Chapter 21 Factor Analysis Flashcards Preview

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Flashcards in Chapter 21 Factor Analysis Deck (16)
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Variability in an item explained by all the identified factors within a solution



Measure of the total variance in the variables accounted for by one factor


Factor analysis (FA)

Method of extracting factors which account for correlations (technically co-variances) between several variables (e.g. items on psychological scales, experimental tests)


Exploratory factor analysis

Use of factor analysis to identify latent (underlying) factors which explain the variance in correlations. Usually performed in a conceptual area where there is as yet no known well-supported factor structure.


Confirmatory factor analysis

Factor analysis performed to support an already identified factor structure. Might be with a larger data set or on a different population from the original.


Factor extraction

Stage in factor analysis when an initial set of factors is developed to explain the correlations between variables


Factor loading

Degree to which an item is associated with a factor in the analysis. A form of partial correlation.


Factor matrix

Table produced by SPSS showing loadings of each factor on each item/variable.


Factor rotation

Adjustment of the factor solution so that factors tie up more closely with the original variables. Can be orthogonal or oblique.


Initial solution

This is obtained in the first of two major steps in a factor analysis. This will provide the information needed for data checking and for deciding on the number of factors to extract.


Oblique factors

Factors which are allowed to correlate with each other.


Orthogonal factors

Factors which are not allowed to correlate with each other; geometrically at right angles to one another.


Pattern matrix

Table provided by SPSS which shows the loadings, after rotation, of each factor on each item.


Principal component's analysis (PCA)

A method of data reduction, which can be used to identify groups of indicators (e.g., items on a scale or experimental tests) that are correlated but are not expected to be caused by an underlying factor. Thus, PCA does not find 'latent' factor structure.


Scree test/plot

Plot of factors against their eigenvalues that can be used to assist with identifying the number of factors to extract


Structure matrix

Table provided by SPSS which shows the correlations of factors with items