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Hongxi Li; Shuwei Li; Liuquan Sun; Xinyuan Song – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…
Descriptors: Statistical Analysis, Statistical Studies, Structural Equation Models, Intervals
Hosseinzadeh, Mostafa – ProQuest LLC, 2021
In real-world situations, multidimensional data may appear on large-scale tests or attitudinal surveys. A simple structure, multidimensional model may be used to evaluate the items, ignoring the cross-loading of some items on the secondary dimension. The purpose of this study was to investigate the influence of structure complexity magnitude of…
Descriptors: Item Response Theory, Models, Simulation, Evaluation Methods
Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?
Schweizer, Karl; Troche, Stefan – Educational and Psychological Measurement, 2018
In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…
Descriptors: Investigations, Difficulty Level, Factor Analysis, Models
Hamaker, E. L.; Grasman, R. P. P. P. – Psychometrika, 2012
Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a "hot hand" and a…
Descriptors: Psychological Patterns, Statistical Inference, Data, Simulation
Gemici, Sinan; Bednarz, Alice; Lim, Patrick – International Journal of Training Research, 2012
Quantitative research in vocational education and training (VET) is routinely affected by missing or incomplete information. However, the handling of missing data in published VET research is often sub-optimal, leading to a real risk of generating results that can range from being slightly biased to being plain wrong. Given that the growing…
Descriptors: Vocational Education, Educational Research, Data, Statistical Analysis
Savalei, Victoria; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a…
Descriptors: Structural Equation Models, Data, Computation, Maximum Likelihood Statistics

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