CHRIST THE KING COLLEGE

Gingoog City
Basic Education Department
S.Y. 2020-2021

MODULE 6 IN INQUIRIES, INVESTIGATION AND IMMERSION

WEEK NO. 6

STUDENT’S NAME: Ella moore B. Sotto QUARTER : ____________

GRADE AND SECTION: _________________ Student’s Contact #: ______________
(For Oral Recitation Purposes)

FOCUS COMPETENCY ESSENTIAL LEARNING COMPETENCY (MELC)

This module contains a comprehensive explanation about some of the essential elements in
conducting a research study more specifically on the Chapter 3. It presents guidelines crafting research
methodology, research design and other elements essential in the crafting Chapter 3.

At the end of this module, you are expected to:

1. describes adequately research design (either quantitative or qualitative), sample, instrument
used in quantitative research, data collection and analysis procedures.
2. presents written research methodology.

TABLE OF CONTENTS
MODULE 5: FINDING ANSWERS TO THE RESEARCH QUESTIONS (QUANTITAIVE)
Lesson 1: INTERPRETATION OF DATA
Lesson 2: QUANTITATIVE DATA ANALYSIS METHODS

Lesson I: Interpretation of Data

Topic Learning Outcomes: By the end of this lesson, I will be able to:
1. determine the guidelines in interpreting the data gathered and,
2. apply the guidelines in interpreting data.

Interpretation of data refers to the implementation of certain procedures through which data results from
surveys is reviewed, analyze for the purpose of achieving at valid and evident based conclusion. The
interpretation of data denotes a meaning to the information analyzed and determines its significance and
implications sto the study.
The first stage of analyzing data is data preparation, where the main goal is to transform raw data into something
meaningful, significant and user friendly. It includes the following steps:

Step 1: Data Validation

The goal of data validation is to check whether the gathered data was performed according to the set standards.
It is a four-step process, which includes
 Fraud - to ensure whether each respondents was actually interviewed.
 Screening - to check that respondents were chosen according to the standard research criteria.
 Procedure - to make sure whether the data collection process was followed
 Completeness - to make sure that the interviewer asked the respondent all the necessary questions,
rather than just choosing a few ones

To do this, you as a researcher would have to choose a random sample of completed surveys and validate the
data collected rather than have the whole population as the respondents.

For instance, suppose a survey with 900 respondents divided into 9 barangays. The researcher can pick a sample
of 50 random respondents from each barangay.

1
Step 2: Data Editing
Usually, many data sets include errors. For example, respondents may fill fields incompletely or skip them. To
ensure that these errors will not occur, the researcher should conduct the initial data checking and edit the raw
research data to identify and clean out any points that may become the barrier to come up with an accurate
results.
For example, an error could be fields in the data information that were left empty by respondents. While
editing and checking the data, it is important to ensure that empty data/information will be removed or has to
be filled in

Step 3: Data Coding

This is the number one significant process in data preparation. Data coding refers to grouping and assigning
values/codes to responses from the conducted survey.
For example, if a researcher has interviewed 1000 people and now wants to find the average daily allowance of
the respondents, the researcher will create daily allowance brackets and categorize the daily allowance of each of
the respondent as per codes. (For example, respondents who has a daily allowance of Php10.00 - below Php20.00
and Php20.00 – below Php3000 would have their daily allowance coded as 1, Php10.00 – below Php20.00 as 2,
Php20.00 – below Php30.00 as 2, etc.)
Then during analysis, the researcher can come up with simplified daily allowance, rather than having many
ranges of individual daily allowances.
Quantitative data interpretation comprises studying the results taken from various questions in a survey. The
results are commonly shown numerically and by percentage in the data tables.
After doing the three steps mention above, the data is now ready for the analysis. The two most widely used
quantitative data analysis methods are descriptive statistics and inferential statistics.

Lesson II: Quantitative Data Analysis Method

Topic Learning Outcomes: By the end of this lesson, I will be able to:
1. examine various means in analyzing quantitative data and,
2. analyze quantitative data using the given methods.

Data collection comprises a major area of the research process. This data however has to be analyzed to have it` s
meaning. There are many methods of analyzing quantitative data collected in surveys. They are:

Cross-tabulation
This is the most commonly used quantitative data analysis methods. It is the most preferred method since it uses
a basic tabular form to draw inferences between different data-sets of dependent and independent variable. It
contains data that have some connection with each other.

“The Question Pro” (2008)

Steps to conduct Quantitative Data Analysis
For quantitative data, raw data has to show in a significant manner using analysis methods. Quantitative data
should be analyzed in order to find evidential/factual data that would help in facilitating the research process.

Relate measurement scales with variables:

Associate scales of measurement such as Nominal, Ordinal, Interval and Ratio with the variables – dependent
and independent variables. This step is of utmost important to arrange the data in proper sequence/order. Data
can be entered/encoded into an excel sheet to organize it in a specific data format.

Connect descriptive statistics with data:

Connect descriptive statistics to contain available data. It can be hard to establish a pattern in the raw data.
Some commonly used descriptive statistics are:
Mean - An average of values for a specific variable
2
 Median - A midpoint of the value scale for a variable
Mode - For a variable, the most common value
Frequency - Number of times a particular value is observed in the scale
Minimum and Maximum Values - Lowest & highest values for the scale
Percentages - Format to express scores and set of values for variables
Range- the highest and lowest value in a set of values.

Decide a measurement scale:

It is important to decide the measurement scale to conclude a descriptive statistic for the specific variable.
For example, a nominal variable score will never have a mean or median and so the descriptive statistics will
vary. Descriptive statistics will suit in a situation where the results are not to be generalized to the whole
population.

Select appropriate tables to represent data and analyze collected data:

After deciding on a suitable measurement scale, researchers can use a tabular format to represent data. This data
can be analyzed using various techniques such as Cross-tabulation.

Descriptive statistics provide absolute/whole numbers. However, they do not explain the reasoning behind
those numbers. Before applying descriptive statistics, it’s important to think about which one is the most
appropriate for your research question and what you want to present. For instance, a percentage is a good way to
present the age distribution of respondents.
It should be noted that visual presentations of data findings are insignificant unless a sound decision is made
regarding scales of measurement.
Before any 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. The varying scales include:
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.).
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.
Quantitative Data Example
I updated my laptop 2 times in a year. Our youngest sister grew by 5 inches last year
68 people uploaded the latest mobile application. 35% people prefer shopping online instead of going
to the mall

Descriptive statistics are most helpful when the research is limited to the sample and does not need to be
generalized to a larger population. For example, if you are comparing the percentage of adults vaccinated in four
different barangays, then descriptive statistics is enough. Since descriptive analysis is mostly used for analyzing
single variable, it is often called univariate analysis.
The importance of data interpretation is evident and this is why it needs to be done correctly. Data is very likely
to arrive from multiple sources and tends to enter the analysis process with tapsy turvy ordering. Data analysis
tends to be extremely subjective. 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”.

Example of Data Analysis:

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 barangays of El Salvador City ages 22 to 60
years old. The following data were given.

3
Interpretation of Data (Table 1)
Table 1 reveals that almost 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 product and services.

Interpretation of Data (Table 2)

Table 2 shows that 61.33 percent of the respondents are female compared to 38.67 percent males. This is
representative of the current gender distribution of the population in the Philippines. According to Philippine
Statistics Authority (PSA) in 2015 of the total population in the Philippines, 50.40% are males and the rest are
females. This gender distribution is common among most countries where male becomes more in population
than female (Skelton, 2012).

Broadening of Concept

How crucial the process of data-analysis is in doing a research? How does it affect the holistic sense of research?
Values Integration Activity (4-Pronged Integration)
Answer: As we all know the process of analyzing data is one of the most important things in doing
research owing to lends credibility to the data. This will give confidence and back up with references
and gives it a theoretical base to stand on. It is the base on which the entire study will rely upon. This
will totally affect the researchers providing an insight to clearly understand for whom and for what
purpose you are conducting the analysis, this will analytics assist human in making decision therefore
conducting the analytics to produce the best results for the decision to be made is an important of the
process, as is appropriately presenting the result.

How does comprehensive analysis of things link us towards excellence?

As I understand the question, we all know that analysis is part of the problem solution process and for me
analyzing is something that is patiently waiting or understand the situation “Situation awareness”
This can be useful for us to step further. Or to become excellent because from analyzing we can define the
problem and we can also generate alternative solutions which lead us towards excellent.

Biblical Passage: Hebrews 4:12 

4
  “For the word of God is living and active, sharper than any two-edged sword, piercing to the
division of soul and of spirit, of joints and of marrow, and discerning the thoughts and intentions
of the heart."

SELF-ASSESSMENT: With your respective research group, review and revise the Chapter4 of the
research study you have conducted during your Practical Research 2 subject. Communicate with your
groupmates as it is a group task.

I am Group 6 leader namo si Jamie sir.

SUMMARY: On the space provided below, create your own hashtag about the module you have answered and
incorporate a single sentence explanation.

#Analyzing means proper conducting

____________________________________________________________
___________________.

REFERENCE/S:
Bhatia, M. 2018.Your Guide to Qualitative and Quantitative Data Analysis Method shorturl.at/gsDP9
Bhat, A.2019. Five Methods Used for Quantitative Data Collection. shorturl.at/abmqZ
Griffiths, J.R.2008. Quantitative Data Analysis:Learn Higher. shorturl.at/rxHPW
Lebiad, M.2018. A Guide to the Methods, Benefits & Problems to the Interpretation of Data.
shorturl.at/bjpH2
Surendran, A. 2019.Quantitative Data: Definition, Types, Analysis and Examples. shorturl.at/ehCHV
Suttle, Rick. 2020.Quantitative Data Interpretation. accessed January 29, 2020.
http://smallbusiness.chron.com/quantitative-data-interpretation3300.html Suttle, R. Quantitative Data
Interpretation.
FEEDBACK/COMMENTS:

Monitored by:

_______________________________ Date: _____________________

Parent’s Signature over Printed Name

5
For questions and concerns, you may contact the subject teacher using the following medium:
Gmail- jlguimaras@gmail.com
Cellphone number- 09976185389
Messenger account- John Louie P. Guimaras