how to write data analysis 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.

Table of Contents

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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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results/findings/analysis chapter is
• What you need to include in your results chapter
• How to structure your results chapter
• A few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this 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 (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Quantitative Data Analysis

9 Presenting the Results of Quantitative Analysis

Mikaila Mariel Lemonik Arthur

This chapter provides an overview of how to present the results of quantitative analysis, in particular how to create effective tables for displaying quantitative results and how to write quantitative research papers that effectively communicate the methods used and findings of quantitative analysis.

Writing the Quantitative Paper

Standard quantitative social science papers follow a specific format. They begin with a title page that includes a descriptive title, the author(s)’ name(s), and a 100 to 200 word abstract that summarizes the paper. Next is an introduction that makes clear the paper’s research question, details why this question is important, and previews what the paper will do. After that comes a literature review, which ends with a summary of the research question(s) and/or hypotheses. A methods section, which explains the source of data, sample, and variables and quantitative techniques used, follows. Many analysts will include a short discussion of their descriptive statistics in the methods section. A findings section details the findings of the analysis, supported by a variety of tables, and in some cases graphs, all of which are explained in the text. Some quantitative papers, especially those using more complex techniques, will include equations. Many papers follow the findings section with a discussion section, which provides an interpretation of the results in light of both the prior literature and theory presented in the literature review and the research questions/hypotheses. A conclusion ends the body of the paper. This conclusion should summarize the findings, answering the research questions and stating whether any hypotheses were supported, partially supported, or not supported. Limitations of the research are detailed. Papers typically include suggestions for future research, and where relevant, some papers include policy implications. After the body of the paper comes the works cited; some papers also have an Appendix that includes additional tables and figures that did not fit into the body of the paper or additional methodological details. While this basic format is similar for papers regardless of the type of data they utilize, there are specific concerns relating to quantitative research in terms of the methods and findings that will be discussed here.

In the methods section, researchers clearly describe the methods they used to obtain and analyze the data for their research. When relying on data collected specifically for a given paper, researchers will need to discuss the sample and data collection; in most cases, though, quantitative research relies on pre-existing datasets. In these cases, researchers need to provide information about the dataset, including the source of the data, the time it was collected, the population, and the sample size. Regardless of the source of the data, researchers need to be clear about which variables they are using in their research and any transformations or manipulations of those variables. They also need to explain the specific quantitative techniques that they are using in their analysis; if different techniques are used to test different hypotheses, this should be made clear. In some cases, publications will require that papers be submitted along with any code that was used to produce the analysis (in SPSS terms, the syntax files), which more advanced researchers will usually have on hand. In many cases, basic descriptive statistics are presented in tabular form and explained within the methods section.

The findings sections of quantitative papers are organized around explaining the results as shown in tables and figures. Not all results are depicted in tables and figures—some minor or null findings will simply be referenced—but tables and figures should be produced for all findings to be discussed at any length. If there are too many tables and figures, some can be moved to an appendix after the body of the text and referred to in the text (e.g. “See Table 12 in Appendix A”).

Discussions of the findings should not simply restate the contents of the table. Rather, they should explain and interpret it for readers, and they should do so in light of the hypothesis or hypotheses that are being tested. Conclusions—discussions of whether the hypothesis or hypotheses are supported or not supported—should wait for the conclusion of the paper.

Creating Effective Tables

When creating tables to display the results of quantitative analysis, the most important goals are to create tables that are clear and concise but that also meet standard conventions in the field. This means, first of all, paring down the volume of information produced in the statistical output to just include the information most necessary for interpreting the results, but doing so in keeping with standard table conventions. It also means making tables that are well-formatted and designed, so that readers can understand what the tables are saying without struggling to find information. For example, tables (as well as figures such as graphs) need clear captions; they are typically numbered and referred to by number in the text. Columns and rows should have clear headings. Depending on the content of the table, formatting tools may need to be used to set off header rows/columns and/or total rows/columns; cell-merging tools may be necessary; and shading may be important in tables with many rows or columns.

Here, you will find some instructions for creating tables of results from descriptive, crosstabulation, correlation, and regression analysis that are clear, concise, and meet normal standards for data display in social science. In addition, after the instructions for creating tables, you will find an example of how a paper incorporating each table might describe that table in the text.

Descriptive Statistics

When presenting the results of descriptive statistics, we create one table with columns for each type of descriptive statistic and rows for each variable. Note, of course, that depending on level of measurement only certain descriptive statistics are appropriate for a given variable, so there may be many cells in the table marked with an — to show that this statistic is not calculated for this variable. So, consider the set of descriptive statistics below, for occupational prestige, age, highest degree earned, and whether the respondent was born in this country.

To display these descriptive statistics in a paper, one might create a table like Table 2. Note that for discrete variables, we use the value label in the table, not the value.

If we were then to discuss our descriptive statistics in a quantitative paper, we might write something like this (note that we do not need to repeat every single detail from the table, as readers can peruse the table themselves):

This analysis relies on four variables from the 2021 General Social Survey: occupational prestige score, age, highest degree earned, and whether the respondent was born in the United States. Descriptive statistics for all four variables are shown in Table 2. The median occupational prestige score is 47, with a range from 16 to 80. 50% of respondents had occupational prestige scores scores between 35 and 59. The median age of respondents is 53, with a range from 18 to 89. 50% of respondents are between ages 37 and 66. Both variables have little skew. Highest degree earned ranges from less than high school to a graduate degree; the median respondent has earned an associate’s degree, while the modal response (given by 39.8% of the respondents) is a high school degree. 88.8% of respondents were born in the United States.

Crosstabulation

When presenting the results of a crosstabulation, we simplify the table so that it highlights the most important information—the column percentages—and include the significance and association below the table. Consider the SPSS output below.

Table 4 shows how a table suitable for include in a paper might look if created from the SPSS output in Table 3. Note that we use asterisks to indicate the significance level of the results: * means p < 0.05; ** means p < 0.01; *** means p < 0.001; and no stars mean p > 0.05 (and thus that the result is not significant). Also note than N is the abbreviation for the number of respondents.

If we were going to discuss the results of this crosstabulation in a quantitative research paper, the discussion might look like this:

A crosstabulation of respondent’s class identification and their highest degree earned, with class identification as the independent variable, is significant, with a Spearman correlation of 0.419, as shown in Table 4. Among lower class and working class respondents, more than 50% had earned a high school degree. Less than 20% of poor respondents and less than 40% of working-class respondents had earned more than a high school degree. In contrast, the majority of middle class and upper class respondents had earned at least a bachelor’s degree. In fact, 50% of upper class respondents had earned a graduate degree.

Correlation

When presenting a correlating matrix, one of the most important things to note is that we only present half the table so as not to include duplicated results. Think of the line through the table where empty cells exist to represent the correlation between a variable and itself, and include only the triangle of data either above or below that line of cells. Consider the output in Table 5.

Table 6 shows what the contents of Table 5 might look like when a table is constructed in a fashion suitable for publication.

If we were to discuss the results of this bivariate correlation analysis in a quantitative paper, the discussion might look like this:

Bivariate correlations were run among variables measuring age, occupational prestige, the highest year of school respondents completed, and family income in constant 1986 dollars, as shown in Table 6. Correlations between age and highest year of school completed and between age and family income are not significant. All other correlations are positive and significant at the p<0.001 level. The correlation between age and occupational prestige is weak; the correlations between income and occupational prestige and between income and educational attainment are moderate, and the correlation between education and occupational prestige is strong.

To present the results of a regression, we create one table that includes all of the key information from the multiple tables of SPSS output. This includes the R 2 and significance of the regression, either the B or the beta values (different analysts have different preferences here) for each variable, and the standard error and significance of each variable. Consider the SPSS output in Table 7.

The regression output in shown in Table 7 contains a lot of information. We do not include all of this information when making tables suitable for publication. As can be seen in Table 8, we include the Beta (or the B), the standard error, and the significance asterisk for each variable; the R 2 and significance for the overall regression; the degrees of freedom (which tells readers the sample size or N); and the constant; along with the key to p/significance values.

If we were to discuss the results of this regression in a quantitative paper, the results might look like this:

Table 8 shows the results of a regression in which age, occupational prestige, and highest year of school completed are the independent variables and family income is the dependent variable. The regression results are significant, and all of the independent variables taken together explain 15.6% of the variance in family income. Age is not a significant predictor of income, while occupational prestige and educational attainment are. Educational attainment has a larger effect on family income than does occupational prestige. For every year of additional education attained, family income goes up on average by $3,988.545; for every one-unit increase in occupational prestige score, family income goes up on average by $522.887. [1]

• Choose two discrete variables and three continuous variables from a dataset of your choice. Produce appropriate descriptive statistics on all five of the variables and create a table of the results suitable for inclusion in a paper.
• Using the two discrete variables you have chosen, produce an appropriate crosstabulation, with significance and measure of association. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a correlation matrix. Create a table of the results suitable for inclusion in a paper.
• Using the three continuous variables you have chosen, produce a multivariate linear regression. Create a table of the results suitable for inclusion in a paper.
• Write a methods section describing the dataset, analytical methods, and variables you utilized in questions 1, 2, 3, and 4 and explaining the results of your descriptive analysis.
• Write a findings section explaining the results of the analyses you performed in questions 2, 3, and 4.
• Note that the actual numberical increase comes from the B values, which are shown in the SPSS output in Table 7 but not in the reformatted Table 8. ↵

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

Need Help Locating Statistics?

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Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Handbook of Research Methods in Health Social Sciences pp 985–997 Cite as

Writing Quantitative Research Studies

• Ankur Singh 2 ,
• Adyya Gupta 3 &
• Karen G. Peres 4  
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Summarizing quantitative data and its effective presentation and discussion can be challenging for students and researchers. This chapter provides a framework for adequately reporting findings from quantitative analysis in a research study for those contemplating to write a research paper. The rationale underpinning the reporting methods to maintain the credibility and integrity of quantitative studies is outlined. Commonly used terminologies in empirical studies are defined and discussed with suitable examples. Key elements that build consistency between different sections (background, methods, results, and the discussion) of a research study using quantitative methods in a journal article are explicated. Specifically, recommended standard guidelines for randomized controlled trials and observational studies for reporting and discussion of findings from quantitative studies are elaborated. Key aspects of methodology that include describing the study population, sampling strategy, data collection methods, measurements/variables, and statistical analysis which informs the quality of a study from the reviewer’s perspective are described. Effective use of references in the methods section to strengthen the rationale behind specific statistical techniques and choice of measures has been highlighted with examples. Identifying ways in which data can be most succinctly and effectively summarized in tables and graphs according to their suitability and purpose of information is also detailed in this chapter. Strategies to present and discuss the quantitative findings in a structured discussion section are also provided. Overall, the chapter provides the readers with a comprehensive set of tools to identify key strategies to be considered when reporting quantitative research.

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Singh, A., Gupta, A., Peres, K.G. (2019). Writing Quantitative Research Studies. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_117

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

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Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

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• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process .

The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.

For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.

This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.

Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.

Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method . You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.

In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.

A set of analytical software can be used to assist with analysis of quantitative data. The following table  illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

Advantages and disadvantages of popular quantitative analytical software

Quantitative data analysis with the application of statistical software consists of the following stages [1] :

• Preparing and checking the data. Input of data into computer.
• Selecting the most appropriate tables and diagrams to use according to your research objectives.
• Selecting the most appropriate statistics to describe your data.
• Selecting the most appropriate statistics to examine relationships and trends in your data.

It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish” [2] .

Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

[2] Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.

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• Indian J Anaesth
• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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What is Data Analysis? Definition, Tools, Examples

Appinio Research · 11.04.2024 · 35min read

Have you ever wondered how businesses make decisions, scientists uncover new discoveries, or governments tackle complex challenges? The answer often lies in data analysis. In today's data-driven world, organizations and individuals alike rely on data analysis to extract valuable insights from vast amounts of information. Whether it's understanding customer preferences, predicting future trends, or optimizing processes, data analysis plays a crucial role in driving informed decision-making and problem-solving. This guide will take you through the fundamentals of analyzing data, exploring various techniques and tools used in the process, and understanding the importance of data analysis in different domains. From understanding what data analysis is to delving into advanced techniques and best practices, this guide will equip you with the knowledge and skills to harness the power of data and unlock its potential to drive success and innovation.

What is Data Analysis?

Data analysis is the process of examining, cleaning, transforming, and interpreting data to uncover insights, identify patterns, and make informed decisions. It involves applying statistical, mathematical, and computational techniques to understand the underlying structure and relationships within the data and extract actionable information from it. Data analysis is used in various domains, including business, science, healthcare, finance, and government, to support decision-making, solve complex problems, and drive innovation.

Importance of Data Analysis

Data analysis is crucial in modern organizations and society, providing valuable insights and enabling informed decision-making across various domains. Here are some key reasons why data analysis is important:

• Informed Decision-Making:  Data analysis enables organizations to make evidence-based decisions by providing insights into past trends, current performance, and future predictions.
• Improved Efficiency:  By analyzing data, organizations can identify inefficiencies, streamline processes, and optimize resource allocation, leading to increased productivity and cost savings.
• Identification of Opportunities:  Data analysis helps organizations identify market trends, customer preferences, and emerging opportunities, allowing them to capitalize on new business prospects and stay ahead of competitors.
• Risk Management:  Data analysis enables organizations to assess and mitigate risks by identifying potential threats, vulnerabilities, and opportunities for improvement.
• Performance Evaluation:  Data analysis allows organizations to measure and evaluate their performance against key metrics and objectives, facilitating continuous improvement and accountability.
• Innovation and Growth:  By analyzing data, organizations can uncover new insights, discover innovative solutions, and drive growth through product development, process optimization, and strategic initiatives.
• Personalization and Customer Satisfaction:  Data analysis enables organizations to understand customer behavior, preferences, and needs, allowing them to deliver personalized products, services, and experiences that enhance customer satisfaction and loyalty .
• Regulatory Compliance:  Data analysis helps organizations ensure compliance with regulations and standards by monitoring and analyzing data for compliance-related issues, such as fraud, security breaches, and data privacy violations.

Overall, data analysis empowers organizations to harness the power of data to drive strategic decision-making, improve performance, and achieve their goals and objectives.

Understanding Data

Understanding the nature of data is fundamental to effective data analysis. It involves recognizing the types of data, their sources, methods of collection, and the crucial process of cleaning and preprocessing data before analysis.

Types of Data

Data can be broadly categorized into two main types: quantitative and qualitative data .

• Quantitative data:  This type of data represents quantities and is measurable. It deals with numbers and numerical values, allowing for mathematical calculations and statistical analysis. Examples include age, height, temperature, and income.
• Qualitative data:  Qualitative data describes qualities or characteristics and cannot be expressed numerically. It focuses on qualities, opinions, and descriptions that cannot be measured. Examples include colors, emotions, opinions, and preferences.

Understanding the distinction between these two types of data is essential as it influences the choice of analysis techniques and methods.

Data Sources

Data can be obtained from various sources, depending on the nature of the analysis and the project's specific requirements.

• Internal databases:  Many organizations maintain internal databases that store valuable information about their operations, customers, products, and more. These databases often contain structured data that is readily accessible for analysis.
• External sources:  External data sources provide access to a wealth of information beyond an organization's internal databases. This includes data from government agencies, research institutions, public repositories, and third-party vendors. Examples include census data, market research reports, and social media data.
• Sensor data:  With the proliferation of IoT (Internet of Things) devices, sensor data has become increasingly valuable for various applications. These devices collect data from the physical environment, such as temperature, humidity, motion, and location, providing real-time insights for analysis.

Understanding the available data sources is crucial for determining the scope and scale of the analysis and ensuring that the data collected is relevant and reliable.

Data Collection Methods

The process of collecting data can vary depending on the research objectives, the nature of the data, and the target population. Various data collection methods are employed to gather information effectively.

• Surveys :  Surveys involve collecting information from individuals or groups through questionnaires, interviews, or online forms. Surveys are versatile and can be conducted in various formats, including face-to-face interviews, telephone interviews, paper surveys, and online surveys.
• Observational studies:  Observational studies involve observing and recording behavior, events, or phenomena in their natural settings without intervention. This method is often used in fields such as anthropology, sociology, psychology, and ecology to gather qualitative data.
• Experiments:  Experiments are controlled investigations designed to test hypotheses and determine cause-and-effect relationships between variables. They involve manipulating one or more variables while keeping others constant to observe the effect on the dependent variable.

Understanding the strengths and limitations of different data collection methods is essential for designing robust research studies and ensuring the quality and validity of the data collected. For businesses seeking efficient and insightful data collection, Appinio offers a seamless solution.

With its user-friendly interface and comprehensive features, Appinio simplifies the process of gathering valuable insights from diverse audiences. Whether conducting surveys, observational studies, or experiments, Appinio provides the tools and support needed to collect, analyze, and interpret data effectively.

Ready to elevate your data collection efforts? Book a demo today and experience the power of real-time market research with Appinio!

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Data Cleaning and Preprocessing

Data cleaning and preprocessing are essential steps in the data analysis process aimed at improving data quality, consistency, and reliability.

• Handling missing values:  Missing values are common in datasets and can arise due to various reasons, such as data entry errors, equipment malfunction, or non-response. Techniques for handling missing values include deletion, imputation, and predictive modeling.
• Dealing with outliers:  Outliers are data points that deviate significantly from the rest of the data and may distort the analysis results. It's essential to identify and handle outliers appropriately using statistical methods, visualization techniques, or domain knowledge.
• Standardizing data:  Standardization involves scaling variables to a common scale to facilitate comparison and analysis. It ensures that variables with different units or scales contribute equally to the analysis results. Standardization techniques include z-score normalization, min-max scaling, and robust scaling.

By cleaning and preprocessing the data effectively, you can ensure that it is accurate, consistent, and suitable for analysis, leading to more reliable and actionable insights.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial phase in the data analysis process, where you explore and summarize the main characteristics of your dataset. This phase helps you gain insights into the data, identify patterns, and detect anomalies or outliers. Let's delve into the key components of EDA.

Descriptive Statistics

Descriptive statistics provide a summary of the main characteristics of your dataset, allowing you to understand its central tendency, variability, and distribution. Standard descriptive statistics include measures such as mean, median, mode, standard deviation, variance, and range.

• Mean: The average value of a dataset, calculated by summing all values and dividing by the number of observations. Mean = (Sum of all values) / (Number of observations)
• Median:  The middle value of a dataset when it is ordered from least to greatest.
• Mode:  The value that appears most frequently in a dataset.
• Standard deviation:  A measure of the dispersion or spread of values around the mean. Standard deviation = Square root of [(Sum of squared differences from the mean) / (Number of observations)]
• Variance: The average of the squared differences from the mean. Variance = Sum of squared differences from the mean / Number of observations
• Range:  The difference between the maximum and minimum values in a dataset.

Descriptive statistics provide initial insights into the central tendencies and variability of the data, helping you identify potential issues or areas for further exploration.

Data Visualization Techniques

Data visualization is a powerful tool for exploring and communicating insights from your data. By representing data visually, you can identify patterns, trends, and relationships that may not be apparent from raw numbers alone. Common data visualization techniques include:

• Histograms:  A graphical representation of the distribution of numerical data divided into bins or intervals.
• Scatter plots:  A plot of individual data points on a two-dimensional plane, useful for visualizing relationships between two variables.
• Box plots:  A graphical summary of the distribution of a dataset, showing the median, quartiles, and outliers.
• Bar charts:  A visual representation of categorical data using rectangular bars of varying heights or lengths.
• Heatmaps :  A visual representation of data in a matrix format, where values are represented using colors to indicate their magnitude.

Data visualization allows you to explore your data from different angles, uncover patterns, and communicate insights effectively to stakeholders.

Identifying Patterns and Trends

During EDA, you'll analyze your data to identify patterns, trends, and relationships that can provide valuable insights into the underlying processes or phenomena.

• Time series analysis:  Analyzing data collected over time to identify temporal patterns, seasonality, and trends.
• Correlation analysis:  Examining the relationships between variables to determine if they are positively, negatively, or not correlated.
• Cluster analysis:  Grouping similar data points together based on their characteristics to identify natural groupings or clusters within the data.
• Principal Component Analysis (PCA):  A dimensionality reduction technique used to identify the underlying structure in high-dimensional data and visualize it in lower-dimensional space.

By identifying patterns and trends in your data, you can uncover valuable insights that can inform decision-making and drive business outcomes.

Handling Missing Values and Outliers

Missing values and outliers can distort the results of your analysis, leading to biased conclusions or inaccurate predictions. It's essential to handle them appropriately during the EDA phase. Techniques for handling missing values include:

• Deletion:  Removing observations with missing values from the dataset.
• Imputation:  Filling in missing values using methods such as mean imputation, median imputation, or predictive modeling.
• Detection and treatment of outliers:  Identifying outliers using statistical methods or visualization techniques and either removing them or transforming them to mitigate their impact on the analysis.

By addressing missing values and outliers, you can ensure the reliability and validity of your analysis results, leading to more robust insights and conclusions.

Data Analysis Examples

Data analysis spans various industries and applications. Here are a few examples showcasing the versatility and power of data-driven insights.

Business and Marketing

Data analysis is used to understand customer behavior, optimize marketing strategies, and drive business growth. For instance, a retail company may analyze sales data to identify trends in customer purchasing behavior, allowing them to tailor their product offerings and promotional campaigns accordingly.

Similarly, marketing teams use data analysis techniques to measure the effectiveness of advertising campaigns, segment customers based on demographics or preferences, and personalize marketing messages to improve engagement and conversion rates.

Healthcare and Medicine

In healthcare, data analysis is vital in improving patient outcomes, optimizing treatment protocols, and advancing medical research. For example, healthcare providers may analyze electronic health records (EHRs) to identify patterns in patient symptoms, diagnoses, and treatment outcomes, helping to improve diagnostic accuracy and treatment effectiveness.

Pharmaceutical companies use data analysis techniques to analyze clinical trial data, identify potential drug candidates, and optimize drug development processes, ultimately leading to the discovery of new treatments and therapies for various diseases and conditions.

Finance and Economics

Data analysis is used to inform investment decisions, manage risk, and detect fraudulent activities. For instance, investment firms analyze financial market data to identify trends, assess market risk, and make informed investment decisions.

Banks and financial institutions use data analysis techniques to detect fraudulent transactions, identify suspicious activity patterns, and prevent financial crimes such as money laundering and fraud. Additionally, economists use data analysis to analyze economic indicators, forecast economic trends, and inform policy decisions at the national and global levels.

Science and Research

Data analysis is essential for generating insights, testing hypotheses, and advancing knowledge in various fields of scientific research. For example, astronomers analyze observational data from telescopes to study the properties and behavior of celestial objects such as stars, galaxies, and black holes.

Biologists use data analysis techniques to analyze genomic data, study gene expression patterns, and understand the molecular mechanisms underlying diseases. Environmental scientists use data analysis to monitor environmental changes, track pollution levels, and assess the impact of human activities on ecosystems and biodiversity.

These examples highlight the diverse applications of data analysis across different industries and domains, demonstrating its importance in driving innovation, solving complex problems, and improving decision-making processes.

Statistical Analysis

Statistical analysis is a fundamental aspect of data analysis, enabling you to draw conclusions, make predictions, and infer relationships from your data. Let's explore various statistical techniques commonly used in data analysis.

Hypothesis Testing

Hypothesis testing is a method used to make inferences about a population based on sample data. It involves formulating a hypothesis about the population parameter and using sample data to determine whether there is enough evidence to reject or fail to reject the null hypothesis.

Common types of hypothesis tests include:

• t-test:  Used to compare the means of two groups and determine if they are significantly different from each other.
• Chi-square test:  Used to determine whether there is a significant association between two categorical variables.
• ANOVA (Analysis of Variance):  Used to compare means across multiple groups to determine if there are significant differences.

Correlation Analysis

Correlation analysis is used to measure the strength and direction of the relationship between two variables. The correlation coefficient, typically denoted by "r," ranges from -1 to 1, where:

• r = 1:  Perfect positive correlation
• r = -1:  Perfect negative correlation
• r = 0:  No correlation

Common correlation coefficients include:

• Pearson correlation coefficient:  Measures the linear relationship between two continuous variables.
• Spearman rank correlation coefficient:  Measures the strength and direction of the monotonic relationship between two variables, particularly useful for ordinal data.

Correlation analysis helps you understand the degree to which changes in one variable are associated with changes in another variable.

Regression Analysis

Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It aims to predict the value of the dependent variable based on the values of the independent variables. Common types of regression analysis include:

• Linear regression:  Models the relationship between the dependent variable and one or more independent variables using a linear equation. It is suitable for predicting continuous outcomes.
• Logistic regression:  Models the relationship between a binary dependent variable and one or more independent variables. It is commonly used for classification tasks.

Regression analysis helps you understand how changes in one or more independent variables are associated with changes in the dependent variable.

ANOVA (Analysis of Variance)

ANOVA is a statistical technique used to analyze the differences among group means in a sample. It is often used to compare means across multiple groups and determine whether there are significant differences between them. ANOVA tests the null hypothesis that the means of all groups are equal against the alternative hypothesis that at least one group mean is different.

ANOVA can be performed in various forms, including:

• One-way ANOVA:  Used when there is one categorical independent variable with two or more levels and one continuous dependent variable.
• Two-way ANOVA:  Used when there are two categorical independent variables and one continuous dependent variable.
• Repeated measures ANOVA:  Used when measurements are taken on the same subjects at different time points or under different conditions.

ANOVA is a powerful tool for comparing means across multiple groups and identifying significant differences that may exist between them.

Machine Learning for Data Analysis

Machine learning is a powerful subset of artificial intelligence that focuses on developing algorithms capable of learning from data to make predictions or decisions.

Introduction to Machine Learning

Machine learning algorithms learn from historical data to identify patterns and make predictions or decisions without being explicitly programmed. The process involves training a model on labeled data (supervised learning) or unlabeled data (unsupervised learning) to learn the underlying patterns and relationships.

Key components of machine learning include:

• Features:  The input variables or attributes used to train the model.
• Labels:  The output variable that the model aims to predict in supervised learning.
• Training data:  The dataset used to train the model.
• Testing data:  The dataset used to evaluate the performance of the trained model.

Supervised Learning Techniques

Supervised learning involves training a model on labeled data, where the input features are paired with corresponding output labels. The goal is to learn a mapping from input features to output labels, enabling the model to make predictions on new, unseen data.

Supervised learning techniques include:

• Regression:  Used to predict a continuous target variable. Examples include linear regression for predicting house prices and logistic regression for binary classification tasks.
• Classification:  Used to predict a categorical target variable. Examples include decision trees, support vector machines, and neural networks.

Supervised learning is widely used in various domains, including finance, healthcare, and marketing, for tasks such as predicting customer churn, detecting fraudulent transactions, and diagnosing diseases.

Unsupervised Learning Techniques

Unsupervised learning involves training a model on unlabeled data, where the algorithm tries to learn the underlying structure or patterns in the data without explicit guidance.

Unsupervised learning techniques include:

• Clustering:  Grouping similar data points together based on their features. Examples include k-means clustering and hierarchical clustering.
• Dimensionality reduction:  Reducing the number of features in the dataset while preserving its essential information. Examples include principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE).

Unsupervised learning is used for tasks such as customer segmentation, anomaly detection, and data visualization.

Model Evaluation and Selection

Once a machine learning model has been trained, it's essential to evaluate its performance and select the best-performing model for deployment.

• Cross-validation:  Dividing the dataset into multiple subsets and training the model on different combinations of training and validation sets to assess its generalization performance.
• Performance metrics:  Using metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve to evaluate the model's performance on the validation set.
• Hyperparameter tuning:  Adjusting the hyperparameters of the model, such as learning rate, regularization strength, and number of hidden layers, to optimize its performance.

Model evaluation and selection are critical steps in the machine learning pipeline to ensure that the deployed model performs well on new, unseen data.

Advanced Data Analysis Techniques

Advanced data analysis techniques go beyond traditional statistical methods and machine learning algorithms to uncover deeper insights from complex datasets.

Time Series Analysis

Time series analysis is a method for analyzing data collected at regular time intervals. It involves identifying patterns, trends, and seasonal variations in the data to make forecasts or predictions about future values. Time series analysis is commonly used in fields such as finance, economics, and meteorology for tasks such as forecasting stock prices, predicting sales, and analyzing weather patterns.

Key components of time series analysis include:

• Trend analysis:  Identifying long-term trends or patterns in the data, such as upward or downward movements over time.
• Seasonality analysis:  Identifying recurring patterns or cycles that occur at fixed intervals, such as daily, weekly, or monthly seasonality.
• Forecasting:  Using historical data to make predictions about future values of the time series.

Time series analysis techniques include:

• Autoregressive integrated moving average (ARIMA) models.
• Exponential smoothing methods.
• Seasonal decomposition of time series (STL).

Predictive Modeling

Predictive modeling involves using historical data to build a model that can make predictions about future events or outcomes. It is widely used in various industries for customer churn prediction, demand forecasting, and risk assessment. This involves involves:

• Data preparation:  Cleaning and preprocessing the data to ensure its quality and reliability.
• Feature selection:  Identifying the most relevant features or variables contributing to the predictive task.
• Model selection:  Choosing an appropriate machine learning algorithm or statistical technique to build the predictive model.
• Model training:  Training the model on historical data to learn the underlying patterns and relationships.
• Model evaluation:  Assessing the performance of the model on a separate validation dataset using appropriate metrics such as accuracy, precision, recall, and F1-score.

Common predictive modeling techniques include linear regression, decision trees, random forests, gradient boosting, and neural networks.

Text Mining and Sentiment Analysis

Text mining, also known as text analytics, involves extracting insights from unstructured text data. It encompasses techniques for processing, analyzing, and interpreting textual data to uncover patterns, trends, and sentiments. Text mining is used in various applications, including social media analysis, customer feedback analysis, and document classification.

Key components of text mining and sentiment analysis include:

• Text preprocessing:  Cleaning and transforming raw text data into a structured format suitable for analysis, including tasks such as tokenization, stemming, and lemmatization.
• Sentiment analysis:  Determining the sentiment or opinion expressed in text data, such as positive, negative, or neutral sentiment.
• Topic modeling:  Identifying the underlying themes or topics present in a collection of documents using techniques such as latent Dirichlet allocation (LDA).
• Named entity recognition:  Identifying and categorizing entities mentioned in text data, such as names of people, organizations, or locations.

Text mining and sentiment analysis techniques enable organizations to gain valuable insights from textual data sources and make data-driven decisions.

Network Analysis

Network analysis, also known as graph analysis, involves studying the structure and interactions of complex networks or graphs. It is used to analyze relationships and dependencies between entities in various domains, including social networks, biological networks, and transportation networks.

Key concepts in network analysis include:

• Nodes:  Represent entities or objects in the network, such as people, websites, or genes.
• Edges:  Represent relationships or connections between nodes, such as friendships, hyperlinks, or interactions.
• Centrality measures:  Quantify the importance or influence of nodes within the network, such as degree centrality, betweenness centrality, and eigenvector centrality.
• Community detection:  Identify groups or communities of nodes that are densely connected within themselves but sparsely connected to nodes in other communities.

Network analysis techniques enable researchers and analysts to uncover hidden patterns, identify key influencers, and understand the underlying structure of complex systems.

Data Analysis Software and Tools

Effective data analysis relies on the use of appropriate tools and software to process, analyze, and visualize data.

What Are Data Analysis Tools?

Data analysis tools encompass a wide range of software applications and platforms designed to assist in the process of exploring, transforming, and interpreting data. These tools provide features for data manipulation, statistical analysis, visualization, and more. Depending on the analysis requirements and user preferences, different tools may be chosen for specific tasks.

Popular Data Analysis Tools

Several software packages are widely used in data analysis due to their versatility, functionality, and community support. Some of the most popular data analysis software include:

• Python:  A versatile programming language with a rich ecosystem of libraries and frameworks for data analysis, including NumPy, pandas, Matplotlib, and scikit-learn.
• R:  A programming language and environment specifically designed for statistical computing and graphics, featuring a vast collection of packages for data analysis, such as ggplot2, dplyr, and caret.
• Excel:  A spreadsheet application that offers basic data analysis capabilities, including formulas, pivot tables, and charts. Excel is widely used for simple data analysis tasks and visualization.

These software packages cater to different user needs and skill levels, providing options for beginners and advanced users alike.

Data Collection Tools

Data collection tools are software applications or platforms that gather data from various sources, including surveys, forms, databases, and APIs. These tools provide features for designing data collection instruments, distributing surveys, and collecting responses.

Examples of data collection tools include:

• Google Forms:  A free online tool for creating surveys and forms, collecting responses, and analyzing the results.
• Appinio :  A real-time market research platform that simplifies data collection and analysis. With Appinio, businesses can easily create surveys, gather responses, and gain valuable insights to drive decision-making.

Data collection tools streamline the process of gathering and analyzing data, ensuring accuracy, consistency, and efficiency. Appinio stands out as a powerful tool for businesses seeking rapid and comprehensive data collection, empowering them to make informed decisions with ease.

Ready to experience the benefits of Appinio? Book a demo and get started today!

Data Visualization Tools

Data visualization tools enable users to create visual representations of data, such as charts, graphs, and maps, to communicate insights effectively. These tools provide features for creating interactive and dynamic visualizations that enhance understanding and facilitate decision-making.

Examples of data visualization tools include Power BI, a business analytics tool from Microsoft that enables users to visualize and analyze data from various sources, create interactive reports and dashboards, and share insights with stakeholders.

Data visualization tools play a crucial role in exploring and presenting data in a meaningful and visually appealing manner.

Data Management Platforms

Data management platforms (DMPs) are software solutions designed to centralize and manage data from various sources, including customer data, transaction data, and marketing data. These platforms provide features for data integration, cleansing, transformation, and storage, allowing organizations to maintain a single source of truth for their data.

Data management platforms help organizations streamline their data operations, improve data quality, and derive actionable insights from their data assets.

Data Analysis Best Practices

Effective data analysis requires adherence to best practices to ensure the accuracy, reliability, and validity of the results.

• Define Clear Objectives:  Clearly define the objectives and goals of your data analysis project to guide your efforts and ensure alignment with the desired outcomes.
• Understand the Data:  Thoroughly understand the characteristics and limitations of your data, including its sources, quality, structure, and any potential biases or anomalies.
• Preprocess Data:  Clean and preprocess the data to handle missing values, outliers, and inconsistencies, ensuring that the data is suitable for analysis.
• Use Appropriate Tools:  Select and use appropriate tools and software for data analysis, considering factors such as the complexity of the data, the analysis objectives, and the skills of the analysts.
• Document the Process:  Document the data analysis process, including data preprocessing steps, analysis techniques, assumptions, and decisions made, to ensure reproducibility and transparency.
• Validate Results:  Validate the results of your analysis using appropriate techniques such as cross-validation, sensitivity analysis, and hypothesis testing to ensure their accuracy and reliability.
• Visualize Data:  Use data visualization techniques to represent your findings visually, making complex patterns and relationships easier to understand and communicate to stakeholders.
• Iterate and Refine:  Iterate on your analysis process, incorporating feedback and refining your approach as needed to improve the quality and effectiveness of your analysis.
• Consider Ethical Implications:  Consider the ethical implications of your data analysis, including issues such as privacy, fairness, and bias, and take appropriate measures to mitigate any potential risks.
• Collaborate and Communicate:  Foster collaboration and communication among team members and stakeholders throughout the data analysis process to ensure alignment, shared understanding, and effective decision-making.

By following these best practices, you can enhance the rigor, reliability, and impact of your data analysis efforts, leading to more informed decision-making and actionable insights.

Data analysis is a powerful tool that empowers individuals and organizations to make sense of the vast amounts of data available to them. By applying various techniques and tools, data analysis allows us to uncover valuable insights, identify patterns, and make informed decisions across diverse fields such as business, science, healthcare, and government. From understanding customer behavior to predicting future trends, data analysis applications are virtually limitless. However, successful data analysis requires more than just technical skills—it also requires critical thinking, creativity, and a commitment to ethical practices. As we navigate the complexities of our data-rich world, it's essential to approach data analysis with curiosity, integrity, and a willingness to learn and adapt. By embracing best practices, collaborating with others, and continuously refining our approaches, we can harness the full potential of data analysis to drive innovation, solve complex problems, and create positive change in the world around us. So, whether you're just starting your journey in data analysis or looking to deepen your expertise, remember that the power of data lies not only in its quantity but also in our ability to analyze, interpret, and use it wisely.

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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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• Computational science
• 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

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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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