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Research Methods in MIS: Experimentation Dr. Deepak Khazanchi Acknowledgment: Some of the information in this presentation is Based on Cooper and Schindler (2000) and Sproull (1996).
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Variables in Experiments Independent variables Treatment or experimental variable: The independent variable that is manipulated by the researcher so that different groups of subjects receive different kinds or amounts. Dependent variables
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Advantages of an Experiment? Researcher’s ability to manipulate the independent variable Contamination from extraneous variables can be controlled more efficiently Convenience Cost Replication
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Disadvantages of Experiments Artificiality of the laboratory Generalization from nonprobability samples Larger budgets needed Restricted to problems of the present or immediate future Ethical limits to manipulation of people
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Experimentation Process Select relevant variables Specify the treatment levels Control the experimental environment Choose the experimental design Select and assign the subjects Pilot-test, revise, and test Analyze the data
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Ways to Assign Subjects Random Assignment Matching Assignment Quota matrix
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Does a Measure Accomplish What it Claims? Internal validity External validity
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Threats to Internal Validity History Maturation Testing Instrumentation Selection Statistical Regression Experimental Mortality
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Threats to External Validity The Reactivity of Testing on X Interaction of Selection and X Other Biasing Effects on X Artificial setting of testing Respondents knowledge of testing
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Experimental Designs Preexperimental designs True experimental designs Field experiments
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Design Symbols X the introduction of an experimental stimulus to the respondent 0 a measure or observation activity R an indication that sample units have been randomly assigned
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Preexperimental Designs One-shot case study One-group pretest-posttest design Static group comparison
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True Experimental Designs Pretest-posttest control group design Posttest-only control group design
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Operational Extensions of True Designs Completely randomized designs Randomized block design Latin square Factorial design Covariance analysis
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Field Experiments: Quasi- or Semi-Experiments Non Equivalent Control Group Design Separate Sample Pretest-Posttest Design Group Time Series Design
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