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DATA ANALYSIS TECHNIQUES
IN QUANTITATIVE AND
QUALITATIVE RESEARCH
HARVEY R. PADRIGANO
Salcedo Vocational High School 313509 / Salcedo I District
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SESSION OBJECTIVES
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Identify data analysis techniques commonly
used in quantitative and qualitative research.
Differentiate the use of each data analysis
technique.
Compute for the value of quantitative data
analysis techniques.
Apply the appropriate data analysis technique to
be used in the given problem.
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Activity 1: Classifying Data Analysis Techniques
1. Study the words that will be flashed on the screen.
2. Classify the words under qualitative and
quantitative.
3. Post your answers on the comment section in the
Google Meet platform.
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Questions for Analysis
1. How did you find the activity?
2. Was it easy or difficult? Why or Why not?
3. How were you able to classify the words?
4. What helped you in classifying the words?
5. When do we appropriately use these data
analysis techniques?
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Quantitative Data Analysis using…
Descriptive
Statistics
It used in describing a set of data. This
includes frequency count, percentage,
measures of central tendency (mean,
median and mode) and measures of
variability (range, variance and
standard deviation).
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Quantitative Data Analysis Techniques
Frequency counts
Percentage
Measure of the number of
times that an event or
observation occurs.
A part of a whole
expressed in hundredths.
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Quantitative Data Analysis Techniques
Mean
Median
The average of the set of
numbers.
The middle most value
when data are arranged
(increasing or decreasing
order).
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Quantitative Data Analysis Techniques
Mode
Range
The value/s that appear/s
most often.
The difference between
the largest and smallest
values in a set of data.
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Quantitative Data Analysis Techniques
Variance
Standard
Deviation
The squared value of the
standard deviation.
The measure of the
dispersion of the set of
data from its mean.
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Activity 2: Practice Makes Perfect But Nobody’s
Perfect 😉
1. A research data will be flashed on the screen from a
random survey on demographic profile of eight (8)
respondents.
2. Study the data provided and answer the questions to
be flashed on the screen.
3. Post your answers on the comment section in the
Google Meet platform.
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Respondent Sex Age Height Weight Marital
Status
Total
Monthly
Expenses
1 F 33 173 cm 72 kg Married Php 20,000
2 F 30 169 cm 65 kg Single Php 23,000
3 M 29 180 cm 82 kg Single Php 21,500
4 F 28 181 cm 90 kg Single Php 29,000
5 M 29 179 cm 88 kg Married Php 30,000
6 F 31 165 cm 57 kg Married Php 18,000
7 M 25 173 cm 72 kg Married Php 19,500
8 F 29 185 cm 87 kg Single Php 22,500
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Answer the following questions:
1. What is the highest monthly expense?
2. What is the average age of the respondents?
3. How many are single?
4. What is the sex of the tallest respondent?
5. What is the average height of the female
respondents?
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Analysis 2: Practice Makes Perfect But Nobody’s
Perfect 😉
Processing Questions:
A. What did you use in computing the
gathered data?
B. How did you get the answer in each
question?
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What if you are asked…
1. Is there a significant difference between the
first and second semester general average
grade of Grade 12 learners?
2. Is there a significant relationship between the
length and weight of milkfish bangus (Chanos
chanos)?
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Quantitative Data Analysis using…
Inferential
Statistics
Data are analyzed from a sample to make
inferences in the larger collection of the
population. The purpose is to answer or test the
hypotheses. Hypothesis testing is, thus, a
procedure for making rational decisions about
the reality of observed facts. A hypothesis (plural
hypotheses) is a proposed explanation for a
phenomenon. It has two types: null and
alternative hypothesis.
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To use the Parametric test, there are some
conditions that should be met. The data must be
normally distributed and the level of measurement
must be either ratio or interval data. However,
Non-parametric tests do not require normality
of the distribution, hence can be used for ordinal or
even nominal data.
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Steps to follow in Hypothesis Testing:
1 State the null and alternative hypotheses.
Null Hypothesis (H0)
Alternative Hypothesis
(Ha or H1)
Is a denial of existence of significant difference,
effect or relationship. This states that there is no
significant difference, effect or relationship
between or among variables.
Is the affirmation of existence of significant
difference, effect or relationship. This states that
there is a significant difference, effect or
relationship between or among variables
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Steps to follow in Hypothesis Testing:
2 Indicate the level of significance and type of test.
For social science/behavioural research, 0.05 level of
significance should be considered.
For physical/mathematical science research, 0.05 level of
significance should be considered.
0.05 is the commonly used significance level in accepting or
rejecting the null hypothesis.
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Steps to follow in Hypothesis Testing:
3 Determine the appropriate statistical technique
to be used.
PARAMETRIC TESTS
Pearson r
It measures the strengths and direction of
the linear relationship of the two
interval/ratio variables. . Do not use this
test when correlating ordinal or nominal
data.
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t Test
PARAMETRIC TESTS
A t-test is
done for 30
samples or
less.
t-test of dependent samples/ Paired-samples t-
test/ one sample t-test
It determines whether the means of two dependent
groups differ significantly.
t-test of independent samples
It determines whether the means of two independent
groups differ significantly.
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PARAMETRIC TESTS
f-test
A z-test is a statistical test used to determine whether two
population means are different when the variances are known
and the sample size is large (>30).
z-test
It is the analysis of variance (ANOVA). This is used in
comparing the means of three or more independent groups.
One-way ANOVA is used when there is only one variable
involved. The two-way ANOVA is used when two variables are
involved.
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NON-PARAMETRIC TESTS
Spearman rho
It measures relationship between ordinal
variables. This test of correlation does not
require the stringent assumption of
normality.
Chi-square test
It compares the expected or theoretical frequencies
of categories from a population distribution to the
observed, or actual frequencies from a distribution
to determine whether there is a difference between
what was expected and what was observed.
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Median Test
NON-PARAMETRIC TESTS
Used to
compare
medians of
samples
Two-Sample Case
It is used to compare the median of two independent
samples. This is the counterpart of the t-test under
parametric test.
Multi-Sample Case
This is a straightforward extension of the
median test for two independent samples.
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NON-PARAMETRIC TESTS
Kruskal-Wallis H-Test
It is used to compare 3 or more independent
groups. This is a nonparametric test which does not
require normal distribution. This is an alternative
for the F-test (ANOVA) in parametric test.
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Steps to follow in Hypothesis Testing:
4 Identify the approach to be used in decision making as to
the use of critical (tabular) value or p-value approach.
Using critical
value approach in
hypothesis
testing
• If the computed value is greater than the critical
(tabular) value, then we reject the null hypothesis.
It means that the difference/relationship is
significant.
• If the computed value is lesser than or equal to the
critical (tabular) value, then we fail to reject the
null hypothesis. It means that the
difference/relationship is not significant.
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Steps to follow in Hypothesis Testing:
Using p-value
method of
hypothesis testing
• If the p-value is less than or equal to the level of
significance, then we reject the null hypothesis. It
means that the difference/relationship is
significant.
• If the p-value is greater than the level of
significance, then we fail to reject the null
hypothesis. It means that the
difference/relationship is not significant.
5 Write conclusion.
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Approach to be used Type of Value Symbol Type of Value
Decision
for Ho
Significance
Using critical value in hypothesis
testing
Computed Value > Critical (tabular) Value Reject Significant
Computed Value ≤ Critical (tabular) ValueFail to reject Not Significant
Using p-value in hypothesis testing
p-value ≤ α Reject Significant
p-value > α Fail to reject Not Significant
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Activity 3: Let’s Compute
Test the relationship between the length and
weight of Milk fish (Chanos chanos). Use 0.05 level
of significance. The p-value is ________.
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Activity 3: Let’s Compute (continuation)
Test the significant difference of the first
and second semester general average grade
of the Grade 12 learners. Use 0.05 level of
significance. The p-value is _________.
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Activity 4 : My Living Experiences
Guide Questions:
1. What made you decide to teach in Senior High School (SHS)?
2. What are the most significant experiences you encountered in
teaching Research in the SHS? Why do you consider these
experiences significant?
3. What are your memorable experiences as a research teacher?
Why do you consider these memorable?
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Analysis 4: Processing Questions
1. What common themes do you think will emerge
if you will use the questions provided in an
interview?
2. What data analysis technique would you use?
3. How would you analyze the data?
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Steps in Qualitative Data Analysis
Step 1 As soon as data is collected it is critical that you immediately process the
information and record detailed notes.
These notes could include:
* Things that stuck out to you
* Time/date details
* Other observations
* Highlights from the interaction
It is important to do this while the interaction is still fresh in your mind so
that you can record your thoughts and reactions as accurately as possible.
* It is helpful to make a reflection sheet template that you carry
with you and complete after each interaction so that it is standardized across
all data collection points.
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Steps in Qualitative Data Analysis
Step 2 Qualitative data analysis should begin as soon as
you begin collecting the first piece of information.
The moment the first pieces of data are collected you
should begin reviewing the data and mentally processing it
for themes or patterns that were exhibited. It is important
to do this early so that you will be focused on these
patterns and themes as they appear in subsequent data you
collect.
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Steps in Qualitative Data Analysis
Step 3
Data Reduction
Qualitative studies generally produce a wealth of
data but not all of it is meaningful. After data has
been collected, you will need to undergo a data
reduction process in order to identify and focus in
on what is meaningful. This is the process of
reducing and transforming your raw data.
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Steps in Qualitative Data Analysis
Step 4 Identifying Meaningful Patterns and Themes
In order for qualitative data to be analyzable it must first be grouped into the
meaningful patterns and/or themes that you observed. This process is the
core of qualitative data analysis.
This process is generally conducted in two primary ways:
* Content analysis
* Thematic analysis
The type of analysis is highly dependent on the nature of the research
questions and the type(s) of data you collected. Sometimes a study will use
one type of analysis and other times, a study may use both types
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Steps in Qualitative Data Analysis
Step 5
Data Display
After identifying themes or content patterns,
assemble, organize, and compress the data into
a display that facilitates conclusion drawing.
The display can be a graphic, table/matrix, or
textual display.
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Steps in Qualitative Data Analysis
Step 6 Conclusion Drawing and Verification
Conclusion drawing and verification are the final step in qualitative data
analysis.
To draw reasonable conclusions, you wili need to (Krathwohl, 1998; Miles and
Huberman, 1994; NSF, 1997):
* Step back and interpret what all of your findings mean
* Determine how your findings help answer the research
question(s)
* Draw implications from your findings
To verify these conclusions, you must revisit the data (multiple times) to
confirm the conclusions that you have drawn.
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Alternatives in Qualitative Data Analysis
Content Analysis
This refers to the process of categorizing verbal or
behavioral data to classify, summarize and tabulate
the data.
Narrative Analysis
This method involves the reformulation of stories
presented by respondents by taking in to account the
context of each case and different experiences of each
respondent. In other words, narrative analysis is the
revision of primary qualitative data by researcher.
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Alternatives in Qualitative Data Analysis
Discourse Analysis A method of analysis of naturally occurring
talk and all types of written text.
Framework Analysis
This is more advanced method that consists
of several stages such as familiarization,
identifying a thematic framework, coding,
charting, mapping and interpretation.
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Alternatives in Qualitative Data Analysis
Grounded Theory
The grounded theorist first summarizes
observations into conceptual categories and
tests the coherence of these categories
directly in the research setting with more
observations. Over time, as the researcher
refines and links the conceptual categories,
a theory evolves (Glaser & Strauss 1967;
Huberman & Miles 1994:436).
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Alternatives in Qualitative Data Analysis
Photo Voice
This creates the possibility of “observing” the social
world through photographs and films and of
interpreting the resulting images as a “text”.
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Sample in Qualitative Data Analysis
Uluöz, E., 2020. Opinions Of The Faculty Of Sport Sciences Students On The Changes In Education System During COVID-19 Pandemic: A Qualitative
Research.. [online] Eric.ed.gov. Available at: https://eric.ed.gov/?q=OPINIONS+SPORTS&id=EJ1263479 [Accessed 9 November 2020].
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Sample in Qualitative Data Analysis
Uluöz, E., 2020. Opinions Of The Faculty Of Sport Sciences Students On The Changes In Education System During COVID-19 Pandemic: A Qualitative
Research.. [online] Eric.ed.gov. Available at: https://eric.ed.gov/?q=OPINIONS+SPORTS&id=EJ1263479 [Accessed 9 November 2020].
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Sample in Qualitative Data Analysis
Uluöz, E., 2020. Opinions Of The Faculty Of Sport Sciences Students On The Changes In Education System During COVID-19 Pandemic: A Qualitative
Research.. [online] Eric.ed.gov. Available at: https://eric.ed.gov/?q=OPINIONS+SPORTS&id=EJ1263479 [Accessed 9 November 2020].
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APPLICATION 1: Test Me!
1. The facilitator will be flashing research problems.
2. Brainstorm on what data analysis technique should
be used for the problems.
3. Post your answers on the comment section in the
Google Meet platform.
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1. Profile of Grade 11 teachers in terms of age, gender, civil status.
2. Teachers’ Perceptions on the implementation of SHS using 3-
point scale.
3 Academic Performance of Grade 12 Students in Research 2.
4. Significant relationship between the first and second semester
grades of Grade-12 learners.
5. Significant difference between Pretest and Posttest results in
Mathematics.
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6. Significant difference between the Performance of Male and
Female Students in Statistics.
7 Number of absences that a SHS-student incurred in a semester.
8. Scores in Statistics and Probability of a Grade-12 Class.
9. Significant difference between the scores in Statistics and
Probability of the Grade-12 learners when grouped by sex.
10. Lived Experiences of Grade-12 students to their chosen strand.
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ANSWERS
1. Age-Mean & SD; Gender & Civil Status-Frequency counts & Percentage
2. Mean & SD
3. Mean & SD
4. Pearson r
5. t-test for dependent samples
6. t-test for independent samples
7. Frequency counts / Percentage
8. Mean / SD / Frequency counts / Percentage
9. t-test of independent samples
10. Content or thematic analysis
