Factor Analysis
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**Introduction**
*"Factor Analysis is a method for modeling observed variables, and their covariance structure, in terms of a smaller number of underlying unobservable (latent) “factors.” The factors typically are viewed as broad concepts or ideas that may describe an observed phenomenon."* ([Linked from Penn State on 6/27/2018][1])
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**Methodology & Applications**
- Process of data analysis with Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) (SAS):
- [Exploratory or Confirmatory Factor Analysis? (Suhr, 2006)][2]
- The *Psych* Package in R Studio
- [An Overivew of the *Psych* Package (Revelle, 2017)][3]
- Dealing with the common situation of analysing ordinal data as derived from Likert-type scales.
- [Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR. (Baglin, 2014)][4]
- Confirmatory factor analysis (CFA) for examining relations among ordinal variables (e.g., Likert-type items).
- [An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data. (Flora & Curran, 2004)][5]
- Ordinal Explortatory Factor Analysis
- [Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR. (Baglin, 2014)][6]
[1]: https://onlinecourses.science.psu.edu/stat505/node/74/
[2]: http://www2.sas.com/proceedings/sugi31/200-31.pdf
[3]: http://personality-project.org/r/overview.pdf
[4]: https://researchbank.rmit.edu.au/view/rmit:24699
[5]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153362/
[6]: https://pareonline.net/getvn.asp?v=19&n=5