A case study is an in-depth, detailed examination of a particular case within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time to an enormous undertaking.
Social research is a research conducted by social scientists following a systematic plan. Social research methodologies can be classified as quantitative and qualitative.
Social mobility is the movement of individuals, families, households, or other categories of people within or between social strata in a society. It is a change in social status relative to one's current social location within a given society. This movement occurs between layers or tiers in an open system of social stratification. Open stratification systems are those in which at least some value is given to achieved status characteristics in a society. The movement can be in a downward or upward direction. Markers for social mobility, such as education and class, are used to predict, discuss, and learn more about an individual or a group's mobility in society.
James Samuel Coleman was an American sociologist, theorist, and empirical researcher, based chiefly at the University of Chicago.
Structural equation modeling (SEM) is a label for a diverse set of methods used by scientists in both experimental and observational research across the sciences, business, and other fields. It is used most in the social and behavioral sciences. A definition of SEM is difficult without reference to highly technical language, but a good starting place is the name itself.
Christopher Winship is Diker-Tishman Professor of sociology at Harvard University, and principal of the Hauser Center for Nonprofit Organizations at Harvard. He is best known for his contributions to quantitative methods in sociology and, since 1995, has served as editor of Sociological Methods and Research. He received the 2006 Paul Lazarsfeld Award from the Methodology Section of the American Sociological Association, which recognizes outstanding contributions over a career to sociological methodology.
The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis, though he discussed it only in the context of completely randomized experiments. Rubin extended it into a general framework for thinking about causation in both observational and experimental studies.
Stephen Webb Raudenbush is the Lewis-Sebring Professor of Sociology and Chairman of the Committee on Education at the University of Chicago. He is best known for his development and application of hierarchical linear models (HLM) in the field of education but he has also published on other subjects such as health and crime. Hierarchical linear models, which go by many other names, are used to study many natural processes. To use an example from education, a three level hierarchical model might account for the fact that students are nested in classrooms which are nested in schools. With the right data one could go further and note that schools are nested in districts which are nested in states. Repeated measures of the same individuals can be studied with these models as observations nested in people.
While epidemiology is "the study of the distribution and determinants of states of health in populations", social epidemiology is "that branch of epidemiology concerned with the way that social structures, institutions, and relationships influence health." This research includes "both specific features of, and pathways by which, societal conditions affect health".
James M. Robins is an epidemiologist and biostatistician best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. He is the 2013 recipient of the Nathan Mantel Award for lifetime achievement in statistics and epidemiology.
David Amiel Freedman was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose wide-ranging research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models. He published extensively on methods for causal inference and the behavior of standard statistical models under non-standard conditions – for example, how regression models behave when fitted to data from randomized experiments. Freedman also wrote widely on the application—and misapplication—of statistics in the social sciences, including epidemiology, public policy, and law.
David Collier is an American political scientist specializing in comparative politics. He is Chancellor's Professor Emeritus at the University of California, Berkeley. He works in the fields of comparative politics, Latin American politics, and methodology. His father was the anthropologist Donald Collier.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Critical realism is a philosophical approach to understanding science initially developed by Roy Bhaskar (1944–2014). It specifically opposes forms of empiricism and positivism by viewing science as concerned with identifying causal mechanisms. In the last decades of the twentieth century it also stood against various forms of postmodernism and poststructuralism by insisting on the reality of objective existence. In contrast to positivism's methodological foundation, and poststructuralism's epistemological foundation, critical realism insists that (social) science should be built from an explicit ontology. Critical realism is one of a range of types of philosophical realism, as well as forms of realism advocated within social science such as analytic realism and subtle realism.
Ronald Breiger is an American sociologist and a Regents Professor, a professor of sociology and government and public policy, an affiliate of the interdisciplinary graduate program in statistics and data science, and an affiliate of the interdisciplinary graduate program in applied mathematics at the University of Arizona. Prior to coming to Arizona he served on the faculties of Harvard University and Cornell University. He is well cited in the fields of social networks, social stratification, mathematical sociology, organizational sociology and cultural sociology and, with Linton Freeman, edited the influential academic journal Social Networks from 1998 to 2006. In 2005 he was the recipient of the Georg Simmel Distinguished Career Award of the International Network for Social Network Analysis,. In 2018 he received the James S. Coleman Distinguished Career Achievement Award of the American Sociological Association (ASA) Section on Mathematical Sociology. In 2020 he was the recipient of the Paul F. Lazarsfeld Award of the ASA Section on Methodology, recognizing a scholar who has made a career of outstanding contributions to methodology in sociology.
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.
Causality: Models, Reasoning and Inference is a book by Judea Pearl. It is an exposition and analysis of causality. It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in several fields including statistics, computer science and epidemiology. In this book, Pearl espouses the Structural Causal Model (SCM) that uses structural equation modeling. This model is a competing viewpoint to the Rubin causal model. Some of the material from the book was reintroduced in the more general-audience targeting The Book of Why.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis
Jennie E. Brand is an American sociologist and social statistician. She studies stratification, social inequality, education, social demography, disruptive events, and quantitative methods, including causal inference. Brand is currently Professor of Sociology and Statistics at the University of California, Los Angeles (UCLA), where she directs the California Center for Population Research and co-directs the Center for Social Statistics.
