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Showing 1 to 15 of 57 results Save | Export
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Liu, Xiaoling; Cao, Pei; Lai, Xinzhen; Wen, Jianbing; Yang, Yanyun – Educational and Psychological Measurement, 2023
Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical ([omega]H) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices…
Descriptors: Correlation, Measurement Techniques, Prediction, Regression (Statistics)
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Educational and Psychological Measurement, 2022
Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article…
Descriptors: Structural Equation Models, Factor Structure, Statistical Bias, Error of Measurement
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Lee, Bitna; Sohn, Wonsook – Educational and Psychological Measurement, 2022
A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…
Descriptors: Goodness of Fit, Factor Structure, Monte Carlo Methods, Factor Analysis
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Beauducel, André; Kersting, Martin – Educational and Psychological Measurement, 2020
We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor…
Descriptors: Factor Structure, Factor Analysis, Sample Size, Intelligence
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Liang, Xinya – Educational and Psychological Measurement, 2020
Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both…
Descriptors: Factor Structure, Bayesian Statistics, Structural Equation Models, Goodness of Fit
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Fujimoto, Ken A. – Educational and Psychological Measurement, 2019
Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the…
Descriptors: Bayesian Statistics, Item Response Theory, Psychometrics, Models
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Gonzalez, Oscar; MacKinnon, David P. – Educational and Psychological Measurement, 2018
Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to…
Descriptors: Statistical Analysis, Monte Carlo Methods, Measurement, Models
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Yang, Yanyun; Xia, Yan – Educational and Psychological Measurement, 2019
When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a…
Descriptors: Scores, Sample Size, Bayesian Statistics, Item Analysis
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Hayduk, Leslie – Educational and Psychological Measurement, 2014
Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously…
Descriptors: Factor Analysis, Goodness of Fit, Factor Structure, Structural Equation Models
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Raykov, Tenko; Pohl, Steffi – Educational and Psychological Measurement, 2013
A method for examining common factor variance in multiple-component measuring instruments is outlined. The procedure is based on an application of the latent variable modeling methodology and is concerned with evaluating observed variance explained by a global factor and by one or more additional component-specific factors. The approach furnishes…
Descriptors: Statistical Analysis, Factor Structure, Scores, Models
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Raykov, Tenko; Pohl, Steffi – Educational and Psychological Measurement, 2013
A procedure for examining essential unidimensionality in multicomponent measuring instruments is discussed. The method is based on an application of latent variable modeling and is concerned with the extent to which a common factor for all components of a given scale accounts for their correlations. The approach provides point and interval…
Descriptors: Measures (Individuals), Statistical Analysis, Factor Structure, Correlation
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Brown, Allison R.; Finney, Sara J.; France, Megan K. – Educational and Psychological Measurement, 2011
The Hong Psychological Reactance Scale (HPRS) purports to measure reactance: a motivational state experienced when a behavioral freedom is threatened with elimination. To date, five studies have examined the psychometric properties of the HPRS, but reached different conclusions regarding its factor structure. The current study further investigated…
Descriptors: Measures (Individuals), Motivation, Psychometrics, Factor Structure
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Liu, Yan; Zumbo, Bruno D. – Educational and Psychological Measurement, 2012
There is a lack of research on the effects of outliers on the decisions about the number of factors to retain in an exploratory factor analysis, especially for outliers arising from unintended and unknowingly included subpopulations. The purpose of the present research was to investigate how outliers from an unintended and unknowingly included…
Descriptors: Factor Analysis, Factor Structure, Evaluation Research, Evaluation Methods
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Wilkins, Jesse L. M. – Educational and Psychological Measurement, 2010
Quantitative literacy is a habit of mind that is characterized by the interrelationship among a person's everyday understanding of mathematics, his or her beliefs about mathematics, and his or her disposition toward mathematics. To assess quantitative literacy, it is important to devise measurement tools that provide valid and reliable information…
Descriptors: Numeracy, Factor Structure, Factor Analysis, High School Students
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Ng, Kok-Mun; Wang, Chuang; Kim, Do-Hong; Bodenhorn, Nancy – Educational and Psychological Measurement, 2010
The authors investigated the factor structure of the Schutte Self-Report Emotional Intelligence (SSREI) scale on international students. Via confirmatory factor analysis, the authors tested the fit of the models reported by Schutte et al. and five other studies to data from 640 international students in the United States. Results show that…
Descriptors: Emotional Intelligence, Factor Structure, Measures (Individuals), Factor Analysis
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