Class_6 Experimental Research

7/28/2019 Class_6 Experimental Research

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Dr. Sasmita Mishra

KSOM, KIIT University


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An experiment is generally usedto infer a causality. In an

experiment, a researcher activelymanipulates one or more causalvariables and measures their

effects on the dependentvariable of interest.


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CR determines the cause and effect relationships.

For example, a 5% increase in price of the product will have noappreciable impact on the quantity demanded by customers.

DR is not suitable for establishing, causality. Therefore, toestablish causal relationship the experiments are used.

Causal research is appropriate for understanding whichvariables are the cause/s (independent variables) and whichvariables are the effect (dependent variables)

Like DR, causal research requires a planned and structureddesign. Causal design, in which the causal or independentvariables are manipulated in a relatively controlled environment.

The main method of causal research is experimentation.

To understand CRD, one must understand the scientific notionof causality.


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The SNC of causality is different from the common day notion.

1) The common notion: there is a single cause of an event such

that the statement X is the cause of Y implies that X is indeed

the cause. The SNC holds that X would only be one of thecauses/conditions.

2) CN implies a completely deterministic relationship. TheSNC implies a probababilistic relationship. The SNCsuggests that X can be a cause of Y if the occurrence of X

makes the occurrence of Y more likely or more probable.

3) Finally, the SNC implies that we never prove that X is acause of Y. Rather we infer, that a relationship exists.

SN: causality is inferred, it is neverdemonstrated conclusively. Three kinds of evidences, concomitant variation, time of

occurrence of variables, and elimination of other possiblecausal factors.


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Concomitant variation

Time order of occurrence of variables

Elimination ofother possible causalfactors


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1. Concommitment Variation:

If a statement is "X is a cause of Y" then the concomitantvariation as to the validity of this statement refers to theextent of which X and Y occur together or vary together inthe way predicted by the hypothesis.

Suppose there is a positive relationship between the

quality of dealers and market share. The hypothesis, inthis connection is "The success of a companys marketingefforts is highly dealer dependent.

Where the company has good dealers, it has market

penetration and where the co. has poor dealers, the co.has unsatisfactory market penetration.

If X is to be considered a cause of Y, one should expect tofind the following


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Research investigated market penetration in each salesterritory

Perfect evidence of CV would be provided if all gooddealers have satisfactory market share and all poor

dealers unsatisfactory market shares. The "pure" case will rarely be found in practice, as the

other causal factors will effect some deviation from aone-to-one correspondence between X and Y.

DealerQuality X MARKET SHARE - YSatisfactory Unsatisfactory Total

Good 40(67%) 20(33%) 60(100%)Poor 10(25%) 30(75%) 40(100%)


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2. Time order of Occurrence of VariablesThis is conceptually simple

One event cannot be cause of another if it occurs after the

other event.By definition, an effect cannot be produced by an event

that occurs only after the effect has taken place.

However, it is possible for each term in the relationship to

be both a cause and an event of the other term

3. The Elimination of other possible causes

The researcher has to eliminate any other variable or

variables, which may affect the dependent variable under

study. If it is not done then the causal relationship betweenthe independent and dependent variable may not be proved.

These evidences can be provided through causal research,

which is carried out through controlled experiments.


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Independent variables: Independentvariables are also known as explanatoryvariables or treatments. The levels ofthese variables are manipulated (changed)

by researchers to measure their effect onthe dependent variable. Test units: Test units are those entities on

which treatments are applied.

Dependent variables: These variablesmeasures the effect of treatments(independentvariable) on the test units.


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Experiment: An experiment is executed when theresearcher manipulates one or more independentvariables and measures their effect on thedependent variables while controlling the effect ofthe extraneous variables.

Extraneous variables: These are the variables otherthan the independent variables which influencethe response of test units to treatments.

Examples: Store size, government policies,temperature, food intake, geographical location, etc.


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Internal validity: Internal validity tries toexamine whether the observed effect on adependent variable is actually caused bythe treatments (independent variables) in

question. External validity: External validity refers to

the generalization of the results of anexperiment. The concern is whether the

result of an experiment can begeneralized beyond the experimentalsituations.


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History Maturation Testing Instrumentation Statistical regression Selection bias Test unit mortality


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The environment at the time of test may bedifferent from the environment of the realworld where these results are to begeneralized.

Population used for experimentation of thetest may not be similar to the populationwhere the results of the experiments are tobe applied.

Results obtained in a 56 week test may not

hold in an application of 12 months. Treatment at the time of the test may be

different from the treatment of the real world.


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Randomization Matching Use of experimental designs Statistical control


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Laboratory Environment - In a laboratoryexperiment, the researcher conducts theexperiment in an artificial environmentconstructed exclusively for the

experiment.

Field Environment - The field experimentis conducted in actual market conditions.There is no attempt to change the real-lifenature of the environment.


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Pre-experimental designs do not make use of anyrandomization procedures to control the extraneous variables.Therefore, the internal validity of such designs is questionable.

One-shot case study:

X O One-group pre-testpost-test design:

O1 X O2

Static group comparison:

Group 1 - X O1

Group 2 - O2


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In quasi-experimental design, the researchercan control when measurements are taken andon whom they are taken. However, this designlacks complete control of scheduling of

treatment and also lacks the ability torandomize test units exposure to treatments.

Time series design:

O1 O2 O3 O4 X O5 O6 O7 O8

Contd..


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Multiple time series design:

Experimental Group: O1 O2 O3 O4 X O5 O6

O7 O8

Control Group: O1 O2 O3 O4 O5 O6O7 O8


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In true experimental designs, researcherscan randomly assign test units andtreatments to an experimental group. Here,the researcher is able to eliminate the effect

of extraneous variables from both theexperimental and control group.

Pre-testpost-test control group:Experimental Group: R O1 X O2Control Group: R O3 O4


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Post-testonly control group design:

Experimental Group: R X O1Control Group: R O2

Solomon four-group design:

Experimental Group 1 : R O1 X O2Control Group 1: R O

3

O4Experimental Group 2: R X O5

Control Group 2: R O6


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Statistical designs allow for statistical controland analysis of external variables.

Completely randomized design

Randomized block design

Latin square design

Factorial design


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Thank You

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Class_6 Experimental Research