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exploratory_factor_analysis_khang_updated.pdf

# exploratory_factor_analysis_khang_updated.pdf - Exploratory...

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1 D.B. Khang _ HSRS _ EFA _ Page 1 Exploratory factor analysis Coverage: The purpose and requirements of EFA Deriving and interpreting the factors Validating the EFA findings Using EFA results for further analysis Practice with SPSS Required readings: Hair et al.: Multivariate data analysis, Ch. 3 Recommended readings: Hardy and Bryman: Handbook of data analysis, Ch. 2 Objectives Upon completing this chapter, you should be able to do the following: Understand the applications and uses of exploratory factor analysis (EFA) techniques in research. Verify the requirements and assumptions of EFA before use. Use SPSS to derive the factors from a given data set using various options and techniques available. Interpret the results and assess the overall fit of the factors obtained. Explain the additional uses of EFA. D.B. Khang _ HSRS _ EFA _ Page 2

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2 Factor Analysis What is a factor? A construct (or dimension) that underlies (or is measured by) some variables that are highly correlated. Statistically, a factor is a linear combination (or variate) of these variables. Exploratory Factor Analysis (EFA): tools to analyze the correlations of a large number of variables to define the underlying structure by identifying factors (i.e. groups of highly correlated variables) assumed to represent dimensions in the data These dimensions can guide in creating new composite measures to reduce the number of variables These dimensions may also correspond to concepts that cannot be adequately described by a single measure Confirmatory Factor Analysis (CFA): We start (conceptually) with a number of factors, and the variables representing these factors We can then test how well our specification of the factors matches the actual data D.B. Khang _ HSRS _ EFA _ Page 3 Objectives of Exploratory Factor Analysis Objectives of EFA Data summarization = derives underlying dimensions that, when interpreted and understood, describe the data in a much smaller number of concepts than the original individual variables. This is particularly useful in conceptualizing new and abstract latent constructs. Data reduction = extends the process of data summarization by deriving an empirical value (factor score) for each dimension (factor) and then substituting this value for the original values. Benefits of using EFA with other techniques: Better understanding of variables, Reduced number of variables, New insights in conceptual foundations and interpretation D.B. Khang _ HSRS _ EFA _ Page 4
3 Exercise 1 Review the interpretations of the 13 variables X 6 through X 18 in HBAT data set and find out if some of them may be grouped as representing similar constructs? Develop the correlation matrix of the variables to check if the variables by groups are indeed strongly correlated?

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• Summer '16
• Bhatti
• EFA, D.B. Khang

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