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Types of Data (Measurement)
AMA Collegiate Marketing Research Certificate Program
Module Objectives
• To introduce the four types of data and their characteristics
• Provide examples• Traits and measurement
strengths
Types of Data/Measurement Scales
These scales differ on what they measure and how the data may be analyzed and interpreted
• 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
Nominal Data
Nominal Data
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
Nominal Data
Merely measure the presence or absence of something
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
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?
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
Nominal Data• Nominal data are usually
represented “descriptively” • Graphic representations include
tables, bar graphs, pie charts
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
Measurement Traits/Strength
• Weakest type of data• Frequencies, mode• Cross-tabulations• Common to use as
grouping variable for other analyses (gender and average satisfaction)
Ordinal Data
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?
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
Ranking Scale is Ordinal
• Favorite Movie Type?– Drama, Action,
Comedy, Romance, Mystery, Horror
– 2nd favorite, 3rd 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 2nd and 1 point for 3rd.
WEBSITE# Most Visited
# 2nd Most
# 3rd Most
Ranking Score
Facebook 134 130 147 809Google 110 90 77 587Twitter 100 100 67 567Yahoo 77 98 88 515Four-square 9 12 51 102
Other Examples (Ordered Responses)
• Favorite restaurants• Most important buying
attributes• Most preferred buying
channels (retail, online, catalog)
• Most pressing concerns• Highest quality, 2nd…• Most watched, 2nd…• Most visited, 2nd….
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 1st and 2nd, 2nd and 3rd, etc.)
Interval Data
• 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
Interval Data
–IQ tests • A person can’t have zero
intelligence or zero self esteem
• 120 IQ not twice as intelligent as 60
Semantic Differential
ImportanceQuality
Some Examples of Interval Scales
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.
Ratio Data
Ratio data measured on a continuous scale and does have a true zero point
• Examples: Exact….
• Age
• Weight
• Height
Ratio Data
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!
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)
The levels of measurement can be placed in hierarchical order
Hierarchical Data Order
• 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
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?
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 Fire