By Donna Harrington
Measures which are trustworthy, legitimate and will be used throughout various populations are very important to social paintings study, however the improvement of recent measures is a dear and time-consuming strategy. An array of current measures promises a cheap substitute, yet on the way to take this expedient step with self belief, researchers needs to make sure that the prevailing degree is acceptable for the recent learn. Confirmatory manufacturing unit research (CFA) is a technique to take action, and during this essentially written pocket advisor Donna Harrington offers social paintings researchers with a vital roadmap to the highlights of CFA's powers and the way to harness them. CFA has 4 basic functions-- psychometric evaluate of measures, build validation, trying out process results, and trying out size invariance-- all of which Harrington makes awfully available. She comprises an easy-to-follow evaluation of the strategy, step by step publications to making a CFA version and assessing its healthy, and transparent causes of the necessities for utilizing CFA, in addition to underscoring the problems which are essential to ponder in substitute events, reminiscent of whilst a number of teams are concerned. Real-world examples, screenshots from the Amos software that may be used to behavior CFA, and examining feedback for every bankruptcy make the fabric obtainable for even the greenest beginner. This pocket consultant is best for readers who plan to behavior CFA analyses and want a short, non-technical creation to the subject to get them began sooner than entering into the extra distinctive and technical literature, in addition to readers who don't plan to behavior CFA analyses, yet are looking to be an expert shoppers of study literature that makes use of CFA.
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Additional info for Confirmatory Factor Analysis (Pocket Guides to Social Work Research Methods)
Mean Substitution One approach to handling missing data is to substitute the variable mean for all missing values on that variable. This used to be a very common approach, and it is still an option for handling missing data in many procedures, such as factor analysis, in SPSS and other software packages. However, this is not a good approach, and it should be avoided (Shafer & Graham, 2002). Imputation of Missing Data Another method for handling missing data is to impute missing values before the data are used for the CFA analysis.
However, as a measure is used over time, CFA can be used to replicate EFA or other analyses that have been conducted on 21 22 Conﬁrmatory Factor Analysis the measure. In the Professional Opinion Scale (POS) example discussed in Chapter 1, Abbott’s (2003) initial CFA was based both on underlying theory and an earlier EFA, whereas the Greeno et al. (2007) CFA was based on Abbott’s (2003) earlier CFA work. Conﬁrmatory factor analysis may not be an appropriate analysis to use if there is no strong underlying foundation on which to base the model, and more preliminary work, such as EFA or theory development, may be needed.
Air Force Family Advocacy Program (FAP) workers (Bean, Harrington, & Pintello, 1998; Harrington, Bean, Pintello, & Mathews, 2001). The sample includes 139 FAP workers and the response rate for the survey was 74%. Before continuing, it is important to note that this sample size is considered medium (Kline, 2005) for this analysis (although one can ﬁnd published CFA articles with similar and even smaller size samples). Therefore, it is offered only as an example data set that readers can play with, not one from which conclusions should be drawn.