Home About us Contact | |||
DIF Analyses (dif + analysis)
Selected AbstractsUsing Dimensionality-Based DIF Analyses to Identify and Interpret Constructs That Elicit Group DifferencesEDUCATIONAL MEASUREMENT: ISSUES AND PRACTICE, Issue 1 2005Mark J. Gierl In this paper I describe and illustrate the Roussos-Stout (1996) multidimensionality-based DIF analysis paradigm, with emphasis on its implication for the selection of a matching and studied subtest for DIF analyses. Standard DIF practice encourages an exploratory search for matching subtest items based on purely statistical criteria, such as a failure to display DIF. By contrast, the multidimensional DIF paradigm emphasizes a substantively-informed selection of items for both the matching and studied subtest based on the dimensions suspected of underlying the test data. Using two examples, I demonstrate that these two approaches lead to different interpretations about the occurrence of DIF in a test. It is argued that selecting a matching and studied subtest, as identified using the DIF analysis paradigm, can lead to a more informed understanding of why DIF occurs. [source] Testing Features of Graphical DIF: Application of a Regression Correction to Three Nonparametric Statistical TestsJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 4 2006Daniel M. Bolt 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 ,2test, 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. [source] Identifying Sources of Differential Item and Bundle Functioning on Translated Achievement Tests: A Confirmatory AnalysisJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 2 2001Mark J. Gierl 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. [source] |