1. Types of Data

2. Four types of data Alphabetical / Categorical / Nominal data:
Information falls only in certain categories, not in-between categories
No inferences possible between groups except that one group may contain more / less observations than the other
Only reporting frequencies, percentages and mode makes sense (descriptive statistics)
Chi Square measure of Association (inferential Statistics)
Examples: gender, age groups, income groups, etc.

3. Four types of data
Rank order data:
Ranked according to some logic, e.g. preference, etc.
Again an in-between rank does not make sense.
Difference between say rank 1 and 2 need not necessarily be of the same magnitude as the difference between rank 3 and 4.
Only reporting frequencies, percentages and mode makes sense (descriptive statistics); Spearman Rho coefficient of correlation (Inferential statistics)
Examples: brand preferences, class rank on test, etc.

4. Four types of data Interval Level
Numerical data in which the numbers denote the amount of presence / absence of a trait.
zero point does not necessarily mean complete absence of the trait
In-between numbers make sense
Magnitude of difference between numbers on the scale is constant.
All descriptive and inferential statistics possible
Examples: attitude, satisfaction, temperature, etc.

5. Four types of data Ratio level data
Interval level data with a meaningful zero point meaning complete absence of the trait
Magnitude of the difference between numbers of the scale is constant AND the zero point denotes complete absence of the trait being measured.
All descriptive and inferential statistics possible
Examples: sales, profits, weight, height, etc.

6. Type of data? Age in years Recall order of brands Age groups Ad. costs
Income
Number of students in various classes
Income groups
Time
Name
Test grades
SAT scores
Number of players in a team
Attitude to brand
Number of students in WU
Number of ads recalled
Calories