Exploratory Factor Analysis QUESTIONS WITH COMPLETE SOLUTIONS

Give the three functions of exploratory factor analysis. correct answer: 1- Simplification of a complex data set - Organizes similar variables by assessing shared variance in responses - Hypothetical constructs: Not directly measured in study - Meaning of constructs is based of content of variables and relevant theory 2 - Can help clarify what construct(s) variables are measuring - Allows us to judge whether variables measure what we think they do 3 - Summarizing Data with Factors - Factors directly summarize commonalities among different measures - Unobserved "latent variable" - Operationally defined in terms of how measures relate to one another - Interpretation of a factor (i.e., what it means) depends on the variables the factor is derived from - Not all measures contribute equally to a factor - Factor loading refers to the correlation between an observed measure and the latent (unobserved) factor Summary: - the broad aim of EFA is to simplify a data set - Reduces the number of measured variables to a smaller number of unobservable latent variables (i.e., hypothetical constructs) Give a conceptual overview of how exploratory factor analysis works. correct answer: - EFA is interested in relationships between different variables - Are there patterns in the way our variables correlate with one another? - Unique Variance vs. Communality - Uniqueness: Proportion of variance that is not shared with other variables - Communality: Proportion of variance that is shared with other variables - Pattern of communalities determines the factor structure - Reflects sub-groups of variables that correlate highly with each other, but have low correlations with other variables - From Shared Variance to Factors - We assume patterns in our observed variables are driven by unobserved mental processes - But there can be several ways of measuring a single mental process - Reflected in shared variance among several different variables (i.e., factors) - Goal of EFA is to arrive at a parsimonious factor structure - Factors are clearly distinguishable, but are not too numerous! - Two Step Process - Extraction of Factors: capture as much shared variance as possible across all the extracted factors