Research methodology

1. D N Roopa Department of Mechanical Engineering,
JSSATEB
Workshop
on
Research Data Analysis
Objective of the workshop: To provide skills and knowledge which
will allow the students to learn the analysis of both primary research data
and secondary research data .
Outcome of the workshop: The workshop helps students to gain the
practical insight of data analysis and develops a greater understanding
about research survey and analysis
Target audience: 4th semester MBA students (Finance, HR and
Marketing specialization)

2. • Primary Source of Data – (research you conduct yourself)
• Secondary Source of Data – (research is a type of research
that has already been compiled, gathered, organized and published by
others)

3. • Primary Data Collection
•Interviews (telephone or face-to-face)
•Surveys using questionnaire (online or mail)
•Focus groups etc

4. • Secondary Data Collection
• Reports and studies by government agencies
• Report by trade associations or other
businesses/industry. Etc…

5. Step 1: Design and administer a questionnaire
Step 2: Enter the raw data in a data management program such
as Microsoft Excel, SPSS, and STATA, Tableau, etc
However, before entering the data, it is good practice
create a codebook for the raw data.

6. • The columns represent the variables.
• Rows represent the observations (respondents to the questionnaire).
• Assign a Unique Identification Number (UIN) to each filled questionnaire before data entry
begins.
• When writing the variable names, you can either use the question number or short form of
the question.
• For example, “what is your gender” can be entered as “gender” as the variable name. This
is preferred because it is easier to know what the variable is about without referring back
to the questionnaire.

7. • Questionnaire

8. • Activity – Create a template in excel referring this
questionnaire
"C:UsersD N RoopaDesktopMBA WorkshopActivity sheet.xlsx"

9. • The question represents a specific type of data and level of measurement,
which determines how it is coded.
• After specifying the variable names in the column headers, we will add the raw
data for each questionnaire in rows; each questionnaire on its own row. The
sheet with the raw data is called the “data view.”
• To create the codebook, open another sheet called “variable view” where all
the codes for the responses will be created.

10. •Nominal: the data can only be categorized
•Ordinal: the data can be categorized and ranked
•Interval: the data can be categorized, ranked, and evenly
spaced
•Ratio: the data can be categorized, ranked, evenly spaced, and
has a natural zero.
"C:UsersD N
RoopaDesktopMBA
WorkshopActivity sheet.xlsx"

11.  It is best practice to create the codebook before the questionnaire is administered
because it will help us to think through the codes that will be used, and how the data
will be measured and analyzed.
 Additionally, it will help us recognize any responses that would be problematic to code
and analyze and therefore give us a chance to correct the mistakes before the data
collection is undertaken.
• As seen in the image above, questions 1, 4, 5, 8, and 12 were not coded because of the
nature of their data.
• The variable “gender” was coded 1 for male, and 2 for female. It does not matter what
codes one uses because gender is a nominal variable and therefore there is no meaningful
order in the codes used.
• This applies to the variables on family business, business category and effects of covid-19
on business.
• The variable “education” is an ordinal variable therefore the codes used must have a
meaningful order. A person moves from pre-primary to primary to secondary and tertiary
levels of education. Therefore the code used for pre-primary education should be the
lowest and the code for tertiary education should be the highest.

12. • Sample size

13. • Histogram
• Descriptive Statistics
• Anova
• F-Test
• t-Test
• Correlation
• Regression

14. What is a Hypothesis Test?
Hypothesis testing helps identify ways to reduce costs
and improve quality. Hypothesis testing asks the
question: Are two or more sets of data the same or
different, statistically.
For companies working to improve operations, hypothesis
tests help identify differences between machines,
formulas, raw materials, etc. and whether the differences
are statistically significant or not. Without such testing,
teams can run around changing machine settings,
formulas and so on causing more variation. These knee-
jerk responses can amplify variation and cause more
problems than doing nothing at all.

15. Three Types of Hypothesis Tests
•Classical Method - comparing a test statistic to a critical
value
•p Value Method - the probability of a test statistic being
contrary to the null hypothesis
•Confidence Interval Method - is the test statistic
between or outside of the confidence interval

16. How to Conduct a Hypothesis Test
1.Define the null (H0) and an alternate (Ha) hypothesis.
2.Conduct the test.
3.Using data from the test:
1. Calculate the test statistic and the critical value (t-Test, F-test, z-Test, ANOVA, etc.).
2. Calculate a p value and compare it to a significance level (a) or confidence level (1-a).
4.Interpret the results to determine if you "cannot reject null hypothesis (accept null hypothesis)" or
"reject the null hypothesis."