Quantitative Data Analysis

1
Chapter 14

• Quantitative Data Analysis

2
Chapter Outline

• Quantification of data
• Univariate Analysis
• Subgroup Comparisons
• Bivariate Analysis
• Introduction to Multivariate Analysis

3
Developing Code Categories

• Two basic approaches
• Beginning with a coding scheme derived from the
  research purpose.
• Generate codes from the data.

4
Codebook Construction

• Purposes
• Primary guide used in the coding process.
• Guide for locating variables and interpreting
  codes in the data file during analysis.

5
Entering Data

• Data entry specialists enter the data into an
  SPSS data matrix or Excel spreadsheet.
• Optical scan sheets.
• Part of the process of data collection.

6
Quantitative 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.

7
Univariate Analysis

• Describing a case in terms of the distribution of
  attributes that comprise it.
• Example
• Gender - number of women, number of men.

8
Presenting Univariate Data

• Goals
• Provide reader with the fullest degree of detail
  regarding the data.
• Present data in a manageable from.

9
Subgroup Comparisons

• Describe subsets of cases, subjects or
  respondents.
• Examples
• "Collapsing" response categories.
• Handling "don't knows."

10
Bivariate Analysis

• Describe a case in terms of two variables
  simultaneously.
• Example
• Gender
• Attitudes toward equality for men and women

11
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.

12
Multivariate Analysis

• Analysis of more than two variables
  simultaneously.
• Can be used to understand the relationship
  between two variables more fully.