Group No.6-Roll No.2013034-Rohit Murarka

Cross Tabulation

Cross Tabulation (Chi-Squared)

Introduction

Cross Tabulation is a powerful technique that helps you to describe  the relationships between categorical (nominal or ordinal) variables. With Cross Tabulation, we can produce the following statistics:

• Observed Counts and Percentages
• Expected Counts and Percentages
• Residuals
• Chi-Square
• Relative Risk and Odds Ratio for a 2 x 2 table
• Kappa Measure of agreement for an R x R table

Examples will be used to demonstrate how to produce these statistics using SPSS. The data set used for the demonstration comes with SPSS and it is called GSS93.sav. It has 67 variables and 1500 cases (observations). Open this data file which is located in the SPSS folder. Study the data file in order to understand it before performing the following exercises.

Exercise 1: An R x C Table with Chi-Square Test of Independence

Chi-Square tests the hypothesis that the row and column variables are independent, without indicating strength or direction of the relationship. Like most statistics test, to use the Chi-Square test successfully, certain assumptions must be met. They are:

• No cell should have expected value (count) less than 0, and
• No more than 20% of the cells have expected values (counts) less than 5

In the SPSS file, there is a variable called relig short for religion (Protestant, Catholic, Jewish, None, Other) and another one called region4 (Northeast, Midwest, South, West). In this example, we want to find out if religious preferences vary by region of the country.

To produce the output, from the menu choose:

1. Analyze -> Descriptive Statistics -> Crosstabs….
2. Row(s): Religious Preferences 
3. Column(s): Region [region4]
4. Statistics… select Chi-Square, click Continue then OK

In the SPSS output, Pearson chi-square, likelihood-ratio chi-square, and linear-by-linear association chi-square are displayed. Fisher's exact test and Yates' corrected chi-square are computed for 2x2 tables.