Exam 19: Factor Analysis

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The factor matrix is also called the factor pattern matrix.

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Selecting surrogate variables works well if one factor loading for a variable is clearly higher than all other factor loadings.

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

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If the variables are standardized,the factor model may be presented as ________.

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For the factor analysis to be appropriate,the variables must be correlated.

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Which of the following statements is not true about factor rotation?

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When selecting variables to serve as surrogate variables,you should look for the variable with the highest loading on a factor.

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

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The various methods of factor analysis are differentiated by the approach used to derive the weights or factor score coefficients.

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The factor scores for the ith factor may be estimated as follows: ________.

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Factor analysis can be used in which of the following circumstances?

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Communality is ________.

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The unrotated factor matrix seldom results in factors that can be interpreted because the factors are correlated with many variables.

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A ________ is a lower triangle matrix showing the simple correlations,r,between all possible pairs of variables included in the analysis.

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Factors can be estimated so that their factor scores are not correlated and the first factor accounts for the highest variance in the data,the second factor the second highest and so on.

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What are the steps involved in conducting factor analysis (Figure 19.3 in the text)?

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

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