Week 9 - Explanatory Factor Analysis Flashcards
What are latent variables?
Things that can not be measured directly. Usually the focus of psychology researchers.
What are the three main uses of factor analysis?
- To understand the structure of a set of variables
- To construct a questionaire to measure an underlying variable variable
- To reduce a data set to a more manageable size while retaining as much of the original information as possible
What is factor loading?
The regression coefficient of a variable for the linear model that describes a latent variable or factor in factor analysis.
What are common factors?
A factor that affects all measured variables and, therefore, explains the correlations between those variables.
What are unique factors?
A factor that affects only one of many measured variables and, therefore, cannot explain the correlations between those variables
What is a factor matrix?
General term for the structure matrix in principal component analysis
What are factor scores?
A single score from an individual entity representing their performance on some latent variable
What is the Anderson-Rubin method?
A way of calculating factor scores which produce scores that are uncorrelated and standardized with a mean of 0 and a standard deviation of 1
What is confirmatory factor analysis?
A version of factor analysis in which specific hypotheses about structure and relations between the latent variables that underlie the data are tested
<p>What is common variance?</p>
Variance shared by two or more variables.
What is unique variance?
Variance that is specific to a particular variable (not shared with other variables). We tend to use the term ‘unique variance’ to refer to variance that can be reliably attributed to only one measure otherwise it is called random variance.
What is random variance?
Variance that is unique to a particular variable but not reliably so.
What is communality?
The proportion of a variable’s variance that is common variance.
What is a scree plot?
A graph plotting each factor in a factor analysis (x-axis) against its associated eigenvalue (y-axis). It shows the relative importance of each factor.
What is Kaiser’s criterion?
A method of extraction in factor analysis based on the idea of retaining factors with associated eigenvalues greater than 1.
What is rotation?
A process in factor analysis for improving the interpret-ability of factors. In essence, an attempt is made to transform the factors that emerge from the analysis in such a way as to maximise factor loadings that are already large, and minimise factor loadings that are already small.
What is orthogonal rotation?
A method of rotation in factor analysis that keeps the underlying factors independent (not correlated)
What is oblique rotation?
A method of rotation in factor analysis that allows the underlying factors to be correlated.
What is the factor transformation matrix?
A matrix used in factor analysis. It can be thought of as containing the angles through which factors are rotated in factor rotation.
What is the KMO?
The Kaiser-Meyer-Olkin measure of sampling adequacy. It is calculated to represent the ratio of the squared correlation between variables to the squared partial correlation between variables.
What is the Bartlett’s test?
The test examines whether a variance - covariance matrix is proportional to an identity matrix. It effectively tests whether the diagonal elements of variance-covariance matrix are equal.
What is singularity?
A term used to describe variables that are perfectly correlated.
