Measuring anxiety by ordered categorical items in data with subgroup structure: the case of the Hungarian version of the trait anxiety scale of the state-trait anxiety inventory for children (staic-h)

Hagtvet, Knut A.; Sipos, Kornel

doi:10.1080/1061580031000151611

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…In a previous article that compares IRT-LR-DIF and MIMIC models with small focal-group N (Woods, 2009a), MIMIC models were set up so that only uniform, not nonuniform, DIF could be detected. This is consistent with the way MIMIC models are described for testing DIF in extant literature (Chen & Anthony, 2003;Christensen et al, 1999;Finch, 2005;Fleishman, Spector, & Altman, 2002;Gelin, 2005;Grayson, Mackinnon, Jorm, Creasey, & Broe, 2000;Hagtvet & Sipos, 2004;MacIntosh & Hashim, 2003;Mast & Lichtenberg, 2000;B. O. Muthén, 1985B.…”

supporting

confidence: 85%

Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models

Woods

Grimm

2011

Applied Psychological Measurement

115

155

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In extant literature, multiple indicator multiple cause (MIMIC) models have been presented for identifying items that display uniform differential item functioning (DIF) only, not nonuniform DIF. This article addresses, for apparently the first time, the use of MIMIC models for testing both uniform and nonuniform DIF with categorical indicators. A latent variable interaction is added to the MIMIC model to test for nonuniform DIF. The approach is tested in simulations with small focal-group N and illustrated with an empirical example using a scale about agoraphobic cognitions. MIMIC-interaction models are compared with MIMIC models without the interaction as well as likelihood ratio DIF testing using item response theory (IRT-LR-DIF). The most important finding is that when the latent moderated structural equations approach is used to estimate the interaction, the Type I error in MIMIC-interaction DIF models is severely inflated.

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supporting

confidence: 85%

Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models

Woods

Grimm

2011

Applied Psychological Measurement

115

155

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“…Items with a "large" MI for the " i parameter are flagged and may be regressed on the grouping variable in a subsequent model. Some practitioners have used similar MI-based approaches (Hagtvet & Sipos, 2004;Mast & Lichtenberg, 2000).…”

Section: Details Of the Testing Proceduresmentioning

confidence: 99%

Evaluation of MIMIC-Model Methods for DIF Testing With Comparison to Two-Group Analysis

Woods

2009

Multivariate Behavioral Research

165

201

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“…As shown in Figure 6, the five items in the rectangles represent observed variables designed to measure , here, math ability. Chen & Anthony, 2003;Christensen et al, 1999;Finch, 2005;Fleishman, Spector, & Altman, 2002;Gelin, 2005;Grayson, Mackinnon, Jorm, Creasey, & Broe, 2000;Hagtvet & Sipos, 2004;MacIntosh & Hashim, 2003;Mast & Lichtenberg, 2000; O. Muthén, Kao, & Burstein, 1991;Oishi, 2006;Schroeder & Moolchan, 2007;Shih & Wang, 2009;Wang & Shih, 2010).…”

Section: Figure 5 Mimic Modelmentioning

confidence: 99%

Evaluation of Two Types of Differential Item Functioning in Factor Mixture Models With Binary Outcomes

Lee

Beretvas

2014

Educational and Psychological Measurement

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Conventional differential item functioning (DIF) detection methods (e.g., the Mantel–Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as personality or response patterns. The factor mixture model (FMM) is designed to detect unobserved sources of heterogeneity in factor models. The current study investigated use of the FMM for detecting between-class latent DIF and class-specific observed DIF. Factors that were manipulated included the DIF effect size and the latent class probabilities. The performance of model fit indices (Akaike information criterion [AIC], Bayesian information criterion [BIC], sample size–adjusted BIC, and consistent AIC) were assessed for their detection of the correct DIF model. The recovery of DIF parameters was also assessed. Results indicated that use of FMMs with binary outcomes performed well in terms of the DIF detection and for recovery of large DIF effects. When class probabilities were unequal with small DIF effects, performance decreased for fit indices, power, and the recovery of DIF effects compared with equal class probability conditions. Inflated Type I errors were found for non-DIF items across simulation conditions. Results and future research directions for applied and methodological are discussed.

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Measuring anxiety by ordered categorical items in data with subgroup structure: the case of the Hungarian version of the trait anxiety scale of the state-trait anxiety inventory for children (staic-h)

Cited by 12 publications

References 28 publications

Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models

Testing for Nonuniform Differential Item Functioning With Multiple Indicator Multiple Cause Models

Evaluation of MIMIC-Model Methods for DIF Testing With Comparison to Two-Group Analysis

Evaluation of Two Types of Differential Item Functioning in Factor Mixture Models With Binary Outcomes

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