1. Data Analysis Express: Data Analysis Express: Practical Application using SPSS

2. Data of Interest National Insurance Company – 1000 questionnaires sent – 285 respondents Questionnaire Presentation – Copy given in class

3. SPSS Data Set 2 Views : Variable and Data. Raw Variable (labels and values) Transformed Variable (compute and recode)

4. Preliminary Data Analysis: Basic Descriptive Statistics Preliminary data analysis examines the central tendency and the dispersion of the data on each variable in the data set Measurement level dictates what to do Feeling for the data What can we do: limitations on next slide? Run descriptives. (outputs 1)

5. Measures of Central Tendency and Dispersion for Different Types of Variables

6. Crosstabs: Frequencies in specific condition. Most of the time with categorical variables Examples to run

7. Cross-Tabulations- Comparing frequencies: Chi-square Contingency Test Technique used for determining whether there is a statistically significant relationship between two categorical (nominal or ordinal) variables

8. Need to Conduct Chi-square Test to Reach a Conclusion The hypotheses are: – H 0 :There is no association between educational level and willingness to recommend National to a friend (the two variables are independent of each other). – H a :There is some association between educational level and willingness to recommend National to a friend (the two variables are not independent of each other). – Let’s do it….

9. Computed Chi- square value P-value National Insurance Company Study

10. National Insurance Company Study --P-Value Significance The actual significance level (p-value) = 0.019 the chances of getting a chi-square value as high as 10.007 when there is no relationship between education and recommendation are less than 19 in 1000. The apparent relationship between education and recommendation revealed by the sample data is unlikely to have occurred because of chance. We can safely reject null hypothesis.

11. Precautions in Interpreting Cross Tabulation Results Two-way tables cannot show conclusive evidence of a causal relationship Watch out for small cell sizes Increases the risk of drawing erroneous inferences when more than two variables are involved

12. Comparing Means Mainly T-tests and ANOVAs T-test on OQ and gender.

13. Independent T-tests Independent Variable with 2 categories max. Equality of variance (cf output) 88% of chance that the difference of.04 is due to chance (random effect). Cannot reject the null hypothesis.

14. Analysis of Variance ANOVA is appropriate in situations where the independent variable is set at certain specific levels (called treatments in an ANOVA context) and metric measurements of the dependent variable are obtained at each of those levels

15. Example 24 Stores Chosen randomly for the study 8 Stores randomly chosen for each treatment Treatment 1 Store brand sold at the regular price Treatment 2 Store brand sold at 50¢ off the regular price Treatment 3 Store brand sold at 75¢ off the regular price monitor sales of the store brand for a week in each store

16. Table 15.2 Unit Sales Data Under Three Pricing Treatments

17. ANOVA –Grocery Store Hypothesis Grocery Store Example – H o  1 =  2 =  3 – H a At least one  is different from one or more of the others Hypotheses for K Treatment groups or samples – H o  1 =  2 = ………..  k – H a At least one  is different from one or more of the others

18. Exhibit 15.1 SPSS Computer Output for ANOVA Analysis

19. Exhibit 15.1 SPSS Computer Output for ANOVA Analysis (Cont’d) There is less than a.001 probability of obtaining an F- value as high as 137.447

20. ANOVA OQ recommendation and OQ, individual variable OQ and EDUC (Graph)..and post hoc