1. Data Analysis
AMA Collegiate
Marketing Research Certificate Program

2. Module Objectives Introduce key data analysis procedures
Provide a basic understanding of how to interpret data

3. Data Analysis Metrics

4. What Will We Be Covering?
Frequencies
Mode
Median
Mean
Standard Deviation
Cross-Tabulations
T-Tests
One-way ANOVA
Data Type Plays a Key Role in Data Analysis and Analytics

5. Frequencies

6. Frequencies = Data Counts and Percent
Relevant for all types of data

7. Mode

8. Mode = Most Frequent Response

9. Median

10. Median = Middle Response
Numerical value that identifies split of high vs. low half of sample responses
Notes:
(1) has 32.1% of responses
(2) 45+ has 47% of responses
(3) Thus, 50th percentile falls in the age range (32.1% - 53%)
Median = 35-44

11. Mean

12. Mean = Average Convenience Store Food Must be interval or ratio data1 2 3 4 5
Mean
Not Fresh
0.3%
1.8%
18.2%
38.4%
41.4%
Fresh
4.19
Bad Value
1.6%
21.6%
40.9%
35.6%
Good Value
4.10
Taste Bad
0.4%
1.9%
20.2%
42.5%
35.0%
Taste Good
Low Quality
0.5%
2.9%
26.1%
40.7%
29.8%
High Quality
3.96
Few Take-Home Options
1.4%
5.0%
29.5%
35.9%
28.2%
Many Take-Home Options
3.85
Little Variety
4.0%
29.3%
38.9%
27.5%
Large Variety
3.89
Unhealthy
1.1%
7.4%
33.2%
31.9%
26.3%
Healthy
3.75
Few Choices for Children
6.9%
35.7%
30.6%
25.8%
Many Choices for Children
3.73
Must be interval or ratio data
Also frequencies, mode (3), median (4) for children food choices

13. Standard Deviation

14. Standard Deviation = Variation/Dispersion
Represents how much variation or dispersion there is in the distribution of responses. Less variation = lower standard deviation.
Standard deviation is “standardized” with a mean “0” and standard deviation of “1”

15. Standard Deviation and Margin of Error
Need interval or ratio data
Margin of error goes up as standard deviation goes up
Harder to detect group difference (i.e., male vs. female satisfaction) as standard deviation goes up
+/- 3%
+/- 5%
+/- 7%

16. Cross-Tabulations

17. Cross-Tabulations
Comparing the distribution of responses across two categorical variables
Goal is to see if there is a statistical differences in the distribution of responses across groups
Male vs. females and preferred movie type
Age groups and smart phone ownership
Home owners vs. renters and Cable vs. Dish

18. Cross-Tabulations Considerations
Typically two categorical variables
Gender (M/F) and Movies (Comedy, Drama, Romance)
Although you can use interval scales in cross-tabulations, t-tests and ANOVA are more commonly used because of the ability to calculate means
Identify independent and dependent variable
V = which groups you are comparing (gender, age)
DV = what are you comparing (movies, smart phone)

19. Example: Buying Channel by Year
Need to determine statistical significance

20. SPSS Output Where Buy: Hobbyist vs. Professional Conclusion: Because
p of .000 < .05, can conclude that hobbyists are more likely to buy in store than professionals
(74.6% vs. 68.5%)

21. T-Tests

22. T-Tests Comparison of mean scores across two groups
Grouping variable = Independent variables
Grouping variable must be categorical
Male vs. female, smart phone vs. no smart phone
Dependent variable = must be something for which you can calculate a mean (interval/ratio)
Average satisfaction score, average importance rating, average number of times dining out per week

23. Independent Sample T-test
Two different (independent) groups
Male vs. female
Smart phone buyers vs. non-smart phone buyers
Hobbyists
Professionals
Quality
4.1
4.7
Price
4.6
4.0
Service
4.8
4.9
Speed of Orders
4.2
Conclusions: Professionals put greater importance on quality, service and speed; Hobbyists on price

24. SPSS Output Purchase Likelihood Hobbyist Vs. Professional
Conclusion: Professionals are more likely to buy power tools. No significant difference for wood
Sig (p value) must be < than .05

25. Paired T-test Paired T-test = same group, two different scores
Customer satisfaction 2011 vs (same customers)
Comparison of attributes vs. competitor (see below)
Our Brand
Brand X
Quality
4.9
4.2
Price
4.1
4.6
Service
4.8
Speed of Orders
4.7
4.3

26. SPSS Output Comparing Perceptions of Price vs. Other Attributes
Conclusion: Perceptions of price are lower than hand tool variety, power tool variety, and product quality

27. One-way ANOVA

28. One-Way ANOVA Comparison of means across multiple groups
Purchase likelihood by
Age: by 18-24, and 45+ years old
Bus type: Government, university, manufacturing
Conclusion: Each has a more likely purchase:
G = Phones
U = Computers
M = Services
Government
University
Manufacturing
Computers
3.7
4.1
3.6
Phone Systems
3.4
3.3
Services
3.8
3.0
4.4

29. SPSS Output Conclusion: At least one significant difference exists
Comparing Overall Value by Channel
Conclusion: At least one significant difference exists
Conclusion: Retail buyers have the highest perceptions of overall value; catalog second (but need to look at contrasts)

30. You will get to see how to run these analyses in SPSS, including click by click tutorials and results