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Kim, Yongnam; Steiner, Peter M. – Sociological Methods & Research, 2021
For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain…
Descriptors: Scores, Graphs, Causal Models, Statistical Bias
Su, Dan; Steiner, Peter M. – Sociological Methods & Research, 2020
Factorial surveys use a population of vignettes to elicit respondents' attitudes or beliefs about different hypothetical scenarios. However, the vignette population is frequently too large to be assessed by each respondent. Experimental designs such as randomized block confounded factorial (RBCF) designs, D-optimal designs, or random sampling…
Descriptors: Surveys, Vignettes, Factor Analysis, Research Design
Steiner, Peter M.; Kim, Yongnam; Hall, Courtney E.; Su, Dan – Sociological Methods & Research, 2017
Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…
Descriptors: Graphs, Causal Models, Quasiexperimental Design, Randomized Controlled Trials