Factor Analysis and Assessment EFA and CFA

Factor Analysis and Assessment

In testing, factor analysis is a mathematical strategy to analyze groups of items within a large test to see how well they relate to each other. The goal will be to reduce the large number of items to a set of factors that appear to measure different but related constructs; hence, factor analysis is a method of data reduction. (Sutton, 2020)

A large test of various abilities may be analyzed for ways to group different abilities. Short tests of vocabulary, verbal analogies, and synonyms might form a factor that a researcher could label as "Verbal Abilities."

A factor is a group of variables that are highly correlated with each other and, although different, they appear to have something in common. Researchers choose names for groups of variables based on the content of the variables in the factor. In large research projects, each participant may have scores on a large number of variables. Factor analysis can be used to identify patterns among the variables. Thus, it may be possible to reduce 30 variables to 5 or 6 groups of variables (that is factors).

A research database may contain several variables considered relevant to understanding the risk of child sexual abuse. Such variables may include prior abuse by a person in a close relationship to the child, age of a child, family problems, child problems, family structure, parenting difficulties, sex of the child, and so forth. Theoretically, researchers could look for patterns that may suggest ways to identify key risk factors. (Sutton, 2020)

Exploratory Factor Analysis (EFA)

In the early phases of creating a test or questionnaire, researchers use EFA to explore or discover the structure of the measure. That is, they are looking for the number of factors that best fit the set of data.

Confirmatory Factor Analysis (CFA)

After the data have been explored and the number of factors that best fit the data have been determined, researchers perform a CFA on a new sample. The purpose of CFA is to confirm or reject the factor structure previously thought to be the best fit for the data.

Link to an Index of Statistical Concepts in Psychology, Counseling, and Education

Reference

Sutton, G. W. (2020). Applied statistics: Concepts for counselors, second edition. Springfield, MO: Sunflower.

AMAZON Paperback ISBN-10: 168821772X, ISBN-13: 978-168217720

More information: Book website: counselorstatistics

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Personal Self-Concept Questionnaire (PSQ)

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