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Suyoung Kim; Sooyong Lee; Jiwon Kim; Tiffany A. Whittaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits.…
Descriptors: Social Science Research, Behavioral Sciences, Structural Equation Models, Statistical Analysis
Kurtz, Jaime L. – Teaching of Psychology, 2016
All students, from college freshmen to advanced graduate students, have asked themselves, "Will this decision make me happy?" The vast majority of them have been wrong. Affective forecasting, the process of predicting future feelings, is a topic of great interest to students due to its applicable and highly relatable nature. This article…
Descriptors: Prediction, Affective Behavior, Psychological Patterns, Error of Measurement
Ledford, Jennifer R.; Ayres, Kevin M.; Lane, Justin D.; Lam, Man Fung – Journal of Special Education, 2015
Momentary time sampling (MTS), whole interval recording (WIR), and partial interval recording (PIR) are commonly used in applied research. We discuss potential difficulties with analyzing data when these systems are used and present results from a pilot simulation study designed to determine the extent to which these issues are likely to be…
Descriptors: Intervals, Research Methodology, Sampling, Time
Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P. – MDRC, 2014
A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…
Descriptors: Regression (Statistics), Research Design, Quasiexperimental Design, Research Methodology
Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M. – Society for Research on Educational Effectiveness, 2013
Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…
Descriptors: Probability, Scores, Statistical Analysis, Statistical Bias