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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing 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, other interesting articles, 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 generalized 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).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. 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 visualize your data and check for any trends or outliers.

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

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

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 standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized 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 analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized 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 standardized 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.

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
• 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 .

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.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize 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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November 28th, 2014

Stand up and be counted: why social science should stop using the qualitative/quantitative dichotomy.

14 comments | 33 shares

Estimated reading time: 5 minutes

Over the past year, I’ve met with many doctoral students and junior faculty in my travels around the United States and Europe, all of them eager to share information with me about their research. Invariably, at every stop, at least one person will volunteer the information that “I’m doing a qualitative study of…” When I probe for what’s behind this statement, I discover a diversity of data collection and analysis strategies that have been concealed by the label “qualitative.” They are doing participant observation ethnographic fieldwork, archival data collection, long unstructured interviews, simple observational studies, and a variety of other approaches. What seems to link this heterogeneous set is an emphasis on not using the latest high-powered statistical techniques to analyze data that’s been arranged in the form of counts of something or other. The implicit contrast category to “qualitative” is “quantitative.” Beyond that, however, commonalities are few.

Here I want to offer my own personal reflections on why I urge abandoning the dichotomy between “qualitative” and “quantitative,” although I hope readers will consult the important recent essays by  Pearce  and  Morgan  for more comprehensive reviews of the history of this distinction. For a variety of reasons, some people began making a distinction more than four decades ago between what they perceived as two types of research – – quantitative and qualitative – – with research generating data that could be manipulated statistically seen as generally more scientific.

Image credit: Libby Levi for opensource.com via flickr.com (CC BY-SA)

I’ve endured this distinction for so long that I had begun to take it for granted, a seemingly fixed property in the firmament of social science data collection and analysis strategies. However, I’ve never been happy with the distinction and about a decade ago, began challenging people who label themselves this way. I was puzzled by the responses I received, which often took on a remorseful tone, as if somehow researchers had to apologize for the methodological strategies they had chosen. To the extent that my perception is accurate, I believe their tone stems from the persistent way in which non-statistical approaches have been marginalized in many departments. However, it also seemed as though the people I talked with had accepted the evaluative nature of the distinction. As  Lamont and Swidler  might say, these researchers had bought into “methodological tribalism.”

Such responses upset me so much that I have now taken to asking people, so, you are saying you don’t “count” things? And, accordingly, you do research that “doesn’t count”?!

Why would any researcher accept such second-class status for what they do? Cloaking one’s research with the label of “qualitative” implicitly contrasts it with a higher order and more desirable brand of research, labeled “quantitative.” This is nonsense, of course, for several reasons.

First, methods of data collection do not automatically determine methods of data analysis. Information collected through ethnography (see  Kleinman, Copp, and Henderson ), perusal of archival documents, semi-structured interviews, unstructured interviews, study of photographs and maps, and other methods that might initially yield non-numerical information can often be coded into categories that can subsequently be statistically manipulated. For example, the recording and processing of ethnographic notes by programs such as  NVivo  and  Atlas.ti  yields systematic information that can be coded, classified, and categorized and then “counted” in a variety of ways. Thus, an ethnographer interested in a numerical indicator of social status within an emergent group could count the number of instances of deferential speech directed toward a (presumed) high status person or the number of interruptions made by (presumed) high status people into the conversations of others. Note that the meaning of what has been observed derives not from “counting” something but rather from understanding how to interpret what was observed, with the counts helping to judge the strength of the interpretation. The interpretation depends upon a researcher’s understanding of the social context for what was observed.

I believe some of the controversy over what standards to apply to assessing so-called “qualitative” research stems from observers confounding methods of data collection with methods of data analysis. For example,  Lareau  was quite critical of  LaRossa’s  suggestions to authors and reviewers of “qualitative” manuscripts because she felt that he had imported some terms from “quantitative” research that were inappropriate for what she viewed as good “qualitative” research, e.g. terms such as “hypothesis” and “variables.” She saw his suggestions as imposing “a relatively narrow conception of what it means to be scientific.” Although she supported his call to improve our analytic understanding of social processes, she also seemed to view data collection and data analysis as inextricably intertwined in the research process. I do not share this view. Even in  grounded theory approaches , practitioners distinguish between the collection and analysis of data, although there might be very little time lag between “collection” and “analysis.”

To be clear: not all information collected through the various methods I’ve described can be neatly ordered and classified into categories subject to statistical manipulation. Forcing interpretive reports into a Procrustean bed of cross tabulations and correlations makes no sense. Skillful analysts working with deep knowledge of the social processes they are studying can construct narratives without numbers. It all depends on the question they want to answer, and how.

Second, standards of evidence required to support empirical generalizations do not differ by the method of data collection. Researchers who claim “qualitative” status for their research must meet the same  standards of validity and reliability as other researchers . Regardless of whether information is collected through highly structured computerized surveys or semi structured interviewing in the field, researchers must still demonstrate that their indicators are valid and reliable. Ethnographers must provide sufficient information via “thick description” to convince readers that they were in a position to actually observe the interaction they are interpreting, just as demographers using federal census data must convince readers that the questions they are interpreting were framed without bias.

When did this pattern of apologies for “qualitative” research start? Based on my own experience and  Morgan’s review , I would say “something happened” in the late 1970s. Back in 1965, when I was doing a one-year ethnography class with John Lofland, at the University of Michigan, no one used the term “qualitative research.” My colleagues in the course – all of whom were doing field-work based MA theses, as I recall – might have described their work as doing “grounded theory,” as we were using a mimeographed version of  Glaser and Strauss’s book . The ethnography class was an alternative track to the  Detroit Area Study  survey research course, and neither course was described as a substitute for the required statistics courses. Field work and survey research were just alternative ways of collecting data.

I published several articles from that ethnography class, including  a paper in the inaugural issue of Urban Life and Culture , begun in 1972. That journal is now called the  Journal of Contemporary Ethnography . As I recall, no one ever asked me why I was doing “qualitative research,” nor did I ever describe it in those terms. The journal  Qualitative Sociology  began in 1978 and so I assume the phrase “qualitative sociology” was beginning to percolate into sociological writings around that time. In 1983, Lance Kurke and I published a  non-participant observational study of four executives  in Management Science , replicating  Henry Mintzberg’s research  from the early 1970s. We described it as a “field study” during which we had spent one week with each executive and mentioned that we had also conducted semi-structured interviews with them. The phrase “qualitative research” did not appear in the article. The terms “field study” and “non-participant observation” captured perfectly how the project was carried out.

Am I “blaming the victims” for their continuing to use the labels “qualitative” and “quantitative”? To be clear, there are several institutional factors that explain why some people persist in using this term, and they clearly transcend individual characteristics, as I have found them used not only in North America but also globally.  Mario Small  offered at least three explanations. First, the availability of large data sets has made it easier for researchers to conduct statistical analyses in their research. Second, journal reviewers apply inappropriate standards when they try to evaluate research that uses ethnographic or other less frequently used data collection techniques. Third, foundations and agencies have gravitated toward research labeled as “quantitative” because they see it as higher prestige or more policy relevant. My colleague,  Laura Lopez-Sanders , noted a fourth possible reason: departments that offer a “methods sequence” often overlook or downplay ethnographic and other methods that are seen as not leading to easily quantifiable data for statistical analysis. Students pick up on the implicit message that statistical methods carry a higher priority than other methods of analysis.

To the extent that institutional norms and practices keep alive the implicit message that there really are “quantitative” and “qualitative” methods, they will be available for use by graduate students and junior faculty. Their availability, however, does not mandate their use. The point of this blog post is to make readers more reflexive about how they choose to describe their work.

So, if you are doing an ethnography, constructing a narrative using historical records, rummaging through old archives, building agent based models, or doing just about anything else, for that matter, tell that to the next person who asks what kind of research you do. Don’t automatically say “I’m doing qualitative research.” You might want to describe in some detail what data collection and data analysis methods you are actually using. Explain the fit between the questions you’re asking and the type of empirical evidence you are gathering and how you are analyzing it. If the person says, “oh, you are doing qualitative research,” tell them you don’t know what they mean. You’re just doing good research.

This piece originally appeared on the Work in Progress blog of the American Sociological Association’s Organizations, Occupations, and Work Section and is reposted with permission.

Note: This article gives the views of the author, and not the position of the Impact of Social Science blog, nor of the London School of Economics. Please review our  Comments Policy  if you have any concerns on posting a comment below.

About the Author

Howard E. Aldrich   is Kenan Professor of Sociology, Adjunct Professor of Business at the University of North Carolina, Chapel Hill, Faculty Research Associate at the Department of Strategy & Entrepreneurship, Fuqua School of Business, Duke University, and Fellow, Sidney Sussex College, Cambridge University. His main research interests are entrepreneurship, entrepreneurial team formation, gender and entrepreneurship, and evolutionary theory.

About the author

14 Comments

• Pingback: the Q words | orgtheory.net

I share the sentiments expressed by the author as I have had the difficulty of explaining ‘action research’ to colleagues who carry out ‘traditional quantitative research’. Action research may utilize many forms of research within the two areas of both qualitative and quantitative research. Should we abandon the ‘Q’ words then or should we combine them to achieve the benefits that both provide?What are the feelings on ‘mixed methods’?

Great article discussing the importance of ‘qualitative’ research. Whilst i agree on the importance of rigour, when ‘qualitative’ research is paradigmatically different from quantative research, using language from quantitative studies like validity and reliability actually reiforces the (ostensive) primacy of quantitative research. Other evaluative criteria such as transparency, theoretical contribution etc. are arguably better suited to ‘qualitative’ research that takes a hermeneutic or critical lens.

I have my concerns when terms are rejected only because they are used in quantitative research. Reliability might be difficult to implement in frameworks that question intersubjectivity, but validity asks for whether we are examining what we actually want to analyze (in simple terms). I find this highly relevant for any empirical research. Qualitative research should part ways with quantitative research when it comes to ways to assess validity (e.g., Onwuegbuzie, Anthony and Beth L. Leech (2007): Validity and Qualitative Research: An Oxymoron? Quality and Quantity 41 (2): 233-249. doi: 10.1007/s11135-006-9000-3)

I think the terms “quantitative” and “qualitative” carry important information about the basis of one’s (causal) arguments. In short and simplifying, they communicate whether inferences are based on a number (or some numbers) summarizing data, or whether they are based on a diverse body of evidence. Besides, the problem that Aldrich sees, which I agree is there, does not disappear if we stop using the Q words. Without them, it becomes a small-n vs large-n distinction (n being the number of cases) or a depth vs breadth distinction. The people that now favor quantitative over qualitative research then favor breadth and large-n studies over depth and small-n studies. For these reasons, I see more benefits than downsides in using the Q words.

While it makes sense to say that the use of multiple approaches to research design would enlighten us to see the multiple dimensions of knowledge, the distinctions between qualitative and quantitative ‘traditions’ are too broad to reconcile. A reconciliation is possible only if both the traditions agree on the nature of truth and the best way to access it – the war has been on for quite sometime with each side self-declaring victory from day 1.

Interesting topic. I totally agree. I tried to disentangle qual/quant approach, method, data, analysis etc n the publication below. It was extremely difficult to move beyond the qual/quant binary without reverting to that terminology.The devil, I would argue is in the detail (or the understanding) rather than in the terms used.

https://www.researchgate.net/publication/262380458_Qualitative_Research_Rules__Using_Qualitative_and_Ethnographic_Methods_to_Access_the_Human_Dimensions_of_Technology

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Aldrich based his views on the fact that qualitative software packages tend to translate categories and codes into quantities, thus making decisions about results contingent upon the quantities.Aldrich should also consider a situation where the data collected through ethnography, for instance, is not analysed using any software, but rather using an approach of merely synthesizing the result.

interestingly , the value that in my training ​​the distinction between qualitative and quantitative approaches had, is before a descriptive value ( production methods and data analysis) , a political value : quantitative research, at least here in South America , is associated to an objectivist research often involving an invisibilization of epistemological and political questions (ie , the effects of the conceptual categories in use or the investigation as a whole). In that sense it seems that the first pedestal is taken by qualitative aproaches ( although their ability to influence the public debate remains limited). In any event, I share the idea to stop using this distinction to describe an investigation, but only at the conversational level with our colleagues, because at the time of writing something that same approach would be likely to lead to a operationalism without depths.

• Pingback: Howard ALDRICH – Por qué las ciencias sociales deberían abandonar la dicotomía cualitativa/cuantitativa (2014) – CIENCIAS ANTROPOLÓGICAS
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I have a few reactions to this post. The first is to the point about standards of validity and reliability being the same across methodologies, i.e., the obligation of the researcher to convince the reader that the researcher was in a position to assess the phenomenon for which s/he claims to have findings. This is true for both qual and quant.

Per Aldrich’s point about qualitative data being countable, I also thought of Hodson’s Dignity at Work book (and several other papers using the ethnography database he and colleagues created). What a powerful dataset that has been for building theory and evidence in Orgs and Work!

To his point about “the interpretation depends upon a researcher’s understanding of the social context for what was observed” – this reminds me of the philosophy behind calibrating measures in QCA. The methodological precedent there is, the researcher selects a threshold/cut point for being “in” or “out” of the given condition, drawing on his/her knowledge of the context to justify it.

Finally, a related pattern I thought of that seems to signal institutional norms in the inferior treatment of qual methods is that in management journals, the ordering of qual-then-quant in journal articles is the norm. “Qual,” as it were, can never “prove the day;” it can only generate questions. Which I think is wrong. See Kaplan’s (2015) paper on the ability of both quant and qual methods to generate questions, which to me suggests that there is no reason why mixed methods studies need to always follow the same ordering.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

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

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Quasi-experimental Research Design

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

Experimental Research Design

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

Survey Research

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

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Quantitative Research

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

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

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

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

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

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

How to Conduct Quantitative Research

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

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

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

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

Purpose of Quantitative Research

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

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

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

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

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

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Undergraduate
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• Summer schools
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• Meet, visit and discover LSE

MSc Operations Research & Analytics

• Graduate taught
• Department of Mathematics
• Application code G2U1
• Starting 2024
• Home full-time: Open
• Overseas full-time: Open
• Location: Houghton Street, London

The MSc Operations Research & Analytics provides you with the skills needed to apply mathematical methods to real-world analytics problems faced by companies, governments, and other institutions.

With study in practice and theory, you will gain deep insight into analytics problems. On the practical side, you will learn how to model a range of real-world problems using optimisation, simulation, and statistics, with specialist software taught with accompanying computer lab sessions. On the theoretical side, you will learn to recognise canonical underlying mathematical problems, and how to solve them with state-of-the-art methods. You will also have the opportunity to undertake a Project in Operations Research & Analytics, working in a consultancy role in a host organisation, where you will turn a real problem faced by the organisation into a mathematical model whose solution provides tangible benefit. Alternatively, you may choose to write a dissertation, supervised by a faculty member. The programme is designed for students with strong quantitative backgrounds wishing to deepen and broaden their mathematical knowledge while gaining applicable skills in high demand in the marketplace.

Programme details

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Entry requirements

Minimum entry requirements for msc operations research & analytics.

An upper second class honours (2:1) degree in a relevant discipline (or equivalent). Students should normally have taken university courses including calculus, linear algebra, and statistics. Appropriate work experience will also be considered.

Competition for places at the School is high. This means that even if you meet our minimum entry requirement, this does not guarantee you an offer of admission.

If you have studied or are studying outside of the UK then have a look at our  Information for International Students  to find out the entry requirements that apply to you.

Assessing your application

We welcome applications from all suitably qualified prospective students and want to recruit students with the very best academic merit, potential and motivation, irrespective of their background.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including predicted and achieved grades) - statement of academic purpose - two academic references - CV

See further information on supporting documents

You may also have to provide evidence of your English proficiency, although you do not need to provide this at the time of your application to LSE.   See our English language requirements .

When to apply

Applications for this programme are considered on a rolling basis, meaning the programme will close once it becomes full. There is no fixed deadline by which you need to apply, however, to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details. 

Fees and funding

Every graduate student is charged a fee for their programme.

The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover living costs or travel or fieldwork.

Tuition fees 2024/25 MSc Operations Research & Analytics

Home students: £29,472 Overseas students: £29,472

The Table of Fees shows the latest tuition amounts for all programmes offered by the School.

For this programme, the tuition fee is the same for all students regardless of their fee status. However any financial support you are eligible for will depend on whether you are classified as a home or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information about fee status classification.

Fee reduction

Students who completed undergraduate study at LSE and are beginning taught graduate study at the School are eligible for a  fee reduction  of around 10 per cent of the fee.

Scholarships and other funding

The School recognises that the  cost of living in London  may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students.

This programme is eligible for needs-based awards from LSE, including the  Graduate Support Scheme ,  Master's Awards , and  Anniversary Scholarships . 

Selection for any funding opportunity is based on receipt of an offer for a place and submitting a Graduate Financial Support application, before the funding deadline. Funding deadline for needs-based awards from LSE:  25 April 2024 .

This programme is also eligible for   Economic and Social Research Council (ESRC) funding  when you apply as part of a 1+3 research programme. Selection for the ESRC funding is based on receipt of an application for a place – including all ancillary documents, before the funding deadline.

Funding deadline for the ESRC funding:  15 January 2024.

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas.  Find out more about financial support.

Government tuition fee loans and external funding

A postgraduate loan is available from the UK government for eligible students studying for a first master’s programme, to help with fees and living costs. Some other governments and organisations also offer tuition fee loan schemes.

Find out more about tuition fee loans

Further information

Fees and funding opportunities

Information for international students

LSE is an international community, with over 140 nationalities represented amongst its student body. We celebrate this diversity through everything we do.  

If you are applying to LSE from outside of the UK then take a look at our Information for International students . 

1) Take a note of the UK qualifications we require for your programme of interest (found in the ‘Entry requirements’ section of this page). 

2) Go to the International Students section of our website. 

3) Select your country. 

4) Select ‘Graduate entry requirements’ and scroll until you arrive at the information about your local/national qualification. Compare the stated UK entry requirements listed on this page with the local/national entry requirement listed on your country specific page.

Programme structure and courses

You will take three compulsory courses and will choose courses from a range of options within the Department and across other relevant departments, including Management and Statistics. 

(* denotes half unit)  

Fundamentals of Operations Research * Introduces a range of Operations Research techniques including linear programming, the simplex method and duality, Markov chains, queueing theory and birth and death processes, inventory models and dynamic programming.

Modelling in Operations Research * Provides hands-on training in the art of converting real-world problems to optimisation and simulation models, inputting the models into specialist software, solving the optimisation problem or exercising the simulation model, and deriving applicable conclusions about the original problem.

Data Analysis and Statistical Methods * Studies common techniques of statistical inference, together with theoretical justification. The techniques are then applied to linear and logistic regression and basic time series models. Statistical software R constitutes an integral part of the course and provides hands-on experience of data analysis. 

Either Project in Operations Research & Analytics A project in a host organisation taking a consultancy role. Or Dissertation in Operations Research & Analytics An independent research project of 10,000 words on an approved topic of your choice.

Courses to the value of one and a half units from a range of options.

For the most up-to-date list of optional courses please visit the relevant School Calendar page .

You must note, however, that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s  Calendar , or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the  updated graduate course and programme information page.

Teaching and assessment

Contact hours and independent study.

Teaching will combine traditional lectures with seminars. Several of the courses, including two of the three compulsory ones, will involve training in a programming language or use of specialised computational tools. These parts of those courses will have accompanying computer lab sessions in which students will actively develop their programming skills by applying them to a range of problems in OR. Most courses on the degree are quantitative, but one optional course may, depending on your choice, study OR-related methods or applications from a qualitative perspective. During the summer, you are required to do either a project in Operations Research & Analytics or a Dissertation in Operations Research & Analytics. The project involves work in a host organisation (in business, government, health, or a social non-profit organisation), in a consultancy role, typically turning a real problem faced by the organisation into a mathematical model whose solution provides tangible benefit. You will be marked on a project report. The Dissertation requires study of an area of research, or an application of advanced techniques, and a report of findings. 

Within your programme you will take a number of courses, including half unit courses and full unit courses, to a total of 4 units. In half unit courses, on average, you can expect 35 contact hours in total and for full unit courses, 40-60 contact hours in total. This includes sessions such as lectures, seminars or workshops. Hours vary from course to course and you can view indicative details in the  Calendar  within the Teaching section of each  course guide .

You are also expected to complete independent study outside of class time. This requires you to manage the majority of your study time yourself, reading, thinking, solving problems, doing software exercise, and undertaking research.

Teaching methods

LSE is internationally recognised for its teaching and research and therefore employs a rich variety of teaching staff with a range of experience and status. Courses may be taught by members of faculty, such as assistant, associate, and full professors. Many departments now also employ guest teachers and visiting members of staff, LSE teaching fellows, and graduate teaching assistants who are usually doctoral research students and in the majority of cases teach on undergraduate courses only. You can view indicative details for the teacher responsible for each course in the relevant  course guide .

All taught courses are required to include formative coursework which is unassessed. It is designed to help prepare you for summative assessment which counts towards the course mark and to the degree award. LSE uses a range of formative assessment, such as essays, problem sets, case studies, reports, quizzes, mock exams and many others. Summative assessment may be conducted during the course or by final examination at the end of the course. An indication of the formative coursework and summative assessment for each course can be found in the relevant  course guide .

Academic support

You will also be assigned an academic mentor who will be available for guidance and advice on academic or personal concerns.

There are many opportunities to extend your learning outside the classroom and complement your academic studies at LSE.  LSE LIFE  is the School’s centre for academic, personal and professional development. Some of the services on offer include: guidance and hands-on practice of the key skills you will need to do well at LSE: effective reading, academic writing and critical thinking; workshops related to how to adapt to new or difficult situations, including development of skills for leadership, study/work/life balance and preparing for the world of work; and advice and practice on working in study groups and on cross-cultural communication and teamwork.

LSE is committed to enabling all students to achieve their full potential and the School’s  Disability and Wellbeing Service  provides a free, confidential service to all LSE students and is a first point of contact for all disabled students.

Student support and resources

We’re here to help and support you throughout your time at LSE, whether you need help with your academic studies, support with your welfare and wellbeing or simply to develop on a personal and professional level.

Whatever your query, big or small, there are a range of people you can speak to who will be happy to help.  

Department librarians   – they will be able to help you navigate the library and maximise its resources during your studies. 

Accommodation service  – they can offer advice on living in halls and offer guidance on private accommodation related queries.

Class teachers and seminar leaders  – they will be able to assist with queries relating to specific courses. 

Disability and Wellbeing Service  – they are experts in long-term health conditions, sensory impairments, mental health and specific learning difficulties. They offer confidential and free services such as  student counselling,  a  peer support scheme  and arranging  exam adjustments.  They run groups and workshops.  

IT help  – support is available 24 hours a day to assist with all your technology queries.   

LSE Faith Centre  – this is home to LSE's diverse religious activities and transformational interfaith leadership programmes, as well as a space for worship, prayer and quiet reflection. It includes Islamic prayer rooms and a main space for worship. It is also a space for wellbeing classes on campus and is open to all students and staff from all faiths and none.   

Language Centre  – the Centre specialises in offering language courses targeted to the needs of students and practitioners in the social sciences. We offer pre-course English for Academic Purposes programmes; English language support during your studies; modern language courses in nine languages; proofreading, translation and document authentication; and language learning community activities.

LSE Careers  ­ – with the help of LSE Careers, you can make the most of the opportunities that London has to offer. Whatever your career plans, LSE Careers will work with you, connecting you to opportunities and experiences from internships and volunteering to networking events and employer and alumni insights. 

LSE Library   –   founded in 1896, the British Library of Political and Economic Science is the major international library of the social sciences. It stays open late, has lots of excellent resources and is a great place to study. As an LSE student, you’ll have access to a number of other academic libraries in Greater London and nationwide. 

LSE LIFE  – this is where you should go to develop skills you’ll use as a student and beyond. The centre runs talks and workshops on skills you’ll find useful in the classroom; offers one-to-one sessions with study advisers who can help you with reading, making notes, writing, research and exam revision; and provides drop-in sessions for academic and personal support. (See ‘Teaching and assessment’). 

LSE Students’ Union (LSESU)  – they offer academic, personal and financial advice and funding.  

PhD Academy   – this is available for PhD students, wherever they are, to take part in interdisciplinary events and other professional development activities and access all the services related to their registration. 

Sardinia House Dental Practice   – this   offers discounted private dental services to LSE students.  

St Philips Medical Centre  – based in Pethwick-Lawrence House, the Centre provides NHS Primary Care services to registered patients.

Student Services Centre  – our staff here can answer general queries and can point you in the direction of other LSE services.  

Student advisers   – we have a  Deputy Head of Student Services (Advice and Policy)  and an  Adviser to Women Students  who can help with academic and pastoral matters.

Student life

As a student at LSE you’ll be based at our central London campus. Find out what our campus and London have to offer you on academic, social and career perspective. 

Student societies and activities

Your time at LSE is not just about studying, there are plenty of ways to get involved in  extracurricular activities . From joining one of over 200 societies, or starting your own society, to volunteering for a local charity, or attending a public lecture by a world-leading figure, there is a lot to choose from. 

The campus 

LSE is based on one  campus  in the centre of London. Despite the busy feel of the surrounding area, many of the streets around campus are pedestrianised, meaning the campus feels like a real community. 

Life in London 

London is an exciting, vibrant and colourful city. It's also an academic city, with more than 400,000 university students. Whatever your interests or appetite you will find something to suit your palate and pocket in this truly international capital. Make the most of career opportunities and social activities, theatre, museums, music and more. 

Want to find out more? Read why we think  London is a fantastic student city , find out about  key sights, places and experiences for new Londoners . Don't fear, London doesn't have to be super expensive: hear about  London on a budget . 

Student stories

To read all our Alumni Stories,  see our webpage here .

Philipp Loick - MSc Operations Research & Analytics 2017-18

Having a background in finance and economics, I aimed for a Masters programme where I could develop mathematical and programming skills to solve industry problems in operations research and data science. Enrolling in the Operations Research and Analytics programme at LSE was the right choice for this goal.

The programme features a diverse student body with the majority of students having majored in mathematics with some engineering and finance students. Even though only a one-year programme, the programme achieved a good balance between theoretical foundations and industry applications and allowed us to study topics such as combinatorial optimization, advanced statistics or algorithmic techniques for data mining.

The high academic level and relevance of the programme is due to the academic staff, who have excellent academic credentials, partially have worked for renowned industry companies and are well connected in the academic community. Graduating from the programme, I had an offer from BCG Gamma, the advanced analytics team of BCG, which I rejected for a PhD in discrete mathematics.

Alexander Saftschuk - MSc Operations Research & Analytics 2017-18

I came to the LSE with the main goal of improving my quantitative problem-solving skills, and subsequently landing a job in investment banking. The School and societies provided extremely good network opportunities, which really helped to land the job that I aimed at. After only two months at the LSE I landed a job offer with one of the top global investment banks. However, upon finishing the Operations Research & Analytics programme I quickly realised that I would rather pursue a career in data science, and once again the university's reputation opened doors for me last minute. Currently I work as a Data Analyst in the Telenor Digital data science team in Norway. There I code various machine learning algorithms in R, all of which I have all learned during this degree. 

Overall I can say that coming from a non-quantitative, business background I have learned more in this one-year Masters than I did in my entire three years of my bachelor degree. The programme was challenging but manageable. In particular, I highly appreciated how much face time I received from all of my professors, as well as the professor who supervised my thesis. The decision to come to the LSE and studying Operations Research & Analytics was one of the best I have made so far and I can highly recommend LSE and the degree. 

Kate Lavrinenko - MSc Operations Research & Analytics 2017-18

Studying this Masters was my third MSc, after studying Applied Mathematics and Economics, four years of experience in Economics and Finance, moving country, two kids, and four years at home with them. It was a challenging experience to find myself among young, inspired and able students from around the world. It also took some time to get used to the pace of study, and to network with people and share skills and knowledge. I needed some psychological help at the start of the journey and I had an opportunity to get it at LSE, which makes me feel grateful. 

I liked that the programme was flexible in what courses you could choose in order to make it fit your personal interests and academic goals. I encourage students to research and think hard about their course choices before starting the programme. Also, it is useful to have an understanding of which direction you wish to head in (e.g. academic or business) so you can utilise LSE’s resources properly. 

I found the careers events to be very valuable in my experience here. For example, I met a member of the Data Science team from Deloitte and after many rounds and following my MA425 Project there in the summer, I found myself with a full time job after finishing the course.

I enjoyed my journey, my job, and my experience with LSE. Whenever I get a new research heavy task, I start dreaming whether I could eventually turn it into a PhD, so my journey is not over.

Preliminary reading

You are not  required to do any preliminary reading in advance of this programme, but if you wish to read some material before arriving, we can make a few suggestions. 

If you do not have experience of  computer programming, you could learn the language R, which you will use in ST447 Data Analysis and Statistical Methods . Once you learn any language it is easy to learn others, and programming will be useful in your career. Programming will also give you a sense of what computers can and cannot do, that will be useful in all algorithmic courses. Good starting points are Introductory Statistics with R  by Peter Dalgaard, and the Coursera course .

Linear algebra plays a major role in several key courses and in the field of OR generally. It is expected that you are comfortable with the basic notions (linear independence, rank, determinants, solutions of systems of equations, eigenvalues and eigenvectors). These will not be reviewed in the course; you can review this material independently. There are many good textbooks to choose from; a suitable one is Linear Algebra by Martin Anthony and Michele Harvey. 

Quick Careers Facts for the Department of Mathematics

Median salary of our PG students 15 months after graduating: £39,500

• Financial and Professional Services              
• Information, Digital Technology and Data            
• Accounting and Auditing              
• Real Estate, Environment and Energy 
• Advertising, Marketing, PR, Media, Entertainment, Publishing and Journalism

Top 5 sectors our students work in:

The data was collected as part of the Graduate Outcomes survey, which is administered by the Higher Education Statistics Agency (HESA). Graduates from 2020-21 were the fourth group to be asked to respond to Graduate Outcomes. Median salaries are calculated for respondents who are paid in UK pounds sterling and who were working in full-time employment.

This programme is ideal preparation for a range of careers in quantitative positions in consultancy, management, finance, government and business, anywhere in the world.

Further information on graduate destinations for this programme

Support for your career

Many leading organisations give careers presentations at the School during the year, and LSE Careers has a wide range of resources available to assist students in their job search. Find out more about the  support available to students through LSE Careers .

Find out more about LSE

Discover more about being an LSE student - meet us in a city near you, visit our campus or experience LSE from home. 

Experience LSE from home

Webinars, videos, student blogs and student video diaries will help you gain an insight into what it's like to study at LSE for those that aren't able to make it to our campus.  Experience LSE from home . 

Come on a guided campus tour, attend an undergraduate open day, drop into our office or go on a self-guided tour.  Find out about opportunities to visit LSE . 

LSE visits you

Student Marketing, Recruitment and Study Abroad travels throughout the UK and around the world to meet with prospective students. We visit schools, attend education fairs and also hold Destination LSE events: pre-departure events for offer holders.  Find details on LSE's upcoming visits . 

How to apply

Virtual Graduate Open Day

Register your interest

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Distinguished Winner - Gabriel honored for postpartum depression research

The Fellows of the Academy of Management and the Community for Responsible Research in Business and Management named an article by Allison Gabriel, Thomas J. Howatt Chair in Management in the Mitchell E. Daniels, Jr. School of Business, and four other researchers as a Distinguished Winner of the 2024 Award for Responsible Research in Management. The paper appeared in the Journal of Applied Psychology . It previously was recognized as one of 13 nominees from more than 2,500 articles considered for the Rosabeth Moss Kanter Award for Excellence in Work-Family Research.

Co-authors on the paper were Jamie Ladge, professor of management and organization at Boston College; Laura Little, Chick-fil-A Distinguished Professor for Leadership Advancement at the University of Georgia; Rebecca MacGowan, assistant professor of management at the University of Arkansas; and Elizabeth Stillwell, assistant professor of employment relations and human resource management at London School of Economics. Their paper was one of three Distinguished Winners selected from 134 scholarly works, an honor presented to studies that academics deem scientifically rigorous and practicing executives consider meaningful and actionable.

“Sensemaking through the storm: How postpartum depression shapes personal work-family narratives” found that mothers diagnosed with postpartum depression experienced an imposing identity that is unexpected and undesirable. The study showed that coping with the identity ultimately gave way to important outcomes in the work and home domains, most notably that women were able to better enact self-compassion toward themselves, compassion toward others, and became critical supports for co-workers going through personal hardships.

Gabriel joined the Daniels School faculty in 2023. She has received several research honors, including the 2021 Academy of Management Organizational Behavior Division Cummings Scholarly Achievement Award, the 2021 Society for Industrial and Organizational Psychology Distinguished Early Career Contributions-Science Award, and the 2020 Academy of Management Human Resources Division Early Career Award. She was recognized in 2018 by the website Poets & Quants as a “Top 50 Undergraduate Professor” when she was on the faculty at the University of Arizona.

Gabriel is director of Purdue’s Center for Working Well , a research center focused on the challenges facing modern workforces and geared toward promoting personal well-being while creating sustainable performance.

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Dedicated digital citizen curriculum needed to help pupils navigate online dangers and tackle ‘digital divide’

It is essential that any incoming government invest in training and sustained support for a digital citizenship curriculum

Schools urgently need a digital citizenship and critical media literacy curriculum, taught by trained educators, to help adults and children navigate the world of AI, deep-fakes, disinformation, online bullying and false advertising say LSE researchers in a new report (1).

Professor Shakuntala Banaji and Dr Fiona Abades Barclay from the Department of Media and Communications at LSE were funded by the Department of Science, Innovation and Technology (DSIT) to carry out an independent evaluation of the effectiveness of Common Sense Media’s free Digital Citizenship Curriculum in primary and secondary schools(2).

Their research found significant inequalities in digital knowledge and experience, not only between groups of pupils, but also between teachers and schools. Under-resourced schools were the least likely to have taught modules on digital citizenship. The need for further funding and rigorous evaluation of critical learning in this area is urgent.

Children from less digitally immersed backgrounds found it more challenging to engage with the digital citizenship curriculum. These children were from households where children and adults had less access to digital tools and technologies for socioeconomic reasons , as well as    households where there were a surfeit of digital technologies but minimal supervision in using them.

The impact of this persistent ‘digital citizenship divide’ was particularly noticeable amongst younger primary school pupils.

In contrast, many teenagers from urban inner-city households, where technology is ingrained in their daily life, showed enthusiasm and affinity for learning about digital citizenship. They also had a greater level of digital awareness around concepts such as privacy.

LSE’s research suggests that children and teenagers from households with access to digital technologies, but where adults take a considered choice to regulate and discuss its use, were amongst the most tech-savvy and civic minded in their comprehension of the principles around privacy, fairness and healthy balance in the use of digital technologies or fake news. Ensuring that all children have access to such spaces for discussion and exploration is an imperative for educators.

Shakuntala Banaji, Professor of Media, Culture and Social Change at LSE and co-author of the report said: “If primary school children haven’t been taught good digital habits and how to stay safe online by their schools, and supported in these by families, it is more likely that issues such as lack of sleep through excessive digital media use, or being bullied online for extended periods will go unchallenged as they hit their teens.

“In the schools where we saw a positive interest in the children's digital well-being, the pupils demonstrated consistently a greater degree of empowerment to set themselves limits in terms of their use of digital technologies and to ask critical questions about who owns their data, who produces fake news, and how to challenge online hate. This is why it is essential that any incoming government invest in training and sustained support for a digital citizenship curriculum, so all schools and teachers can confidently deliver this kind of education.”

The researchers designed a set of rigorous evaluation tools, involving both qualitative and quantitative methods, to carefully investigate more than 200 children’s pre-existing digital knowledge and then evaluated changes through quizzes and interviews. They found that pupils’ ability to navigate the online world improved after being taught Common Sense’s Digital Citizenship Curriculum for as little as six weeks.

Lord Ed Vaizey of Didcot, Chair of Common Sense Media UK, said: “There’s a reason teachers around the world love our digital literacy curriculum. LSE’s independent research has confirmed its positive impact on students, which will shape our society for the better, especially as we see an uptick in generative AI adoption, the spread of misinformation and disinformation, and an ongoing youth mental health crisis.

"At Common Sense, we believe it is critical to help children understand these issues from an early age, and LSE's report supports the idea that we must do more to prepare children for their future roles as citizens of our digital world." 

Pupils of all ages consistently wanted more time to talk about and question adult digital habits and choices. They told the researchers that parents also often need help with excessive screen use, susceptibility to misinformation and inability to access the latest tools and digital language.

One year 10 teacher said: “I know a lot of [the children] actually went home and discussed it because I had a lot of feedback from parents saying that a lot of them actually spoke to their parents about scrolling.”

Behind the article

(1)  LSE-Common Sense Digital Citizenship Curriculum Evaluation Report LSE — Common Sense Digital Citizenship Curriculum Evaluation Report high res web version V6

(2) The researchers assessed pupil’s digital citizenship, media literacy and attitudes towards misinformation and disinformation and before after the teaching Common Sense’s Digital Citizenship Curriculum. The research was conducted between May and December 2023 in two UK primary and two UK secondary schools in and around London and Essex using qualitative and quantitative methods.