The document discusses the process of interpreting and presenting research findings, emphasizing the importance of data analysis in both qualitative and quantitative research. It outlines various methods and steps involved in data interpretation, such as identifying themes, applying scales of measurement, and using visual formats like graphs and tables for clarity. Additionally, it highlights the significance of constructing a conceptual framework to guide research design and provide a coherent understanding of the variables involved.

1. FINDING THE ANSWERS TO
THE RESEARCH QUESTIONS
(Interpretation and Presentation of Results)
TOPIC 6

2. LEARNING COMPETENCIES:
a. interpret data gathered using
suitable techniques and conceptualize
framework of the research paper;
b. Know the importance of data
interpretation; and
c. frame conceptual framework for
qualitative research.

3. Name the following pictures presented below. Identify if it is
whether a bar graph, line graph, pie graph or table.

4. Interpreting the Data
Interpretation is the process of attaching
meaning to the data. After identifying and
analyzing, coding and organizing a
presentation, and identifying the themes
and patterns, the next step is to interpret
the results. In this process, the results are
synthesized into a coherent whole.

5. Interpreting the Data
Meaning and significance are attached to
the analysis of data. The themes and
patterns serve to explain the findings. The
implications of the study are highlighted in
this section as a preface for the final parts of
the research paper which are the summary
of the findings, conclusion, and
recommendations.

6. The following are the steps in interpreting
research findings:
a. Points or important findings should be listed.
b. The lessons learned and new things should be
noted.
c. Quotes or descriptive examples given by the
participants should be included.
d. The new found knowledge from other settings,
programs, or reviewed literatures should be
applied.

7. The following are the levels of interpretation as
considered in organizing the discussion of the
results (Ducut & Pangilinan, 2006):
a. Level 1. Data collected are compared and
contrasted and any unexpected results may be
included. Comments on certain shortcomings
of the study may be made but they should not
overly emphasize the flaws.

8. b. Level 2. The internal validity of the results,
as well as their consistency or reliability, is
explained. The causes or factors that may have
influenced the results are described.
c. Level 3. The external validity of the results,
that is, their generally or applicability of the
external condition is explained.
d. Level 4. The data are related to theoretical
research or with reviewed literature.

9. The importance of data interpretation is evident and
this is why it needs to be done properly. Data is very
likely to arrive from multiple sources and has a
tendency to enter the analysis process with haphazard
ordering. Data analysis tends to be extremely
subjective. That is to say, the nature and goal of
interpretation will vary from business to business,
likely correlating to the type of data being analyzed.
While there are several different types of processes
that are implemented based on individual data nature,
the two broadest and most common categories are
“quantitative analysis” and “qualitative analysis”.

10. Yet, before any serious data interpretation
inquiry can begin, it should be understood that
visual presentations of data findings are
irrelevant unless a sound decision is made
regarding scales of measurement. Before any
serious data analysis can begin, the scale of
measurement must be decided for the data as
this will have a long-term impact on data
interpretation ROI. The varying scales include:

11. •Nominal Scale: non-numeric categories that
cannot be ranked or compared quantitatively.
Variables are exclusive and exhaustive.
•Ordinal Scale: exclusive categories that are
exclusive and exhaustive but with a logical
order. Quality ratings and agreement ratings
are examples of ordinal scales (i.e., good,
very good, fair, etc., OR agree, strongly agree,
disagree, etc.).

12. •Interval: a measurement scale where data is
grouped into categories with orderly and
equal distances between the categories.
There is always an arbitrary zero point.
•Ratio: contains features of all three.

13. How to Interpret 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.

14. There are various data interpretation methods
one can use. In this part, we will look at the
two main methods of interpretation of data:
with a qualitative and a quantitative analysis.
Qualitative Data Interpretation Qualitative
data analysis can be summed up in one word
– categorical. With qualitative analysis, data is
not described through numerical values or
patterns, but through the use of descriptive
context (i.e., text).

15. Typically, narrative data is gathered by
employing a wide variety of person-to-person
techniques. These techniques include:
•Observations: detailing behavioral patterns
that occur within an observation group.
These patterns could be the amount of time
spent in an activity, the type of activity and
the method of communication employed.

16. •Documents: much like how patterns of
behavior can be observed, different types of
documentation resources can be coded and
divided based on the type of material they
contain.
•Interviews: one of the best collection
methods for narrative data. Enquiry
responses can be grouped by theme, topic or
category. The interview approach allows for
highly-focused data segmentation.

17. A key difference between qualitative and
quantitative analysis is clearly noticeable in the
interpretation stage. Qualitative data, as it is
widely open to interpretation, must be “coded”
so as to facilitate the grouping and labeling of
data into identifiable themes. As person-to-
person data collection techniques can often
result in disputes pertaining to proper analysis,
qualitative data analysis is often summarized
through three basic principles: notice things,
collect things, think about things.

18. Quantitative Data Interpretation
If quantitative data interpretation could be summed
up in one word (and it really can’t) that word would
be “numerical.” There are few certainties when it
comes to data analysis, but you can be sure that if
the research you are engaging in has no numbers
involved, it is not quantitative research. Quantitative
analysis refers to a set of processes by which
numerical data is analyzed. More often than not, it
involves the use of statistical modeling such as
standard deviation, mean and median.

19. Let’s quickly review the most common statistical
terms:
•Mean: a 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. Other terms that can
be used to describe the concept are arithmetic
mean, average and mathematical expectation.

20. •Standard deviation: this is another statistical
term commonly appearing in quantitative
analysis. Standard deviation reveals the
distribution of the responses around the
mean. It describes the degree of consistency
within the responses; together with the
mean, it provides insight into data sets.

21. •Frequency distribution: this is a
measurement gauging the rate of a response
appearance within a data set. When using a
survey, for example, frequency distribution
has the capability of determining the number
of times a specific ordinal scale response
appears (i.e., agree, strongly agree, disagree,
etc.). Frequency distribution is extremely
keen in determining the degree of consensus
among data points.

22. Typically, quantitative data is measured by visually
presenting correlation tests between two or more
variables of significance. Different processes can
be used together or separately, and comparisons
can be made to ultimately arrive at a conclusion.
Other signature interpretation processes of
quantitative data include:
•Regression analysis
•Cohort analysis
•Predictive and prescriptive analysis

23. 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. From businesses, to
newlyweds researching their first home, data collection
and interpretation provides limitless benefits for a wide
range of institutions and individuals.

24. Data analysis and interpretation, regardless of
method and qualitative/quantitative status, may
include the following characteristics:
•Data identification and explanation
•Comparing and contrasting of data
•Identification of data outliers
•Future predictions

25. Data analysis and interpretation, in the end,
helps improve processes and identify
problems. It is difficult to grow and make
dependable improvements without, at the very
least, minimal data collection and
interpretation. What is the key word?
Dependable. What are a few of the business
benefits of digital age data analysis and
interpretation?

26. 1) Informed decision-making: A decision
is only as good as the knowledge that
formed it. Informed data decision making
has the potential to set industry leaders
apart from the rest of the market pack.

27. 2) Anticipating needs with trends
identification: data insights provide
knowledge, and knowledge is power. The
insights obtained from market and consumer
data analyses have the ability to set trends for
peers within similar market segments.
3) Cost efficiency: Proper implementation of
data analysis processes can provide
businesses with profound cost advantages
within their industries.

28. 4) Clear foresight: companies that collect and
analyze their data gain better knowledge
about themselves, their processes and
performance. They can identify performance
challenges when they arise and take action to
overcome them. Data interpretation through
visual representations lets them process their
findings faster and make better-informed
decisions on the future of the company.

30. Various methods of data presentation can be used
to present data and facts based on available data
set. Widely used format and data presentation
techniques are mentioned below:
1. As Text – Raw data with proper formatting,
categorization, indentation is most extensively used
and is a very effective way of presenting data. Text
format is widely found in books, reports, research
papers and in this article itself.

31. a.With the rearranged data, pertinent data worth
mentioning can be easily recognized. The following
is one way of presenting data in textual form.
b.Stem-and-leaf Plot is a table which sorts data
according to a certain pattern. It involves
separating a number into two parts. In a two-digit
number, the stem consists of the first digit, and the
leaf consists of the second digit. While in a three-
digit number, the stem consists of the first two
digits, and the leaf consists of the last digit. In a
one-digit number, the stem is zero

32. Below is the stem-and-leaf plot of the ungrouped data given in
the example:
• Stem Leaves
0 -9
1 -7,8
2 -0,3,3,4,5,6,7,8,9
3 -4,4,5,5,7,8,8,8,8,9,9,9
4 -2,3,3,4,4,5,5,5,6,6,6,8,9
5 -0,0,0
Utilizing the stem-and-leaf plot, we can readily see the order of
the data. Thus, we can say that the top ten got scores 50, 50,
50, 49, 48, 46, 46, 46,45, and 45 and the ten lowest scores are 9,
17, 18, 20, 23,23,24,25,26, and 27.

33. 2. In Tabular Form – Tabular form is generally used
to differentiate, categorise, relate different datasets.
It can be a simple pros & cons table, or a data with
corresponding value such as annual GDP, a bank
statement, monthly expenditure etc. Quantitative
data usually require such tabular form.
A Frequency Distribution Table (FDT)- is a table
which shows the data arranged into different
classes(or categories) and the number of cases(or
frequencies) which fall into each class.

39. A complete FDT has class mark or midpoint (x), class boundaries (c.b),
relative frequency or percentage frequency, and the less than
cumulative frequency (cf).

40. 3. In Graphical Form – Data can further be
presented in a simpler and even easier form by
means of using graphics. The input for such
graphical data can be another type of data itself or
some raw data. For example, a bar graph & pie chart
takes tabular data as input. The tabular data in such
case is processed data itself but provides limited
use. Converting such data or raw data into graphical
form directly makes it quicker and easier to
interpret.

41. a. Bar Charts/Bar Graphs: These are one of the
most widely used charts for showing the grown of a
company over a period. There are multiple options
available like stacked bar graphs and the option of
displaying a change in numerous entities.
b. Line Chart: These are best for showing the
change in population, i.e., for showing the trends.
These also work well for explaining the growth of
multiple areas at the same time.

42. c. Pie Charts: These work best for representing the
share of different components from a total 100%.
For eg. contribution of different sectors to GDP, the
population of different states in a country, etc.
d. Combo Chart: As the name suggests it is a
combination of more than one chart type. The one
shown in the figure below is a combination of line
and bar graph. These save space and are at times
more effective than using two different charts. There
can even be 3 or more charts depending on the
requirement.

43. Conceptual Framework
A 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.
You should construct one before you actually begin
your investigation.

44. "Miles and Huberman (1994) defined a
conceptual framework as a visual or
written product, one that “explains, either
graphically or in narrative form, the main
things to be studied—the key factors,
concepts, or variables—and the
presumed relationships among them” (p.
18).

45. Here, I use the term in a broader sense,
to refer to the actual ideas and beliefs
that you hold about the phenomena
studied, whether these are written down
or not; this may also be called the
“theoretical framework” or “idea context”
for the study.

46. A valuable guide to developing a
conceptual framework and using this
throughout the research process, with
detailed analyses of four actual studies, is
Ravitch and Riggan, Reason & Rigor: How
Conceptual Frameworks Guide Research
(2011).
(Full disclosure: Sharon Ravitch is a former student of mine, and I
wrote the foreword for the book.)

47. The most important thing to understand
about your conceptual framework is that
it is primarily a conception or model of
what is out there that you plan to study,
and of what is going on with these things
and why—a tentative theory of the
phenomena that you are investigating.

48. The function of this theory is to inform the
rest of your design— to help you to assess
and refine your goals, develop realistic and
relevant research questions, select
appropriate methods, and identify potential
validity threats to your 3 Conceptual
Framework What Do You Think Is Going On?
40 Qualitative Research Design conclusions.
It also helps you justify your research."
Retrieved from:https://academicguides.waldenu.edu/library/conceptualframework

49. •Purpose of Conceptual Framework
1. Identify relevant variables
2. Define variables
3. Have an idea of analysis

50. How to develop conceptual
framework for a qualitative
research study?

51. Qualitative research’s conceptual
framework can be developed based on
your research problem, objective &
question(s).
The goal of the conceptual framework is
to illustrate your research approach in
some pictorial & text forms to ease
readers’ understanding of your research
approach.

52. •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.

53. The pieces of the conceptual framework are borrowed
but the researcher provides the structure. To develop
the structure you could:
•Identify the key words used in the subject area of
your study.
•Draw out the key things within something you have
already written about the subject area - literature
review.
•Take one key concept, idea or term at a time and
brainstorm all the other things that might be related
and then go back and select those that seem most
relevant.

54. Whichever is used it will take time and a number of iterations
and the focus is both on the content and the inter-
relationships.
• How does it look? It can take the form of Equation or a
diagram or may simply description of how the variables are
related. Diagram may take the form of…

58. Thank you! 