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Aim The threats of novel coronavirus disease 2019 (COVID‐19) have caused fears worldwide. The Fear of COVID‐19 Scale (FCV‐19S) was recently developed to assess the fear of COVID‐19. Although many studies found that the FCV‐19S is psychometrically sound, it is unclear whether the FCV‐19S is invariant across countries. The present study aimed to examine the measurement invariance of the FCV‐19S across eleven countries. Design Cross‐sectional study. Methods Using data collected from prior research on Bangladesh (N = 8,550), United Kingdom (N = 344), Brazil (N = 1,843), Taiwan (N = 539), Italy (N = 249), New Zealand (N = 317), Iran (N = 717), Cuba (N = 772), Pakistan (N = 937), Japan (N = 1,079) and France (N = 316), comprising a total 15,663 participants, the present study used the multigroup confirmatory factor analysis (CFA) and Rasch differential item functioning (DIF) to examine the measurement invariance of the FCV‐19S across country, gender and age (children aged below 18 years, young to middle‐aged adults aged between 18 and 60 years, and older people aged above 60 years). Results The unidimensional structure of the FCV‐19S was confirmed. Multigroup CFA showed that FCV‐19S was partially invariant across country and fully invariant across gender and age. DIF findings were consistent with the findings from multigroup CFA. Many DIF items were displayed for country, few DIF items were displayed for age, and no DIF items were displayed for gender. Conclusion Based on the results of the present study, the FCV‐19S is a good psychometric instrument to assess fear of COVID‐19 during the pandemic period. Moreover, the use of FCV‐19S is supported in at least ten countries with satisfactory psychometric properties.

Introduction: We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to selfreported race, Hispanic ethnicity, age, years of education, and sex. Methods: We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Results: Five items were found to have DIF related to language. These same items also had DIF related to other covariates. Discussion: The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Student well-being has gradually become a topic of interest in higher education, and the accurate, valid, and reliable measure of well-being constructs is crucial in the South African context. This study examined item bias and configural, metric and scalar invariance of the Satisfaction With Life Scale (SWLS) for South African first-year university students. A cross-sectional design was used. A sample of 780 first-year South African university students was included. Confirmatory factor analysis, differential item functioning (DIF) measurement invariance, and internal consistency were tested. A one-factor structure was confirmed. Item 1 of the SWLS was particularly problematic concerning bias (uniform and non-uniform bias). Measurement invariance was established; however, Item 1 was again problematic, resulting in only partial metric and scalar invariance. The scale was reliable (α ≥ 0.70). This study contributes to the limited research on the specific psychometric properties of the SWLS in a diverse Higher Education setting. The results could assist with valid and reliable measurement when developing interventions to enhance student well-being.

There has been an increasing amount of research examining problematic shopping behavior (PSB), often referred to in the psychological literature as “compulsive buying” or “shopping addiction.” A popular scale for assessing the risk of PSB is the seven-item Bergen Shopping Addiction Scale (BSAS). To expand our knowledge of the psychometric properties of this instrument, the present study employed Item Response Theory (IRT) and differential item functioning analyses (DIF) while concurrently attempting to determine a preliminary cut-off point. A relatively large community sample completed the BSAS online ( N  = 968, M age  = 29.5 years, SD age  = 9.36, 32.5% women). IRT analyses showed differences regarding the BSAS items’ discrimination, difficulty, and precision, with a raw score exceeding 23 (out of 28) indicating a higher risk of shopping addiction. Finally, while most BSAS items operated equally among males and females, Item 2 ( mood modification ) required a higher level of shopping addiction behaviors to be endorsed by males. The BSAS functions as a reliable assessment of the risk of shopping addiction, particularly between average and high levels of the trait. Clinical implications are discussed in light of these findings.

Background When studying student performance across different countries or cultures, an important aspect for comparisons is that of score comparability. In other words, it is imperative that the latent variable (i.e., construct of interest) is understood and measured equivalently across all participating groups or countries, if our inferences regarding performance can be regarded as valid. Relatively fewer studies examined an item-level approach to measurement equivalence, particularly in settings where a large number of groups is included. Methods This simulation study examines item-level differential item functioning (DIF) in the context of international large-scale assessment (ILSA) using a generalized logistic regression approach. Manipulated factors included the number of groups (10 or 20), magnitude of DIF, percent of DIF items, the nature of DIF, as well as the percent of affected groups with DIF. Results Results suggested that the number of groups did not have an effect of the performance of the method (high power and low Type I error rates); however, other factors had impacted the accuracy. Specifically, Type I error rates were inflated in non-DIF conditions, while they were very conservative in all of the DIF conditions. Power was generally high, in particular in conditions where DIF magnitude was large, with one exception – in conditions where DIF was introduced in difficulty parameters and the percent of DIF items was 60. Conclusions Our findings presented a mixed picture with respect to the performance of the generalized logistic regression method in the context of large number of groups with large sample sizes. In the presence of DIF, the method was successful in distinguishing between DIF and non-DIF, as evidenced by low Type I error and high power rates. On the other hand, however, in the absence of DIF, the method yielded increased Type I errors.

Differential item functioning (DIF) attempts to identify items for which subpopulations of examinees exhibit performance differentials that are not consistent with the performance differentials seen among those subpopulations on a reliable measure of the construct of interest. DIF assessment requires a rule for scoring items and a matching variable on which different subpopulations can be viewed as comparable for purposes of assessing their performance on items. Typically, DIF is operationally defined as a difference in item performance between subpopulations, eg, Spanishspeakers and English-speakers, which exist after members of the different subpopulations have been matched on some one-dimensional matching variable such as total score. This work defines DIF, describes 2 standard procedures for measuring DIF, applies these DIF procedures to the Mini-Mental State Examination, and contrasts DIF with score equity analysis (SEA). The description of DIF assessment presented in this paper is applicable to any examination question that has responses that can be ordered, eg, with respect to correctness or severity.

Background Rasch analysis with a focus on Differential Item Functioning (DIF) is increasingly used for examination of psychometric properties of health outcome measures. To take account of DIF in order to retain precision of measurement, split of DIF-items into separate sample specific items has become a frequently used technique. The purpose of the paper is to present and summarise recent advances of analysis of DIF in a unified methodology. In particular, the paper focuses on the use of analysis of variance (ANOVA) as a method to simultaneously detect uniform and non-uniform DIF, the need to distinguish between real and artificial DIF and the trade-off between reliability and validity. An illustrative example from health research is used to demonstrate how DIF, in this case between genders, can be identified, quantified and under specific circumstances accounted for using the Rasch model. Methods Rasch analyses of DIF were conducted of a composite measure of psychosomatic problems using Swedish data from the Health Behaviour in School-aged Children study for grade 9 students collected during the 1985–2014 time periods. Results The procedures demonstrate how DIF can be identified efficiently by ANOVA of residuals, and how the magnitude of DIF can be quantified and potentially accounted for by resolving items according to identifiable groups and using principles of test equating on the resolved items. The results of the analysis also show that the real DIF in some items does affect person measurement estimates. Conclusions Firstly, in order to distinguish between real and artificial DIF, the items showing DIF initially should not be resolved simultaneously but sequentially. Secondly, while resolving instead of deleting a DIF item may retain reliability, both options may affect the content validity negatively. Resolving items with DIF is not justified if the source of the DIF is relevant for the content of the variable; then resolving DIF may deteriorate the validity of the instrument. Generally, decisions on resolving items to deal with DIF should also rely on external information.

BackgroundAlcohol use disorder (AUD) is highly prevalent but underrecognized and undertreated in primary care settings. Alcohol Symptom Checklists can engage patients and providers in discussions of AUD-related care. However, the performance of Alcohol Symptom Checklists when they are used in routine care and documented in electronic health records (EHRs) remains unevaluated.ObjectiveTo evaluate the psychometric performance of an Alcohol Symptom Checklist in routine primary care.DesignCross-sectional study using item response theory (IRT) and differential item functioning analyses of measurement consistency across age, sex, race, and ethnicity.PatientsPatients seen in primary care in the Kaiser Permanente Washington Healthcare System who reported high-risk drinking on the Alcohol Use Disorder Identification Test Consumption screening measure (AUDIT-C ≥ 7) and subsequently completed an Alcohol Symptom Checklist between October 2015 and February 2020.Main MeasureAlcohol Symptom Checklists with 11 items assessing AUD criteria defined in the Diagnostic and Statistical Manual for Mental Disorders, 5th edition (DSM-5), completed by patients during routine medical care and documented in EHRs.Key ResultsAmong 11,464 patients who screened positive for high-risk drinking and completed an Alcohol Symptom Checklist (mean age 43.6 years, 30.5% female), 54.1% reported ≥ 2 DSM-5 AUD criteria (threshold for AUD diagnosis). IRT analyses demonstrated that checklist items measured a unidimensional continuum of AUD severity. Differential item functioning was observed for some demographic subgroups but had minimal impact on accurate measurement of AUD severity, with differences between demographic subgroups attributable to differential item functioning never exceeding 0.42 points of the total symptom count (of a possible range of 0–11).ConclusionsAlcohol Symptom Checklists used in routine care discriminated AUD severity consistently with current definitions of AUD and performed equitably across age, sex, race, and ethnicity. Integrating symptom checklists into routine care may help inform clinical decision-making around diagnosing and managing AUD.

This study examines the presence of item bias in actuarial methods used to assess recidivism risk by focusing on their use among female offenders. Starting with the risk assessment and clinical analysis method for offenders in Quebec (ORAC-PCQ), an analysis of differential item functioning (DFI) was carried out using the Raju method integrated into a Rasch model. The goal was to determine if certain items functioned differently according to sex, which could compromise the fairness and validity of the scores produced. The results reveal the presence of DFI in several items, suggesting that the method might assess risk differently for men and women. Furthermore, the analysis highlights different risk profiles: women present more issues related to victimization, and instability in the home and at work; whereas men demonstrate a more significant history of criminality and antisocial behaviour. The results of this study will help correctional staff refine their criminological assessments while also orienting the adaptation of social reintegration programs according to gender. (PsycInfo Database Record (c) 2026 APA, all rights reserved) (Source: journal abstract)

Rasch analysis is a procedure to develop and validate instruments that aim to measure a person’s traits. However, manual Rasch analysis is a complex and time-consuming task, even more so when the possibility of differential item functioning (DIF) is taken into consideration. Furthermore, manual Rasch analysis by construction relies on a modeler’s subjective choices. As an alternative approach, we introduce a semi-automated procedure that is based on the optimization of a new criterion, called in-plus-out-of-questionnaire log likelihood with differential item functioning (IPOQ-LL-DIF), which extends our previous criterion. We illustrate our procedure on artificially generated data as well as on several real-world datasets containing potential DIF items. On these real-world datasets, our procedure found instruments with similar clinimetric properties as those suggested by experts through manual analyses.