Confirmatory Factor Analysis (Pocket Guides to Social Work by Donna Harrington
By Donna Harrington
Measures which are trustworthy, legitimate and will be used throughout varied populations are very important to social paintings learn, however the improvement of latest measures is a dear and time-consuming strategy. An array of present measures provides an economical replacement, yet that allows you to take this expedient step with self assurance, researchers needs to make sure that the present degree is suitable for the hot examine. Confirmatory manufacturing facility research (CFA) is a method to take action, and during this in actual fact written pocket consultant 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 fundamental functions-- psychometric evaluate of measures, build validation, trying out strategy results, and checking out dimension invariance-- all of which Harrington makes tremendously available. She contains an easy-to-follow evaluation of the tactic, step by step publications to making a CFA version and assessing its healthy, and transparent motives of the necessities for utilizing CFA, in addition to underscoring the problems which are essential to examine in replacement occasions, similar to while a number of teams are concerned. Real-world examples, screenshots from the Amos software that may be used to behavior CFA, and analyzing feedback for every bankruptcy make the fabric obtainable for even the greenest amateur. This pocket advisor is very best for readers who plan to behavior CFA analyses and want a quick, non-technical creation to the subject to get them begun ahead of entering into the extra special 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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Extra resources for Confirmatory Factor Analysis (Pocket Guides to Social Work Research Methods)
Given the number and variety of these articles now available, it is likely that readers can ﬁnd a similar article in their area of interest. , Tabachnick & Fidell, 2007) provide a combined introduction to PCA and factor analysis, focusing on the similarities between the two analyses. Grimm and Yarnold (1994, 2000) provide nontechnical introductions to PCA, EFA, and CFA as well as SEM and testing the validity of measurement, respectively. 19 20 Conﬁrmatory Factor Analysis Koeske (1994) provides an excellent discussion of construct validity, including recommendations for the consistent use of validity terminology within social work research.
Small sample sizes may result in technical problems when running the analysis (such as non-convergence or improper solutions) and low power (Kline, 2005). , groups of indicators) for categorical items. However, as noted earlier, Brown (2006) recommends against using item parcels or any of the multiple rules of thumb for determining what the sample size should be. Lee and Song (2004) conducted a simulation study comparing ML and Bayesian estimation with small sample sizes. They concluded that ML is not recommended with small sample sizes, even when data are normally distributed; however, the Bayesian approach is recommended with small sample sizes as long as the sample size is two to three times as large as the number of unknown parameters to be estimated.
Some of the estimation methods that you may see in the literature include maximum likelihood (ML), weighted least squares (WLS), generalized least squares (GLS), and unweighted least squares (ULS). 0 and may appear in the literature, both are used with multivariate normal data (Kline, 2005), and if data are multivariate normal, then ML is a better estimation procedure to use, so we will not discuss GLS and ULS. 0. Maximum Likelihood Maximum likelihood (ML) is the most commonly used estimation method.