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Raykov, Tenko; Anthony, James C.; Menold, Natalja – Educational and Psychological Measurement, 2023
The population relationship between coefficient alpha and scale reliability is studied in the widely used setting of unidimensional multicomponent measuring instruments. It is demonstrated that for any set of component loadings on the common factor, regardless of the extent of their inequality, the discrepancy between alpha and reliability can be…
Descriptors: Correlation, Evaluation Research, Reliability, Measurement Techniques
Raykov, Tenko; Calvocoressi, Lisa – Educational and Psychological Measurement, 2021
A procedure for evaluating the average R-squared index for a given set of observed variables in an exploratory factor analysis model is discussed. The method can be used as an effective aid in the process of model choice with respect to the number of factors underlying the interrelationships among studied measures. The approach is developed within…
Descriptors: Factor Analysis, Structural Equation Models, Statistical Analysis, Selection
Raykov, Tenko; Marcoulides, George A.; Tong, Bing – Educational and Psychological Measurement, 2016
A latent variable modeling procedure is discussed that can be used to test if two or more homogeneous multicomponent instruments with distinct components are measuring the same underlying construct. The method is widely applicable in scale construction and development research and can also be of special interest in construct validation studies.…
Descriptors: Models, Statistical Analysis, Measurement Techniques, Factor Analysis
Raykov, Tenko; Marcoulides, George A.; Millsap, Roger E. – Educational and Psychological Measurement, 2013
A multiple testing method for examining factorial invariance for latent constructs evaluated by multiple indicators in distinct populations is outlined. The procedure is based on the false discovery rate concept and multiple individual restriction tests and resolves general limitations of a popular factorial invariance testing approach. The…
Descriptors: Testing, Statistical Analysis, Factor Analysis, Statistical Significance

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