Investigating Local Dependence with Conditional Covariance Functions

Douglas, J. A.; Kim, Hae Rim; Habing, Brian; Gao, Furong

doi:10.2307/1165318

Cited by 29 publications

(31 citation statements)

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“…Habing andDouglas et al (1998) discuss similar results. The only exception is when p = 0, as the following theorem demonstrates.…”

Section: Frameworksupporting

confidence: 62%

“…Kim (1994), Gao (1998) and Habing and Donoghue (1998) have applied the method to assess bias. An estimate of the bias is bia-"~s = (I/B) ~ (2~)u.…”

Section: Adjustment For Biasmentioning

confidence: 99%

“…Some recent developments include Bradlow, Wainer and Wang (1999), Douglas, Kim, Habing and Gao (1998), Ip (2000), Hoskens and De Boeck (1997), and Tuerlinckx and De Boeck (1998). In other words, local dependency within a cluster may arise.…”

Section: Introductionmentioning

confidence: 99%

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Testing for local dependency in dichotomous and polytomous item response models

2001

Psychometrika

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“…Habing andDouglas et al (1998) discuss similar results. The only exception is when p = 0, as the following theorem demonstrates.…”

Section: Frameworksupporting

confidence: 62%

“…Kim (1994), Gao (1998) and Habing and Donoghue (1998) have applied the method to assess bias. An estimate of the bias is bia-"~s = (I/B) ~ (2~)u.…”

Section: Adjustment For Biasmentioning

confidence: 99%

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Testing for local dependency in dichotomous and polytomous item response models

2001

Psychometrika

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show abstract

“…Douglas, Kim, Habing, and Gao [18], Ip [19,20] or Hoskens and De Boeck [21]. We have chosen not to discuss this issue in any detail here, since it goes beyond the intended scope of this introductory paper.…”

Section: Introductionmentioning

confidence: 99%

Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers

Štochl

Jones

Croudace

2012

BMC Med Res Methodol

198

191

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BackgroundMokken scaling techniques are a useful tool for researchers who wish to construct unidimensional tests or use questionnaires that comprise multiple binary or polytomous items. The stochastic cumulative scaling model offered by this approach is ideally suited when the intention is to score an underlying latent trait by simple addition of the item response values. In our experience, the Mokken model appears to be less well-known than for example the (related) Rasch model, but is seeing increasing use in contemporary clinical research and public health. Mokken's method is a generalisation of Guttman scaling that can assist in the determination of the dimensionality of tests or scales, and enables consideration of reliability, without reliance on Cronbach's alpha. This paper provides a practical guide to the application and interpretation of this non-parametric item response theory method in empirical research with health and well-being questionnaires.MethodsScalability of data from 1) a cross-sectional health survey (the Scottish Health Education Population Survey) and 2) a general population birth cohort study (the National Child Development Study) illustrate the method and modeling steps for dichotomous and polytomous items respectively. The questionnaire data analyzed comprise responses to the 12 item General Health Questionnaire, under the binary recoding recommended for screening applications, and the ordinal/polytomous responses to the Warwick-Edinburgh Mental Well-being Scale.Results and conclusionsAfter an initial analysis example in which we select items by phrasing (six positive versus six negatively worded items) we show that all items from the 12-item General Health Questionnaire (GHQ-12) – when binary scored – were scalable according to the double monotonicity model, in two short scales comprising six items each (Bech’s “well-being” and “distress” clinical scales). An illustration of ordinal item analysis confirmed that all 14 positively worded items of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) met criteria for the monotone homogeneity model but four items violated double monotonicity with respect to a single underlying dimension.Software availability and commands used to specify unidimensionality and reliability analysis and graphical displays for diagnosing monotone homogeneity and double monotonicity are discussed, with an emphasis on current implementations in freeware.

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“…In the presence of test speededness, the parameters of item response models can be poorly estimated, particularly for items located near the end of tests (Douglas, Kim, Habing, & Gao, 1998; Oshima, 1994). Item response theory (IRT) applications that are particularly sensitive to the accuracy of the item parameter estimates, such as differential item functioning (DIF) analysis, standard‐setting studies, or even item analysis, can produce inaccurate and misleading results when speededness is present, especially for the end‐of‐test items.…”

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confidence: 99%

A Comparison of Item Calibration Procedures in the Presence of Test Speededness

Suh

Cho

Wollack

2012

J Educational Measurement

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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.

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Investigating Local Dependence with Conditional Covariance Functions

Cited by 29 publications

References 0 publications

Testing for local dependency in dichotomous and polytomous item response models

Testing for local dependency in dichotomous and polytomous item response models

Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers

A Comparison of Item Calibration Procedures in the Presence of Test Speededness

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