1. Quantifying Data Advanced Social Research (soci5013)
Peter Njuguna
Source: Course Pack Chapter 14 (Page 383 – 395)

2. Overview Data Analysis Process Statistical Quantitative
Mostly computer aided nowadays
Process
Mass observations
Quantification (through coding)
Coding error reduction (Data cleaning)

3. Introduction Social Science data (largely non-numeric)
Machine Readability, Manipulation
Logic of data manipulation in quantitative analysis
Biological & Physical science data (mostly numeric attributes, eg counts, pH, length, temp.,..)
Baseline: The logic remains same even with development of more powerful technology
Computers are tools to enhance research. They understand only the basics

4. Computers in Social Research
France (1801) Joseph marie-Jacquard (automatic loom, punched cards, weaving patterns)
USA (1790) 10-year census – under 4 mil. People
1880 Over 62 million. (9 years to tabulate!)
1890 Herman Hollerith: Punched card system (Results reported in 6 weeks)
Tabulating Machine Co. + mergers = IBM
Baseline: Information coding, storage, Retrieval.
Today’s computer data analysis: Converting observations into machine readable form, electronic data storage, retrieval, manipulation and presentation
Statistical Analysis (Some programs specific for social Science eg SPSS)

5. Coding for Quantitative Analysis
Social science methods (interviews, questionnaires, .)
Open-ended & closed-ended questions : Non-numeric responses
Coding reduces responses to limited set of attributes to enable analysis
use pre-established coding: Comparable with others
coding from the data set (responses): Flexibility response coverage
Coding system should be appropriate to theoretical concepts
If data coded to maintain detail, can be combined where detail not necessary, but not vice versa

6. Developing code categories
Well developed coding scheme
Derived from research purpose
Existing coding scheme (comparable)
Generate codes from your data
Many possible schemes (cf. pg 388, 389), specific to your research purpose
Review for recoding as you progress
Code categories should be;
Exhaustive
Mutually exclusive
Coder reliability (including yourself) crucial

7. Codebook construction
Codebook (describes location of variables; assignment of codes to attributes)
Primary guide in coding process
Guide for locating variables & interpreting codes in data file during analysis
Contains
Variable names,
Full descriptions (cf. exact wording of questions)
Categorized response options

8. Coding and data entry options (1)
Transfer sheets
Useful technique especially with complex questionnaires and other data sources
Source Course pack pg 391
Case #
01 (Variable1 eg Gender)
02 (Variable 2 eg educ)
03 (Variable3 eg religiosity)
Case 1(eg Peter)
Attribute 1 (Male)
Case 2
etc
Case 3
Etc..

9. Coding and data entry options (2)
Edge-coding
Direct data entry (pre-coded questionnaires)
Data entry by interviewers
e.g. CATIs
Closed-ended data ready for analysis
Open-ended responses - additional coding step before analysis
Coding to optical scan sheets
Coder error high
Low scanner tolerance
Direct coding on op sheets by respondent
Connecting with data analysis program
eg SPSS – blank data sheets – entry – analysis
Create data set (spreadsheet, etc) – import & export
Compatibility options well developed

10. Screening and elimination of errors (Data cleaning)
Errors almost inevitable
Incorrect coding
Incorrect reading of codes
Sensing of marks, etc
Two types of data cleaning methods
Possible code cleaning
By checking for errors as data is entered (“beep!”)
Testing for illegitimate codes in stored data files
Contingency cleaning
That only cases relevant to attribute have such entries (cf. No of pregnancies in men) inappropriate.
Can be ignored sometimes (significance, discretion)
Remember that “dirty” data almost always produces misleading results ….

11. YOUR DATA IS READY FOR ANALYSIS …
AT LONG LAST, ….
YOUR DATA IS READY FOR ANALYSIS …
GO!