Quantitative Research in Education

Abstract

In the past few decades, educational practices have changed drastically, particularly regarding how information and learning are delivered and processed. Education research frequently employs quantitative methods. Quantitative education research provides numerical data that can prove or disprove a theory, and administrators can easily share the quantitative findings with other academics and districts. While the study may be based on relative sample size, educators and researchers can extrapolate the results from quantitative data to predict outcomes for larger student populations and groups. Educational research has a long history of utilising measurement and statistical methods. Commonly quantitative methods encompass a variety of statistical tests and instruments. Educators and students could transition to the digital era and research-based knowledge, including quantitative research in advanced higher education, as the technology has advanced. The quantitative research methods in education emphasise basic group designs for research and evaluation, analytic methods for exploring relationships between categorical and continuous measures, and statistical analysis procedures for group design data. The essential is to evaluate quantitative analysis and provide the research process, sampling techniques, the advantages and disadvantages of quantitative research in the article.

Full text

Interdisciplinary Research: Collaborative Insights VOLUME 2
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Quantitative Research in Education
Dr. Supaprawat Siripipatthanakula,1, Dr. Muthmainnahb,2,
Dr. Andi Asrifanc,3, Dr. Sutithep Siripipattanakuld,4, Dr.
Pichart Kaewpuange,5, Dr. Patcharavadee Sriboonruangf,6,
Pongsakorn Limnag,7, Parichat Jaipongh,8, Tamonwan
Sitthiponi,9
a University of Geomatika, Malaysia, drsupaprawat@gmail.com
bUniversitas Al Asyariah Mandar, Indonesia,
muthmainnahunasman@gmail.com
cUniversitas Muhammadiyah Sidenreng Rappang,
andiasrifan@gmail.com
d Kasetsart University, Thailand, fedustt@ku.ac.th
e Phranakhon Rajabhat University, Thailand, pichart@pnru.ac.th
f Kasetsart University, fagrpds@ku.ac.th
g Rangsit University, Thailand, palmlimna@gmail.com
hManipal GlobalNxt University, Malaysia,
iam.parichatt@gmail.com
i City University, Malaysia, tamonwan.f@gmail.com

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Introduction
A comprehensive presentation of quantitative research
design and statistical methods in education and related fields is
crucial. Researchers in education, counselling, rehabilitation,
psychology, sociology, social work, and human development can
also benefit from advanced quantitative research (Dimitrov, 2008).
A concern for generalisation dominates quantitative research; for
generalizability and repeatability, identification of sample size is
essential. Population and Sample facilitate document analysis.
Coding is used to analyse the data, and descriptive statistics
present the results. The characteristics of the sample were
determined by randomisation in the frequently employed
random sampling technique (Delice, 2010). In social science and
education, survey research is one of the most prevalent
Abstract
In the past few decades, educational practices have changed drastically,
particularly regarding how information and learning are delivered and
processed. Education research frequently employs quantitative methods.
Quantitative education research provides numerical data that can prove or
disprove a theory, and administrators can easily share the quantitative
findings with other academics and districts. While the study may be based on
relative sample size, educators and researchers can extrapolate the results
from quantitative data to predict outcomes for larger student populations and
groups. Educational research has a long history of utilising measurement and
statistical methods. Commonly quantitative methods encompass a variety of
statistical tests and instruments. Educators and students could transition to
the digital era and research-based knowledge, including quantitative
research in advanced higher education, as the technology has advanced. The
quantitative research methods in education emphasise basic group designs
for research and evaluation, analytic methods for exploring relationships
between categorical and continuous measures, and statistical analysis
procedures for group design data. The essential is to evaluate quantitative
analysis and provide the research process, sampling techniques, the
advantages and disadvantages of quantitative research in the article.
Keywords: Quantitative Research, Education, Learning, Technology,
Statistical Analysis

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32
techniques. It is an efficient method for collecting large amounts
of data and is adaptable in that a wide variety of topics can be
investigated. However, the notion that survey research is simple
is incorrect. Because only probability samples are unbiased, it is
necessary to carefully consider the nature of the population and
how to sample from it. In survey research, nonresponse is
common and can lead to bias, as can poorly designed
questionnaires. Avoiding double negatives, ambiguous or unclear
questions, and double questions, keeping questionnaires brief and
being culturally sensitive can help minimise bias (Muijs, 2004).
Undoubtedly, awareness of ethical concerns in educational
research has increased in recent years. It is reflected in the
prominence accorded to such issues in standard texts on
educational research methods (Jones, 2012). High-quality research
is being conducted, contributing significantly to the mosaic of
evidence that serves as the foundation of available education
knowledge. Improving the quality of research and research
synthesis in education is crucial to how philosophies and methods
for evaluating the credibility of research evidence can be used to
enhance educational research (Bernard et al., 2004). The evolution
of the critical quantitative paradigm with a focus on extending
this strategy to new populations and methods. Alongside this
expansion of essential quantitative work, however, are ongoing
challenges and tensions for researchers interested in the future use
of quantitative criticism by institutional researchers and higher
education scholars (Wells & Stage, 2015). Education and
educational psychology research have significantly contributed to
our understanding of the interconnected processes that shape
students' educational experiences. An essential aspect of these
efforts has been mapping the emergence, antecedents, and
outcomes of crucial change processes that underpin learning and
achievement (Hodis, & Hancock, 2016). Quantitative research
permits researchers to learn more about the demographics of a
population, to measure and examine attitudes and behaviors, to
document trends, and to explain what is known from anecdotal
evidence. Measurements such as frequencies (i.e., counts),
percentages, proportions, and relationships provide

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quantification and evidence for variables. It is important to note,
however, that quantitative approach does not explain why people
think, feel, or act in particular ways. In other words, quantitative
research emphasizes patterns across data sets or study groups, but
not the motives underlying observed behaviors. To fill these
knowledge gaps, qualitative research methods such as focus
groups, interviews, and open-ended survey questions are useful
(Goertzen, 2017). This article provides quantitative research on
research process, sampling, advantages-disadvantages of the
quantitative research, which is essential for assessing quantitative
analysis.
RESEARCH PROCESS: Validity and Reliability of
Measurements
Typically, a research process consists of the following steps:
1) Identifying the problem. 2) Defining the research question (s)
and objective (s). 3) Developing a research proposal. 4) Outlining
a research design. 5) Developing an instrument and conducting a
pilot study to test the instrument for validity and reliability. 6)
Data collection (through surveys). 7) Data Analysis (Khalid et al.,
2012). The research process is proposed in figure 1 as the following
steps 1) Define the Problem 2) Research Design 3) Review
Literature 4) Instrumentation 5) Planning a Sample 6) Data
Collection 7) Datal Analysis.

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Figure 1. Research Process
The primary research instruments, such as the
questionnaire, typically employed in quantitative research
designs are presented and elaborated upon. It is believed that
utilising various data collection techniques and obtaining
information from multiple sources (learners, teachers, program
staff, etc.) can enhance the validity and reliability of the data and
the interpretation (Zohrabi, 2013). Social science research, the
questionnaire is one of the most common data collection tools. The

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primary purpose of questionnaires in research is to collect
pertinent data most reliably and validly possible. Thus, the
validity and reliability of a survey or questionnaire constitute an
essential aspect of research methodology, also known as precision
and consistency. Frequently, novice researchers are unsure how
to choose and test the validity of their research instruments, such
as a questionnaire or survey (Taherdoost, 2016). The reliability of
measurement relates to its consistency. Each time a participant
completes an instrument designed to measure motivation; the
participant's responses should be relatively consistent. Validity is
the degree to which a quantitative study accurately measures a
concept. A survey that purports to investigate depression, but
measures anxiety would not be considered valid (Heale &
Twycross, 2015). Cronbach’s Alphas and factor loadings are
typically set at 0.7 for measurements’ reliability and validity,
respectively (Chana et al., 2021; Nuanchaona et al., 2021;
Jandawapee et al., 2022; Sitthipon et al., 2022; Napawut et al.,
2022).
Sampling Techniques
Table 1. Probability Sampling and Non-Probability Sampling
(Etikan et al., 2016; Sharma, 2017)
Probability Sampling
Non-Probability Sampling
Simple Random Sampling
Quota Sampling
Systematic Sampling
Purposive Sampling
Stratified Sampling
Convenience Sampling
Cluster Sampling
Snowball Sampling

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Simple Random Sampling
In simple random sampling, each member of the
population has an equal chance of being selected for the sample.
Using a random number table or a computer-generated list of
random numbers, data is selected. It is also possible to use the
lottery method, currency notes, etc. (Acharya et al., 2013).
Systematic Sampling
Systematic sampling is among the most common sampling
methods. Popularity of the systematic design is primarily
attributable to its usability. Compared to simple random
sampling, it is easier to collect a systematic sample, particularly
when sample units are selected in the field. Moreover, when
explicit or implicit stratification is present in the sampling frame,
systematic sampling can provide more accurate estimators than
simple random sampling (Mostafa & Ahmad, 2018).
Stratified Sampling
Stratified sampling is a method of probability sampling
that is utilized in sample surveys. Elements of the target
population are divided into distinct groups or strata, wherein
elements within each stratum are similar regarding survey-
relevant characteristics. Stratification is also used to improve the
effectiveness of a sample design in terms of survey expenses and
estimator precision (Parsons, 2014).
Cluster Sampling
A cluster random sample is a two-step procedure in which
the entire population is divided into clusters or groups, which are
typically geographic areas or districts such as villages, schools,
wards, blocks, etc. It is utilized more frequently in
epidemiological research than clinical research. It is most useful
for use in large-scale national surveys. The clusters are selected at
random. All members of the cluster are included in the sample.
Usually, it requires a larger sample size. Cluster sampling is
advantageous when the population is dispersed, and it is

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impractical to sample and select a representative sample of all the
elements (Acharya et al., 2013).
Quota Sampling
In quota sampling, the entire population is divided into
relevant strata, such as gender, age, and social class, among
others. These strata are referred to as "quota controls" and are
selected based on their relevance to the topic of interest. External
data such as census results are used to estimate the number of
individuals in each population stratum. The total number of units
in each stratum of the sample is then equal to the product of the
proportion of the population corresponding to that stratum and
the predetermined sample size (Yang & Banamah, 2014).
Purposive Sampling
Purposive sampling, also known as judgment sampling, is
the deliberate selection of a participant based on the qualities that
individual possesses. It is a method that does not require
underlying theories or a predetermined number of participants.
Simply put, the researcher decides what must be known and then
seeks out people who possess the necessary knowledge or
experience and are willing to share it (Etikan et al., 2016).
Convenience Sampling
Convenience sampling, also known as Accidental
Sampling, is a type of nonprobability or non-random sampling in
which members of the target population who meet certain
practical criteria, such as easy accessibility, geographical
proximity, availability at a given time, or willingness to
participate, are included in the study (Etikan et al., 2016).
Snowball Sampling
Snowball sampling technique is one of the most popular
qualitative sampling techniques. Researchers typically begin with
a small number of initial contacts (seeds) who meet the research

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criteria and are invited to participate in the study. The willing
participants are then asked to recommend other contacts who
meet the research criteria and who may also be willing
participants, who in turn recommend other potential participants
(Parker et al., 2019).
Sample Size Determination
The sample size could be determined by known population
A standard survey will usually have a confidence level
of 95% and margin of error at 5 %. The sample size will
be calculated from the formula of
Krejcie and Morgan (1970) for the known number of
populations.
N= Number of Population = 3,584 cases n
= sample size
X = 1.96 X2 = 3.8416 P = 0.5 E
= 0.05
n = 348.524
Table 2 for determining the sample size for a known
number of populations
)1()1(
)1(
22
2
PPXNE
PNPX
n−+−
−
=
N
S
N S
N S
N S
N S
10
10
100 80
280
162
800 260
2800 338
15
14
110 86
290
165
850 265
3000 341

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20
19
120 92
300
169
900 269
3500 346
25
24
130 97
320
175
950 274
4000 351
30
28
140
103
340
181
1000 278
4500 354
35
32
150
108
360
186
1100 285
5000 357
40
36
160
113
380
191
1200 291
6000 361
45
40
180
118
400
196
1300 297
7000 364
50
44
190
123
420
201
1400 302
8000 367
55
48
200
127
440
205
1500 306
9000 368
60
52
210
132
460
210
1600 310
10000 373
65
56
220
136
480
214
1700 313
15000 375
70
59
230
140
500
217
1800 317
20000 377
75
63
240
144
550
225
1900 320
30000 379
80
66
250
148
600
234
2000 322
40000 380
85
70
260
152
650
242
2200 327
50000 381
90
73
270
155
700
248
2400 331
75000 382
95
76
270
159
750
256
2600 335
100000 384

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At 95% of confidence level (Z-score or X = 1.96) and the
margin of error at 5%.
The sample size could be determined by using the formula for
unknown population (Zikmund, 2003: 425)
Sample size
n = Sample size
z = z score based on researcher desire level
of confidence is 95%
Therefore, z = 1.96
p = this is the proportion of correction.
According to the population parameter, the
researcher determines the value that will make p
(1-p) as large as possible
e = an acceptable sample error, which be
estimated at 5 %
Formula n = (Z)2 (p) (1-p) / (e) 2
= (1.96)2 (0.5) (0.5) /
(0.05) 2
= 384.16
The required sample size for the study
would be more than 384.
Descriptive Statistics
Descriptive statistics are utilized to organize data
summaries by describing the relationship between variables
within a sample or population. Calculating descriptive statistics is
a crucial first step in conducting research and must always
precede making inferences based on statistical comparisons.
Types of variables (nominal, ordinal, interval, and ratio) and
measures of frequency, central tendency, dispersion/variation,
and position are included in descriptive statistics. Because
descriptive statistics condense data into a more manageable
summary, they allow health-care decision-makers to evaluate

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specific populations in a more manageable format (Kaur et al.,
2018).
Inferential Statistics
In education and psychology research, the investigator
typically collects data and then applies descriptive and inferential
statistics. Using the null hypothesis significance testing
procedure, a researcher might compute group means and draw
conclusions about the populations from which the groups were
drawn. An alternative inferential statistical procedure is executed
before rather than after data collection (Trafimow & MacDonald,
2017).

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a Table 3.
Types
of
statistic
al tests
based
on the
data
type,
sample
size,
and
data
distrib
ution
(Marsh
all &
Jonker,
2011).T
ype of
data
b Numb
er of
sample
s
c Statist
ical
test
d Binary
e One or
paired
f McNe
mar’s
test
g
h Two
indepe
ndent
sample
s
i Chi-
squar
ed or
Odds
Ratio
j Nomin
al
k One or
paired
l Stuart
test
m
n Two
indepe
ndent
sample
s
o Chi-
squar
ed test

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p Ordinal
q One or
paired
r Wilco
xon
test
s
t Two
indepe
ndent
sample
s
u Mann
-
Whitn
ey U-
test
v Interva
l/ratio
w One or
paired
x Wilco
xon
test
(non-
norma
l)
y
z
aa Paired
t-test
(norm
al)
bb
cc Two
indepe
ndent
sample
s
dd Mann
-
Whitn
ey U-
test
(non-
norma
l)
ee
ff
gg Unpai
red t-
test
(norm
al)
The inferential statistics are including 1) correlation and
regression. 2) Chi-Square Procedures for Categorical Frequency
Data Analysis 3) t-Test for Two Corresponding Samples 4)

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ANOVA One-Way Analysis 6) Two-Way ANOVA Analysis 7)
Factor Analysis 8) Structural Equation Model (SEM) (Lowry, 2014;
Awang et al., 2016; Henseler, 2017); Muthmainnah, M. (2023).
The advantages of a quantitative design are shown in the table
proposed by
the Authors based on Choy (2014) and Zikmund et al.
(2003)
Advantages of a quantitative
survey study
Details
Reliability by critical analysed
Reliability is the consistency
of quantitative design
measurements, in which the
qualitative approach cannot
be made. Cronbach’s Alpha is
a measure of internal
consistency. (Cronbach’s
Alpha is higher than 0.7 is
exceptionally reliable)
Fast-Short time frame for
administered survey
Self-administered survey
questionnaires are distributed
to respondents (many
participants) responsible for
reading and filling in. A
quantitative study's data
collection process saves time
because there is no
participation between the
researcher and respondents,
but the qualitative approach
takes more time and needs the
interviewer.

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Facilitated numerical data for
groups and extents of strongly
agree or strongly disagree
from respondents (Likert’s
rating scale).
The main variables in
quantitative research
were measured using a
five-Point Likert Scale,
classified as follows:
Strongly Agree with a
value of 5, Agree with a
value of 4, Neutral with a
value of 3, Disagree with
a value of 2, and lastly
Strongly Disagree with a
value of 1. So, it is
beneficial for a
quantitative study if you
are adopted a rating
scale but do not in a
qualitative design.
Inexpensive
Survey questionnaires may
be distributed to respondents
by using (1) or (2)
1) Printed questionnaires
2) Online questionnaires
Both methods of survey
questionnaires are
inexpensive if compared with
a qualitative study. Regarding
qualitative research, the
interviewer may generate the
cost and timing of travelling in
data collection.

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Efficient
1) The printed
questionnaires are worthy of
the costs.
2) There is no cost when
using Online questionnaires
Both methods of survey
questionnaires are efficient
compared with a qualitative
study.
2) Accurate
frequency and mean of
assessing information
about the sample
(Descriptive Statistics)
3) Accurate means of
assessing information on
the population:
Inferential statistics
(Pearson, Simple Linear
Regression (SLR),
Multiple Linear
Regression (MLR) and
Struct Equation Model
(SEM)
e
1) Chi-square is a tool to test
hypotheses univariate
analysis in the difference in
the sample's number
(frequency).
2) t-test is a tool to test
the hypotheses
univariate analysis in
differences of the mean
between two groups.
3) ANOVA is a tool to test
hypotheses through
univariate analysis of the
mean difference in more
than two groups.
4) Pearson is the tool to test
the correlation in bivariate
analysis.
5) Simple Linear
Regression is the tool to
test the variables'
prediction or relationship
in bivariate analysis.
6) Multiple Linear
Regression is a tool to test
the variables' prediction or

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relationship in multivariate
analysis.
7) Structural Equation Model
(SEM) is the tool to test the
model's hypotheses, both
direct and indirect effects of
variables.
8) Exploratory Factor
Analysis (EFA) or
Confirmatory Factor
Analysis (CFA) is the tool to
test the validity of the
constructs.
These statistics are
advantages of the
quantitative study in which
qualitative research cannot
be done.
Good statistical
significance
Scientific significance to
accept or reject the
hypothesis is at a confidence
level of 95% and an error of
0.05.
The good statistical
significance is only for
quantitative research, in
which a qualitative study
cannot be done.
Representing a large
population
Inferential statistics benefit
quantitative analysis in
which a qualitative approach
cannot be made. The results
represent a large population
sample if it is from a suitable
sampling technique.

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The disadvantages of a quantitative design are shown in
the table proposed by Authors based on Choy (2014) and
Zikmund (2003).
Disadvantages of a
quantitative survey study
Details
No human perception and
belief.
The quantitative design has a
disadvantage over qualitative
research because there is not
much participation between
the distributor (researcher)
and respondents.
Lack of resources for large-
scale analysis
Few studies support the
research on a large scale a
quantitative analysis. Many
studies were selected in small
areas to ensure the
population's representation.
No depth description or
definitions
The questionnaire items
in a survey need to be
precise because there are
no details or definitions.
Costs of printing
questionnaire
Although the printed
questionnaires are
inexpensive, it still generates
cost. The online questionnaire
does not create a cost, but
ensuring the sample
represents the population is
difficult. These are also the
disadvantages of the
questionnaire in quantitative
survey research.

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Inflexible design because the
questionnaires are not
changed through the
collection process.
Suppose the
instrumentation is completed,
also reliability and validity are
tested on the measurements in
a questionnaire. The
questionnaire items could not
be changed through the data
collection process. If not, the
researcher must do all
methods again.
The respondents may not
precisely answer controversial
questions.
If some questionnaire items
are unclear, the respondents
may respond with the wrong
answer regarding the invalid.
Bias error
Bias error may be from the
respondent's answering bias
because of love or hate. It is
not representing the actual
evaluation.
Conclusion
Quantitative research encompasses a variety of methods
for systematically investigating social phenomena using statistical
or numerical data. Consequently, quantitative analysis involves
measurement and assumes that the phenomenon under
investigation can be measured. The purpose of quantitative
research is to examine data for trends and relationships and to
validate measurements. It is important to note that the
quantitative approach does not explain why people think, feel, or
act in particular ways. In other words, quantitative research
emphasises patterns across data sets or study groups but not the
motives underlying observed behaviours. Conversely, qualitative

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50
research methods such as focus groups, interviews, and open-
ended survey questions help fill the knowledge gaps that
quantitative research could not be employed to do. The expansion
of the critical quantitative paradigm to new populations and
methods. Alongside this expansion of essential quantitative work,
however, researchers interested in the future use of quantitative
criticism by institutional researchers and higher education
scholars face ongoing challenges and tensions. Education and
educational psychology research have made substantial
contributions to our understanding of the interconnected
processes that shape the educational experiences of students.
Mapping the emergence, antecedents, and outcomes of crucial
change processes that underpin learning and achievement has
been an essential aspect of these efforts.
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The Role of Community Leaders in the
Family Planning Village (KB) Program in
Bulukumba
Sumrah AP a,1, Musfirah b,2
Universitas Muhammadiyah Bulukumba,
andisumrah@umbulukumba.ac.id Indonesia
Introduction
By 2030, it is estimated that Indonesia will face three
demographic trends. First, the rate of population growth will not
be achieved because the large population in Indonesia will
continue to increase. Second, the occurrence of demographic gains
since the late 1980s and it is estimated that 2030 will be at its peak
so as to produce a demographic bonus, before the percentage of
the working age population then declines. Finally, the mobility of
the permanent population to the mobility of the non-permanent
population in Indonesia will experience a shift in the situation.
These three demographic trends will have a major impact on
Indonesia's progress towards achieving its sustainable
development goals by 2030.
A very high population increase if not followed by an
increase in the quality of human resources will be a disaster for
Indonesia. Disasters of poverty, high crime and other social
problems will emerge. Not only social problems will arise, other
problems will also emerge. Therefore, the government created a
program to intervene in the problem of uncontrolled population