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

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

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 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

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) 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..

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

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 ….

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