Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

Monroe, Scott; Cai, Li

doi:10.1080/00273171.2015.1032398

Cited by 59 publications

(52 citation statements)

References 37 publications

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“…The model was fit using maximum marginal likelihood estimation via the Bock-Aitkin EM algorithm (Bock & Aitkin, 1981), as implemented in the mirt R package (Chalmers, 2012). Model fit was assessed using the limited-information C2 statistic (L. Cai & Monroe, 2014;Monroe & Cai, 2015) as well as C2-based approximate fit indices. The guidelines for adequate fit (i.e., RMSEA2 < 0.089 and SRMR < 0.05) proposed by Maydeu-Olivares & Joe (2014) were used to judge the fit of the IRT model.…”

Section: Statistical Analysesmentioning

confidence: 99%

Measuring Depression in Autistic Adults: Psychometric Validation of the Beck Depression Inventory–II

2020

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Depressive disorders are common in autistic adults, but few studies have examined the extent to which common depression questionnaires are psychometrically appropriate for use in this population. Using item response theory, this study examined the psychometric properties of the Beck Depression Inventory–II (BDI-II) in a sample of 947 autistic adults. BDI-II latent trait scores exhibited strong reliability, construct validity, and moderate ability to discriminate between depressed and nondepressed adults on the autism spectrum (area under the receiver operating characteristic curve = 0.796 [0.763, 0.826], sensitivity = 0.820 [0.785, 0.852], specificity = 0.653 [0.601, 0.699]). These results collectively indicate that the BDI-II is a valid measure of depressive symptoms in autistic adults, appropriate for quantifying depression severity in research studies or screening for depressive disorders in clinical settings. A free online score calculator has been created to facilitate the use of BDI-II latent trait scores for clinical and research applications (available at https://asdmeasures.shinyapps.io/bdi_score/ ).

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Section: Statistical Analysesmentioning

confidence: 99%

Measuring Depression in Autistic Adults: Psychometric Validation of the Beck Depression Inventory–II

2020

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“…For these conditions, the M 2 statistic is significant, and the RMSEA values are all .02. In the structural equation modeling literature, an RMSEA of .02 is often interpreted as indicating "close" fit (Browne & Cudeck, 1993), but more stringent guidelines may be necessary for categorical data models (Monroe & Cai, 2015). More clearly, the large rejection rates for S − X 2 (ranging from .63 to .80) and X 2 L D (ranging from .39 to .65) indicate the 1PL provides poor fit.…”

Section: Item Model Misspecifiedmentioning

confidence: 99%

Performance of Person‐Fit Statistics Under Model Misspecification

Hong

Monroe

Falk

2019

J Educational Measurement

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In educational and psychological measurement, a person‐fit statistic (PFS) is designed to identify aberrant response patterns. For parametric PFSs, valid inference depends on several assumptions, one of which is that the item response theory (IRT) model is correctly specified. Previous studies have used empirical data sets to explore the effects of model misspecification on PFSs. We further this line of research by using a simulation study, which allows us to explore issues that may be of interest to practitioners. Results show that, depending on the generating and analysis item models, Type I error rates at fixed values of the latent variable may be greatly inflated, even when the aggregate rates are relatively accurate. Results also show that misspecification is most likely to affect PFSs for examinees with extreme latent variable scores. Two empirical data analyses are used to illustrate the importance of model specification.

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“…Overall, the contribution of this paper is to two research areas: CSEM and multiple imputation in SEM. First, while a number of researchers have focused mainly on estimation and test statistic for CSEM (e.g., Forero et al, 2009;Maydeu-Olivares & Joe, 2014;Monroe & Cai, 2015), issues regarding missing data have not been explored solely for ordinal indicators in the SEM literature. Second, as Lee and Cai (2012) and Enders and Mansolf (2018) have pointed out, multiple imputation inference in SEM is an area that has rarely received attention despite the prevalent usage of multiple imputation in practice.…”

Section: Examplementioning

confidence: 99%

Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

Chung

Cai

2019

Multivariate Behavioral Research

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The use of responses from questionnaires is ubiquitous in social and behavioral science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques, wherein missing data are replaced by plausible values from the their proper posterior distribution given the observed data. Instead of the standard procedure in structural equation modeling (SEM), which requires researchers to fit their model to imputed data sets as many times as the number of imputations and then combine parameter estimates and standard errors at the end, we propose a new and simpler approach that is computationally more convenient. It has a number of additional benefits such as the availability of fit indices. Motivated by Lee and Cai (2012), who proposed an alternative method for statistical inference under MI in SEM with continuous variables, we extend their approach to the case of categorical variables. Within the context of ordered categorical data, the main idea is summarized as follows. Assume we have thresholds and polychoric correlations computed from M imputed data set. Our goal is to perform estimation and inference with these M different thresholds and polychoric correlations. We can easily average the ii thresholds and polychoric correlations; however, the weight matrix for obtaining the correct statistic in CSEM requires reflecting the between-imputation variance on top of simple averaging of asymptotic covariance matrices of the thresholds and polychoric correlations. Finally, applying Browne (1984)'s Proposition 4 leads us to obtain the correct test statistic,T B. We further considerT YB , a small-sample adjustment ofT B (Yuan & Bentler, 1997). We demonstrate our proposed statistics performance and their power to detect model misspecification via simulation studies. In addition, we illustrate our findings with two empirical data sets. iii The thesis of Seung Won Chung is approved.

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Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

Cited by 59 publications

References 37 publications

Measuring Depression in Autistic Adults: Psychometric Validation of the Beck Depression Inventory–II

Measuring Depression in Autistic Adults: Psychometric Validation of the Beck Depression Inventory–II

Performance of Person‐Fit Statistics Under Model Misspecification

Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

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