2. Content
Steps and the the process of analyzing and
interpreting the data
Reporting the findings of Data Analysis
Methods of interpret the results
3. Preparing and organizing the data for
Analysis
1. Score the data: Scoring data means that the
researcher assigns a numeric score (or value) to
each response category for each question on the
instruments used to collect data
2. Coding the data: By using a codebook, data
coding is the process of listing the variables or
questions that indicates how the researcher will
code or score responses from instruments or
checklists
4. 3. Determine the types of scores;
– Single Item: A single-item score is an individual score
assigned to each question for each participant in your
study. These scores provide a detailed analysis of each
person’s response to each question on an instrument.
– Summed Score: Summed scores are the scores of an
individual added over several questions that measure the
same variable. Researchers add the individual items to
compute an overall score of the variable
– Difference Score: Net or difference scores are scores in a
quantitative study that represent a difference or change for
each individual. Some gains may be more meaningful than
others.
5. 4. Select the Statistical Programme:
SYSTAT, SAS, SPSS, STATA, PSPP, ROOT, SOFA
Statistics, Stan, Torch, Weka, Xlisp-Stat, Statbook etc.
5. Input/Import the data:
Inputting the data occurs when the researcher transfers
the data from the responses on instruments to a
computer file for analysis. For those new to this process,
this grid is similar to a spreadsheet table used in many
popular software packages i.e. MS excel or MS Assess
6. 6. Data Cleaning:
Cleaning the data is the process of inspecting the data
for scores (or values) that are outside the accepted
range.
7. 7. Assessing the database for missing data
Missing data are data missing in the database
because participants do not supply it.
– Eliminate participants with missing scores from
the data analysis
– Substitute numbers for missing data in the
database for individuals.
8. Analyze the data
1. Descriptive Statistics:
a) Central tendencies in the data; mean, mode, median,
b) Spread of scores; variance, S. D., and range,
c) Comparison: z scores, percentile rank etc.
9. 2. Inferential Statistics:
a) Analyze data from sample to draw conclusions about population.
B) Compare two/more groups on the independent variable in terms of the
dependent variable.
11. Example of Hypothesis Testing
Research question: Is there significant difference on average income of
KU Master Degrees and Others University Master Degrees?
* Annual Income of Treatment Rs. 450,000, and Control Rs.200,000
12. If hypothesis testing is carried out to examine the mean
difference of average income between the treatment
and control group.
• Null hypothesis (H o): There is significant difference
on average income between KU and Others.
• Alternative hypothesis (H1): There is not significant
difference on average income KU and Others.
Hypothesis
Testing.....
13. • This implies that there is significant difference on
variances of income between KU and others. It
guides us to use t test with equal variance not
assumed.
• We have, T = 6.564 ; Difference = 156.197
• Significant (2 tailed) = p value= .000 and Q = 0.05
• Since p<Q, we reject H1 accept H0
• Therefore, there is a significant different on average
income between treatment and Control.
Hypothesis Testing.....
14. How Interpreting the Results..?
a) Table Generation:
The tables summarize
statistical information
(Use Relevant only),
Particulars B.S. 2071 B.S. 2060
Mean Income of
KU Masters
Rs. 1050,000 Rs. 400,000
b) Figure and Visual:
Figures, charts, pictures,
drawings and plot of the
variables and their
relationships.
c) Detailed explanations:
Detailed information about the specific results of the descriptive
and inferential statistical analyses (Substantial Argument only)
15. How to explain the results findings...?
• Summarize the Major Results: A summary is a statement that
reviews the major conclusions to each of the research questions
or hypotheses.
• Explain Why the Results Occurred: Why the results turned
out, often this explanation is based on returning to predictions
made from a theory or conceptual framework.
• Spill over Effects: Further explain what type of effects is spill
over and overcome to made effect
• Advance Limitations: Limitations are potential weaknesses or
problems with the study identified by the researcher.
• Suggest Future Research: Future research directions and
suggestions made about additional studies that need to be
conducted based on the results of the present research.
16. Questions to My Teacher
• For the quantifying the figures, is descriptive and
inferential statistics support and relevant ?
• Generous assumption and argument is frequently
used to interpret the results of inferential statistics? Is
it valid?
• How do we do the test on the economic growth,
Income Situation or to any others monetary value?
Because how will the test be valid without considering
the time value of money?
17. Thanks for listening me,
Making inquisitive me,
Getting interact me,
Finally encouraging me.
But, I don’t forgive you,
Because as my true friend,
You don’t like this,
I know how much you love me.
