Discriminant Function Analysis (Statistical Associates Blue Book Series 27)
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Discriminant Function Analysis (Statistical Associates Blue Book Series 27)
DISCRIMINANT FUNCTION ANALYSIS
Discriminant function analysis, also known as discriminant analysis or simply DA, is used to classify cases into the values of a categorical dependent, usually a dichotomy. If discriminant function analysis is effective for a set of data, the classification table of correct and incorrect estimates will yield a high percentage correct. Discriminant function analysis is found in SPSS under Analyze>Classify>Discriminant. If the specified grouping variable has two categories, the procedure is considered "discriminant analysis" (DA). If there are more than two categories the procedure is considered "multiple discriminant analysis" (MDA).
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Below is the unformatted table of contents.
Table of Contents
Overview6 Key Terms and Concepts7 Variables7 Discriminant functions7 Pairwise group comparisons8 Output statistics8 Examples9 SPSS user interface9 The "Statistics" button10 The "Classify" button10 The "Save" button13 The "Bootstrap" button13 The "Method" button14 SPSS Statistical output for two-group DA16 The "Analysis Case Processing Summary" table16 The "Group Statistics" table16 The "Tests of Equality of Group Means" table16 The "Pooled Within-Group Matrices" and "Covariance Matrices" tables.18 The "Box's Test of Equality of Covariance Matrices" tables18 The "Eigenvalues" table19 The "Wilks' Lambda" table21 The "Standardized Canonical Discriminant Function Coefficients" table21 The "Structure Matrix" table23 The "Canonical Discriminant Functions Coefficients" table23 The "Functions at Group Centroids" table24 The "Classification Processing Summary" table24 The "Prior Probabilities for Groups" table25 The "Classification Function Coefficients" table25 The "Casewise Statistics" table26 Separate-groups graphs of canonical discriminant functions27 The "Classification Results" table27 SPSS Statistical output for three-group MDA28 Overview and example28 MDA and DA similarities28 The "Eigenvalues" table29 The "Wilks' Lambda" table29 The "Structure Matrix" table30 The "Territorial Map"31 Combined-groups plot34 Separate-groups plots34 SPSS Statistical output for stepwise discriminant analysis35 Overview35 Example35 Stepwise discriminant analysis in SPSS36 Assumptions41 Proper specification41 True categorical dependent variables41 Independence41 No lopsided splits41 Adequate sample size41 Interval data42 Variance42 Random error42 Homogeneity of variances (homoscedasticity)42 Homogeneity of covariances/correlations42 Absence of perfect multicollinearity43 Low multicollinearity of the independents43 Linearity43 Additivity43 Multivariate normality43 Frequently Asked Questions44 Isn't discriminant analysis the same as cluster analysis?44 When does the discriminant function have no constant term?44 How important is it that the assumptions of homogeneity of variances and of multivariate normal distribution be met?44 In DA, how can you assess the relative importance of the discriminating variables?44 Dummy variables45 In DA, how can you assess the importance of a set of discriminating variables over and above a set of control variables? (What is sequential discriminant analysis?)45 What is the maximum likelihood estimation method in discriminant analysis (logistic discriminate function analysis)?45 What are Fisher's linear discriminant functions?46 I have heard DA is related to MANCOVA. How so?46 How does MDA work?46 How can I tell if MDA worked?46 For any given MDA example, how many discriminant functions will there be, and how can I tell if each is significant?47 What are Mahalonobis distances?47 How are the multiple discriminant scores on a single case interpreted in MDA?47 And 5 more pages of topics on MDA. Coverage: SPSS Pagecount: 52