DATA INTERPRETATION - Qualitative and Quantitative

Why interpret research
results?
Numbers do
not speak for
themselves.
For example,
*What does it mean that 55 youth reported change in behavior,
*25 % of participants rated the program a 5 and 75 % rated it a 4?
What do these numbers mean?

The intelligence and logic of
the researcher are required in
this part. The analysis and
interpretation will be the
bases of the findings of the
study.
DATA INTERPRETATION

Interpretation refers to the task of
drawing inferences from the data
that has been collected, analyzed,
and presented after an analytical
and or experimental study.
What is Interpretation in Research?
It involves making “inferences pertinent to the research
relations” investigated from where generalizations are drawn
(Calderon & Gonzales, 1993).

Interpretation reflects the researcher’s
own understanding of the research
results which are guided by logic and
reason, established theories and
previous findings.
It is simply the process of
attaching meaning to the
data.

Qualitative
Data
What are the two types of data to
interpret?
Quantitative
Data

Qualitative
Data
requirements
and limitations
This requires
* UNDERSTANDING
* DIGESTING
* SYNTHESIZING
* CONCEPTUALIZING
* DESCRIPTIONS OF
BEHAVIORS, EXPERIENCES,
AND IDEAS
Qualitative Interpretation tends to be
more subjective in nature and many times
can be influenced by the researcher’s
biases.
(Leed & Ormrod, 2001)
The interpretation includes
the voices of the
participants. The
researchers should neither
say more than what the data
says nor less than the data
before them (Chenail,2012)

Types and Examples of Qualitative Data
• Open-ended Approach
What is your highest qualification?
• Closed-ended Approach
Example 1. : What is your highest
qualification?
- LPT
- CPA
- RN
- PhD
Example 2: Which of the following
payment platforms are you familiar with?
-Paypal
-Gcash
-Paymaya
NOMINAL DATA (Nominal Scale) -
attributes or properties and cannot be
calculated
ORDINAL DATA
- Variables have natural ordered categories
and distances between the categories are
not known
Question with a 5 point Likert Scale:
Example 1: How will you rate the new
BTS meal of Mc Donalds?
1. Very good
2. Good
3. Neutral
4. Bad
5. Very Bad
Example 2: How will you rate you
experience working with us?
1. Very good
2. Good
3. Neutral
4. Bad
5. Very Bad

Quantitative
Data
This is also known as numerical
data. Interpreting this in order to
make predictions is known as
Inferential Statistics.
Some measures used in
inferential statistics include the
standard error of the mean,
estimators, and the p-value.

Quantitative Data
• Answers to closed-ended
Survey Items
Example: Rating Scale from 1-5 for a certain
product, where, 1=Horrible, 2=Bad,
3=Average, 4= Good, and 5=Excellent
whereas a rating of 2.5 would mean the
product is below average.
• Attendance Data
Example: SF2 Daily Attendance Report of
Learners
• Scores on Standardized
Instruments
Example 1: RPMS-IPCRF Resuls-based
Performance Management System-
Individual Performance Commitment
and Review Form
- Other examples: Psychological Tests
and Inventories
• Demographic Data
-age, monthly income, asset and liability values
Note: Demographic data such as Sex, Gender,
Ethnicity/Nationality, Civil Status,
Employment status are usually Qualitative in
nature

There are levels of
interpretation considered in
organizing the discussion of
the results from the data.
Level 1 - data to be collected are compared
and contrasted
Data Interpretation

Levels of Interpreting the Data
Level 2-
explain the
internal validity,
consistency and
reliability of
results
Level 1-
data collected are
compared and
contrast
Level 3-
explain the
external validity of
the results, the
applicability to
Level 4-
relate or connect
the interpretation of
data with theory or
with review of

Sample Interpretation of Data
The term
“significant” here
simply means
“noticeably large”.
Considering that 124
out of 138 or 89.85 %
of students
experienced present
signs of depression.
Quantitative
Data
Interpretation

Important reminders in
interpreting a data:
1. Never use the word “proof” or “proves”
-No single scientific investigation ever proves anything.
2. Be objective and critical.
-Data-driven thinking and not personal
3. Never twist or alter the analysis of the data gathered.
-Unexpected result does not mean a bad or incorrect research
4. Ask technical assistance from professionals
-Have your interpretation be checked by a Statistician/Mathematician, a
reputable Researcher, and/or a Grammarian
-

It is absolutely essential that the task
of, interpretation be accomplished with
PATIENCE in an impartial manner and
also in correct perspective.
-Prof. A. Balasubramanian, 2017

“Data are just summaries of
thousands of stories-tell a few
of those stories to help make
the data meaningful.”
- Chip & Dan Heath

DATA INTERPRETATION - Qualitative and Quantitative

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