The Effects of Purification and the Evaluation of Differential Item Functioning With the Likelihood Ratio Test

Methodology ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 134-145 ◽  
Author(s):  
Fabiola González-Betanzos ◽  
Francisco J. Abad
Keyword(s):  
Differential Item Functioning ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Type I Error ◽  
Error Rates ◽  
Ratio Test ◽  
Type I ◽  
Two Stage ◽  
Item Functioning ◽  
Size Type

The current research compares the effects of several strategies to establish the anchor subtest when detecting for differential item functioning (DIF) using the IRT likelihood ratio test in one- and two-stage procedures. Two one-stage strategies were examined: (1) “One item” and (2) “All other items” used as anchor. Additionally, two two-stage strategies were tested: (3) “One anchor item with posterior anchor test augmentation” and (4) “All other items with purification.” The strategies were compared in a simulation study, where sample sizes, DIF size, type of DIF, and software implementation (MULTILOG vs. IRTLRDIF) were manipulated. Results indicated that Procedure (1) was more efficient than (2). Purification was found to improve Type I error rates substantially with the “all other items” strategy, while “posterior anchor test augmentation” did not yield a significant improvement. In relation to the effect of the software used, we found that MULTILOG generally offers better results than IRTLRDIF.

scholarly journals A Likelihood Ratio Test For The Homogeneity of Between-Study Variance in Network Meta-Analysis

2021 ◽  
Author(s):  
Dapeng Hu ◽  
Chong Wang ◽  
Annette O'Connor
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Random Effects ◽  
Type I Error ◽  
Meta Analysis ◽  
Indirect Evidence ◽  
Point Estimate ◽  
Ratio Test ◽  
Type I ◽  
Statistical Heterogeneity

Abstract Background: Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. Methods: In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). Results: The type I error rate was well controlled in the Monte Carlo simulation. The homogeneous between-study variance assumption is unrealistic both statistically and practically in the network meta-analysis BRD. The point estimate and conffdence interval of relative effect sizes are strongly inuenced by this assumption. Conclusions: Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

scholarly journals Detecting Differential Item Functioning Using Multiple-Group Cognitive Diagnosis Models

2020 ◽  
Vol 45 (1) ◽  
pp. 37-53
Author(s):  
Wenchao Ma ◽  
Ragip Terzi ◽  
Jimmy de la Torre
Keyword(s):  
Differential Item Functioning ◽  
Type I Error ◽  
Search Algorithm ◽  
Real Data ◽  
Error Rates ◽  
Cognitive Diagnosis ◽  
Type I ◽  
Wald Tests ◽  
Multiple Group ◽  
Item Functioning

This study proposes a multiple-group cognitive diagnosis model to account for the fact that students in different groups may use distinct attributes or use the same attributes but in different manners (e.g., conjunctive, disjunctive, and compensatory) to solve problems. Based on the proposed model, this study systematically investigates the performance of the likelihood ratio (LR) test and Wald test in detecting differential item functioning (DIF). A forward anchor item search procedure was also proposed to identify a set of anchor items with invariant item parameters across groups. Results showed that the LR and Wald tests with the forward anchor item search algorithm produced better calibrated Type I error rates than the ordinary LR and Wald tests, especially when items were of low quality. A set of real data were also analyzed to illustrate the use of these DIF detection procedures.

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