MIMIC Methods for Assessing Differential Item Functioning in Polytomous Items

Wang, Wen Chung; Shih, Ching-Lin

doi:10.1177/0146621609355279

Cited by 48 publications

(62 citation statements)

References 28 publications

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“…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). Thus, Figure 6 depicts only uniform DIF in the MIMIC-DIF model.…”

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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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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“…But as demonstrated in Wang and Shih [25], there is also evidence about the effectiveness of the iterative purification method. For the permutation method we used for the detection of anchor items, it is known to be sometimes conservative [37], thus it will take more extreme test statistics to reject a null hypothesis and the actual error rate is much less than the prescribed alpha level.…”

Section: Discussionmentioning

confidence: 99%

“…The importance of anchor items detection has been previously discussed [25] and is not trivial when there is a large amount of DIF in the items of interest. Zenisky, Hambleton and Robin [26] found that when at least 30% of items have DIF, lack of identification of anchor items can result in a substantial change in items that are detected as having DIF.…”

Section: Methodsmentioning

confidence: 99%

Differential Item Functioning by Survey Language Among Older Hispanics Enrolled in Medicare Managed Care

et al. 2011

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Objective To propose a permutation based approach of anchor item detection and evaluate differential item functioning (DIF) related to language of administration (English vs. Spanish) for nine questions assessing patients’ perceptions of their providers from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Medicare 2.0 survey. Method and Study Design CAHPS 2.0 health plan survey data collected from 703 Hispanics who completed the survey in Spanish were matched on personal characteristics to 703 Hispanics that completed the survey in English. Steps to be followed for the detection of anchor items using the permutation tests are proposed and these tests in conjunction with item response theory were used for the identification of anchor items and DIF detection. Results Four of the questions studied were selected as anchor items and three of the remaining questions were found to have DIF (p<.05). The three questions with DIF asked about seeing the doctor within 15 minutes of the appointment time, respect for what patients had to say, and provider spending enough time with patients. Conclusion Failure to account for language differences in CAHPS survey items may result in misleading conclusions about disparities in health care experiences between Spanish and English speakers. Statistical adjustments are needed when using the items with DIF.

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“…Since the large sample made most DIF contrasts statistically significant, we chose a clinically significant DIF contrast that was based on ≥ .7 logit difference for all comparisons which is approximately half of a standard deviation (SD = 1.35) for the persons. Standards for what is considered an important DIF effect size vary from about .4 to .6 logits (see Longford, Holland, Thayer; 1993; Paek, 2002; Draba, 1977; Elder, McNamara, & Congdon, 2003; Scheunemann & Subhiyah, 1998; Wang, 2000). In this paper, we used the criterion of .7 logit or larger since we believed that most would agree that this is a large, important DIF contrast, and half a standard deviation is a common criterion for clinical significance (Cohen, 1988; Norman, Sloan, & Wyrwich, 2003; Wolf, 1986).…”

Section: Methodsmentioning

confidence: 99%

Dimensionality, hierarchical structure, age generalizability, and criterion validity of the GAIN's Behavioral Complexity Scale.

Conrad¹,

Mazza²,

Riley³

et al. 2012

Psychological Assessment

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This study used Rasch measurement model criteria and traditional psychometric strategies to examine key psychometric properties of the Behavioral Complexity Scale (BCS), a widely used measure of externalizing disorders that focuses on attention deficit, hyperactivity and conduct disorders. Using a sample of 7,435 persons being screened for substance use disorders, the BCS was found to: (1) be unidimensional, (2) have a hierarchical severity structure, (3) be generalizable to both youth and adults, (4) and meet hypothesized correlations with criterion variables. The BCS performed well as a unidimensional measure. The Rasch severity hierarchy of attention deficit to hyperactivity to conduct disorders provided a perspective that suggested that a dimensional measure could be used as an alternative and, in some ways, as an improvement, to categorical diagnosis and common dimensional approaches. The finding of three low severity conduct disorder items also supported a revision of categorical criteria, especially in substance use disorders.

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MIMIC Methods for Assessing Differential Item Functioning in Polytomous Items

Cited by 48 publications

References 28 publications

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

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

Differential Item Functioning by Survey Language Among Older Hispanics Enrolled in Medicare Managed Care

Dimensionality, hierarchical structure, age generalizability, and criterion validity of the GAIN's Behavioral Complexity Scale.

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