Using Parallel Splits with Self-Report and Other Measures to Enhance Precision in Generalizability Theory Analyses
Bibliographic Data
| ID | 21352295 |
|---|---|
| Authors | Walter P Vispoel (0000-0002-9415-251X, University of Iowa, corresponding author), Guanlan Xu (0000-0001-6749-7697, University of Iowa), Wei Schneider (0000-0002-6936-9821, University of Iowa), Wei S Schneider (University of Iowa) |
| Year | 2022 |
| Volume | 104 |
| Issue | 3 |
| Pages | 303-319 |
| Publication date | 2022-05-04 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Personality Assessment (JOURNAL) |
| Journal identifiers | ISSN: 0022-3891 • E-ISSN: 1532-7752 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00223891.2021.1938589 |
| PMID | 34323619 |
| OpenAlex | W3184574581 |
| Language | EN |
| Citations received | 5 |
| References cited | 31 |
Although generalizability theory (G-theory) provides indices of reliability that take multiple sources of measurement error into account, those indices are typically conservative in nature because they reflect random rather than classical parallelism. One way to address these shortcomings is to use parallel splits rather than items as the unit of analysis in G-theory designs. In this article, we provide the most extensive treatment to date in how to effectively integrate parallel splits into an extended set of G-theory designs using data from the newly developed version of the Big Five Inventory (BFI-2; Soto & John). Results revealed that properly designed splits approximated classical parallelism while improving overall score consistency and reducing key components of measurement error. Variance components within appropriately chosen G-theory designs also provided effective means to evaluate the quality of splits and determine the best ways to improve score consistency and reduce specific sources of measurement error. To help readers in applying these techniques, we provide a comprehensive instructional supplement with code in R for creating parallel splits, analyzing all illustrated designs, and modifying those designs for other objectively or subjectively scored measures
Algorithm · Classical test theory · Consistency (knowledge bases) · Generalizability theory · Item response theory · Parallel computing · Parallelism (grammar) · Programming language · Psychometrics · Reliability (semiconductor) · Reliability engineering · Set (abstract data type) · Statistics · Variance (accounting) · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics · Mental Health Research Topics · Psychometric Methodologies and Testing
Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks
Forming intervals of predicted total scores for cut-off scores evaluation
Analyzing and Comparing Univariate, Multivariate, and Bifactor Generalizability Theory Designs for Hierarchically Structured Personality Traits
The Hexaco Adjective Scale
Estimating Item Wording Effects in Self-Report Measures with Generalizability Theory-Based SEMs
Generalizability Theory
Mini-Markers
Why the items versus parcels controversy needn’t be one.
Why item parcels are (almost) never appropriate
Short forms of the Texas Social Behavior Inventory (TSBI), an objective measure of self-esteem
Null
Measurement and Control of Response Bias
Expanding G-Theory Models to Incorporate Congeneric Relationships
Congeneric and (Essentially) Tau-Equivalent Estimates of Score Reliability
My Current Thoughts on Coefficient Alpha and Successor Procedures
To Parcel or Not to Parcel
Using Generalizability Theory to Disattenuate Correlation Coefficients for Multiple Sources of Measurement Error
Coefficient alpha and the internal structure of tests
Between facets and domains
The next Big Five Inventory (BFI-2)
| Citations per year | 2.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |