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1.
This study investigated the extent to which log-linear smoothing could improve the accuracy of common-item equating by the chained equipercentile method in small samples of examinees. Examinee response data from a 100-item test were used to create two overlapping forms of 58 items each, with 24 items in common. The criterion equating was a direct equipercentile equating of the two forms in the full population of 93,283 examinees. Anchor equatings were performed in samples of 25, 50, 100, and 200 examinees, with 50 pairs of samples at each size level. Four equatings were performed with each pair of samples: one based on unsmoothed distributions and three based on varying degrees of smoothing. Smoothing reduced, by at least half, the sample size required for a given degree of accuracy. Smoothing that preserved only two moments of the marginal distributions resulted in equatings that failed to capture the curvilinearity in the population equating.  相似文献   

2.
The impact of log‐linear presmoothing on the accuracy of small sample chained equipercentile equating was evaluated under two conditions . In the first condition the small samples differed randomly in ability from the target population. In the second condition the small samples were systematically different from the target population. Results showed that equating with small samples (e.g., N < 25 or 50) using either raw or smoothed score distributions led to considerable large random equating error (although smoothing reduced random equating error). Moreover, when the small samples were not representative of the target population, the amount of equating bias also was quite large. It is concluded that although presmoothing can reduce random equating error, it is not likely to reduce equating bias caused by using an unrepresentative sample. Other alternatives to the small sample equating problem (e.g., the SiGNET design) which focus more on improving data collection are discussed.  相似文献   

3.
The purpose of this study was to investigate the power and Type I error rate of the likelihood ratio goodness-of-fit (LR) statistic in detecting differential item functioning (DIF) under Samejima's (1969, 1972) graded response model. A multiple-replication Monte Carlo study was utilized in which DIF was modeled in simulated data sets which were then calibrated with MULTILOG (Thissen, 1991) using hierarchically nested item response models. In addition, the power and Type I error rate of the Mantel (1963) approach for detecting DIF in ordered response categories were investigated using the same simulated data, for comparative purposes. The power of both the Mantel and LR procedures was affected by sample size, as expected. The LR procedure lacked the power to consistently detect DIF when it existed in reference/focal groups with sample sizes as small as 500/500. The Mantel procedure maintained control of its Type I error rate and was more powerful than the LR procedure when the comparison group ability distributions were identical and there was a constant DIF pattern. On the other hand, the Mantel procedure lost control of its Type I error rate, whereas the LR procedure did not, when the comparison groups differed in mean ability; and the LR procedure demonstrated a profound power advantage over the Mantel procedure under conditions of balanced DIF in which the comparison group ability distributions were identical. The choice and subsequent use of any procedure requires a thorough understanding of the power and Type I error rates of the procedure under varying conditions of DIF pattern, comparison group ability distributions.–or as a surrogate, observed score distributions–and item characteristics.  相似文献   

4.
Two simulation studies investigated Type I error performance of two statistical procedures for detecting differential item functioning (DIF): SIBTEST and Mantel-Haenszel (MH). Because MH and SIBTEST are based on asymptotic distributions requiring "large" numbers of examinees, the first study examined Type 1 error for small sample sizes. No significant Type I error inflation occurred for either procedure. Because MH has the potential for Type I error inflation for non-Rasch models, the second study used a markedly non-Rasch test and systematically varied the shape and location of the studied item. When differences in distribution across examinee group of the measured ability were present, both procedures displayed inflated Type 1 error for certain items; MH displayed the greater inflation. Also, both procedures displayed statistically biased estimation of the zero DIF for certain items, though SIBTEST displayed much less than MH. When no latent distributional differences were present, both procedures performed satisfactorily under all conditions.  相似文献   

5.
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   

6.
In a previous simulation study of methods for assessing differential item functioning (DIF) in computer-adaptive tests (Zwick, Thayer, & Wingersky, 1993, 1994), modified versions of the Mantel-Haenszel and standardization methods were found to perform well. In that study, data were generated using the 3-parameter logistic (3PL) model and this same model was assumed in obtaining item parameter estimates. In the current study, the 3PL data were used but the Rasch model was assumed in obtaining the item parameter estimates, which determined the information table used for item selection. Although the obtained DIF statistics were highly correlated with the generating DIF values, they tended to be smaller in magnitude than in the 3PL analysis, resulting in a lower probability of DIF detection. This reduced sensitivity appeared to be related to a degradation in the accuracy of matching. Expected true scores from the Rasch-based computer-adaptive test tended to be biased downward, particularly for lower-ability examinees  相似文献   

7.
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   

8.
《教育实用测度》2013,26(3):241-261
This simulation study compared two procedures to enable an adaptive test to select items in correspondence with a content blueprint. Trait level estimates obtained from testlet-based and constrained adaptive tests administered to 10,000 simulated examinees under two trait distributions and three item pool sizes were compared to the trait level estimates obtained from traditional adaptive tests in terms of mean absolute error, bias, and information. Results indicate that using constrained adaptive testing requires an increase of 5% to 11% in test length over the traditional adaptive test to reach the same error level and, using testlets requires an increase of 43% to 104% in test length over the traditional adaptive test. Given these results, the use of constrained computerized adaptive testing is recommended for situations in which an adaptive test must adhere to particular content specifications.  相似文献   

9.
Logistic regression has recently been advanced as a viable procedure for detecting differential item functioning (DIF). One of the advantages of this procedure is the considerable flexibility it offers in the specification of the regression equation. This article describes incorporating two ability estimates into a single regression analysis, with the result that substantially fewer items exhibit DIF. A comparable analysis is conducted using the Mantel-Haenszel with similar results. It is argued that by simultaneously conditioning on two relevant ability estimates, more accurate matching of examinees in the reference and focal groups is obtained, and thus multidimensional item impact is not mistakenly identified as DIF.  相似文献   

10.
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   

11.
Administering tests under time constraints may result in poorly estimated item parameters, particularly for items at the end of the test (Douglas, Kim, Habing, & Gao, 1998; Oshima, 1994). Bolt, Cohen, and Wollack (2002) developed an item response theory mixture model to identify a latent group of examinees for whom a test is overly speeded, and found that item parameter estimates for end-of-test items in the nonspeeded group were similar to estimates for those same items when administered earlier in the test. In this study, we used the Bolt et al. (2002) method to study the effect of removing speeded examinees on the stability of a score scale over an II-year period. Results indicated that using only the nonspeeded examinees for equating and estimating item parameters provided a more unidimensional scale, smaller effects of item parameter drift (including fewer drifting items), and less scale drift (i.e., bias) and variability (i.e., root mean squared errors) when compared to the total group of examinees.  相似文献   

12.
Frequency distributions of test scores may appear irregular and, as estimates of a population distribution, contain a substantial amount of sampling error. Techniques for smoothing score distributions are available that have the capacity to improve estimation. In this article, estimation/smoothing methods that are flexible enough to fit a wide variety of test score distributions are reviewed. The methods are a kernel method, a strong true–score model–based method, and a method that uses polynomial log–linear models. The use of these methods is then reviewed, and applications of the methods are presented that include describing and comparing test score distributions, estimating norms, and estimating equipercentile equivalents in test score equating. Suggestions for further research are also provided.  相似文献   

13.
In the logistic regression (LR) procedure for differential item functioning (DIF), the parameters of LR have often been estimated using maximum likelihood (ML) estimation. However, ML estimation suffers from the finite-sample bias. Furthermore, ML estimation for LR can be substantially biased in the presence of rare event data. The bias of ML estimation due to small samples and rare event data can degrade the performance of the LR procedure, especially when testing the DIF of difficult items in small samples. Penalized ML (PML) estimation was originally developed to reduce the finite-sample bias of conventional ML estimation and also was known to reduce the bias in the estimation of LR for the rare events data. The goal of this study is to compare the performances of the LR procedures based on the ML and PML estimation in terms of the statistical power and Type I error. In a simulation study, Swaminathan and Rogers's Wald test based on PML estimation (PSR) showed the highest statistical power in most of the simulation conditions, and LRT based on conventional PML estimation (PLRT) showed the most robust and stable Type I error. The discussion about the trade-off between bias and variance is presented in the discussion section.  相似文献   

14.
This study investigated the effectiveness of equating with very small samples using the random groups design. Of particular interest was equating accuracy at specific scores where performance standards might be set. Two sets of simulations were carried out, one in which the two forms were identical and one in which they differed by a tenth of a standard deviation in overall difficulty. These forms were equated using mean equating, linear equating, unsmoothed equipercentile equating, and equipercentile equating using two through six moments of log-linear presmoothing with samples of 25, 50, 75, 100, 150, and 200. The results indicated that identity equating was preferable to any equating method when samples were as small as 25. For samples of 50 and above, the choice of an equating method over identity equating depended on the location of the passing score relative to examinee performance. If passing scores were located below the mean, where data were sparser, mean equating produced the smallest percentage of misclassified examinees. For passing scores near the mean, all methods produced similar results with linear equating being the most accurate. For passing scores above the mean, equipercentile equating with 2- and 3-moment presmoothing were the best equating methods. Higher levels of presmoothing did not improve the results.  相似文献   

15.
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.  相似文献   

16.
When tests are designed to measure dimensionally complex material, DIF analysis with matching based on the total test score may be inappropriate. Previous research has demonstrated that matching can be improved by using multiple internal or both internal and external measures to more completely account for the latent ability space. The present article extends this line of research by examining the potential to improve matching by conditioning simultaneously on test score and a categorical variable representing the educational background of the examinees. The responses of male and female examinees from a test of medical competence were analyzed using a logistic regression procedure. Results show a substantial reduction in the number of items identified as displaying significant DIF when conditioning is based on total test score and a variable representing educational background as opposed to total test score only.  相似文献   

17.
This study evaluated exact testing (Agresti, 1992) as a method for conducting Mantel-Haenszel DIF analyses (Holland & Thayer, 1988) with relatively small samples. Sample-size restrictions limit the standard asymptotic Mantel-Haenszel for many practical applications; however, new developments in computing technology have made exact testing procedures feasible. The highly discrete distributions that are likely to occur in small-sample DIF analyses could yield very different results for asymptotic versus exact methods. It is therefore important to determine under controlled conditions the extent to which the exact approach is effective in correctly identifying DIF. A series of computer simulations were conducted in which 3 levels of induced bias (IRT b -parameter differences between groups of .25, .50, and .75) and 4 sample sizes (reference group = 500, focal group = 25, 50, 100, and 200) were investigated. Power comparisons at .01 and .05 alpha levels were carried out between the exact testing procedure and the conventional Mantel-Haenszel  相似文献   

18.
本文旨在考察HSK应试者的专业背景是否会对他们的阅读成绩产生影响。运用MH方法和SIBTEST方法对2009年HSK(初中等)考试阅读题目进行DIF筛查,把专业背景为自然科学的HSK考生设为目标组,专业背景为人文社会科学的HSK考生设为参照组。MH方法的结果是没有找到含有DIF的题目;SIBTEST方法的结果如下:第一轮DIF筛查检测到一个题目,第二轮DBF筛查检测到一组题目。这组题目有利于人文社会学科专业背景的被试。就检测DIF的方法而言,本研究认为SIBTEST方法更加敏感,DBF检验更加适合像阅读理解测验这样的一组或多组相互关联的题目。  相似文献   

19.
Most currently accepted approaches for identifying differentially functioning test items compare performance across groups after first matching examinees on the ability of interest. The typical basis for this matching is the total test score. Previous research indicates that when the test is not approximately unidimensional, matching using the total test score may result in an inflated Type I error rate. This study compares the results of differential item functioning (DIF) analysis with matching based on the total test score, matching based on subtest scores, or multivariate matching using multiple subtest scores. Analysis of both actual and simulated data indicate that for the dimensionally complex test examined in this study, using the total test score as the matching criterion is inappropriate. The results suggest that matching on multiple subtest scores simultaneously may be superior to using either the total test score or individual relevant subtest scores.  相似文献   

20.
Results obtained from computer-adaptive and self-adaptive tests were compared under conditions in which item review was permitted and not permitted. Comparisons of answers before and after review within the "review" condition showed that a small percentage of answers was changed (5.23%), that more answers were changed from wrong to right than from right to wrong (by a ratio of 2.92:1), that most examinees (66.5%) changed answers to at least some questions, that most examinees who changed answers improved their ability estimates by doing so (by a ratio of 2.55 to 1), and that review was particularly beneficial to examineees at high ability levels. Comparisons between the "review" and "no-review" conditions yielded no significant differences in ability estimates or in estimated measurement error and provided no trustworthy evidence that test anxiety moderated the effects of review on those indexes. Most examinees desired review, but permitting it increased testing time by 41%.  相似文献   

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