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Lee, Hyung Rock; Sung, Jaeyun; Lee, Sunbok – International Journal of Assessment Tools in Education, 2021
Conventional estimators for indirect effects using a difference in coefficients and product of coefficients produce the same results for continuous outcomes. However, for binary outcomes, the difference in coefficient estimator systematically underestimates the indirect effects because of a scaling problem. One solution is to standardize…
Descriptors: Statistical Analysis, Computation, Regression (Statistics), Scaling
Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
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Günhan, Burak Kürsad; Friede, Tim; Held, Leonhard – Research Synthesis Methods, 2018
Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network…
Descriptors: Meta Analysis, Regression (Statistics), Statistical Inference, Probability
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Devlieger, Ines; Talloen, Wouter; Rosseel, Yves – Educational and Psychological Measurement, 2019
Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the…
Descriptors: Regression (Statistics), Computation, Goodness of Fit, Statistical Inference
Sales, Adam C.; Hansen, Ben B. – Journal of Educational and Behavioral Statistics, 2020
Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, "R," approaches a cut point, "c," from either side. Alternative methods target the average treatment effect in a small region around "c," at the cost of an assumption that treatment assignment,…
Descriptors: Regression (Statistics), Computation, Statistical Inference, Robustness (Statistics)
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Geldhof, G. John; Anthony, Katherine P.; Selig, James P.; Mendez-Luck, Carolyn A. – International Journal of Behavioral Development, 2018
The existence of several accessible sources has led to a proliferation of mediation models in the applied research literature. Most of these sources assume endogenous variables (e.g., M, and Y) have normally distributed residuals, precluding models of binary and/or count data. Although a growing body of literature has expanded mediation models to…
Descriptors: Regression (Statistics), Statistical Analysis, Evaluation Methods, Correlation
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Banjanovic, Erin S.; Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2016
Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…
Descriptors: Computation, Statistical Analysis, Effect Size, Sampling
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Keiffer, Greggory L.; Lane, Forrest C. – European Journal of Training and Development, 2016
Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…
Descriptors: Probability, Scores, Statistical Analysis, Statistical Inference
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Showalter, Daniel A.; Mullet, Luke B. – Mid-Western Educational Researcher, 2017
Selection bias is a persistent, and often hidden, problem in educational research. It is the primary obstacle standing in between increasingly available large education datasets and the ability to make valid causal inferences to inform policymaking, research, and practice (Stuart, 2010). This article provides an accessible discussion on the…
Descriptors: Educational Research, Selection Criteria, Selection Tools, Bias
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Wahyuddin, Wawan – Higher Education Studies, 2016
This study wants to examine the relationship between teacher competence and emotional intelligence that held by teachers to increase the teacher performance Madrasah Tsanawiyah at district of Serang Banten. This research was conducted with the quantitative method, through analysis descriptive and inferential. Samples the research were teachers…
Descriptors: Teacher Competencies, Relationship, Emotional Intelligence, Islam
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Weller, Susan C. – Field Methods, 2015
This article presents a simple approach to making quick sample size estimates for basic hypothesis tests. Although there are many sources available for estimating sample sizes, methods are not often integrated across statistical tests, levels of measurement of variables, or effect sizes. A few parameters are required to estimate sample sizes and…
Descriptors: Sample Size, Statistical Analysis, Computation, Hypothesis Testing
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Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent – Journal of Educational and Behavioral Statistics, 2015
When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Bayesian Statistics, Statistical Inference
Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent – Grantee Submission, 2015
When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix [sigma] of group-level varying coefficients are often degenerate. One can do better, even…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Bayesian Statistics, Statistical Inference
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Jarosz, Andrew F.; Wiley, Jennifer – Journal of Problem Solving, 2014
The purpose of this paper is to provide an easy template for the inclusion of the Bayes factor in reporting experimental results, particularly as a recommendation for articles in the "Journal of Problem Solving." The Bayes factor provides information with a similar purpose to the "p"-value--to allow the researcher to make…
Descriptors: Problem Solving, Bayesian Statistics, Statistical Inference, Computation
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Sosu, Edward M.; Rydzewska, Ewelina – Educational Studies, 2017
This study explores the nature of parental attitudinal beliefs towards educational inclusion and the factors that determine these beliefs. Participants were drawn from the Growing Up in Scotland Survey (N = 2200). Results indicate that majority of parents held positive generalised belief towards including children with additional support needs…
Descriptors: Parent Attitudes, Beliefs, Educational Attitudes, Inclusion
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