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Jason A. Schoeneberger; Christopher Rhoads – American Journal of Evaluation, 2025
Regression discontinuity (RD) designs are increasingly used for causal evaluations. However, the literature contains little guidance for conducting a moderation analysis within an RDD context. The current article focuses on moderation with a single binary variable. A simulation study compares: (1) different bandwidth selectors and (2) local…
Descriptors: Regression (Statistics), Causal Models, Evaluation Methods, Multivariate Analysis
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Aarnes Gudmestad; Thomas A. Metzger – Language Learning, 2025
In this Methods Showcase Article, we illustrate mixed-effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future-time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step-by-step…
Descriptors: Second Language Learning, Spanish, Applied Linguistics, Predictor Variables
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W. Holmes Finch – Educational and Psychological Measurement, 2024
Dominance analysis (DA) is a very useful tool for ordering independent variables in a regression model based on their relative importance in explaining variance in the dependent variable. This approach, which was originally described by Budescu, has recently been extended to use with structural equation models examining relationships among latent…
Descriptors: Models, Regression (Statistics), Structural Equation Models, Predictor Variables
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André Beauducel; Norbert Hilger; Tobias Kuhl – Educational and Psychological Measurement, 2024
Regression factor score predictors have the maximum factor score determinacy, that is, the maximum correlation with the corresponding factor, but they do not have the same inter-correlations as the factors. As it might be useful to compute factor score predictors that have the same inter-correlations as the factors, correlation-preserving factor…
Descriptors: Scores, Factor Analysis, Correlation, Predictor Variables
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Sang-June Park; Youjae Yi – Journal of Educational and Behavioral Statistics, 2024
Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…
Descriptors: Interaction, Predictor Variables, Causal Models, Mathematical Models
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Lanqi Wang; Chengan Yuan; Shahad Alsharif; Qing Archer Zhang; Yang Du – Remedial and Special Education, 2024
Single-case comparative studies could help identify efficient instructional procedures for individuals with disabilities. However, previous literature reported inconsistent efficiency results if multiple comparisons were conducted, indicating that within-participant replication was uncommon. In this review, we examined single-case comparative…
Descriptors: Regression (Statistics), Research Methodology, Intervention, Program Effectiveness
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Myoung-jae Lee; Goeun Lee; Jin-young Choi – Sociological Methods & Research, 2025
A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…
Descriptors: Probability, Least Squares Statistics, Regression (Statistics), Causal Models
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Francesco Innocenti; Math J. J. M. Candel; Frans E. S. Tan; Gerard J. P. van Breukelen – Journal of Educational and Behavioral Statistics, 2024
Normative studies are needed to obtain norms for comparing individuals with the reference population on relevant clinical or educational measures. Norms can be obtained in an efficient way by regressing the test score on relevant predictors, such as age and sex. When several measures are normed with the same sample, a multivariate regression-based…
Descriptors: Sample Size, Multivariate Analysis, Error of Measurement, Regression (Statistics)
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Robert B. Olsen; Larry L. Orr; Stephen H. Bell; Elizabeth Petraglia; Elena Badillo-Goicoechea; Atsushi Miyaoka; Elizabeth A. Stuart – Journal of Research on Educational Effectiveness, 2024
Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs…
Descriptors: Accuracy, Predictor Variables, Randomized Controlled Trials, Regression (Statistics)
James R. Izzo – ProQuest LLC, 2024
Curriculum-based measurements (CBM) are used in schools to screen students for academic difficulties. Oral reading fluency curriculum-based measures (CBM-R) are the most popular, yet several studies have identified predictive bias in CBM-Rs in relation to key variables (e.g., race, sex). Despite a massive increase in the use of these measures as…
Descriptors: Predictor Variables, Bias, Curriculum Based Assessment, Reading Tests
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JiYoon Kim; Saebuyl An; Sehee Hong – Asia Pacific Education Review, 2024
The main objective of the study was to explore the main predictors that influence academic stress among fourth-grade elementary school students using data from the Panel Study on Korean Children via a least absolute shrinkage and selection operator (LASSO) regularized penalty regression model. The study examined 280 explanatory variables using the…
Descriptors: Stress Variables, Elementary School Students, Regression (Statistics), Predictor Variables