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Confirmatory Factor Analysis
Abstract
Measures that are reliable, valid, and can be used across diverse populations are vital to social work research, but the development of new measures is an expensive and time-consuming process. An array of existing measures can provide a cost-effective alternative, but in order to take this expedient step with confidence, researchers must ensure that the existing measure is appropriate for the new study. Confirmatory factor analysis (CFA) is one of the ways to do so. CFA has four primary functions—psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance. This book provides an overview of the method, step-by-step guides to creating a CFA model and assessing its fit, and explanations of the requirements for using CFA, as well the book underscores the issues that are necessary to consider when using multiple groups or equivalent and multilevel models. Real-world examples, screenshots from the Amos software program that can be used to conduct CFA, and reading suggestions for each chapter form part of the book.
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From What Needs Attention? Prioritizing Drivers of Developers' Trust and Adoption of Generative AI (2025) · cites this paper as [59]
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Cited by
Clarke, A., Friede, T., Putz, R., Ashdown, J., Martin, S., Blake, A., Adi, Y., Parkinson, J., Flynn, P., Platt, S., et al. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-487 · Cited by 493
Teachers' AI-TPACK: exploring the relationship between knowledge elements
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024) · Sustainability · doi:10.3390/su16030978 · Cited by 330
Psychological well‐being and the human conserved transcriptional response to adversity
Fredrickson, B. L., Grewen, K. M., Algoe, S. B., Firestine, A. M., Arevalo, J. M. G., Ma, J., & Cole, S. W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121839 · Cited by 205
Annear, M. J., Toye, C., Elliott, K.-E. J., McInerney, F., Eccleston, C., & Robinson, A. (2017) · BMC Geriatrics · doi:10.1186/s12877-017-0552-y · Cited by 212
Artificial Intelligence, AI literacy, Digital literacy, AI literacy scale
ÇELEBİ, C., YILMAZ, F., DEMİR, U., & KARAKUŞ, F. (2023) · Öğretim Teknolojisi ve Hayat Boyu Öğrenme Dergisi - Instructional Technology and Lifelong Learning · doi:10.52911/itall.1401740 · Cited by 77
Metadata
- Authors
- Harrington, D.
- Venue
- Oxford University Press eBooks
- Publisher
- Oxford University Press
- Published
- 2008-10-10
- OpenAlex
- W4298169675
- Cited by
- 1,105
- References
- 0
- FWCI
- 2.97
- Type
- book
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
