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Olsson, Ulf – Practical Assessment, Research & Evaluation, 2022
We discuss analysis of 5-grade Likert type data in the two-sample case. Analysis using two-sample "t" tests, nonparametric Wilcoxon tests, and ordinal regression methods, are compared using simulated data based on an ordinal regression paradigm. One thousand pairs of samples of size "n"=10 and "n"=30 were generated,…
Descriptors: Regression (Statistics), Likert Scales, Sampling, Nonparametric Statistics
van Dorresteijn, Chevy; Kan, Kees-Jan; Smits, Niels – Assessment & Evaluation in Higher Education, 2023
When higher education students are assessed multiple times, teachers need to consider how these assessments can be combined into a single pass or fail decision. A common question that arises is whether students should be allowed to take a resit. Previous research has found little to no clear learning benefits of resits and therefore suggested they…
Descriptors: College Students, Student Evaluation, Pretests Posttests, Regression (Statistics)
Xu, Ziqian; Hai, Jiarui; Yang, Yutong; Zhang, Zhiyong – Grantee Submission, 2022
Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past…
Descriptors: Social Networks, Network Analysis, Data Analysis, Bayesian Statistics
Bogaert, Jasper; Loh, Wen Wei; Rosseel, Yves – Educational and Psychological Measurement, 2023
Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error…
Descriptors: Factor Analysis, Regression (Statistics), Structural Equation Models, Error of Measurement
Koziol, Natalie A.; Goodrich, J. Marc; Yoon, HyeonJin – Educational and Psychological Measurement, 2022
Differential item functioning (DIF) is often used to examine validity evidence of alternate form test accommodations. Unfortunately, traditional approaches for evaluating DIF are prone to selection bias. This article proposes a novel DIF framework that capitalizes on regression discontinuity design analysis to control for selection bias. A…
Descriptors: Regression (Statistics), Item Analysis, Validity, Testing Accommodations
Yesiltas, Gonca; Paek, Insu – Educational and Psychological Measurement, 2020
A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were…
Descriptors: Simulation, Sample Size, Item Analysis, Scores
Abulela, Mohammed A. A.; Rios, Joseph A. – Applied Measurement in Education, 2022
When there are no personal consequences associated with test performance for examinees, rapid guessing (RG) is a concern and can differ between subgroups. To date, the impact of differential RG on item-level measurement invariance has received minimal attention. To that end, a simulation study was conducted to examine the robustness of the…
Descriptors: Comparative Analysis, Robustness (Statistics), Nonparametric Statistics, Item Analysis
Hemmert, Giselmar A. J.; Schons, Laura M.; Wieseke, Jan; Schimmelpfennig, Heiko – Sociological Methods & Research, 2018
The literature proposes numerous so-called pseudo-R[superscript 2] measures for evaluating "goodness of fit" in regression models with categorical dependent variables. Unlike ordinary least square-R[superscript 2], log-likelihood-based pseudo-R[superscript 2]s do not represent the proportion of explained variance but rather the…
Descriptors: Regression (Statistics), Sample Size, Predictor Variables, Benchmarking
Li, Zhushan – Journal of Educational Measurement, 2014
Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…
Descriptors: Test Bias, Sample Size, Statistical Analysis, Regression (Statistics)
Stamey, James D.; Beavers, Daniel P.; Sherr, Michael E. – Sociological Methods & Research, 2017
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is…
Descriptors: Bayesian Statistics, Classification, Models, Correlation
Tipton, Elizabeth – Society for Research on Educational Effectiveness, 2014
Replication studies allow for making comparisons and generalizations regarding the effectiveness of an intervention across different populations, versions of a treatment, settings and contexts, and outcomes. One method for making these comparisons across many replication studies is through the use of meta-analysis. A recent innovation in…
Descriptors: Replication (Evaluation), Robustness (Statistics), Meta Analysis, Regression (Statistics)
Tipton, Elizabeth; Pustejovsky, James E. – Society for Research on Educational Effectiveness, 2015
Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…
Descriptors: Randomized Controlled Trials, Sample Size, Effect Size, Hypothesis Testing
Ugille, Maaike; Moeyaert, Mariola; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2014
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…
Descriptors: Effect Size, Statistical Bias, Sample Size, Regression (Statistics)
Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel – Psicologica: International Journal of Methodology and Experimental Psychology, 2012
In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…
Descriptors: Regression (Statistics), Models, Simulation, Least Squares Statistics
Park, Sangwook – ProQuest LLC, 2011
Many studies have been conducted to evaluate the performance of DIF detection methods, when two groups have different ability distributions. Such studies typically have demonstrated factors that are associated with inflation of Type I error rates in DIF detection, such as mean ability differences. However, no study has examined how the direction…
Descriptors: Test Bias, Regression (Statistics), Sample Size, Simulation

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