LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position

Genge, Ewa

doi:10.3390/su13084130

Cited by 2 publications

(3 citation statements)

References 67 publications

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“…where |I 0 | is the number of items in the set I 0 . The estimation in (11) corresponds to the assumption of additive DIF effects in logarithmized item discriminations (see (7)). The mean µ 2 of the second group is estimated by:…”

Section: Log-mean-mean Linkingmentioning

confidence: 99%

“…The analysis of educational and psychological tests is an important field in the social sciences. The test items (i.e., tasks presented in these tests) are often modeled using item response theory (IRT; [1][2][3]; for applications, see, e.g., [4][5][6][7][8][9][10][11][12][13][14]) models. In this article, the two-parameter logistic (2PL; [15]) IRT model is investigated to compare two groups on test items.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

Robitzsch

2021

Foundations

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This article investigates the comparison of two groups based on the two-parameter logistic item response model. It is assumed that there is random differential item functioning in item difficulties and item discriminations. The group difference is estimated using separate calibration with subsequent linking, as well as concurrent calibration. The following linking methods are compared: mean-mean linking, log-mean-mean linking, invariance alignment, Haberman linking, asymmetric and symmetric Haebara linking, different recalibration linking methods, anchored item parameters, and concurrent calibration. It is analytically shown that log-mean-mean linking and mean-mean linking provide consistent estimates if random DIF effects have zero means. The performance of the linking methods was evaluated through a simulation study. It turned out that (log-)mean-mean and Haberman linking performed best, followed by symmetric Haebara linking and a newly proposed recalibration linking method. Interestingly, linking methods frequently found in applications (i.e., asymmetric Haebara linking, recalibration linking used in a variant in current large-scale assessment studies, anchored item parameters, concurrent calibration) perform worse in the presence of random differential item functioning. In line with the previous literature, differences between linking methods turned out be negligible in the absence of random differential item functioning. The different linking methods were also applied in an empirical example that performed a linking of PISA 2006 to PISA 2009 for Austrian students. This application showed that estimated trends in the means and standard deviations depended on the chosen linking method and the employed item response model.

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Section: Log-mean-mean Linkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

Robitzsch

2021

Foundations

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“…Notably, MMLLC poses the weakest assumptions about the data-generating distribution G, but it relies on a possible doubtful discrete representation of the θ distribution. Classifying persons into different discrete ability levels might be conceptually appealing in empirical applications [93,94].…”

Section: With Located Latent Classes (Mmllc)mentioning

confidence: 99%

A Comprehensive Simulation Study of Estimation Methods for the Rasch Model

Robitzsch

2021

Stats

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The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are systematically compared through a comprehensive simulation study: Different alternatives of joint maximum likelihood (JML) estimation, different alternatives of marginal maximum likelihood (MML) estimation, conditional maximum likelihood (CML) estimation, and several limited information methods (LIM). The type of ability distribution (i.e., nonnormality), the number of items, sample size, and the distribution of item difficulties were systematically varied. Across different simulation conditions, MML methods with flexible distributional specifications can be at least as efficient as CML. Moreover, in many situations (i.e., for long tests), penalized JML and JML with ε adjustment resulted in very efficient estimates and might be considered alternatives to JML implementations currently used in statistical software. Moreover, minimum chi-square (MINCHI) estimation was the best-performing LIM method. These findings demonstrate that JML estimation and LIM can still prove helpful in applied research.

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LC and LC-IRT Models in the Identification of Polish Households with Similar Perception of Financial Position

Cited by 2 publications

References 67 publications

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning

A Comprehensive Simulation Study of Estimation Methods for the Rasch Model

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