The document discusses data analysis and interpretation, emphasizing the importance of processing and summarizing collected data to reach conclusions related to research questions. It categorizes data analysis into descriptive and inferential types, highlighting their distinct purposes and methodologies. The text also touches on the significance of proper interpretation of findings and the role of tabulation in simplifying and presenting data effectively.

1. Data Analysis & Interpretation
The data, collected for research, has to be processed,
analyzed and interpreted to develop a solution to the
research question.
Data analysis is a practice in which unorganized or
unfinished data is ordered and organized so that useful
information can be extracted from it.
It is the most enjoyable part of carrying out the
research since after all of the hard works and waiting
the researcher gets the chance to find out the answers.
So analyzing the data and interpreting the results are
the “reward” for the work of collecting the data.

2. Types of Data analysis
Data analysis can be categorized into two:
Descriptive analysis
Inferential analysis

3. Descriptive analysis
 Descriptive analysis describe the main features of a collection
of data quantitatively
 Provides simple summaries about the sample and the measures
 Descriptive analysis includes the numbers, tables, charts, and
graphs used to describe, organize, summarize, and present raw
data
 Describes the frequency and/or percentage distribution of a
single variable
 Tells how many and what percent
Example:33% of the respondents are male and
67% are female

4. …….Descriptive analysis
 Descriptive analysis are most often used to examine:
- Central tendency of data, i.e. where data tend to fall,
as measured by the mean, median, and mode.
- Dispersion of data, i.e. how spread out data are, as
measured by the variance and the standard deviation.
- Skew of data, i.e. how concentrated data are at the low
or high end of the scale, as measured by the skew index.
- Kurtosis of data, i.e. how concentrated data are
around a single value, as measured by the kurtosis
index.

5. Inferential analysis
 Inferential analysis is the process of drawing conclusions from
data that are subject to random variation
 Used to analyze data from randomly selected samples
 Used to try to infer from the sample data what the population
might think
 Risk of error because your sample may be different from the
population as a whole
 To make an inference, you first need to estimate the
probability of that error

6. …..With descriptive analysis you are simply
describing what is or what the data shows. With
inferential analysis, you are trying to reach
conclusions that extend beyond the immediate data
alone. we use inferential analysis to make judgments
of the probability that an observed difference between
groups is a dependable one or one that might have
happened by chance in this study. Thus, we use
inferential analysis to make inferences from our data
to more general conditions; we use descriptive
analysis simply to describe what's going on in our
data.

7. Interpretation
 After collecting and analyzing data, the
researcher has to accomplish the task of
drawing inferences. It has to be done
very carefully, otherwise misleading
conclusions may be drawn. It is through
the task of interpretation the researcher
draws inferences from the collected facts.

8. ….Interpretation
Need of Interpretation
 Usefulness and utility of research findings lie in
proper interpretation
 Researcher can well understand the principle
that works beneath his findings
 Leads to the establishment of explanatory
concepts for future research
 Researcher can better appreciate what and why
his findings are..

9. Tabulation
 Tabulation is the process of arranging data in
systematic manner in the form of rows and
columns.
 Classified data is condensed in the form of a
table so that it may be more easily understood.
 The main objective of tabulation is to
systematize data and make them simple and
comparable.

10. ….Tabulation
Objectives
 To simplify complex data
 To facilitate comparison
 To economize space
 To facilitate statistical analysis
 To facilitate presentation

11. ….Tabulation
The most important thing about a table is
that it should clearly communicate
information. To achieve this there are
certain conventions that must be followed.
 Tables must be clear and easy to read
 Must be ruled or presented as a computer
generated table an of an appropriate size for
the information.

12. ….Tabulation
 Must have a title which describes the data in
the table. This title should be underlined or in
bold type.
 Columns and rows should be clearly headed.
When appropriate the left column or top row
should contain the independent variable and
the bottom row or right column should contain
the dependent variable.

13. ….Tabulation
 Units should be displayed in column / row
headings only.
 Missing values should be displayed as -, and
zeros as 0. Thee should be no blanks in a table
conveying experimental results.
 Numbers should be listed neatly below each
other and should be to the same number of
decimal places.

14. ….Tabulation
Main parts of a table
 Table No.
 Title of table
 Captions
 Stubs
 Body of the table
 Head notes
 Foot notes
 Source note

16. ----Box Head----
----Row
Captions----
------Column Captions-----
---Stub
Entries---
-----The Body-----

17. Generalization
In research, once the data is
properly arranged using tabulation, it
starts showing some pattern. On the
basis of this pattern, the researcher
may assume about the nature of the
population. The process of making
such assumptions is known as
generalization.

18. ….Generalization
 It is a broad statement about the population based
on provided information, observations and
experiences
 A valid generalization is build on
- supporting facts
- several examples
- past experiences
- logic and reasoning
 For a valid generalization, the sample drawn
should be typical and representative.

19. ….Generalization
Types of Generalization
Valid generalizations
Faulty generalizations

20. ….Generalization
Valid generalizations
 Supported by facts
 Agrees with what you already know about the
topic
 Uses logic and reasoning
 Proven with several examples

21. ….Generalization
Faulty generalizations
 Not supported by facts
 Watch for the key words: none, all, always,
never, everyone, nobody