Data Analysis in Quantitative and Qualitative Research

Outcomes
By the end of this chapter, the learner
will be able to:
• Identify appropriate methods in
planning the data analysis research
• Define the different types of data
• Identify the data analysis implications
of each form of data
• Justify appropriate approaches in the
analysis of quantitate and qualitative
data
• Be aware of computer-assisted
methods of data analysis.Let’s start with
the first set of slides
1

THE QUANTITATIVE DATA ANALYSIS
▸ Quantitative data alludes to all
information that can be
deduced in numerical qualities,
going from the numerical
recurrence of event to
complex introduction of
information in
terms of diagrams and graphs.
3

This process will require the researcher
to:
▸ Seek advice regarding statistics and statistical analyses should
they not have knowledge in the field.
▸ Utilize, if possible, computer-based analysis software and seek if
necessary, the assistance of experts in the field.
▸ Study the statistical concepts relevant to the needs of the study.
4

 Descriptive statistics enable a concise portrayal of the information
regarding measurements, for example rate, frequencies, means
and standard deviations.
 Inferential statistics/measurements go further.
 While descriptive statistics describe a sample’s attributes based on
the information gathered from respondents, inferential
measurements are utilized to obtain information about the
population from which the sample was drawn based on the data
outlined in the descriptive measurements.
5

Four categories of quantitative
data:
▸ Nominal measures are descriptive measures that
serve only to indicate the alternative states of the
variable.
▸ Ordinal measures are rank estimates, usually reflect
choices made by the subject or categories
predetermined by the researcher. Ordinal measures
can be logically rank ordered and the different
attributes represent relatively more or less of the
specific variable. 7

Four categories of quantitative
data: (Cont.)
▸ Interval measures refer to those variables of which
the attributes are not only rank-ordered, but they are
separate by a uniform distance between them.
▸ Ratio measures are based on an absolute scale, which
has a fixed zero point. This means that the scale
readings are exactly proportional to the variables
being measured. Ratio measures represent the
highest possible level of precision and are amenable
to all forms of statistical analysis.
8

▸ Categorical data cannot be measured numerically but can be
classified into sets (categories) according to specified criteria
(e.g. gender, religion, profession, qualification) or placed in
rank order (e.g. level of experience, consumer preference, etc.).
Nominal and ordinal data fall into this group.
▸ Quantifiable data is data of which the values can be measured
numerically. The more precise the measurement the greater
the range of statistical techniques that can be used to analyze
the data. Interval and ratio measures fall into this group.
Generally it is better in quantitatively-oriented studies to
collect data that enables highest possible level of statistical
analysis.
9

Coding of data
▸ All data should be recorded using numerical codes to
categorise responses to each item on the research instrument.
▸ Simultaneously, a codebook should be designed to maintain a
record of the codes for each variable.
▸ For instance, for the gender of respondents, the code used may
be 1=male and 2=female or for an item which uses a Likert
scale, 1=strongly agree, 2=agree, 3-uncertain, 4=disagree and
5=strongly disagree. All missing data should be indicated
through codes. For example, if a respondent did not indicate
his/her sex, the code 9 may be used. Items with missing data
are then excluded from subsequent analysis of data.
10

Statistical analysis
▸ Once the data has been coded into a format that can be entered
on a spreadsheet or a statistical analysis package, the
appropriate procedure may be used to process the data into a
format that can be analyzed (frequencies, tables, diagrams, etc.).
▸ The tables and diagrams most relevant in addressing the needs
of the study will eventually appear in the student’s/researcher’s
dissertation. Tables and diagrams can be used in an exploratory
analysis of data to identify trends, show proportions and the
distribution of values and to visually compare the relationship
among variables.
11

▸ With descriptive statistics, the researcher can
describe (and compare) variables numerically
with means and standard deviations, while
with inferential statistics, the researcher can
reach conclusions about how the data
collected relates to the original research
objectives and hypothesis and how these
results might be generalized to the research
population.
12

GUIDELINES FOR
THE SUCCESSFUL
APPLICATION OF
STATISTICS IN
THE ANALYSIS OF
DATA
13

1. Statistics must be appropriate to integrate
into the study and not use as a device to create
an impression of scientific analysis and
objectivity.
2. The researcher must develop a good working
knowledge of the principles underlying the
statistical procedures in the study to analyze
data to enable appropriate use and meaningful
interpretation of the results.

3. The researcher who has access to statistical software
packages such as SPSS for Windows should first work
through examples of coding and analyzing research
instruments before proceeding with an analysis of the
research data.
4. Before the researcher embarks on any kind of
statistical analysis, they should determine the
characteristics of the data in terms of its type (i.e.
nominal, ordinal, interval or ratio) and its distribution
(normal or skewed) since these inherent properties will
determine what kind of statistical test is possible to
use. 15

The analysis of univariate data
Univariate data analysis involves the analysis of a single
variable, usually with descriptive statistics such as the
calculation of:
• frequencies
• percentages
• means (the arithmetic ‘average’ of data)
• median and mode
• standard deviations.
16

The analysis of bivariate data
17

The mean, often called the average, of a numerical set of data, is
simply the sum of the data values divided by the number of values.
This is also referred to as the arithmetic mean. The mean is the
balance point of a distribution.
Mean = sum of the values
the number of values
18

Example
Problem
Stephen has been working on programing and updating a Web site
for his company for the past 15 months. The following numbers
represent the number of hours Stephen has worked on this Web site
for each of the past 7 months:
24, 25, 31, 50, 53, 66, 78
What is the mean (average) number of hours that Stephen worked
on this Web site each month?
19

Step 1: Add the numbers to determine the total
number of hours he worked.
24 + 25 + 33 + 50 + 53 + 66 + 78 = 329
Step 2: Divide the total by the number of months.
329 = 47
7 20

The median is the number that falls in the middle position once
the data has been organized. Organized data means the
numbers are arranged from smallest to largest or from largest
to smallest. The median for an odd number of data values is the
value that divides the data into two halves. If n represents the
number of data values and n is an odd number, then the median
will be found in this position.
n plus 1
2
21

Example
Problem
Find the median of the following data:
12, 2, 16, 8, 14, 10, 6
22

Step 1: Organize the data, or arrange the numbers from smallest
to largest.
2, 6, 8, 10, 12, 14, 16
Step 2: Since the number of data values is odd, the median will be
found in the n plus 1 position.
2
n plus 1 = 7 + 1 = 8 = 4
2 2 2
Step 3: In this case, the median is the value that is found in the
fourth position of the organized data.
2, 6, 8, 10, 12, 14, 16
23

Problem
Find the mean and median of
the following data:
7, 9, 3, 4, 11, 1, 8, 6, 1, 4
24

The mode of a set of data is simply
the value that appears most
frequently in the set.
26

Example
Problem
Find the mode of the following data:
76, 81, 79, 80, 78, 83, 77, 79, 82, 75
27
There is no need to organize the data, unless you think that it
would be easier to locate the mode if the numbers were
arranged from least to greatest. In the above data set, the
number 79 appears twice, but all the other numbers appear
only once. Since 79 appears with the greatest frequency, it is
the mode of the data values.

Data Analysis in Quantitative and Qualitative Research

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