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10 Advantages & Disadvantages of Quantitative Research

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis.

10 Advantages & Disadvantages of Quantitative Research

Quantitative Research

When researchers look at gathering data, there are two types of testing methods they can use: quantitative research, or qualitative research. Quantitative research looks to capture real, measurable data in the form of numbers and figures; whereas qualitative research is concerned with recording opinion data, customer characteristics, and other non-numerical information.

Quantitative research is a powerful tool for those looking to gather empirical data about their topic of study. Using statistical models and math, researchers evaluate their hypothesis. An integral component of quantitative research - and truly, all research - is the careful and considered analysis of the resulting data points.

There are several key advantages and disadvantages to conducting quantitative research that should be considered when deciding which type of testing best fits the occasion.

5 Advantages of Quantitative Research

  • Quantitative research is concerned with facts & verifiable information.

Quantitative research is primarily designed to capture numerical data - often for the purpose of studying a fact or phenomenon in their population. This kind of research activity is very helpful for producing data points when looking at a particular group - like a customer demographic. All of this helps us to better identify the key roots of certain customer behaviors. 

Businesses who research their customers intimately often outperform their competitors. Knowing the reasons why a customer makes a particular purchasing decision makes it easier for companies to address issues in their audiences. Data analysis of this kind can be used for a wide range of applications, even outside the world of commerce. 

  • Quantitative research can be done anonymously. 

Unlike qualitative research questions - which often ask participants to divulge personal and sometimes sensitive information - quantitative research does not require participants to be named or identified. As long as those conducting the testing are able to independently verify that the participants fit the necessary profile for the test, then more identifying information is unnecessary. 

  • Quantitative research processes don't need to be directly observed.

Whereas qualitative research demands close attention be paid to the process of data collection, quantitative research data can be collected passively. Surveys, polls, and other forms of asynchronous data collection generate data points over a defined period of time, freeing up researchers to focus on more important activities. 

  • Quantitative research is faster than other methods.

Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects. Less delays and a larger sample size ensures you will have a far easier go of managing your data collection process.

  • Quantitative research is verifiable and can be used to duplicate results.

The careful and exact way in which quantitative tests must be designed enables other researchers to duplicate the methodology. In order to verify the integrity of any experimental conclusion, others must be able to replicate the study on their own. Independently verifying data is how the scientific community creates precedent and establishes trust in their findings.

5 Disadvantages of Quantitative Research

  • Limited to numbers and figures.

Quantitative research is an incredibly precise tool in the way that it only gathers cold hard figures. This double edged sword leaves the quantitative method unable to deal with questions that require specific feedback, and often lacks a human element. For questions like, “What sorts of emotions does our advertisement evoke in our test audiences?” or “Why do customers prefer our product over the competing brand?”, using the quantitative research method will not derive a meaningful answer.

  • Testing models are more difficult to create.

Creating a quantitative research model requires careful attention to be paid to your design. From the hypothesis to the testing methods and the analysis that comes after, there are several moving parts that must be brought into alignment in order for your test to succeed. Even one unintentional error can invalidate your results, and send your team back to the drawing board to start all over again.

  • Tests can be intentionally manipulative.  

Bad actors looking to push an agenda can sometimes create qualitative tests that are faulty, and designed to support a particular end result. Apolitical facts and figures can be turned political when given a limited context. You can imagine an example in which a politician devises a poll with answers that are designed to give him a favorable outcome - no matter what respondents pick.

  • Results are open to subjective interpretation.

Whether due to researchers' bias or simple accident, research data can be manipulated in order to give a subjective result. When numbers are not given their full context, or were gathered in an incorrect or misleading way, the results that follow can not be correctly interpreted. Bias, opinion, and simple mistakes all work to inhibit the experimental process - and must be taken into account when designing your tests. 

  • More expensive than other forms of testing. 

Quantitative research often seeks to gather large quantities of data points. While this is beneficial for the purposes of testing, the research does not come free. The grander the scope of your test and the more thorough you are in it’s methodology, the more likely it is that you will be spending a sizable portion of your marketing expenses on research alone. Polling and surveying, while affordable means of gathering quantitative data, can not always generate the kind of quality results a research project necessitates. 

Key Takeaways 

Numerical data quantitative research process:

Numerical data is a vital component of almost any research project. Quantitative data can provide meaningful insight into qualitative concerns. Focusing on the facts and figures enables researchers to duplicate tests later on, and create their own data sets.

To streamline your quantitative research process:

Have a plan. Tackling your research project with a clear and focused strategy will allow you to better address any errors or hiccups that might otherwise inhibit your testing. 

Define your audience. Create a clear picture of your target audience before you design your test. Understanding who you want to test beforehand gives you the ability to choose which methodology is going to be the right fit for them. 

Test, test, and test again. Verifying your results through repeated and thorough testing builds confidence in your decision making. It’s not only smart research practice - it’s good business.

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Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

What is Quantitative Research

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

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What is Quantitative Research? Definition, Examples, Key Advantages, Methods and Best Practices

By Nick Jain

Published on: May 17, 2023

What is Quantitative Research

Table of Contents

What is Quantitative Research?

Quantitative research examples, quantitative research: key advantages, quantitative research methodology, 7 best practices to conduct quantitative research.

Quantitative research stands as a powerful research methodology dedicated to the systematic collection and analysis of measurable data. Through rigorous statistical and mathematical techniques, this method extracts insights from structured surveys, controlled experiments, or other defined data-gathering methods.

The primary objective of quantitative research is to measure and quantify variables, relationships, and patterns within the dataset. By testing hypotheses, making predictions, and drawing generalizable conclusions, it plays a crucial role in fields such as psychology, sociology, economics, and education. This approach often involves significant sample sizes, ensuring robust results.

Explore the depth of quantitative research with this comprehensive guide, offering practical examples and applications to demonstrate its real-world impact. Stay updated with the latest trends and developments in quantitative research as we continually refine our insights to provide you with the most relevant and cutting-edge information.

Quantitative Research: Key Characteristics

Below are the key characteristics of quantitative research:

  • Objectivity: Quantitative research is grounded in the principles of objectivity and empiricism, which means that the research is focused on observable and measurable phenomena, rather than personal opinions or experiences.
  • Structured approach: Quantitative research follows a structured and systematic approach to data collection and analysis, using clearly defined variables, hypotheses, and research questions.
  • Numeric data: Quantitative research uses numerical data to describe and analyze the phenomena under study, such as statistical analysis, surveys, and experiments.
  • Large sample size: Quantitative research often involves large sample sizes to ensure statistical significance and to generalize findings to a larger population.
  • Standardized data collection: Quantitative research typically involves standardized data collection methods, such as surveys or experiments, to minimize potential sources of bias and increase reliability.
  • Deductive reasoning: Quantitative research uses deductive reasoning, where the researcher tests a specific hypothesis based on prior knowledge and theory.
  • Replication: Quantitative research emphasizes the importance of replication, where other researchers can reproduce the study’s methods and obtain similar results.
  • Statistical analysis: Quantitative research involves statistical analysis to analyze the data and test the research hypotheses, often using software programs to assist with data analysis.
  • Precision: Quantitative research aims to be precise in its measurement and analysis of data. It seeks to quantify and measure the specific aspects of a phenomenon being studied.
  • Generalizability: Quantitative research aims to generalize findings from a sample to a larger population. It seeks to draw conclusions that apply to a broader group beyond the specific sample being studied.

Below are 3 examples of quantitative research:

A study investigating the effectiveness of a new training program for employees in a company. The study uses a quasi-experimental design, where one group of employees receives the new training program, and another group does not. The researchers measure the employees’ job performance before and after the training program and compare the results between the two groups using statistical analysis.

A study examining the relationship between physical exercise and mental health. The study collects data from a sample of individuals, asking them to report their frequency and duration of exercise, as well as their level of mental health. The researchers then use statistical analysis to determine if there is a significant correlation between exercise and mental health, controlling for other variables such as age and gender.

A study investigating the impact of a new teaching method on student learning outcomes. The study uses a quasi-experimental design, where one group of students receives the new teaching method, and another group receives the traditional teaching method. The researchers collect pre-test and post-test data on the student’s learning outcomes and analyze the results using statistical methods to determine if there is a significant difference between the two groups.

Learn more: What is Quantitative Market Research?

Quantitative Research: Key Advantages

The advantages of quantitative research make it a valuable research method in a variety of fields, particularly in fields that require precise measurement and testing of hypotheses.

  • Precision: Quantitative research aims to be precise in its measurement and analysis of data. This can increase the accuracy of the results and enable researchers to make more precise predictions.
  • Test hypotheses: Quantitative research is well-suited for testing specific hypotheses or research questions, allowing researchers to draw clear conclusions and make predictions based on the data.
  • Quantify relationships: Quantitative research enables researchers to quantify and measure relationships between variables, allowing for more precise and quantitative comparisons.
  • Efficiency: Quantitative research often involves the use of standardized procedures and data collection methods, which can make the research process more efficient and reduce the amount of time and resources required.
  • Easy to compare: Quantitative research often involves the use of standardized measures and scales, which makes it easier to compare results across different studies or populations.
  • Ability to detect small effects: Quantitative research is often able to detect small effects that may not be observable through qualitative research methods, due to the use of statistical analysis and large sample sizes.

Quantitative research is a type of research that focuses on collecting and analyzing numerical data to answer research questions. There are two main methods used to conduct quantitative research:

1. Primary Method

There are several methods of primary quantitative research, each with its own strengths and limitations.

Surveys: Surveys are a common method of quantitative research and involve collecting data from a sample of individuals using standardized questionnaires or interviews. Surveys can be conducted in various ways, such as online, by mail, by phone, or in person. Surveys can be used to study attitudes, behaviors, opinions, and demographics.

One of the main advantages of surveys is that they can be conducted on a large scale, making it possible to obtain representative data from a population. However, surveys can suffer from issues such as response bias, where participants may not provide accurate or truthful answers, and nonresponse bias, where certain groups may be less likely to participate in the survey.

Experiments: Experiments involve manipulating one or more variables to determine their effects on an outcome of interest. Experiments can be carried out in controlled laboratory settings or in real-world field environments. Experiments can be used to test causal relationships between variables and to establish cause-and-effect relationships.

One of the main advantages of experiments is that they provide a high level of control over the variables being studied, which can increase the internal validity of the study. However, experiments can suffer from issues such as artificiality, where the experimental setting may not accurately reflect real-world situations, and demand characteristics, where participants may change their behavior due to the experimental setting.

Observational studies: Observational studies involve observing and recording data without manipulating any variables. Observational studies can be conducted in various settings, such as naturalistic environments or controlled laboratory settings. Observational studies can be used to study behaviors, interactions, and phenomena that cannot be manipulated experimentally.

One of the main advantages of observational studies is that they can provide rich and detailed data about real-world phenomena. However, observational studies can suffer from issues such as observer bias, where the observer may interpret the data in a subjective manner, and reactivity, where the presence of the observer may change the behavior being observed.

Content analysis: Content analysis involves analyzing media or communication content, such as text, images, or videos, to identify patterns or trends. Content analysis can be used to study media representations of social issues or to identify patterns in social media data.

One of the main advantages of content analysis is that it can provide insights into the cultural and social values reflected in media content. However, content analysis can suffer from issues such as the subjectivity of the coding process and the potential for errors or bias in the data collection process.

Psychometrics: Psychometrics involves the development and validation of standardized tests or measures, such as personality tests or intelligence tests. Psychometrics can be used to study individual differences in psychological traits and to assess the validity and reliability of psychological measures.

One of the main advantages of psychometrics is that it can provide a standardized and objective way to measure psychological constructs. However, psychometrics can suffer from issues such as the cultural specificity of the measures and the potential for response bias in self-report measures.

2. Secondary Method

Secondary quantitative research methods involve analyzing existing data that was collected for other purposes. This can include data from government records, public opinion polls, or market research studies. Secondary research is often quicker and less expensive than primary research, but it may not provide data that is as specific to the research question.

One of the main advantages of secondary data analysis is that it can be a cost-effective way to obtain large amounts of data. However, secondary data analysis can suffer from issues such as the quality and relevance of the data, and the potential for missing or incomplete data.

Learn more: What is Quantitative Observation?

7 Best Practices to Conduct Quantitative Research

Here are the key best practices that should be followed when conducting quantitative research:

1. Clearly define the research question: The research question should be specific, measurable, and focused on a clear problem or issue.

2. Use a well-designed research design: The research design should be appropriate for the research question, and should include a clear sampling strategy, data collection methods, and statistical analysis plan.

3. Use validated and reliable instruments: The instruments used to collect data should be validated and reliable to ensure that the data collected is accurate and consistent.

4. Ensure informed consent: Participants should be fully informed about the purpose of the research, their rights, and how their data will be used. Informed consent should be obtained before data collection begins.

5. Minimize bias: Researchers should take steps to minimize bias in all stages of the research process, including study design, data collection, and data analysis.

6. Ensure data security and confidentiality: Data should be kept secure and confidential to protect the privacy of participants and prevent unauthorized access.

7. Use appropriate statistical analysis: Statistical analysis should be appropriate for the research question and the data collected. Accurate and clear reporting of results is imperative in quantitative research.

Learn more: What is Qualitative Research?

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Advantages and Disadvantages of Quantitative Research

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Quantitative research is the process of gathering observable data to answer a research question using statistical , computational, or mathematical techniques. It is often seen as more accurate or valuable than qualitative research, which focuses on gathering non-numerical data.

Qualitative research looks at opinions, concepts, characteristics, and descriptions. Quantitative research looks at measurable, numerical relationships. Both kinds of research have their advantages and disadvantages .

How Can Businesses Use Quantitative Research?

Research benefits small businesses by helping you make informed decisions. Conducting market research should be a regular part of any business plan, allowing you to grow efficiently and make good use of your available resources.

Businesses can use research to:

  • Learn more about customer opinions and buying patterns .
  • Test new products and services before launching them.
  • Make decisions about product packaging, branding, and other visual elements.
  • Understand patterns in your market or industry.
  • Analyze the behavior of your competitors.
  • Identify the best use of your marketing resources.
  • Compare how successful different promotions will be before scaling up.
  • Decide on where new locations or stores should be.

When deciding what type of research will benefit your business, it is important to consider the advantages and disadvantages of quantitative research.

Advantages of Quantitative Research

The use of statistical analysis and hard numbers found in quantitative research has distinct advantages in the research process.

  • Can be tested and checked. Quantitative research requires careful experimental design and the ability for anyone to replicate both the test and the results. This makes the data you gather more reliable and less open to argument.
  • Straightforward analysis. When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.
  • Prestige. Research that involves complex statistics and data analysis is considered valuable and impressive because many people don't understand the mathematics involved. Quantitative research is associated with technical advancements like computer modeling, stock selection, portfolio evaluation, and other data-based business decisions. The association of prestige and value with quantitative research can reflect well on your small business.

Disadvantages of Quantitative Research

However, the focus on numbers found in quantitative research can also be limiting, leading to several disadvantages.

  • False focus on numbers. Quantitative research can be limited in its pursuit of concrete, statistical relationships, which can lead to researchers overlooking broader themes and relationships. By focusing solely on numbers, you run the risk of missing surprising or big-picture information that can benefit your business.
  • Difficulty setting up a research model. When you conduct quantitative research, you need to carefully develop a hypothesis and set up a model for collecting and analyzing data. Any errors in your set up, bias on the part of the researcher, or mistakes in execution can invalidate all your results. Even coming up with a hypothesis can be subjective, especially if you have a specific question that you already know you want to prove or disprove.
  • Can be misleading. Many people assume that because quantitative research is based on statistics it is more credible or scientific than observational, qualitative research. However, both kinds of research can be subjective and misleading. The opinions and biases of a researcher are just as likely to impact quantitative approaches to information gathering. In fact, the impact of this bias occurs earlier in the process of quantitative research than it does in qualitative research.

Tips for Conducting Quantitative Research

If you decide to conduct quantitative research for your small business,

  • Work with a professional. Professional market researchers and data analysts are trained in how to conduct survey research and run statistical models. To ensure that your research is well-designed and your results are accurate, work with a professional. If you can't afford to hire researchers for the length of the project, look for someone who can help just with set-up or analysis.
  • Have a clear research question. To save time and resources, have a clear idea of what question you want answered before you begin researching. You can find areas that need research by looking at your marketing plan and identifying where you struggle to make an informed decision.
  • Don't be afraid to change your model. Research is a process, and needing to change direction or start over doesn't mean you have failed or done something wrong. Often, successful research will raise new questions. Keep track of those new questions so that you can continue answering them as you move forward.
  • Combine quantitative and qualitative research. Successfully running a small business relies on understanding people, and the behavior of your customers and competitors cannot be reduced to numbers. As you conduct quantitative research, try to collect qualitative data as well. This can take the form of open-ended questions on surveys, panel discussions, or even just keeping track of opinions or concerns that customers share. By combining the two types of research, you'll end up with the best possible picture of how your business can grow and succeed within its market.

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Qualitative vs. quantitative research

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

  • Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
  • Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
  • Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

  • Interviews : Asking open-ended questions verbally to respondents.
  • Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
  • Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
  • Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

  • Use quantitative research if you want to confirm or test something (a theory or hypothesis )
  • Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

  • Average scores ( means )
  • The number of times a particular answer was given
  • The correlation or causation between two or more variables
  • The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

  • Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
  • Thematic analysis : Closely examining the data to identify the main themes and patterns
  • Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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research advantages quantitative

Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

research advantages quantitative

  • Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
  • Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
  • Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
  • Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
  • Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
  • Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

  • Primary quantitative research methods
  • Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

  • Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
  • Multiple samples can be analyzed and compared using a cross-sectional survey research method.
  • Multiple variables can be evaluated using this type of survey research.
  • The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

  • In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
  • Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
  • In situations where the sequence of events is highly essential, longitudinal surveys are used.
  • Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

  • The relationship between stress and depression.
  • The equation between fame and money.
  • The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

  • The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

  • Traditional research methods are more effective than modern techniques.
  • Systematic teaching schedules help children who struggle to cope with the course.
  • It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

  • Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
  • Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
  • Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
  • Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

  • Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
  • Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
  • Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
  • Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
  • Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

  • Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
  • Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
  • Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
  • Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
  • QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
  • SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
  • Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

  • SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
  • Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
  • Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
  • TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

  • Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
  • Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
  • Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
  • Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
  • Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

  • A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
  • Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

advantages-of-quantitative-research

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

Tips to conduct quantitative research

  • Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
  • Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
  • Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
  • Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

research advantages quantitative

Quantitative Research

  • Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
  • Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
  • Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
  • Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
  • Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
  • Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
  • Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

  • Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
  • Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
  • Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
  • Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
  • Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
  • Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
  • Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

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Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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  • What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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13 Pros and Cons of Quantitative Research Methods

Quantitative research utilizes mathematical, statistical, and computational tools to derive results. This structure creates a conclusiveness to the purposes being studied as it quantifies problems to understand how prevalent they are.

It is through this process that the research creates a projectable result which applies to the larger general population.

Instead of providing a subjective overview like qualitative research offers, quantitative research identifies structured cause-and-effect relationships. Once the problem is identified by those involved in the study, the factors associated with the issue become possible to identify as well. Experiments and surveys are the primary tools of this research method to create specific results, even when independent or interdependent factors are present.

These are the quantitative research pros and cons to consider.

List of the Pros of Quantitative Research

1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine. That means the information under study can be analyzed very quickly when compared to other research methods. The need to separate systems or identify variables is not as prevalent with this option either.

2. The samples of quantitative research are randomized. Quantitative research uses a randomized process to collect information, preventing bias from entering into the data. This randomness creates an additional advantage in the fact that the information supplied through this research can then be statistically applied to the rest of the population group which is under study. Although there is the possibility that some demographics could be left out despite randomization to create errors when the research is applied to all, the results of this research type make it possible to glean relevant data in a fraction of the time that other methods require.

3. It offers reliable and repeatable information. Quantitative research validates itself by offering consistent results when the same data points are examined under randomized conditions. Although you may receive different percentages or slight variances in other results, repetitive information creates the foundation for certainty in future planning processes. Businesses can tailor their messages or programs based on these results to meet specific needs in their community. The statistics become a reliable resource which offer confidence to the decision-making process.

4. You can generalize your findings with quantitative research. The issue with other research types is that there is no generalization effect possible with the data points they gather. Quantitative information may offer an overview instead of specificity when looking at target groups, but that also makes it possible to identify core subjects, needs, or wants. Every finding developed through this method can go beyond the participant group to the overall demographic being looked at with this work. That makes it possible to identify trouble areas before difficulties have a chance to start.

5. The research is anonymous. Researchers often use quantitative data when looking at sensitive topics because of the anonymity involved. People are not required to identify themselves with specificity in the data collected. Even if surveys or interviews are distributed to each individual, their personal information does not make it to the form. This setup reduces the risk of false results because some research participants are ashamed or disturbed about the subject discussions which involve them.

6. You can perform the research remotely. Quantitative research does not require the participants to report to a specific location to collect the data. You can speak with individuals on the phone, conduct surveys online, or use other remote methods that allow for information to move from one party to the other. Although the number of questions you ask or their difficulty can influence how many people choose to participate, the only real cost factor to the participants involves their time. That can make this option a lot cheaper than other methods.

7. Information from a larger sample is used with quantitative research. Qualitative research must use small sample sizes because it requires in-depth data points to be collected by the researchers. This creates a time-consuming resource, reducing the number of people involved. The structure of quantitative research allows for broader studies to take place, which enables better accuracy when attempting to create generalizations about the subject matter involved. There are fewer variables which can skew the results too because you’re dealing with close-ended information instead of open-ended questions.

List of the Cons of Quantitative Research

1. You cannot follow-up on any answers in quantitative research. Quantitative research offers an important limit: you cannot go back to participants after they’ve filled out a survey if there are more questions to ask. There is a limited chance to probe the answers offered in the research, which creates fewer data points to examine when compared to other methods. There is still the advantage of anonymity, but if a survey offers inconclusive or questionable results, there is no way to verify the validity of the data. If enough participants turn in similar answers, it could skew the data in a way that does not apply to the general population.

2. The characteristics of the participants may not apply to the general population. There is always a risk that the research collected using the quantitative method may not apply to the general population. It is easy to draw false correlations because the information seems to come from random sources. Despite the efforts to prevent bias, the characteristics of any randomized sample are not guaranteed to apply to everyone. That means the only certainty offered using this method is that the data applies to those who choose to participate.

3. You cannot determine if answers are true or not. Researchers using the quantitative method must operate on the assumption that all the answers provided to them through surveys, testing, and experimentation are based on a foundation of truth. There are no face-to-face contacts with this method, which means interviewers or researchers are unable to gauge the truthfulness or authenticity of each result.

A 2011 study published by Psychology Today looked at how often people lie in their daily lives. Participants were asked to talk about the number of lies they told in the past 24 hours. 40% of the sample group reported telling a lie, with the median being 1.65 lies told per day. Over 22% of the lies were told by just 1% of the sample. What would happen if the random sampling came from this 1% group?

4. There is a cost factor to consider with quantitative research. All research involves cost. There’s no getting around this fact. When looking at the price of experiments and research within the quantitative method, a single result mist cost more than $100,000. Even conducting a focus group is costly, with just four groups of government or business participants requiring up to $60,000 for the work to be done. Most of the cost involves the target audiences you want to survey, what the objects happen to be, and if you can do the work online or over the phone.

5. You do not gain access to specific feedback details. Let’s say that you wanted to conduct quantitative research on a new toothpaste that you want to take to the market. This method allows you to explore a specific hypothesis (i.e., this toothpaste does a better job of cleaning teeth than this other product). You can use the statistics to create generalizations (i.e., 70% of people say this toothpaste cleans better, which means that is your potential customer base). What you don’t receive are specific feedback details that can help you refine the product. If no one likes the toothpaste because it tastes like how a skunk smells, that 70% who say it cleans better still won’t purchase the product.

6. It creates the potential for an unnatural environment. When carrying out quantitative research, the efforts are sometimes carried out in environments which are unnatural to the group. When this disadvantage occurs, the results will often differ when compared to what would be discovered with real-world examples. That means researchers can still manipulate the results, even with randomized participants, because of the work within an environment which is conducive to the answers which they want to receive through this method.

These quantitative research pros and cons take a look at the value of the information collected vs. its authenticity and cost to collect. It is cheaper than other research methods, but with its limitations, this option is not always the best choice to make when looking for specific data points before making a critical decision.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research, on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

RESEARCH THEMATICANALYSISMETHOD

Key Features

  • Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
  • Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
  • The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
  • The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
  • The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

  • Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
  • The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
  • Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
  • The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

  • Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
  • Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
  • Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
  • Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

  • Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
  • The research aims for objectivity (i.e., without bias) and is separated from the data.
  • The design of the study is determined before it begins.
  • For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
  • Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

  • Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
  • Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
  • Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
  • Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

  • Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
  • Useful for testing and validating already constructed theories.
  • Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
  • Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
  • Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

  • Designing qualitative research
  • Methods of data collection and analysis
  • Introduction to quantitative and qualitative research
  • Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
  • Qualitative research in health care: Analysing qualitative data
  • Qualitative data analysis: the framework approach
  • Using the framework method for the analysis of
  • Qualitative data in multi-disciplinary health research
  • Content Analysis
  • Grounded Theory
  • Thematic Analysis

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Quantitative and Qualitative Research

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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Quantitative research

Affiliation.

  • 1 Faculty of Health and Social Care, University of Hull, Hull, England.
  • PMID: 25828021
  • DOI: 10.7748/ns.29.31.44.e8681

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Keywords: Experiments; measurement; nursing research; quantitative research; reliability; surveys; validity.

  • Biomedical Research / methods*
  • Double-Blind Method
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Home » Quantitative Research: Definition, Methods, and Examples

Quantitative Research: Definition, Methods, and Examples

June 13, 2023 max 8min read.

Quantitative Research

This article covers:

What Is Quantitative Research?

Quantitative research methods .

  • Data Collection and Analysis

Types of Quantitative Research

  • Advantages and Disadvantages of Quantitative Research

Examples of Quantitative Research

Picture this: you’re a product or project manager and must make a crucial decision. You need data-driven insights to guide your choices, understand customer preferences, and predict market trends. That’s where quantitative research comes into play. It’s like having a secret weapon that empowers you to make informed decisions confidently.

Quantitative research is all about numbers, statistics, and measurable data. It’s a systematic approach that allows you to gather and analyze numerical information to uncover patterns, trends, and correlations. 

Quantitative research provides concrete, objective data to drive your strategies, whether conducting surveys, analyzing large datasets, or crunching numbers.

In this article, we’ll dive and learn all about quantitative research; get ready to uncover the power of numbers.

Quantitative Research Definition:

Quantitative research is a systematic and objective approach to collecting, analyzing, and interpreting numerical data. It measures and quantifies variables, employing statistical methods to uncover patterns, relationships, and trends.

Quantitative research gets utilized across a wide range of fields, including market research, social sciences, psychology, economics, and healthcare. It follows a structured methodology that uses standardized instruments, such as surveys, experiments, or polls, to collect data. This data is then analyzed using statistical techniques to uncover patterns and relationships.

The purpose of quantitative research is to measure and quantify variables, assess the connections between variables, and draw objective and generalizable conclusions. Its benefits are numerous:

  • Rigorous and scientific approach : Quantitative research provides a comprehensive and scientific approach to studying phenomena. It enables researchers to gather empirical evidence and draw reliable conclusions based on solid data.
  • Evidence-based decision-making : By utilizing quantitative research, researchers can make evidence-based decisions. It helps in developing informed strategies and evaluating the effectiveness of interventions or policies by relying on data-driven insights.
  • Advancement of knowledge : Quantitative research contributes to the advancement of knowledge by building upon existing theories. It expands understanding in various fields and informs future research directions, allowing for continued growth and development.

Here are various quantitative research methods:

Survey research : This method involves collecting data from a sample of individuals through questionnaires, interviews, or online surveys. Surveys gather information about people’s attitudes, opinions, behaviors, and characteristics.

Experimentation: It is a research method that allows researchers to determine cause-and-effect relationships. In an experiment, participants randomly get assigned to different groups. While the other group does not receive treatment or intervention, one group does. The outcomes of the two groups then get measured to analyze the effects of the treatment or intervention.

Here are the steps involved in an experiment:

  • Define the research question. What do you want to learn about?
  • Develop a hypothesis. What do you think the answer to your research question is?
  • Design the experiment. How will you manipulate the variables and measure the outcomes?
  • Recruit participants. Who will you study?
  • Randomly assign participants to groups. This ensures that the groups are as similar as possible.
  • Apply the treatments or interventions. This is what the researcher is attempting to test the effects of.
  • Measure the outcomes. This is how the researcher will determine whether the treatments or interventions had any effect.
  • Analyze the data. This is how the researcher will determine whether the results support the hypothesis.
  • Draw conclusions. What do the results mean?
  • Content analysis : Content analysis is a systematic approach to analyzing written, verbal, or visual communication. Researchers identify and categorize specific content, themes, or patterns in various forms of media, such as books, articles, speeches, or social media posts.
  • Secondary data analysis : It is a research method that involves analyzing data already collected by someone else. This data can be from various sources, such as government reports, previous research studies, or large datasets like surveys or medical records. 

Researchers use secondary data analysis to answer new research questions or gain additional insights into a topic.

Data Collection and Analysis for Quantitative Research

Quantitative research is research that uses numbers and statistics to answer questions. It often measures things like attitudes, behaviors, and opinions.

There are three main methods for collecting quantitative data:

  • Surveys and questionnaires: These are structured instruments used to gather data from a sample of people.
  • Experiments and controlled observations: These are conducted in a controlled setting to measure variables and determine cause-and-effect relationships.
  • Existing data sources (secondary data): This data gets collected from databases, archives, or previous studies.

Data preprocessing and cleaning is the first step in data analysis. It involves identifying and correcting errors, removing outliers, and ensuring the data is consistent.

Descriptive statistics is a branch of statistics that deals with the description of the data. It summarizes and describes the data using central tendency, variability, and shape measures.

Inferential statistics again comes under statistics which deals with the inference of properties of a population from a sample. It tests hypotheses, estimates parameters, and makes predictions.

Here are some of the most common inferential statistical techniques:

  • Hypothesis testing : This assesses the significance of relationships or differences between variables.
  • Confidence intervals : This estimates the range within which population parameters likely fall.
  • Correlation and regression analysis : This examines relationships and predicts outcomes based on variables.
  • Analysis of variance (ANOVA) : This compare means across multiple groups or conditions.

Statistical software and tools for data analysis can perform complex statistical analyses efficiently. Some of the most popular statistical software packages include SPSS, SAS, and R.

Here are some of the main types of quantitative research methodology:

  • Descriptive research describes a particular population’s characteristics, trends, or behaviors. For example, a descriptive study might look at the average height of students in a school, the number of people who voted in an election, or the types of food people eat.
  • Correlational research checks the relationship between two or more variables. For example, a correlational study might examine the relationship between income and happiness or stress and weight gain. Correlational research can show that two variables are related but cannot show that one variable causes the other.
  • Experimental research is a type of research that investigates cause-and-effect relationships. In an experiment, researchers manipulate one variable (the independent variable) and measure the impact on another variable (the dependent variable). This allows researchers to make inferences about the relationship between the two variables.
  • Quasi-experimental research is similar to experimental research. However, it does not involve random assignment of participants to groups. This can be due to practical or ethical considerations, such as when assigning people to receive a new medication randomly is impossible. In quasi-experimental research, researchers try to control for other factors affecting the results, such as the participant’s age, gender, or health status.
  • Longitudinal research studies change patterns over an extended time. For example, a longitudinal study might examine how children’s reading skills develop over a few years or how people’s attitudes change as they age. But longitudinal research can be expensive and time-consuming. Still, it can offer valuable insights into how people and things change over time.

 Advantages and Disadvantages of Quantitative Research

Here are the advantages and downsides of quantitative research:

Advantages of Quantitative Research:

  • Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation.
  • Generalizability: Quantitative research often involves large sample sizes, which increases the likelihood of obtaining representative data. The study findings are more likely to apply to a wider population.
  • Replicability: Using standardized procedures and measurement instruments in quantitative research enhances replicability. This means that other researchers can repeat the study using the same methods to test the reliability of the findings.
  • Statistical analysis: Quantitative research employs various statistical techniques for data analysis. This allows researchers to identify data patterns, relationships, and associations. Additionally, statistical analysis can provide precision and help draw objective conclusions.
  • Numerical precision: Quantitative research produces numerical data that can be analyzed using mathematical calculations. This numeric precision allows for clear comparisons and quantitative interpretations.

Disadvantages of Quantitative Research :

  • Lack of Contextual Understanding : Quantitative research often focuses on measurable variables, which may limit the exploration of complex phenomena. It may overlook the social, cultural, and contextual factors that could influence the research findings.
  • Limited Insight : While quantitative research can identify correlations and associations, it may not uncover underlying causes or explanations of these relationships. It may provide answers to “what” and “how much,” but not necessarily “why.”
  • Potential for Simplification : The quantification of data can lead to oversimplification, as it may reduce complex phenomena into numerical values. This simplification may overlook nuances and intricacies important to understanding the research topic fully.
  • Cost and Time-Intensive : Quantitative research requires significant resources. It includes time, funding, and specialized expertise. Researchers must collect and analyze large amounts of numerical data, which can be lengthy and expensive.
  • Limited Flexibility : A systematic and planned strategy typically gets employed in quantitative research. It signifies the researcher’s use of a predetermined data collection and analysis approach. As a result, you may be more confident that your study gets conducted consistently and equitably. But it may also make it more difficult for the researcher to change the research plan or pose additional inquiries while gathering data. This could lead to missing valuable insights.

Here are some real-life examples of quantitative research:

  • Market Research : Quantitative market research is a type of market research that uses numerical data to understand consumer preferences, buying behavior, and market trends. This data typically gets gathered through surveys and questionnaires, which are then analyzed to make informed business decisions.
  • Health Studies : Quantitative research, such as clinical trials and epidemiological research, is vital in health studies. Researchers collect numerical data on treatment effectiveness, disease prevalence, risk factors, and patient outcomes. This data is then analyzed statistically to draw conclusions and make evidence-based recommendations for healthcare practices.
  • Educational Research : Quantitative research is used extensively in educational studies to examine various aspects of learning, teaching methods, and academic achievement. Researchers collect data through standardized tests, surveys, or observations. The reason for this approach is to analyze factors influencing student performance, educational interventions, and educational policy effectiveness.
  • Social Science Surveys : Social science researchers often employ quantitative research methods. The aim here is to study social phenomena and gather data on individuals’ or groups’ attitudes, beliefs, and behaviors. Large-scale surveys collect numerical data, then statistically analyze to identify patterns, trends, and associations within the population.
  • Opinion Polls : Opinion polls and public opinion research rely heavily on quantitative research techniques. Polling organizations conduct surveys with representative samples of the population. The companies do this intending to gather numerical data on public opinions, political preferences, and social attitudes. The data then gets analyzed to gauge public sentiment and predict election outcomes or public opinion on specific issues.
  • Economic Research : Quantitative research is widely used in economic studies to analyze economic indicators, trends, and patterns. Economists collect numerical data on GDP, inflation, employment, and consumer spending. Statistical analysis of this data helps understand economic phenomena, forecast future trends, and inform economic policy decisions.

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Qualitative research is about understanding and exploring something in depth. It uses non-numerical data, like interviews, observations, and open-ended survey responses, to gather rich, descriptive insights. Quantitative research is about measuring and analyzing relationships between variables using numerical data.

Quantitative research gets characterized by the following:

  • The collection of numerical information
  • The use of statistical analysis
  • The goal of measuring and quantifying phenomena
  • The purpose of examining relationships between variables
  • The purpose of generalizing findings to a larger population
  • The use of large sample sizes
  • The use of structured surveys or experiments
  • The usage of statistical techniques to analyze data objectively

The primary goal of quantitative research is to gather numerical data and analyze it statistically to uncover patterns, relationships, and trends. It aims to provide objective and generalizable insights using systematic data collection methods, standardized instruments, and statistical analysis techniques. Quantitative research seeks to test hypotheses, make predictions, and inform decision-making in various fields.

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15 Advantages and Disadvantages of Quantitative Research

Quantitative research involves information that deals with quantities and numbers. That is different from the qualitative approach, which is known for observation and description. You can measure quantitative results, but you cannot do so for the qualitative work.

The research takes on a systematic approach that relies on an empirical investigation of observable phenomena. It uses statistical models, computational techniques, and mathematics to develop and employ hypotheses or theories regarding specific ideas. The process of measurement is central to the success of this work.

It is used widely in psychology, sociology, and marketing as a way to provide evidence that a hypothesis is correct. Instead of relying on instinct or opinion, this method of research seeks out facts before suggesting an outcome. That is why the research gets closely affiliated with the scientific method.

Several advantages and disadvantages of quantitative research are worth reviewing when there is a hypothesis under consideration.

List of the Advantages of Quantitative Research

1. The quantitative approach allows you to reach a higher sample size. When you have the ability to study a larger sample size for any hypothesis, then it is easier to reach an accurate generalized conclusion. The additional data that you receive from this work gives the outcome greater credibility because the statistical analysis has more depth to review. A larger sample makes it less likely that outliers in the study group can adversely impact the results you want to achieve impartially.

2. You can collect information quickly when using quantitative research. Researchers collect information for the quantitative research process in real-time scenarios so that statistical analysis can occur almost immediately. Experiments, surveys, and interviews provide immediate answers that become useful from a data-centered approach. Fewer delays in the acquisition of these resources makes it easier to find correlations that eventually lead toward a useful conclusion.

Quantitative research doesn’t require the separation of systems or the identification of variables to produce results. That’s why it is a straightforward process to implement.

3. Quantitative research uses randomized samples. When research participants suspect that a study wants to achieve a specific result, then their personal bias can enter into the data spectrum. The answers provided on the included materials are partial truths or outright lies as a way to manipulate the work. That’s why the quantitative approach is so useful when trying to study a specific hypothesis within a large population demographic.

This approach uses a randomized process to collect information. That excludes bias from appearing in most situations. It also provides an advantage in the fact that the data can then get statistically applied to the rest of the demographic being studied. There is always a risk of error to consider, but it is this method that typically supplies the most factual results.

4. Results duplication is possible when using quantitative research. When opinions are a valid substitute for facts, then anything becomes possible. Quantitative research eliminates this problem because it only focuses on actual data. The work validates itself because the results always point toward the same data, even though randomized conditions exist. There can be minute variations found over time, but the general conclusions that researchers develop when using this process stay accurate.

That’s why this information is useful when looking at the need for specific future outcomes. The facts provide statistics that are suitable to consider when difficult decisions must get made.

5. Quantitative research can focus on facts or a series of information. Researchers can use the quantitative approach to focus on a specific fact that they want to study in the general population. This method is also useful when a series of data points are highly desirable within a particular demographic. It is a process that lets us understand the reasons behind our decisions, behaviors, or actions from a societal viewpoint.

When we can comprehend the meaning behind the decisions that people make, then it is easier to discover pain points or specific preferences that require resolution. Then the data analysis can extend to the rest of the population so that everyone can benefit from this work.

6. The research performed with the quantitative approach is anonymous. As long as researchers can verify that individuals fit in the demographic profile of their study group, there is no need to provide personal information. The anonymous nature of quantitative research makes it useful for data collection because people are more likely to share an honest perspective when there are guarantees that their feedback won’t come back to haunt them. Even when interviews or surveys are part of this work, the personal information is a screening tool instead of an identifying trademark.

7. Quantitative research doesn’t require direct observation to be useful. Researchers must follow specific protocols when using the quantitative method, but there isn’t a requirement to directly observe each participant. That means a study can send surveys to individuals without the need to have someone in the room while they provide answers. This advantage creates a better response rate because people have more time and less pressure to complete the work.

Although the difficulty of the questions asked or the length of a survey or interview can be barriers to participation, the amount of data that researchers collect from the quantitative process is always useful.

List of the Disadvantages of Quantitative Research

1. This method doesn’t consider the meaning behind social phenomena. The quantitative approach wants to find answers to specific questions so that a particular hypothesis can be proven or disproven. It doesn’t care about the motives that people have when sharing an opinion or making a decision. The goal of this information collecting process is to paint a present-time picture of what is happening in the selected demographic. That means this option cannot measure the ways in which society changes or how people interpret their actions or that of others.

2. Every answer provided in this research method must stand on its own. Quantitative research does not give you the option to review answers with participants. The replies provided to researchers must stand by themselves, even if the information seems confusing or it is invalid. Instead of following a tangent like other methods use, the quantitative option has very few opportunities to ask for clarity.

Part of this disadvantage is due to the anonymous nature of the data that researchers collect. If an answer provides inconclusive results, then there is no way to guarantee the validity of what was received. It is even possible to skew results when a question might be incorrectly formatted.

3. Quantitative research sometimes creates unnatural environments. Quantitative research works well when a verifiable environment is available for study. Researchers can then take advantage of the decisions made in that arena to extrapolate data that is useful for review. There can be times when this approach generates an unnatural scenario based on the questions asked or the approaches used to solicit information. Just as a participant can attempt to skew results by providing falsified answers, researchers can attempt the same result by influencing the design of the work in its initial stages.

4. Some efforts at randomization will not create usable information. The quantitative approach doesn’t look for the reason why variables exist in specific environments. Its goal is to find the different aspects of a demographic in a particular setting to extrapolate data that can be used for generalization purposes. Although the impact of randomization adds validity to the final result, there can be times when the information isn’t usable.

One person might decide to purchase pizza because they’ve had a long day at work and don’t feel like cooking at home. Another individual could make the same decision because it’s Tuesday, and they always purchase pizza on that day. A third household might become customers of a pizzeria because they are celebrating a family birthday. Quantitative data looks at the fact that everyone bought pizza, and it doesn’t care about the reasons why.

5. There is no access to specific feedback. Quantitative research could be best described as a pass-fail grade. You know for certain that a majority of a population demographic will feel a specific way about a particular situation because of the data that researchers collect. You know that everyone purchases pizza, but what you don’t know is how many people enjoyed the experience and will come back for another transaction in the future.

The statistics that researchers gather when using this approach are useful for generalizations that let you see if goods or services earn a passing grade in a specific demographic. What this data cannot produce are specific feedback incidents that allow for positive refinement.

6. Quantitative research studies can be very expensive. If the price is an issue when research work must be done, then the quantitative approach has a significant barrier to consider. A single result may cost more than $100,000 when corporate interests are seeking more data to analyze. One of the most popular methods when using this approach is to use a focus group. Working with groups of participants to solicit answers is about 40% cheaper than other information collection methods, but it is still a problematic approach for small businesses to manage.

There are some affordable methods to use when considering the quantitative research method, such as online polling or emails, but you don’t have any guarantees that the respondents fit into your targeted demographic.

7. Answer validity always creates a cloud of doubt on the final results. Researchers have no meaningful way to determine if the answers someone gives during a quantitative research effort are accurate. This work always gets based on the assumption that everyone is honest and each situation. Since direct observation isn’t always possible with this approach, the data always has a tinge of doubt to it, even when generalizing the results to the rest of the population.

This disadvantage is the reason why you see so many duplicated quantitative research efforts. When the same results occur multiple times, then there is more confidence in the data produced. If different outcomes happen, then researchers know that there are information concerns that require management.

8. Individual characteristics don’t always apply to the general population. Researchers are always facing the risk that the answers or characteristics given in a quantitative study aren’t an accurate representation of the entire population. It is relatively easy to come to false conclusions or correlations because of the assumptions that are necessary for this work. Even the randomized sampling that takes place to remove bias from the equation isn’t 100% accurate. The only certainty that we have from this data is that if we gather enough of it, then the averages that come out of the data analysis offer a path toward something usable.

The use of quantitative research is uncontroversial in most biological and physical sciences. It often gets compared with qualitative methods because the same truth applies to that approach. Each one gets used when it is the most appropriate option.

It is more controversial to use the quantitative method in the social sciences where individuality is sometimes more important than demographical data.

We use quantitative methods to provide testable and precise expressions to qualitative ideas. Then we use the qualitative methods to understand the conclusions that we generate from the statistical analysis of the quantitative approach.

That’s why we review the advantages and disadvantages of quantitative research whenever data collection is necessary. It allows us to focus on facts instead of opinion in a way that we can duplicate in future studies.

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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

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Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

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Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

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Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

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

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

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  • Published: 16 February 2024

Graduate students need more quantitative methods support

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Graduate students in psychology need hands-on support to conduct research using quantitative techniques that exceed their curricular training. If supervisors are not willing or able to provide this support, student-led projects must be redesigned to leverage basic statistical skills learned in the classroom.

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How to Save for Retirement—and Why Most of Us Haven’t (or Can’t) Save Enough

Video via iStock/D-Keine

Whether it’s because of a broken Social Security benefits system, pervasive economic inequality, or poor retirement advice, not everyone’s later years will be so golden

People say getting old isn’t for the faint of heart. Well, neither is retirement—financially speaking, anyway. Saving is hard. Few jobs offer traditional pensions anymore. A 401(k) puts the burden of financial management largely on the employee. And Social Security is a labyrinth of complex regulations and difficult calculations, administered by a seemingly indifferent bureaucracy.

Retirees and those getting ready to retire must navigate all of this at a time of life when they may not be at their strongest, physically, mentally, or emotionally. But they need to be. As the last of the 73 million baby boomers turn 65 in the next seven years, challenges to the system will only increase.

“You’re probably going to have an increase in the poverty rate,” says retirement planning evangelist Laurence Kotlikoff , William Fairfield Warren Professor of Economics in the Boston University College of Arts & Sciences. “People are retiring, some quite early, and retiring with very little. Baby boomers just don’t have enough savings. And Social Security is just not gonna be that big for lots of people.” 

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Twenty-seven percent of adults in 2021 considered themselves to be retired, even though some were still working in some capacity, according to the Federal Reserve . Some 92 percent of retirees over the age of 65 collected Social Security, and two-thirds drew from retirement accounts or pensions. 

A look at the stats covering the number of older people living in poverty seems to provide good news, having declined from one in three people in the 1960s to only about 10 percent of older people today. Still too many, but a big improvement.

But within that picture is a cautionary tale, says Deborah Carr , a CAS professor of sociology and director of the Center for Innovation in Social Science .

“If you look at just that 10 percent, 3 percent of white married men live in poverty, but over a third of women of color living on their own are in poverty,” Carr says. “So, 10 percent is a good news story overall, but we want to recall that there are some deep pockets of poverty among older people.”

Of course, millions of Americans retire just fine, but BU researchers are working to identify the challenges that keep many others from fully enjoying what are supposed to be their golden years.

Conventional Retirement Advice: A “Bait and Switch”

For most people, it all starts with saving, whether in a bank, under a mattress, or, most commonly today, with an individual retirement account. But Kotlikoff says most middle- and working-class people simply don’t save enough.

“Even after Social Security contributions, and after 401(k) contributions, they should probably be saving another 15 percent of their take-home pay, which is very tough,” Kotlikoff says, noting that perhaps 40 percent of those eligible for a 401(k) don’t take advantage. “Most people are saving nothing.”

Total US personal savings, exclusive of Social Security contributions and 401(k)s, only accounted for 4.1 percent of disposable personal income as of April 2023, according to Forbes Advisor , roughly a third below the 6.2 percent a decade earlier. And Kotlikoff says too much of that money is going in the wrong places.

Conventional planning is an elaborate bait and switch designed to sell you investment products that are highly expensive and overly risky. Laurence Kotlikoff

“Everyone who expects to sustain their living standard through retirement needs to do economics-based financial planning,” Kotlikoff says. Where conventional retirement planning involves setting financial and lifestyle goals, then trying to hit them—sometimes through risky investment strategies—he says the economics-based approach is about determining a sustainable living standard based on your resources, and adjusting your spending as your circumstances change, so you don’t outlive your savings.

“Conventional planning is an elaborate bait and switch designed to sell you investment products that are highly expensive and overly risky,” Kotlikoff says.

5 Tips for Smarter Retirement Savings

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Plan Within Your Means

Base your calculations on your assets and calculated benefits, not the lifestyle you’d like to have, to determine a sustainable living standard.

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Save, Save, Save

Save in addition to your retirement program, as much as 15 percent of your salary.

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Use Planning Tools

The information in this article and others like it is only for informational purposes, so make sure you use a financial planning tool or work with an expert on a routine basis to do lifetime financial planning. MaxiFi Planner , is available for free to all BU staff and faculty .

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Collect Benefits Later

Collect Social Security and other benefits at the right time—usually later than you think—to save thousands. Kotlikoff has developed software that can help you find your sweet spot.

Inequality Doesn’t Start at 65

Many people cannot save enough, no matter how hard they try. “The economic inequalities we see among people 65 and older are just a continuation of things that have happened earlier,” says Carr.

In 2020, women’s annual earnings were 82.3 percent of men’s, according to the Bureau of Labor Statistics. So, no surprise that among people invested in a 401(k) plan for at least 10 years, the average balance for women was just under three-quarters of men’s. And life events can take a toll, too, says Carr: a woman who gets divorced at an early age—living on her own, paying for a child, perhaps not receiving a lot of child support—is already taking potentially insurmountable financial hits. 

“It starts really that early. When you’re living check to check you cannot amass wealth, so you have no buffer as you move along,” says Carr, a life course sociologist who uses survey data and quantitative methods to study social factors affecting health and well-being in later life. Her books include Aging in America (University of California Press, 2023) and Golden Years? Social Inequality in Later Life (Russell Sage, 2019).

Most working-class people don’t have a pension now, Carr says, “and if they do have a pension, they can’t afford to put anything in it. And so that’s part of the reason why they just amass less over time. So, it’s the intersection of gender inequality, race inequality going back to much earlier years.”

Our Retirement Safety Net Is Full of Holes

Social Security is supposed to be the great retirement safety net, and for many it is. But decisions about when to retire or take Social Security can be difficult—and so can getting good advice.

“Social Security is broke beyond belief,” says Kotlikoff, whose research focuses on macroeconomics and public finance, as well as economic inequality and Social Security. “Its unfunded liability is $65.9 trillion—twice the size of official government debt. Paying for all projected benefits through time requires an immediate and permanent hike in the combined employer-employee FICA tax [Federal Insurance Contributions Act, a federal payroll tax] from 12.4 percent to 17.0 percent.”

But even if the government somehow manages to fully fund its obligation, simply figuring out when and what you should collect and making it happen is a challenge.

“For far too many of America’s seniors with any but the simplest situations, negotiating the complexities and outright scams of the Social Security system on their own is nigh impossible,” says Kotlikoff. “Kafka could not have designed a more complex set of provisions with hidden catch-22s that can haunt you— in the form of clawbacks —decades after you start collecting benefits.” Not to mention that wrong answers are epidemic for those who ask the Social Security Administration for help making their way through the labyrinth.

Kotlikoff recommends waiting longer to retire and to begin collecting, often until age 70, which can deliver a much larger monthly payout than starting at 62 or 65. In one of his regular Forbes columns , Kotlikoff wrote that, “Your Social Security retirement benefit rate will climb by at least 8 percent for each year that you wait to start collecting until you reach 70.” He offers MaximizeMySocialSecurity.com to households for $39 a year, which provides specific plans to maximize benefits.

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But even careful saving won’t change Social Security’s formulas, or the basic demographic facts.

“Social Security is complex as Larry has told you. And even beyond that, Social Security doesn’t benefit all people or all women in similar ways,” Carr says. A widow can get 100 percent of their late spouse’s benefits, for example, but a divorcee only gets half and only if they were married for more than 10 years.

“We know that divorce is something that historically was more common among low-income people,” she says. “We can’t think about late-life economic inequalities as instantly appearing on your 65th birthday.”

And those inequalities are already having devastating consequences, particularly when it comes to where older Americans call home.

Photo: A young white man wearing a collared shirt and tie poses for a formal portrait headshot

“There is clear evidence that we are in the midst of a growing crisis of homelessness and housing insecurity among older adults,” says Thomas Byrne , a BU School of Social Work associate professor of social welfare policy whose research focuses on the causes, consequences, and policy solutions to homelessness and housing insecurity. “This crisis is driven in part by the substantial rise in housing costs throughout the country over the past decade or so, which have far outstripped overall inflation and increases in income.”

In other words, a rent hike can throw older adults who have been stably housed their entire lives into a housing crisis, especially people who worked in lower-paying jobs, collect less from Social Security, and may not have a pension or private retirement plan, says Byrne.

There is clear evidence that we are in the midst of a growing crisis of homelessness and housing insecurity among older adults. Thomas Byrne

“We should be very concerned about what this means for society as a whole, since research shows that older adults experiencing homelessness have geriatric conditions that are on par with members of the general population who are 20 years older,” Byrne says.

How Cognitive Financial Skills Change with Age

Society also needs to factor how older Americans manage (or don’t) those critical financial decisions. Literature on the topic is pretty sparse, says Preeti Sunderaraman , a BU Chobanian & Avedisian School of Medicine assistant professor of neurology. Under a five-year grant from the National Institutes of Health, she has begun to study financial decision-making by older adults—both those with dementia and a control group of healthy individuals.

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Her interest evolved out of earlier work with people who had traumatic brain injuries and often did poorly managing their money. “I’m not looking at investment decisions or retirement decisions, I’m looking at how people make everyday financial decisions,” Sunderaraman says. Things like having a budget when shopping at the store.

“We really don’t have a good way to gauge a person’s financial abilities,” she says. “So, I started thinking, how do we make this assessment more objective and in sync with today’s technological advances. At the same time, I also found in my research that different cognitive abilities are related to different aspects of finances. There’s not one ability like memory that is the only factor related to good decision-making, you need attention, you need numeracy.”

Sunderaraman’s latest research involves presenting older adults with a simulated credit card task developed in collaboration with computer scientists, economists, geriatricians, and psychologists. Subjects get an online account and are asked to log in and download and review a statement. There are errors embedded in the statement, and she records how people do on the task and their level of financial awareness.

The type of errors you make may be driven by where the pathology is in your brain, Sunderaraman says. If you have Alzheimer’s disease pathology, you’re going to make certain types of financial errors, such as forgetting to pay bills, whereas if you have frontotemporal dementia, you might buy things excessively. 

“We talk about the baby boom generation, right? And this is exactly what is happening,” says Sunderaraman. “This wave is going to go on for another two decades at least. People as they grow older are at higher risk for cognitive decline.”

What Families Can Do to Help Older Relatives Flourish

The implications are important not only because of the costs to society of bad planning, but also to each individual’s life.

“You’re not just dealing with everyday finances,” Sunderaraman says. “You’re dealing with, OK, I have this whole pot of money because of [my] retirement fund, life insurance, what do I do with that money? Should I sell the house? Should I get a reverse mortgage? What happens when I have to go into a facility? All of that stuff. And who do I trust at that stage to help me with my money?”

5 Tips for Making the Best Financial Decisions as You Age

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Know what you’ve got: conduct a comprehensive assessment of your assets and expected expenses.

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Name a power of attorney who could manage your funds in the event you no longer can.

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Keep a trusted family member informed about key financial details, like where to find your account numbers, so they can step in if your health fails.

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Family members of older adults should also be proactive in discussing financial issues, like advanced care plans and wills, early and often.

Carr, who studies how and why human beings flourish in their lives, says families need to be proactive at that stage.

“These things have to be part of the family conversation, what you discuss at Thanksgiving,” she says. “Do your aging parents have a will? Do they need advanced care planning help? And underscoring [that] you’re not asking because you want their inheritance, this is not a ploy to grab the money.”

The goal, after all, is the same one that motivates the years of retirement planning—all that number crunching and sifting through Social Security rules—which is being able to enjoy our later years.

“If we can take financial worry out of the equation, that absolutely frees up people’s mental space and time to focus on the relationships and experiences that enable flourishing, whether it is time with friends or hobbies or family,” Carr says. “Whatever can be done to minimize economic strain and suffering enables people to pursue their passions even at later ages.”

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Working from home can bring big health benefits, study finds

A review of 1,930 papers into home working found major pluses, but also downsides such as antisocial hours and being overlooked for promotion

Working from home allows people to eat more healthily, feel less stressed and have lower blood pressure, according to a large-scale review of academic literature on post-pandemic workplaces.

Yet remote workers are also more likely to eat snacks, drink more, smoke more and put on weight, the study found. And employers who believe that people working from home are lazy should think again – they are less likely to take time off sick, tend to work longer hours and to work evenings and weekends.

The review , funded by the National Institute for Health and Care Research Health Protection Research Unit in Emergency Preparedness and Response – a partnership between the UK Health Security Agency, King’s College London, and the University of East Anglia – considered 1,930 academic papers on home working, teleworking and other types of hybrid and home working in an effort to distil the often contradictory research.

Prof Neil Greenberg, a psychiatrist at King’s College London and one of the study’s authors, said the study showed that workers and employers needed to start considering home working with the same seriousness as they did office working.

“In the old days of office working, people realised that if you put everyone in the same room with no sound-proofing, it was all unpleasant and you didn’t have a very productive workforce,” he said.

“Now that we’ve shifted to a home working culture, it makes sense for organisations and the government to make sure that people who are home working are doing it in as effective a way as possible.”

The review, published in the Journal of Occupational Health , identified three themes – the working environment at home, the effect on workers’ lives and careers, and the effect on their health. Greenberg said the research showed that there were winners and losers in many areas of home working. The working environment depended on how much space there was at home, the available equipment and on how much control workers had over their day.

People on higher incomes often enjoyed home working more, but those with more responsibilities at home such as childcare or housework – often women and those living alone – tended to be more stressed.

“Overall, people felt more productive at home,” Greenberg said. “It was particularly good for creative things, but much more difficult dealing with tedious matters. A lot of people worried about career prospects – this feeling that if you’re not present in the office, you’re going to get overlooked.”

Effects on health were clearer. The transition to home working during Covid was linked “with an increase in intake of vegetables, fruit, dairy, snacks, and self-made meals; younger workers and females benefited the most in terms of healthier eating,” the paper said.

One of the studies reviewed found that 46.9% of employees working from home had gained weight, and another put the figure at 41%. Most of the papers reviewed showed that homeworkers were more sedentary.

Greenberg said: “Managers needed to think about finding ways to support their homeworkers and help create their working environment.

“There’s a great adage in science that at some point, we need to stop admiring the problem and actually think about solutions,” he said. “We know quite a lot now. So we need to ask ‘what is the best training for an individual who’s going to become a partial homeworker?’ What we don’t need to do is to ask ‘would it be helpful to train someone to homework?’ The answer is clearly yes.”

Since the end of Covid restrictions in 2022, some companies have insisted that employees return to the office full-time, with firms such as JP Morgan requiring managers to be in five days a week.

“If companies like JP Morgan are afraid that people at home will be slacking, or won’t be doing a good job, and they can’t keep an eye on them, then I think that is an outdated concept,” Greenberg said.

Refusing WFH options will mean that talented employees may find other jobs, and makes companies less flexible in the event of future crises, such as another health emergency or strikes or severe weather conditions that prevent people from reaching their offices, he added.

“If they are doing it merely out of fear, then they risk being left behind,” he said. “We looked at a huge amount of evidence of the years and what our review shows is that there are ways to make the home working approach actually work well for the organisation and also for the employee.”

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What can super-healing species teach us about regeneration?

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When Albert E. Almada PhD ’13 embarks on a new project, he always considers two criteria instilled in him during his time as a graduate student in the Department of Biology at MIT.

“If you want to make a big discovery, you have to approach it from a unique perspective — a unique angle,” Almada says. “You also have to be willing to dive into the unknown and go to the leading edge of your field.”

This is not without its challenges — but with an innovative spirit, Almada says, one can find ways to apply technologies and approaches to a new area of research where a roadmap doesn’t yet exist.

Now an assistant professor of orthopedic surgery and stem cell biology and regenerative medicine at the Keck School of Medicine of the University of Southern California (USC), Almada studies the mechanics of how stem cells rebuild tissues after trauma and how stem cell principles are dysregulated and drive conditions like degenerative disease and aging, exploring these topics through an evolutionary lens. 

He’s also trying to solve a mystery that has intrigued scientists for centuries: Why can some vertebrate species like fish, salamanders, and lizards regenerate entire body parts, but mammals cannot?  Almada’s laboratory  at USC tackles these critical questions in the musculoskeletal system. 

Almada’s fascination with muscle development and regeneration can be traced back to growing up in southern California. Almada’s brother had a degenerative muscle disease called  Duchenne muscular dystrophy — and, while Almada grew stronger and stronger, his brother grew weaker and weaker. Last summer, Almada’s brother, unfortunately, lost his battle with his disorder at the age of 41. 

“Watching his disease progress in those early years is what inspired me to become a scientist,” Almada recalls. “Sometimes science can be personal.” 

Almada went to the University of California at Irvine for his undergraduate degree, majoring in biological sciences. During his summers, he participated in the  Undergraduate Research Program  (URP) at the  Cold Spring Harbor Laboratory and the MIT Summer Research Program-Bio (now the Bernard S. and Sophie G. Gould MIT Summer Research Program in Biology, BSG-MSRP-Bio ), where he saw the passion, rigor, and drive that solidified his desire to pursue a PhD. 

Despite his interest in clinical applications, skeletal muscle, and regenerative biology, Almada was drawn to the Department of Biology at MIT , which is focused on basic fundamental research.

“I was willing to bet that it all came down to understanding basic cellular processes and things going wrong with the cell and how it interacts with its environment,” he says. “The MIT biology program really helped me define an identity for myself and gave me a template for how to tackle clinical problems from a molecular perspective.”

Almada’s PhD thesis work was based on a curious finding that  Phillip Sharp , Institute Professor emeritus, professor emeritus of biology, and intramural faculty at the Koch Institute for Integrative Cancer Research, had made in 2007 — that transcription, the process of copying DNA into a messenger molecule called RNA, can occur in both directions at gene promoters. In one direction, it was long understood that fully formed mRNA is transcribed and can be used as a blueprint to make a protein. The transcription Sharp observed, in the opposite direction, results in a very short RNA that is not used as a gene product blueprint. 

Almada’s project dug into what those short RNA molecules are — their structure and sequence, and why they’re not produced the same way that coding messenger RNA is. In two papers published in  PNAS  and  Nature , Almada and colleagues discovered that a balance between splicing and transcription termination signals controls the length of an RNA. This finding has wider implications because toxic RNAs are produced and can build up in several degenerative diseases; being able to splice out or shorten RNAs to remove the harmful segments could be a potential therapeutic treatment.

“That experience convinced me that if I want to make big discoveries, I have to focus on basic science,” he says. “It also gave me the confidence that if I can succeed at MIT, I can succeed just about anywhere and in any field of biology.” 

At the time Almada was in graduate school, there was a lot of excitement about transcription factor reprogramming. Transcription factors are the proteins responsible for turning on essential genes that tell a cell what to be and how to behave; a subset of them can even theoretically turn one cell type into another. 

Almada began to wonder whether a specialized set of transcription factors instructs stem cells to rebuild tissues after trauma. After MIT, Almada moved on to a postdoctoral position in the lab of  Amy Wagers , a leader in muscle stem cell biology at Harvard University, to immerse himself in this problem.

In many tissues in our bodies, a population of stem cells typically exists in an inactive, non-dividing state called quiescence. Once activated, these stem cells interact with their environment, sense damage signals, and turn on programs of proliferation and differentiation, as well as self-renewal, which is critical to maintaining a pool of stem cells in the tissue.

One of the biggest mysteries in the field of regenerative biology is how stem cells transition from dormancy into that activated, highly regenerative state. The body’s ability to turn on stem cells, including those in the skeletal muscle system, declines as we age and is often dysregulated in degenerative diseases — diseases like the one Almada’s brother suffered from. 

In a study Almada published in Cell Reports  several years ago, he identified a family of transcription factors that work together to turn on a critical regenerative gene program within hours of muscle trauma. This program drives muscle stem cells out of quiescence and speeds up healing. 

“Now my lab is studying this regenerative program and its potential dysregulation in aging and degenerative muscle diseases using mouse and human models,” Almada says. “We’re also drawing parallels with super-healing species like salamanders and lizards.” 

Recently, Almada has been working on characterizing the molecular and functional properties of stem cells in lizards, attempting to understand how the genes and pathways differ from mammalian stem cells. Lizards can regenerate massive amounts of skeletal muscle from scratch — imagine if human muscle tissue could be regrown as seamlessly as a lizard’s tail can. He is also exploring whether the tail is unique, or if stem cells in other tissues in lizards can regenerate faster and better than the tail, by comparing analogous injuries in a mouse model. 

“This is a good example of approaching a problem from a new perspective: We believe we’re going to discover new biology in lizards that we can use to enhance skeletal muscle growth in vulnerable human populations, including those that suffer from deadly muscle disorders,” Almada says.

In just three years of starting his faculty position at USC, his work and approach have already received recognition in academia, with junior faculty awards from the Baxter Foundation and the Glenn Foundation/American Federation of Aging Research. He also received his  first RO1 award  from the National Institutes of Health with nearly $3 million in funding. Almada and his first graduate student, Alma Zuniga Munoz, were also awarded the  HHMI Gilliam Fellowship  last summer. Zuniga Munoz is  the first to be recognized  with this award at USC; fellowship recipients, student and advisor pairs, are selected with the goal of preparing students from underrepresented groups for leadership roles in science.

Almada himself is a second-generation Mexican American and has been involved in mentoring and training throughout his academic career. He was a graduate resident tutor for Spanish House at MIT and currently serves as the chair of the Diversity, Equity, and Inclusion Committee in the Department of Stem Cell Biology and Regenerative Medicine at USC; more than half of his lab members identify as members of the Hispanic community.

“The focus has to be on developing good scientists,” Almada says. “I learned from my past research mentors the importance of putting the needs of your students first and providing a supportive environment for everyone to excel, no matter where they start.” 

As a mentor and researcher, Almada knows that no question and no challenge is off limits — foundations he built in Cambridge, where his graduate studies focused on teaching him to think, not just do.

“Digging deep into the science is what MIT taught me,” he says. “I’m now taking all of my knowledge in molecular biology and applying it to translationally oriented questions that I hope will benefit human health and longevity.”

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Do you cite what you tweet? Investigating the relationship between tweeting and citing research articles

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Madelaine Hare , Geoff Krause , Keith MacKnight , Timothy D. Bowman , Rodrigo Costas , Philippe Mongeon; Do you cite what you tweet? Investigating the relationship between tweeting and citing research articles. Quantitative Science Studies 2024; doi: https://doi.org/10.1162/qss_a_00296

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The last decade of altmetrics research has demonstrated that altmetrics have a low to moderate correlation with citations, depending on the platform and the discipline, among other factors. Most past studies used academic works as their unit of analysis to determine whether the attention they received on Twitter was a good predictor of academic engagement. Our work revisits the relationship between tweets and citations where the tweet itself is the unit of analysis, and the question is to determine if, at the individual level, the act of tweeting an academic work can shed light on the likelihood of the act of citing that same work. We model this relationship by considering the research activity of the tweeter and its relationship to the tweeted work. Results show that tweeters are more likely to cite works affiliated with their same institution, works published in journals in which they also have published, and works in which they hold authorship. It finds that the older the academic age of a tweeter the less likely they are to cite what they tweet, though there is a positive relationship between citations and the number of works they have published and references they have accumulated over time.

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    research advantages quantitative

  3. Advantages And Disadvantages Of Quantitative Research

    research advantages quantitative

  4. Quantitative Research| Characteristics Advantages of Quantitative

    research advantages quantitative

  5. Advantages of Quantitative Research

    research advantages quantitative

  6. Quantitative Research: The Ultimate Guide

    research advantages quantitative

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  1. Quantitative Research: Its Characteristics, Strengths, and Weaknesses

COMMENTS

  1. 10 Advantages & Disadvantages of Quantitative Research

    Quantitative research can capture vast amounts of data far quicker than other research activities. The ability to work in real-time allows analysts to immediately begin incorporating new insights and changes into their work - dramatically reducing the turn-around time of their projects.

  2. What Is Quantitative Research?

    Advantages of quantitative research Disadvantages of quantitative research Other interesting articles Frequently asked questions about quantitative research Quantitative research methods You can use quantitative research methods for descriptive, correlational or experimental research.

  3. Quantitative Research

    by Muhammad Hassan Table of Contents Quantitative Research Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions. This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected.

  4. What is Quantitative Research? Definition, Examples, Key Advantages

    By Nick Jain Published on: May 17, 2023 Table of Contents What is Quantitative Research? Quantitative Research Examples Quantitative Research: Key Advantages Quantitative Research Methodology 7 Best Practices to Conduct Quantitative Research What is Quantitative Research?

  5. What Is Quantitative Research? (With Advantages and Disadvantages)

    Updated June 24, 2022 Quantitative research is a way to conduct studies and examine data for trends and patterns. Researchers using quantitative methods often attempt to interpret the meaning of the data to find potential causal relationships between different variables.

  6. Advantages and Disadvantages of Quantitative Research

    The use of statistical analysis and hard numbers found in quantitative research has distinct advantages in the research process. Can be tested and checked. Quantitative research requires careful experimental design and the ability for anyone to replicate both the test and the results.

  7. What is Quantitative Research? Definition, Methods, Types, and Examples

    March 23, 2023 Divya Sreekumar Quantitative research is used to validate or test a hypothesis through the collection and analysis of data. (Image by Freepik) If you're wondering what is quantitative research and whether this methodology works for your research study, you're not alone.

  8. Qualitative vs. Quantitative Research

    Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions. Data collection methods Quantitative and qualitative data can be collected using various methods.

  9. Quantitative Research: What It Is, Practices & Methods

    There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are: Collect Reliable and Accurate Data: Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the ...

  10. What Is Quantitative Research?

    Quantitative research is the opposite of qualitative research, which involves collecting and analysing non-numerical data (e.g. text, video, or audio). Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc. Quantitative research question examples.

  11. 13 Pros and Cons of Quantitative Research Methods

    List of the Pros of Quantitative Research 1. Data collection occurs rapidly with quantitative research. Because the data points of quantitative research involve surveys, experiments, and real-time gathering, there are few delays in the collection of materials to examine.

  12. A Practical Guide to Writing Quantitative and Qualitative Research

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

  13. Qualitative vs Quantitative Research: What's the Difference?

    Advantages of Quantitative Research What is the difference between quantitative and qualitative? The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods.

  14. PDF Introduction to quantitative research

    Mixed-methods research is a flexible approach, where the research design is determined by what we want to find out rather than by any predetermined epistemological position. In mixed-methods research, qualitative or quantitative components can predominate, or both can have equal status. 1.4. Units and variables.

  15. Quantitative research

    Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.. Associated with the natural, applied, formal, and social sciences this research strategy promotes the objective empirical investigation of ...

  16. Why Is Quantitative Research Important?

    Advantages of Quantitative Research When to Use Quantitative Research Becoming a Quantitative Researcher What Is the Basic Methodology for a Quantitative Research Study? Quantitative research is structured around the scientific method.

  17. Broadening horizons: Integrating quantitative and qualitative research

    Quantitative research is very well suited to establishing cause-and-effect relationships, to testing hypotheses and to determining the opinions, attitudes and practices of a large population, whereas qualitative research lends itself very well to developing hypotheses and theories and to describing processes such as decision making or communicat...

  18. Quantitative and Qualitative Research

    What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns.Quantitative research gathers a range of numeric data.

  19. Quantitative research

    Abstract. This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys - the principal research designs in quantitative research - are ...

  20. Quantitative Research: Definition, Methods, and Examples

    Advantages of Quantitative Research: Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation. Generalizability: Quantitative research often involves large sample sizes, which increases the ...

  21. 15 Advantages and Disadvantages of Quantitative Research

    List of the Advantages of Quantitative Research. 1. The quantitative approach allows you to reach a higher sample size. When you have the ability to study a larger sample size for any hypothesis, then it is easier to reach an accurate generalized conclusion. The additional data that you receive from this work gives the outcome greater ...

  22. Strengths and Limitations of Qualitative and Quantitative Research Methods

    Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and ...

  23. Quantitative and Qualitative Approaches to Generalization and

    We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing.

  24. Graduate students need more quantitative methods support

    Graduate students in psychology need hands-on support to conduct research using quantitative techniques that exceed their curricular training. If supervisors are not willing or able to provide ...

  25. How to Save for Retirement—and Why Most of Us Haven't (or Can't) Save

    Whether it's because of a broken Social Security benefits system, pervasive economic inequality, or poor retirement advice, not everyone's later years will be so golden ... a life course sociologist who uses survey data and quantitative methods to study social factors affecting health and well-being in later life. Her books include Aging in ...

  26. Working from home can bring big health benefits, study finds

    The review, funded by the National Institute for Health and Care Research Health Protection Research Unit in Emergency Preparedness and Response - a partnership between the UK Health Security ...

  27. What can super-healing species teach us about regeneration?

    Almada's PhD thesis work was based on a curious finding that Phillip Sharp, Institute Professor emeritus, professor emeritus of biology, and intramural faculty at the Koch Institute for Integrative Cancer Research, had made in 2007 — that transcription, the process of copying DNA into a messenger molecule called RNA, can occur in both ...

  28. Do you cite what you tweet? Investigating the relationship between

    Abstract. The last decade of altmetrics research has demonstrated that altmetrics have a low to moderate correlation with citations, depending on the platform and the discipline, among other factors. Most past studies used academic works as their unit of analysis to determine whether the attention they received on Twitter was a good predictor of academic engagement. Our work revisits the ...