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1.
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   

2.
Trend estimation in international comparative large‐scale assessments relies on measurement invariance between countries. However, cross‐national differential item functioning (DIF) has been repeatedly documented. We ran a simulation study using national item parameters, which required trends to be computed separately for each country, to compare trend estimation performances to two linking methods employing international item parameters across several conditions. The trend estimates based on the national item parameters were more accurate than the trend estimates based on the international item parameters when cross‐national DIF was present. Moreover, the use of fixed common item parameter calibrations led to biased trend estimates. The detection and elimination of DIF can reduce this bias but is also likely to increase the total error.  相似文献   

3.
In one study, parameters were estimated for constructed-response (CR) items in 8 tests from 4 operational testing programs using the l-parameter and 2- parameter partial credit (IPPC and 2PPC) models. Where multiple-choice (MC) items were present, these models were combined with the 1-parameter and 3-parameter logistic (IPL and 3PL) models, respectively. We found that item fit was better when the 2PPC model was used alone or with the 3PL model. Also, the slopes of the CR and MC items were found to differ substantially. In a second study, item parameter estimates produced using the IPL-IPPC and 3PL-2PPC model combinations were evaluated for fit to simulated data generated using true parameters known to fit one model combination or ttle other. The results suggested that the more flexible 3PL-2PPC model combination would produce better item fit than the IPL-1PPC combination.  相似文献   

4.
Detection of differential item functioning (DIF) on items intentionally constructed to favor one group over another was investigated on item parameter estimates obtained from two item response theory-based computer programs, LOGIST and BILOG. Signed- and unsigned-area measures based on joint maximum likelihood estimation, marginal maximum likelihood estimation, and two marginal maximum a posteriori estimation procedures were compared with each other to determine whether detection of DIF could be improved using prior distributions. Results indicated that item parameter estimates obtained using either prior condition were less deviant than when priors were not used. Differences in detection of DIF appeared to be related to item parameter estimation condition and to some extent to sample size.  相似文献   

5.
As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the effort-moderated item response theory (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., two-parameter logistic [2PL]) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (Assumption 1) and is unrelated to the underlying ability of examinees (Assumption 2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of Assumption 1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating Assumption 2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating Assumption 2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared with the 2PL model.  相似文献   

6.
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.  相似文献   

7.
In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized p -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life data set.  相似文献   

8.
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.  相似文献   

9.
The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates.  相似文献   

10.
Methods of uniform differential item functioning (DIF) detection have been extensively studied in the complete data case. However, less work has been done examining the performance of these methods when missing item responses are present. Research that has been done in this regard appears to indicate that treating missing item responses as incorrect can lead to inflated Type I error rates (false detection of DIF). The current study builds on this prior research by investigating the utility of multiple imputation methods for missing item responses, in conjunction with standard DIF detection techniques. Results of the study support the use of multiple imputation for dealing with missing item responses. The article concludes with a discussion of these results for multiple imputation in conjunction with other research findings supporting its use in the context of item parameter estimation with missing data.  相似文献   

11.
We developed an empirical Bayes (EB) enhancement to Mantel-Haenszel (MH) DIF analysis in which we assume that the MH statistics are normally distributed and that the prior distribution of underlying DIF parameters is also normal. We use the posterior distribution of DIF parameters to make inferences about the item's true DIF status and the posterior predictive distribution to predict the item's future observed status. DIF status is expressed in terms of the probabilities associated with each of the five DIF levels defined by the ETS classification system: C–, B–, A, B+, and C+. The EB methods yield more stable DIF estimates than do conventional methods, especially in small samples, which is advantageous in computer-adaptive testing. The EB approach may also convey information about DIF stability in a more useful way by representing the state of knowledge about an item's DIF status as probabilistic.  相似文献   

12.
The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called latent DIF analysis, is provided and its applications in the literature are surveyed. Then, the methodological issues pertaining to latent DIF analysis are described, including mixture item response models, parameter estimation, and latent DIF detection methods. Finally, recommended steps for latent DIF analysis are illustrated using empirical data.  相似文献   

13.
In the presence of test speededness, the parameter estimates of item response theory models can be poorly estimated due to conditional dependencies among items, particularly for end‐of‐test items (i.e., speeded items). This article conducted a systematic comparison of five‐item calibration procedures—a two‐parameter logistic (2PL) model, a one‐dimensional mixture model, a two‐step strategy (a combination of the one‐dimensional mixture and the 2PL), a two‐dimensional mixture model, and a hybrid model‐–by examining how sample size, percentage of speeded examinees, percentage of missing responses, and way of scoring missing responses (incorrect vs. omitted) affect the item parameter estimation in speeded tests. For nonspeeded items, all five procedures showed similar results in recovering item parameters. For speeded items, the one‐dimensional mixture model, the two‐step strategy, and the two‐dimensional mixture model provided largely similar results and performed better than the 2PL model and the hybrid model in calibrating slope parameters. However, those three procedures performed similarly to the hybrid model in estimating intercept parameters. As expected, the 2PL model did not appear to be as accurate as the other models in recovering item parameters, especially when there were large numbers of examinees showing speededness and a high percentage of missing responses with incorrect scoring. Real data analysis further described the similarities and differences between the five procedures.  相似文献   

14.
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15.
This article examines nonmathematical linguistic complexity as a source of differential item functioning (DIF) in math word problems for English language learners (ELLs). Specifically, this study investigates the relationship between item measures of linguistic complexity, nonlinguistic forms of representation and DIF measures based on item response theory difficulty parameters in a state fourth-grade math test. This study revealed that the greater the item nonmathematical lexical and syntactic complexity, the greater are the differences in difficulty parameter estimates favoring non-ELLs over ELLs. However, the impact of linguistic complexity on DIF is attenuated when items provide nonlinguistic schematic representations that help ELLs make meaning of the text, suggesting that their inclusion could help mitigate the negative effect of increased linguistic complexity in math word problems.  相似文献   

16.
The validity of inferences based on achievement test scores is dependent on the amount of effort that examinees put forth while taking the test. With low-stakes tests, for which this problem is particularly prevalent, there is a consequent need for psychometric models that can take into account differing levels of examinee effort. This article introduces the effort-moderated IRT model, which incorporates item response time into proficiency estimation and item parameter estimation. In two studies of the effort-moderated model when rapid guessing (i.e., reflecting low examinee effort) was present, one based on real data and the other on simulated data, the effort-moderated model performed better than the standard 3PL model. Specifically, it was found that the effort-moderated model (a) showed better model fit, (b) yielded more accurate item parameter estimates, (c) more accurately estimated test information, and (d) yielded proficiency estimates with higher convergent validity.  相似文献   

17.
ABSTRACT

Differential item functioning (DIF) analyses have been used as the primary method in large-scale assessments to examine fairness for subgroups. Currently, DIF analyses are conducted utilizing manifest methods using observed characteristics (gender and race/ethnicity) for grouping examinees. Homogeneity of item responses is assumed denoting that all examinees respond to test items using a similar approach. This assumption may not hold with all groups. In this study, we demonstrate the first application of the latent class (LC) approach to investigate DIF and its sources with heterogeneous (linguistic minority groups). We found at least three LCs within each linguistic group, suggesting the need to empirically evaluate this assumption in DIF analysis. We obtained larger proportions of DIF items with larger effect sizes when LCs within language groups versus the overall (majority/minority) language groups were examined. The illustrated approach could be used to improve the ways in which DIF analyses are typically conducted to enhance DIF detection accuracy and score-based inferences when analyzing DIF with heterogeneous populations.  相似文献   

18.
Mantel-Haenszel and SIBTEST, which have known difficulty in detecting non-unidirectional differential item functioning (DIF), have been adapted with some success for computerized adaptive testing (CAT). This study adapts logistic regression (LR) and the item-response-theory-likelihood-ratio test (IRT-LRT), capable of detecting both unidirectional and non-unidirectional DIF, to the CAT environment in which pretest items are assumed to be seeded in CATs but not used for trait estimation. The proposed adaptation methods were evaluated with simulated data under different sample size ratios and impact conditions in terms of Type I error, power, and specificity in identifying the form of DIF. The adapted LR and IRT-LRT procedures are more powerful than the CAT version of SIBTEST for non-unidirectional DIF detection. The good Type I error control provided by IRT-LRT under extremely unequal sample sizes and large impact is encouraging. Implications of these and other findings are discussed.  相似文献   

19.
This study examined the extent to which log-linear smoothing could improve the accuracy of differential item functioning (DIF) estimates in small samples of examinees. Examinee responses from a certification test were analyzed using White examinees in the reference group and African American examinees in the focal group. Using a simulation approach, separate DIF estimates for seven small-sample-size conditions were obtained using unsmoothed (U) and smoothed (S) score distributions. These small sample U and S DIF estimates were compared to a criterion (i.e., DIF estimates obtained using the unsmoothed total data) to assess their degree of variability (random error) and accuracy (bias). Results indicate that for most studied items smoothing the raw score distributions reduced random error and bias of the DIF estimates, especially in the small-sample-size conditions. Implications of these results for operational testing programs are discussed.  相似文献   

20.
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.  相似文献   

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