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Quantifying Parsimony in Structural Equation Modeling
| Content Provider | Scilit |
|---|---|
| Author | Preacher, Kristopher J. |
| Copyright Year | 2006 |
| Description | Fitting propensity (FP) is defined as a model's average ability to fit diverse data patterns, all else being equal. The relevance of FP to model selection is examined in the context of structural equation modeling (SEM). In SEM it is well known that the number of free model parameters influences FP, but other facets of FP are routinely excluded from consideration. It is shown that models possessing the same number of free parameters but different structures may exhibit different FPs. The consequences of this fact are demonstrated using illustrative examples and models culled from published research. The case is made that further attention should be given to quantifying FP in SEM and considering it in model selection. Practical approaches are suggested. |
| Related Links | https://kuscholarworks.ku.edu/bitstream/1808/1484/1/preacher_2006b.pdf |
| Ending Page | 259 |
| Page Count | 33 |
| Starting Page | 227 |
| ISSN | 00273171 |
| e-ISSN | 15327906 |
| DOI | 10.1207/s15327906mbr4103_1 |
| Journal | Multivariate behavioral research |
| Issue Number | 3 |
| Volume Number | 41 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2006-09-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Multivariate Behavioral Research Mathematical Social Sciences Diverse Quantifying Model Selection Fit Equation Modeling Structural Equation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Experimental and Cognitive Psychology Statistics and Probability Arts and Humanities Medicine |
