Quantitative data analysis

Tahmina Ferdous Tanny, Lecturer, Dept. of Public Administration, Jagannath University, Dhaka-1100 Page 1
Quantitative Research
In quantitative research, the investigator identifies a research problem based on trends in the
field or on the need to explain why something occurs. Describing a trend means that the research
problem can be answered best by a study in which the researcher seeks to establish the overall
tendency of responses from individuals and to note how this tendency varies among people. For
example, you might seek to learn how voters describe their attitudes toward a bond issue. Results
from this study can inform how a large population views an issue and the diversity of these
views (Cresswell, 2001, p-13)
Characteristics of Quantitative Research
In quantitative research the major characteristics are (Cresswell, 2001, p-13):
 Describing a research problem through a description of trends or a need for an
explanation of the relationship among variables.
 Providing a major role for the literature through suggesting the research questions to be
asked and justifying the research problem and creating a need for the direction (purpose
statement and research questions or hypotheses) of the study.
 Creating purpose statements, research questions, and hypotheses that are specific, narrow,
measurable, and observable.
 Collecting numeric data from a large number of people using instruments with preset
questions and responses.
 Analyzing trends, comparing groups, or relating variables using statistical analysis, and
interpreting results by comparing them with prior predictions and past research.
 Writing the research report using standard, fixed structures and evaluation criteria, and
taking an objective, unbiased approach.
Quantitative Analysis
Quantitative analysis involves the techniques by which researchers convert data to numerical
forms and subject them to statistical analyses (Babbie, 2001). It involves the numerical
representation and manipulation of observations for the purpose of describing and explaining the
phenomena that those observations reflect.

• Involves techniques
• Involve task of converting data into knowledge.
1. Quantification of data
It is the process of converting data to a numerical format. This involves converting social
science data into a machine-readable form—a form that can be read and manipulated by
computers and similar machines used in quantitative analysis. Today, quantitative analysis is
almost always handled by computer programs such as SPSS and Micro Case (Babbie, 2011,
page, 422).
1.1 Coding
To conduct a quantitative analysis, researcher often must engage in a coding process after the
data have been collected. For example, open-ended questionnaire items result in non-numerical
responses which need to be coded before analysis. Suppose, for example, that a survey
researcher asks respondents, ―What is your occupation?‖ The responses to such a question will
vary considerably. Although he or she can assign a separate numerical code to each reported
occupation, this procedure will not facilitate analysis. The variable occupation has many pre-
established coding schemes which differentiate and combines both to compare research results
with other studies.
One coding scheme distinguishes
Professional and managerial occupations Clerical occupation Semi-skilled occupations
Another coding scheme distinguishes sectors of economy
Manufacturing health education commerce

1.2 Developing code categories
It means the categorization of data into distinct field. For example, if we are willing to conduct a
survey in a self-administered campus about the existing problems facing by students. We can
categorize the responses from students as financial concern, academic concern and non academic
concern.
Financial concern Academic concern Non-academic concern
1.3 Codebook construction
The end product of the coding process is the conversion of data items into numerical codes.
These, codes represent attributes composing variables. A codebook is a document that describes
the locations of variables and lists the assignments of codes to the attributes composing those
variables. For example, if we conduct a survey on then it will be the following:
1.4 Data entry
Transforming data into quantitative form, researchers interested in quantitative analysis need to
convert data into a machine-readable format, so that computers can read and manipulate the data.
Tuition is too high
Books cost too much
Not enough financial aid
Not enough classes offered
Advisors are not available
Too many requirements
Cafeteria food is infected
Not enough parking
spaces
Cockroaches in the dorm
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.Every week

Data-entry specialists (including yourself) could enter the data into, say, an SPSS data matrix or
into an Excel spreadsheet to be imported later into SPSS.
Types of Variables Analysis
2.0 Univariate analysis
Univariate analysis is the analysis of a single variable for purposes of description. Frequency
distribution, averages, and measures of dispersion would be examples of univariate analysis, as
distinguished from bivariate and multivariate analysis.
Univariate analysis covers the following points:
2.1 Frequency Distribution
A description of the number of times that the various attributes of a variable are observed in a
sample is called a frequency distribution. The report that 53 percent of a sample were men and
47 percent were women would be a simple example of a frequency distribution. It gives
researcher some general picture about the dispersion, as well as maximum and minimum
response.
2.2 Central Tendency
A measure of central tendency is a single value that attempts to describe a set of data by
identifying the central position within that set of data. As such, measures of central tendency are
sometimes called measures of central location.
Univariate analysis
One variable
E.g. Age, gender, income etc.
Bivariate analysis
Two variables
E.g. gender & CGPA
Multivariate analysis
Several variables
E.g. Age, education, and
prejudice

2.2.1 Mean
An average computed by summing the values of several observations and dividing by the
number of observations. If you now have a grade number of observations point average of 4.0
based on 10 courses, and you get an F in this course, your new grade point (mean) average will
be 3.6
2.2.2 Mode
An average representing the most frequently observed value or attribute. If a sample contains
1,000 Protestants, 275 Catholics, and 33 Jews, Protestant is the modal category.
2.2.3 Median
An average representing the value of the ―middle‖ case in a rank-ordered set of observations. If
the ages of five men are 16, 17, 20, 54, and 88, the median would be 20. (The mean would be 39)
Figure: Three averages (Babbie, 2001, page, 430)

2.3 Dispersion
Dispersion refers to the way values are distributed around some central value, such as an
average. The simplest measure of dispersion is the range: the distance separating the highest
from the lowest value.
Range
The simplest measure of dispersion is the range: the distance separating the highest
from the lowest value. The range is a simple example of a measure of dispersion. Thus, we may
report that the mean age of a group is 37.9, and the range is from 12 to 89 (Babbie, 2001, page,
431)
Variance
To describe the variability of the distribution.
Standard deviation
A measure of dispersion around the mean. It is an index of the amount of variability in a set of
data. Higher SD means data are more dispersed. Lower SD means that they are more bunched
together. Figure 14-4 illustrates the basic idea. Notice that the professional golfer not only has a
lower mean score but is also more consistent represented by the smaller standard deviation. The
duffer, on the other hand, has a higher average and is also less consistent: sometimes doing much
better, sometimes much worse (Babbie, 2001, page, 432)
Dispersion
Range Variance Standard Deviation

2.4 Continuous & Discrete Variables
Continuous variable: A variable whose attributes form a steady progression, such as age or
income. Thus, the ages of a group of people might include 21, 22, 23, 24, and so forth and could
even be broken down into fractions of years.
E.g. Income & age
Scale: Interval & Ratio
Discrete Variables: A variable whose attributes are separate from one another, or discontinuous,
as in the case of gender or religious affiliation. Thus, in age (a continuous variable), the attributes
progress steadily from 21 to 22 to 23, and so forth, whereas there is no progression from male to
female in the case of gender.
E.g. Marital status, gender & nationality.
Scale: Nominal & Ordinal

Modes should be calculated for nominal data, medians for interval data, and means for ratio data,
not for nominal data.
2.6 Sub group comparison
Univariate analyses describe the units of analysis of a study and, if they are a sample drawn from
some larger population, allow us to make descriptive inferences about the larger population. The
subgroup comparisons tell us how diﬀerent groups in the population response to questions and
see a pattern in the result (Babbie, 2011, page: 433).
For example table represents whether marijuana should be legalized or not by age of
respondents:
Marijuana Legalization by Age of Respondents
Source: General Social Survey, 2004, National Opinion Research Center.
In response, 33.4 percent said it should and 66.6 percent said t.it shouldn‘t.
2.7 Collapsing” Response Categories
It means combining the two appropriate range of variation to get better picture or meaningful
analyses. Consider an example: Attitudes toward the United Nations: How is the UN doing in
solving the problems it has had to face?
Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6
Under 21 21-35 36-54 55 & older
Should be legalized 27% 40% 37% 24%
Should not be legalized 73 60 63 76
100%= (34) (238) (338) (265)

Part of the problem with Table lies in the table relatively small percentages of respondents
selecting the two extreme response categories: the UN is doing a very good or a very poor job.
This procedure is inappropriate in that it ignores all those respondents who gave the most
positive answer of all: ―very good job.‖ In a situation like this, you should combine or ―collapse‖
the two ends of the range of variation combine ―very good‖ with ―good‖ and ―very poor‖ with
―poor.‖ If you were to do this in the analysis of your own data, it would be wise to add the raw
frequencies together and recompute percentages for the combined categories (Babbie, 2011,
page, 434)
After collapsing extreme categories
Source: ―5-Nation Survey Finds Hope for U.N.,‖New York Times, June 26, 1985, p. 6
2.8 Handling “Don’t Knows” option
Whether to include or exclude the ‗don‘t knows‘ is harder to decide. It‘s usually a good idea to
give people the option of saying ―don‘t know‖ or ―no opinion‖ when asking for their opinions on
issues. In any event, the truth contained within your data is that a certain percentage said they
didn‘t know and the remainder divided their opinions in whatever manner they did (Babbie,
2011, page, 436).
3.0 Bivariate Analysis
The analysis of two variables simultaneously, for the purpose of determining the empirical
relationship between them. The construction of a simple percentage table or the computation of a
simple correlation coefficient are examples of bivariate analyses. However, as with univariate
analysis the purpose of subgroup comparisons is largely descriptive. Most bivariate analysis in
social research adds on another element: determining relationships between the variables

themselves (Babbie, 2011, page, 436-37). For example: Religious Attendance Reported by Men
and Women in 2004. Table describes the church attendance of men & women as reported in
1990 General Social Survey. It shows: comparatively & descriptively – that women in the study
attended church more often as compared to men.
Source: Babbie, 2011, page, 437
3.1 Constructing and Reading Bivariate Tables
Steps involved in constructing of explanatory bivariate tables:
1. The cases are divided into groups according to attributes of the independent variable.
2. Each of these subgroups is then described in terms of attributes of the independent
variable.
3. Finally, the table is read by comparing the independent variable subgroups with one
another in terms of a given attribute of the dependent variable.
Table: Gender and attitudes toward equality for men and women. Source: (Babbie, 2011, 439)

4.0 Multivariate analysis
The analysis of the simultaneous relationships among several variables. Examining
simultaneously the effects of Religious Attendance, Gender, and Age would be an example of
multivariate analysis (Babbie, 2011, page, 441).
.
Source: General Social Survey, 1972 – 2006, National Opinion Research Center
5.0 Sociological diagnostics
Sociological diagnostics is a quantitative analysis technique for determining the nature of social
problems such as ethnic or gender discrimination (Babbie, 2011, page, 442)
It can be used to replace opinions with facts and to settle debates with data analysis. For example
Issues of gender and income. Because family pattern, women as group have participated less in
in the labor force and many only begin outside the home after completing certain child-rearing
tasks.
Reference
Babbie E. (2011). The Practice of Social Research, (Twelfth ed.). California: Wadsworth
Cengage Learning.
http://www.slideshare.net/asmasemma/quantitative-data-analysis
Religious
attendance
Gender
Age

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