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Timothy R. Konold; Elizabeth A. Sanders; Kelvin Afolabi – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Measurement invariance (MI) is an essential part of validity evidence concerned with ensuring that tests function similarly across groups, contexts, and time. Most evaluations of MI involve multigroup confirmatory factor analyses (MGCFA) that assume simple structure. However, recent research has shown that constraining non-target indicators to…
Descriptors: Evaluation Methods, Error of Measurement, Validity, Monte Carlo Methods
Manuel T. Rein; Jeroen K. Vermunt; Kim De Roover; Leonie V. D. E. Vogelsmeier – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive…
Descriptors: Measurement Techniques, Factor Analysis, Scores, Validity
Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This article is concerned with the question of whether the missing data mechanism routinely referred to as missing completely at random (MCAR) is statistically examinable via a test for lack of distributional differences between groups with observed and missing data, and related consequences. A discussion is initially provided, from a formal logic…
Descriptors: Data Analysis, Statistical Analysis, Probability, Structural Equation Models
Johnson, Emily C.; Meade, Adam W.; DuVernet, Amy M. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Confirmatory factor analytic tests of measurement invariance (MI) require a referent indicator (RI) for model identification. Although the assumption that the RI is perfectly invariant across groups is acknowledged as problematic, the literature provides relatively little guidance for researchers to identify the conditions under which the practice…
Descriptors: Measurement, Validity, Factor Analysis, Models
Saris, Willem E.; Satorra, Albert; van der Veld, William M. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes…
Descriptors: Structural Equation Models, Validity, Goodness of Fit, Evaluation Methods
Ferrando, Pere J.; Lorenzo-Seva, Urbano; Chico, Eliseo – Structural Equation Modeling: A Multidisciplinary Journal, 2009
This article proposes procedures for simultaneously assessing and controlling acquiescence and social desirability in questionnaire items. The procedures are based on a semi-restricted factor-analytic tridimensional model, and can be used with binary, graded-response, or more continuous items. We discuss procedures for fitting the model (item…
Descriptors: Factor Analysis, Response Style (Tests), Questionnaires, Test Items
Hayduk, Leslie A.; Robinson, Hannah Pazderka; Cummings, Greta G.; Boadu, Kwame; Verbeek, Eric L.; Perks, Thomas A. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Researchers using structural equation modeling (SEM) aspire to learn about the world by seeking models with causal specifications that match the causal forces extant in the world. This quest for a model matching existing worldly causal forces constitutes an ontology that orients, or perhaps reorients, thinking about measurement validity. This…
Descriptors: Validity, Structural Equation Models, Reliability, Causal Models
Hox, Joop J.; Kleiboer, Annet M. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This study describes a comparison between retrospective questions and daily diaries inquiring about positive and negative support in spousal interactions. The design was a multitrait-multimethod matrix with trait factors of positive and negative support, and method factors of retrospective questions and daily asked questions. Five questions were…
Descriptors: Comparative Analysis, Interviews, Diaries, Spouses
Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction
Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A structural equation modeling based method is outlined that accomplishes interval estimation of individual optimal scores resulting from multiple-component measuring instruments evaluating single underlying latent dimensions. The procedure capitalizes on the linear combination of a prespecified set of measures that is associated with maximal…
Descriptors: Scores, Structural Equation Models, Reliability, Validity
Schumacker, Randall E. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Amos 5.0 (Arbuckle, 2003) permits exploratory specification searches for the best theoretical model given an initial model using the following fit function criteria: chi-square (C), chi-square--df (C--df), Akaike Information Criteria (AIC), Browne-Cudeck criterion (BCC), Bayes Information Criterion (BIC) , chi-square divided by the degrees of…
Descriptors: Computer Software, Structural Equation Models, Models, Search Strategies