Experimental DesignSubmitted to:Mr. Ajesh P. Joseph,School of Social Work,Marian College,Kuttikkanam.Submitted by:Bimal Antony,1stMSW,School of Social Work,Marian College,Kuttikkanam.Date of Submission:19thApril 2011.
2Experimental DesignIntroductionAn experiment is a process or study that results in the collection of data. The results ofexperiments are not known in advance. Usually, statistical experiments are conducted insituations in which researchers can manipulate the conditions of the experiment and can controlthe factors that are irrelevant to the research objectives. For example, a rental car companycompares the tread wear of four brands of tires, while also controlling for the type of car, speed,road surface, weather, and driver.Experimental design is the process of planning a study to meet specified objectives.Planning an experiment properly is very important in order to ensure that the right type of dataand a sufficient sample size and power are available to answer the research questions of interestas clearly and efficiently as possible.Purpose of Experimental Research DesignThe aim of the experimental research is to investigate the possible cause-and-effectrelationship by manipulating one independent variable to influence the other variable(s) in theexperimental group, and by controlling the other relevant variables, and measuring the effects ofthe manipulation by some statistical means. By manipulating the independent variable, theresearcher can see if the treatment makes a difference on the subjects.If the average scores of two groups prove to be significantly different, and if there are notany explanations for this difference, then it can be concluded that the effect of the treatmentcaused this difference. This is where experimental research differs from correlational research,For instance, correlational studies only describe or predict the strong relationship betweensocioeconomic level and the academic achievement but cannot prove the direct cause-and-effectrelationship between these two variables. It is the experimental research which can demonstratethat by changing the independent variable, a change is possible on the dependent variable.In educational research the most frequently studied dependent variables are achievement,motivation, attention, interest in learning, participation and attitudes. The common independentvariables that are manipulated are teaching methods, types of assignments, types of teachingmaterials such as text books and visual aids, types of rewards, types of questions used by theteacher, and evaluation techniques. There are however, some independent variables such as ageand gender that cannot be manipulated. When the independent variable that is chosen cannot bemanipulated, either a comparative research is conducted, or a second independent variable ischosen for manipulation in order to conduct an experimental study.
3Experimental DesignCategories of Experimental DesignBefore-After or Pre-test - Post-test Experimental DesignThis is normally called classical experimental design. It is more reliable and it representsthe so-called four-cell design (Fig. 13.1). Here, before experimentation all the groups areselected, observed and measured. There is one independent variable - the treatment - and onedependent variable. Subjects are assigned randomly to the control group and experimental group.Then the dependent variable is measured for both the groups.After pretest, the treatment is introduced to the subjects in the experimental group only.The dependent variable is then measured again for both the groups and compared. This is thePost- test. This design suffers from one limitation, that is, it does not ensure to be free from theinfluence of external factors.After-only or Pre-test only Experimental DesignAfter-only or Pre-test only Experimental Design In this type of design the study is carriedout under social conditions, which are not at all in the control of the physical or naturalconditions. Two groups of subjects, who are similar in all conditions, are chosen. One is calledexperimental group and the other is called control group. Experiment is carried out on theexperimental group as per the pre-determined method.After the prescribed period both groups are observed and the results are measured. Theresults are compared and changes that are observed in the experimental group are recognised as aresult of manipulating the variable in theexperiment.Quasi or Ex-Post Facto Experimental DesignThe name of the quasi experiment has been given to those situations in which theexperimenter cannot randomly assign subjects to experimental groups, but can still manipulatethe independent variable. However when even such manipulation is impossible - that is to say,when the stimulus is also beyond the control of the researcher – we can no longer speak ofexperimentation, what we have is purely and simply, an analysis of co-variation. Never the lessthere are research situations which, although lacking both features of experimentation (i.e.randomisation and manipulation) involve a design that closely resembles that of experimentation.Such designs are called ex post facto.‘Ex Post Facto’ is a Latin phrase which means ‘done or made after a thing but retroaction upon it’. In this the experimenter does not achieve the change which studies, he invariablychances upon the effect after it has already occurred. In Library Science, children reading habitsand behaviour of a fresh reader in the library can be studied with the application of this design.
4Experimental DesignCompletely Randomized DesignFrequently an investigator wishes to compare three or more treatments in a singleexperiment. In a survey, too, he may wish to study several populations; for example, he may beinterested in IQ scores from a standard test for students at five schools, Such comparisons couldbe accomplished by looking at the samples two at a time and comparing the means. Althoughfeasible, this is an inefficient method of comparison for more than two populations.One reason for its inefficiency is that the standard deviation for the difference betweenthe two, sample means is not calculated from all the samples but instead uses samples only fromthe two populations under immediate consideration. Second, we feel intuitively that we shallalmost find a significant difference between at least one pair of means (the extreme ones, e.g.) ifwe consider enough identical populations. We can no longer trust our level of significance.Therefore, instead of using two samples at a time, we wish to make a single test to findout whether the students from the five schools are from five populations having the samepopulation mean.Completely randomized design is primarily concerned with tests for population means.To study the means, it is necessary to "analyze the variance".Randomized Complete Block DesignIn the completely randomized design, treatments are assigned at random. For example, ifthe treatments are three drugs and there are 24 patients, eight patients are assigned at random toeach of the three treatments.The 24 patients may vary widely in initial condition, and their initial condition may affecttheir response to the drugs. In the completely randomized design, we try to take care of thesedifferences among the patients by assigning them at random into groups of eight patients.Unfortunately, it is possible that all the patients receiving drug 1 may be comparatively healthyand all those receiving drug 2 may be comparatively unhealthy, even though the assignment wasrandomly made. By randomization, however, at least we have given each drug an equal chancewith respect to the initial condition of the groups. Further more, we can expect that if theexperiment is large enough, randomization will roughly equalize the initial condition of the threegroups. Besides initial condition, the experimenter may feel that other factors might influence theresponse to the drugs (e.g., age or weight).A block design is a much used method for dealing with factors that are known to beimportant and which the investigator wishes to eliminate rather than to study.In the randomized complete block design, still with three treatments and 24 patients, thepatients are divided into eight blocks, each consisting of three patients. These blocks are farmedso that each block is as homogeneous as possible. Each block consists of as many experimentalunits as there are treatments-three, in this case. The blocks might be easily formed on the basis ofage, for example, with blocks 1 and 8 consisting of the three youngest and the three oldestpatients, respectively. The individuals in a particular block are as alike as possible. On the otherhand, there may be wide differences between the individuals for different blocks.
5Experimental DesignLatin Square DesignIn the randomized complete block design, the effect of a single factor was removed. It isoccasionally possible to remove .the effects of two factors simultaneously in the sameexperiment by using the Latin Square design. In order to use the Latin square design, however, itis necessary to assume that no interaction exists between the treatment effect and either blockeffect. In addition, the number of treatments must be equal to the number of categories for eachof the two factors. We might, for instance, wish to test four detergents, using four methods ofapplication, at four hospitals. A 4X4 Latin square design could then be employed, using eachdetergent exactly once with each method and exactly once in each hospital. The assignment ofdetergent could be made as shown in the following table; the roman numeral in the ith row andjth column indicates the detergent that will be used by the ith application method in the jthhospital. As assigned in the Table 2, the first detergent is used in hospital 1 by method 1, inhospital 2 by method 4, in hospital 3 by method 3, and in hospital 4 by method 2. Only 16observations are needed because of the balanced arrangement used and because of theassumption of no interaction.Factorial DesignOften a researcher can use a single experiment advantageously to study two or moredifferent kinds of treatments. For example, in investigating performance of two types of seeds,he may wish to vary the level of fertilizer used during the experiment. If he chose three levels offertilizer-low, medium, and high-one factor would be "type of seed", the second factor "level offertilizer". A factorial design, with two factors, would consist of employing all six treatmentsformed by using each type of seed with each level of fertilizer. Factorial designs can involvemore than two factors; however, we consider here the case of two factors only.A factorial design can also be used in a survey. For example, we might wish to comparethree methods of teaching operations research, and at the same time compare the fast four grades.We might have records on standardized tests for two classes in each grade taught by eachmethod. The class mean improvement from initial test to final test could be the measure ofsuccess. Our data would then consist of two observations on each of 12 (3 x 4) differenttreatment combinations.The characteristic of the factorial design is that every level of one factor is used incombination with every level of the other factor. The design is effective for studying the twofactors in combination. This implies that factorial designs are appropriate in finding out whetherinteractions exist between factors.Some factors can be measured quantitatively, and different levels for them are chosen onan ordered scale; level of fertilizer, dosage level; and temperature are all factors of this type.Other factors involve no obvious underlying continuum and can be said to be qualitative; drugand type of seed are factors of the second type.
6Experimental DesignSolomon Four Group DesignThe Solomon four group design is a way of avoiding some of the difficulties associatedwith the pretest-posttest design.This design contains two extra control groups, which serve to reduce the influence ofconfounding variables and allow the researcher to test whether the pretest itself has an effect onthe subjects.The Solomon four group test is a standard pretest-posttest two-group design and theposttest only control design. The various combinations of tested and untested groups withtreatment and control groups allows the researcher to ensure that confounding variables andextraneous factors have not influenced the resultsThe first two groups of the Solomon four group design are designed and interpreted inexactly the same way as in the pretest-post-test design, and provide the same checks uponrandomization.The comparison between the posttest results of groups C and D, marked by line ‘D’,allows the researcher to determine if the actual act of pretesting influenced the results. If thedifference between the posttest results of Groups C and D is different from the Groups A and Bdifference, then the researcher can assume that the pretest has had some effect upon the results.The comparison between the Group B pretest and the Group D posttest allows theresearcher to establish if any external factors have caused a temporal distortion. For example, itshows if anything else could have caused the results shown and is a check upon causality.The Comparison between Group A posttest and the Group C posttest allows theresearcher to determine the effect that the pretest has had upon the treatment. If the posttestresults for these two groups differ, then the pretest has had some effect upon the treatment andthe experiment is flawed.The comparison between the Group B posttest and the Group D posttest shows whetherthe pretest itself has affected behavior, independently of the treatment. If the results aresignificantly different, then the act of pretesting has influenced the overall results and is in needof refinement.Analysis of CovarianceAnalysis of covariance is a combination of the two techniques-analyses of variance andregression. It is the simultaneous study of several regressions.The purpose of analysis of covariance is to remove the effect of one or more unwantedfactors in an analysis of variance. For example, in studying the heights of three populations ofchildren (cyanotic heart disease children, sibs of heart-disease children, and "well children"), we
7Experimental Designmay wish to eliminate the effect of age. A variable whose effect one wishes to eliminate bymeans of a covariance analysis is called a covariate or a concomitant variable.ADVANTAGES, DISADVANTAGES AND LIMITATIONS OFEXPERIMENTAL METHODExperimentation has two basic advantages; firstly it is the research method that bestenables us to tackle the problem of the causal relationship; secondly it allows isolationof specific phenomena, which could not be studied, systematically in their natural setting,owing to the presence of other factors that hide, confuse and distort them.Advantages_ Its power to determine causal relationship is much better that that of all othermethods._ The influence of extraneous variables can be more effectively controlled._ The element of human error is more reduced._ More conditions may be created and tested in this method._ This method yields generally exact measurements and it can be repeated.Disadvantages_ It is very difficult to establish comparable control group and experimental group._ The scope of experimentation with human beings is extremely difficult._ Experiment is often difficult to design as it tends to be expensive and time consuming._ Experimentation can be used only in studies of the present but not in studies relatingto the past or future.Limitations_ Experimentation is applicable to certain phenomena and certain social situations._ Experimentation cannot be conducted if the independent variable cannot bemanipulated._ This approach is generally suitable to ‘micro’ issues (involving interpersonalrelationships) rather than to ‘macro’ situations (on account of the difficulty ofmanipulating institutions or social groups).An experiment conducted in a laboratory under artificial conditions may not trulyrepresent a situation. It is too simplistic to consider that there are only two variables. In naturalsciences it works where other variables can be kept under control. In behavioural or socialsciences the situation is too complex as best put by John W. Best (p. 92). Despite its appealingsimplicity and apparent logic, it did not provide an adequate method for studying complexproblem. It assumed a highly artificial and restricted relationship between single variables.Rarely, if ever, are human events the result of single causes
8Experimental DesignThey are usually the result of the interaction of many variables, and an attempt to limitvariables so that one can be isolated and observed proves impossible. Hence, there are manylimitations and suppositions when applied to social situations including library and informationmanagement.Human beings cannot be put to experimentation on many psychological and ethicalgrounds. Human beings when under a test or observation can easily manipulate their naturalbehaviour. They cannot be put in test tubes and titrated like chemicals. Then a human beingexperimenter has naturally inherent biases (likings and disliking) when other humans areconcerned. They tend to take sides in heart of hearts. Hence observations and conclusions maynot reflect the objective reality.