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SOCB05H3 (99)
Lecture 6

# Lecture 6: Quantitative Data Analysis

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School
University of Toronto Scarborough
Department
Sociology
Course
SOCB05H3
Professor
Katie Stuart- Lahman
Semester
Summer

Description
SOCB05 – Lecture 6 – June 13 2013 Quantitative Data Analysis Quantitative Analysis  Numerical representation and manipulation of observations for the purpose of describing and explaining Preparing the Data  Generating Quantitative Data o Respondents fill out survey/census questionnaires -> coding and codebook -> Data Entry -> Data Cleaning -> Data Analysis -> Write a report o SASS, SPSS, Stata: Writing a computer code to tell it how to analyse the data. Need to input your own data. o Quantifiable: Convert data into numeric form. Assign numbers to variables and it’s various attributes.  Example: Gender – Male=0 , Female=1 o Coding: Translate data into something that computers/statistical software can recognize  These numbers usually don’t mean anything, it’s just for categorization purposes.  If you do it to rank something, number it so it makes sense. o Codebook: Document that describes the location of a variable within a dataset and lists the codes assigned to the attributes composing those variables o Data Entry  Data Entry specialists enter the data into statistical software or Excel spreadsheet  Optical scan sheets (Scantron)  Sometimes it is part of the process of data collection.  Once it’s put into the computer you can see basic information about the variables. o Data Cleaning  No matter how careful someone is, there will always be mistakes. Either by the researcher, during the entry, or during the study.  Possible Code Cleaning  The process of checking to see that only the codes assigned to particular attributes appear in the data file.  Make sure that only the numbers that are supposed to be there, are there. Every respondent should fit into the numbers chosen.  Contingency Cleaning  Checking that only those cases that should have data entered for a particular variable do in fact have such data.  Example: When you ask a question that has a second part to it, only those that responded in a way to require the second question.  Example: Asking a question about how many children you have given birth to, only women should answer it. No men should have an answer for this. o Analysing the Data  Univariate Analysis  The examination of the distribution of cases of only one variable  Descriptive purposes only  One-way frequency distributions  Summarize distribution of a variable by reporting the number of times each score of a variable occurred (frequency)  General rule for categories of frequency distribution: o Exhaustive
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