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Table-1: Factor loadings for Exploratory Factor Analysis with varimax rotation Results of the PANAS (N=145).
In the next step, factor analysis was applied to a dataset of 18 variables.
Some of the topics discussed in this chapter include evaluation of the correlation matrix, sources of variance in factor analysis models, determination of the factor extraction method, principal component analysis, common factor analysis and criteria for selecting the number of factors to retain.
The relationship between several classical subspace dimensionality reduction algorithms and factor analysis framework is discussed in this section.
For exploratory factor analysis of survey data, the method of principal component analysis is used along with Varimax rotation method to reduce the variables to a minimum number of factors.
Finally, it is appropriate to note that the researchers did not make any decision on the dimensionality of MBI-HSS through the exploratory factor analysis, but rather used the confirmatory factor analysis to decide on the factorial structure of the MBI.
As the data are not normal we can apply Factor Analysis provided there is inter- correlation between the variables.
Hence, the aim of the present paper was to determine the relationship of lactation milk yield (LMY) with somatic cell count (SCC), and udder traits using various statistical analysis techniques, such as multiple linear regression, stepwise regression, jointly use of factor analysis in multiple regression, and regression tree, comprehensively.
The appropriateness of factor analysis was determined with Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity (Sharma, 1996).
2007 and 2009), includes a total of 97 items in its original version, grouped into three dimensions and seven factors through an exploratory factor analysis.