R Tutorial : Introduction to EFA

Hi, I'm Jennifer Brusso and I'm a psychometrician, which is a fancy way to say I work in the field of measurement. Psychometrics is the study of unobservable variables, psycho meaning of the mind, and metrics meaning related to measurement. Researchers develop measures to capture unobservable variables such as personality or iq, and factor analysis is a valuable tool for use both during and after the development process. In Chapter one, you'll learn how to examine the statistical properties of a measure designed around one construct. In our first lesson, you'll start by running a unidimensional exploratory factor analysis, or EFA, on examinees responses to questions, which we'll refer to as items. Next, you'll look at the results of the EFA to examine two key pieces of output items. Factor loadings quantify their relationship to the underlying factor, which tells you how well each item is performing individuals. Factor scores provide an estimate of the amount of the underlying factor each examinee possesses, which helps assign scores to examinees. You may be familiar with some related ways of analyzing data. Factor analysis can be thought of as midway between classical test theory and structural equation modeling. Whereas classical test theory reports scores as the unweighted sum of item scores and factor analysis assigns item weights according to the correlation matrix, these correlations allow us to infer the presence of a latent variable or variables. Structural equation modeling extends this approach to model the relationships between latent variables. It's important to note that there are two different types of factor analysis, exploratory and confirmatory. Exploratory factor analysis is used during measure development to explore factor structure and determine which items do a good job of measuring the construct. Confirmatory factor analysis is used to validate a measure after development. This course primarily uses the PSYC package which was developed by William Revell. You can load the package using the library function. You'll use the GCBS data set in the first chapter. This data set contains 2,495 responses. 2 the 15 multiple choice questions or items which are designed to test respondents level of belief in conspiracies. Items in the GCBS data set are categorized into five conspiracy facets. For example item 2 the government permits or perpetrates acts of terrorism on its own soil. Disguising its involvement is a government malfeasance item. Item 8 as evidence of alien contact is being concealed from the public is an extraterrestrial cover up item. The 15 items are hypothesized to reflect five lower order factors corresponding to their five types. These five factors share a single, higher order factor Conspiracist Belief Hierarchical factor structures like this require structural equation modeling to estimate, but exploratory and confirmatory factor analysis can estimate either a single factor or five factor structure. In Chapter one, you'll ignore the five lower order factors and use a single factor EFA to estimate the item's relationship to conspiracist belief. This analysis will give you information about how well each item measures a single underlying factor and information about how much of the factor each examinee possesses. You'll learn how to deal with multiple factors in later chapters. The FA function is your ticket to running EFAs in the PsyC package. The object created from this function contains lots of valuable information such as items, factor loadings, individuals, factor scores, and fit statistics. In the first lesson of this chapter, you'll learn how to use the FA function to run a single factor efa, access and interpret its output, and diagram the results. Now that we've covered the basic theory behind factor analysis, let's get.