Inquiries

1. FINDING THE ANSWERS
TO THE RESEARCH
QUESTIONS
Lesson 2 – Interpretation and Presentation of Results
QUARTER 4 WEEK 2
INQUIRIES, INVESTIGATIONS
AND IMMERSION

2. CLASS
DISCUSSION
TOPICS:
-Interpretation and
Presentation of the Results

3. INTERPRETATION
Is the process of attaching
meaning to the data.

4. The following are the STEPS IN
INTERPRETING RESEARCH
FINDINGS:
1. Points or important findings should
be listed
2. The lessons learned and new things
should be noted.

5. 3. Quotes or descriptive examples
given by the participants should be
included.
4. The new found knowledge from
other settings, programs, or
reviewed literatures should be
applied.

6. SCALES OF MEASUREMENT
1. NOMINAL SCALE – non-
numeric categories that
cannot be ranked or
compared quantitatively.

7. 2. ORDINAL SCALE –
exclusive categories that are
exclusive and exhaustive but
with a logical order.

8. 3. INTERVAL – a
measurement scale where
data is grouped into
categories with orderly and
equal distances between the
categories.

9. 4. RATIO – contains features
of all three.

10. HOW TO INTERPRET A DATA?
When interpreting data, an analyst
must try to discern the differences
between correlation, causation and
coincidences, as well as many other
bias – but he also has to consider all
the factors involved that may have led
to a result.

11. TWO MAIN METHODS OF
INTERPRETATION OF DATA:
Qualitative Analysis
Quantitative Analysis

12. QUALITATIVE DATA INTERPRETATION
1. OBSERVATIONS – detailing behavioral
patterns that occur within an
observation group.
2. DOCUMENTS – different types of
documents resources can be coded and
divided based on the type of material they
contain.

13. 3. INTERVIEWS – one of the
best collection methods for
narrative data.

14. QUANTITATIVE DATA INTERPRETATION
1. MEAN – represents a numerical average
for a set of responses.
- When dealing with a data set (or multiple
data sets), a mean will represent a central
value of a specific set of numbers.
- It is the sum of the values divided by the
number of values within the data set.

15. 2. STANDARD DEVIATION – this is
another statistical term commonly
appearing in quantitative analysis.
- Reveals the distribution of the
responses around the mean.
- It describes the consistency within the
responses; together with the mean, t
provides insight into data sets.

16. 3. FREQUENCY DISTRIBUTION – this
is a measurement gauging the rate of
a response appearance within a data
set.
- Keen in determining the degree of
consensus among data points.

17. OTHER SIGNATURE INTERPRETATION
PROCESSES OF QUANTITATIVE DATA:
1. Regression Analysis
2. Cohort Analysis
3. Predictive and Prescriptive
Analysis

18. WHY DATA INTERPRETATION IS IMPORTANT?
-The purpose of collection and
interpretation is to acquire useful
and usable information and to
make the most informed
decisions possible.

19. WHAT ARE A FEW OF THE BUSINESS BENEFITS
OF DIGITAL AGE DATA ANALYSIS AND
INTERPRETATION?
1. Informed decision-making
2. Anticipating needs with trends
identification
3. Cost efficiency
4. Clear foresight

20. PRESENTING DATA FOR INTERPRETATION
Textual Method
-Rearrangement
from lowest to
highest
-Stem-and-leaf
plot
Tabular Method
-Frequency Distribution
Table (FDT)
-Relative FDT
-Cumulative FDT
-Contingency FDT

21. PRESENTING DATA FOR INTERPRETATION
Graphical Method
-Bar chart
-Histogram
-Frequency polygon
-Pie chart
-Less than, greater than

22. VARIOUS METHODS OF DATA PRESENTATION
1. As Text – Raw data with proper
formatting, categorization,
indention is most extensively
used and is a very effective way
of presenting data.

23. 2. In Tabular Form – is used to
differentiate, categorize,
relate different datasets.

24. FREQUENCY DISTRIBUTION
TABLE (FDT) – is a table which
shows the data arranged into
different classes and the number
of cases which fall into each
class.

25. Table 1.1 Frequency Distribution for the Ages of 50
Students Enrolled in Statistics
Age Frequency
12 2
13 13
14 27
15 4
16 3
17 1
N = 50

26. 3. In Graphical Form – Data
can further be presented in a
simpler and even easier form
by means of using graphs.

27. a. Bar charts / Bar graphs
b. Line chart
c. Pie charts
d. Combo chart

28. CONCEPTUAL FRAMEWORK
- Is used to illustrate what you expect
to find through your research,
including how the variables you are
considering might relate to each
other.

29. PURPOSE OF CONCEPTUAL FRAMEWORK
1. Identify relevant variables
2. Define variables
3. Have an idea of analysis

30. STEPS IN DEVELOPING CONCEPTUAL FRAMEWORK
1. Identifying the relevant concept
2. Defining those concepts
3. Operationalizing the concepts
4. Identifying any moderating or intervening
variables
5. Identifying the relationships between
variables

31. Different Forms of Conceptual Framework
1. Overlapping domains framework

32. 2. Sequential framework

33. 3. Ecological framework