TYPES OF VARIABLES
RELIABILITY AND VALIDITY
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Tuesday, April 12, 2011
EXPERIMENTAL RESEARCH METHODS
TYPES OF VARIABLES
RELIABILITY AND VALIDITY
CONDUCTING AN EXPERIMENT
Science revolves around experiments, and learning the best way of conducting an experiment is crucial to obtaining useful and valid results.
by Martyn Shuttleworth (2008)
When scientists speak of experiments, in the strictest sense of the word, they mean a true experiment, where the scientist controls all of the factors and conditions.
Real world observations, and case studies, should be referred to as observational research, rather than experiments.
For example, observing animals in the wild is not a true experiment, because it does not isolate and manipulate an independent variable.
THE BASIS OF CONDUCTING AN EXPERIMENT
With an experiment, the researcher is trying to learn something new about the world, an explanation of ‘why’ something happens.
The experiment must maintain internal and external validity, or the results will be useless.
When designing an experiment, a researcher must follow all of the steps of the scientific method, from making sure that the hypothesis is valid and testable, to using controls and statistical tests.
Whilst all scientists use reasoning, operationalization and the steps of the scientific process, it is not always a conscious process.
Experience and practice mean that many scientists follow an instinctive process of conducting an experiment, the ‘streamlined’ scientific process. Following the basic steps will usually generate valid results, but where experiments are complex and expensive, it is always advisable to follow the rigorous scientific protocols. Conducting an experiment has a number of stages, where the parameters and structure of the experiment are made clear.
Whilst it is rarely practical to follow each step strictly, any aberrations must be justified, whether they arise because of budget, impracticality or ethics.
STAGE ONE
After deciding upon a hypothesis, and making predictions, the first stage of conducting an experiment is to specify the sample groups. These should be large enough to give a statistically viable study, but small enough to be practical.
Ideally, groups should be selected at random, from a wide selection of the sample population. This allows results to be generalized to the population as a whole.
In the physical sciences, this is fairly easy, but the biological and behavioral sciences are often limited by other factors.
For example, medical trials often cannot find random groups. Such research often relies upon volunteers, so it is difficult to apply any realistic randomization. This is not a problem, as long as the process is justified, and the results are not applied to the population as a whole.
If a psychological researcher used volunteers who were male students, aged between 18 and 24, the findings can only be generalized to that specific demographic group within society.
STAGE TWO
The sample groups should be divided, into a control group and a test group, to reduce the possibility of confounding variables.
This, again, should be random, and the assigning of subjects to groups should be blind or double blind. This will reduce the chances of experimental error, or bias, when conducting an experiment.
Ethics are often a barrier to this process, because deliberately withholding treatment, as with the Tuskegee study, is not permitted.
Again, any deviations from this process must be explained in the conclusion. There is nothing wrong with compromising upon randomness, where necessary, as long as other scientists are aware of how, and why, the researcher selected groups on that basis.
STAGE THREE
This stage of conducting an experiment involves determining the time scale and frequency of sampling, to fit the type of experiment.
For example, researchers studying the effectiveness of a cure for colds would take frequent samples, over a period of days. Researchers testing a cure for Parkinson’s disease would use less frequent tests, over a period of months or years.
STAGE FOUR
The penultimate stage of the experiment involves performing the experiment according to the methods stipulated during the design phase.
The independent variable is manipulated, generating a usable data set for the dependent variable.
STAGE FIVE
The raw data from the results should be gathered, and analyzed, by statistical means. This allows the researcher to establish if there is any relationship between the variables and accept, or reject, the null hypothesis.
These steps are essential to providing excellent results. Whilst many researchers do not want to become involved in the exact processes of inductive reasoning, deductive reasoning and operationalization, they all follow the basic steps of conducting an experiment. This ensures that their results are valid.
Read more: http://www.experiment-resources.com/conducting-an-experiment.html#ixzz1CLJJDaFX
True experimental design
For some of the physical sciences, such as physics, chemistry and geology, they are standard and commonly used. For social sciences, psychology and biology, they can be a little more difficult to set up.
For an experiment to be classed as a true experimental design, it must fit all of the following criteria.
• The sample groups must be assigned randomly.
• There must be a viable control group.
• Only one variable can be manipulated and tested. It is possible to test more than one, but such experiments and their statistical analysis tend to be cumbersome and difficult.
• The tested subjects must be randomly assigned to either control or experimental groups.
ADVANTAGES
The results of a true experimental design can be statistically analyzed and so there can be little argument about the results.
It is also much easier for other researchers to replicate the experiment and validate the results.
For physical sciences working with mainly numerical data, it is much easier to manipulate one variable, so true experimental design usually gives a yes or no answer.
DISADVANTAGES
Whilst perfect in principle, there are a number of problems with this type of design. Firstly, they can be almost too perfect, with the conditions being under complete control and not being representative of real world conditions.
For psychologists and behavioral biologists, for example, there can never be any guarantee that a human or living organism will exhibit ‘normal’ behavior under experimental conditions.
True experiments can be too accurate and it is very difficult to obtain a complete rejection or acceptance of a hypothesis because the standards of proof required are so difficult to reach.
True experiments are also difficult and expensive to set up. They can also be very impractical.
While for some fields, like physics, there are not as many variables so the design is easy, for social sciences and biological sciences, where variations are not so clearly defined it is much more difficult to exclude other factors that may be affecting the manipulated variable.
SUMMARY
True experimental design is an integral part of science, usually acting as a final test of a hypothesis. Whilst they can be cumbersome and expensive to set up, literature reviews, qualitative research and descriptive research can serve as a good precursor to generate a testable hypothesis, saving time and money.
Whilst they can be a little artificial and restrictive, they are the only type of research that is accepted by all disciplines as statistically provable.
Read more: http://www.experiment-resources.com/true-experimental-design.html#ixzz1CLKCFsAs
MODULE R13
EXPERIMENTAL RESEARCH AND DESIGN
Experimental Research - An attempt by the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur.
Experimental Design - A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted.
Steps involved in conducting an experimental study
Identify and define the problem.
Formulate hypotheses and deduce their consequences.
Construct an experimental design that represents all the elements, conditions, and relations of the consequences.
1. Select sample of subjects.
2. Group or pair subjects.
3. Identify and control non experimental factors.
4. Select or construct, and validate instruments to measure outcomes.
5. Conduct pilot study.
6. Determine place, time, and duration of the experiment.
Conduct the experiment.
Compile raw data and reduce to usable form.
Apply an appropriate test of significance.
Essentials of Experimental Research
Manipulation of an independent variable.
An attempt is made to hold all other variables except the dependent variable constant - control.
Effect is observed of the manipulation of the independent variable on the dependent variable - observation.
Experimental control attempts to predict events that will occur in the experimental setting by neutralizing the effects of other factors.
Methods of Experimental Control
Physical Control
Gives all subjects equal exposure to the independent variable.
Controls non experimental variables that affect the dependent variable.
Selective Control - Manipulate indirectly by selecting in or out variables that cannot be controlled.
Statistical Control - Variables not conducive to physical or selective manipulation may be controlled by statistical techniques (example: covariance).
Validity of Experimental Design
Internal Validity asks did the experimental treatment make the difference in this specific instance rather than other extraneous variables?
External Validity asks to what populations, settings, treatment variables, and measurement variables can this observed effect be generalized?
Factors Jeopardizing Internal Validity
History - The events occurring between the first and second measurements in addition to the experimental variable which might affect the measurement.
Example: Researcher collects gross sales data before and after a 5 day 50% off sale. During the sale a hurricane occurs and results of the study may be affected because of the hurricane, not the sale.
Maturation - The process of maturing which takes place in the individual during the duration of the experiment which is not a result of specific events but of simply growing older, growing more tired, or similar changes.
Example: Subjects become tired after completing a training session, and their responses on the Posttest are affected.
Pre-testing - The effect created on the second measurement by having a measurement before the experiment.
Example: Subjects take a Pretest and think about some of the items. On the Posttest they change to answers they feel are more acceptable. Experimental group learns from the pretest.
Measuring Instruments - Changes in instruments, calibration of instruments, observers, or scorers may cause changes in the measurements.
Example: Interviewers are very careful with their first two or three interviews but on
the 4th, 5th, 6th become fatigued and are less careful and make errors.
Statistical Regression - Groups are chosen because of extreme scores of measurements; those scores or measurements tend to move toward the mean with repeated measurements even without an experimental variable.
Example: Managers who are performing poorly are selected for training. Their average Posttest scores will be higher than their Pretest scores because of statistical regression, even if no training were given.
Differential Selection - Different individuals or groups would have different previous knowledge or ability which would affect the final measurement if not taken into account.
Example: A group of subjects who have viewed a TV program is compared with a group which has not. There is no way of knowing that the groups would have been equivalent since they were not randomly assigned to view the TV program.
Experimental Mortality - The loss of subjects from comparison groups could greatly affect the comparisons because of unique characteristics of those subjects. Groups to be compared need to be the same after as before the experiment.
Example: Over a 6 month experiment aimed to change accounting practices, 12 accountants drop out of the experimental group and none drop out of the control group. Not only is there differential loss in the two groups, but the 12 dropouts may be very different from those who remained in the experimental group.
Interaction of Factors, such as Selection Maturation, etc. - Combinations of these factors may interact especially in multiple group comparisons to produce erroneous measurements.
Factors Jeopardizing External Validity or Generalizability
Pre-Testing -Individuals who were pretested might be less or more sensitive to the experimental variable or might have "learned" from the pre-test making them unrepresentative of the population who had not been pre-tested.
Example: Prior to viewing a film on Environmental Effects of Chemical, a group of subjects is given a 60 item antichemical test. Taking the Pretest may increase the effect of the film. The film may not be effective for a nonpretested group.
Differential Selection - The selection of the subjects determines how the findings can be generalized. Subjects selected from a small group or one with particular characteristics would limit generalizability. Randomly chosen subjects from the entire population could be generalized to the entire population.
Example: Researcher, requesting permission to conduct experiment, is turned down by 11 corporations, but the 12th corporation grant permission. The 12th corporation is obviously different then the others because they accepted. Thus subjects in the 12th corporation may be more accepting or sensitive to the treatment.
Experimental Procedures - The experimental procedures and arrangements have a certain amount of effect on the subjects in the experimental settings. Generalization to persons not in the experimental setting may be precluded.
Example: Department heads realize they are being studied, try to guess what the experimenter wants and respond accordingly rather than respond to the treatment.
Multiple Treatment Interference - If the subjects are exposed to more than one treatment then the findings could only be generalized to individuals exposed to the same treatments in the same order of presentation.
Example: A group of CPA’s is given training in working with managers followed by training in working with comptrollers. Since training effects cannot be deleted, the first training will affect the second.
Tools of Experimental Design Used to Control Factors Jeopardizing Validity
Pre-Test - The pre-test, or measurement before the experiment begins, can aid control for differential selection by determining the presence or knowledge of the experimental variable before the experiment begins. It can aid control of experimental mortality because the subjects can be removed from the entire comparison by removing their pre-tests.
However, pre-tests cause problems by their effect on the second measurement and by causing generalizability problems to a population not pre-tested and those with no experimental arrangements.
Control Group -The use of a matched or similar group which is not exposed to the experimental variable can help reduce the effect of History, Maturation, Instrumentation, and Interaction of Factors. The control group is exposed to all conditions of the experiment except the experimental variable.
Randomization - Use of random selection procedures for subjects can aid in control of Statistical Regression, Differential Selection, and the Interaction of Factors. It greatly increases generalizability by helping make the groups representative of the populations.
Additional Groups - The effects of Pre-tests and Experimental Procedures can be partially controlled through the use of groups which were not pre-tested or exposed to experimental arrangements. They would have to be used in conjunction with other pre-tested groups or other factors jeopardizing validity would be present.
The method by which treatments are applied to subjects using these tools to control factors jeopardizing validity is the essence of experimental design.
Tools of Control
Internal Sources Pre-Test/
Post Test Control Group Randomization Additional
Groups
History X
Maturation X
Pre-Testing X
Measuring Instrument X
Statistical Regression X X
Differential Selection X X
Experimental Mortality X
Interaction of Factors X X
External Sources
Pre-Testing X
Differential Selection X X
Procedures X
Multiple Treatment
Experimental Designs
Pre-Experimental Design - loose in structure, could be biased
Aim of the Research Name of the Design Notation Paradigm Comments
To attempt to explain a consequent by an antecedent One-shot experimental case study X » O An approach that prematurely links antecedents and consequences. The least reliable of all experimental approaches.
To evaluate the influence of a variable One group pretest-posttest O » X » O An approach that provides a measure of change but can provide no conclusive results.
To determine the influence of a variable on one group and not on another Static group comparison Group 1: X » O
Group 2: - » O Weakness lies in no examination of pre-experimental equivalence of groups. Conclusion is reached by comparing the performance of each group to determine the effect of a variable on one of them.
True Experimental Design - greater control and refinement, greater control of validity
Aim of the Research Name of the Design Notation Paradigm Comments
To study the effect of an influence on a carefully controlled sample Pretest-posttest control group R - - [ O » X » O
[ O » - » O This design has been called "the old workhorse of traditional experimentation." If effectively carried out, this design controls for eight threats of internal validity. Data are analyzed by analysis of covariance on posttest scores with the pretest the covariate.
To minimize the effect of pretesting Solomon four-group design R - - [ O » X » O
[ O » - » O
[- » X » O
[ - » - » O This is an extension of the pretest-posttest control group design and probably the most powerful experimental approach. Data are analyzed by analysis of variance on posttest scores.
To evaluate a situation that cannot be pretested Posttest only control group R - - [ X » O
[ - » O An adaptation of the last two groups in the Solomon four-group design. Randomness is critical. Probably, the simplest and best test for significance in this design is the t-test.
Quasi-Experimental Design - not randomly selected
Aim of the Research Name of the Design Notation Paradigm Comments
To investigate a situation in which random selection and assignment are not possible Nonrandomized control group pretest-posttest O » X » O
O » - » O One of the strongest and most widely used quasi-experimental designs. Differs from experimental designs because test and control groups are not equivalent. Comparing pretest results will indicate degree of equivalency between experimental and control groups.
To determine the influence of a variable introduced only after a series of initial observations and only where one group is available Time series experiment O » O » X » O » O If substantial change follows introduction of the variable, then the variable can be suspect as to the cause of the change. To increase external validity, repeat the experiment in different places under different conditions.
To bolster the validity of the above design with the addition of a control group Control group time series O » O » X » O » O
O » O » - » O » O A variant of the above design by accompanying it with a parallel set of observations without the introduction of the experimental variable.
To control history in time designs with a variant of the above design Equivalent time-samples [X1 » O1] »[X0 » O2] » [x1 » O3] An on-again, off-again design in which the experimental variable is sometimes present, sometimes absent.
Correlational and Ex Post Facto Design
Aim of the Research Name of the Design Notation Paradigm Comments
To seek for cause-effect relationships between two sets of data Causal-comparative correlational studies -»
Oa ¥ Ob
«- A very deceptive procedure that requires much insight for its use. Causality cannot be inferred merely because a positive and close correlation ratio exists.
To search backward from consequent data for antecedent causes Ex post facto studies This approach is experimentation in reverse. Seldom is proof through data substantiation possible. Logic and inference are the principal tools of this design
Leedy, P.D. (1997). Practical research: Planning and design (6th ed.). Upper Saddle River, NJ: Prentice-Hall, Inc., p. 232-233.
SELF ASSESSMENT
1. Define experimental research.
Define experimental design.
2. List six steps involved in conducting an experimental study.
3. Describe the basis of an experiment.
4. Name three characteristics of experimental research.
5. State the purpose of experimental control.
6. State three broad methods of experimental control.
7. Name two type of validity of experimental design.
8. Define eight factors jeopardizing internal validity of a research design.
9. Define four factors jeopardizing external validity.
10. Describe the tools of experimental design used to control the factors jeopardizing validity of a research design.
11. Define the essence of experimental design.
12. Name and describe the four types of experimental designs.
EXPERIMENTAL
RESEARCH
METHODS
John Davis, Ph.D.
Department of Psychology
Metropolitan State College of Denver
These notes and outlines are part of a site on psychological research methods. They are intended as a brief introduction and overview for undergraduate students in psychological research methods courses.
Click here to go to the HOME PAGE for this site.
Click here to go to the CONTENTS of OUTLINES AND DESCRIPTIONS of research methods.
Click on the topic you want
Part 1. THE MODEL AND GOALS OF EXPERIMENTAL RESEARCH METHODS
Part 2. VARIABLES AND VALIDITY IN EXPERIMENTAL DESIGNS
Part 3. HYPOTHESES IN EXPERIMENTAL DESIGNS, including alternative hypotheses
Part 4. EXPERIMENTAL DESIGNS
Part 5. SINGLE-SUBJECT DESIGNS
THE MODEL UNDERLYING EXPERIMENTAL RESEARCH METHODS
Experimental research designs are founded on the assumption that the world works according to causal laws. These laws are essentially linear, though complicated and interactive. The goal of experimental research is to establish these cause-and-effect laws by isolating causal variables.
A softer view of the philosophical assumptions behind experimental designs is that SOMETIMES and IN SOME WAYS, the world works according to causal laws. Such cause-and-effect relationships may not be a final view of reality, but demonstrating cause and effect is useful in some circumstances.
Both of these views agree that some (if not all) important psychological questions are questions about what causes what. Experimental research designs are the tools to use for these questions.
The GOAL OF EXPERIMENTAL RESEARCH METHODS is to establish cause-and-effect relationships between variables.
We hypothesize that the Independent Variable caused the changes in the Dependent Variable. However, these changes or effects may have been caused by many other factors or Alternative Hypotheses.
The PURPOSE, therefore, of experimental designs is to eliminate alterntive hypotheses. If we can successfully eliminate all alternative hypotheses, we can argue--by a process of elimination--that the Independent Variable is the cause.
Good experimental designs are those which eliminate more alternative hypotheses.
FOR EXAMPLE: Say I am testing whether a new form of psychotherapy is successful at improving mental health. I hypothesize that this psychotherapy is the cause of improved mental health in the research participants.
I will use an experimental design to eliminate all (or as many as possible) alternative hypotheses. If I can eliminate alternative explanations, I will be able to make the case that the psychotherapy was the cause of the improvements in the research participants.
EXPERIMENTAL RESEARCH METHODS
John Davis, Ph.D.
Part Two: Types of Variables and Validity
Contents
TYPES OF VARIABLES
RELIABILITY AND VALIDITY
Types of Variables
1. Independent Variable (IV): IV has levels, conditions, or treatments. Experimenter may manipulate conditions or measure and assign subjects to conditions; supposed to be the Cause
In the example, it is the psychotherapy.
2. Dependent Variable (DV): measured by the experimenter; the Effect or result.
In the example, it is the mental health of the participants.
3. Control Variables: held constant by the experimenter to eliminate them as potential causes.
For instance, if I use only research participants who have been problems with anxiety or depression, this diagnosis would be a control variable.
4. Random Variables: allowed to vary freely to eliminate them as potential causes.
Many other characteristics of the research participants, as long as they really do vary freely. Examples might include age, personality type, or career goals.
5.
Confounding Variables: vary systematically with the independent variable; may also be a cause. Good experimental designs eliminate them.
Say I divide the research participants into two groups, one of which gets the new psychotherapy (the experimental group) and one of which does not (the control group). If there is some systematic difference between these two groups, it will not be a fair test.
If those in the psychotherapy group know they are getting a new treatment and therefore expect to get better while those in the control group know they are not getting any treatment and expect to get worse, the expectations will be a confounding variable. If the experimental group does improve, we will not know whether it was because of the psychotherapy itself (the Independent Variable) or because of the participants' expectations (a Confounding Variable).
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RELIABILITY AND VALIDITY
1. RELIABILITY
• Are the results of the experiment repeatable?
• If the experiment were done the same way again, would it produce the same results?
• Reliability is a requirement before the validity of the experiment can be established.
2.
INTERNAL VALIDITY
• Accuracy or truth-value
• Does the research design lead to true statements?
• Did the independent variable cause the effects in the dependent variable?
• In experimental research, this usually means eliminating alternative hypotheses.
• In the example evaluating a new psychotherapy, the issue of internal validity is whether the psychotherapy really was the causal factor in improving participants' mental health.
3.
EXTERNAL VALIDITY
• Generalizability
• Can the results can be applied in another setting or to another population of research participants?
Science revolves around experiments, and learning the best way of conducting an experiment is crucial to obtaining useful and valid results.
by Martyn Shuttleworth (2008)
When scientists speak of experiments, in the strictest sense of the word, they mean a true experiment, where the scientist controls all of the factors and conditions.
Real world observations, and case studies, should be referred to as observational research, rather than experiments.
For example, observing animals in the wild is not a true experiment, because it does not isolate and manipulate an independent variable.
THE BASIS OF CONDUCTING AN EXPERIMENT
With an experiment, the researcher is trying to learn something new about the world, an explanation of ‘why’ something happens.
The experiment must maintain internal and external validity, or the results will be useless.
When designing an experiment, a researcher must follow all of the steps of the scientific method, from making sure that the hypothesis is valid and testable, to using controls and statistical tests.
Whilst all scientists use reasoning, operationalization and the steps of the scientific process, it is not always a conscious process.
Experience and practice mean that many scientists follow an instinctive process of conducting an experiment, the ‘streamlined’ scientific process. Following the basic steps will usually generate valid results, but where experiments are complex and expensive, it is always advisable to follow the rigorous scientific protocols. Conducting an experiment has a number of stages, where the parameters and structure of the experiment are made clear.
Whilst it is rarely practical to follow each step strictly, any aberrations must be justified, whether they arise because of budget, impracticality or ethics.
STAGE ONE
After deciding upon a hypothesis, and making predictions, the first stage of conducting an experiment is to specify the sample groups. These should be large enough to give a statistically viable study, but small enough to be practical.
Ideally, groups should be selected at random, from a wide selection of the sample population. This allows results to be generalized to the population as a whole.
In the physical sciences, this is fairly easy, but the biological and behavioral sciences are often limited by other factors.
For example, medical trials often cannot find random groups. Such research often relies upon volunteers, so it is difficult to apply any realistic randomization. This is not a problem, as long as the process is justified, and the results are not applied to the population as a whole.
If a psychological researcher used volunteers who were male students, aged between 18 and 24, the findings can only be generalized to that specific demographic group within society.
STAGE TWO
The sample groups should be divided, into a control group and a test group, to reduce the possibility of confounding variables.
This, again, should be random, and the assigning of subjects to groups should be blind or double blind. This will reduce the chances of experimental error, or bias, when conducting an experiment.
Ethics are often a barrier to this process, because deliberately withholding treatment, as with the Tuskegee study, is not permitted.
Again, any deviations from this process must be explained in the conclusion. There is nothing wrong with compromising upon randomness, where necessary, as long as other scientists are aware of how, and why, the researcher selected groups on that basis.
STAGE THREE
This stage of conducting an experiment involves determining the time scale and frequency of sampling, to fit the type of experiment.
For example, researchers studying the effectiveness of a cure for colds would take frequent samples, over a period of days. Researchers testing a cure for Parkinson’s disease would use less frequent tests, over a period of months or years.
STAGE FOUR
The penultimate stage of the experiment involves performing the experiment according to the methods stipulated during the design phase.
The independent variable is manipulated, generating a usable data set for the dependent variable.
STAGE FIVE
The raw data from the results should be gathered, and analyzed, by statistical means. This allows the researcher to establish if there is any relationship between the variables and accept, or reject, the null hypothesis.
These steps are essential to providing excellent results. Whilst many researchers do not want to become involved in the exact processes of inductive reasoning, deductive reasoning and operationalization, they all follow the basic steps of conducting an experiment. This ensures that their results are valid.
Read more: http://www.experiment-resources.com/conducting-an-experiment.html#ixzz1CLJJDaFX
True experimental design
For some of the physical sciences, such as physics, chemistry and geology, they are standard and commonly used. For social sciences, psychology and biology, they can be a little more difficult to set up.
For an experiment to be classed as a true experimental design, it must fit all of the following criteria.
• The sample groups must be assigned randomly.
• There must be a viable control group.
• Only one variable can be manipulated and tested. It is possible to test more than one, but such experiments and their statistical analysis tend to be cumbersome and difficult.
• The tested subjects must be randomly assigned to either control or experimental groups.
ADVANTAGES
The results of a true experimental design can be statistically analyzed and so there can be little argument about the results.
It is also much easier for other researchers to replicate the experiment and validate the results.
For physical sciences working with mainly numerical data, it is much easier to manipulate one variable, so true experimental design usually gives a yes or no answer.
DISADVANTAGES
Whilst perfect in principle, there are a number of problems with this type of design. Firstly, they can be almost too perfect, with the conditions being under complete control and not being representative of real world conditions.
For psychologists and behavioral biologists, for example, there can never be any guarantee that a human or living organism will exhibit ‘normal’ behavior under experimental conditions.
True experiments can be too accurate and it is very difficult to obtain a complete rejection or acceptance of a hypothesis because the standards of proof required are so difficult to reach.
True experiments are also difficult and expensive to set up. They can also be very impractical.
While for some fields, like physics, there are not as many variables so the design is easy, for social sciences and biological sciences, where variations are not so clearly defined it is much more difficult to exclude other factors that may be affecting the manipulated variable.
SUMMARY
True experimental design is an integral part of science, usually acting as a final test of a hypothesis. Whilst they can be cumbersome and expensive to set up, literature reviews, qualitative research and descriptive research can serve as a good precursor to generate a testable hypothesis, saving time and money.
Whilst they can be a little artificial and restrictive, they are the only type of research that is accepted by all disciplines as statistically provable.
Read more: http://www.experiment-resources.com/true-experimental-design.html#ixzz1CLKCFsAs
MODULE R13
EXPERIMENTAL RESEARCH AND DESIGN
Experimental Research - An attempt by the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur.
Experimental Design - A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted.
Steps involved in conducting an experimental study
Identify and define the problem.
Formulate hypotheses and deduce their consequences.
Construct an experimental design that represents all the elements, conditions, and relations of the consequences.
1. Select sample of subjects.
2. Group or pair subjects.
3. Identify and control non experimental factors.
4. Select or construct, and validate instruments to measure outcomes.
5. Conduct pilot study.
6. Determine place, time, and duration of the experiment.
Conduct the experiment.
Compile raw data and reduce to usable form.
Apply an appropriate test of significance.
Essentials of Experimental Research
Manipulation of an independent variable.
An attempt is made to hold all other variables except the dependent variable constant - control.
Effect is observed of the manipulation of the independent variable on the dependent variable - observation.
Experimental control attempts to predict events that will occur in the experimental setting by neutralizing the effects of other factors.
Methods of Experimental Control
Physical Control
Gives all subjects equal exposure to the independent variable.
Controls non experimental variables that affect the dependent variable.
Selective Control - Manipulate indirectly by selecting in or out variables that cannot be controlled.
Statistical Control - Variables not conducive to physical or selective manipulation may be controlled by statistical techniques (example: covariance).
Validity of Experimental Design
Internal Validity asks did the experimental treatment make the difference in this specific instance rather than other extraneous variables?
External Validity asks to what populations, settings, treatment variables, and measurement variables can this observed effect be generalized?
Factors Jeopardizing Internal Validity
History - The events occurring between the first and second measurements in addition to the experimental variable which might affect the measurement.
Example: Researcher collects gross sales data before and after a 5 day 50% off sale. During the sale a hurricane occurs and results of the study may be affected because of the hurricane, not the sale.
Maturation - The process of maturing which takes place in the individual during the duration of the experiment which is not a result of specific events but of simply growing older, growing more tired, or similar changes.
Example: Subjects become tired after completing a training session, and their responses on the Posttest are affected.
Pre-testing - The effect created on the second measurement by having a measurement before the experiment.
Example: Subjects take a Pretest and think about some of the items. On the Posttest they change to answers they feel are more acceptable. Experimental group learns from the pretest.
Measuring Instruments - Changes in instruments, calibration of instruments, observers, or scorers may cause changes in the measurements.
Example: Interviewers are very careful with their first two or three interviews but on
the 4th, 5th, 6th become fatigued and are less careful and make errors.
Statistical Regression - Groups are chosen because of extreme scores of measurements; those scores or measurements tend to move toward the mean with repeated measurements even without an experimental variable.
Example: Managers who are performing poorly are selected for training. Their average Posttest scores will be higher than their Pretest scores because of statistical regression, even if no training were given.
Differential Selection - Different individuals or groups would have different previous knowledge or ability which would affect the final measurement if not taken into account.
Example: A group of subjects who have viewed a TV program is compared with a group which has not. There is no way of knowing that the groups would have been equivalent since they were not randomly assigned to view the TV program.
Experimental Mortality - The loss of subjects from comparison groups could greatly affect the comparisons because of unique characteristics of those subjects. Groups to be compared need to be the same after as before the experiment.
Example: Over a 6 month experiment aimed to change accounting practices, 12 accountants drop out of the experimental group and none drop out of the control group. Not only is there differential loss in the two groups, but the 12 dropouts may be very different from those who remained in the experimental group.
Interaction of Factors, such as Selection Maturation, etc. - Combinations of these factors may interact especially in multiple group comparisons to produce erroneous measurements.
Factors Jeopardizing External Validity or Generalizability
Pre-Testing -Individuals who were pretested might be less or more sensitive to the experimental variable or might have "learned" from the pre-test making them unrepresentative of the population who had not been pre-tested.
Example: Prior to viewing a film on Environmental Effects of Chemical, a group of subjects is given a 60 item antichemical test. Taking the Pretest may increase the effect of the film. The film may not be effective for a nonpretested group.
Differential Selection - The selection of the subjects determines how the findings can be generalized. Subjects selected from a small group or one with particular characteristics would limit generalizability. Randomly chosen subjects from the entire population could be generalized to the entire population.
Example: Researcher, requesting permission to conduct experiment, is turned down by 11 corporations, but the 12th corporation grant permission. The 12th corporation is obviously different then the others because they accepted. Thus subjects in the 12th corporation may be more accepting or sensitive to the treatment.
Experimental Procedures - The experimental procedures and arrangements have a certain amount of effect on the subjects in the experimental settings. Generalization to persons not in the experimental setting may be precluded.
Example: Department heads realize they are being studied, try to guess what the experimenter wants and respond accordingly rather than respond to the treatment.
Multiple Treatment Interference - If the subjects are exposed to more than one treatment then the findings could only be generalized to individuals exposed to the same treatments in the same order of presentation.
Example: A group of CPA’s is given training in working with managers followed by training in working with comptrollers. Since training effects cannot be deleted, the first training will affect the second.
Tools of Experimental Design Used to Control Factors Jeopardizing Validity
Pre-Test - The pre-test, or measurement before the experiment begins, can aid control for differential selection by determining the presence or knowledge of the experimental variable before the experiment begins. It can aid control of experimental mortality because the subjects can be removed from the entire comparison by removing their pre-tests.
However, pre-tests cause problems by their effect on the second measurement and by causing generalizability problems to a population not pre-tested and those with no experimental arrangements.
Control Group -The use of a matched or similar group which is not exposed to the experimental variable can help reduce the effect of History, Maturation, Instrumentation, and Interaction of Factors. The control group is exposed to all conditions of the experiment except the experimental variable.
Randomization - Use of random selection procedures for subjects can aid in control of Statistical Regression, Differential Selection, and the Interaction of Factors. It greatly increases generalizability by helping make the groups representative of the populations.
Additional Groups - The effects of Pre-tests and Experimental Procedures can be partially controlled through the use of groups which were not pre-tested or exposed to experimental arrangements. They would have to be used in conjunction with other pre-tested groups or other factors jeopardizing validity would be present.
The method by which treatments are applied to subjects using these tools to control factors jeopardizing validity is the essence of experimental design.
Tools of Control
Internal Sources Pre-Test/
Post Test Control Group Randomization Additional
Groups
History X
Maturation X
Pre-Testing X
Measuring Instrument X
Statistical Regression X X
Differential Selection X X
Experimental Mortality X
Interaction of Factors X X
External Sources
Pre-Testing X
Differential Selection X X
Procedures X
Multiple Treatment
Experimental Designs
Pre-Experimental Design - loose in structure, could be biased
Aim of the Research Name of the Design Notation Paradigm Comments
To attempt to explain a consequent by an antecedent One-shot experimental case study X » O An approach that prematurely links antecedents and consequences. The least reliable of all experimental approaches.
To evaluate the influence of a variable One group pretest-posttest O » X » O An approach that provides a measure of change but can provide no conclusive results.
To determine the influence of a variable on one group and not on another Static group comparison Group 1: X » O
Group 2: - » O Weakness lies in no examination of pre-experimental equivalence of groups. Conclusion is reached by comparing the performance of each group to determine the effect of a variable on one of them.
True Experimental Design - greater control and refinement, greater control of validity
Aim of the Research Name of the Design Notation Paradigm Comments
To study the effect of an influence on a carefully controlled sample Pretest-posttest control group R - - [ O » X » O
[ O » - » O This design has been called "the old workhorse of traditional experimentation." If effectively carried out, this design controls for eight threats of internal validity. Data are analyzed by analysis of covariance on posttest scores with the pretest the covariate.
To minimize the effect of pretesting Solomon four-group design R - - [ O » X » O
[ O » - » O
[- » X » O
[ - » - » O This is an extension of the pretest-posttest control group design and probably the most powerful experimental approach. Data are analyzed by analysis of variance on posttest scores.
To evaluate a situation that cannot be pretested Posttest only control group R - - [ X » O
[ - » O An adaptation of the last two groups in the Solomon four-group design. Randomness is critical. Probably, the simplest and best test for significance in this design is the t-test.
Quasi-Experimental Design - not randomly selected
Aim of the Research Name of the Design Notation Paradigm Comments
To investigate a situation in which random selection and assignment are not possible Nonrandomized control group pretest-posttest O » X » O
O » - » O One of the strongest and most widely used quasi-experimental designs. Differs from experimental designs because test and control groups are not equivalent. Comparing pretest results will indicate degree of equivalency between experimental and control groups.
To determine the influence of a variable introduced only after a series of initial observations and only where one group is available Time series experiment O » O » X » O » O If substantial change follows introduction of the variable, then the variable can be suspect as to the cause of the change. To increase external validity, repeat the experiment in different places under different conditions.
To bolster the validity of the above design with the addition of a control group Control group time series O » O » X » O » O
O » O » - » O » O A variant of the above design by accompanying it with a parallel set of observations without the introduction of the experimental variable.
To control history in time designs with a variant of the above design Equivalent time-samples [X1 » O1] »[X0 » O2] » [x1 » O3] An on-again, off-again design in which the experimental variable is sometimes present, sometimes absent.
Correlational and Ex Post Facto Design
Aim of the Research Name of the Design Notation Paradigm Comments
To seek for cause-effect relationships between two sets of data Causal-comparative correlational studies -»
Oa ¥ Ob
«- A very deceptive procedure that requires much insight for its use. Causality cannot be inferred merely because a positive and close correlation ratio exists.
To search backward from consequent data for antecedent causes Ex post facto studies This approach is experimentation in reverse. Seldom is proof through data substantiation possible. Logic and inference are the principal tools of this design
Leedy, P.D. (1997). Practical research: Planning and design (6th ed.). Upper Saddle River, NJ: Prentice-Hall, Inc., p. 232-233.
SELF ASSESSMENT
1. Define experimental research.
Define experimental design.
2. List six steps involved in conducting an experimental study.
3. Describe the basis of an experiment.
4. Name three characteristics of experimental research.
5. State the purpose of experimental control.
6. State three broad methods of experimental control.
7. Name two type of validity of experimental design.
8. Define eight factors jeopardizing internal validity of a research design.
9. Define four factors jeopardizing external validity.
10. Describe the tools of experimental design used to control the factors jeopardizing validity of a research design.
11. Define the essence of experimental design.
12. Name and describe the four types of experimental designs.
EXPERIMENTAL
RESEARCH
METHODS
John Davis, Ph.D.
Department of Psychology
Metropolitan State College of Denver
These notes and outlines are part of a site on psychological research methods. They are intended as a brief introduction and overview for undergraduate students in psychological research methods courses.
Click here to go to the HOME PAGE for this site.
Click here to go to the CONTENTS of OUTLINES AND DESCRIPTIONS of research methods.
Click on the topic you want
Part 1. THE MODEL AND GOALS OF EXPERIMENTAL RESEARCH METHODS
Part 2. VARIABLES AND VALIDITY IN EXPERIMENTAL DESIGNS
Part 3. HYPOTHESES IN EXPERIMENTAL DESIGNS, including alternative hypotheses
Part 4. EXPERIMENTAL DESIGNS
Part 5. SINGLE-SUBJECT DESIGNS
THE MODEL UNDERLYING EXPERIMENTAL RESEARCH METHODS
Experimental research designs are founded on the assumption that the world works according to causal laws. These laws are essentially linear, though complicated and interactive. The goal of experimental research is to establish these cause-and-effect laws by isolating causal variables.
A softer view of the philosophical assumptions behind experimental designs is that SOMETIMES and IN SOME WAYS, the world works according to causal laws. Such cause-and-effect relationships may not be a final view of reality, but demonstrating cause and effect is useful in some circumstances.
Both of these views agree that some (if not all) important psychological questions are questions about what causes what. Experimental research designs are the tools to use for these questions.
The GOAL OF EXPERIMENTAL RESEARCH METHODS is to establish cause-and-effect relationships between variables.
We hypothesize that the Independent Variable caused the changes in the Dependent Variable. However, these changes or effects may have been caused by many other factors or Alternative Hypotheses.
The PURPOSE, therefore, of experimental designs is to eliminate alterntive hypotheses. If we can successfully eliminate all alternative hypotheses, we can argue--by a process of elimination--that the Independent Variable is the cause.
Good experimental designs are those which eliminate more alternative hypotheses.
FOR EXAMPLE: Say I am testing whether a new form of psychotherapy is successful at improving mental health. I hypothesize that this psychotherapy is the cause of improved mental health in the research participants.
I will use an experimental design to eliminate all (or as many as possible) alternative hypotheses. If I can eliminate alternative explanations, I will be able to make the case that the psychotherapy was the cause of the improvements in the research participants.
EXPERIMENTAL RESEARCH METHODS
John Davis, Ph.D.
Part Two: Types of Variables and Validity
Contents
TYPES OF VARIABLES
RELIABILITY AND VALIDITY
Types of Variables
1. Independent Variable (IV): IV has levels, conditions, or treatments. Experimenter may manipulate conditions or measure and assign subjects to conditions; supposed to be the Cause
In the example, it is the psychotherapy.
2. Dependent Variable (DV): measured by the experimenter; the Effect or result.
In the example, it is the mental health of the participants.
3. Control Variables: held constant by the experimenter to eliminate them as potential causes.
For instance, if I use only research participants who have been problems with anxiety or depression, this diagnosis would be a control variable.
4. Random Variables: allowed to vary freely to eliminate them as potential causes.
Many other characteristics of the research participants, as long as they really do vary freely. Examples might include age, personality type, or career goals.
5.
Confounding Variables: vary systematically with the independent variable; may also be a cause. Good experimental designs eliminate them.
Say I divide the research participants into two groups, one of which gets the new psychotherapy (the experimental group) and one of which does not (the control group). If there is some systematic difference between these two groups, it will not be a fair test.
If those in the psychotherapy group know they are getting a new treatment and therefore expect to get better while those in the control group know they are not getting any treatment and expect to get worse, the expectations will be a confounding variable. If the experimental group does improve, we will not know whether it was because of the psychotherapy itself (the Independent Variable) or because of the participants' expectations (a Confounding Variable).
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RELIABILITY AND VALIDITY
1. RELIABILITY
• Are the results of the experiment repeatable?
• If the experiment were done the same way again, would it produce the same results?
• Reliability is a requirement before the validity of the experiment can be established.
2.
INTERNAL VALIDITY
• Accuracy or truth-value
• Does the research design lead to true statements?
• Did the independent variable cause the effects in the dependent variable?
• In experimental research, this usually means eliminating alternative hypotheses.
• In the example evaluating a new psychotherapy, the issue of internal validity is whether the psychotherapy really was the causal factor in improving participants' mental health.
3.
EXTERNAL VALIDITY
• Generalizability
• Can the results can be applied in another setting or to another population of research participants?
EXPERIMENTAL RESEARCH:
What to be presented:
1) Describe the topic
2) Find article/research that employed experimental (population/procedure)
3) think of one the example ( eg using multimedia is better than using books in the classroom
Notes:
1) Pre test, IQ Test , eg eating icecream ( treatment)
2) Attempts to influence/manipulate variables
3) Best for testing hypothesis of cause and effect relationship
4) In education – use to test effects of various practices:
a. Teaching techniques
b. Organization of curriculum
c. Instructional
5) Independent variables – reading achievement (KSSR)
6) To test a new reading program: Using multimedia: you give them reading test – pretest, them give them post test
Answer:
Why to conduct experiment; basically the researcher is trying to learn something new about the world, an explanation of “why” something happens. The experiment must maintain enternal and external validity otherwise the results will be useless
A. The experimental research means:
1) It is a systematic and scientific approach to the scientific method where scientist manipulates variables
2) An attempt by researcher to maintain control over all factors that may affect the result of an experiment
B. Research that employed experimental:
1) Experimental research is commonly used in science such as sociology and psychology, physics, chemistry, biology and medicine etc
2) 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
What to be presented:
1) Describe the topic
2) Find article/research that employed experimental (population/procedure)
3) think of one the example ( eg using multimedia is better than using books in the classroom
Notes:
1) Pre test, IQ Test , eg eating icecream ( treatment)
2) Attempts to influence/manipulate variables
3) Best for testing hypothesis of cause and effect relationship
4) In education – use to test effects of various practices:
a. Teaching techniques
b. Organization of curriculum
c. Instructional
5) Independent variables – reading achievement (KSSR)
6) To test a new reading program: Using multimedia: you give them reading test – pretest, them give them post test
Answer:
Why to conduct experiment; basically the researcher is trying to learn something new about the world, an explanation of “why” something happens. The experiment must maintain enternal and external validity otherwise the results will be useless
A. The experimental research means:
1) It is a systematic and scientific approach to the scientific method where scientist manipulates variables
2) An attempt by researcher to maintain control over all factors that may affect the result of an experiment
B. Research that employed experimental:
1) Experimental research is commonly used in science such as sociology and psychology, physics, chemistry, biology and medicine etc
2) 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
Internal validity
From Wikipedia, the free encyclopedia
Internal validity is the validity of (causal) inferences in scientific studies, usually based on experiments as experimental validity.[1]
Contents
[hide]
• 1 Details
• 2 Threats to internal validity
o 2.1 Confounding
o 2.2 Selection (bias)
o 2.3 History
o 2.4 Maturation
o 2.5 Repeated testing
o 2.6 Instrument change
o 2.7 Regression toward the mean
o 2.8 Mortality/differential attrition
o 2.9 Selection-maturation interaction
o 2.10 Diffusion
o 2.11 Compensatory rivalry/resentful demoralization
o 2.12 Experimenter bias
• 3 See also
• 4 References
[edit] Details
Inferences are said to possess internal validity if a causal relation between two variables is properly demonstrated.[2][3] A causal inference may be based on a relation when three criteria are satisfied:
1. the "cause" precedes the "effect" in time (temporal precedence),
2. the "cause" and the "effect" are related (covariation), and
3. there are no plausible alternative explanations for the observed covariation (nonspuriousness).[4]
In scientific experimental settings, researchers often manipulate a variable (the independent variable) to see what effect it has on a second variable (the dependent variable)[5] For example, a researcher might, for different experimental groups, manipulate the dosage of a particular drug between groups to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable, and when he can rule out other explanations (or rival hypotheses), then his causal inference is said to be internally valid.[6]
In many cases, however, the magnitude of effects found in the dependent variable may not just depend on
• variations in the independent variable,
• the power of the instruments and statistical procedures used to measure and detect the effects, and
• the choice of statistical methods (see: Statistical conclusion validity).
Rather, a number of variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations (a) for the effects found and/or (b) for the magnitude of the effects found. Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of Internal Validity.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the scientific study. As a rule of thumb, conclusions based on correlations or associations may only allow for lesser degrees of internal validity than conclusions drawn on the basis of direct manipulation of the independent variable. And, when viewed only from the perspective of Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the "gold standard" of scientific research. By contrast, however, the very strategies employed to control these factors may also limit the generalizability or External Validity of the findings.
[edit] Threats to internal validity
[edit] Confounding
A major threat to the validity of causal inferences is Confounding: Changes in the dependent variable may rather be attributed to the existence or variations in the degree of a third variable which is related to the manipulated variable. Where Spurious relationships cannot be ruled out, rival hypothesis to the original causal inference hypothesis of the researcher may be developed.
[edit] Selection (bias)
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent. For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity. For example, a researcher created two test groups, the experimental and the control groups. The subjects in both groups are not alike with regard to the independent variable but similar in one or more of the subject-related variables.
[edit] History
Events outside of the study/experiment or between repeated measures of the dependent variable may affect participants' responses to experimental procedures. Often, these are large scale events (natural disaster, political change, etc) that affect participants' attitudes and behaviors such that it becomes impossible to determine whether any change on the dependent measures is due to the independent variable, or the historical event.
[edit] Maturation
Subjects change during the course of the experiment or even between measurements. For example, young children might mature and their ability to concentrate may change as they grow up. Both permanent changes, such as physical growth and temporary ones like fatigue, provide "natural" alternative explanations; thus, they may change the way a subject would react to the independent variable. So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable.
[edit] Repeated testing
Repeatedly measuring the participants may lead to bias. Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeately taking (the same or similar) intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides good rival hypotheses.
[edit] Instrument change
The instrument used during the testing process can change the experiment. This also refers to observers being more concentrated or primed. If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
[edit] Regression toward the mean
Main article: Regression toward the mean
This type of error occurs when subjects are selected on the basis of extreme scores (one far away from the mean) during a test. For example, when children with the worst reading scores are selected to participate in a reading course, improvements at the end of the course might be due to regression toward the mean and not the course's effectiveness. If the children had been tested again before the course started, they would likely have obtained better scores anyway. Likewise, extreme outliers on individual scores are more likely to be captured in one instance of testing but will likely evolve into a more normal distribution with repeated testing.
[edit] Mortality/differential attrition
This error occurs if inferences are made on the basis of only those participants that have participated from the start to the end. However, participants may have dropped out of the study before completion, and maybe even due to the study or programme or experiment itself. For example, the percentage of group members having quit smoking at post-test was found much higher in a group having received a quit-smoking training program than in the control group. However, in the experimental group only 60% have completed the program. If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.
[edit] Selection-maturation interaction
This occurs when the subject-related variables, color of hair, skin color, etc., and the time-related variables, age, physical size, etc., interact. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.
[edit] Diffusion
If treatment effects spread from treatment groups to control groups, a lack of differences between experimental and control groups may be observed. This does not mean, however, that the independent variable has no effect or that there is no relationship between dependent and independent variable.
[edit] Compensatory rivalry/resentful demoralization
Behaviour in the control groups may alter as a result of the study. For example, control group members may work extra hard to see that expected superiority of the experimental group is not demonstrated. Again, this does not mean that the independent variable produced no effect or that there is no relationship between dependent and independent variable. Vice-versa, changes in the dependent variable may only be effected due to a demoralized control group, working less hard or motivated, not due to the independent variable.
[edit] Experimenter bias
Experimenter bias occurs when the individuals who are conducting an experiment inadvertently affect the outcome by non-consciously behaving differently to members of control and experimental groups. It is possible to eliminate the possibility of experimenter bias through the use of double blind study designs, in which the experimenter is not aware of the condition to which a participant belongs.
[edit] See also
• External validity
• Construct validity
• Content validity
• Statistical conclusion validity
• Validity in statistics
• Ecological validity
[edit] References
Constructs such as ibid. and loc. cit. are discouraged by Wikipedia's style guide for footnotes, as they are easily broken. Please improve this article by replacing them with named references (quick guide), or an abbreviated title.
1. ^ Mitchell, M. and Jolley, J. (2001). Research Design Explained (4th Ed) New York:Harcourt.
2. ^ Brewer, M. (2000). Research Design and Issues of Validity. In Reis, H. and Judd, C. (eds.) Handbook of Research Methods in Social and Personality Psychology. Cambridge:Cambridge University Press.
3. ^ Shadish, W., Cook, T., and Campbell, D. (2002). Experimental and Quasi-Experimental Designs for Generilized Causal Inference Boston:Houghton Mifflin.
4. ^ ibid.
5. ^ Levine, G. and Parkinson, S. (1994). Experimental Methods in Psychology. Hillsdale, NJ:Lawrence Erlbaum.
6. ^ Liebert, R. M. & Liebert, L. L. (1995). Science and behavior: An introduction to methods of psychological research. Englewood Cliffs, NJ: Prentice Hall.
Retrieved from "http://en.wikipedia.org/wiki/Internal_validity"
Categories: Causal inference | Validity (statistics)
From Wikipedia, the free encyclopedia
Internal validity is the validity of (causal) inferences in scientific studies, usually based on experiments as experimental validity.[1]
Contents
[hide]
• 1 Details
• 2 Threats to internal validity
o 2.1 Confounding
o 2.2 Selection (bias)
o 2.3 History
o 2.4 Maturation
o 2.5 Repeated testing
o 2.6 Instrument change
o 2.7 Regression toward the mean
o 2.8 Mortality/differential attrition
o 2.9 Selection-maturation interaction
o 2.10 Diffusion
o 2.11 Compensatory rivalry/resentful demoralization
o 2.12 Experimenter bias
• 3 See also
• 4 References
[edit] Details
Inferences are said to possess internal validity if a causal relation between two variables is properly demonstrated.[2][3] A causal inference may be based on a relation when three criteria are satisfied:
1. the "cause" precedes the "effect" in time (temporal precedence),
2. the "cause" and the "effect" are related (covariation), and
3. there are no plausible alternative explanations for the observed covariation (nonspuriousness).[4]
In scientific experimental settings, researchers often manipulate a variable (the independent variable) to see what effect it has on a second variable (the dependent variable)[5] For example, a researcher might, for different experimental groups, manipulate the dosage of a particular drug between groups to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable, and when he can rule out other explanations (or rival hypotheses), then his causal inference is said to be internally valid.[6]
In many cases, however, the magnitude of effects found in the dependent variable may not just depend on
• variations in the independent variable,
• the power of the instruments and statistical procedures used to measure and detect the effects, and
• the choice of statistical methods (see: Statistical conclusion validity).
Rather, a number of variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations (a) for the effects found and/or (b) for the magnitude of the effects found. Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of Internal Validity.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the scientific study. As a rule of thumb, conclusions based on correlations or associations may only allow for lesser degrees of internal validity than conclusions drawn on the basis of direct manipulation of the independent variable. And, when viewed only from the perspective of Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the "gold standard" of scientific research. By contrast, however, the very strategies employed to control these factors may also limit the generalizability or External Validity of the findings.
[edit] Threats to internal validity
[edit] Confounding
A major threat to the validity of causal inferences is Confounding: Changes in the dependent variable may rather be attributed to the existence or variations in the degree of a third variable which is related to the manipulated variable. Where Spurious relationships cannot be ruled out, rival hypothesis to the original causal inference hypothesis of the researcher may be developed.
[edit] Selection (bias)
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent. For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity. For example, a researcher created two test groups, the experimental and the control groups. The subjects in both groups are not alike with regard to the independent variable but similar in one or more of the subject-related variables.
[edit] History
Events outside of the study/experiment or between repeated measures of the dependent variable may affect participants' responses to experimental procedures. Often, these are large scale events (natural disaster, political change, etc) that affect participants' attitudes and behaviors such that it becomes impossible to determine whether any change on the dependent measures is due to the independent variable, or the historical event.
[edit] Maturation
Subjects change during the course of the experiment or even between measurements. For example, young children might mature and their ability to concentrate may change as they grow up. Both permanent changes, such as physical growth and temporary ones like fatigue, provide "natural" alternative explanations; thus, they may change the way a subject would react to the independent variable. So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable.
[edit] Repeated testing
Repeatedly measuring the participants may lead to bias. Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeately taking (the same or similar) intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides good rival hypotheses.
[edit] Instrument change
The instrument used during the testing process can change the experiment. This also refers to observers being more concentrated or primed. If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
[edit] Regression toward the mean
Main article: Regression toward the mean
This type of error occurs when subjects are selected on the basis of extreme scores (one far away from the mean) during a test. For example, when children with the worst reading scores are selected to participate in a reading course, improvements at the end of the course might be due to regression toward the mean and not the course's effectiveness. If the children had been tested again before the course started, they would likely have obtained better scores anyway. Likewise, extreme outliers on individual scores are more likely to be captured in one instance of testing but will likely evolve into a more normal distribution with repeated testing.
[edit] Mortality/differential attrition
This error occurs if inferences are made on the basis of only those participants that have participated from the start to the end. However, participants may have dropped out of the study before completion, and maybe even due to the study or programme or experiment itself. For example, the percentage of group members having quit smoking at post-test was found much higher in a group having received a quit-smoking training program than in the control group. However, in the experimental group only 60% have completed the program. If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.
[edit] Selection-maturation interaction
This occurs when the subject-related variables, color of hair, skin color, etc., and the time-related variables, age, physical size, etc., interact. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.
[edit] Diffusion
If treatment effects spread from treatment groups to control groups, a lack of differences between experimental and control groups may be observed. This does not mean, however, that the independent variable has no effect or that there is no relationship between dependent and independent variable.
[edit] Compensatory rivalry/resentful demoralization
Behaviour in the control groups may alter as a result of the study. For example, control group members may work extra hard to see that expected superiority of the experimental group is not demonstrated. Again, this does not mean that the independent variable produced no effect or that there is no relationship between dependent and independent variable. Vice-versa, changes in the dependent variable may only be effected due to a demoralized control group, working less hard or motivated, not due to the independent variable.
[edit] Experimenter bias
Experimenter bias occurs when the individuals who are conducting an experiment inadvertently affect the outcome by non-consciously behaving differently to members of control and experimental groups. It is possible to eliminate the possibility of experimenter bias through the use of double blind study designs, in which the experimenter is not aware of the condition to which a participant belongs.
[edit] See also
• External validity
• Construct validity
• Content validity
• Statistical conclusion validity
• Validity in statistics
• Ecological validity
[edit] References
Constructs such as ibid. and loc. cit. are discouraged by Wikipedia's style guide for footnotes, as they are easily broken. Please improve this article by replacing them with named references (quick guide), or an abbreviated title.
1. ^ Mitchell, M. and Jolley, J. (2001). Research Design Explained (4th Ed) New York:Harcourt.
2. ^ Brewer, M. (2000). Research Design and Issues of Validity. In Reis, H. and Judd, C. (eds.) Handbook of Research Methods in Social and Personality Psychology. Cambridge:Cambridge University Press.
3. ^ Shadish, W., Cook, T., and Campbell, D. (2002). Experimental and Quasi-Experimental Designs for Generilized Causal Inference Boston:Houghton Mifflin.
4. ^ ibid.
5. ^ Levine, G. and Parkinson, S. (1994). Experimental Methods in Psychology. Hillsdale, NJ:Lawrence Erlbaum.
6. ^ Liebert, R. M. & Liebert, L. L. (1995). Science and behavior: An introduction to methods of psychological research. Englewood Cliffs, NJ: Prentice Hall.
Retrieved from "http://en.wikipedia.org/wiki/Internal_validity"
Categories: Causal inference | Validity (statistics)
QUANTITATIVE RESEARCH METHODOLOGIES
Experimental research:
Introduction:
ER – An attempt by researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur
Briefly:
(Pg 262 – text book): The basic idea of ER is ….try something and systematically observe what happens……
Details explanation:
Formal experiments consist of two basic conditions;
1) At least two ( often more ) conditions or methods are compared to access the effects of particular conditions/treatments (the independent variable)
2) The independent variable is directly manipulated by the researcher
PURPOSE OF EXPERIMENTAL RESEARCH:
1) Best for testing hypothesis of cause and effect relationship
2) Why to conduct experiment; basically the researcher is trying to learn something new about the world, an explanation of “why” something happens
ESSENTIALS OF EXPERIMENT RESEARCH”
The uniqueness of ER
• The only research that that directly attempts to influence a particular variable
• When it is applied; is a best type for testing hypotheses about cause and effect relationship
• The independent variable is also referred as the experimental or treatment variable; while dependant variable also known as outcome variable ( refers to the results or outcomes of the study)
o One group receiving treatment we name it as experimental group, the other one group, which is not receiving the treatment – control group
• Major characteristic that distinguishes it from other type of research is the researchers manipulate the independent variable.
• Decision can be made on the treatment ( what going to happen to the subject of the study ); to whom and to what extent the selected group should be treated;
• after the treatment has been administered for an appropriate lenghth of time; researchers observe or measure the groups receiving different treatments ( by means of a posttest of some sort ) to see if they differ ( @ whether the treatment made any difference). If do differ that means the treatment did have an effect and is likely the cause of the difference
•
STEPS INVOLVED IN CONDUCTING AN EXDPERIMENT
1) Identify the problem – mention the one that ida/her friend going to explain
2) Formulate hypotheses and deduce (reasoning) the consequences
3) Construct the experimental design that represents all the elements, conditions and relations of the consequences
CHARACTERISTICS OF EXPERIMENTAL RESEARCH:
1) One of the most powerful research methodologies that researchers can use, best way to establish cause-and-effect relationships among variables ( pg 261 buku teks)
2) Must involve two groups of subjects; an experimental group and a control or a comparison group.
3) The experimental group receives treatment ( as what prof said: eating ice cream) while control group /comparison group receives no treatment/different treatment
4) The control group is always important because the result enables researcher to determine whether the treatment has had an effect or whether one treatment is more effective than another
5) Manipulation of an independent variable; researcher deliberately and directly determines what forms the independent variable will take and then which group will get which form-(example pg 263 1st para last sentences)
6) Randomization; Important aspect in ER is the random assignment of subjects to groups- every individual who is participating in experiment has an equal chance of being assigned to any of the experimental or control conditions being compared; random selection every member of a population has an equal chance of being selected to be a member of the sample ( pg 263 )
7) Control of variables; Using ER has far more control:
a. Determine the treatment
b. Select sample
c. Assign individual to groups
d. Decide which group will get treatment
e. Try to control other factors besides the treatments that might influence the outcome of the study
f. Observe/measure the effect of the treatment on the groups when the treatment is completed
• Experimental Design
ED – A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted.
All these 5 steps are essential to providing excellent results.
Stage one:
1) After deciding upon hypothesis / making predictions, is to specify sample groups. Should be large enough to give a statistically viable study, but small enough to be practical
2) Group should be selected randomly, to allow results to be generalized to the population as a whole, example : if use volunteers aged bet 18-24, the findings can only be generalized to that age group only
Stage two
1. Sample groups should be divided into two groups : control group and a test group ( to reduce the possibility of confounding variables). Assigning subjects to groups also should be blind or double blind to reduce chances of experimental error, or bias
Stage three
1. This stage involves determining the time scale and frequency of sampling ( either days, months / year)
Stage 4
This is the the penultimate stage of the experiment involves performing the exdperiment and according to the methods stipulated during the design phase
Stage 5
1. The raw data should be gathered and analyzed by statistical means. This allows researcher to establish ( if any relationship between the variables and accept or reject the null hypothesis )
ACTUAL EXAMPLES:
(REFER TO PG 262 OF THE TEXT BOOK – 6 EXAMPLES)
NOTES DLM BUKU TEXT – PG 264 ONWARDS ( LAST PARA)
VALIDITY:
1) The design can take a variety of forms, the good designs able to control threats to internal validity to avoid difficulty in assessing effectiveness of the independent variables. Designs that do not have controls for threats are weak and researchers have difficulty assessing the effectiveness of the independent variable
2)
Experimental research:
Introduction:
ER – An attempt by researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur
Briefly:
(Pg 262 – text book): The basic idea of ER is ….try something and systematically observe what happens……
Details explanation:
Formal experiments consist of two basic conditions;
1) At least two ( often more ) conditions or methods are compared to access the effects of particular conditions/treatments (the independent variable)
2) The independent variable is directly manipulated by the researcher
PURPOSE OF EXPERIMENTAL RESEARCH:
1) Best for testing hypothesis of cause and effect relationship
2) Why to conduct experiment; basically the researcher is trying to learn something new about the world, an explanation of “why” something happens
ESSENTIALS OF EXPERIMENT RESEARCH”
The uniqueness of ER
• The only research that that directly attempts to influence a particular variable
• When it is applied; is a best type for testing hypotheses about cause and effect relationship
• The independent variable is also referred as the experimental or treatment variable; while dependant variable also known as outcome variable ( refers to the results or outcomes of the study)
o One group receiving treatment we name it as experimental group, the other one group, which is not receiving the treatment – control group
• Major characteristic that distinguishes it from other type of research is the researchers manipulate the independent variable.
• Decision can be made on the treatment ( what going to happen to the subject of the study ); to whom and to what extent the selected group should be treated;
• after the treatment has been administered for an appropriate lenghth of time; researchers observe or measure the groups receiving different treatments ( by means of a posttest of some sort ) to see if they differ ( @ whether the treatment made any difference). If do differ that means the treatment did have an effect and is likely the cause of the difference
•
STEPS INVOLVED IN CONDUCTING AN EXDPERIMENT
1) Identify the problem – mention the one that ida/her friend going to explain
2) Formulate hypotheses and deduce (reasoning) the consequences
3) Construct the experimental design that represents all the elements, conditions and relations of the consequences
CHARACTERISTICS OF EXPERIMENTAL RESEARCH:
1) One of the most powerful research methodologies that researchers can use, best way to establish cause-and-effect relationships among variables ( pg 261 buku teks)
2) Must involve two groups of subjects; an experimental group and a control or a comparison group.
3) The experimental group receives treatment ( as what prof said: eating ice cream) while control group /comparison group receives no treatment/different treatment
4) The control group is always important because the result enables researcher to determine whether the treatment has had an effect or whether one treatment is more effective than another
5) Manipulation of an independent variable; researcher deliberately and directly determines what forms the independent variable will take and then which group will get which form-(example pg 263 1st para last sentences)
6) Randomization; Important aspect in ER is the random assignment of subjects to groups- every individual who is participating in experiment has an equal chance of being assigned to any of the experimental or control conditions being compared; random selection every member of a population has an equal chance of being selected to be a member of the sample ( pg 263 )
7) Control of variables; Using ER has far more control:
a. Determine the treatment
b. Select sample
c. Assign individual to groups
d. Decide which group will get treatment
e. Try to control other factors besides the treatments that might influence the outcome of the study
f. Observe/measure the effect of the treatment on the groups when the treatment is completed
• Experimental Design
ED – A blueprint of the procedure that enables the researcher to test his hypothesis by reaching valid conclusions about relationships between independent and dependent variables. It refers to the conceptual framework within which the experiment is conducted.
All these 5 steps are essential to providing excellent results.
Stage one:
1) After deciding upon hypothesis / making predictions, is to specify sample groups. Should be large enough to give a statistically viable study, but small enough to be practical
2) Group should be selected randomly, to allow results to be generalized to the population as a whole, example : if use volunteers aged bet 18-24, the findings can only be generalized to that age group only
Stage two
1. Sample groups should be divided into two groups : control group and a test group ( to reduce the possibility of confounding variables). Assigning subjects to groups also should be blind or double blind to reduce chances of experimental error, or bias
Stage three
1. This stage involves determining the time scale and frequency of sampling ( either days, months / year)
Stage 4
This is the the penultimate stage of the experiment involves performing the exdperiment and according to the methods stipulated during the design phase
Stage 5
1. The raw data should be gathered and analyzed by statistical means. This allows researcher to establish ( if any relationship between the variables and accept or reject the null hypothesis )
ACTUAL EXAMPLES:
(REFER TO PG 262 OF THE TEXT BOOK – 6 EXAMPLES)
NOTES DLM BUKU TEXT – PG 264 ONWARDS ( LAST PARA)
VALIDITY:
1) The design can take a variety of forms, the good designs able to control threats to internal validity to avoid difficulty in assessing effectiveness of the independent variables. Designs that do not have controls for threats are weak and researchers have difficulty assessing the effectiveness of the independent variable
2)
ENGLISH LANGUAGE COMPETENCE AMONG SECURITY PERSONNEL IN FACULTY OF MEDICINE UNIVERSITY TECHNOLOGY MARA
1. CHAPTER 1
1.1. INTRODUCTION AND THEORITICAL FRAMEWORK
Faculty of Medicine is a new faculty in UiTM , located in Selayang, Sg Buloh Selangor and Telok Intan, Perak. Consist of 500 staff ( majority are medical doctors ) and administrative staff and 400 students. and Selangor.
The security personnel who are working with UiTM must have basic qualification Sijil Rendah Pelajaran (SRP) , but now many of them are Sijil Pelajaran Malaysia (SPM) holders. How ever, for security duty, UiTM has not stated as a policy ; that is qualification in the English Language is not a compulsory requirement. Because of that, not many of them able to speak or write using English Language well.
As time goes by, at faculty of Medicine, majority of the staff especially medical doctors, students who are studying and majoring in medical or even visitors who are coming to our campus are using English language as their spoken language. This situation is now become a culture and every staff including security personnel must able to speak and communicate with them using English because basic duty as security personnel, is greeting people who enter or coming to UiTM.
UiTM is now playing a vital role to be one of the world class universities and must posses a world class standard that is using L2 ( English language ) as its L1. Every one on campus must be able to speak or communicate among them using international language - English language; with other staff, students and visitors.
However, not many security guards are able to communicate using the English language. Therefore, this study intends to look into the matter; what to be done in making sure all the staff is at par with others on campus, not just in identifying their capability but also to identify weaknesses and strength in planning the suitable programs for them.
1.2. PROBLEM STATEMENT
Presently Faculty of Medicine UiTM stressed on the need to have a workforce that can fulfill UiTM inspirations, that is to achieve world Class University. Security department is working very hard in educating all of our security personnel towards achieving this inspiration by telling them to speak using English language with their colleagues, other staff and students at the university.
No studies have been made on the matter yet; for example: are they capable in using standard/proper English language?, facing problems in pronouncing words? / Grammatical error? There has been no specific mechanism to measure their capabilities in using the language so far.
1.3. STATEMENT OF PURPOSE
This study is carried out with the following objectives, taking into consideration some aspects in the problem statement:
i. To identify the level of competence of security personnel in using English language when they communicate or speak
ii. To identify steps taken to improve English language among security personnel
iii. To identify the pattern of English language used when communicating with others
1.4. RESEARCH QUESTIONS
i. What level of English competence they posses when communicating
ii. What steps taken to improve English language among the security personnel
iii. What pattern of English language used when communicating
2. CHAPTER 2
2.1. REVIEW OF LITERATURE
1. THE IMPORTANCE OF ENGLISH AT WORK FOR THE SECURITY GUARDS AT UiTM
3. CHAPTER 3
RESEARCH DESIGN AND METHODOLOGY
3.1. RESEARCH DESIGN
This study will involve only quantitative design. The questionnaire will be designed and distributed among the security personnel working on campus at Faculty of Medicine UiTM Selayang, Sg Buloh and Telok Intan campuses. The data will be analyzed using graph and chart.
3.2. SAMPLE SIZE
Target sample will consist of 70 of the security guards which will be picked up at random in UiTM Selayang, Sg. Buloh and Telok Intan. However, not all security guards can be met at one time because they work in shift work.
3.3. INSTRUMENTATION
Questionnaire shall be distributed to the security guards in UiTM Selayang, Sg. Buloh and Telok Intan, by hand, because they are working at the same place. Each questionnaire shall be attached with written envelope. Respondent will send back using self address envelope to surveyor . I will make each respondent tick/answer each question.
3.3.1. The instrument will be used:
3.3.1.1. Questionnaire
3.4. DATA COLLECTION
This study will be carried out at UiTM Selayang, Sg. Buloh and Telok Intan distribute questionnaire and collect back. The duration for distributing/collecting questionnaire will be done in one month. Security guards are working on shift and one day able to meet 7-8 of them.
3.5. DATA ANALYSIS
3.5.1. Data collection:
3.5.1.1. The data from the questionnaire will be treated on frequency. The score of the test will be evaluated and analyzed to identify suitable programs or courses for them
3.6. RESEARCH ETHICS
The data from respondent will be treated as confidential. Permission will be asked to carry out research.
3.7. CONCLUSION
Based to the findings, proper programs can be arranged like competency tests etc. All the security guards must attend all the programs would be arranged to develop competency.
References:
Mohd.Faizal Hanapiah.(2002, September 24-26). English Language and the language of development. The Star Online. Paper presented at International Conference IPBA
Ahmad Sarji A.A.editor,(1993), Human Resource Development: The Education and Training Aspects by Ungku Abdul Aziz. Malaysia’s vision 2020. Petaling Jaya: Pelanduk Publication
Ridge, B. (2004). Bangsa Malaysia and Recent Malaysian English Language Policies. Journal of Current Issues in Language Planning, Vol.5,No.4, p. 414-417.
1. CHAPTER 1
1.1. INTRODUCTION AND THEORITICAL FRAMEWORK
Faculty of Medicine is a new faculty in UiTM , located in Selayang, Sg Buloh Selangor and Telok Intan, Perak. Consist of 500 staff ( majority are medical doctors ) and administrative staff and 400 students. and Selangor.
The security personnel who are working with UiTM must have basic qualification Sijil Rendah Pelajaran (SRP) , but now many of them are Sijil Pelajaran Malaysia (SPM) holders. How ever, for security duty, UiTM has not stated as a policy ; that is qualification in the English Language is not a compulsory requirement. Because of that, not many of them able to speak or write using English Language well.
As time goes by, at faculty of Medicine, majority of the staff especially medical doctors, students who are studying and majoring in medical or even visitors who are coming to our campus are using English language as their spoken language. This situation is now become a culture and every staff including security personnel must able to speak and communicate with them using English because basic duty as security personnel, is greeting people who enter or coming to UiTM.
UiTM is now playing a vital role to be one of the world class universities and must posses a world class standard that is using L2 ( English language ) as its L1. Every one on campus must be able to speak or communicate among them using international language - English language; with other staff, students and visitors.
However, not many security guards are able to communicate using the English language. Therefore, this study intends to look into the matter; what to be done in making sure all the staff is at par with others on campus, not just in identifying their capability but also to identify weaknesses and strength in planning the suitable programs for them.
1.2. PROBLEM STATEMENT
Presently Faculty of Medicine UiTM stressed on the need to have a workforce that can fulfill UiTM inspirations, that is to achieve world Class University. Security department is working very hard in educating all of our security personnel towards achieving this inspiration by telling them to speak using English language with their colleagues, other staff and students at the university.
No studies have been made on the matter yet; for example: are they capable in using standard/proper English language?, facing problems in pronouncing words? / Grammatical error? There has been no specific mechanism to measure their capabilities in using the language so far.
1.3. STATEMENT OF PURPOSE
This study is carried out with the following objectives, taking into consideration some aspects in the problem statement:
i. To identify the level of competence of security personnel in using English language when they communicate or speak
ii. To identify steps taken to improve English language among security personnel
iii. To identify the pattern of English language used when communicating with others
1.4. RESEARCH QUESTIONS
i. What level of English competence they posses when communicating
ii. What steps taken to improve English language among the security personnel
iii. What pattern of English language used when communicating
2. CHAPTER 2
2.1. REVIEW OF LITERATURE
1. THE IMPORTANCE OF ENGLISH AT WORK FOR THE SECURITY GUARDS AT UiTM
3. CHAPTER 3
RESEARCH DESIGN AND METHODOLOGY
3.1. RESEARCH DESIGN
This study will involve only quantitative design. The questionnaire will be designed and distributed among the security personnel working on campus at Faculty of Medicine UiTM Selayang, Sg Buloh and Telok Intan campuses. The data will be analyzed using graph and chart.
3.2. SAMPLE SIZE
Target sample will consist of 70 of the security guards which will be picked up at random in UiTM Selayang, Sg. Buloh and Telok Intan. However, not all security guards can be met at one time because they work in shift work.
3.3. INSTRUMENTATION
Questionnaire shall be distributed to the security guards in UiTM Selayang, Sg. Buloh and Telok Intan, by hand, because they are working at the same place. Each questionnaire shall be attached with written envelope. Respondent will send back using self address envelope to surveyor . I will make each respondent tick/answer each question.
3.3.1. The instrument will be used:
3.3.1.1. Questionnaire
3.4. DATA COLLECTION
This study will be carried out at UiTM Selayang, Sg. Buloh and Telok Intan distribute questionnaire and collect back. The duration for distributing/collecting questionnaire will be done in one month. Security guards are working on shift and one day able to meet 7-8 of them.
3.5. DATA ANALYSIS
3.5.1. Data collection:
3.5.1.1. The data from the questionnaire will be treated on frequency. The score of the test will be evaluated and analyzed to identify suitable programs or courses for them
3.6. RESEARCH ETHICS
The data from respondent will be treated as confidential. Permission will be asked to carry out research.
3.7. CONCLUSION
Based to the findings, proper programs can be arranged like competency tests etc. All the security guards must attend all the programs would be arranged to develop competency.
References:
Mohd.Faizal Hanapiah.(2002, September 24-26). English Language and the language of development. The Star Online. Paper presented at International Conference IPBA
Ahmad Sarji A.A.editor,(1993), Human Resource Development: The Education and Training Aspects by Ungku Abdul Aziz. Malaysia’s vision 2020. Petaling Jaya: Pelanduk Publication
Ridge, B. (2004). Bangsa Malaysia and Recent Malaysian English Language Policies. Journal of Current Issues in Language Planning, Vol.5,No.4, p. 414-417.
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