Exam 19: Factor Analysis

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Factor analysis is somewhat similar to discriminant analysis in that each variable is expressed as a linear combination of underlying factors.

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Which of the following applications is appropriate for using factor analysis?

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D

m represents ________ in the factor model, Xi = Ai1 F1 + Ai2 F2 + Ai3 F3 + ...+ Aim Fm + ViUi,.

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In order to use factor analysis, it is important that the variables be appropriately measured on an ordinal or nominal scale.

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When using eigenvalues to determine the number of factors, only factors with eigenvalues greater than .05 are retained.

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________ is an approach to factor analysis that estimates the factors based only on the common variance.

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The factor matrix is also called the factor pattern matrix.

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If the goal of factor analysis is to reduce the original set of variables to a smaller set of composite variables for use in subsequent multivariate analysis, it is useful to ________.

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Which is not a method of factor analysis?

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Bartlett's test of sphericity is one test of the appropriateness of the factor model.

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When selecting surrogate variables from variables with similarly high loadings, the choice should be based on ________.

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Factor analysis may not be appropriate in all of the following situations except ________.

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Only in the case of principal components analysis is it possible to compute exact factor scores.

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Percentage of variance accounted for, scree plot, and a priori determination are all procedures for determining the number of factors.

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Interpretation is facilitated by identifying the variables that have small loadings on the same factor.

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A factor model that provides a good fit to the data has many large residuals.

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Which statement is not true about principal components analysis (PCA)?

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Rotation does not affect the communalities and the percentage of total variance explained.

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The ________ represents the total variance explained by each factor.

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Factor scores should be computed if the goal of factor analysis is to use the results in subsequent multivariate analysis.

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