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Levin, Joel R.; Ferron, John M.; Gafurov, Boris S. – Journal of Education for Students Placed at Risk, 2022
The present simulation study examined the statistical properties (namely, Type I error and statistical power) of various novel randomized single-case multiple-baseline designs and associated randomized-test analyses for comparing the A- to B-phase immediate abrupt outcome changes in two independent intervention conditions. It was found that with…
Descriptors: Statistical Analysis, Error of Measurement, Intervention, Program Effectiveness
Emma Somer; Carl Falk; Milica Miocevic – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon's correction and…
Descriptors: Scores, Structural Equation Models, Comparative Analysis, Sample Size
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
Inga Laukaityte; Marie Wiberg – Practical Assessment, Research & Evaluation, 2024
The overall aim was to examine effects of differences in group ability and features of the anchor test form on equating bias and the standard error of equating (SEE) using both real and simulated data. Chained kernel equating, Postratification kernel equating, and Circle-arc equating were studied. A college admissions test with four different…
Descriptors: Ability Grouping, Test Items, College Entrance Examinations, High Stakes Tests
Kristin Porter; Luke Miratrix; Kristen Hunter – Society for Research on Educational Effectiveness, 2021
Background: Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs)…
Descriptors: Statistical Analysis, Hypothesis Testing, Computer Software, Randomized Controlled Trials
Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2023
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random…
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions
Deke, John; Finucane, Mariel; Thal, Daniel – National Center for Education Evaluation and Regional Assistance, 2022
BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…
Descriptors: Bayesian Statistics, Educational Research, Data Interpretation, Hypothesis Testing
Greifer, Noah – ProQuest LLC, 2018
There has been some research in the use of propensity scores in the context of measurement error in the confounding variables; one recommended method is to generate estimates of the mis-measured covariate using a latent variable model, and to use those estimates (i.e., factor scores) in place of the covariate. I describe a simulation study…
Descriptors: Evaluation Methods, Probability, Scores, Statistical Analysis
Olivera-Aguilar, Margarita; Rikoon, Samuel H.; Gonzalez, Oscar; Kisbu-Sakarya, Yasemin; MacKinnon, David P. – Educational and Psychological Measurement, 2018
When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to…
Descriptors: Error of Measurement, Statistical Analysis, Factor Analysis, Simulation
Kim, Sooyeon; Livingston, Samuel A. – ETS Research Report Series, 2017
The purpose of this simulation study was to assess the accuracy of a classical test theory (CTT)-based procedure for estimating the alternate-forms reliability of scores on a multistage test (MST) having 3 stages. We generated item difficulty and discrimination parameters for 10 parallel, nonoverlapping forms of the complete 3-stage test and…
Descriptors: Accuracy, Test Theory, Test Reliability, Adaptive Testing
Suero, Manuel; Privado, Jesús; Botella, Juan – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…
Descriptors: Evaluation Methods, Theories, Simulation, Statistical Analysis
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
Hidalgo, Ma Dolores; Benítez, Isabel; Padilla, Jose-Luis; Gómez-Benito, Juana – Sociological Methods & Research, 2017
The growing use of scales in survey questionnaires warrants the need to address how does polytomous differential item functioning (DIF) affect observed scale score comparisons. The aim of this study is to investigate the impact of DIF on the type I error and effect size of the independent samples t-test on the observed total scale scores. A…
Descriptors: Test Items, Test Bias, Item Response Theory, Surveys
Miratrix, Luke; Feller, Avi; Pillai, Natesh; Pati, Debdeep – Society for Research on Educational Effectiveness, 2016
Modeling the distribution of site level effects is an important problem, but it is also an incredibly difficult one. Current methods rely on distributional assumptions in multilevel models for estimation. There it is hoped that the partial pooling of site level estimates with overall estimates, designed to take into account individual variation as…
Descriptors: Probability, Models, Statistical Distributions, Bayesian Statistics
Westlund, Erik; Stuart, Elizabeth A. – American Journal of Evaluation, 2017
This article discusses the nonuse, misuse, and proper use of pilot studies in experimental evaluation research. The authors first show that there is little theoretical, practical, or empirical guidance available to researchers who seek to incorporate pilot studies into experimental evaluation research designs. The authors then discuss how pilot…
Descriptors: Use Studies, Pilot Projects, Evaluation Research, Experiments