Replication Analysis in Exploratory Factor Analysis: What It Is and Why It Makes Your Analysis Better
Saved in:
| Title: | Replication Analysis in Exploratory Factor Analysis: What It Is and Why It Makes Your Analysis Better |
|---|---|
| Language: | English |
| Authors: | Osborne, Jason W., Fitzpatrick, David C. |
| Source: | Practical Assessment, Research & Evaluation. Nov 2012 17(15). |
| Availability: | Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2012 |
| Document Type: | Journal Articles Reports - Evaluative Tests/Questionnaires |
| Descriptors: | Factor Analysis, Replication (Evaluation), Reliability, Factor Structure, Questionnaires |
| ISSN: | 1531-7714 |
| Abstract: | Exploratory Factor Analysis (EFA) is a powerful and commonly-used tool for investigating the underlying variable structure of a psychometric instrument. However, there is much controversy in the social sciences with regard to the techniques used in EFA (Ford, MacCallum, & Tait, 1986; Henson & Roberts, 2006) and the reliability of the outcome. Simulations by Costello and Osborne (2005), for example, demonstrate how poorly some EFA analyses replicate, even with clear underlying factor structures and large samples. Thus, we argue that researchers should routinely examine the stability or volatility of their EFA solutions to gain more insight into the robustness of their solutions and insight into how to improve their instruments while still at the exploratory stage of development. (Contains 4 tables and 3 footnotes.) |
| Abstractor: | As Provided |
| Entry Date: | 2013 |
| Accession Number: | EJ990689 |
| Database: | ERIC |
| Abstract: | Exploratory Factor Analysis (EFA) is a powerful and commonly-used tool for investigating the underlying variable structure of a psychometric instrument. However, there is much controversy in the social sciences with regard to the techniques used in EFA (Ford, MacCallum, & Tait, 1986; Henson & Roberts, 2006) and the reliability of the outcome. Simulations by Costello and Osborne (2005), for example, demonstrate how poorly some EFA analyses replicate, even with clear underlying factor structures and large samples. Thus, we argue that researchers should routinely examine the stability or volatility of their EFA solutions to gain more insight into the robustness of their solutions and insight into how to improve their instruments while still at the exploratory stage of development. (Contains 4 tables and 3 footnotes.) |
|---|---|
| ISSN: | 1531-7714 |
