This document provides an overview of quantitative data analysis techniques used in sociology. It defines key terms like univariate analysis, bivariate analysis, and multivariate analysis. Univariate analysis examines one variable at a time through measures like frequency distributions, averages, and standard deviation. Bivariate analysis examines the relationship between two variables using cross-tabulation tables. Multivariate analysis examines relationships between multiple variables simultaneously. The document also discusses data coding, codebook construction, and ethical considerations in 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
 Sociological Diagnostics
 Ethics and Quantitative Data Analysis
 Quick Quiz

3. Quantification of Data
 Quantification Analysis – The numerical
representation and manipulation of
observations for the purpose of
describing and explaining the
phenomena that those observations
reflect.

4.  Age
 1 = 1
 2 = 2
 3 = 3
 4 = 4
 5 = 5
 Sex
 Male = 1
 Female = 2
 Political Affiliation
 Democrat = 1
 Republican = 2
 Independent = 3
 Region of Country
 West = 1
 Midwest = 2
 South = 3
 Northeast = 4

5.  Develop Code Categories
1. Use well-developed coding scheme.
2. Generate codes from your data.

6.  Codebook Construction
 Codebook – The document used in data
processing and analysis that tells the
location of different data items in a data
file.

7.  The codebook also identifies the locations of
data items and the meaning of the codes used.
 Purposes of the Codebook
1. Primary guide in the coking processes
2. Guide for locating variables

8. Figure 14.1

9. ATTEND
How often do you attend religious services?
0. Never
1. Less than once a year
2. About once or twice a year
3. Several times a year
4. About once a month
5. 2-3 times a month
6. Nearly every week
7. Every week
8. Several times a week
9. Don’t know, No answer
Abbreviated Variable
Name
NumericalLabel
Definition of the
Variable
Variable
Attributes

10.  Data Entry
 Excel
 SPSS

11. Univariate Analysis
 Univariate Analysis – The analysis of a
single variable, for purposes of
description (examples: frequency
distribution, averages, and measures of
dispersion).
 Example: Gender
 The number of men in a sample/population and
the number of women in a sample/population.

12.  Distributions
 Frequency Distributions – A description of
the number of times the various attributes
of a variable are observed in a sample.

13. Figure 14.3

14. Figure 14.4

15.  Central Tendency
 Average – An ambiguous term generally
suggesting typical or normal – a central
tendency (examples: mean, median,
mode).

16.  Mean – an average computed by summing
the values of several observations and
dividing by the number of observations.
 Mode- an average representing the most
frequently observed value or attribute.
 Median – an average representing the value
of the “middle” case in a rank-ordered set
of observations.

17.  Practice: The following list represents the
scores on a mid-term exam.
 100, 94, 88, 91, 75, 61, 93, 82, 70, 88, 71, 88
 Determine the mean.
 Determine the mode.
 Determine the median.

18. Figure 14.5

19.  Dispersion – The distribution of values
around some central value, such as an
average.
 Standard Deviation – A measure of
dispersion around the mean, calculated so
that approximately 68 percent of the cases
will lie within plus or minus one standard
deviation from the mean, 95 percent within
two, and 99.9 percent within three standard
deviations.

20. Figure 14.6

21.  Continuous Variable – A variable whose
attributes form a steady progression,
such as age of income.
 Discrete Variable – A variable whose
attributes are separate from one
another, such as gender or political
affiliation.

22.  Detail versus Manageability
 Provide reader with fullest degree of detail,
balanced with presenting data in a
manageable form.

23. Subgroup Comparisons
 Description of subsets of cases, subjects
or respondents.
 “Collapsing” Response Categories
 Handling “Don’t Knows”
 Numerical Descriptions in Qualitative
Research

24. Bivariate Analysis
 Bivariate Analysis – The analysis of two
variables simultaneously, for the
purpose of determine the empirical
relationship between them.

25.  Constructing a Bivariate Table
1. Determine logical direction of relationship
(independent variable and dependent
variable).
2. Percentage down versus percentage
across.

26. Figure 14.7
 Percentaging a
Table

27.  Constructing and Reading Bivariate Tables
 Example: Gender and Attitude toward
Sexual Equality
1. The cases are divided into men and women.
2. Each gender subgrouping is described in
terms of approval or disapproval of sexual
equality.
3. Men and women are compared in terms of the
percentages approving of sexual equality.

28.  Contingency Table – A format for
presenting the relationship among
variables as percentage distributions.

29.  Guidelines for Presentation of Tables
1. A table should have a heading or title that
describes what is contained in the table.
2. Original content should be clearly
presented.
3. The attributes of each variable should be
clearly indicated.
4. The base on which percentage are
computed should be indicated.
5. Missing data should be indicated in the
table.

30. Introduction to Multivariate
Analysis
 Multivariate Analysis – The analysis of
the simultaneous relationships among
several variables.

31. Quick Quiz

32. 1. To conduct a quantitative analysis,
researchers often must engage in a _____
after the data have been collected.
A. coding process
B. case-oriented analysis
C. experimental analysis
D. field research study

33. Answer: A.
To conduct a quantitative analysis,
researchers often must engage in a
coding process after the data have been
collected.

34. 2. Which of the following describe the
analysis of more than two variables?
A. experimental designs
B. quasi-experimental designs
C. qualitative evaluations
D. multivariate analysis

35. Answer: D.
Multivariate analyses describe the analysis
of more than two variables.

36. 3. The process of converting data to
numerical format is called _____.
A. feminist research
B. qualification
C. quantification

37. ANSWER: C.
The process of converting data to
numerical format is called quantification.

38. 4. Which of the following are basic
approaches to the coding process?
A. You can begin with a well developed
coding scheme.
B. You can generate codes from your data.
C. both of the above
D. none of the above

39. ANSWER: C.
The following are basic approaches to the
coding process: you can begin with a well
developing coding scheme and/or you can
generate codes from your data.

40. 5. A _____ is a document that describes
the locations of variables and lists the
assignments of codes to the attributes
composing those variables.
A. cross-case analysis
B. codebook
C. constant comparative method
D. monitoring study

41. ANSWER: B.
A codebook is a document that describes
the locations of variables and lists the
assignments of codes to the attributes
composing those variables.

42. 6. The _____ is an average computed by
summing the values of several
observations and divided by the number
of observations.
A. frequency
B. mean
C. median
D. mode

43. ANSWER: B.
The mean is an average computed by
summing the values of several
observations and divided by the number
of observations.

44. 7. Which of the following are aimed at
explanation?
A. multivariate analysis
B. bivariate analysis
C. univariate analysis
D. both A and B

45. ANSWER: D.
Multivariate analysis and bivariate analysis
are aimed at explanation.

46. 8. The multivariate techniques can serve
as power tools for
A. predicting behavior.
B. diagnosing social problems.
C. reacting to issues.
D. all of the above

47. ANSWER: B.
The multivariate techniques can serve as
powerful tools for diagnosing social
problems.