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Monday, July 27, 2020 | History

2 edition of Identifying nonuniform DIF in polytomously scored test items found in the catalog.

Identifying nonuniform DIF in polytomously scored test items

Judith A Spray

Identifying nonuniform DIF in polytomously scored test items

by Judith A Spray

  • 311 Want to read
  • 38 Currently reading

Published by American College Testing Program in Iowa City, Iowa .
Written in English

    Subjects:
  • Test bias -- Computer simulation,
  • Examinations -- Scoring -- Evaluation,
  • Educational tests and measurements

  • Edition Notes

    StatementJudy Spray, Tim Miller
    SeriesACT research report series -- 94-1
    ContributionsMiller, Timothy R, American College Testing Program
    The Physical Object
    Paginationii, 16 p. :
    Number of Pages16
    ID Numbers
    Open LibraryOL14962586M

      Differential item functioning (DIF) is a direct threat to the MI of test items and occurs when item parameters differ across equal ability groups, resulting in the differential likelihood of a particular (e.g., correct) item response (Raju et al., ). DIF detection generally focus on the identification of uniform and nonuniform DIF, where. Contents. About this Book About the Authors

    Background. The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention. The DFIT Framework. The DFIT framework provides estimates of differential functioning at the item and scale levels. 3, 4 Before the DFIT framework can be applied, however, separate IRT item parameter estimates for a reference group (eg, English) and a comparison (focal) group (eg, Spanish) must be obtained. Because the 2 sets of item parameters are obtained from separately estimated IRT models.

    For the nonuniform DIF items, logistic regression was 50% accurate in the small sample short test case and 75% accurate in the large sample long test case, but MH wa s not able to detect any nonuniform DIF item. Groups with Skewed Ability D istribution s Monaco () conducted a Monte Carlo simulation study to investigate the role of. 6 From the first line, you see that most items except the last 5 are dichotomously scored (the maximum score is 1); the last 5 are polytomously scored and the maximum is 4. From the second line, the fact that there is a 1 in each column means that every item on the test in included in the DIF analysis.


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Identifying nonuniform DIF in polytomously scored test items by Judith A Spray Download PDF EPUB FB2

Identifying nonuniform DIF in polytomously scored test items. Iowa City, Iowa: American College Testing Program, © (OCoLC) Document Type: Book: All Authors / Contributors: Judith A Spray; Timothy R Miller; American College Testing Program.

Identifying Nonuniform DIF in Polytomously Scored Test Items. which nonuniform DIF occurs, its detection is still important. Mantel-Haenszel procedure are available for DIF identification in polytomous items, depending upon whether the responses can be treated as nominal or ordinal.

Mantel and Haenszel (). Computer simulations under three conditions of polytomous differential item functioning (DIF) compared the ability of three different statistical procedures to detect nonuniform DIF. The procedures were a nominal and an ordinal extension of the Mantel-Haenszel statistic, and logistic discriminant function analysis.

Results showed that only the logistic discriminant function analysis could Cited by: Identifying Nonuniform DIF in Polytomously Scored Test Items The use of polytomously scored items in addition to, or in place of the more traditional correct/incorrect item formats, requires reconsideration of some of the psychometric procedures that are specific to the dichotomous situation.

In particular, the identification of differential item. Identifying nonuniform DIF in polytomously scored test items (American College Testing Research Report Series ). Iowa City, IA: American College Testing Program. Cited by: The purpose of this article is to present logistic discriminant function analysis as a means of differential item functioning (DIF) identification of items that are polytomously scored.

This study focused on the effectiveness in nonuniform polytomous item DIF detection using Discriminant Logistic Analysis (DLA) and Multinomial Logistic Regression (MLR).

Differential Item Functioning classification for polytomously scored items ability continuum (crossing DIF). So, NUDIF implies that there is an interaction between ability level and group membership.

When DIF items are found in the test, it is necessary to make. It describes effect size indices for DIF and DTF. In principle, the item response theory (IRT) approach to DIF and DTF should be preferred for discrete item responses, whether dichotomously scored or polytomously scored.

The chapter also describes Thissen, Steinberg, and Wainer's likelihood ratio (LR) approach to the IRT study of DIF. the same across item score categories.

For both dichotomous and polytomous items, Models 1, 2, and 3 are also referred to as a no DIF model, a uniform DIF model, and a nonuniform DIF model, respectively.

The logistic regression models are estimated in the macro by Proc Logistic for both dichotomously scored items and polytomously scored items. level. Thus, nonuniform DIF displays an interaction between ability level and group membership. Because of this interaction, nonuniform DIF is much more difficult to interpret.

The identification of nonuniform DIF in polytomous items may be more important than the identification of nonuniform DIF in dichotomous items (Spray & Miller, ).

DIF Assessment for Polytomously Scored Items: A Framework for Classification and Evaluation Show all authors. A consumer's guide to statistics for identifying differential item functioning. Applied Measurement in Education, 2, Logistic regression and its use in detecting nonuniform differential item functioning.

Further research is needed to examine non uniform DIF under different simulated situations for the testlet based, polytomously scored data. As one type of non unif orm DIF, crossing DIF in polytomously scored items might be further investigated using simulated data ated using the current DIF detection models (Penfield,p.

PAGE regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample size The likelihood-ratio test statistic,G2, is obtained to test the uniform and nonuniform DIF.

The methods used are as follows: DIF analytical study From the synthesis of the research related to the study of DIF for dichotomously scored items, it can be concluded that: (1) the study of the effects of DIF can be classified according to various factors, including gender, race, difficulty of the test, the distribution of the examineesâ.

Mueller, ). It is uncommon, however, to be concerned with identifying items that perform differentially. A search of the ERIC and PsychLit electronic databases using a combination of words and descriptors for attitude measurement and differential item functioning failed to turn up more than just a few entries (and most of these were not.

of examinees take a test, statistical power of DIF detection methods may be affected. Researchers have proposed modifications to DIF detection methods to account for small focal group examinee sizes for the case when items are dichotomously scored.

These methods, however, have not been applied to polytomously scored items. Graphs of Test Information Functions. Introduction The item response theory (IRT) models discussed in Chapter 4 apply to dichotomously scored items.

When items are polytomously scored, such as constructed-response items in educational assessments and rating or. IRT methods can also be used to detect DIF in dichotomously scored items.

An alternate approach using logistic regression can be used to identify DIF in both dichotomous and polytomously scored items. This workshop will provide participants with the knowledge necessary to use both IRT and logistic regression to conduct DIF studies. First, the. For items scored polytomously, the nature of DIF can become more complex.

Not only does there exist the possibility of a group-by-conditioning score interaction, but item score level becomes a third, possibly interacting variable.

Thus, DIF in a polytomous item can occur within all of the score. The first was to develop a method of detecting differential item functioning (DIF) within tests containing both dichotomously and polytomously scored items.

The second was related to gender and aimed a) to investigate if those items that were functioning differently for girls and boys showed any characteristic properties and, if so, b.Differential Item Functioning (DIF) has been widely used in healthcare, business management, and educational both can identify uniform and nonuniform DIF for dichotomous and polytomous items.

It is essential to understand Responders in the focal and reference groups were matched on total test or questionnaire scores by.Allalouf, A. Hambleton, R. K. Sireci, S.

G. Identifying the sources of differential item functioning in translated verbal items Journal of Educational Measurement 36 Angoff, W. H. Use of difficulty and discrimination indices for detecting item bias Berk, R. A. Handbook of methods for detecting test bias 96 Baltimore Johns.