1. CORRESPONDENCE ANALYSIS Prepared by Saba Khan Presented to Imtiaz Arif Id:4640

2. What is CorrespondenceAnalysis Correspondence analysis is a that generates graphical representations of the interactions between objects (or categories ) of two categorical variables Correspondence analysis is a related perceptual mapping technique with similar objectives PERCEPTUAL MAPPING:-It is a set of techniques that attempt to identify the perceived relative image of set of objects

3. When you wish to grasp an overall perception of the inter objects associations. It is a descriptive analysis and used according to similar objectives. Used to analyze consumer perception, brand positioning etc. When you have categorical variables. When to use it?3

4. OBJECTIVES OF CA Association among row or column categories Association between both row and column categories

5. REASON The main reason is that it can easily transform table display into graphical display to show the association The table is contingency table because in that we can show the attributes and objects in column and rows In graph we can easily predict which one category is best associated with other category

6. DISADVANTAGES Not suitable for hypothesis testing, because it is a descriptive technique It is a data reduction technique in which there is no other method which tell us the appropriate number of dimensions CA is quite sensitive to the outliers showing low degree of association as compare to factor analysis(doesnt show outliers separately)

7. ASSUMPTIONS1. HOMOGENITY:-In correspondence analysis it is assumed that there is homogeneity between cloumn variable of the analysis. If homogeneity is not present in the analysis, then the result will be misleading.2. DISTRIBUTIONAL ASSUMPTION:- Correspondence analysis is a non-parametric techniques that assumes distributional assumptions

8. ASSUMPTIONSCATEGORY ASSUMPTION:- In correspondence analysis it is assumed that discrete data has many categories Simplest form of non metric data is used. Variation in dimensionality. If continuous data is used, they must be categorized into ranges or transformed into categorical data. Negative values: In correspondence analysis, negative value is not considered.

9. STAGE 4 Deriving CA result:- Terminologies used in correspondence analysis that will help us in our research MASS: Mass is calculated for every single entry in the table as a percentage of total represented by that entry. INERTIA: Inertia measures the variance among the point in the cross table. Inertia is obtained by dividing total chi-square by N (total frequency counts).

10. Capabilities Correspondence Analysis (CA) handles a Categorical Independent variable with a Categorical Dependent variable. CA analyzes the association between two categorical variables by representing categories as points in a 2 or 3 dimensional space graph.11 GMathCorrespondence 1/1/2012

11. Disadvantages and limitations If we want to convert metric data into categorical data by using dummy variables then it will loose its originality and it will not show accurate result

12. We will now go to SPSS for analysis. Retrieve smoking.sav Analyze Data reduction Correspondence ana13