Classification Accuracy of Multidimensional Tests
Quantifying the Impact of Noninvariance
Bibliographic Data
| ID | 21641932 |
|---|---|
| Authors | Mark H C Lai (0000-0002-9196-7406, University of Southern California, corresponding author), Yichi Zhang (0000-0002-3697-833X, University of Southern California) |
| Year | 2022 |
| Volume | 29 |
| Issue | 4 |
| Pages | 620-629 |
| Publication date | 2022-07-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2021.1977936 |
| OpenAlex | W4206830778 |
| Language | EN |
| Citations received | 1 |
| References cited | 35 |
There has been tremendous growth in research on measurement invariance over the past two decades. However, given that psychological tests are commonly used for making classification decisions such as personnel selections or diagnoses, surprisingly, there has been little research on how noninvariance impacts classification accuracy. Millsap and Kwok previously proposed a selection accuracy framework for that purpose, which has been recently extended to categorical data. Their framework, however, only deals with classification using a unidimensional test. In contrast, classification in practice usually involves multidimensional tests (e.g., personality) or multiple tests, with different weights assigned to each dimension. In the current paper, we extend Millsap and Kwok's framework for examining the impact of noninvariance to a multidimensional test on classification. We also provide an R script for the proposed method and illustrate it with a personnel selection example using data from a published report featuring a five-factor personality inventory
Categorical variable · Data mining · Econometrics · Machine learning · Advanced Statistical Methods and Models · Computer Science · Mathematics · Psychology · Psychometric Methodologies and Testing · Artificial Intelligence
Statistical Approaches to Measurement Invariance
Item bias and item response theory
Measurement invariance
A practical and theoretical guide to measurement invariance in aging research
Personality and job performance
Evaluating the Impact of Partial Factorial Invariance on Selection in Two Populations.
Toward a Further Understanding of and Improvement in Measurement Invariance Methods and Procedures
The Mini-Ipip Scales
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance
Measurement Invariance, Factor Analysis and Factorial Invariance
The Big Five Personality Dimensions and Job Performance
A Scaled Difference Chi-Square Test Statistic for Moment Structure Analysis
Multiple factor analysis
Measurement invariance conventions and reporting
Comparative fit indexes in structural models
The validity and utility of selection methods in personnel psychology
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |