Journal article
Investigation of Missing Responses in Q-Matrix Validation
Applied psychological measurement, Vol.42(8), pp.660-676
11/2018
Handle:
https://hdl.handle.net/2376/114514
PMCID: PMC6291893
PMID: 30559573
Abstract
Missing data can be a serious issue for practitioners and researchers who are tasked with Q-matrix validation analysis in implementation of cognitive diagnostic models. The article investigates the impact of missing responses, and four common approaches (treat as incorrect, logistic regression, listwise deletion, and expectation–maximization [EM] imputation) for dealing with them, on the performance of two major Q-matrix validation methods (the EM-based δ-method and the nonparametric Q-matrix refinement method) across multiple factors. Results of the simulation study show that both validation methods perform better when missing responses are imputed using EM imputation or logistic regression instead of being treated as incorrect and using listwise deletion. The nonparametric Q-matrix validation method outperforms the EM-based δ-method in most conditions. Higher missing rates yield poorer performance of both methods. Number of attributes and items have an impact on performance of both methods as well. Results of a real data example are also discussed in the study.
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Details
- Title
- Investigation of Missing Responses in Q-Matrix Validation
- Creators
- Shenghai Dai - Washington State University, Pullman, USADubravka Svetina - Indiana University, Bloomington, USACong Chen - University of Illinois at Urbana–Champaign, USA
- Publication Details
- Applied psychological measurement, Vol.42(8), pp.660-676
- Academic Unit
- UNKNOWN
- Identifiers
- 99900548309201842
- Language
- English
- Resource Type
- Journal article