Search

Background: The construction and evaluation of item banks to measure unidimensional constructs of health-related quality of life (HRQOL) is a fundamental objective of the Patient-Reported Outcomes Measurement Information System (PROMIS) project. Objectives: Item banks will be used as the foundation for developing short-form instruments and enabling computerized adaptive testing. The PROMIS Steering Committee selected 5 HRQOL domains for initial focus: physical functioning, fatigue, pain, emotional distress, and social role participation. This report provides an overview of the methods used in the PROMIS item analyses and proposed calibration of item banks. Analyses: Analyses include evaluation of data quality (eg, logic and range checking, spread of response distribution within an item), descriptive statistics (eg, frequencies, means), item response theory model assumptions (unidimensionality, local independence, monotonicity), model fit, differential item functioning, and item calibration for banking. Recommendations: Summarized are key analytic issues; recommendations are provided for future evaluations of item banks in HRQOL assessment.

Background An important part of examining the adequacy of measures for use in ethnically diverse populations is the evaluation of differential item functioning (DIF) among subpopulations such as those administered the measure in different languages. A number of methods exist for this purpose. Objective The objective of this study was to introduce and demonstrate the identification of DIF using item response theory (IRT) and the likelihood-based model comparison approach. Methods Data come from a sample of community-residing elderly who were part of a dementia case registry. A total of 1578 participants were administered either an English (n = 913) or Spanish (n = 665) version of the 21-item Mini-Mental State Examination. IRT was used to identify language DIF in these items with the likelihood-based model comparison approach. Results : Fourteen of the 21 items exhibited significant DIF according to language of administration. However, because the direction of the identified DIF was not consistent for one language version over the other, the impact at the scale level was negligible. Conclusions IRT and the likelihood-based model comparison approach comprise a powerful tool for DIF detection that can aid in the development, refinement, and evaluation of measures for use in ethnically diverse populations.

Analysis of subgroups such as different ethnic, language, or education groups selected from among a parent population is common in health disparities research. One goal of such analyses is to examine measurement equivalence, which includes both qualitative review of the meaning of items as well as quantitative examination of different levels of factorial invariance and differential item functioning.The purpose of this essay is to review the definitions and assumptions associated with factorial invariance, placing this formulation in the context of bias, fairness, and equity. The connection between the concepts of factorial invariance and item bias (differential item functioning) using a variant of item response theory is discussed. The situations under which different forms of invariance (weak, strong, and strict) are required are discussed.Establishing factorial invariance involves a hierarchy of levels that include tests of weak, strong, and strict invariance. Pattern (metric or weak) factorial invariance implies that the regression slopes are invariant across groups. Pattern invariance requires only invariant factor loadings. Strong factorial invariance implies that the conditional expectation of the response, given the common and specific factors, is invariant across groups. Strong factorial invariance requires that specific factor means (represented as invariant intercepts) also be identical across groups. Strict factorial invariance implies that, in addition, the conditional variance of the response, given the common and specific factors, is invariant across groups. Strict factorial invariance requires that, in addition to equal factor loadings and intercepts, the residual (specific factor plus error variable) variances are equivalent across groups. The concept of measurement invariance that is most closely aligned to that of item response theory considers the latent variable as a common factor measured by manifest variables; the specific factors can be characterized as nuisance variables.Invariance of factor loadings across studied groups is required for valid comparisons of scale score or latent variable means. Strong and strict invariance may be less important in the context of basic research in which group differences in specific factors are indicative of individual differences that are important for scientific exploration. However, for most applications in which the aim is to ensure fairness and equity, strict factorial invariance is required. Health disparities research often focuses on self-reported clinical outcomes such as quality of life that are not observed directly. Latent variable models such as factor analyses are central to establishing valid assessment of such outcomes.

We sought to examine the relationship between functional outcome and process of care for patients with hip fracture.We undertook a prospective cohort study in 4 hospitals of 554 patients treated with surgery for hip fracture.Information on patient characteristics and processes of hospital care collected from the medical record, interviews, and bedside observations. Follow-up information obtained at 6 months on function (using the Functional Independence Measure [FIM]), survival, and readmission.Individual processes of care were generally not associated with adjusted outcomes. A scale of 9 processes related to mobilization was associated with improved adjusted locomotion (P = 0.006), self care (P = 0.022), and transferring (P = 0.007) at 2 months, but the benefits were smaller and not significant by 6 months. These processes were not associated with mortality. The predicted value for the FIM locomotion measure (range, 2-14) at 2 months was 5.9 (95% confidence interval 5.4-6.4) for patients at the 10th percentile of performance on these processes compared with 7.1 (95% confidence interval 6.6, 7.6) at the 90th percentile. Patients who experienced no hospital complications and no readmissions retained the benefits in locomotion at 6 months. Anticoagulation processes were associated with improved transferring at 2 months (P = 0.046) but anticoagulation and other processes of care were not otherwise associated with improved function.Our findings indicate the need to attend to all steps in the care of patients with hip fracture. Additionally, functional outcomes were more sensitive markers of improved process of care, compared with 6-month mortality, in the case of hip fracture.

Reviewed in this article are issues relating to the study of invariance and differential item functioning (DIF). The aim of factor analyses and DIF, in the context of invariance testing, is the examination of group differences in item response conditional on an estimate of disability. Discussed are parameters and statistics that are not invariant and cannot be compared validly in crosscultural studies with varying distributions of disability in contrast to those that can be compared (if the model assumptions are met) because they are produced by models such as linear and nonlinear regression.The purpose of this overview is to provide an integrated approach to the quantitative methods used in this special issue to examine measurement equivalence. The methods include classical test theory (CTT), factor analytic, and parametric and nonparametric approaches to DIF detection. Also included in the quantitative section is a discussion of item banking and computerized adaptive testing (CAT).Factorial invariance and the articles discussing this topic are introduced. A brief overview of the DIF methods presented in the quantitative section of the special issue is provided together with a discussion of ways in which DIF analyses and examination of invariance using factor models may be complementary.Although factor analytic and DIF detection methods share features, they provide unique information and can be viewed as complementary in informing about measurement equivalence.

Background and objectives Reviewed in this article are topics related to the study of invariance and differential item functioning (DIF) that have received relatively little attention in the literature. Several factors influence DIF detection; these include (1) model fit, (2) model assumptions, (3) disability distributions, (4) purification, (5) cutoff values for magnitude measures, and (6) sample and scale size. Methods Approaches to DIF detection are discussed in terms of model assumptions, purification, magnitude and impact, and possible advantages and disadvantages of each method. Conclusions An integrated approach to the examination of measurement equivalence, invariance, and DIF is necessary for measurement in an increasingly multi-ethnic society. Ideally, qualitative analyses should be performed in an iterative fashion to inform about findings of DIF. However, if an already-developed measure is being evaluated, then the steps might be to focus first on dimensional invariance using factor analytic methods, followed by DIF analyses examining both significance and magnitude of DIF, accompanied by formal tests of the impact of DIF. The DIF analytic method selected in the second step might be determined based on the findings summarized in the table presented within this paper.

Various forms of differential item functioning (DIF) in the Mini-Mental State Examination (MMSE) have been identified. Items have been found to perform differently for individuals of different educational levels, racial/ethnic groups, and/or of groups whose first language is not English. The articles in this section illustrate the use of different methods to examine DIF in relation to English and Spanish language administration of the MMSE.The aim of this article is to provide a context for interpretation of the findings contained in the following set of papers examining DIF in the MMSE.The performance of the MMSE, when administered in English and Spanish, was reviewed. "Translation" has been discussed in the context of measurement bias, illustrating the variability in Spanish translations. Presented are the readability of the MMSE, description of the translation method, the study design and sample for the data set used, together with treatment of missing data, and model assumptions related to the analyses described in the accompanying set of papers examining DIF.The examination of item bias in cognitive impairment assessment instruments has practical and theoretical implications in the context of health disparities. Considerable DIF has been identified in the MMSE. A critical factor that may contribute to measurement bias is language translation and conversion. Once DIF has been established consistently in a measure, decisions regarding adjustments proceed. Perhaps the development of guidelines for appropriate adjustments for DIF correction in self-reported measures represents the next challenge in addressing measurement equivalence in crosscultural research.

To illustrate the application of the Differential Item and Test Functioning (DFIT) method using English and Spanish versions of the Mini-Mental State Examination (MMSE).Study participants were 65 years of age or older and lived in North Manhattan, New York. Of the 1578 study participants who were administered the MMSE 665 completed it in Spanish.: The MMSE contains 20 items that measure the degree of cognitive impairment in the areas of orientation, attention and calculation, registration, recall and language, as well as the ability to follow verbal and written commands.After assessing the dimensionality of the MMSE scale, item response theory person and item parameters were estimated separately for the English and Spanish sample using Samejima's 2-parameter graded response model. Then the DFIT framework was used to assess differential item functioning (DIF) and differential test functioning (DTF).Nine items were found to show DIF; these were items that ask the respondent to name the correct season, day of the month, city, state, and 2 nearby streets, recall 3 objects, repeat the phrase no ifs, no ands, no buts, follow the command, "close your eyes," and the command, "take the paper in your right hand, fold the paper in half with both hands, and put the paper down in your lap." At the scale level, however, the MMSE did not show differential functioning.Respondents to the English and Spanish versions of the MMSE are comparable on the basis of scale scores. However, assessments based on individual MMSE items may be misleading.