Development and Demonstration of Multidimensional IRT-Based Internal Measures of Differential Functioning of ltems and Tests

This article defines and demonstrates a framework for studying differential item functioning (DIF) and differential test functioning (DTF) for tests that are intended to be multidimensional The procedure introduced here is an extension of unidimensional differential functioning of items and tests (DFIT) recently developed by Raju, van der Linden, & Fleer (1995). To demonstrate the usefulness of these new indexes in a multidimensional IRT setting, two-dimensional data were simulated with known item parameters and known DIF and DTE The DIF and DTF indexes were recovered reasonably well under various distributional differences of Os after multidimensional linking was applied to put the two sets of item parameters on a common scale. Further studies are suggested in the area of DIF/DTF for intentionally multidimensional tests.     

DIF Detection and Effect Size Measures for Polytomously Scored Items

Data from a large-scale performance assessment ( N = 105,731) were analyzed with five differential item functioning (DIF) detection methods for polytomous items to examine the congruence among the DIF detection methods. Two different versions of the item response theory (IRT) model-based likelihood ratio test, the logistic regression likelihood ratio test, the Mantel test, and the generalized Mantel–Haenszel test were compared. Results indicated some agreement among the five DIF detection methods. Because statistical power is a function of the sample size, the DIF detection results from extremely large data sets are not practically useful. As alternatives to the DIF detection methods, four IRT model-based indices of standardized impact and four observed-score indices of standardized impact for polytomous items were obtained and compared with the R ² measures of logistic regression.     

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model

Bock, Muraki, and Pfeiffenberger (1988) proposed a dichotomous item response theory (IRT) model for the detection of differential item functioning (DIF), and they estimated the IRT parameters and the means and standard deviations of the multiple latent trait distributions. This IRT DIF detection method is extended to the partial credit model (Masters, 1982; Muraki, 1993) and presented as one of the multiple-group IRT models. Uniform and non-uniform DIF items and heterogeneous latent trait distributions were used to generate polytomous responses of multiple groups. The DIF method was applied to this simulated data using a stepwise procedure. The standardized DIF measures for slope and item location parameters successfully detected the non-uniform and uniform DIF items as well as recovered the means and standard deviations of the latent trait distributions.This stepwise DIF analysis based on the multiple-group partial credit model was then applied to the National Assessment of Educational Progress (NAEP) writing trend data.     

Detecting Potentially Biased Test Items: Comparison of IRT Area and Mantel-Haenszel Methods

The purpose of this study was to compare the IRT-based area method and the Mantel-Haenszel method for investigating differential item functioning (DIF), to determine the degree of agreement between the methods in identifying potentially biased items, and, when the two methods led to different results, to identify possible reasons for the discrepancies. Data for the study were the item responses of Anglo American and Native American students who took the 1982 New Mexico High School Proficiency Exam. Two samples of 1,000 students from each group were studied. The major findings were that (a) the consistency of classifications of items into "biased" and "not-biased" categories across replications was 75% to 80% for both methods and (b) when the unreliability of the statistics was taken into account, the two methods led to very similar results. Discrepancies between methods were due to the presence of nonuniform DIF (the Mantel-Haenszel method could not identify these items) and the choice of interval over which DIF was assessed (the IRT method results depended on the choice of interval). The implications for practitioners seem clear: The Mantel-Haenszel method in general provides an acceptable approximation to the IRT-based methods.     

Detection of Differential Item Functioning with Nonlinear Regression: A Non‐IRT Approach Accounting for Guessing

In this article we present a general approach not relying on item response theory models (non‐IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non‐IRT approach, NLR can be seen as a proxy of detection based on the three‐parameter IRT model which is a standard tool in the study field. Hence, NLR fills a logical gap in DIF detection methodology and as such is important for educational purposes. Moreover, the advantages of the NLR procedure as well as comparison to other commonly used methods are demonstrated in a simulation study. A real data analysis is offered to demonstrate practical use of the method.     

Distinguishing between Net and Global DIF in Polytomous Items

In this article, I address two competing conceptions of differential item functioning (DIF) in polytomously scored items. The first conception, referred to as net DIF, concerns between-group differences in the conditional expected value of the polytomous response variable. The second conception, referred to as global DIF, concerns the conditional dependence of group membership and the polytomous response variable. The distinction between net and global DIF is important because different DIF evaluation methods are appropriate for net and global DIF; no currently available method is universally the best for detecting both net and global DIF. Net and global DIF definitions are presented under two different, yet compatible, modeling frameworks: a traditional item response theory (IRT) framework, and a differential step functioning (DSF) framework. The theoretical relationship between the IRT and DSF frameworks is presented. Available methods for evaluating net and global DIF are described, and an applied example of net and global DIF is presented.     

Effect of Unequal Variances in Proficiency Distributions on Type-I Error of the Mantel-Haenszel Chi-square Test for Differential Item Functioning

Empirical studies demonstrated Type-I error (TIE) inflation (especially for highly discriminating easy items) of the Mantel-Haenszel chi-square test for differential item functioning (DIF), when data conformed to item response theory (IRT) models more complex than Rasch, and when IRT proficiency distributions differed only in means. However, no published study manipulated proficiency variance ratio (VR). Data were generated with the three-parameter logistic (3PL) IRT model. Proficiency VRs were 1, 2, 3, and 4. The present study suggests inflation may be greater, and may affect all highly discriminating items (low, moderate, and high difficulty), when IRT proficiency distributions of reference and focal groups differ also in variances. Inflation was greatest on the 21-item test (vs. 41) and 2,000 total sample size (vs. 1,000). Previous studies had not systematically examined sample size ratio. Sample size ratio of 1:1 produced greater TIE inflation than 3:1, but primarily for total sample size of 2,000.     

A Mixture Model Analysis of Differential Item Functioning

Once a differential item functioning (DIF) item has been identified, little is known about the examinees for whom the item functions differentially. This is because DIF focuses on manifest group characteristics that are associated with it, but do not explain why examinees respond differentially to items. We first analyze item response patterns for gender DIF and then illustrate, through the use of a mixture item response theory (IRT) model, how the manifest characteristic associated with DIF often has a very weak relationship with the latent groups actually being advantaged or disadvantaged by the item(s). Next, we propose an alternative approach to DIF assessment that first uses an exploratory mixture model analysis to define the primary dimension(s) that contribute to DIF, and secondly studies examinee characteristics associated with those dimensions in order to understand the cause(s) of DIF. Comparison of academic characteristics of these examinees across classes reveals some clear differences in manifest characteristics between groups.     

A Nested Logit Approach for Investigating Distractors as Causes of Differential Item Functioning

In multiple‐choice items, differential item functioning (DIF) in the correct response may or may not be caused by differentially functioning distractors. Identifying distractors as causes of DIF can provide valuable information for potential item revision or the design of new test items. In this paper, we examine a two‐step approach based on application of a nested logit model for this purpose. The approach separates testing of differential distractor functioning (DDF) from DIF, thus allowing for clearer evaluations of where distractors may be responsible for DIF. The approach is contrasted against competing methods and evaluated in simulation and real data analyses.     

DIF Detection and Interpretation in Large-Scale Science Assessments: Informing Item Writing Practices

Differential item functioning (DIF) analyses are a routine part of the development of large-scale assessments. Less common are studies to understand the potential sources of DIF. The goals of this study were (a) to identify gender DIF in a large-scale science assessment and (b) to look for trends in the DIF and non-DIF items due to content, cognitive demands, item type, item text, and visual-spatial or reference factors. To facilitate the analyses, DIF studies were conducted at 3 grade levels and for 2 randomly equivalent forms of the science assessment at each grade level (administered in different years). The DIF procedure itself was a variant of the "standardization procedure" of Dorans and Kulick (1986) and was applied to very large sets of data (6 sets of data, each involving 60,000 students). It has the advantages of being easy to understand and to explain to practitioners. Several findings emerged from the study that would be useful to pass on to test development committees. For example, when there was DIF in science items, MC items tended to favor male examinees and OR items tended to favor female examinees. Compiling DIF information across multiple grades and years increases the likelihood that important trends in the data will be identified and that item writing practices will be informed by more than anecdotal reports about DIF.     

Identifying Sources of Differential Item and Bundle Functioning on Translated Achievement Tests: A Confirmatory Analysis

Increasingly, tests are being translated and adapted into different languages. Differential item functioning (DIF) analyses are often used to identify non-equivalent items across language groups. However, few studies have focused on understanding why some translated items produce DIF. The purpose of the current study is to identify sources of differential item and bundle functioning on translated achievement tests using substantive and statistical analyses. A substantive analysis of existing DIF items was conducted by an 11-member committee of testing specialists. In their review, four sources of translation DIF were identified. Two certified translators used these four sources to categorize a new set of DIF items from Grade 6 and 9 Mathematics and Social Studies Achievement Tests. Each item was associated with a specific source of translation DIF and each item was anticipated to favor a specific group of examinees. Then, a statistical analysis was conducted on the items in each category using SIBTEST. The translators sorted the mathematics DIF items into three sources, and they correctly predicted the group that would be favored for seven of the eight items or bundles of items across two grade levels. The translators sorted the social studies DIF items into four sources, and they correctly predicted the group that would be favored for eight of the 13 items or bundles of items across two grade levels. The majority of items in mathematics and social studies were associated with differences in the words, expressions, or sentence structure of items that are not inherent to the language and/or culture. By combining substantive and statistical DIF analyses, researchers can study the sources of DIF and create a body of confirmed DIF hypotheses that may be used to develop guidelines and test construction principles for reducing DIF on translated tests.     

Aggregating Polytomous DIF Results Over Multiple Test Administrations

In typical differential item functioning (DIF) assessments, an item's DIF status is not influenced by its status in previous test administrations. An item that has shown DIF at multiple administrations may be treated the same way as an item that has shown DIF in only the most recent administration. Therefore, much useful information about the item's functioning is ignored. In earlier work, we developed the Bayesian updating (BU) DIF procedure for dichotomous items and showed how it could be used to formally aggregate DIF results over administrations. More recently, we extended the BU method to the case of polytomously scored items. We conducted an extensive simulation study that included four “administrations” of a test. For the single‐administration case, we compared the Bayesian approach to an existing polytomous‐DIF procedure. For the multiple‐administration case, we compared BU to two non‐Bayesian methods of aggregating the polytomous‐DIF results over administrations. We concluded that both the BU approach and a simple non‐Bayesian method show promise as methods of aggregating polytomous DIF results over administrations.     

Decisions that make a difference in detecting differential item functioning

There are numerous statistical procedures for detecting items that function differently across subgroups of examinees that take a test or survey. However, in endeavouring to detect items that may function differentially, selection of the statistical method is only one of many important decisions. In this article, we discuss the important decisions that affect investigations of differential item functioning (DIF) such as choice of method, sample size, effect size criteria, conditioning variable, purification, DIF amplification, DIF cancellation, and research designs for evaluating DIF. Our review highlights the necessity of matching the DIF procedure to the nature of the data analysed, the need to include effect size criteria, the need to consider the direction and balance of items flagged for DIF, and the need to use replication to reduce Type I errors whenever possible. Directions for future research and practice in using DIF to enhance the validity of test scores are provided.     

Identifying the Causes of DIF in Translated Verbal Items

Translated tests are being used increasingly for assessing the knowledge and skills of individuals who speak different languages. There is little research exploring why translated items sometimes function differently across languages. If the sources of differential item functioning (DIF) across languages could be predicted, it could have important implications on test development, scoring and equating. This study focuses on two questions: “Is DIF related to item type?”, “What are the causes of DIF?” The data were taken from the Israeli Psychometric Entrance Test in Hebrew (source) and Russian (translated). The results indicated that 34% of the items functioned differentially across languages. The analogy items were the most problematic with 65% showing DIF, mostly in favor of the Russian-speaking examinees. The sentence completion items were also a problem (45% D1F). The main reasons for DIF were changes in word difficulty, changes in item format, differences in cultural relevance, and changes in content.     

A Comparison of Lord's Chi-Square,Raju's Area Measures,and the Likelihood Ratio Test on Detection of Differential Item Functioning

This study compares three procedures for the detection of differential item functioning (DIF) under item response theory (IRT): (a) Lord's chi-square, (b) Raju's area measures, and (c) the likelihood ratio test. Relations among the three procedures and some practical considerations, such as linking metrics and scale purification, are discussed. Data from two forms of a university mathematics placement test were analyzed to examine the congruence among the three procedures. Results indicated that there was close agreement among the three DIF detection procedures.     

Detection of Gender-Related Differential Item Functioning in a Mathematics Performance Assessment

This study used three different differential item functioning (DIF) detection proce- dures to examine the extent to which items in a mathematics performance assessment functioned differently for matched gender groups. In addition to examining the appropriateness of individual items in terms of DIF with respect to gender, an attempt was made to identify factors (e.g., content, cognitive processes, differences in ability distributions, etc.) that may be related to DIF. The QUASAR (Quantitative Under- standing: Amplifying Student Achievement and Reasoning) Cognitive Assessment Instrument (QCAI) is designed to measure students' mathematical thinking and reasoning skills and consists of open-ended items that require students to show their solution processes and provide explanations for their answers. In this study, 33 polytomously scored items, which were distributed within four test forms, were evaluated with respect to gender-related DIF. The data source was sixth- and seventh- grade student responses to each of the four test forms administrated in the spring of 1992 at all six school sites participatingin the QUASARproject. The sample consisted of 1,782 students with approximately equal numbers of female and male students. The results indicated that DIF may not be serious for 3 1 of the 33 items (94%) in the QCAI. For the two items that were detected as functioning differently for male and female students, several plausible factors for DIF were discussed. The results from the secondary analyses, which removed the mutual influence of the two items, indicated that DIF in one item, PPPl, which favored female students rather than their matched male students, was of particular concern. These secondary analyses suggest that the detection of DIF in the other item in the original analysis may have been due to the influence of Item PPPl because they were both in the same test form.     

Logistic Regression and Its Use in Detecting Differential Item Functioning in Polytomous Items

A computer simulation study was conducted to determine the feasibility of using logistic regression procedures to detect differential item functioning (DIF) in polytomous items. One item in a simulated test of 25 items contained DIF; parameters' for that item were varied to create three conditions of nonuniform DIF and one of uniform DIF. Item scores were generated using a generalized partial credit model, and the data were recoded into multiple dichotomies in order to use logistic regression procedures. Results indicate that logistic regression is powerful in detecting most forms of DIF; however, it required large amounts of data manipulation, and interpretation of the results was sometimes difficult. Some logistic regression procedures may be useful in the post hoc analysis of DlF for polytomous items.     

Assessment of the quality and generalizability of the revised UCLA loneliness scale in Chinese and Korean community-dwelling elderly populations using item response theory (IRT)-Rasch modeling and hybrid IRT-logistic regression

ABSTRACT

Objectives: This study aims to test the dimensionality, reliability, and item quality of the revised UCLA loneliness scale as well as to investigate the differential item functioning (DIF) of the three dimensions of the revised UCLA loneliness scale in community-dwelling Chinese and Korean elderly individuals.

Method: Data from 493 elderly individuals (287 Chinese and 206 Korean) were used to examine the revised UCLA loneliness scale. The Research model based on item response theory (IRT) was used to test dimensionality, reliability, and item fit. The hybrid ordinal logistic regression-IRT test was used to evaluate DIF.

Results: Item separation reliability, person reliability, and Cronbach’s alpha met the benchmarks. The quality of the items in the three-dimension model met the benchmark. Eight items were detected as significant DIF items (at α < .01). The loneliness level of Chinese elderly individuals was significantly higher than that of Koreans in Dimensions 1 and 2, while Korean elderly participants showed significantly higher loneliness levels than Chinese participants in Dimension 3. Several collected demographic characteristics and loneliness levels were more highly correlated in Korean elderly individuals than in Chinese elderly individuals.

Conclusion: Analysis using the three dimensions is reasonable for the revised UCLA loneliness scale. Good item quality and the items of this measure suggest that the revised UCLA loneliness can be used to assess the preferred latent traits. Finally, the differences between the levels of loneliness in Chinese and Korean elderly individuals are associated with the factors of loneliness.     

The Effect of Model Misspecification on Classification Decisons Made Using a Computerized Test

Many computerized testing algorithms require the fitting of some item response theory (IRT) model to examinees' responses to facilitate item selection, the determination of test stopping rules, and classification decisions. Some IRT models are thought to be particularly useful for small volume certification programs that wish to make the transition to computerized adaptive testing (CAT). The one-parameter logistic model (1-PLM) is usually assumed to require a smaller sample size than the three-parameter logistic model (3-PLM) for item parameter calibrations. This study examined the effects of model misspecification on the precision of the decisions made using the sequential probability ratio test (SPRT). For this comparison, the 1-PLM was used to estimate item parameters, even though the items' characteristics were represented by a 3-PLM. Results demonstrated that the 1-PLM produced considerably more decision errors under simulation conditions similar to a real testing environment, compared to the true model and to a fixed-form standard reference set of items.     

Item-Bundle DIF Hypothesis Testing: Identifying Suspect Bundles and Assessing Their Differential Functioning

This article proposes two multidimensional IRT model-based methods of selecting item bundles (clusters of not necessarily adjacent items chosen according to some organizational principle) suspected of displaying DIF amplification. The approach embodied in these two methods is inspired by Shealy and Stout's (1993a, 1993b) multidimensional model for DIF. Each bundle selected by these methods constitutes a DIF amplification hypothesis. When SIBTEST (Shealy & Stout, 1993b) confirms DIF amplification in selected bundles, differential bundle functioning (DBF) is said to occur. Three real data examples illustrate the two methods for suspect bundle selection. The effectiveness of the methods is argued on statistical grounds. A distinction between benign and adverse DIF is made. The decision whether flagged DIF items or DBF bundles display benign or adverse DIF/DBF must depend in part on nonstatistical construct validity arguments. Conducting DBF analyses using these methods should help in the identification of the causes of DIF/DBF.