1. Data Management: Quantifying Data & Planning Your Analysis

2. Planning for Analysis

3. Planning for Analysis
A sound research plan successfully matches these elements with the proper techniques
Collect the type of data that is most appropriate to answering your question and fits the other parameters of your project (budget, personnel, etc.)

4. Type of Data & Formatting Technique
Quantitative Data
Must “quantify” the data
Convert (“data reduce”) from collection format into numeric database
Qualitative Data
Must process the data (type/enter/describe)
Convert from audio/video to text
Combination
Process each element as appropriate

5. Type of Data & Analysis Quantitative Data Qualitative Data Combination
Counts, frequencies, tallies
Statistical analyses (as appropriate)
Qualitative Data
Coding
Patterns, themes, theory building
Combination
Process each element as appropriate

6. Quantifying Data
Coding
Processing

7. Quantifying Data
Before we can do any kind of analysis, we need to quantify our data
“Quantification” is the process of converting data to a numeric format
Convert social science data into a “machine-readable” form, a form that can be read & manipulated by computer programs

8. Quantifying Data Some transformations are simple:
Assign numeric representations to nominal or ordinal variables:
Turning male into “1” and female into “2”
Assigning “3” to Very Interested, “2” to Somewhat Interested, “1” to Not Interested
Assign numeric values to continuous variables:
Turning born in 1973 to “35”
Number of children = “02”

9. Developing Code Categories
Some data are more challenging. Open-ended responses must be coded.
Two basic approaches:
Begin with a coding scheme derived from the research purpose.
Generate codes from the data.

10. Coding Quantitative Data
Goal – reduce a wide variety of information to a more limited set of variable attributes:
“What is your occupation?”
Use pre-established scheme: Professional, Managerial, Clerical, Semi-skilled, etc.
Create a scheme after reviewing the data
Assign value to each category in the scheme: Professional = 1, Managerial = 2, etc.
Classify the response: “Secretary” is “clerical” and is coded as “3”

11. Coding Quantitative Data
Points to remember:
If the data are coded to maintain a good amount of detail, they can always be combined (reduced) later
However, if you start off with too little detail, you can’t get it back
If you’re using a survey / questionnaire, it’s a good idea to do your coding on the form so that it can be entered properly (i.e. create a “codebook”)

12. Codebook Construction
Purposes:
Primary guide used in the coding process.
Should note the value assigned to each variable attribute (response)
Guide for locating variables and interpreting codes in the data file during analysis.
If you’re doing your own input, this will also guide data set construction

13. Hands-on Exercise 1
Create a mini-codebook by coding the survey instrument
Note column spaces / locations
Note variable attribute values
Pay attention to the box at the bottom, special instructions

14. Entering Data Optical scan sheets (usually ASCII output).
Limits possible responses
CATI system / On-line: entered while collected
Data entry specialists enter the data into an SPSS data matrix, Excel spreadsheet, or ASCII file.
Typically, work off a coded questionnaire

15. Entering Data In Excel or Access, follow procedures from class:
Format tables with proper variable columns
Enter data for each case
In SPSS
Import an ASCII file and name variables/column headings
Or, create variables/column headings & enter each case

16. Entering Data
ASCII files are useful because they can be transformed or used in almost all analysis programs
Upload to SPSS, Excel, or use directly with SAS

17. Entering Data Into an ASCII file Using notepad
Use your coded survey to show you the proper entry order

18. Entering Data Into an ASCII file
Use the Command prompt (Accessories Command Prompt)
Type “Edit”

19. Entering Data
If you open an ASCII file in Excel, you’ll get a wizard to convert the data
Delimited or Fixed width
If Fixed width, add column breaks
Opens as Excel workbook

20. Hands-on Exercise 2 Complete the survey (fill-in your answers)
Create a ‘dataset’
Enter the data from your survey using either Notepad or the Edit program from the Command prompt

21. Quantitative Analysis

22. Quantitative Analysis
You should choose a level of analysis that is appropriate for your research question
You should choose the type of statistical analysis appropriate for the variables you have
Nominal/Categorical, Ordinal, or Continuous

23. Quantitative Levels of Analysis
Univariate - simplest form,describe a case in terms of a single variable.
Bivariate - subgroup comparisons, describe a case in terms of two variables simultaneously.
Multivariate - analysis of two or more variables simultaneously.

24. Univariate Analysis
Describing a case in terms of the distribution of attributes that comprise it.
Example:
Gender - number of women, number of men.
You should always begin your analysis by running the basic univariate frequencies and checking to be sure data were entered properly

25. Univariate Analysis Frequency distributions
Measures of central tendency
Mean, Median, Mode

26. Presenting Univariate Data
Goals:
Provide reader with the fullest degree of detail regarding the data.
Present data in a manageable from.
Simple and straightforward

27. Subgroup Comparisons
Describe subsets of cases, subjects or respondents.
Examples
"Collapsing" response categories:
Age categories, Open responses, etc.
Handling "don't knows“
Code separately, make missing if appropriate

28. Bivariate Analysis
Describe a case in terms of two variables simultaneously.
Example:
Gender
Attitudes toward equality for men and women
How does a respondent’s gender affect his or her attitude toward equality for men and women?
Crosstabulations / Correlations

29. Constructing Bivariate Tables
Divide cases into groups according to the attributes of the independent variable.
Describe each subgroup in terms of attributes of the dependent variable.
Read the table by comparing the independent variable subgroups in terms of a given attribute of the dependent variable.
DV goes in the rows, IV goes in the columns

30. Bivariate Analysis
Bivariate Tables / Crosstabs are appropriate for all types of variables, but the proper inferential statistic will vary by variable type
Continuous variables are typically made into categorical variables for this type of analysis
Recode variables
Example: Create “Age” (18-34, 35-50, 51-65, 66+)

31. Appropriate Types of Analysis

32. Bivariate Analysis: Correlations
Bivariate correlation analysis is appropriate for continuous variables (interval, ratio)
Other types of variables are often recoded into ‘Dummy’ variables (value 0 or 1) for these purposes
Example: Gender becomes two variables ‘Male’ (1=yes) & ‘Female’ (1=yes)
Present in Correlation Matrix

33. Multivariate Analysis
Analysis of more than two variables simultaneously.
Can be used to understand the relationship between multiple variables more fully.
Most typical: Regression analysis

34. Multivariate Analysis
Ordinal (technically inappropriate but it happens), continuous, dummy variables
Type of regression analysis will depend on the type of variables
OLS (continuous)
Logistic (other types)

35. Plan Your Analysis
Time Management

36. Planning your analysis
Leave enough time for data entry and data formatting
Can take much longer than you expect
In your codebook – note the TYPE of variable for each measurement/question
This will allow you to plan the proper levels and types of analysis

37. Planning your analysis
If your research question requires a level of analysis your variables won’t allow, you’ll need to transform them
Create ‘dummy’ variables
Collapse categories
Determine the level of significance acceptable & apply proper tests

38. Planning your analysis
Proper planning will make things easier later
Take good notes on any transformations, etc. that you do
Save all the elements of your analysis programs