LA MODLISATION DU COMPORTEMENT DU CONSOMMATEUR

1
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
• H0There is no association between educational
  level and willingness to recommend National to a
  friend (the two variables are independent of each
  other).
• HaThere is some association between educational
  level and willingness to recommend National to a
  friend (the two variables are not independent of
  each other).
• Lets do it.

9
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
16
Table 15.2 Unit Sales Data Under Three Pricing
Treatments
17
ANOVA Grocery Store Hypothesis

• Grocery Store Example
• Ho ?1 ?2 ?3
• Ha At least one ? is different from one or
  more of
• the others
• Hypotheses for K Treatment groups or samples
• Ho ?1 ?2 ..?k
• Ha 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 (Contd)
20
ANOVA

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