What I Need to Know

Data is all around us, it's everywhere, and every action we do results in new data

and information. Research data such as questionnaires, Focus Group Interview

(FGI), Focus Group Discussion (FGD), and other related documents should be

collected, observed, or created for analysis to come up with original research results.

You cannot simply move to a conclusion in your research study without doing the

correct process and methodology used to analyze and interpret the data gathered. In

other words, data analysis, interpretation, and implications are needed. As a

researcher, this is very important to you, in the same manner as a doctor to analyze

the condition of the patient before giving him any advice, treatment, and medicines.

Data analysis helps the researcher to come up with a valid and concrete conclusion.

This module will guide you on how to do the interpretation of data and data analysis

methods. It contains activities that can help you enhance your knowledge and skill

in data analysis, interpretation, and implication. You can improve your skills in this

area. Nothing is impossible.

This module contains activities that guide you on the appropriate method of analysis

of data obtained, interpretation, and presentation of results (if applicable).

This module has two lesson lessons in qualitative data analysis:

• Data Analysis method

• Interpretation of Data

What you are expected to learn?

After going completed this module, you are able and expected to

gather analyze data with intellectual honesty using suitable

techniques.

How to learn this module?

• Take your time to read and understand the concepts in this module

• Follow the instruction carefully in every given task

• Answers all the given tests and exercises

• Present an output in every performance task given

• Familiarize yourselves with the given terms

CO_Q2_ Inquiries, Investigation and

Immersion SHS

Module 5

The following terms will be encountered in the lesson:

• Research data- is any information that has been collected, observed, generated,

or created to validate a research study.

• Data analysis- a process that involves examining, and molding collected data for

interpretation to discover relevant information, draw or propose conclusions, and

support decision-making to solve a research problem.

• Data Interpretation- is the process of making sense of numerical data that has

been collected, analyzed, and presented.

• A Conceptual framework is an analytical tool that is used to get a comprehensive

understanding of a phenomenon. It can be in different fields of work and is most

commonly used to visually explain the key concepts or variables and the

relationships between them that need to be studied.

What is Data Analysis in Research?

Data analysis is a way of simplifying numerous and wordy data to a meaningful story

and interpreting it to arrive at an insight to behold. It is a process of converting a

multitude of data into a smaller group of sensible data.

Since it is a process, it involves several stages. To start with, data must be organized.

The next step is to summarize and categorize data together. Through data analysis,

you can find patterns and themes to identify and link ideas. Lastly, is to really

analyze data from the start to finish, or one may go backward in analyzing it.

Most beginner researchers, find data analysis very tasking and time-consuming. It

is very hard to navigate with data, especially if it entails vague data. However, the

end result will fascinate anyone as it will bring about clear, well-structured, and

meaningful data.

Why do we need to analyze data in research?

For a researcher, to tell a story about a problem solved, large-scale data might be too

boring for the spectators. Although they rely mainly on data, it cannot give a clear

picture or answer to some questions. Well-analyzed data will reveal patterns that

may be interesting and worth exploring. Through data analysis, researchers can have

a bigger, meaningful, and beautiful picture of data. Organized and analyzed data can

guide the researcher to find patterns and provide shape and beauty to the story they

want to tell. On the other hand, an open-minded researcher must remain unbiased

in whatever data is gathered. Along the way, unexpected patterns, expressions, and

results may arise. Remember that data analysis can sometimes reveal the most

unexpected yet intriguing stories that were not anticipated at the time of data

collection. As a result, trust the information you have and enjoy the voyage of

exploratory investigation.

Data analysis in qualitative and quantitative research

Qualitative data analysis usually involves texts, phrases, images, objects, and

sometimes symbols. Some details in this part have been discussed in your Practical

Research 1.

On the other hand, quantitative data analysis involves numbers and statistics.

Statistical analysis is the core of quantitative analysis. It deals with basic

calculations including average and median to more sophisticated analyzes like

correlations and regressions.

While descriptive statistics gives details on your specific data set, inferential statistics

aim to make inferences about the population. It makes two common times of

predictions. One is prediction between groups, for example, weight differences

between learners grouped according to their favorite meal. The second is

relationships between variables. For example, the relationship between body weight

and the number of hours a week a person does Zumba dance. In other words,

inferential statistics allows you to connect the dots and make predictions based on

what you observe in your sample data.

A. Define the following Common Inferential Methods

1. T-Tests

2. ANOVA

3. Correlation Analysis

4. Regression Analysis

B. Direction: Read and answer the questions carefully. Write and explain your

answer on your answer sheet.

1. What type of data analysis did you use in your research paper?

2. Identify the methods that you used in analyzing your paper.

3. Whether your research used qualitative data analysis or quantitative data

analysis, present the process of analyzing you did.

Sample interpretation of data using the extracted table from the unpublished

research paper of Ms. Cristy G. Dablo, entitled, “TEENAGE PREGNANCY AND ITS

INTERVENTIONS: MINIMIZING FUTURE RISKS AMONG HIGH SCHOOL

STUDENTS.”

Table 1. Experiences knowing that you are pregnant

“Yung natatakot akong hindi panagutan ng nobyo ko, pero mas natakot

akong hindi matanggap ng parents ko ang aking nobyo dahil ayaw nila sa

kanya.”

{I’m afraid that my boyfriend won’t carry the responsibility, but I am more

afraid of my parents not accepting me as they don’t like my boyfriend}

“Nung nalaman kung buntis ako para akng na down kasi nag overthink

ako sa mga posebling mangyari at hindi ko alam ang aking gagawin.

{As soon as I know that I’m pregnant, I felt so down because I overthink of

possibilities and do not know what to do}

“Natakot ako… kasi mapapahiya ang aking pamilya. Iniisip ko na hindi

ituloy ang aking dinadala. Gusto ko magpakalayo na lang, titigil sap ag

aaral. Nawalan ako ng pag-asa sa buhay ng dahil sa bata.”

{I’m afraid… because I put shame on my family. I thought of aborting my

baby inside my tummy. I want to stay away from them, I want to stop

schooling. I lost hope in my life because of the baby.}

Interpretation for Table 1:

All respondents’ responses were about fear, worries, and apprehensions. Table 1

showed the emotions that respondents felt knowing that they were pregnant at an

early age. Three (3) directly blurted out the feeling of fear, and the rest indirectly said.

Fear on how the parents reacted to the shame they brought up, fear of hopelessness

that the baby shuttered their future dreams, fear on how they raise the child knowing

that they are incapable of supporting themselves. The fear felt a push to worry,

apprehend and think of the worst deed to abort the child.

According to Enyegue (2004) teenagers raised in a culture where parents are really

afraid to broach the topic to their kids are at risk of early pregnancy. With this, many

teens worry about what their families will say when they find out that they are

pregnant. So, they avoid telling their parents or someone else who might be able to

help them find support. This delays their prenatal care, making the pregnancy even

riskier for themselves and their baby. With that fear, abortion came to their thinking

trying to solve the problem, facing the grim realities of teen pregnancy is not pleasant.

Suppose a study is conducted to one of the companies in El Salvador City Misamis

Oriental to determine the factors affecting customer preferences among the residence

of one barangay of El Salvador City ages 22 to 60 years old. The following data were

given.

Table 1

Distribution of Respondents by Age

Age	Frequency	Percent
21 – 30 yrs. old	170	45.33
31 – 40 yrs .old	90	24.00
41 – 50 yrs .old	80	21.33
51 – 60 yrs .old	35	9.33
Total	375	100

Interpretation of Data (Table 1)

Table 1 reveals that 45.33 percent of the respondents are in the age bracket of 21-

30 years old compared to only 9.3 percent in ages 51 – 61 years old and above and

21.33 percent belonged to the 31- 40 age range.

This age profile is important as it also reflects the current age demographic for the

Filipinos according to Philippine Statistics Authority (PSA). There is a much younger

age cohort of teachers entering the workforce.

There is a much younger cohort who has the capacity to purchase products and

Services

REFERENCE:

Inquiries, Investigation and Immersion Quarter 2 – Module 5: Finding the Answers to the Research Questions.

Marjorie B. Yosores Emily A. Tabamo

Rudilyn F. Zambrano Cheryll M. Sabaldana

Jungie G. Palma Cathrine B. Pielago

Christy C. Gabule-Dablo, DScN

Maria Conception Sione E. Alpore

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