QUANTITATIVE
RESEARCH
Quantitative research refers to the systematic empirical
investigation of social phenomena via statistical, mathematical or
computational techniques. The objective of quantitative research is to develop
and employ mathematical models, theories and/or hypotheses
pertaining to phenomena.
Creswell (1994) has given a very concise definition of quantitative
research as a type of research that is `explaining phenomena by collecting
numerical data that are analyzed using mathematically based methods (in particular
statistics).
·
Quantitative research is empirical research where
the data is in the form of
something that can be counted / number (Punch, 1988:
4)
·
Quantitative research process according to Bryman (2005: 63)
is the beginning of
the theory, hypothesis, research design, choosing the research site (s),
choose the subject / respondent research, collect
data, hypotheses, process data, analyze data,
and write a conclusion
- for later
back to the beginning of everything,
the theory.
Some steps in quantitative
research that
a.
Set the object or subject matter
b.
Limiting the object or subject matter
c.
Collecting data or information
d.
Process the data and draw conclusions
e.
Formulate and report results
f.
Research suggests implications
Quantitative
Research Characteristics
In quantitative research the major characteristics are:
·
Describing a research problem through a description of trends or a
need for an explanation of the relationship among variables
·
Providing a major role for the literature through suggesting the
research questions to be asked and justifying the research problem and creating
a need for the direction (purpose statement and research questions or
hypotheses) of the study
·
Creating purpose statements, research questions, and hypotheses
that are specific, narrow, measurable, and observable
·
Collecting numeric data from a large number of people using
instruments with preset questions and responses
·
Analyzing trends, comparing groups, or relating variables using
statistical analysis, and interpreting results by comparing them with prior
predictions and past research
·
Writing the research report using standard, fi xed structures and
evaluation criteria, and taking an objective, unbiased approach.
Basic
Concepts of Quantitative Research
·
Selection and definition of a problem
Research statement: A well-written
statement of a problem includes 1) variables of interest to the researcher, 2)
specific relationship between those variables, and 3) type of subjects
involved. It should be researchable; it
has theoretical and practical significance.
·
Literature review
o
It involves the systematic identification,
location, and analysis of documents containing information related to the
research problem.
o
Why should we conduct a literature review? It
helps researcher become familiar with previous research facilitates the process
of identifying research problems, research strategies, and measuring
instruments. It also facilitates the
interpretation of the results of your study.
·
Statement of a hypothesis
A hypothesis is a tentative explanation for certain behaviors,
phenomena, or events that have occurred or will occur. It states the researchers' expectations
concerning the relationship between the variables in the research problem; it
is a most specific statement of a problem.
Characteristics
of a hypothesis:
ü A research
hypothesis should be based on a sound rationale, that is, it should provide a
reasonable explanation.
ü A good
hypothesis states as clearly and concisely as possible the expected
relationship (or differences ) between two variables and defines those
variables in operational, measurable terms.
ü A well-stated
and defined hypothesis must be testable.
Types of hypothesis:
ü Nondirectional: There is a
significant difference in the achievement of 5th-grade math students who are
instructed using interactive multimedia and those who receive regular
instruction only.
ü Directional: Fifth-grade math
students who are instructed using interactive multimedia achieve at a higher
level than those who receive regular instruction only.
ü Null: There is no
difference in the achievement level of 5th-grade math students who are
instructed using interactive multimedia and those who receive regular
instruction.
There
are several types of quantitative research. For instance, it can be classified
:
1.
survey research
Survey
research uses scientific sampling and questionnaire design to measure
characteristics of the population with statistical precision. you would
normally work with a statistician to build a statistically valid sampling plan,
a researcher to develop a survey instrument and research objectives, and a
reputable field service that has the capacity to conduct large scale interview
projects. It is important to work with experts because the quality of the
survey can be affected by the research instrument.
Psychologists and sociologists often use survey research to analyze behavior, while it is also
used to meet the more pragmatic needs of the media, such as, in evaluating political candidates, public health
officials, professional organizations, and advertising and
marketing directors.
2.
Descriptive
Descriptive
research involves collecting data in order to test hypotheses or answer
questions concerning the current status of the subjects of the study. It determines and reports the way things are.
The
description is used for frequencies, averages and other statistical calculations. Often the best approach, prior
to writing descriptive research, is to conduct a survey investigation.
3.
Correlational
Correlational research purposes according Suryabrata (in Abidin,
2010) is to
detect the extent of variations in
the factors associated
with variations in one or more other factors based
on the correlation coefficient.
Correlational
research attempts to determine whether and to what degree a relationship exists
between two or more quantifiable variables.
However, it never establishes a cause-effect relationship. The relationship is expressed by correlation
coefficient, which is a number between .00 and 1.00.
Characteristics of Correlational Research :
·
The research was conducted when suitable
variables studied complex and / or
can not be investigated by experimental methods or can not be manipulated.
·
This kind of research allows the measurement
of multiple variables simultaneously and mutual relationship in his
realistic circumstances.
·
The output of this study is the high-level or low correlation with each other and not the presence or absence of another relationship.
·
Dapat
digunakan untuk meramalkan variabel tertentu berdasarkan variabel bebas.
Kinds of Correlational Research
:
a.
Relationships research
research relationship, relational, or simple correlation (sometimes
just called correlation only) is used to
investigate the relationship between the results of measurements on two different variables at the same time. This study aimed to determine the extent or degree of relationship between a pair of variables. Basically, the research design is fairly simple relationship, which
is only by collecting scores two
variables of the
same subject, and then calculate the
correlation coefficient. Therefore,
in conducting this study, the researchers first define
a variable to be investigated
levels of relationship.
b.
Predictive research
In the
implementation in the field of education, many situations do require prediction
or forecasting. At the beginning of the new academic year, for example, every
school due to limited facilities, often have to select the applicants to be
accepted as a candidate for the new students.
This type of correlation research focuses on the measurement of one or more variables that can be used to predict
or predict events in the future or
other variables (Borg & Gall in Abidin, 2010).
c.
Correlation Multivariate
Teknik
untuk mengukur dan menyelidiki tingkat hubungan antara kombinasi dari tiga
variabel atau lebih disebut teknik korelasi multivariat.
4.
Comparative
Casual-comparative research explores cause-and-effect relationships
by observing an effect and collecting data after the fact, to explain causes .
Provides a more practical method than the experimental in situations where it
is not always possible to randomly assign groups, yet carries many
disadvantages with the lack of control . If a relationship appears between two
variables using this method, it is difficult to determine which is cause and which
is effect .
Comparative research, simply put, is the act of comparing two or
more things with a view to discovering something about one or all of the things
being compared. This technique often utilizes multiple disciplines in one
study. When it comes to method, the majority agreement is that there is no
methodology peculiar to comparative research.
5.
Experimental
Experimental
research establishes the cause-effect relationship and does the comparison, but
the cause is manipulated. The cause,
independent variable makes the difference.
The effect, dependent variable is dependent on the independent variable.
Experimental
Research is often used where:
- There is time priority in a causal relationship (cause precedes effect)
- There is consistency in a causal relationship (a cause will always lead to the same effect)
- The magnitude of the correlation is great.
(Reference: en.wikipedia.org)
Steps of experimental research :
a.
Identifying the Research Problem
b.
Constructing
the Experiment
c.
Conducting
the Experiment
d.
Analysis
and Conclusions
Method
Selection
of a sample
Population
and sample: So far we have talked about introduction, literature review and
methods in a research. Next we will
discuss the subject. Subjects mean the
population or samples researcher used for study. Population is the group of interest to the
researcher, the group to which she or he would like to the results of the study
to be generalizable. The population
that the researcher would ideally like to generalize to is referred to as the
target population; the population that the researcher can realistically select
from is referred to as the accessible or available population.
If
the group of interest is unmanageably large or geographically scattered, study
of this group could result in considerable expenditure of time money and
effort. Therefore, selecting a sample is
needed and is a very important step in conducting a research study. A “good” sample is one that is representative
of the population from which it was selected.
Regardless of the specific
techniques used for sampling, the steps in sampling are essentially the same:
Identification of the population, Determination of the required sample size,
and Selection of the sample. There are
four basic sampling techniques: random sampling, stratified sampling, cluster
sampling, and systematic sampling.
FOUR sampling techniques are: Random
sampling, Stratified sampling, cluster sampling, and systematic sampling.
Random sampling is the process
of selecting a sample in such a way that all individuals in the defined
population have an equal and independent chance of being selected for the
sample. It is considered as the best
single way to obtain a representative sample that is required by inferential
statistics.
Steps
of random sampling:
1.
Identify and define the population;
2.
Determine the desired sample size;
3.
List all members of the population;
4.
Assign all individuals on the list a
consecutive number from zero to the required number, for example, 000 to 249,
00 to 99;
5.
Select an arbitrary number in the table of
random numbers (close eyes and point);
6.
For the selected number look at only the
appropriate number of digits (2 or 3 digits);
7.
Match the selected number with the number
assigned to individuals in the population, if the number corresponds to the
number of assigned number of individual, the individual is in the sample;
8.
Go to the next number in the column and repeat
step 7;
9.
Repeat step 8 until the desired number of
individuals has been selected for the sample.
Example-- Label
5000 teacher with the number 0000 - 4999, then randomly point a number in the
random number table, match the last four-digit number of the number in the
table with the teacher’s label number.
If it is matched the teacher is selected in the sample. Repeat the process until 500 teachers are
selected as the required sample size.
Stratified sampling is the process
of selecting a sample in such a way that identifies subgroups in the population
are represented in the sample in the same proportion that they exist in the
population. In this process, random
sampling is done more than once; it is done form each subgroup.
Steps
of stratified sampling:
1.
Identify and define the population;
2.
Determine desired sample size;
3.
Identify the variable and subgroups for each
the researcher wants to guarantee appropriate representation;
4.
Classify all members of the population as
members of one of the identified subgroups;
5.
Randomly select an “appropriate” number of
individuals from each of the subgroups, “appropriate” meaning either a
proportional number of individuals or an equal number of individuals.
Example: Suppose one is interested
in comparing the performance of students of different IQ levels, following two
different methods of mathematics instruction.
One can have 300 eighth graders classified in to high (>115), average
(85-115), and low (< 85) IQ group, randomly select 30 students from each
group, and then randomly assign 15 from each 30 students to experimental or
control groups for the study.
Cluster sampling is sampling in
which groups, not individuals, are randomly selected. All the members of selected groups have
similar characteristics.
Steps
of cluster sampling:
1.
Identify and define the population.
2.
Determine the desired sample size.
3.
Identify and define a logical cluster.
4.
List all clusters that comprise the population.
5.
Estimate the average number of population
members per cluster.
6.
Determine the number of clusters needed by
dividing the sample size by the estimated size of a cluster.
7.
Randomly select the needed number of cluster
(using a table of random numbers).
8.
Include in the study all population members in
each selected cluster.
Example:
One needs to have 500 teachers from 5000 for the study. There are 100 schools with 50 teacher in
each. In cluster sampling, one randomly
select 10 schools for study and have 500 teachers in the sample. The problem of this sampling is that
inferential statistics are not appropriate for analyzing data resulting from a
study using cluster sampling.
Inferential statistics generally require random sampling.
System sampling is sampling in
which individuals are selected from a list by taking every Kth
name. If the list of the population is
randomly ordered, this sample can be considered as a random sample
Steps of systematic sampling:
1.
Identify and define the population.
2.
Determine the desired sample size.
3.
Obtain a list of the population.
4.
Determine what k is equal to by dividing the
size of the population by the desired sample size.
5.
Start at some random place at the top of the
population list.
6.
Starting at that point, take every Kith name on
the list until the desired sample size is reached.
7.
If the end of the list is reached before the
desired sample is reached, go back to the top of the list.
Sample size: Sample should be as large
as possible. Minimum acceptable sample
size depends on the type of research.
For causal-comparative, correlational research 30 in each group, and 15
in experimental research are generally recommended as minimum sample size.
Nonprobability sampling that includes:
1)
convenience sampling: the use of volunteers and
existing groups
2)
judgment sampling: the use of believed groups
3)
quota sampling: the use of quota in the
situation of not all members of the population be possible listed for sampling.
Selection of measuring instruments
All
research studies involve data collection.
There are three major ways to collect dada: 1) administrate a
standardized instrument; 2) administrate a self-developed instrument; 3) record
naturally available data.
Validity and Reliability
Validity means the degree to which a test measures that
it is supposed to measure and, consequently, permits appropriate interpretation
of scores. The concept of validity is
that the validity for whom or for what.
Since tests are designed for a variety of purposes, and since validity
can be evaluated only in terms of purpose, the validity is categorized in
several different types: Content, construct, concurrent and predictive
validates.
Content validity: Content validity is the degree to which a
test measures an intended content area.
It requires both item validity and sampling validity. Item validity is concerned with whether the
test items represent measurement in the intended content area, and sampling
validity is concerned with how well the test samples the total content
area. The content validity is determined
by expert judgment.
Construct validity: Construct validity is the degree to
which a test measures an intended
hypothetical construct. It is not easy
task to process a test of construct validity.
Generally, a number of independent studies are required to establish the
credibility of a test of a construct.
Concurrent
validity: It is the degree to which the scores on a
test are related to the scores on another, already established, test administered
at the same time, or to some other valid criterion available at the same time. The concurrent validity is determined by the
resulting number, validity coefficient.
If the coefficient is high, the test has good concurrent validity.
Predictive
validity: Predictive validity is the degree to which a
test can predict how well an individual will do in a future situation. The predictive validity of a test is
determined by establishing the relationship between scores on the test and some
measure of success in the situation of interest. It is determined by the resulting number,
validity coefficient. If the coefficient
is high, the predictive validity is good.
Generally, 0.5 might be acceptable predictive validity in certain
situations, but not in some others.
Reliability is expressed
numerically, usually as a coefficient; a high coefficient indicates high
reliability. High reliability indicates
minimum error variance; if a test has high reliability, then the effect of
errors of measurement has been reduced.
A valid test is always reliable but
a reliable test is not necessarily valid.
In other word, if a test is measuring what it is supposed to be
measuring, it will be reliable and do so every time, but a reliable test can
consistently measure the wrong thing and be invalid.
The reliability is easier than
validity to be assessed. Test-retest,
equivalent-forms, and split-half reliability are all determined through
correlation.
Test-retest reliability is the
degree to which scores are consistent over time.
Reliability can also be expressed in
terms of the standard error of measurement.
Standard error of measurement (degree of variance) is an estimate of how
often you can expect errors of a given size.
A small standard error of measurement indicates high reliability and a
large standard error of measurement indicates low reliability.
Design and
Procedure
Basic
causal-comparative research design
The basic
causal-comparative design involves selecting two groups differing on some
independent variable and comparing them on some dependent variable.
Case A: (E) – (X) – (O)
(C)
-- (O) One
group is with different experiences, the other is not
Case B: (E) – (X1)--(O)
(C) – (X2) –(O) Two
groups with two different experiences
***Symbols: (E) – Experimental group, (X) – Independent
variable
(C) – Control group, (O) – Dependent variable
Three types of
control procedures that can be used in a causal-comparative study
Since the
randomization is not possible in causal-comparative studies (the groups already
exist), three types of control procedures usually adopted in the study to
insure equality of groups.
1)
Matching: If a researcher match on IQ, then a subject
in one group with an IQ at or near 140
would have a match in the other group.
2)
Comparing
homogeneous groups or subgroups: If IQ were an identified extraneous variable,
the researcher might limit groups to contain only subjects with IQ between 85
and 115 (average IQ). Or, each group
might be divided into high (116 and above), average (85-115) and low (84 and
below) IQ subgroups.
3)
A statistic
method, Analysis of covariance (ANCOVA): A
statistic method to be used to equate groups on one or more variables.
Common
experimental research design
1)
Pre-experimental Designs
X -- O One-short
case study
O-- X -- O One
group pretest-post test design
X1 -- O / X2 -- O Static
group comparison
2)
True experimental designs
R -- O -- X1 -- O
R -- O -- X2 -- O Pretest-posttest
control group design
R -- X1 -- O
R -- X2 -- O
Posttest only control
group design
3)
Quasi-experimental group designs
O -- X1 -- O
O -- X2 -- O
Nonequivalent control
group design
O -- O -- O -- O -- X -- O -- O -- O -- O Time series design
X1 -- O -- X2
-- O -- X3 -- O
X3 -- O -- X1
-- O -- X2 -- O
X2 -- O -- X3
-- O -- X1 -- O Counterbalanced design
***Symbols:
(R) – Randon assignment of subjects to group
(X) –
Independent variable (O) – Dependent variable
Five ways to
control extraneous variables
1.
Randomization is effective
in creating equivalent and representative groups that are essentially the same
on all relevant variables thought of by the researcher.
2.
Matching is a
techniques for equating groups on one or more variables the researcher has
identified as being highly related to performance on the dependent variable.
3.
Comparing
homogeneous groups or subgroups to control the extraneous variables.
4.
Using subjects
as their own controls involves exposing the same group to the
different treatments, one treatment at a time.
5.
Analysis of
covariance is a statistical method for equating randomly
formed groups on one or more variables.
Three types of
replication involved in single-subject research
1.
Direct
replication refers to replication by the same
investigator, with the same subject or with different subjects, in a specific
setting.
2.
Systematic
replication refers to replication which follows direct
replication, and which involves different investigators, behaviors, and
settings.
3.
Clinical
replication involves the development of a treatment
package, composed of two or more interventions that have been found to be
effective individually, designed for persons with complex behavior disorders.
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