Social Sciences Research Services
The social sciences—sociology, psychology, political science, education, public policy, and allied fields—study people and societies through surveys, nested populations, and policy interventions. MAS Research brings the measurement, multilevel, and causal methods these questions require, matched to how social data behave.
Social sciences research applies quantitative (and mixed) methods to questions about people, institutions, and society—such as what a survey instrument really measures, how context shapes individual outcomes, or whether a policy or programme worked. Because the data are often survey-based latent constructs, nested populations, and non-random interventions, the field relies on psychometrics, multilevel models, and causal-inference designs.
The social sciences as a research domain
The social sciences study human behaviour, institutions, and society, and the fields we support span sociology, psychology, political science and international relations, education, public administration and public policy, communication and media studies, demography, development and social policy, and allied disciplines such as criminology, social work, and behavioural science. They share a set of methodological challenges that quantitative social science has developed specific tools to meet.
Three features recur. First, much of what the social sciences study is latent—attitudes, beliefs, well-being, trust, ability—measured through survey instruments whose validity has to be established, not assumed. Second, social data are typically nested: students within schools, citizens within regions, respondents within countries, so observations are not independent. Third, the interventions of interest—policies, programmes, reforms—are almost never randomly assigned, making credible causal identification essential. Each of these points to a specific methodological response, which is where matching the method to the question matters most.
How we work in this domain
Our role is to bring the right quantitative method to a social-science research question and execute it to the standard leading social-science journals demand—where reviewers scrutinise measurement validity, the handling of clustered data, and the credibility of causal claims. We work with doctoral researchers, faculty, and research teams across social-science departments, schools of education and public policy, and research institutes, on individual papers, dissertation chapters, and larger programmes.
The through-line is methodological fit. A measurement question is a psychometrics problem; nested populations call for multilevel models; a policy or programme question is a quasi-experimental one—difference-in-differences, regression discontinuity, or instrumental variables; and theory about relationships among constructs calls for structural equation modelling. Where questions call for depth alongside numbers, we also support mixed-methods designs. Below we map the domain’s recurring questions to the methods that answer them.
Matching social-science questions to methods
The recurring empirical questions in social-science research, and the methods best suited to each. Every method links to its dedicated service page.
| Research question | Why it’s hard | Method |
|---|---|---|
| What does my survey instrument actually measure? | The construct is latent, measured with error | Scale development & psychometrics |
| How do several constructs relate in a model? | Unobserved constructs, complex relationships | Structural equation modelling |
| How does context shape individual outcomes? | People nested in schools, regions, countries | Multilevel models |
| Did a policy or programme cause an effect? | Non-random participation; need a counterfactual | Difference-in-differences |
| Treatment set by an eligibility cutoff or score? | Confounding, except near the threshold | Regression discontinuity |
| A key variable is endogenous or self-selected? | Omitted variables, reverse causality | Instrumental variables |
| Do groups differ equivalently on a measure? | A scale may function differently across groups | Measurement invariance |
| What does the evidence across many studies say? | Many studies, mixed findings, varied measures | Meta-analysis |
Across the social-science landscape
We support quantitative and mixed-methods research across the breadth of the domain—each field tending to lean on a characteristic set of methods.
Psychology & Behavioural Science
Attitudes, well-being, and behaviour—the heartland of psychometrics, scale validation, SEM, and experimental methods.
Education
Students nested in classrooms and schools, and programme effects—the classic home of multilevel models and quasi-experimental evaluation.
Sociology & Demography
Social structure, inequality, and population processes—survey, multilevel, and longitudinal methods on large datasets.
Political Science & Public Policy
Policy effects, institutions, and behaviour—causal-inference designs and cross-national comparison with measurement invariance.
Public Administration & Social Policy
Programme and service evaluation—difference-in-differences, RDD, and survey-based measurement of outcomes.
Communication, Criminology & Allied
Media, crime, and social-work questions—survey, content-analytic, multilevel, and causal methods as the data require.
From question to publishable result
We start from your research question and data, and advise on the design before any estimation—because in the social sciences the credibility of a finding rests on sound measurement (validated instruments), correct handling of nested data, and defensible causal identification. We then execute the analysis to current standards, with the validity evidence and robustness checks social-science reviewers expect—and we are candid about what a cross-sectional or observational design can and cannot establish.
Whether you are writing a single paper, a dissertation chapter, or running a larger programme, we provide the statistical work—measurement validation, multilevel and structural models, and causal designs—and, where appropriate, support mixed-methods integration. All with reproducible code and analysis-ready files where appropriate and permitted, and our publication support to help carry the analysis through peer review.
Measurement, clustering, and causation are the three recurring tests. A scale must be shown to measure what it claims; nested data need multilevel methods for correct inference; and a programme effect needs a credible counterfactual, not a before-after comparison. Addressing all three is what makes a social-science finding hold up.
Who we work with
Doctoral researchers and faculty across social-science departments, schools of education and public policy, and research institutes—psychology, sociology, political science, education, public administration, and allied fields—as well as research teams needing methodological depth, across single studies and multi-paper programmes.
Social-science research: common questions
Working on a social-science study?
Tell us the question and the data, and we will match it to the right method—psychometrics and SEM, multilevel models, or a causal design—and execute it to a standard that stands up to social-science review.