Quantifying Data

Advanced Social Research (soci5013)

Peter Njuguna

Source: Course Pack Chapter 14 (Page 383 – 395)

Overview

Data Analysis Statistical Quantitative

Mostly computer aided nowadays

Process1. Mass observations2. Quantification (through coding)3. Coding error reduction (Data cleaning)4. Data Analysis

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

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)

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

Developing code categories

1. Well developed coding scheme Derived from research purpose Existing coding scheme (comparable)

2. 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;a) Exhaustive

b) Mutually exclusive Coder reliability (including yourself) crucial

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

Coding and data entry options (1)

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 etcCase 3

Etc..

Coding and data entry options (2)

2. Edge-coding3. Direct data entry (pre-coded questionnaires)4. Data entry by interviewers

e.g. CATIs Closed-ended data ready for analysis Open-ended responses - additional coding step before

analysis5. Coding to optical scan sheets

Coder error high Low scanner tolerance

6. Direct coding on op sheets by respondent7. Connecting with data analysis program

eg SPSS – blank data sheets – entry – analysis Create data set (spreadsheet, etc) – import & export Compatibility options well developed

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 methods1. Possible code cleaning

By checking for errors as data is entered (“beep!”) Testing for illegitimate codes in stored data files

2. 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 ….

AT LONG LAST, ….

YOUR DATA IS READY FOR ANALYSIS …

GO!