Lecture note for first phase MBA class

1. Chapter Six: Data Analysis & Interpretation
 Once the data are collected, the next logical step of the
research process is data analysis.
 Data Analysis is the process of systematically applying
statistical and/or logical techniques to describe and
illustrate, condense and recap, and evaluate the data.
 Data Analysis is the process of evaluating data using
analytical and logical reasoning to examine each
component of the data collected.
 Data analysis is the science of examining raw data with
the purpose of drawing conclusions about that
information.

2. Con’t
 Data Analysis is an attempt by the researcher
to summarize collected data where as data
Interpretation is an attempt to find
meaning/Implication of the result.
 Depending on the nature of data collected,
the analysis can be either qualitative or
quantitative analysis.

3. Qualitative Data Analysis
 Qualitative research is used to describe behaviors,
actions, feelings, perceptions, and interaction among
people.
 It assumes that respondents or people observed have
unique views of their personal experiences or the
surrounding environment.
 It is used to help us understand lifestyles and cultural
values, actions, and symbols and it heavily rely on
verbal description.
 The main instrument of data collection, interpretation
and written explanation is the researcher him
/herself.

4. Qualitative Data Analysis
Qualitative data analysis is mostly used to:
 Develop an understanding of people or groups
that we know very little about.
 Develop new theories that are relevant to
women, people of color, and other groups in
society that might have been excluded from
previous studies.
 For example, qualitative research can be used to
understand the lives of Women, Low income
communities, people with HIV Aids and so on

5. Qualitative Data Analysis
 Qualitative researches are usually more tentative
than quantitative ones, mainly because it is expected
that a qualitative study will evolve (change) in focus
once the researcher is in the research setting.
 The Data Collection process is not an end in itself.
The culminating activities of qualitative inquiry are
not data collection but analysis, interpretation, and
presentation of findings.
 Qualitative Data Collection and Analysis is more
common in exploratory type of research that looks at
a situation about which little is known.

6. Qualitative Data Analysis
The credibility of qualitative analysis depends on two distinct
but related inquiry elements:
1 Rigorous techniques and methods for gathering high-quality
data that is carefully analysed, with attention to issues of
validity, reliability, and triangulation;
2 The credibility of the researcher, which is dependent on
training, experience, track record, status, and presentation of
self; the extent to which the researcher influences responses is
significant in qualitative research and analysis.
Analysis and Interpretation depends on the perspective
of the researcher. Why?
 The qualitative data analysis involves coding, categorisation,
abstraction, comparison, integration, interpretation,
explanation, description.

7. Qualitative Data analysis
 In terms of research tradition qualitative data analysis is
different from quantitative in that data analysis in
qualitative research is undertaken during and after data
collection. That means analysis not left until the end of
data collection.
 As they collect data the researcher must ask
 Why do the participants act as they do?
 What does this focus mean?
 What else do I want to know?
 What new ideas have emerged?
 Is this new information?

8. Qualitative Data Analysis
 Data Analysis After Collection
 One way is to follow three important steps
1. Become familiar with the data through reading
2. Exam the data in depth to provide detailed
descriptions of the setting, participants, and
activities.
3. Categorizing and coding pieces of data and
grouping them into themes. coding qualitative
data helps to reduce data to a manageable form.

9. Qualitative Data Analysis
 Useful techniques for qualitative data analysis & interpretation
 Extend the analysis by raising questions
 Connect findings to personal experiences
 Seek the advice of “critical” friends.
 Contextualize findings in the research
 Turn to theory
 The researcher shall answer these four questions while
analyzing and interpreting qualitative data
 What is important in the data?
 Why is it important?
 What can be learned from it?
 So what?

10. Ensuring Credibility of Qualitative
Data Analysis and Interpretation
To ensure Credibility of the analysis and interpretation of
qualitative data the following shall be adequately answered.
 Are the data based on one’s own observation?
 Is there corroboration by other’s of the observation?
 In what circumstances was an observation made or reported?
 How reliable are those providing the data?
 What motivations might have influenced a participant’s
report?
 What biases might have influenced how an observation was
made or reported?

11. Exercise
Focus Groups discussion: Form a group of 4
people. One person should be the
interviewer. Another should take notes.
Address the following three questions:
1. What are the best things about Queens’ College?
2. What are the worst things about Queens’ College?
3. How do you think Queens’ College could be
improved?

12. Quantitative Data Analysis
 Before we can do any kind of analysis, we
need to quantify our data.
 “Quantification” is the process of
converting data to a numeric format.

13. Quantitative Data Analysis
Quantitative data analysis Qualitative data analysis
Data reduced to numeric values Data reduced to codes
Primarily rely on research procedures Primarily rely on researcher himself.
Preference for statistical summary
results
Preference for narrative summary
results
Preference for developing hypothesis
at the outset and statistically testing it
Preference to set hypothesis as study
develops and preference for
logical/narrative description.
Preference for random sampling Preference for expert informant
purposive sampling

14. Data Analysis Tools
 Statistical Package for Social Sciences (SPSS)
 STATA
 Eviews

15. Statistical Package for Social Sciences (SPSS)
 SPSS-format data files are organized by cases
(rows) and variables (columns). There are two
viewes-varaible view and data view
• In Variable View, each row is a variable, and each
column is an attribute that is associated with that
variable-it contains ten columns
Name Type Width Decimal Label Value missing Column Align Measure

16. SPSS
 In Data view: columns represent variables,
and rows represent cases (observations).
 Variables are used to represent the different
types of data that you have compiled.
 The response to each question on a survey is
equivalent to a variable.
 Variables come in many different types,
including numbers, strings, currency, and
dates

17. SPSS
 Data can be entered into the Data Editor,
which may be useful for small data files or for
making minor edits to larger data files
 Click the Variable View tab at the bottom of
the Data Editor window.
 You need to define the variables that will be
used.
 Fore example variables like age, marital
status, and income

18. SPSS
For instance:
 In the first row of the first column, type age.
 In the second row, type marital.
 In the third row, type income.
 These variables are automatically given a Numeric
data type. You can also choose other data type
depending on the type of variable
 Click the Data View tab to continue entering the data
for the variables.
 The names that you entered in Variable View are
now the headings for the first three columns in Data
View.

19. SPSS
 Begin entering data in the first row, starting
at the first column-In the age column, type 55,
In the marital column, type 1, In the income
column, type 2000.
 Move the cursor to the second row of the first
column to add the next subject's data
 In the age column, type 53, In the marital
column, type 0, In the income column, type
3000.

20. SPSS
 Currently, the age and marital columns display
decimal points, even though their values are
intended to be integers. To hide the decimal points in
these variables:
►Click the Variable View tab at the bottom of the
Data Editor window.
 In the Decimals column of the age row, type 0 to
hide the decimal.
 In the Decimals column of the marital row, type 0 to
hide the decimal

21. SPSS
 Non-numeric data, such as strings of text, can also be
entered into the Data Editor.
 Click the Variable View tab at the bottom of the Data
Editor window.
 In the first cell of the first empty row, type sex for the
variable name.
 Click the Type cell next to your entry.
 Click the button on the right side of the Type cell to
open the Variable Type dialog box
 Select String to specify the variable type-Click OK to
save your selection and return to the Data Editor.

22. SPSS
 In addition to defining data types, you can also
define descriptive variable labels and value labels for
variable names and data values
 These descriptive labels are used in statistical reports
and charts.
 Labels are meant to provide descriptions of
variables. These descriptions are often longer
versions of variable names.
 These labels are used in your output to identify the
different variables

23. SPSS
 In the Label column of the age row, type
Respondent's Age.
 In the Label column of the marital row, type Marital
Status
 In the Label column of the income row, type
Household Income.
 In the Label column of the sex row, type Gender.
 The Type column displays the current data type for
each variable. The most common data types are
numeric and string, but many other formats are
supported.

24. SPSS
 In the current data file, the income variable is
defined as a numeric type.
 Click the Type cell for the income row, and
then click the button on the right side of the
cell to open the Variable Type dialog box and
select Dollar.
 The formatting options for the currently
selected data type are displayed.
 Click OK to save your changes.

25. SPSS
 Value labels provide a method for mapping
your variable values to a string label. In this
example, there are two acceptable values for
the marital variable.
 A value of 0 means that the subject is single,
and a value of 1 means that he or she is
married
 Click the Values cell for the marital row, and
then click the button on the right side of the
cell to open the Value Labels dialog box.

26. SPSS
 The value is the actual numeric value.
 The value label is the string label that is applied to
the specified numeric value.
 Type 0 in the Value field.
 Type Single in the Label field.
 Click Add to add this label to the list.
 Type 1 in the Value field, and type Married in the
Label field.
 Click Add, and then click OK to save your changes
and return to the Data Editor
 These labels can also be displayed in Data View,
which can make your data more readable.

27. SPSS
 Click the Data View tab at the bottom of
the Data Editor window.
 From the menus choose:
 View
Value Labels

28. SPSS
 Missing or invalid data are generally too
common to ignore. Survey respondents may
refuse to answer certain questions, may not
know the answer, or may answer in an
unexpected format
 If you don't filter or identify these data, your
analysis may not provide accurate results.