lecture 17 , 18 - notes - Group G

lecture 17 , 18 - notes - Group G

 

Brief outline of the various analysis discussed in class

• 1.       1^st level of analysis
• a.       Frequencies
• b.      Crosstabs
• c.       Overlaps
• 2.       Cluster
• a.       K-means
• b.      Hierarchical
• 3.       MDS – Multi Dimensional Scaling (Perceptual Mapping)
• a.       Overall Similarity
• b.      Attribute Based

• 4.       Factor analysis
• 5.       Discriminant Analysis

The purposes of Discriminant analysis (DA)

Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome. However, multiple linear regression is limited to cases where the dependent variable on the Y axis is an interval variable so that the combination of predictors will, through the regression equation, produce estimated mean population numerical Y values for given values of weighted combinations of X values. But many interesting variables are categorical, such as political party voting intention, migrant/non-migrant status, making a profi t or not, holding a particular credit card, owning, renting or paying a mortgage for a house, employed/unemployed, satisfied versus dissatisfied employees, which customers are likely to buy a product or not buy, what distinguishes Stellar Bean clients from Gloria Beans clients, whether a person is a credit risk or not, etc.

Classification table

Classification phase is the final phase. The classification is simply a table in which the rows are the observed categories of the dependent and the columns are the predicted categories. When prediction is perfect all cases will lie on the diagonal. The percentage of cases on the diagonal is the percentage of correct classifications.

Brief Description and difference of Discriminant Analysis, Factor Analysis And Cluster Analysis

Discriminant analysis helps to identify the independent variables that discriminate a nominally scaled dependent variable of interest. The linear combination of independent variables indicates the discriminating function showing the large difference that exists in the two group means. In other words the independent variables measured on an interval or ratio scale discriminate the groups of interest to study.

Factor analysis helps to reduce a vast number of variables to a meaningful, interpretable, and manageable set of factors. A principle component analyses transform all the variables into a set of composite variables that are not correlated to one another. Suppose we have measured in a questionnaire the four concepts of mental health, job satisfaction, life satisfaction, and job involvement with seven questions tapping each. When we factor analyze these 28 items, we should find four factors with the right variables loading on each factor, confirming that we have measured the concepts correctly.

The cluster analysis is used to classify objects or individuals into mutually exclusive and collectively exhaustive groups with high homogeneity within clusters and low homogeneity between clusters. In other words cluster analysis helps to identify objects that are similar to one another, based on some specified criterion. Cluster analysis will cluster individuals by their preferences for each of the different brands.

Lakshmi Sravanthi

Group G

14086 - HR