Data, Tables and Graphs

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

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

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