Deck 10: Discriminant Analysis
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Deck 10: Discriminant Analysis
1
Discriminant analysis seeks to identify a combination of IVs, measured at the nominal level, which best predicts membership in a particular group, as measured by an interval DV.
False
2
Descriptive discriminant analysis refers to the process of classifying participants into groups based on a combination of measures.
False
3
The correlated linear combinations of original variables identified in descriptive discriminant analysis are called discriminant functions.
False
4
If the discriminant analysis is being used for purposes of prediction or classification, the goal of the interpretation of discriminant functions is to determine dimensions that serve as the basis for reliably classifying participants into groups.
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5
Discriminant analysis is often seen as the reverse of MANOVA.
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6
In discriminant analysis, the IVs serve as the grouping variables and the DVs are the predictors.
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7
If the goal of analysis is prediction, the researcher would determine the number of dimensions that maximize the differences among the groups in question.
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8
If prediction is the goal of analysis, the researcher might use discriminant function scores in order to describe group differences.
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9
The main analysis obtained from a discriminant analysis is the summary of the discriminant functions.
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10
A discriminant score is analogous to a factor score.
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11
A canonical correlation is a value that is equivalent to the correlation between the discriminant scores and the levels of the independent variables.
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12
A high value for this correlation indicates a function that does not discriminate well between participants.
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13
Standardized coefficients are the basis for the calculation of discriminant scores in discriminant analysis.
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14
Standardized coefficients are used to assess the relative contributions of individual variables to the discriminant function(s).
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15
The hit rate refers to the number of correct classifications.
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16
Assessing the accuracy of the hit rates cannot be accomplished through the use of the jackknife procedure.
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17
An important consideration when assessing a classification procedure is the cost of classification.
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18
Misclassification is best explained through the statistical analysis rather than being assessed and explicated by the knowledge and intuition of the researcher.
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19
In standard, or direct, discriminant analysis, each predictor is entered into the equation simultaneously and is assigned only its unique association with the groups, as defined by the DV.
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20
In sequential, or hierarchical, discriminant analysis, the predictors are entered into the analysis in an order specified by the researcher.
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21
The assumptions for discriminant analysis are basically the same as those for MANOVA, and there is no need for adjustments when classification is the goal of analysis.
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22
Discriminant analysis is not sensitive to sample size.
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23
Multivariate normality in discriminant analysis implies that the sampling distributions of the linear combinations of dependent variables are normally distributed.
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24
There exists no test of the normality of all linear combinations of sampling distributions of means of predictor variables in discriminant analysis.
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25
Checking for univariate normality is a good starting point for assessing possible violations of homoscedasticity.
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26
Discriminant functions are:
A) Linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
B) Uncorrelated linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
C) Correlated linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
D) Uncorrelated linear combinations of the original dependent variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
A) Linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
B) Uncorrelated linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
C) Correlated linear combinations of the original (predictor) variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
D) Uncorrelated linear combinations of the original dependent variables that maximize the between-to-within association, as measured by the sum-of-squares and cross-products (SSCP) matrices.
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27
The logic behind discriminant analysis involves finding:
A) The function with the smallest eigenvalue.
B) The function with the largest eigenvalue.
C) Discriminant functions that are related.
D) None of the above.
A) The function with the smallest eigenvalue.
B) The function with the largest eigenvalue.
C) Discriminant functions that are related.
D) None of the above.
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28
A discriminant analysis output typically has four parts. Which one of the four described below is incorrect?
A) Preliminary statistics that describe group membership and covariances.
B) Significance tests and strength of relationship statistics for each discriminant function.
C) Discriminant function coefficients.
D) Group classification.
A) Preliminary statistics that describe group membership and covariances.
B) Significance tests and strength of relationship statistics for each discriminant function.
C) Discriminant function coefficients.
D) Group classification.
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29
Discriminant analysis produces a series of preliminary statistics that assist in interpreting the overall analysis results, which include all but which of the following?
A) Table of means and standard deviations for each DV by group.
B) ANOVA analysis testing for group differences among the IVs.
C) Covariance matrices.
D) Box's test.
A) Table of means and standard deviations for each DV by group.
B) ANOVA analysis testing for group differences among the IVs.
C) Covariance matrices.
D) Box's test.
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30
The final step in the interpretation of discriminant analysis results is to:
A) Determine the extent to which group differences support the functions generated by reviewing group variance for each function as presented in the table of Functions at Group centroids.
B) Determine the extent to which group differences support the functions generated by reviewing group means for each function as presented in the table of Functions at Group centroids.
C) Determine the extent to which groups support the functions generated by reviewing group means for each function as presented in the table of Functions at Group centroids.
D) Determine the extent to which groups support the functions generated by reviewing group variance for each function as presented in the table of Functions at Group centroids.
A) Determine the extent to which group differences support the functions generated by reviewing group variance for each function as presented in the table of Functions at Group centroids.
B) Determine the extent to which group differences support the functions generated by reviewing group means for each function as presented in the table of Functions at Group centroids.
C) Determine the extent to which groups support the functions generated by reviewing group means for each function as presented in the table of Functions at Group centroids.
D) Determine the extent to which groups support the functions generated by reviewing group variance for each function as presented in the table of Functions at Group centroids.
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