Types
of Research Design
By:
JON SASTRO
ENGLISH STUDY
PROGARAM FACULTY OF TEACHER
TRAINING
MUHAMMADIYAH UNIVERSITY
OF BENGKULU
2011
Types
of Research Design
1.1 Definition of Research Design
Research design is a formal plan of action for a research project.
Research designs help researchers to lay out their research questions,
methodologies, implementation procedures, and data collection and analysis for
the conduct of a research project.
Generally there are three types of research design: quantitative design,
qualitative design,
and mixed methods design.
Research design is a decision making process. During the decision making
process, the researcher, like an architect, should choose from many design
alternatives and consider over the trade-offs of each approach and decide the
best possible solution. Generally speaking, the research design decisions are
influenced by the questions the investigator is trying to answer, by the
resources such as time, trained personnel, and money that the researcher have
at hand, by the characteristics of the research sites, and also by the
researcher's personal preferences.
2.2 Types of Research Design
2.2.1. Quantitative design
A. Definition
In quantitative research your aim is to determine the relationship
between one thing (an independent variable) and another (a dependent or outcome
variable) in a population. Quantitative research designs are either descriptive
(subjects usually measured once) or experimental (subjects measured before and
after a treatment). A descriptive study establishes only associations between
variables. An experiment establishes causality.
For an accurate estimate of the relationship between variables, a
descriptive study usually needs a sample of hundreds or even thousands of
subjects; an experiment, especially a crossover, may need only tens of
subjects. The estimate of the relationship is less likely to be biased if you
have a high participation rate in a sample selected randomly from a population.
In experiments, bias is also less likely if subjects are randomly assigned to
treatments, and if subjects and researchers are blind to the identity of the
treatments.
Quantitative research is all about
quantifying relationships between variables. Variables are things like weight,
performance, time, and treatment. You measure variables on a sample of
subjects, which can be tissues, cells, animals, or humans. You express the
relationship between variable using effect statistics, such as correlations,
relative frequencies, or differences between means.
Studies aimed at quantifying
relationships are of two types: descriptive and experimental, and
quasi experimental. In a descriptive study, no attempt is made to change
behavior or conditions--you measure things as they are. In an experimental
study you take measurements, try some sort of intervention, then take
measurements again to see what happened.
1.
Descriptive Studies
Descriptive studies are also called observational,
because you observe the subjects without otherwise intervening. The simplest
descriptive study is a case, which reports data on only one subject;
examples are a study of an outstanding athlete or of a dysfunctional
institution. Descriptive studies of a few cases are called case series.
In cross-sectional studies variables of interest in a sample of subjects
are assayed once and the relationships between them are determined. In prospective
or cohort studies, some variables are assayed at the start of a study
(e.g., dietary habits), then after a period of time the outcomes are determined
(e.g., incidence of heart disease). Another label for this kind of study is longitudinal,
although this term also applies to experiments. Case-control studies
compare cases (subjects with a particular attribute, such as an injury
or ability) with controls (subjects without the attribute); comparison
is made of the exposure to something suspected of causing the cases, for
example volume of high intensity training, or number of alcoholic drinks
consumed per day. Case-control studies are also called retrospective,
because they focus on conditions in the past that might have caused subjects to
become cases rather than controls.
A common case-control design in the
exercise science literature is a comparison of the behavioral, psychological or
anthropometric characteristics of elite and sub-elite athletes: you are
interested in what the elite athletes have been exposed to that makes them
better than the sub-elites. Another type of study compares athletes with
sedentary people on some outcome such as an injury, disease, or disease risk
factor.
2.
Experimental Studies
Experimental studies are also known
as longitudinal or repeated-measures studies, for obvious
reasons. They are also referred to as interventions, because you do more
than just observe the subjects.
In the simplest experiment, a time
series, one or more measurements are taken on all subjects before and after
a treatment. A special case of the time series is the so-called single-subject
design, in which measurements are taken repeatedly (e.g., 10 times) before
and after an intervention on one or a few subjects.
Time series suffer from a major
problem: any change you see could be due to something other than the treatment.
For example, subjects might do better on the second test because of their
experience of the first test, or they might change their diet between tests
because of a change in weather, and diet could affect their performance of the
test. The crossover design is one solution to this problem. Normally the
subjects are given two treatments, one being the real treatment, the other a control
or reference treatment. Half the subjects receive the real treatment first, the
other half the control first. After a period of time sufficient to allow any
treatment effect to wash out, the treatments are crossed over. Any effect of
retesting or of anything that happened between the tests can then be subtracted
out by an appropriate analysis. Multiple crossover designs involving
several treatments are also possible.
If the treatment effect is unlikely
to wash out between measurements, a control group has to be used. In
these designs, all subjects are measured, but only some of them--the experimental
group--then receive the treatment. All subjects are then measured again,
and the change in the experimental group is compared with the change in the
control group.
If the subjects are assigned
randomly to experimental and control groups or treatments, the design is known
as a randomized controlled trial. Random assignment minimizes the chance
that either group is not typical of the population. If the subjects are blind
(or masked) to the identity of the treatment, the design is a single-blind
controlled trial. The control or reference treatment in such a study is called
a placebo: the name physicians use for inactive pills or treatments that
are given to patients in the guise of effective treatments. Blinding of
subjects eliminates the placebo effect, whereby people react differently
to a treatment if they think it is in some way special. In a double-blind study,
the experimenter also does not know which treatment the subjects receive until
all measurements are taken. Blinding of the experimenter is important to stop
him or her treating subjects in one group differently from those in another. In
the best studies even the data are analyzed blind, to prevent conscious or
unconscious fudging or prejudiced interpretation.
Ethical considerations or lack of
cooperation (compliance) by the subjects sometimes prevent experiments from
being performed. For example, a randomized controlled trial of the effects of
physical activity on heart disease may not have been performed yet, because it
is unethical and unrealistic to randomize people to 10 years of exercise or
sloth. But there have been many short-term studies of the effects of physical
activity on disease risk factors (e.g., blood pressure)
3. Quasi Experimental
A
quasi-experimental design is one that looks a bit like an experimental design
but lacks the key ingredient -- random assignment. My mentor, Don Campbell,
often referred to them as "queasy" experiments because they give the
experimental purists a queasy feeling. With respect to internal validity,
they often appear to be inferior to randomized experiments. But there is
something compelling about these designs; taken as a group, they are easily
more frequently implemented than their randomized cousins.
2.2.2 Qualitative design
Qualitative data analysis is
primarily an inductive process of comparison in which the categories and
patterns emerge from the data from specific questions that the researcher asks
about the data. The researcher codes the data into categories, and then
identifies (sorts) similarities and distinctions
between categories to discover patterns or relationships among the categories.
Synthesis or analysis is the key to identify patterns. Types of analysis are
called strategies rather than procedures.
Qualitative analysis is no less
rigorous than statistical procedures, nor is it data reduction. The qualitative
researcher does not force data into the researcher's presuppositions, but
instead immerses him or herself in the data to let the data "speak."
Qualitative researchers are expected to monitor and report their analytical
techniques, processes, and reasons for decisions.
There are
five main approaches to analysis, each with subcategories of variations to the
approaches. Based on the research problem, the researcher selects an approach.
1. Descriptive Narration of stories and events focused on
groups & their activities that change over time.
Descriptive Narrations Contain at Least 4 Elements:
1. people
2. incidents
3. participants' language
4. participants' meaning
2. Topology (of shared experiences) classifies different types of
experiences with the same phenomenon.
3. Theme Analysis describes recurring themes found in
the data such as visual qualities, behavioral characteristics, discourse
topics, or participants' expressed concerns.
4. Grounded Theory - theory building analyses that
proposes a theory as an explanation that is grounded in the data.
5. Concept Analysis describes each subcomponent and its
relationship to other subcomponents of the concept
constructs - a complex abstraction that is not directly observable
(meaning is assigned based on prior theory) ex: "public perception"
or "art"
concept - the perspectives of participants (grounded in data) ex:
"museum" or "art"
3. Mix Methods Design
A mixed research design is a general type of
research that includes quantitative and qualitative research data, techniques
and methods. All these paradigm characteristics are mixed in one case study.
This method design involves research that uses mixed data (numbers and text)
and additional means (statistics and text analysis). A mixed method uses both
deductive and inductive scientific method, has multiple
forms of data collecting and produces eclectic and
pragmatic reports.
Two main types of a mixed method are: mixed method
and mixed model research. A mixed research method is a research in which you
use quantitative data for one stage of a research study and qualitative data
for a second stage of a research. A mixed model design is a research in which
you use both quantitative and qualitative data in one or two stages of the
research process. The mixing of quantitative and qualitative approaches happens
in every stage of a research.
In a research it is important to use a mixed
research method for the conducting of a detailed research. The advantages of a
mixed research are:
1)
The strength of the research;
2)
Use of multiple methods in a research helps to research a process or a problem
from all sides;
3)
Usage of different approaches helps to focus on a single process and confirms
the data accuracy. A mixed research complements a result from one type of
research with another one. This research does not miss any available data.
A quantitative component of a mixed research assumes
the usage of deductive scientific method while qualitative component assumes
inductive scientific method. Moreover, a quantitative approach collects
quantitative data based on exact measurement applying structured as well as
validated information collection. For instance, rating scales, closed-ended
items and responses. This approach produces statistical
report with correlations.
A qualitative component uses qualitative
information. For instance, interview, field notes, open-ended questions etc.
This approach considers a researcher to be the major means of information
collection. At the end of a research this approach supposes a narrative report
with context description, quotations taken from research material.
It is important to stress that there are many ways
of research performing. Quantitative and qualitative methods have their
advantages and disadvantages in a research. However, you may summarize the
advantages of both methods and have accurate information on implementation,
findings and conclusions of your research project. Qualitative and quantitative
research methods have different strengths, weaknesses and requirements that
affect researcher’s project accuracy. The aim of a mixed method design is to
summarize positive aspects of two approaches and produce a highly accurate data.
When you use several methods in your research
process, then you can use the strength of every type of information collection
and minimize the weak points of every of both approaches. A mixed method
approach of gathering and evaluation can increase the validity and accuracy of
the information. The article briefly analyzes a mixed method research design
including the major components: quantitative and qualitative approach for the
design of a research. The article proves the effectiveness of a mixed method design.
1.3 Kinds if Sample in
selecting the data
A sample is a finite part of a
statistical population whose properties are studied to gain information about
the whole(Webster, 1985). When dealing with people, it can be defined as a set
of respondents(people) selected from a larger population for the purpose of a
survey.
Bias
and error in sampling a sample is expected to mirror the population from which
it comes, however, there is no guarantee that any sample will be precisely
representative of the population from which it comes. Chance may dictate that a
disproportionate number of untypical observations will be made like for the
case of testing fuses, the sample of fuses may consist of more or less faulty
fuses than the real population proportion of faulty cases. In practice, it is
rarely known when a sample is unrepresentative and should be discarded.
a. Sampling Error
Sampling error comprises the differences between the sample and the
population that are due solely to the particular units that happen to have been
selected.
The main
protection against this kind of error is to use a large enough sample. The
second cause of sampling is sampling bias.
Sampling bias is a tendency to favor the selection of units that have particular
characteristics. Sampling bias is usually the result of a poor sampling plan.
The most notable is the bias of non response when for some reason some units
have no chance of appearing in the sample.
b. Non
sampling error (measurement error) .
A non sampling error is an error that results solely from the manner
in which the observations are made. The simplest example of non sampling error
is inaccurate measurements due to malfunctioning instruments or poor
procedures. For example, consider the observation of human weights. If persons
are asked to state their own weights themselves, no two answers will be of
equal reliability. The people will have weighed themselves on different scales
in various states of poor calibration. An individual`s weight fluctuates
diurnally by several pounds, so that the time of weighing will affect the
answer. The scale reading will also vary with the person`s state of undress.
Responses therefore will not be of comparable validity unless all persons are
weighed under the same circumstances.
c. The Interwiers
effect
No two
interviewers are alike and the same person may provide different answers to
different interviewers. The manner in which a question is formulated can also
result in inaccurate responses. Individuals tend to provide false answers to
particular questions. For example, some people want to feel younger or older
for some reason known to themselves. If you ask such a person their age in
years, it is easier for the individual just to lie to you by over stating their
age by one or more years than it is if you asked which year they were born
since it will require a bit of quick arithmetic to give a false date and a date
of birth will definitely be more accurate.
d. The Respondent
Effect
Respondents might also give incorrect answers to impress the
interviewer. This type of error is the most difficult to prevent because it
results from out right deceit on the part of the respond. An example of this is
what I witnessed in my recent study in which I was asking farmers how much
maize they harvested last year (1995). In most cases, the men tended to lie by
saying a figure which is the recommended expected yield that is 25 bags per
acre. The responses from men looked so uniform that I became suspicious. I
compared with the responses of the wives of these men and their responses were
all different. To decide which one was right, whenever possible I could in a
tactful way verify with an older son or daughter. It is important to
acknowledge that certain psychological factors induce incorrect responses and
great care must be taken to design a study that minimizes their effect. .
e. The Random
Sample
This may be the most important type of sample. A random sample
allows a known probability that each elementary unit will be chosen. For this
reason, it is sometimes referred to as a probability sample. This is the type
of sampling that is used in lotteries and raffles. For example, if you want to
select 10 players randomly from a population of 100, you can write their names,
fold them up, mix them thoroughly then pick ten. In this case, every name had
any equal chance of being picked. Random numbers can also be used.
Types of random samples:
1.A simple
random sample
A simple random sample is obtained by choosing elementary units in
search a way that each unit in the population has an equal chance of being
selected. A simple random sample is free from sampling bias. However, using a
random number table to choose the elementary units can be cumbersome. If the
sample is to be collected by a person untrained in statistics, then
instructions may be misinterpreted and selections may be made improperly.
Instead of using a least of random numbers, data collection can be simplified
by selecting say every 10th or 100th unit after the first unit has been chosen
randomly as discussed below. such a procedure is called systematic random
sampling.
2. A
systematic random sample
A systematic random sample is obtained by selecting one unit on a
random basis and choosing additional elementary units at evenly spaced
intervals until the desired number of units is obtained. For example, there are
100 students in your class
3. A stratified
sample
A stratified sample is obtained by independently selecting a
separate simple random sample from each population stratum. A population can be
divided into different groups may be based on some characteristic or variable
like income of education.
4. A cluster
sample
A cluster sample is obtained by selecting clusters from the
population on the basis of simple random sampling. The sample comprises a
census of each random cluster selected. For example, a cluster may be some
thing like a village or a school, a state.
f. Sample Size
The question of how large a sample should be is a difficult one.
Sample size can be determined by various constraints. For example, the
available funding may per specify the sample size. When research costs are
fixed, a useful rule of thumb is to spent about one half of the total amount
for data collection and the other half for data analysis. This constraint
influences the sample size as well as sample design and data collection
procedures.
Deciding on a sample size for qualitative inquiry can be even more
difficult than quantitative because there are no definite rules to be followed.
It will depend on what you want to know, the purpose of the inquiry, what is at
stake, what will be useful, what will have credibility and what can be done
with available time and resources. With fixed resources which is always the
case, you can choose to study one specific phenomenon in depth with a smaller
sample size or a bigger sample size when seeking breadth. In purposeful
sampling, the sample should be judged on the basis of the purpose and rationale
for each study and the sampling strategy used to achieve the studies purpose.
The validity, meaningfulness, and insights generated from qualitative inquiry
have more to do with the information-richness of the cases selected and the
observational/analytical capabilities of the researcher than with sample size.
Quantitative Research Designs
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Descriptive
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Experimental
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Quasi-experimental
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Qualitative Research Designs
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Historical
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Ethnographic
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Case Studies
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