Experimental
Research
It is a collection of
research designs which use manipulation and controlled testing to understand
causal processes. Generally, one or more variables are manipulated to determine
their effect on a dependent variable.
The experimental method
is a systematic and
scientific approach to research in which the researcher manipulates one or more
variables, and controls and measures any change in other variables.
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.
The word experimental
research has a range of definitions. In the strict sense, experimental research
is what we call a true experiment.
This is an experiment where
the researcher manipulates one variable, and control/randomizes the rest of the
variables. It has a control group, the subjects have been randomly assigned
between the groups, and the researcher only tests one effect at a time. It is
also important to know what variable(s) you want to test and measure.
A very wide definition of
experimental research, or a quasi experiment, is research where the scientist
actively influences something to observe the consequences. Most experiments
tend to fall in between the strict and the wide definition.
A rule of thumb is that
physical sciences, such as physics, chemistry and geology tend to define
experiments more narrowly than social sciences, such as sociology and
psychology, which conduct experiments closer to the wider definition.
Aims
of Experimental Research
Experiments are conducted
to be able to predict phenomenon’s. Typically, an experiment is constructed to
be able to explain some kind of causation. Experimental research is important
to society - it helps us to improve our everyday lives.
BASIC CONCEPTS of Experimental Research
The experimental research
involves some basic concept
And terminologies which are
discussed briefly here:
¨ Experimental group:
A group of individuals,
objects or events, exposed to the influence of
The Factor under
consideration, is called an Experimental group.
¨ Controlled group:
A group of individuals,
objects or events not exposed to the influence
Of the factor under
consideration, is called a Controlled group.
¨ Variable:
a situation, number or quantity that can vary
or be varied.
Types
of Variables
n Independent
variables:
n Dependent
variables:
n Confounding
Variables:
n Intervening
Variables:
n Extraneous
Variables:
Independent
variables:
·
They
are also called Experimental or Treatment Variables.
·
These
are the variables which an experimenter manipulates or controls him/herself and
observes their impact on the phenomenon under consideration.
·
For
example, “the effect of A.V Aids on the academic achievements of the students”,
·
here,
the A.V Aids are independent variables, which are controlled or manipulated by
the researcher.
Dependent variables:
·
They
are also called the Criterion or Outcome Variables.
·
These
are the variables which change, appear or disappear by the influence of the
independent variables.
·
The
dependent variables can only be observed or recorded
·
in
the above example; the academic achievements are dependent upon the effect of A.V.
Aids,
·
here
the academic achievements refer to the dependent variables.
Confounding Variables:
- Those aspects of the study or
sample that might influence the dependent variable (outcome measure) and
whose effect may be confused with effects of the independent variable.
Intervening
Variables
·
Variables
which cannot be controlled or measured directly may have an important effect
upon the outcome.
·
These
modifying variables intervene between the cause and the effect.
§ Anxiety
§ Fatigue
§ Motivation
·
May
be controlled upto some extent by using appropriate designs.
Extraneous variables:
- Those uncontrolled variables that may have a
significant influence upon the results of a study.
- Teacher competence or
enthusiasm
- Age
- Socioeconomic level
- Academic ability
METHODS
TO CONTROL EXTRANEOUS VARIABLES
- Extraneous variables can’t be totally controlled,
however, their effect can be minimized by using some technique like the
following.
- Elimination:
- Randomization:
- Matching cases:
- Balancing cases:
Elimination:
n If possible, remove the
extraneous variables completely, for example consider the same example i.e.
“The effect of A.V. Aids on the academic achievements of students,” Here the
gender factor may be an extraneous variable, so remove this factor completely
i.e. undertake the research only on male or female students. So this method of
removing the extraneous variables is called elimination.
Randomization:
It means to minimize the
effect of extraneous variables by taking random samples. For example, the
extraneous variables may be present in both experimental as well as controlled
groups. So, the effect of extraneous variables will not produce an error in the
results.
Matching cases: It means to take the
groups matching with each other, i.e. the groups are equal in all respects. For
example, we have to study the effect of drill on the improvement of spellings,
so, in order to minimize the effect of extraneous variables; the students of
almost equal intelligence are selected in the experimental as well as
controlled group. This is called matching case, which is done wherever
randomization is not possible.
Balancing cases: It
means that the two groups are balanced with each other. Consider for example,
the above study i.e. “the effect of Drill on the improvement of spellings”. If
there is a group of eight students (the experimental group) in which five
students are of high intelligence and three are feeble minded. So, while taking
the controlled group, there must be a total of eight students including five of
high intelligence and three feeble minded, so that the two groups are matched
equally.
PROCESS AND PROCEDURE OF EXPERIMENTAL
RESEARCH
¨
Studying the problematic situation
¨
Problem selection
¨
Specification of objectives
¨
Hypothesis
¨
Measuring instruments
¨
Planning and selecting experimental design
¨
Pilot-test of the instrument
¨
Execution
¨
Data analysis
¨
Conclusion
¨
Further verification of results
Experimental Design
n An experimental design
means a blue-print of the experimental procedure to be followed, in order to
determine the cause-effect relationship.
Types
of Experimental Designs
n Pre-Experimental
n True Experimental
n Quasi-Experimental
Pre Experimental:
n The essential ingredient of
a true experiment is random assignment of subjects to treatment groups
n Random assignments is a
powerful tool for controlling threats to internal validity
Designs
of Pre-Experimental
·
One-Shot case study
·
Randomized pretest-posttest control group
design
·
The Static Group Comparison Design
True
Experimental
n The
True Experimental designs involve the equivalent experimental and controlled
groups for comparison. In educational and behavioral sciences, it is very
difficult to achieve the two groups exactly similar in all aspects, however,
True Experimental designs are further sub-divided into 2.
Ø Randomized
matched control group design
Ø Factorial
design
Quasi-experimental
Designs
n One
group posttest-only design
n One
group pretest-posttest design
n Non-equivalent
control group design
n Non-equivalent
control group pretest-posttest design
n Time
series
n Single
subject designs (Case study)
Identifying
the Research Problem
After deciding the topic of
interest, the researcher tries to define the research problem. This helps the
researcher to focus on a more narrow research area to be able to study it
appropriately. Defining the research problem helps you to formulate
a research hypothesis, which is tested against the null hypothesis.
The research problem is
often operationalizationed, to define how to measure the research problem. The
results will depend on the exact measurements that the researcher chooses and
may be operationalized differently in another study to test the main
conclusions of the study.
Constructing
the Experiment
There are various aspects
to remember when constructing an experiment. Planning ahead ensures that the
experiment is carried out properly and that the results reflect the real world,
in the best possible way.
Sampling
Groups to Study
Sampling groups correctly
is especially important when we have more than one condition in the experiment.
One sample group often serves as a control group, whilst others are tested
under the experimental conditions.
Deciding the sample groups
can be done in using many different sampling techniques. Population sampling
may chosen by a number of methods, such as randomization,
"quasi-randomization" and pairing.
Reducing sampling errors is
vital for getting valid results from experiments. Researchers often adjust the
sample size to minimize chances of random errors.
Here are some common
sampling techniques:
- probability sampling
- non-probability sampling
- simple random sampling
- convenience sampling
- stratified sampling
- systematic sampling
- cluster sampling
- sequential sampling
- disproportional sampling
- judgmental sampling
- snowball sampling
- quota sampling
Creating
the Design
The research design is
chosen based on a range of factors. Important factors when choosing the design
are feasibility, time, cost, ethics, measurement problems and what you would
like to test. The design of the experiment is critical for the validity of the
results.
Typical
Designs and Features in Experimental Design
- Pretest-Posttest Design
Check whether the groups are different before the manipulation starts and
the effect of the manipulation. Pretests sometimes influence the effect.
- Control Group
Control groups are designed to measure research bias and measurement
effects, such as the Hawthorne Effect or the Placebo Effect. A control
group is a group not receiving the same manipulation as the experimental
group. Experiments frequently have 2 conditions, but rarely more than 3
conditions at the same time.
- Randomized Controlled Trials
Randomized Sampling, comparison between an Experimental Group and a
Control Group and strict control/randomization of all other variables
- Solomon Four-Group Design
With two control groups and two experimental groups. Half the groups have
a pretest and half do not have a pretest. This to test both the effect
itself and the effect of the pretest.
- Between Subjects Design
Grouping Participants to Different Conditions
- Within Subject Design
Participants Take Part in the Different Conditions - See also: Repeated
Measures Design
- Counterbalanced Measures Design
Testing the effect of the order of treatments when no control group is
available/ethical
- Matched Subjects Design
Matching Participants to Create Similar Experimental- and Control-Groups
- Double-Blind Experiment
Neither the researcher, nor the participants, know which is the control
group. The results can be affected if the researcher or participants know
this.
- Bayesian Probability
Using bayesian probability to "interact" with participants is a
more "advanced" experimental design. It can be used for settings
were there are many variables which are hard to isolate. The researcher
starts with a set of initial beliefs, and tries to adjust them to how
participants have responded
Pilot
Study
It may be wise to first
conduct a pilot-study or two before you do the real experiment. This ensures
that the experiment measures what it should, and that everything is set up
right.
Minor errors, which could
potentially destroy the experiment, are often found during this process. With a
pilot study, you can get information about errors and problems, and improve the
design, before putting a lot of effort into the real experiment.
If the experiments involve
humans, a common strategy is to first have a pilot study with someone involved
in the research, but not too closely, and then arrange a pilot with a person
who resembles the subject(s). Those two different pilots are likely to give the
researcher good information about any problems in the experiment.
Conducting
the Experiment
An experiment is typically
carried out by manipulating a variable, called the independent variable,
affecting the experimental group. The effect that the researcher is interested
in, the dependent variable(s), is measured.
Identifying and controlling
non-experimental factors which the researcher does not want to influence the
effects, is crucial to drawing a valid conclusion. This is often done by controlling
variables, if possible, or
randomizing variables to minimize effects that can be traced back to third
variables. Researchers only want to measure the effect of the independent
variable(s) when conducting an experiment, allowing them to conclude that this
was the reason for the effect.
Conducting
the Experiment
An experiment is typically
carried out by manipulating a variable, called the independent variable,
affecting the experimental group. The effect that the researcher is interested
in, the dependent variable(s), is measured.
Identifying and controlling
non-experimental factors which the researcher does not want to influence the
effects, is crucial to drawing a valid conclusion. This is often done by
controlling variables, if possible,
or randomizing variables to minimize effects that can be traced back to third
variables. Researchers only want to measure the effect of the independent
variable(s) when conducting an experiment, allowing them to conclude that this
was the reason for the effect.
The aim of an analysis is
to draw a conclusion, together with other observations. The researcher might
generalize the results to a wider phenomenon, if there is no indication of
confounding variables "polluting" the results.
If the researcher suspects
that the effect stems from a different variable than the independent variable,
further investigation is needed to gauge the validity of the results. An
experiment is often conducted because the scientist wants to know if the
independent variable is having any effect upon the dependent variable.
Variables correlating are not proof that there is causation.
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