Logistic Discriminant Function Analysis for DIF Identification of Polytomously Scored Items

Miller, Timothy; Spray, Judith A.

doi:10.1111/j.1745-3984.1993.tb01069.x

Cited by 97 publications

(108 citation statements)

References 30 publications

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“…If for dichotomous items there are several DIF methods available, for polytomous items analyses options are limited to: the generalized version of the Mantel-Haenszel method (Agresti, 2002), the ordinal logistic regression (Zumbo, 1999) or the analysis of discriminatory logistics function (Miller & Spray, 1993).…”

Section: Methods Used For Polytomous Itemsmentioning

confidence: 99%

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2017

PRU

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A psychological test should be developed to accurately estimate the measured construct, being insensitive to extraneous factors that could affect it. The way in which the various external factors jeopardize the item responses is known as test bias and minimizing these influences is a primary concern for test developers. A relatively recent category of psychometric techniques that aim to identify those items that function differently in a group of test-takers is called Differential Item Functioning (DIF). The purpose of this paper is to present, in a non-technical manner, the conceptual foundation of DIF, and the most common applied techniques. We will discuss methods based on logistic regression, those using Item Response Theory models as well as the Mantel-Haenszel method. We will then discuss the strengths and limitations of each method, conditions of application, and the main software packages used to conduct such analyses.

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Section: Methods Used For Polytomous Itemsmentioning

confidence: 99%

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2017

PRU

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“…These reviewer concerns can be informed by an awareness that comparative studies of DIF methods always require a choice of how the DIF criteria and populations are defined. In prior DIF studies, DIF methods have been compared based on criteria and populations ranging from actual test data (Dorans & Holland, 1993;Hanson & Feinstein, 1995;Lyu et al, 1995;Miller & Spray, 1993;Moses & Miao, 2007;Puhan et al, 2007) to data that have been simulated with degrees of nonuniform DIF and with presumed relationships between observed scores and latent variables (Douglas et al, 1996;Kristjansson et al, 2005;Roussos & Stout, 1996;Shealy & Stout, 1993;Swaminathan & Rogers, 1990).…”

Section: Future Directionsmentioning

confidence: 99%

A Comparison of Methods for Estimating Conditional Item Score Differences in Differential Item Functioning (Dif) Assessments

Moses

Miao

Dorans

2010

ETS Research Report Series

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This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus was on assessing the accuracy of strategies' overall DIF effect sizes and statistical significance tests. A real data simulation was used to evaluate the estimation strategies with 6 items representing DIF and No DIF situations, and with 4 sample size combinations for the reference and focal group data. Results showed that the logistic regression estimation strategy was the most highly recommended strategy in terms of the bias and variability of its estimates and the power of its statistical significance test. The loglinear models strategy had flexibility advantages, but these advantages only offset the greater variability of its estimates and its reduced statistical power when sample sizes were large. The kernel smoothing estimation strategy was the least accurate of the considered strategies due to estimation problems when the reference and focal groups differed in overall ability.Key words: DIF, kernel smoothing, loglinear models, logistic regression ii While the psychometric literature has defined differential item functioning (DIF) as a performance difference between examinee groups at one level of ability (Dorans & Holland, 1993;Lord, 1980;Shepard, 1982), considerable research has focused on developing and comparing DIF detection methods that summarize DIF across a total range of ability (Dorans & Kulick, 1986;Holland & Thayer, 1988;Kristjansson, Aylesworth, McDowell, & Zumbo, 2005;Roussos & Stout, 1996;Shealy & Stout, 1993;Swaminathan & Rogers, 1990;Zumbo, 1999;Zwick, Thayer, & Lewis, 2000). This work usually focuses on overall statistical significance tests of summary DIF indexes and, to a lesser extent, on the use of summary DIF indexes as overall effect sizes. Due to the potential of all summary measures to oversummarize in special circumstances (to be described below), effect sizes and significance tests of overall DIF may benefit by being supplemented with assessments of conditional, ability-level DIF. The purpose of this study was to compare the accuracies of four DIF estimation strategies for estimating conditional DIF (raw data, logistic regression, loglinear models, and kernel smoothing). Assessing Differential Item Functioning (DIF)The assessment of DIF is a determination of whether a studied item, Y, performs differently for reference examinees, R, and focal examinees, F, conditioned on the M levels of a variable that measures reference and focal examinees' overall ability, m X . In this study, Y is dichotomously scored. m X denotes an observed test score that excludes Y and all items containing extensive DIF, or C-DIF (Dorans & Holland, 1993). In typical DIF assessments, the M differences in (1) are summarized rather than individually evalua...

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“…As with SIBTEST and the Generalized MantelHaenszel procedures, Logistic Discriminant Function Analysis (LDFA), proposed by Miller & Spray (1993), is a parametric DIF detection approach which provides both a significance test and a measure of effect size. LDFA is closely related to logistic regression, and it is also modelbased.…”

Section: Introductionmentioning

confidence: 99%

A Psychometric Analysis of an Inventory for Evaluating Students' Leadership Skills

Ibrahim¹

2017

JTTE

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Abstract:The study classified individual items that function differentially according to the magnitude of DIF in dichotomous test and compared the power of Generalized Mantel Haenszel (GMH), Simultaneous Item Bias Test (SIBTEST), and Logistic Discriminant Function Analysis (LDFA) methods in detecting DIF in dichotomous test items. It also determined the relationship between the proportions of test items that function differentially in dichotomous test items when the different methods were used. These were with a view to improving the quality of dichotomous test items construction. The study adopted a survey design. The population consisted of all undergraduate students who registered for EFC 303 (Tests and Measurement) at Obafemi Awolowo University during 2011/2012 Harmattan Semester. The sample consisted of an intact class of 457 Part 3 undergraduate students who registered for the course. A 50-item multiple -choice achievement test designated "Undergraduate Students' Achievement Test (USAT)" was developed by the researcher for the study. A total of 445 scripts were found properly completed. Data collected were analysed using Generalized Mantel Haenszel, Simultaneous Item Bias Test, and Logistic Discriminant Function Analysis. The results showed that there was a significant difference in the performance of the GMH, SIBTEST, and LDFA methods in detecting DIF in dichotomous test items. Further, a non significant relationship existed between the proportions of test items that functioned differentially in the dichotomous test items when the different methods were used. The study concluded that GMH, SIBTEST and LDFA were effective in detecting DIF across dichotomous test items but complement one other in their ability to detect DIF in dichotomous test items.

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Logistic Discriminant Function Analysis for DIF Identification of Polytomously Scored Items

Cited by 97 publications

References 30 publications

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A Comparison of Methods for Estimating Conditional Item Score Differences in Differential Item Functioning (Dif) Assessments

A Psychometric Analysis of an Inventory for Evaluating Students' Leadership Skills

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