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Exam 10: Discriminant Analysis

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Question 1

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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.

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B) False

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Question 2

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Checking for univariate normality is a good starting point for assessing possible violations of homoscedasticity.

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B) False

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Misclassification is best explained through the statistical analysis rather than being assessed and explicated by the knowledge and intuition of the researcher.

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.

In sequential, or hierarchical, discriminant analysis, the predictors are entered into the analysis in an order specified by the researcher.

The hit rate refers to the number of correct classifications.

Assessing the accuracy of the hit rates cannot be accomplished through the use of the jackknife procedure.

The main analysis obtained from a discriminant analysis is the summary of the discriminant functions.

Discriminant analysis is often seen as the reverse of MANOVA.

The logic behind discriminant analysis involves finding:

Standardized coefficients are the basis for the calculation of discriminant scores in discriminant analysis.

A high value for this correlation indicates a function that does not discriminate well between participants.

Discriminant analysis produces a series of preliminary statistics that assist in interpreting the overall analysis results, which include all but which of the following?

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.

A discriminant analysis output typically has four parts. Which one of the four described below is incorrect?

The correlated linear combinations of original variables identified in descriptive discriminant analysis are called discriminant functions.

A discriminant score is analogous to a factor score.

A canonical correlation is a value that is equivalent to the correlation between the discriminant scores and the levels of the independent variables.

Multivariate normality in discriminant analysis implies that the sampling distributions of the linear combinations of dependent variables are normally distributed.

If prediction is the goal of analysis, the researcher might use discriminant function scores in order to describe group differences.