Differential Item Functioning Detection Using the Multiple Indicators, Multiple Causes Method with a Pure Short Anchor

Shih, Ching-Lin; Wang, Wen Chung

doi:10.1177/0146621608321758

Cited by 70 publications

(108 citation statements)

References 36 publications

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“…42 We adopted the usual method to identify DIF items: a DIF contrast (ie, the difference of difficulty between 2 groups) of greater than 0.5 suggests a substantial DIF. 43 We examined the relationship between MARS-5 and the MPR using the Pearson correlation coefficient. Afterward, we constructed 2 regression models to explore the factors for medication adherence measured by the MARS-5 and MPR, respectively.…”

Section: Discussionmentioning

confidence: 99%

Validation of the 5-Item Medication Adherence Report Scale in Older Stroke Patients in Iran

Lin

Ou²,

Nikoobakht

et al. 2018

J Cardiovasc Nurs

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Background: There is a lack of feasible and validated measures to self-assess medication adherence for older patients with stroke. In addition, the potential determinants of medication adherence for older patients with stroke remain unclear. Objectives: The aims of this study were to (1) examine the psychometric properties of a 5-item questionnaire on medication adherence, specifically the 5-item Medication Adherence Report Scale (MARS-5), and (2) explore the determinants of medication adherence. Methods: Stroke patients older than 65 years (N = 523) filled out the MARS-5 and the Hospital Anxiety and Depression Scale. The medication possession rate (MPR) was calculated to measure the objective medication adherence. Several clinical characteristics (stroke types, blood pressure, comorbidity, HbA1c, quantity of prescribed drugs, fasting blood glucose, and total cholesterol) and background information were collected. We used Rasch analysis with a differential item functioning test to examine psychometric properties. Results: All 5 items in the MARS-5 fit in the same construct (ie, medication adherence), no differential item functioning items were displayed in the MARS-5 across gender, and the MARS-5 total score was strongly correlated with the MPR (r = 0.7). Multiple regression models showed that the MARS-5 and the MPR shared several similar determinants. In addition, the variance of the MARS-5 (R 2 = 0.567) was more than that of the MPR (R 2 = 0.300). Conclusions: The MARS-5 is a feasible and

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Section: Discussionmentioning

confidence: 99%

Validation of the 5-Item Medication Adherence Report Scale in Older Stroke Patients in Iran

Lin

Ou²,

Nikoobakht

et al. 2018

J Cardiovasc Nurs

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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%

“…Applied DIF studies have assessed DIF for tests of various lengths ranging from 8 to 50 items (Cohen & Bolt, 2005;Dai, 2009;De Ayala et al, 2002;Finch, 2005;Jackman, 2011;Maij-de Meij et al, 2011;Samuelsen, 2005;Shih & Wang, 2009;Tay et al, 2011). However, a majority of methodological studies using real data have typically used around 20 to 30 items for a test (for example, Cohen & Bolt, 2005;De Ayala et al, 2002;Samuelsen, 2005).…”

Section: Fixed Conditionsmentioning

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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“…Early forms of the MIMIC model were only used for examining uniform DIF for dichotomously or polytomously scored item responses (e.g., Finch, 2005Finch, , 2012Shih and Wang, 2009;Woods et al, 2009). To examine uniform and non-uniform DIF simultaneously, Woods and Grimm (2011) introduced the MIMICinteraction model, which is similar to restricted factor analysis models with an interaction term (Ferrando and Lorenzo-Seva, 2000;Barendse et al, 2010).…”

mentioning

confidence: 99%

Detecting Multidimensional Differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response Theory Likelihood Ratio Test, and Logistic Regression

Bulut

Suh

2017

Front. Educ.

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Differential item functioning (DIF) is typically evaluated in educational and psychological assessments with a simple structure in which items are associated with a single latent trait. This study aims to extend the investigation of DIF for multidimensional assessments with a non-simple structure in which items can be associated with two or more latent traits. A simulation study was conducted with the multidimensional extensions of the item response theory likelihood ratio (IRT-LR) test, the multiple indicators multiple causes (MIMIC) model, and logistic regression for detecting uniform and non-uniform DIF in multidimensional assessments. The results indicated that the IRT-LR test outperformed the MIMIC and logistic regression approaches in detecting non-uniform DIF. When detecting uniform DIF, the MIMIC and logistic regression approaches appeared to perform better than the IRT-LR test in short tests, while the performances of all three approaches were very similar in longer tests. Type I error rates for logistic regression were severely inflated compared with the other two approaches. The IRT-LR test appears to be a more balanced and powerful method than the MIMIC and logistic regression approaches in detecting DIF in multidimensional assessments with a non-simple structure.

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Differential Item Functioning Detection Using the Multiple Indicators, Multiple Causes Method with a Pure Short Anchor

Cited by 70 publications

References 36 publications

Validation of the 5-Item Medication Adherence Report Scale in Older Stroke Patients in Iran

Validation of the 5-Item Medication Adherence Report Scale in Older Stroke Patients in Iran

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

Detecting Multidimensional Differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response Theory Likelihood Ratio Test, and Logistic Regression

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