Business Research Methods William G. Zikmund Chapter 12: Experimental Research Experiment • A research investigation in which conditions are controlled • One independent variable is manipulated (sometimes more than one) • Its effect on a dependent variable is measured • To test a hypothesis Basic Issues of Experimental Design • Manipulation of the Independent Variable • Selection of Dependent Variable • Assignment of Subjects (or other Test Units) • Control Over Extraneous Variables The experimenter has some degree of control over the independent variable. The variable is independent because its value can be manipulated by the experimenter to whatever he or she wishes it to be. Experiment Treatment Alternative manipulations of the independent variable being investigated Independent Variable • The experimenter controls independent variable. • The variable’s value can be manipulated by the experimenters to whatever they wish it to be. Manipulation of Independent Variable • • • • Classificatory Vs. continuous variables Experimental and control groups Treatment levels More than one independent variable Experimental Treatments • The alternative manipulations of the independent variable being investigated Dependent Variable • Its value is expected to be dependent on the experimenter’s manipulation • Criterion or standard by which the results are judged Dependent Variable • Selection – e.g... sales volume, awareness, recall, • Measurement Test Units • Subjects or entities whose response to the experimental treatment are measured or observed. Two Types of Experimental Error • Constant errors • Random errors Field versus Laboratory Experiments Establishing Control Physical Control – Holding the value or level of extraneous variables constant throughout the course of an experiment. Statistical Control – Adjusting for the effects of confounding variables by statistically adjusting the value of the dependent variable for each treatment conditions. Design Control – Use of the experimental design to control extraneous causal factors. Eg. Demand Demand Characteristics • Experimental procedures that intentionally hint to subjects something about the experimenter’s hypothesis Demand Characteristics • Guinea pig effect • Hawthorne effect Field Vs. Laboratory Experiment Laboratory Experiment Field Experiment Artificial-Low Realism Natural-High Realism Few Extraneous Variables Many Extraneous Variables High control Low control Low Cost High Cost Short Duration Long Duration Subjects Aware of Participation Subjects Unaware of Participation Control Groups Isolate extraneous variation When does an Experiment have Internal Validity? Internal Validity - The ability of an experiment to answer the question whether the experimental treatment was the sole cause of changes in a dependent variable Did the manipulation do what it was supposed to do? Factors Influencing Internal Validity • • • • • • History Maturation Testing Instrumentation Selection Mortality Isolating Extraneous Variation with a Control Group • History Effects • Maturation Effects • Mortality Effects Type of Extraneous Variable Example History - Specific events in the environment between the Before and After measurement that are beyond the experimenter’s control A major employer closes its plant in test market area Maturation - Subjects change during the course of the experiment Subjects become tired Testing - The Before measure alerts or sensitizes subject to nature of experiment or second measure. Questionnaire about the traditional role of women triggers enhanced awareness of women in an experiment. Instrument - Changes in instrument result in response bias New questions about women are interpreted differently from earlier questions. Selection - Sample selection error because of differential selection comparison groups Control group and experimental group is self-selected group based on preference for soft drinks Mortality - Sample attrition; some subjects withdraw from experiment Subjects in one group of a hair dying study marry rich widows and move to Florida How can Internal Validity Increase? Increasing Internal Validity • Control group • Random assignment • Pretesting and posttesting • Posttest only What are the Different Basic Experimental Designs? Quasi-Experimental Designs • One Shot Design (After Only) • One Group Pretest-Posttest • Static Group Design One Shot Design (After Only) X O1 One Group Pretest-Posttest O1 X O2 Static Group Design Experimental Group Control Group X O1 O2 Three Good Experimental Designs • Pretest - Posttest Control Group Design • Posttest Only Control Group • Solomon Four Group Design Pretest-Posttest Control Group Design Experimental Group R O1 X O2 Control Group R O3 O4 Posttest Only Control Group Experimental Group R Control Group R X O1 O2 One-Shot Design Internal Validity Problems • History – weak • Instrumentation – not relevant • Maturation • Selection – weak – weak • Testing – not relevant • Mortality – weak One-Group Pretest-Posttest Internal Validity Problems • History • Instrumentation – weak – weak • Maturation • Selection – weak • Testing – weak – controlled • Mortality – controlled Static-Group Design Internal Validity Problems • History – controlled • Maturation – possible source of concern • Testing – controlled • Instrumentation – controlled • Selection – weak • Mortality – weak Pretest-Posttest Control Internal Validity Problems • History – controlled • Maturation – controlled • Testing – controlled • Instrumentation – controlled • Selection – controlled • Mortality – controlled Solomon Four-Group Design Internal Validity Problems • History – controlled • Maturation – controlled • Testing – controlled • Instrumentation – controlled • Selection – controlled • Mortality – controlled Posttest-Only Control Internal Validity Problems • History – controlled • Maturation – controlled • Testing – controlled • Instrumentation – controlled • Selection – controlled • Mortality – controlled Solomon Four Group Design Experimental Group 1: Control Group 1: Experimental Group 2: Control Group 2: R O1 X O2 R O3 O4 R X O5 R O6 Advanced Experimental Designs are More Complex • • • • Completely randomized Randomized block design Latin square Factorial Completely Randomized Design • An experimental design that uses a random process to assign subjects (test units) and treatments to investigate the effects of only one independent variable. Completely Randomized Designs Control: no music Average minutes shopper spends in store 16 Experimental treatment: slow music 18 Experimental treatment: fast music 12 Independent Variable A Level 1 Group A Level 2 Level 3 Group B Group C Completely Randomized Design With a pretest posttest Group A R O1 X1 O2 Group B R O3 X2 O4 Group C R O5 X3 O6 Completely Randomized Design With a posttest Group A R X1 O1 Group B R X2 O2 Group C R X3 O3 Randomized Block Design • An extension of the completely randomized design in which a single extraneous variable that might affect test units’ response to the treatment has been identified and the effects of this variable are isolated by blocking out its effects. Randomized Block Design Independent Variables Blocking variable Control: no music Mornings and afternoons Evening hours Experimental treatment slow music Experimental treatment: fast music Factorial Design • An experiment that investigates the interaction of two or more variables on a single dependent variable. Independent Variable 1 Independent Variable 2 No Music No Music cart signs Grocery cart signs Slow Music Fast Music Factorial Design -- Roller Skates Package Design Price Red Gold $25 $30 $35 Cell 1 Cell 2 Cell 3 Cell 4 Cell 5 Cell 6 Effects • Main effect • The influence of a single independent variable on a dependent variable. • Interaction effect • The influence on a dependent variable by combinations of two or more independent variables. 2 x 2 Factorial Design Ad A Ad B Men 65 Main Effects > of Gender Women 65 70 60 Main Effects of Ad Interaction Between Gender and Advertising Copy 100 90 80 70 60 50 40 30 20 10 Ad A Ad B Independent Variable 2 Independent Variable 1 Level 1 Level 2 Level 1 Group A Group B Level 2 Group C Group D 2 x 2 Factorial with a Pretest Posttest Group A R O1 X11 O2 Group B R O3 X21 O4 Group C R O5 X12 O6 Group D R O7 X22 O8 2 x 2 Factorial Design with a Posttest Measure Group A R X11 O1 Group B R X21 O2 Group C R X12 O3 Group D R X22 O4 A Test Market Experiment on Pricing Sales in Units (thousands) Regular Price $.99 Test Market A, B, or C Test Market D, E, or F Test Market G, H, or I Test Market J, K, or L Mean Grand Mean Reduced Price $.89 Cents-Off Coupon Regular Price 130 118 87 84 145 143 120 131 153 129 96 99 X1=104.75 X=119.58 X2=134.75 X1=119.25 Latin Square Design • A balanced, two-way classification scheme that attempts to control or block out the effect of two or more extraneous factors by restricting randomization with respect to the row and column effects. Order of Usage SUBJECT 1 1 2 3 2 3 A B C B C A C A B
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