QUANTITATIVE RESEARCH

1. QUANTITATIVE
RESEARCH
ARDRIAN D. MALANGEN, MAEd,CLSSYB
Associate Professor II

2. LEARNING OBJECTIVES
 At the end of the webinar the participants should be able to
1. Discuss briefly and determine the purpose of the study
2. Give examples of comparison and relationship
3. Identify and give examples of the different types of data
4. Identify the types of statistics and level of measurements
5. Determine the four basic methods of sampling and
common statistical tools used in quantitative research.

3. PURPOSE
COMPARISON
RELATIONSHIP
IS THERE A
DIFFERENCE?
IS THERE A
CONNECTION?

4. COMPARISON
(Looking for difference)
MALES
VS.
FEMALES
CONTROL
GROUP
AND
TREATMENT
GROUP
STEM
VS.
HUMSS

5. RELATIONSHIP
(Looking for Connection)
Height
and
Flexibility Test scores
and Grade
Point
Average
Age and the
number of
Hours he/she
exercise per
week

6. TYPES OF DATA
QUALITATIVE
CHARACTERISTICS
QUANTITATIVE
NUMERICAL

7. CATEGORICAL
(QUALITATIVE)
YES OR NO
ANSWER
EDUCATIONAL
LEVEL
BRAND NAME
CIVIL STATUS SEX

8. Surveys or Questionnaires
 Online surveys are commonly used to carry out
investigations on certain topics. The data gathered in
some cases are categorical.
 Examples of Categorical Data
1. How many siblings do you have? ____________
 The above is an example of an open-ended nominal
data collection form. The response may be quantitative
but will possess qualitative properties.
 Motives for employees to work better:
 Companies who want to improve employee productivity
may use this method to discover what motivates
employees to work better. For example:

9. 2. What motivates you to work better? (Others specify)
 Peer motivation
 Recognition
 Professional growth opportunities
 Friendly work culture
 Others _____
 Motives for travelling:
 Travel and tourism companies ask their customers or target
audience this question to inform marketing strategies.
3. What are your motives for travelling? (Others specify)
 Business
 Leisure
 Family
 Study
 Health
 Others _____

10.  Proficiency level
 Employees measure a job applicant's proficiency level in
skills required to perform well in the job. This helps in
choosing the best applicant for the job.
4. What is your proficiency level in excel?
 Advanced
 Intermediate
 Novice
5. How will you rate the desert served tonight?
 Very good
 Good
 Neutral
 Bad
 Very bad

11. CONTINUOUS/DISCRETE
(QUANTITATIVE)
AGE
HEIGHT
TEST
SCORES
PRICE
HOURS
OF SLEEP

12. TYPES OF STATISTICS
Descriptive Statistics-summarizes or
described the important characteristics of a
known set data .
Ex. The National Statistics Office conducts
surveys to determine the average age,
income and other characteristics of the
Filipino population,
Inferential Statistics- uses sample data to
make inferences about a population. It
consists of generalizing from samples to
population, performing hypothesis testing,
determining relationships among variables,
and making predictions.

13. QUANTITATIVE DATA
 Listed below are some examples of quantitative data that
can help understand exactly what this pertains:
 1.) I updated my phone 6 times in a quarter.
 2.) My teenager grew by 3 inches last year.
 3.) 83 people downloaded the latest mobile application.
 4.) My aunt lost 18 pounds last year.
 5.) 150 respondents were of the opinion that the new
product feature will not be successful.
 6.) There will be 30% increase in revenue with the
inclusion of a new product.
 7.) 500 people attended the seminar.
 8.) 54% people prefer shopping online instead of going to
the mall

15. LEVEL OF MEASUREMENT
 Nominal Level- this is characterized by data that consist of names,
labels or categories only.
 Ordinal Level- this involves data that may be arranged in some
order, but differences between data values either cannot be
determined or meaningless, an example is the grading system
involving letters(A,B,C,D,E,F
 Interval Level- This is the same as the ordinal, with an additional
property that we can determine meaningful amounts of differences
between the data.
 Ratio Level- this is an interval level modified to include the inherent
zero starting point. The differences and ratios of data are
meaningful. Ex. Measure of height, weight and area.
 Data collection and Sampling Techniques-data can be collected
in different ways. The most common is trough survey-telephone,
mailed- questionaire,or personal interview. There are also other
methods of collecting data: surveying records or direct observation.

16. LEVEL OF MEASUREMENTS

17. MEASUREM
ENT LEVEL
NAMED ORDER DIFFERENCE TRUE ZERO
NOMINAL
ORDINAL
INTERVAL
RATIO

18. FOUR BASIC METHODS OF SAMPLING
Random Sampling- this is done by using
chance methods or random numbers. For
example, number each subject in the population.
Systematic Sampling- this is done by
numbering each subject of the population and
then selecting every nth number. For example
there are 5000 families in a city. Fifty families are
needed as a sample for an experiment. Since
5000÷50 = 100, then k= 100. This means that
every 100th subject would be selected.

19.  Stratified Sampling- if a population has distinct
groups, it is possible to divide the population into these
groups and to draw from each the groups. The groups
are called strata. Strata are designed so that members
in each groups are more homogenous. This technique
is particularly useful in populations that can be
stratified into groups by gender, race, age, strand or
program.
 Cluster Sampling-this method uses intact groups
called clusters. Suppose a medical researcher wants
to study the patients in metro manila.
 It would be very costly and time consuming to obtain a
random sample since they would be spread over
different parts of Metro Manila. Rather, a few hospitals
could be selected at random and the patients in these
hospitals would be studied in a cluster.

20. STATISTICAL TOOLS
COMPARISON
1. DEPENDENT SAMPLE
2. INDEPENDENT SAMPLE
DEPENDENT SAMPLE
PARAMETRIC
•T – test for dependent Means/Paired t- test
NON PARAMETRIC
•Wilcoxon Signed rank Test

21. DEPENDENT SAMPLE
 PARAMETRIC
 T – test for dependent Means/Paired t- test
 It is a parametric approach (or large sample approach)
used to compare means of paired groups (dependent
groups or match groups.)
 Examples:
 The researcher wants to determine the significant
difference between the pre-test(before intervention
program)and post-test (after intervention program) results
in general mathematics?
 Is there a significant difference between the mean
performance of experimental group before and after
remediation class?

22.  NON PARAMETRIC
 Wilcoxon Signed rank Test
 It is a non parametric approach(used one group) to test
difference before and after treatment using ordinal data.
Examples:
 A study assessed the effectiveness of a new drug designed
to reduce repetitive behaviours in children affected with
autism before and after 1 week of treatment.
 A company test the effectiveness of a newly developed
sunscreen formula. An experiment is performed with 12
subjects/participants each of whom has the old formula
applied to the left arm(L) and the new formula applied to the
right arm (R). Each individual is exposed to one hour of sun
and the degree of redness (0-10) on each arm is compared.

23. INDPENDENT SAMPLES
PARAMETRIC
Z – test
Independent Sample T-test
ANOVA (Analysis of Variance)
NON PARAMETRIC
Mann- Witney U test
Kruskal Wallis

24. PARAMETRIC
Z – test
 It is a statistical test for the mean of a population. It can be
used when the sample size is greater than or equal to 30 (n
≥ 30), when the population is normally distributed and σ
(population deviation) is known.
 Examples :
 1. A diet clinic states that there is an average loss of 24
pounds for those who stay on the program for 20 weeks.
The standard deviation is 5 pounds. The clinic tries a new
diet, reducing salt intake to see whether that strategy will
produce a greater weight loss. A group of 40 volunteers
loses an average of 16.3 pounds each over 20 weeks.
Should the clinic change the new diet? Use 0. 05 level of
significance.

25.  2. A manufacturer claims that the average lifetime of his light
bulbs is 3 years and 36 months. The standard deviation is 8
months. Fifty bulbs are selected, and the average lifetime is
found to be 32 months. Should the manufacturer’s statement
be rejected at 0.05 level significance.
 Independent Sample T-test
 - It compares the means of two unrelated/independent
groups in order to determine whether there is a statistical
evidence that the associated population means are
significantly different. The Independent Samples t Test is a
parametric test.
 EXAMPLES:
 1. Is there a significant difference between smoking and
perceived academic performance of the grade 11 students?
 2. Is there a significant difference in the mathematics
performance of the Grade 11 students when group according
to sex?

26. ANOVA( Analysis of Variance)
 It compares the means of more than two independent
groups in order to determine whether there is statistical
evidence that the associated population means are
significantly different. One-Way ANOVA is a parametric test.
It is omnibus.
This test is also known as:
 One-Factor ANOVA
 One-Way Analysis of Variance
 Between Subjects ANOVA
The One-Way ANOVA is often used to analyze data from
the following types of studies:
 Field studies
 Experiments
 Quasi-experiments

27. Examples :
1. Is there a significant difference on the effectiveness of
delivery mode of instruction methods used when they
group according to strands?
2. Do the three groups of learners on the new normal mode
of instruction significantly differ before and after learning
statistics and probability?
Mann -Whitney U test
-The Mann-Whitney U test is used to compare differences
between two independent groups when the dependent
variable is either ordinal or continuous, but not normally
distributed.
1. Examples:
1. Is there a significant difference between the ranks/level of
effectiveness of Online distance E-learning (ODEL)
when group according to sex?

28.  2. Is there a significant difference between the ranks of
treatment A and Treatment B?
 Kruskal-Wallis test
 - It is a rank-based nonparametric test that can be used to
determine if there are statistically significant differences
between two or more groups of an independent variable on
a continuous or ordinal dependent variable.
 1. Is there a significant difference between the Salary and
Job position of employees in company ABC?
 2. Is there a significant difference on the satisfaction
ratings in assistance during COVID-19 lockdown when
group into class (lower, middle and upper)?

29. RELATIONSHIP
PARAMETRIC
1. Pearson Product Moment Correlation
 NON PARAMETRIC
1. Chi – Square Test
2. Spearman Rho
 -Cramer’s V

30.  PARAMETRIC
 1. Pearson Product Moment Correlation
 -Measure the strength/degree/level of association or
relationship between two variables.
 -The value of a correlation coefficient ranges between -1
and 1
 -Whether a statistically significant linear relationship exists
between two continuous variables
 -The strength of a linear relationship (i.e., how close the
relationship is to being a perfectly straight line)
 -The direction of a linear relationship (increasing or
decreasing)
 -The level of measurement used is ratio and interval
data/level.

31. EXAMPLES:
 1. Is there a significant relationship between the number of
absences incurred by a student and his or her final grade in a
Statistics class?
 2. Is there a significant relationship between the weight and
systolic blood pressure of some selected Grade 11 students?
 NON PARAMETRIC
 1. Chi – Square Test
 -Determines whether there is an association between categorical
variables (i.e., whether the variables are independent or related). It
is a nonparametric test. This test is also known as Chi-Square Test
of Association.
 -This test utilizes a contingency table to analyze the data. A
contingency table (also known as a cross-tabulation, crosstab,
or two-way table) is an arrangement in which data is classified
according to two categorical variables. The categories for one
variable appear in the rows, and the categories for the other
variable appear in columns. Each variable must have two or more
categories. Each cell reflects the total count of cases for a specific
pair of categories.

32.  1. Is there a significant relationship between jogging and
systolic blood pressure?
 2. A researcher wants to determine whether there is a
relationship between sex and the amount of alcohol
consumed .
 Spearman Rho/Spearman rank correlation
 -Spearman's Rho is a non-parametric test used to measure
the strength of association between two rank variables,
where the value r = 1 means a perfect positive correlation
and the value r = -1 means a perfect negative correlation.
 Example:
 1. Is there a significant relationship between weekly
exercise time (rank) and grades (rank) of the students?