The document discusses data analysis methods for quantitative and qualitative data. It explains that quantitative data analysis involves coding, tabulating, and describing data using statistical techniques like frequency distribution, measures of central tendency, and standard deviation. More advanced quantitative methods include correlation, analysis of variance, and regression. Qualitative data analysis involves coding and organizing subjective data into themes to understand respondents' perspectives. Both quantitative and qualitative data analysis aim to understand the data and answer research questions, but qualitative analysis relies more on interpretation of textual data rather than numerical calculations.

1. Chapter IV
RESULTS AND FINDINGS
Chapter
Content

2. Data analysis is a process of understanding data or known facts or
assumptions serving as the basis of any claims or conclucions you
have about something. You collect these data in many ways:
observation, interview, documentary analysis, and research
instruments like questionnaires, test, etc. Your primary aim in analyzing
recorded data is to find out if they exist or operate to give answers to
the research questions you raised prior to your acts of collecting them.
In analyzing data, you go through coding and collating. Coding is your
act of using symbols like letters or words to represent arbitrary or
subjective data (emotions, opinions, attitudes) to ensure secrecy or
privacy of the data. Collating, on the other hand, is your way of brining
together the coded data. Giving the data an orderly appearance is
putting them in a graph, specifically a table of responses.

3. QUANTITATIVE DATA ANALYSIS
 All facts or information about people, places, things, events
and so on, and when these data appear not in words, images
or pictures, but in numerical forms such as fractions,
numbers and percentages, they become quantitative data.
 It is time consuming because it involves series of
examinations, classifications, mathematical calculations, and
graphical recording, among others.

4. ▷STEPS IN QUANTITATIVE DATA ANALYSIS
Step 1: Preparing the Data
a. Coding System
- To analyze data means to quantify or change the verbally
expressed data into numerical information. Converting
words , images, or pictures into numbers. If not, it is very
impossible to do mathematical operations of division,
multiplication or subtraction.
Ex. a. Gender: 1 - male and 2 - female
b. Educational Attainment: 2 – elementary , 4 - high school
, 6 – college, 9 – MA and 12 for Ph.D. level

5. b. Data Tabulation
- For
easy classification
and distribution of
number based on a
certain criterion,
you have to collate
them with the help
of a graph called
Table.
Gender Male: 11 (46%)
Female: 13 (54%)
Course Fine Arts: 9 (37%)
Architecture: 6 (25%)
Journalism: 4 (17%)
Comm. Arts: 5 (20%)
Role in the 2016
Seminar Workshop on
Arts
Speaker: 4 (17%)
Organizer: 3 (12%)
Demonstrator: 5 (20%)
Participant: 12 (50%)
Attended in 2016 Arts
Summer Workshop
Yes: 18 (75%)
No: 6 (25%)
Sample Size - 24

6. Step 2: Analyzing the Data
Data coding and tabulation are the two important things you
have to do in preparing the data for analysis. Before immersing yourself
into studying every component of the data, decide on the kind of
quantitative analysis you have to use.
1. Descriptive Statistical Technique - provides a summary of the
orderly or sequential data obtained from the sample through the
data –gathering instrument used. The results of the analysis reveal
the following aspects of an item in a set of data (Morgan 2014;
Punch 2014; Walsh 2010):
 Frequency Distribution – gives you the frequency of distribution
and percentage of the occurrence of an item in asset of data. In
other words, it gives you the number of responses given repeatedly
for one question.

7. Question: Do you find the Senators’ attendance in 2015
legislative sessions awful?
Measurement
Scale
Code Frequency
Distribution
Percent
Distribution
Strongly Agree 1 14 58%
Agree 2 3 12%
Neutral 3 2 8%
Disagree 4 1 4%
Strongly Disagree 5 4 17%

8.  Measure of Central Tendency – indicates the different positions or
values of the items, such that in a category of data, you find an item or
items serving as the:
 Mean – average of all the items or scores
Example: 3 +8 + 9 + 2 + 3 + 10 + 3
38/7 = 5.43
 Median – the score in the middle of the set of items that cuts or
divides the set into two groups. (arranged from lowest to
highest) Example: 2, 3, 3, 3, 8, 9, 10 = 3
 Mode – refers to the item or score in the data set that has the
most repeated appearance in the set.
Example: 3

9.  Standard Deviation – shows the extent of the difference of the data
from the mean. An examination of this gap between the mean and the
data gives you an idea about the extent of the similarities and
differences between the respondents. These are mathematical
operations that you have to do to determine the standard deviation.
 Step 1. Compute the Mean
 Step 2. Compute the deviation (difference) between each
respondent’s answer (data item) and the mean. The plus (+) sign
appears before the number if the difference is higher; negative (-)
sign, if the difference is lower.
 Step 3. Compute the square of each deviation.
 Step 4. Compute the sum of squares by adding the squared figures.

10.  Step 5. Divide the sum of squares by the number of data items to get
the variance.
 Step 6. Compute the square root of variance figure to get standard
deviation.
Standard Deviation of the category of the data
collected from selected faculty members of one
university
Step 1: Mean: 7
(Step 2) (Step 3)
Date Item Deviation Square of Dev.
1 - 8 64
2 - 5 25
6 - 1 1
6 - 1 1
8 + 8 1
6 - 1 1
6 - 1 1
(Step 2) (Step 3)
Date Item Deviation Square of Dev.
14 + 7 49
16 + 9 81
Total: 317
Step 4: Sum of Squares: 317
Step 5: Variance = 36 (317 + 9)
Step 6: Standard Dev. – 6 (square root of 6)

11. 2. Advanced Quantitative Analytical Methods
An analysis of quantitative data that involves the use of more
complex statistical methods needing computer software like the SPSS,
STATA or MINTAB, among others, occurs graduate-level students
taking their MA of PhD degrees. Some of the advanced methods of
quantitative data analysis are the ff: (Argyrous 2011, Levin and Fox
2014; Godwin 2014):
a. Correlation – uses statistical analysis to yield results that can
describe the relationship of two variables. The results, however,
are incapable of establishing casual relationships.
b. Analysis of Variance (ANOVA) – the results of this statistical
analysis are sued to determine if the difference in the means or
averages of two categories of data are statistically significant.

12. Example: If the means of the grades of a student attending tutorial
lessons is significantly different from the mean of the grades of a
student not attending tutorial lessons.
c. Regression – has some similarities with correlation, in that, it also
shows the nature of relationship of variables, but gives more
extensive result than that of correlation. Aside from indicating the
presence of relationship between two variables, it determines
whether a variable is capable of predicting the strength of the
relation between the treatment (independent variable) and the
Outcome (dependent variable). Just like correlation, regression is
capable of establishing cause-effect relationship.
Example: If reviewing with music (treatment variable) is a
statistically significant predictor of the extent of the concept learning
(outcome variable) of a person.

13. ▷QUALITATIVE DATA ANALYSIS
In a qualitative research, you analyze or study data thatreflect the
respondents’ thoughts, feelings, attitudes, or views about something.
These are subjective data that are expressed in words, and these words
serve as the unit of analysis in a qualitative type of research. You examine
these subjective data to understand how related or relevant they are to
your problem or specific research questions.
You collect qualitative data through interviews, observations, or
content analysis and then subject them to data analysis. In your data
collecting activities, you indispensably experience a lot of things vis-à-vis
the sources of data, such as their sizes, shapes, ideas, feelings, attitudes,
an so on.

14. Data analysis is a process of understanding data or known facts or
assumptions serving as the basis of any claims or conclucions you have
about something. You collect these data in many ways: observation,
interview, documentary analysis, and research instruments like
questionnaires, test, etc. Your primary aim in analyzing recorded data is to
find out if they exist or operate to give answers to the research questions
you raised prior to your acts of collecting them.
In analyzing data, you go through coding and collating. Coding is your act
of using symbols like letters or words to represent arbitrary or subjective
data (emotions, opinions, attitudes) to ensure secrecy or privacy of the
data. Collating, on the other hand, is your way of brining together the
coded data. Giving the data an orderly appearance is putting them in a
graph, specifically a table of responses. If you record these data through
verbal language or graphic means, you get to immerse yourself in a
qualitative data analysis, not quantitative data

15. analysis, for the latter deals with data expressed in numerical forms.
(Lyder 2013).
Qualitative data analysis is a time-consuming process. It makes you
deal with data coming from wide sources of information. It is good if all the
data you collected from varies sources of knowledge work favorably for
your research study, but, ironically, some of these may not have strong
relations to your research questions. Data analysis in a qualitative
research is a rigorous act of thematic or theoretical organization of ideas
or information into a certain format that is capable of presenting groups of
responses. Analyzing the data and synthesizing them based on one
principle idea, theory, or pattern demand a lot of time and effort, let alone
the methodical ways you have to adhere to in presenting the results of a
long a written discussion containing verbal or graphical explanations of
your findings. (Letherby 2012; Silverman 2013; Litchman 2013)

16. ACTIVITY 5: C4 CRAFTING TIME
Chapter IV
Results and Findings
Sir Von Christopher Chua
Font style – Arial
Font size:
Chapter Name - 14, Bold
Title of the parts - 12, bold
Body - 11
Spacing - 1.5
Margin - Top, bottom, right – 1
- Left = 1.5

17. GOOD LUCK
KEEP SAFE
GOD BLESS
Sir A