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1.
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.  相似文献   

2.
《教育实用测度》2013,26(4):329-349
The logistic regression (LR) procedure for differential item functioning (DIF) detection is a model-based approach designed to identify both uniform and nonuniform DIF. However, this procedure tends to produce inflated Type I errors. This outcome is problematic because it can result in the inefficient use of testing resources, and it may interfere with the study of the underlying causes of DIF. Recently, an effect size measure was developed for the LR DIF procedure and a classification method was proposed. However, the effect size measure and classification method have not been systematically investigated. In this study, we developed a new classification method based on those established for the Simultaneous Item Bias Test. A simulation study also was conducted to determine if the effect size measure affects the Type I error and power rates for the LR DIF procedure across sample sizes, ability distributions, and percentage of DIF items included on a test. The results indicate that the inclusion of the effect size measure can substantially reduce Type I error rates when large sample sizes are used, although there is also a reduction in power.  相似文献   

3.
We make a distinction between the operational practice of using an observed score to assess differential item functioning (DIF) and the concept of departure from measurement invariance (DMI) that conditions on a latent variable. DMI and DIF indices of effect sizes, based on the Mantel-Haenszel test of common odds ratio, converge under restricted conditions if a simple sum score is used as the matching or conditioning variable in a DIF analysis. Based on theoretical results, we demonstrate analytically that matching on a weighted sum score can significantly reduce the difference between DIF and DMI measures over what can be achieved with a simple sum score. We also examine the utility of binning methods that could facilitate potential operational use of DIF with weighted sum scores. A real data application was included to show this feasibility.  相似文献   

4.
This article considers potential problems that can arise in estimating a unidimensional item response theory (IRT) model when some test items are multidimensional (i.e., show a complex factorial structure). More specifically, this study examines (1) the consequences of model misfit on IRT item parameter estimates due to unintended minor item‐level multidimensionality, and (2) whether a Projection IRT model can provide a useful remedy. A real‐data example is used to illustrate the problem and also is used as a base model for a simulation study. The results suggest that ignoring item‐level multidimensionality might lead to inflated item discrimination parameter estimates when the proportion of multidimensional test items to unidimensional test items is as low as 1:5. The Projection IRT model appears to be a useful tool for updating unidimensional item parameter estimates of multidimensional test items for a purified unidimensional interpretation.  相似文献   

5.
本研究旨在从一维和多维的角度检测国际教育成效评价协会(IEA)儿童认知发展状况测验中中译英考题的项目功能差异(DIF)。我们分析的数据由871名中国儿童和557名美国儿童的测试数据组成。结果显示,有一半以上的题目存在实质的DIF,意味着这个测验对于中美儿童而言,并没有功能等值。使用者应谨慎使用该跨语言翻译的比较测试结果来比较中美两国考生的认知能力水平。所幸约有半数的DIF题目偏向中国,半数偏向美国,因此利用测验总分所建立的量尺,应该不至于有太大的偏误。此外,题目拟合度统计量并不能足够地检测到存在DIF的题目,还是应该进行特定的DIF分析。我们探讨了三种可能导致DIF的原因,尚需更多学科专业知识和实验来真正解释DIF的形成。  相似文献   

6.
This study presents a new approach to synthesizing differential item functioning (DIF) effect size: First, using correlation matrices from each study, we perform a multigroup confirmatory factor analysis (MGCFA) that examines measurement invariance of a test item between two subgroups (i.e., focal and reference groups). Then we synthesize, across the studies, the differences in the estimated factor loadings between the two subgroups, resulting in a meta-analytic summary of the MGCFA effect sizes (MGCFA-ES). The performance of this new approach was examined using a Monte Carlo simulation, where we created 108 conditions by four factors: (1) three levels of item difficulty, (2) four magnitudes of DIF, (3) three levels of sample size, and (4) three types of correlation matrix (tetrachoric, adjusted Pearson, and Pearson). Results indicate that when MGCFA is fitted to tetrachoric correlation matrices, the meta-analytic summary of the MGCFA-ES performed best in terms of bias and mean square error values, 95% confidence interval coverages, empirical standard errors, Type I error rates, and statistical power; and reasonably well with adjusted Pearson correlation matrices. In addition, when tetrachoric correlation matrices are used, a meta-analytic summary of the MGCFA-ES performed well, particularly, under the condition that a high difficulty item with a large DIF was administered to a large sample size. Our result offers an option for synthesizing the magnitude of DIF on a flagged item across studies in practice.  相似文献   

7.
Investigations of differential distractor functioning (DDF) can provide valuable information concerning the location and possible causes of measurement invariance within a multiple‐choice item. In this article, I propose an odds ratio estimator of the DDF effect as modeled under the nominal response model. In addition, I propose a simultaneous distractor‐level (SDL) test of invariance based on the results of the distractor‐level tests of DDF. The results of a simulation study indicated that the DDF effect estimator maintained good statistical properties under a variety of conditions, and the SDL test displayed substantially higher power than the traditional Mantel‐Haenszel test of no DIF when the DDF effect varied in magnitude and/or size across the distractors.  相似文献   

8.
Lord's Wald test for differential item functioning (DIF) has not been studied extensively in the context of the multidimensional item response theory (MIRT) framework. In this article, Lord's Wald test was implemented using two estimation approaches, marginal maximum likelihood estimation and Bayesian Markov chain Monte Carlo estimation, to detect uniform and nonuniform DIF under MIRT models. The Type I error and power rates for Lord's Wald test were investigated under various simulation conditions, including different DIF types and magnitudes, different means and correlations of two ability parameters, and different sample sizes. Furthermore, English usage data were analyzed to illustrate the use of Lord's Wald test with the two estimation approaches.  相似文献   

9.
Three types of effects sizes for DIF are described in this exposition: log of the odds-ratio (differences in log-odds), differences in probability-correct, and proportion of variance accounted for. Using these indices involves conceptualizing the degree of DIF in different ways. This integrative review discusses how these measures are impacted in different ways by item difficulty, item discrimination, and item lower asymptote. For example, for a fixed discrimination, the difference in probabilities decreases as the difference between the item difficulty and the mean ability increases. Under the same conditions, the log of the odds-ratio remains constant if the lower asymptote is zero. A non-zero lower asymptote decreases the absolute value of the probability difference symmetrically for easy and hard items, but it decreases the absolute value of the log-odds difference much more for difficult items. Thus, one cannot set a criterion for defining a large effect size in one metric and find a corresponding criterion in another metric that is equivalent across all items or ability distributions. In choosing an effect size, these differences must be understood and considered.  相似文献   

10.
This study examined the effect of sample size ratio and model misfit on the Type I error rates and power of the Difficulty Parameter Differences procedure using Winsteps. A unidimensional 30-item test with responses from 130,000 examinees was simulated and four independent variables were manipulated: sample size ratio (20/100/250/500/1000); model fit/misfit (1 PL and 3PLc =. 15 models); impact (no difference/mean differences/variance differences/mean and variance differences); and percentage of items with uniform and nonuniform DIF (0%/10%/20%). In general, the results indicate the importance of ensuring model fit to achieve greater control of Type I error and adequate statistical power. The manipulated variables produced inflated Type I error rates, which were well controlled when a measure of DIF magnitude was applied. Sample size ratio also had an effect on the power of the procedure. The paper discusses the practical implications of these results.  相似文献   

11.
This paper illustrates that the psychometric properties of scores and scales that are used with mixed‐format educational tests can impact the use and interpretation of the scores that are reported to examinees. Psychometric properties that include reliability and conditional standard errors of measurement are considered in this paper. The focus is on mixed‐format tests in situations for which raw scores are integer‐weighted sums of item scores. Four associated real‐data examples include (a) effects of weights associated with each item type on reliability, (b) comparison of psychometric properties of different scale scores, (c) evaluation of the equity property of equating, and (d) comparison of the use of unidimensional and multidimensional procedures for evaluating psychometric properties. Throughout the paper, and especially in the conclusion section, the examples are related to issues associated with test interpretation and test use.  相似文献   

12.
Inspection of differential item functioning (DIF) in translated test items can be informed by graphical comparisons of item response functions (IRFs) across translated forms. Due to the many forms of DIF that can emerge in such analyses, it is important to develop statistical tests that can confirm various characteristics of DIF when present. Traditional nonparametric tests of DIF (Mantel-Haenszel, SIBTEST) are not designed to test for the presence of nonuniform or local DIF, while common probability difference (P-DIF) tests (e.g., SIBTEST) do not optimize power in testing for uniform DIF, and thus may be less useful in the context of graphical DIF analyses. In this article, modifications of three alternative nonparametric statistical tests for DIF, Fisher's χ 2 test, Cochran's Z test, and Goodman's U test ( Marascuilo & Slaughter, 1981 ), are investigated for these purposes. A simulation study demonstrates the effectiveness of a regression correction procedure in improving the statistical performance of the tests when using an internal test score as the matching criterion. Simulation power and real data analyses demonstrate the unique information provided by these alternative methods compared to SIBTEST and Mantel-Haenszel in confirming various forms of DIF in translated tests.  相似文献   

13.
Although logistic regression became one of the well‐known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying note on those three tests when logistic regression is applied for DIF detection.  相似文献   

14.
One approach to measuring unsigned differential test functioning is to estimate the variance of the differential item functioning (DIF) effect across the items of the test. This article proposes two estimators of the DIF effect variance for tests containing dichotomous and polytomous items. The proposed estimators are direct extensions of the noniterative estimators developed by Camilli and Penfield (1997) for tests composed of dichotomous items. A small simulation study is reported in which the statistical properties of the generalized variance estimators are assessed, and guidelines are proposed for interpreting values of DIF effect variance estimators.  相似文献   

15.
Assessment of Differential Item Functioning for Performance Tasks   总被引：1，自引：0，他引：1  
  相似文献   

16.
This article concerns the simultaneous assessment of DIF for a collection of test items. Rather than an average or sum in which positive and negative DIF may cancel, we propose an index that measures the variance of DIF on a test as an indicator of the degree to which different items show DIF in different directions. It is computed from standard Mantel-Haenszel statistics (the logodds ratio and its variance error) and may be conceptually classified as a variance component or variance effect size. Evaluated by simulation under three item response models (IPL, 2PL, and 3PL), the index is shown to be an accurate estimate of the DTF generating parameter in the case of the 1PL and 2PL models with groups of equal ability. For groups of unequal ability, the index is accurate under the I PL but not the 2PL condition; however, a weighted version of the index provides improved estimates. For the 3PL condition, the DTF generating parameter is underestimated. This latter result is due in part to a mismatch in the scales of the log-odds ratio and IRT difficulty.  相似文献   

17.
The aim of this study is to assess the efficiency of using the multiple‐group categorical confirmatory factor analysis (MCCFA) and the robust chi‐square difference test in differential item functioning (DIF) detection for polytomous items under the minimum free baseline strategy. While testing for DIF items, despite the strong assumption that all but the examined item are set to be DIF‐free, MCCFA with such a constrained baseline approach is commonly used in the literature. The present study relaxes this strong assumption and adopts the minimum free baseline approach where, aside from those parameters constrained for identification purpose, parameters of all but the examined item are allowed to differ among groups. Based on the simulation results, the robust chi‐square difference test statistic with the mean and variance adjustment is shown to be efficient in detecting DIF for polytomous items in terms of the empirical power and Type I error rates. To sum up, MCCFA under the minimum free baseline strategy is useful for DIF detection for polytomous items.  相似文献   

18.
Many large‐scale assessments are designed to yield two or more scores for an individual by administering multiple sections measuring different but related skills. Multidimensional tests, or more specifically, simple structured tests, such as these rely on multiple multiple‐choice and/or constructed responses sections of items to generate multiple scores. In the current article, we propose an extension of the hierarchical rater model (HRM) to be applied with simple structured tests with constructed response items. In addition to modeling the appropriate trait structure, the multidimensional HRM (M‐HRM) presented here also accounts for rater severity bias and rater variability or inconsistency. We introduce the model formulation, test parameter recovery with a focus on latent traits, and compare the M‐HRM to other scoring approaches (unidimensional HRMs and a traditional multidimensional item response theory model) using simulated and empirical data. Results show more precise scores under the M‐HRM, with a major improvement in scores when incorporating rater effects versus ignoring them in the traditional multidimensional item response theory model.  相似文献   

19.
The development of the DETECT procedure marked an important advancement in nonparametric dimensionality analysis. DETECT is the first nonparametric technique to estimate the number of dimensions in a data set, estimate an effect size for multidimensionality, and identify which dimension is predominantly measured by each item. The efficacy of DETECT critically depends on accurate, minimally biased estimation of the expected conditional covariances of all the item pairs. However, the amount of bias in the DETECT estimator has been studied only in a few simulated unidimensional data sets. This is because the value of the DETECT population parameter is known to be zero for this case and has been unknown for cases when multidimensionality is present. In this article, integral formulas for the DETECT population parameter are derived for the most commonly used parametric multidimensional item response theory model, the Reckase and McKinley model. These formulas are then used to evaluate the bias in DETECT by positing a multidimensional model, simulating data from the model using a very large sample size (to eliminate random error), calculating the large-sample DETECT statistic, and finally calculating the DETECT population parameter to compare with the large-sample statistic. A wide variety of two- and three-dimensional models, including both simple structure and approximate simple structure, were investigated. The results indicated that DETECT does exhibit statistical bias in the large-sample estimation of the item-pair conditional covariances; but, for the simulated tests that had 20 or more items, the bias was small enough to result in the large-sample DETECT almost always correctly partitioning the items and the DETECT effect size estimator exhibiting negligible bias.  相似文献   

20.
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.  相似文献   

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