1. Types of Data (Measurement) AMA Collegiate Marketing Research Certificate Program

2. Module Objectives To introduce the four types of data and their characteristics Provide examples Traits and measurement strengths

3. Types of Data/Measurement Scales These scales differ on what they measure and how the data may be analyzed and interpreted

4. When collecting or gathering data we collect data from individual cases on particular variables A variable is a unit of data collection whose value can vary Variables can be defined into types according to the level of mathematical scaling that can be carried out on the data There are four types of data or levels of measurement: Nominal, Ordinal, Interval and Ratio data Introduction: Types of Data

5. Nominal Data

6. Data comprised of categories that cannot be rank ordered – each category is just different –Male or female –Own Home vs. Rent vs. other –Bought car in last 12 months/didn’t buy –Own a smart phone/don’t own

7. Nominal Data Merely measure the presence or absence of something

8. Nominal Data: An Example Nominal categories aren’t hierarchical, one category isn’t “better” or “higher” than another Marital status □ □ Single (never married) □ □ Married □ □ Divorced □ □ Widow/Widower

9. Nominal Data Assignment of numbers to the categories has no mathematical meaning Male could be coded “0” and female “1” or maybe “1” vs. “2” What is your gender?

10. Nominal Data Must ensure that each category is mutually exclusive and the system of measurement needs to be exhaustive –18-24 years old, 24-34 (oops) –18-24, 25-34, 35-44, 45+ (correct) *Assuming no minors in study

11. Nominal Data Nominal data are usually represented “descriptively” Graphic representations include tables, bar graphs, pie charts

12. Other Examples Home ownership status Race/ethnicity Any kind of behavior that is yes/no (have you been to the movies in last 30 days) Children in household Employment status Media usage Purchase channels Activities/hobbies

13. Measurement Traits/Strength Weakest type of data Frequencies, mode Cross-tabulations Common to use as grouping variable for other analyses (gender and average satisfaction)

14. Ordinal Data

15. Ordinal data are data comprised of categories that can be rank ordered (i.e., categories can be ranked above or below each other) = hierarchical data Ordinal Data  Less than $40,000  $60,000 - $79,999  $100,000 - $119,999  $40,000 - $59,999  $80,000 - $99,999  $120,000 or More What is your annual HOUSEHOLD income?

16. Ordinal Data: Example Most Viewed Website Second Most Third Most Forth Most Note: If you also had number of visitors you would have a ratio scale as well

17. Ranking Scale is Ordinal Favorite Movie Type? –Drama, Action, Comedy, Romance, Mystery, Horror –2 nd favorite, 3 rd favorite ….. Most Visited Website? –2nd most –3rd most Can also create a Ranking Score (i.e., like NCAA sports). Example, 3 points for each most important, 2 points for 2 nd and 1 point for 3 rd. WEBSITE# Most Visited # 2nd Most # 3rd MostRanking Score Facebook134130147809 Google1109077587 Twitter100 67567 Yahoo779888515 Four-square91251102

18. Other Examples (Ordered Responses) Favorite restaurants Most important buying attributes Most preferred buying channels (retail, online, catalog) Most pressing concerns Highest quality, 2 nd … Most watched, 2 nd … Most visited, 2 nd ….

19. Measurement Traits/Strength Frequencies, mode, median Ordering, preference, importance ranking - - offers additional insight beyond nominal data But can’t measure distance (only asked to rank order importance - - so don’t know how much more important between 1 st and 2 nd, 2 nd and 3 rd, etc.)

20. Interval Data

21. Interval data are measured on a continuous scale with no true zero point (a complete absence of the phenomenon being measured) Equal distance between interval points on scale Interval Data

22. –IQ tests A person can’t have zero intelligence or zero self esteem 120 IQ not twice as intelligent as 60

23. Semantic Differential Importance Quality Some Examples of Interval Scales

24. Measurement Traits/Strength Frequencies, mode, median, PLUS mean/average and standard deviation –Mean agreement of 4.5 on 5-point scale Ordering: Mean satisfaction of 4.5 > 4.3 But can’t measure comparative distance –Satisfaction average of 4.0 isn’t twice as high as 2.0 –Why: no absolute zero point Agreement scale can be 0-4, 1-5, -2 to +2, etc.

25. Ratio Data

26. Ratio data measured on a continuous scale and does have a true zero point Examples: Exact…. Age Weight Height Ratio Data

27. Other Examples (Exact Responses) Number of times dined out last month (could be none) Hours spent on Internet each day (could be none) Last price paid for dinner (could have been free) Remember, must have a true zero point!

28. Measurement Traits/Strength Frequencies, mode, median, mean/average/standard deviation PLUS, also allows for ABSOLUTE comparisons If Jimmy goes to two movies per week and Scott sees four movies, then Scott sees twice as many movies as Jimmy (2:1 Ratio)

29. The levels of measurement can be placed in hierarchical order Hierarchical Data Order

30. Nominal data are the least complex and indicate whether objects are the same or different Ordinal data maintain the principles of nominal data but add a measure of order to what is being observed Interval data build on ordinal by adding more information on the range between each observation by allowing us to measure the distance between objects Ratio data add to interval with including an absolute zero Hierarchical Data Order Summary

31. The data type or level of measurement influences the type of statistical analysis techniques that can be used when analyzing data It is possible to recode or adjust certain types of data into others Important?

32. Application? ALWAYS employ the highest level/strength of measurement available given (1) response rate and (2) ease of answering/remembering “We are only as strong as we are united, as weak as we are divided.” ― J.K. Rowling, Harry Potter and the Goblet of FireJ.K. RowlingHarry Potter and the Goblet of Fire