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Download full textFatih Orçan – International Journal of Assessment Tools in Education, 2025
Factor analysis is a statistical method to explore the relationships among observed variables and identify latent structures. It is crucial in scale development and validity analysis. Key factors affecting the accuracy of factor analysis results include the type of data, sample size, and the number of response categories. While some studies…
Descriptors: Factor Analysis, Factor Structure, Item Response Theory, Sample Size
Kilic, Abdullah Faruk; Uysal, Ibrahim; Atar, Burcu – International Journal of Assessment Tools in Education, 2020
This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance…
Descriptors: Factor Analysis, Computation, Methods, Sample Size
Kilic, Abdullah Faruk; Dogan, Nuri – International Journal of Assessment Tools in Education, 2021
Weighted least squares (WLS), weighted least squares mean-and-variance-adjusted (WLSMV), unweighted least squares mean-and-variance-adjusted (ULSMV), maximum likelihood (ML), robust maximum likelihood (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items,…
Descriptors: Factor Analysis, Computation, Least Squares Statistics, Maximum Likelihood Statistics
Li, Feifei – ETS Research Report Series, 2017
An information-correction method for testlet-based tests is introduced. This method takes advantage of both generalizability theory (GT) and item response theory (IRT). The measurement error for the examinee proficiency parameter is often underestimated when a unidimensional conditional-independence IRT model is specified for a testlet dataset. By…
Descriptors: Item Response Theory, Generalizability Theory, Tests, Error of Measurement
Direct linkFinch, Holmes – Applied Psychological Measurement, 2010
The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor…
Descriptors: Item Response Theory, Computation, Factor Analysis, Models
Finch, Holmes – Applied Psychological Measurement, 2005
This study compares the ability of the multiple indicators, multiple causes (MIMIC) confirmatory factor analysis model to correctly identify cases of differential item functioning (DIF) with more established methods. Although the MIMIC model might have application in identifying DIF for multiple grouping variables, there has been little…
Descriptors: Identification, Factor Analysis, Test Bias, Models
