cluster in quadrant III, associated with brGDGTs IIIa and IIIa’ ( Figure 3A ). The contrasting distributions of brGDGTs between lake surface sediments and their corresponding watershed soils shows that there is in situ production of brGDGTs in lakes ( Figures 2 , 3A ). Even though lake sediments comprise both soil and lacustrine sourced brGDGTs, the prevalence of brGDGTs IIIa and IIIa’ in lake surface sediments, juxtaposed with the dominance of brGDGT Ia in soils, signifies that lakes and soils in Chile have distinctly different brGDGT distributions. We hypothesize that this observation is likely due to distinct roles and processes governing brGDGT production and preservation in these two environments, especially because this pattern is consistent with observations from diverse locations (e.g., Tierney et al., 2010 ; Buckles et al., 2014a ; Buckles et al., 2014b ; Loomis et al., 2014 ; Weber et al., 2015 ; Hu et al., 2016 ; Li et al., 2017 ; Yao et al., 2020 ; Wang et al., 2021 ).

Figure 2 . Fractional abundances of brGDGTs from lake surface sediments and their respective watershed soils. (A–O) Sampling sites from north to south in Chile.

Figure 3 . Principal component analysis (PCA) bi-plots of principal component 1 (PC1) and principal component 2 (PC2) showing the scores of each sample (lakes - blue circles and soils - orange triangles) and the loadings of each brGDGT for each sample set. Vertical and horizontal dashed reference lines indicate where the x- and y -intercept are 0. (A) Chilean samples. (B) Samples from northeastern China ( Yao et al., 2020 ). (C) Samples from Lake Yangzonghai in southwestern China ( Wu et al., 2021 ). (D) Samples from the Eastern Canadian Arctic ( Raberg et al., 2022 ). (E) Samples from across China ( Wang et al., 2023 ). (F) Global compilation of lake surface sediment and soil samples.

The clear differences in brGDGT distributions between Chilean lake surface sediments and soils indicate that we can potentially distinguish between these two sources in lake sediment records. This capability would enable us to track the changing contributions of each source over time and, if needed, adjust for minor inputs from one source if we can establish appropriate proxies.

3.2 Comparison of the ΣIIIa/ΣIIa ratio in lakes and soils

The ΣIIIa/ΣIIa ratio in Chilean soils follows the thresholds established in marine sediments for soil- and marine-sourced brGDGTs ( Figure 4A ). Notably, all the soil samples fall below the ΣIIIa/ΣIIa threshold of 0.59, a criterion typically used to identify soil-derived brGDGTs ( Xiao et al., 2016 ). Further, all but two lake surface sediment samples have ΣIIIa/ΣIIa values above the 0.59 threshold of soils. Even so, the ΣIIIa/ΣIIa values of the lake surface sediments and soils of these two samples (Cipreces and Cajunco) are distinctly different ( Table 1 ). Further, a within subjects robust t -test reveals that the ΣIIIa/ΣIIa ratios between each of the paired lake surface sediment and soil samples are significantly different ( t Yuen (8) = 9.13, p = 1.66e-05, δ ^ R - avg AKP = 2.40, CI 95% [1.89, 5.43], n pairs = 15). Given that soils adhere to the established ΣIIIa/ΣIIa soil threshold, using it as a criterion to evaluate the influence of soil-sourced brGDGTs on lacustrine paleorecords in Chilean lakes and, consequently, the effects on paleotemperature reconstruction may be beneficial for the majority of Chilean lakes.

Figure 4 . The ΣIIIa/ΣIIa ratio applied to each sample set. The horizontal dashed line denotes the upper limit of soil-sourced brGDGTs and the solid line denotes the lower limit of marine/aquatic-sourced brGDGTs as established for marine sediments by Xiao et al. (2016) . (A) Chilean samples. (B) Samples from northeastern China ( Yao et al., 2020 ). (C) Samples from Lake Yangzonghai in southwestern China ( Wu et al., 2021 ). (D) Samples from the Eastern Canadian Arctic ( Raberg et al., 2022 ). (E) Samples from across China ( Wang et al., 2023 ). (F) Global compilation of lake surface sediment and soil samples.

Table 1 . ΣIIIa/ΣIIa ratio for Chilean lake surface sediments and watershed soils.

When we extend our analysis to previously published paired local and regional datasets as well as the global datasets of lake surface sediments and soils, the distinction between the two sources, as indicated by the established marine thresholds of ΣIIIa/ΣIIa ratio, becomes less clear ( Figures 4B–F ). Although four prior studies ( Yao et al., 2020 ; Wu et al., 2021 ; Raberg et al., 2022 ; Wang et al., 2023 ) investigated paired lake surface sediments and soils and concluded that brGDGTs in lake sediments primarily originated from lacustrine sources, the effectiveness of using the ΣIIIa/ΣIIa ratio to differentiate between sources varies. Specifically, while the samples from Yao et al. (2020) adhere to the established threshold for soil-derived brGDGTs, the other three studies do not ( Figures 4B–E ). Furthermore, there is significant overlap between lake surface sediments and soils in the PCA bi-plots for the studies where the ΣIIIa/ΣIIa ratio fails, i.e., Wu et al. (2021) , Raberg et al. (2022) and Wang et al. (2023) ( Figures 3B–E ). These observations provide strong evidence that the established marine thresholds for aquatic and soil origins are not universally applicable to lake sediments and relying on this ratio alone may not be enough to correctly characterize source contributions.

The compilation of globally distributed 692 lake surface sediments and 773 soils shows that approximately 85% of soil samples and 49% of lake samples display ΣIIIa/ΣIIa ratios below the 0.59 threshold of soils ( Figure 4F ). In contrast, only 35% of lake samples have ΣIIIa/ΣIIa values exceeding the upper threshold used to identify marine-derived brGDGTs (i.e., ΣIIIa/ΣIIa >0.92; Xiao et al., 2016 ). Not only does this contrast with the findings for marine sediments where 90% of marine sediments had ΣIIIa/ΣIIa values >0.92, but also shows considerable overlap between soil and lake surface sediment samples. This overlap complicates the use of the ΣIIIa/ΣIIa ratio in lake sediments overall. This presents two potential scenarios: either 1) almost half of global lakes are significantly influenced by soil-derived brGDGTs, or 2) the ΣIIIa/ΣIIa ratio does not offer as distinct a differentiation for lakes as it does for marine sediments.

The first scenario contradicts a prior study which suggested that only ca. 10% of global lakes are significantly affected by soil-sourced brGDGTs, as extrapolated from a calculation based on 26 Chinese lakes that assumes that crenarchaeol, its isomer crenarchaeol’, and C 33 n -alkane can be used as tracers for soil input of brGDGTs ( Wang et al., 2023 ). This calculation may be an oversimplification in certain contexts. Specifically, in our Chilean samples, we did not observe a significant difference in C 33 n-alkane concentrations between lakes and soils (within subjects robust t -test; t Yuen (8) = −1.62, p = 0.14, δ ^ R - avg AKP = −0.51, CI 95% [−1.20, −5.68e-03], n pairs = 15). This suggests that lake sediments may be dominated by lipids originating from soil sources. However, the distribution of brGDGTs between the two archives shows significant differences, contradicting the data on C 33 n-alkanes. Consequently, accounting for the C 33 n -alkane in the equation may lead to an overestimation of soil-sourced brGDGTs and skew paleotemperature reconstructions that attempt to account for their influence if applied to Chilean lakes.

A probable explanation for this discrepancy and something to consider in future attempts at calculating source contributions are the delivery mechanisms of the two lipid classes. Specifically, leaf waxes are delivered to lake sediments via three primary mechanisms: attached to deposited leaves, wind-driven abrasion and deposition, and the erosion and deposition of soil-derived waxes ( Diefendorf and Freimuth, 2017 ). In contrast, soil-sourced brGDGTs are mainly transported to lakes through erosion and runoff ( Blaga et al., 2010 and references therein) and to a lesser extent wind ( Fietz et al., 2013 ; Yamamoto et al., 2016 ). Changes in the primary delivery mechanisms of each lipid class would clearly affect the proportions of each that are measured in lake sediments, as the mechanisms of each may not be directly comparable to one another in both contemporary and historical contexts.

Taken altogether, the second scenario offers the most parsimonious explanation—the ΣIIIa/ΣIIa ratio does not provide as clear a distinction for lake sediments as it does for marine sediments. This inference is supported not only by the substantial overlap observed in the global datasets of lake surface sediments and soils but is also underscored by the local and regional paired studies. These studies demonstrated that brGDGTs in lake sediments originated primarily from lacustrine sources, despite there being significant overlap between soils and lake surface sediments when the ΣIIIa/ΣIIa ratio is applied ( Figures 4B–D ).

3.3 Influences on the ΣIIIa/ΣIIa ratio in lake sediments

Complications in utilizing the ΣIIIa/ΣIIa ratio to distinguish between lake surface sediments and soils also arise due to the influence of water depth on the abundance of brGDGT IIIa relative to IIa. Previous research ( Yao et al., 2020 ; Stefanescu et al., 2021 ) indicates that the abundance of brGDGT IIIa increases with greater water depth in lakes. However, in the Chilean lakes studied, ranging from 6.5 to 41.2 m in depth (mean = 21.02 m; Supplementary Table S1 ), we found no correlation between water depth and the fA of brGDGT IIIa ( t Student (13) = 1.52, p = 0.15, r ^ Winzorized = 0.39, CI 95% [−0.15, 0.75], n pairs = 15), nor between water depth and the ΣIIIa/ΣIIa ratio ( t Student (13) = 0.97, p = 0.35, r ^ Winzorized = 0.26, CI 95% [−0.29, 0.68], n pairs = 15).

Further, when analyzing data from published studies, we found no consistent trend. For instance, Yao et al. (2020) observed a significant positive correlation between water depth and the fA of brGDGT IIIa ( t Student (11) = 3.75, p = 3.23e-03, r ^ Winzorized = 0.75, CI 95% [0.34, 0.92], n pairs = 13), as well as the ΣIIIa/ΣIIa ratio ( t Student (11) = 5.27, p = 2.63e-04, r ^ Winzorized = 0.85, CI 95% [0.55, 0.95], n pairs = 13), in lakes from eastern China. However, in Lake Yangzonghai ( Wu et al., 2021 ) southwestern China, while there was a significant positive correlation between water depth and the fA of brGDGT IIIa ( t Student (33) = 4.90, p = 2.50e-05, r ^ Winzorized = 0.65, CI 95% [0.40, 0.81], n pairs = 35), there was a significant negative correlation between water depth and the ΣIIIa/ΣIIa ratio ( t Student (33) = −3.16, p = 3.36e-03, r ^ Winzorized = −0.48, CI 95% [-0.70, −0.18], n pairs = 35), contrary to findings from lakes in eastern China. In a broader study across China ( Wang et al., 2023 ), only weak correlations were found between water depth and the fA of brGDGT IIIa ( t Student (73) = 2.43, p = 0.02, r ^ Winzorized = 0.27, CI 95% [0.05, 0.47], n pairs = 75], npairs = 75) and the ΣIIIa/ΣIIa ratio ( t Student (73) = 0.75, p = 0.46, r ^ Winzorized = 0.09, CI 95% [-0.14, 0.31], n pairs = 75). These findings suggest that the relationship between brGDGT IIIa abundance and water depth may be site-specific and not universal.

Additionally, two of the studies provided dissolved oxygen (DO) concentrations ( Wu et al., 2021 ; Wang et al., 2023 ). Correlation analysis showed a weak positive correlation between DO and the ΣIIIa/ΣIIa ratio in Lake Yangzonghai ( t Student (33) = 2.28, p = 0.03, r ^ Winzorized = 0.37, CI 95% [0.04, 0.62], n pairs = 35), but no significant correlation was found in lakes from across China ( t Student (73) = 0.89, p = 0.38, r ^ Winzorized = 0.10, CI 95% [−0.13, 0.32], n pairs = 75). Thus, it appears that DO concentrations do not significantly influence the ΣIIIa/ΣIIa ratio.

3.4 The ΣIIIa/ΣIa ratio as a source indicator

We also explored the potential use of the ΣIIIa/ΣIa ratio (the sum of the fractional abundances of brGDGTs IIIa and IIIa’ divided by the fractional abundance of brGDGT Ia) as a source indicator. This exploration was prompted by the dominance and alignment of brGDGTs IIIa and IIIa’ in Chilean lake surface sediments and in other paired studies, contrasting with the prevalent alignment of watershed soils with brGDGT Ia in the PCA bi-plot ( Figure 3A ). The ΣIIIa/ΣIa ratio yields similar outcomes to the ΣIIIa/ΣIIa ratio in Chilean lake surface sediments and soils, maintaining a distinct separation between them ( Figure 5A ). However, when applied to the other paired studies as well as the global lake surface sediments and soils, this ratio fails to provide a clearer distinction than the ΣIIIa/ΣIIa ratio ( Figures 5B–F ). This lack of clarity, for both ratios, can be attributed to the greater overlap in brGDGT distributions between the two archives, as evidenced in the respective lake surface sediment and soils PCA bi-plot ( Figures 3B–F ).

Figure 5 . The ΣIIIa/ΣIa ratio applied to each sample set. (A) Chilean samples. (B) Samples from northeastern China ( Yao et al., 2020 ). (C) Samples from Lake Yangzonghai in southwestern China ( Wu et al., 2021 ). (D) Samples from the Eastern Canadian Arctic ( Raberg et al., 2022 ). (E) Samples from across China ( Wang et al., 2023 ). (F) Global compilation of lake surface sediment and soil samples.

3.5 Implications

The findings of this study provide valuable insights into the distribution of brGDGTs in Chilean lake surface sediments and their corresponding watershed soils. Understanding these distributions is essential for interpreting paleoclimatological conditions accurately.

To assess the impact of soil-sourced brGDGTs on lacustrine temperature reconstruction in Chile, we employed both lake surface sediment brGDGTs and soil brGDGTs in five recent lacustrine-based temperature calibration models ( Martínez-Sosa et al., 2021 ; Raberg et al., 2021 ; O’Beirne et al., 2023 ; Zhao et al., 2023 ). The results revealed a significant discrepancy: using soil brGDGTs in the models led to a substantial overestimation (by > 10°C) of mean annual air temperature (MAAT) compared to using lake surface sediment brGDGTs ( Figure 6 ). This finding underscores the necessity of employing environment-specific calibration models, as advocated in previous studies ( Tierney et al., 2010 ; Zink et al., 2010 ; Pearson et al., 2011 ; Sun et al., 2011 ; Loomis et al., 2012 ; Wang et al., 2016 ; 2021 ; Dang et al., 2018 ; Russell et al., 2018 ; Martínez-Sosa et al., 2021 ; Raberg et al., 2021 ; Lei et al., 2023 ; O’Beirne et al., 2023 ; Zhao et al., 2023 ). Additionally, these results highlight the necessity of assessing the origin of brGDGTs in lake sediments and applying the most appropriate environment-specific calibration model, as overestimating soil-sourced brGDGTs in lake sediments could skew temperature reconstructions towards much warmer temperatures which would lead to incorrect interpretations—this may be especially important during periods of significant environmental change, such as glacial-interglacial transitions, or other periods of vegetation change or human impacts as noted by Martin et al. (2019) . Therefore, it is crucial to carefully account for source changes in paleorecords and consider contemporary distribution differences between sources, along with other proxy data like carbon-to-nitrogen ratios, trace metals, and sediment grain size to substantiate source change interpretations.

Figure 6 . Boxplot distributions of air temperature (in Celsius) sourced from the CR2MET database ( https://www.cr2.cl/datos-productos-grillados ) for mean annual air temperature (MAAT) along with temperature reconstructions derived from five recent lacustrine-based temperature calibration models ( Raberg et al., 2021 [Full set]; Martínez-Sosa et al., 2021 [MBT’ 5ME ]; Zhao et al., 2023 [mid-high latitude (MHL) MBT’ 5ME and MHL multiple linear regression (MLR); O’Beirne et al., 2023 [cluster-specific random forest (RF)]). In the plot, ‘lakes’ depict the temperature range when brGDGTs from lake sediments were utilized in the calibration models, while ‘soils’ represent the reconstructed temperature range when corresponding watershed soils were employed in the calibration models. For the sample locations in Chile, there are no months where air temperature is below freezing, therefore MAAT is equal to months above freezing (MAF) temperature.

In our Chilean samples, the adherence of soils to established threshold for the ΣIIIa/ΣIIa ratio supports its use in evaluating the impact of soil-sourced brGDGTs on lacustrine sediment core records and brGDGT-based paleotemperature reconstructions. However, applying these marine thresholds globally presents challenges, as there is significant overlap between soil and lake samples, suggesting a potentially significant influence of soil-derived brGDGTs in almost half of the world’s lakes. The limitations of established marine thresholds are further highlighted by several studies showing considerable overlap between paired lake sediments and soils, despite brGDGTs in lake sediments originating primarily from lacustrine sources ( Yao et al., 2020 ; Wu et al., 2021 ; Raberg et al., 2022 ; Wang et al., 2023 ). Hence, caution is warranted when relying solely on established marine thresholds for discerning brGDGT sources using the ΣIIIa/ΣIIa ratio. Instead, establishing local or regional thresholds through direct comparisons between brGDGT distributions in lakes and corresponding watershed soils is more advisable.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding authors.

Author contributions

MB: Conceptualization, Data curation, Formal Analysis, Investigation, Validation, Visualization, Writing–original draft, Writing–review and editing. WS: Data curation, Formal Analysis, Investigation, Validation, Writing–review and editing. SC: Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing–review and editing. AA: Investigation, Writing–review and editing. ET: Investigation, Writing–review and editing. JM: Investigation, Writing–review and editing. JW: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Funding for this project was provided by the Agencia Nacional de Investigación y Desarrollo de Chile (ANID) Fondecyt 1160719, 1190398, and 1201277 to SC and AA The University of Pittsburgh provided additional support through a Central Research Development Fund grant to JW. An Andrew Mellon Predoctoral Fellowship through the University of Pittsburgh provided research support to WS.

Acknowledgments

We thank the Corporación Nacional Forestal (CONAF) for providing access to National Parks. We would also like to thank the editor and reviewers for their constructive comments. SC acknowledges the “Fondo Interno para la Adquisición de Equipamiento Científico de la Universidad Católica de la Santísima Concepción—FIAEC 2019,” and the FAA (2/2019) received from UCSC to complete this work.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2024.1383146/full#supplementary-material

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Keywords: biomarker, branched GDGTs, lake, soil, Chile

Citation: O’Beirne MD, Scott WP, Contreras S, Araneda A, Tejos E, Moscoso J and Werne JP (2024) Distribution of branched glycerol dialkyl glycerol tetraether (brGDGT) lipids from soils and sediments from the same watershed are distinct regionally (central Chile) but not globally. Front. Earth Sci. 12:1383146. doi: 10.3389/feart.2024.1383146

Received: 06 February 2024; Accepted: 05 April 2024; Published: 17 April 2024.

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Copyright © 2024 O’Beirne, Scott, Contreras, Araneda, Tejos, Moscoso and Werne. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sergio Contreras, [email protected] ; Josef P. Werne, [email protected]

This article is part of the Research Topic

Application of Lipid Biomarkers and Compound-Specific Isotopes to Reconstruct Paleoenvironmental Changes in Terrestrial and Marine Sedimentary Records