An Examination of Statistical Power in Multigroup Dynamic Structural Equation Models
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| Title: | An Examination of Statistical Power in Multigroup Dynamic Structural Equation Models |
|---|---|
| Language: | English |
| Authors: | Prindle, John J., McArdle, John J. |
| Source: | Structural Equation Modeling: A Multidisciplinary Journal. 2012 19(3):351-371. |
| Availability: | Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Physical Description: | |
| Page Count: | 21 |
| Publication Date: | 2012 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Statistical Analysis, Structural Equation Models, Goodness of Fit, Monte Carlo Methods, Sample Size, Differences |
| DOI: | 10.1080/10705511.2012.687661 |
| ISSN: | 1070-5511 |
| Abstract: | This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of approximation (RMSEA) power approximation procedures were used to compare the effects of parameter manipulations and how researchers should interpret findings. The chi-square power of perfect fit calls for at least 270 individuals to detect moderate differences, whereas the RMSEA procedure of close fit seems to require as many as 1,450 participants. It is shown that parameters that provide input into the change score that the transfer leads to affect power versus indirect pathways. A discussion of differences in approximation values and future research directions follows. (Contains 3 tables and 4 figures.) |
| Abstractor: | As Provided |
| Number of References: | 49 |
| Entry Date: | 2012 |
| Accession Number: | EJ978545 |
| Database: | ERIC |
| Abstract: | This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of approximation (RMSEA) power approximation procedures were used to compare the effects of parameter manipulations and how researchers should interpret findings. The chi-square power of perfect fit calls for at least 270 individuals to detect moderate differences, whereas the RMSEA procedure of close fit seems to require as many as 1,450 participants. It is shown that parameters that provide input into the change score that the transfer leads to affect power versus indirect pathways. A discussion of differences in approximation values and future research directions follows. (Contains 3 tables and 4 figures.) |
|---|---|
| ISSN: | 1070-5511 |
| DOI: | 10.1080/10705511.2012.687661 |
