Core Symptom Index (CSI): testing for bifactor model and differential item functioning

2019 ◽  
Vol 31 (12) ◽  
pp. 1769-1779
Author(s):  
Nahathai Wongpakaran ◽  
Tinakon Wongpakaran ◽  
Surang Lertkachatarn ◽  
Thanitha Sirirak ◽  
Pimolpun Kuntawong
Keyword(s):  
Differential Item Functioning ◽  
Depressive Disorders ◽  
Bifactor Model ◽  
Ordinal Scale ◽  
Common Variance ◽  
Symptom Index ◽  
Core Symptom ◽  
Item Functioning ◽  
Specific Factors ◽  
Explained Common Variance

ABSTRACTObjectives:The Core Symptom Index (CSI) is designed to measure anxiety, depression and somatization symptoms. This study examined the construct validity of CSI using confirmatory factor analysis (CFA) including a bifactor model and explored differential item functioning (DIF) of the CSI. The criterion and concurrent validity were evaluated.Methods:In all, 803 elderly patients, average age 69.24 years, 70% female, were assessed for depressive disorders and completed the CSI and the geriatric depression scale (GDS). A series involving CFA for ordinal scale was applied. Factor loadings and explained common variance were analyzed for general and specific factors; and Omega was calculated for model-based reliability. DIF was analyzed using the Multiple-Indicator Multiple-Cause model. Pearson’s correlation, ANOVA, and ROC analysis were used for associations and to compare CSI and GDS in predicting major depressive disorders (MDD).Results:The bifactor model provided the best fit to the data. Most items loaded on general rather than specific factors. The explained common variance was acceptable, while Omega hierarchical for the subscale and explained common variance for the subscales were low. Two DIF items were identified; ‘crying’ for sex items and ‘self-blaming’ for education items. Correlation among CSI and clinical disorders and the GDS were found. AUC for the GDS was 0.83, and for the CSI was 0.81.Conclusion:CSI appears sufficiently unidimensional. Its total score reflected a single general factor, permitting users to interpret the total score as a sufficient reliable measure of the general factors. CSI could serve as a screening tool for MDD.

Evaluating Bifactor Models of Psychopathology Using Model-Based Reliability Indices

2019 ◽  
Author(s):  
Matthew Constantinou ◽  
Peter Fonagy
Keyword(s):  
Bifactor Model ◽  
Common Variance ◽  
Reliability Indices ◽  
Bifactor Models ◽  
Study Characteristics ◽  
Specific Factors ◽  
P Factor ◽  
Explained Common Variance ◽  
Internalizing And Externalizing ◽  
Variance Explained

There is has been a rapid increase in quantitative researchers applying the bifactor model to psychopathology data. The bifactor model, which typically includes a general p factor and internalizing and externalizing residual factors, consistently demonstrates superior model fit to competing models, including the correlated factors model, which typically includes internalizing and externalizing factors. However, the bifactor model’s superior fit might stem from its tendency to overfit noise and flexibly fit most datasets. An alternative approach to evaluating bifactor models that does not rely on fit statistics is model-based reliability assessment. Reliability indices, including omega/omega hierarchical, explained common variance, and percent uncontaminated correlations can be used to determine the viability of the general and specific psychopathology factors and the extent that the underlying data structure and its measurement is multidimensional. In this methodological review, we identified 49 studies published between 2009 and 2019 that applied the bifactor model to at least two separate symptom domains and calculated reliability indices from the standardized factor loading matrices. We also predicted variation in the p factor’s strength, indexed by the explained common variance, from study characteristics. We found that psychopathology measures tend to be multidimensional, with 57% of the variance explained by the p factor and the remaining variance explained by specific factors. By contrast, most of the variance in observed total scores (74%) was explained by the p factor, while relatively little of the variance in in observed subscale scores (37%) was explained by specific factors beyond the p factor. Finally, 62% of the variability in the p factor’s strength could be predicted by study characteristics, most notably the informant (in a simultaneous regression model), but also age, percent uncontaminated correlations, and the number of items (in separate regression models). We conclude that the latent structure of psychopathology is multidimensional, but its measurement is governed by a single dimension, the strength of which is predicted by study characteristics, particularly the informant.

Evaluating the Magnitude of Differential Item Functioning in Polytomous Items

1996 ◽  
Vol 21 (3) ◽  
pp. 187-201 ◽  
Author(s):  
Rebecca Zwick ◽  
Dorothy T. Thayer
Keyword(s):  
Hypothesis Testing ◽  
Differential Item Functioning ◽  
Standard Error ◽  
Simulated Data ◽  
Ordinal Scale ◽  
Polytomous Items ◽  
Testing Procedures ◽  
Test Items ◽  
Item Functioning ◽  
Error Formulas

Several recent studies have investigated the application of statistical inference procedures to the analysis of differential item functioning (DIF) in polytomous test items that are scored on an ordinal scale. Mantel’s extension of the Mantel-Haenszel test is one of several hypothesis-testing methods for this purpose. The development of descriptive statistics for characterizing DIF in polytomous test items has received less attention. As a step in this direction, two possible standard error formulas for the polytomous DIF index proposed by Dorans and Schmitt were derived. These standard errors, as well as associated hypothesis-testing procedures, were evaluated though application to simulated data. The standard error that performed better is based on Mantel’s hypergeometric model. The alternative standard error, based on a multinomial model, tended to yield values that were too small.

Erfassung von mathematischen Kompetenzen im Vorschulalter mit MARKO-D

Diagnostica ◽  
2021 ◽  
Vol 67 (1) ◽  
pp. 13-23
Author(s):  
Ariana Garrote ◽  
Elisabeth Moser Opitz
Keyword(s):  
Differential Item Functioning ◽  
Item Functioning

Zusammenfassung. In dieser Studie wurde der Test MARKO-D (Mathematik- und Rechenkonzepte im Vorschulalter–Diagnose) mit einer Stichprobe von Kindern aus der deutschsprachigen Schweiz ( N = 555) im ersten und zweiten Kindergartenjahr erprobt und es wurde analysiert, ob sich die Altersnormen der deutschen Stichprobe auf die Schweiz übertragen lassen. Zudem wurde der Test mit einer Teilstichprobe ( n = 87) hinsichtlich Messinvarianz über die Zeit untersucht. Die Ergebnisse des eindimensionalen Rasch-Modells zeigen, dass das Instrument für die Schweiz geeignet ist. Die Testleistungen hängen jedoch vom Kindergartenbesuch ab. Für die Schweiz müssten deshalb nebst Altersnormen auch Normen pro Kindergartenhalbjahr verwendet werden. Die Analyse mittels Differential Item Functioning ergab, dass 17 von 55 Items von großer Messvarianz über die Zeit betroffen sind. Um das Instrument für Längsschnittuntersuchungen einsetzen zu können, müsste es weiterentwickelt werden.

Differential Item Functioning in Brief Instruments of Disordered Eating

2019 ◽  
Vol 35 (6) ◽  
pp. 823-833 ◽  
Author(s):  
Desiree Thielemann ◽  
Felicitas Richter ◽  
Bernd Strauss ◽  
Elmar Braehler ◽  
Uwe Altmann ◽  
...  
Keyword(s):  
Differential Item Functioning ◽  
Disordered Eating ◽  
Structural Equation ◽  
Young Female ◽  
Eating Attitudes ◽  
Equation Model ◽  
German Population ◽  
Test Fairness ◽  
Item Functioning ◽  
Multiple Indicator

Abstract. Most instruments for the assessment of disordered eating were developed and validated in young female samples. However, they are often used in heterogeneous general population samples. Therefore, brief instruments of disordered eating should assess the severity of disordered eating equally well between individuals with different gender, age, body mass index (BMI), and socioeconomic status (SES). Differential item functioning (DIF) of two brief instruments of disordered eating (SCOFF, Eating Attitudes Test [EAT-8]) was modeled in a representative sample of the German population ( N = 2,527) using a multigroup item response theory (IRT) and a multiple-indicator multiple-cause (MIMIC) structural equation model (SEM) approach. No DIF by age was found in both questionnaires. Three items of the EAT-8 showed DIF across gender, indicating that females are more likely to agree than males, given the same severity of disordered eating. One item of the EAT-8 revealed slight DIF by BMI. DIF with respect to the SCOFF seemed to be negligible. Both questionnaires are equally fair across people with different age and SES. The DIF by gender that we found with respect to the EAT-8 as screening instrument may be also reflected in the use of different cutoff values for men and women. In general, both brief instruments assessing disordered eating revealed their strengths and limitations concerning test fairness for different groups.

An IRT Investigation of the Validity of Non-Patient Analogue Research Using the Beck Depression Inventory

1995 ◽  
Vol 11 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Sean M. Hammond
Keyword(s):  
Differential Item Functioning ◽  
Beck Depression Inventory ◽  
Rating Scale ◽  
Latent Trait ◽  
Scale Model ◽  
Item Functioning ◽  
Two Samples ◽  
Patient Groups ◽  
Analogue Research ◽  
Rating Scale Model

This paper presents an IRT analysis of the Beck Depression Inventory which was carried out to assess the assumption of an underlying latent trait common to non-clinical and patient samples. A one parameter rating scale model was fitted to data drawn from a patient and non-patient sample. Findings suggest that while the BDI fits the model reasonably well for the two samples separately there is sufficient differential item functioning to raise serious duobts of the viability of using it analogously with patient and non-patient groups.

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