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Showing 1 to 15 of 73 results Save | Export
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Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2022
This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features…
Descriptors: Comparative Analysis, Statistical Analysis, Sample Size, Measurement Techniques
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Sedat Sen; Allan S. Cohen – Educational and Psychological Measurement, 2024
A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's…
Descriptors: Goodness of Fit, Item Response Theory, Sample Size, Classification
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Kárász, Judit T.; Széll, Krisztián; Takács, Szabolcs – Quality Assurance in Education: An International Perspective, 2023
Purpose: Based on the general formula, which depends on the length and difficulty of the test, the number of respondents and the number of ability levels, this study aims to provide a closed formula for the adaptive tests with medium difficulty (probability of solution is p = 1/2) to determine the accuracy of the parameters for each item and in…
Descriptors: Test Length, Probability, Comparative Analysis, Difficulty Level
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Fangxing Bai; Ben Kelcey – Society for Research on Educational Effectiveness, 2024
Purpose and Background: Despite the flexibility of multilevel structural equation modeling (MLSEM), a practical limitation many researchers encounter is how to effectively estimate model parameters with typical sample sizes when there are many levels of (potentially disparate) nesting. We develop a method-of-moment corrected maximum likelihood…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Sample Size, Faculty Development
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Logan, Jessica; Jiang, Hui; Yeomans-Maldonado, Gloria – AERA Online Paper Repository, 2018
In education research, conceptual models are becoming more complex and more challenging to fit. In response, many researchers have turned to multi-step model fitting processes; first fitting confirmatory factor analyses (CFA) to their data, then extracting the latent factor scores; converting them to observed variables to use in additional…
Descriptors: Factor Analysis, Sample Size, Correlation, Statistical Bias
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Finch, W. Holmes – Educational and Psychological Measurement, 2020
Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools…
Descriptors: Factor Analysis, Goodness of Fit, Social Sciences, Comparative Analysis
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Kogar, Hakan – Journal of Education and Learning, 2018
The aim of the present research study was to compare the findings from the nonparametric MSA, DIMTEST and DETECT and the parametric dimensionality determining methods in various simulation conditions by utilizing exploratory and confirmatory methods. For this purpose, various simulation conditions were established based on number of dimensions,…
Descriptors: Evaluation Methods, Nonparametric Statistics, Statistical Analysis, Factor Analysis
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Green, Samuel; Xu, Yuning; Thompson, Marilyn S. – Educational and Psychological Measurement, 2018
Parallel analysis (PA) assesses the number of factors in exploratory factor analysis. Traditionally PA compares the eigenvalues for a sample correlation matrix with the eigenvalues for correlation matrices for 100 comparison datasets generated such that the variables are independent, but this approach uses the wrong reference distribution. The…
Descriptors: Factor Analysis, Accuracy, Statistical Distributions, Comparative Analysis
Enders, Craig K.; Hayes, Timothy; Du, Han – Grantee Submission, 2018
Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random…
Descriptors: Data Analysis, Statistical Bias, Sample Size, Correlation
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Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael – Applied Developmental Science, 2017
Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…
Descriptors: Factor Analysis, Structural Equation Models, Correlation, Sample Size
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MacLennan, Helen; Piña, Anthony; Gibbons, Sean – Journal of Education for Business, 2018
The doctor of business administration (DBA) degree is widely considered as a professional doctorate or professional doctoral degree that is designed to prepare business practitioners. The doctor of philosophy (PhD) in business, by contrast, is thought to prepare research scholars and higher education faculty members. Prior research conducted by…
Descriptors: Business Administration Education, Doctoral Dissertations, Content Analysis, Comparative Analysis
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Toprak, Emre; Gelbal, Selahattin – International Journal of Assessment Tools in Education, 2020
This study aims to compare the performances of the artificial neural network, decision trees and discriminant analysis methods to classify student achievement. The study uses multilayer perceptron model to form the artificial neural network model, chi-square automatic interaction detection (CHAID) algorithm to apply the decision trees method and…
Descriptors: Comparative Analysis, Classification, Artificial Intelligence, Networks
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Huang, Francis L. – Educational and Psychological Measurement, 2018
Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…
Descriptors: Multivariate Analysis, Sampling, Statistical Inference, Data Analysis
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Harrison, Sean; Jones, Hayley E.; Martin, Richard M.; Lewis, Sarah J.; Higgins, Julian P. T. – Research Synthesis Methods, 2017
Meta-analyses combine the results of multiple studies of a common question. Approaches based on effect size estimates from each study are generally regarded as the most informative. However, these methods can only be used if comparable effect sizes can be computed from each study, and this may not be the case due to variation in how the studies…
Descriptors: Meta Analysis, Sample Size, Effect Size, Comparative Analysis
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Lee, Soo; Suh, Youngsuk – Journal of Educational Measurement, 2018
Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect…
Descriptors: Item Response Theory, Sample Size, Models, Error of Measurement
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