1. Quantifying Data

2. Data Entry Define variables, enter case data, conduct runs
Coding and Recoding
If numeric values not pre-assigned, decide on coding system
If there is open-ended data, would need to decide how to deal with responses
Defining your variables

3. Data Cleaning
Reread each set of responses back (immediately) to confirm accuracy
“Possible-code cleaning”
easiest way to check is to run a frequency distribution
Contingency cleaning
On the “if” questions
“Sort” by response
do you recycle… then check the “what do you recycle” variable
Can also run cross tabs and make sure cells are empty

4. Basic Analysis – Measures of Central Tendency
Mean: sum of values divided by the number of cases
simple average
Median: middle attribute in a list of observed attributes
extreme cases eliminated
Mode: most frequently occurring attribute
used with nominal variables, i.e.. sex
most respondents were women
usually report with percentage, 60% were women

5. Cross Tabs Used often with Bivariate data Convention usually places
“independent variables” across top in columns
“dependent variables” in rows below

6. Coding and data entry options
Transfer sheets are special forms ruled off in 80 columns
Edge coding involves recording code #'s in margins of questionnaires
Direct data entry involves entering data directly into computer; eliminating transfer sheets
Data entry by interviewer (CATI)
Optical scan sheets

7. Coding
What is it?
It is the assignment of numerical values to information or responses gathered by a research instrument
Codebook: describes the locations of variables and lists the codes assigned to the attributes of the variables

8. Data Management Process
concerned with the process by which raw data gathered by some instrument are converted into numbers for analysis purposes

9. Collect information with data gathering instrument
Use codebook to transfer this information to a transfer sheet or code sheet (optional)
Create data file from information on code sheet by entering data from a computer keyboard
Check/clean up data file for accuracy
Data cleaning done by
Computer edit programs
Examine distributions
Contingency cleaning

10. What about open-ended items?
Read through responses a create a preliminary code based on responses
If more than 10% of responses fall into "other" category, code needs to be revised to include many of these responses

11. Elementary Quantitative Analyses
To understand the meaning of univariate, bivariate, and multivariate analysis
To become familiar with the meaning of several univariate and bivariate statistics

12. Analysis Strategies Why do we have to have them?
People who read our ‘research’ are interested in the highlights
Should try to communicate findings in an understandable and ‘painless fashion’

13. Three types of analysis
Univariate analysis
the examination of the distribution of cases on only one variable at a time (e.g., college graduation)
Bivariate analysis
the examination of two variables simultaneously (e.g., the relation between gender and college graduation)
Multivariate analysis
the examination of more than two variables simultaneously (e.g., the relationship between gender, race, and college graduation)

14. “Purpose” Univariate analysis Purpose: description Bivariate analysis
Purpose: determining the empirical relationship between the two variables
Multivariate analysis
Purpose: determining the empirical relationship among the variables

15. Types of Statistics
Techniques that summarize and describe characteristics of a group or make comparisons of characteristics between groups are knows as descriptive statistics.
Inferential statistics are used to make generalizations or inferences about a population based on findings from a sample.
The choice of a type of analysis is based on the evaluation questions, the type of data collected, and the audience who will receive the results. 

16. Univariate Analysis
Involves examination of the distribution of cases on only ONE variable at a time
Frequency distributions are listings of the number of cases in each attribute of a variable
Ungrouped frequency distribution
Grouped frequency distribution
Proportions express number of cases of the criterion variable as part of the total population; frequency of criterion variable divided by N

17. Percentages are simple 100 X proportion
Or [100 X (frequency of criterion variable divided by N)]
Rates make comparisons more meaningful by controlling for population differences

18. Measures of Central Tendency
Measures of central tendency reflect the central tendencies of a distribution
Mode reflects the attribute with the greatest frequency
Median reflects the attribute that cuts the distribution in half
Mean reflects the average; sum of attributes divided by # of cases

19. Measures of Dispersion
Measures of dispersion reflect the spread or distribution of the distribution
Range is the difference between largest & smallest scores; high – low
Variance is the average of the squared differences between each observation and the mean
Standard deviation is the square root of variance

20. Types of Variables Continuous: increase steadily in tiny fractions
Discrete: jumps from category to category

21. Subgroup Comparisons
Somewhere between univariate & bivariate, are Subgroup Comparisons
Present descriptive univariate data for each of several subgroups
Ratios: compare the number of cases in one category with the number in another

22. Bivariate Analysis
Bivariate analysis focus on the relationship between two variables

23. Contingency Tables
Format: attributes of independent variable are used as column headings and attributes of the dependent variable are used as row headings
Guidelines for presenting & interpreting contingency tables
Contents of table described in title
Attributes of each variable clearly described
Base on which percentages are computed should be shown
Norm is to percentage down & compare across
Table should indicate # of cases omitted from analysis

24. Multivariate Analysis
Multivariate Analysis allow the separate and combined effects of the independent variable to be examined