A Comparison of Item Parameter Standard Error Estimation Procedures for Unidimensional and Multidimensional Item Response Theory Modeling
Bibliographic Data
| ID | 20282000 |
|---|---|
| Authors | Insu Paek (0000-0002-2552-9475, Florida State University, Tallahassee, FL, USA), Li Cai (0000-0002-6098-1168, University of California, Los Angeles, CA, USA) |
| Year | 2014 |
| Volume | 74 |
| Issue | 1 |
| Pages | 58-76 |
| Publication date | 2014-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164413500277 |
| OpenAlex | W2092003689 |
| Language | EN |
| Citations received | 7 |
| References cited | 18 |
The present study was motivated by the recognition that standard errors ( SEs) of item response theory (IRT) model parameters are often of immediate interest to practitioners and that there is currently a lack of comparative research on different SE (or error variance–covariance matrix) estimation procedures. The present study investigated item parameter SEs based on three error variance–covariance matrix estimation procedures for unidimensional and multidimensional IRT models: Fisher information, empirical cross-product, and supplemented expectation maximization. This study centers on the direct comparisons of SEs from different procedures and complements a recent study by Tian, Cai, Thissen, and Xin by providing insights and suggestions on the nature of the differences and similarities as well as on practical matters such as the computational cost. The simulation results show that all three procedures produced similar results with respect to bias in the SE estimates for most conditions. When the number of items is large and sample size is small, empirical cross-product, which was the most computationally efficient procedure, appeared to be affected most, producing slight upward bias
Analysis of covariance · Covariance · Covariance matrix · Econometrics · Fisher information · Item response theory · Psychometrics · Sample size determination · Standard error · Statistics · Variance (accounting) · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Multi-Criteria Decision Making · Psychometric Methodologies and Testing
On the reliability of point estimation of model parameters
On Lagrange Multiplier Tests in Multidimensional Item Response Theory
It Might Not Make a Big DIF
Investigating Confidence Intervals of Item Parameters When Some Item Parameters Take Priors in the 2PL and 3PL Models
A Note on the Conversion of Item Parameters Standard Errors
Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model
Profile-likelihood Confidence Intervals in Item Response Theory Models
Statistical Theories of Mental Test Scores
Marginal Maximum Likelihood Estimation of Item Parameters
Full-Information Item Bi-Factor Analysis
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Numerical Differentiation Methods for Computing Error Covariance Matrices in Item Response Theory Modeling
Characterizing Sources of Uncertainty in Item Response Theory Scale Scores
The Langer-Improved Wald Test for DIF Testing With Multiple Groups
Covariance Structure Model Fit Testing Under Missing Data
| Unique citing works | 7 |
|---|---|
| Citations per year | 0,7 |
| Citation span | 2016 - 2023 (8) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 6 |