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McNeish, Daniel – Journal of Experimental Education, 2018
Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML…
Descriptors: Growth Models, Sampling, Sample Size, Hierarchical Linear Modeling
Gürdogan-Bayir, Ömür; Bozkurt, Mahmut – Online Submission, 2018
In Turkey, it is seen that there are a number of experimental studies conducted in relation to the course of Social Studies to reveal the influence of cooperative teaching approaches on achievement. However, in literature, there is no research combining the findings obtained in these studies. In this respect, with the help of the meta-analysis…
Descriptors: Foreign Countries, Social Studies, Cooperative Learning, Instructional Effectiveness
Wu, Wei; Jia, Fan; Kinai, Richard; Little, Todd D. – International Journal of Behavioral Development, 2017
Spline growth modelling is a popular tool to model change processes with distinct phases and change points in longitudinal studies. Focusing on linear spline growth models with two phases and a fixed change point (the transition point from one phase to the other), we detail how to find optimal data collection designs that maximize the efficiency…
Descriptors: Longitudinal Studies, Data Collection, Models, Change
Sarkar, Jyotirmoy; Rashid, Mamunur – Educational Research Quarterly, 2017
The standard deviation (SD) of a random sample is defined as the square-root of the sample variance, which is the "mean" squared deviation of the sample observations from the sample mean. Here, we interpret the sample SD as the square-root of twice the mean square of all pairwise half deviations between any two sample observations. This…
Descriptors: Sample Size, Sampling, Visualization, Geometric Concepts
Ayodele, Alicia Nicole – ProQuest LLC, 2017
Within polytomous items, differential item functioning (DIF) can take on various forms due to the number of response categories. The lack of invariance at this level is referred to as differential step functioning (DSF). The most common DSF methods in the literature are the adjacent category log odds ratio (AC-LOR) estimator and cumulative…
Descriptors: Statistical Analysis, Test Bias, Test Items, Scores
Craig, Brandon – ProQuest LLC, 2017
The purpose of this study was to determine if using a multistage approach for the empirical selection of anchor items would lead to more accurate DIF detection rates than the anchor selection methods proposed by Kopf, Zeileis, & Strobl (2015b). A simulation study was conducted in which the sample size, percentage of DIF, and balance of DIF…
Descriptors: Simulation, Sample Size, Item Response Theory, Item Analysis
Harbaugh, Allen G.; Liu, Min – AERA Online Paper Repository, 2017
This research examines the effects of nonattending response pattern contamination and select response style patterns on measures of model fit (CFI) and internal reliability (Cronbach's [alpha]). A simulation study examines the effects resulting from percentage of contamination, number of manifest items measured and sample size. Initial results…
Descriptors: Factor Analysis, Response Style (Tests), Goodness of Fit, Test Reliability
Pustejovsky, James Eric; Furman, Gleb – AERA Online Paper Repository, 2017
In linear regression models estimated by ordinary least squares, it is often desirable to use hypothesis tests and confidence intervals that remain valid in the presence of heteroskedastic errors. Wald tests based on heteroskedasticity-consistent covariance matrix estimators (HCCMEs, also known as sandwich estimators or simply "robust"…
Descriptors: Hypothesis Testing, Sample Size, Regression (Statistics), Computation
Tong, Xin; Zhang, Zhiyong – Grantee Submission, 2017
Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even…
Descriptors: Observation, Models, Statistical Distributions, Monte Carlo Methods
Bolin, Jocelyn H.; Finch, W. Holmes; Stenger, Rachel – Educational and Psychological Measurement, 2019
Multilevel data are a reality for many disciplines. Currently, although multiple options exist for the treatment of multilevel data, most disciplines strictly adhere to one method for multilevel data regardless of the specific research design circumstances. The purpose of this Monte Carlo simulation study is to compare several methods for the…
Descriptors: Hierarchical Linear Modeling, Computation, Statistical Analysis, Maximum Likelihood Statistics
Cao, Chunhua; Kim, Eun Sook; Chen, Yi-Hsin; Ferron, John; Stark, Stephen – Educational and Psychological Measurement, 2019
In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of…
Descriptors: Hierarchical Linear Modeling, Structural Equation Models, Computation, Identification
Dikmen, Melih – International Journal of Contemporary Educational Research, 2021
The relationship between foreign language anxiety (FLA) and English as a foreign language (EFL) learners' performance is constantly being examined through experimental and theoretical studies. The aim of this study is to examine the association between FLA and EFL performance through meta-analysis. As a result of an extensive literature review, 69…
Descriptors: English (Second Language), Second Language Learning, Test Anxiety, Language Skills
Luh, Wei-Ming; Guo, Jiin-Huarng – Journal of Experimental Education, 2016
This article discusses the sample size requirements for the interaction, row, and column effects, respectively, by forming a linear contrast for a 2×2 factorial design for fixed-effects heterogeneous analysis of variance. The proposed method uses the Welch t test and its corresponding degrees of freedom to calculate the final sample size in a…
Descriptors: Sample Size, Interaction, Statistical Analysis, Sampling
Rysstad, Anne Langseth; Pedersen, Arve Vorland – Journal of Autism and Developmental Disorders, 2016
A larger distribution of left-handedness in the population of Autism Spectrum Disorder has been repeatedly reported. Despite of this, the sample sizes in the individual study's are too small for any generalization to be made. Using both description-based and citation-based searches, the present review combines the individual results in order to…
Descriptors: Autism, Pervasive Developmental Disorders, Handedness, Sample Size
Huang, Francis L. – School Psychology Quarterly, 2018
The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their…
Descriptors: Hierarchical Linear Modeling, School Psychology, Misconceptions, Correlation

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