1. Data, Tables and Graphs
Presentation

2. Types of data Qualitative and quantitative
Qualitative is descriptive (nominal, categories), labels or words
Quantitative involves numbers
Data: information to be analyzed

3. Types of data Discrete and continuous
Discrete: takes on only whole number values
Continuous: can take on decimal (fractional) values

4. Coding schemes
Coding schemes are numbers assigned to characteristics of the data to be analyzed
Best to use numeric coding schemes

5. Example: age, race and gender, coding scheme
Age: recorded as a two digit number
Race:
Coded as a single digit number using a coding scheme:
African American 2. Hispanic 3. White
4. Asian 5. Other

6. Example: continued Gender 1. male 2. female
Andy is a 22 year old white male
Age: 22, Race: 3, Gender: 1
Coded as: 2232

7. Data file Usually rectangular
Variable values recorded for the unit of analysis
We will use SPSS as an example: Statistical Package for the Social Sciences

8. Data file: example ID Age Sex Race IQ Hand MS 1 22 3 102 2 34 110 60
112 4 54 92 5 39
120

9. Data file Each row is the unit of analysis (usually a subject)
Each column is a variable
Every variable should be given a label (name)
If it is a nominal variable, each value should have a value label

10. Example of value label Unit of analysis: subject
Variable: marital status
Values might include: single, married, divorced, widowed
Each value should be coded as a number, and the label provided

11. Missing value
Data is often incomplete—there will be missing information
There should be a code to indicate if a piece of data (a variable) is missing for a particular subject (often 0 is used)
Example: no IQ score available, coded as a 0, indicated in the data file

12. Simple descriptive statistics
Frequency: number of times a value occurs
If there are 48 females and 52 males in a sample, f = 48 for females and 52 for males
Proportion = f/N, P = 48/100 for females, or .48
Percent: % = f/N * 100

13. Qualitative (nominal)
Frequency distributions
Tables and graphs
Always label tables and graphs

14. Table 1. Gender of Sample Frequency Proportion Percent Male 52 .52 52%
Female 48
.48
48%

15. Pictorial representations
Pie charts
Bar charts

16. Displaying two variables in a table
Crosstabs
Race and gender, as an example

17. Quantitative data Tables and graphs Ungrouped data
Each value is displayed
Count: each value
Frequency: number of times each value occurs

18. Quantitative Frequency: number of times each value occurs
Cumulative frequency: arrange the numbers in ascending (or descending), and sum the frequencies going down the table
Indicates how many scores are less than a given score (cf)

19. Quantitative: tables Proportion, cumulative proportion
Percent, cumulative percent

20. Graphs, quantitative, ungrouped
Histogram
Bar graphs
Line graphs: frequency
Cumulative

21. Quantitative, grouped data
Sometimes cumbersome to list each value—too many values
Example: age—could be 0 to 90+
Set up group intervals, i.e., 0-5, 6-10, etc.
Rules:
1. first and last interval should not have a 0 frequency

22. Grouped data Mutually exclusive and exhaustive
All intervals should be the same width
Important rule, not in the book: when collecting data, do not group (collapse)—information is lost. You can always group later

23. Interval width No hard and fast rules—what seems to be most meaningful
Appearance also a consideration
As a start, use the formula, width = range of scores (highest-lowest), divided by the number of intervals

24. Continuous data
If data is continuous, actually decimal values are possible
Must develop a rule for handling this
For example, use a rounding rule