2. Primary Data Collection: Experimentation Chapter 7

3. What is an Experiment? Example of a magazine company printing two cover designs and evaluation in the office Example of the same magazine company printing two cover designs and measuring sales in two different cities Maker of Grape Jelly trying various formulations

4. Laboratory Experiment Field Experiment Study in a realistic Situation – Natural setting Study in a controlled Situation – outside the natural setting Experiment

5. Experiments Studies in which conditions are controlled so that one or more independent variable can be manipulated to test a hypothesis about a dependent variable. Randomization. Manipulation of A treatment variable (x), followed by observation of response variable or dependent variable (y). Goal is to obtain an experimental effect. Experiment must be designed to control for other variables to establish causal relationship.

7. Causal relationship is key Manipulation of variable(s) to observe the effect on another variable Conditions for causality Concomitant Variation Temporal order Spurious factors Correlation vs. Causation

8. Observing an association If X, then Y and If not X, then not Y

9. Non-spurious We say that a relationship between two variables is spurious when it is actually due to changes in a third variable, so what appears to be a direct connection is in fact not one. i.e. If we measure children’s shoe sizes and their academic knowledge, for example, we will find a positive association. Does that mean that shoe size causes academic knowledge? What about this? Do schools with better resources produce better students?

10. Correlation vs. Causation Correlation= degree of association between two variable They must vary together: when one goes up (or down) the other must go up (or down) Linear relationship The correlation coefficient can range between +1 and -1. Positive values indicate a relationship between X and Y variables so that as X increases so does Y. Negative values mean the relationship between X and Y is such that as values for X increase, values for Y decrease. A value near zero means that there is a random, nonlinear relationship between the two variables r- coefficient of correlation

11. Experimental Setting- Issues Notation Design and Treatment Experimental Effects Control groups vs. Experimental groups.

12. Basic Issues Control Factors Randomization Statistical Control Experimental Validity Internal Validity External Validity

13. Basic Symbols and Notations O denotes a formal observation or measurement X denotes exposure of test units participating in the study to the experimental manipulation of treatment EGdenotes an experimental group of test units that are exposed to the experimental treatment. CGdenotes a control group of test units participating in the experiment but not exposed to the experimental treatment. Rdenotes random assignment of test units and experimental treatments to groups. Increases reliability

15. Experimental Designs One Group, After-only Design EGX O 1 Two Group, After-only Design EGX O 1 - - - - - - - - - - - - - - CG O 2

16. Experimental Designs (Contd.) One-group Before-After Design EG O 1 X O 2 Two-group, Before-after Design EG O 1 X O 2 - - - - - - - - CG O 3 O 4

17. True-experimental Designs Two-group After-only Design EG R X O 1 - - - - - - - - CG R O 2

18. True-experimental Designs Two-group Before-After Design EG R O 1 X O 2 - - - - - - - - CG R O 3 O 4

19. Marketing Research Seminar Internal Validity The degree to which plausible alternative causes have been controlled for Are the observed effects on the D.V. a cause of the treatment? Or could they have been caused by something else?

20. Internal validity

21. Threats to Validity History Treatment Maturation Instrument Variation Selection Bias Mortality Testing Effects Regression to the Mean

22. Threats to Internal Validity History: Events external to the experiment that affect responses of the people involved in the experiment (weather, news reports, time of day) -The “cohort effect”: members of one experimental condition experience historical situations different from others Example: Linda McCartney’s death might have affected responses to breast cancer PSAs more for her age cohort; Members of the WW II generation are more responsive to calls for volunteerism and community activism

23. Marketing Research Seminar Threats to Internal Validity Treatment Effect: Awareness of being in the test causes subjects to act different than they otherwise would Types of treatment effects: The Hawthorne Effect: special attention received in experiment produces the result Demand Effect: awareness of test produces response desired by researchers

24. Marketing Research Seminar Threats to Internal Validity Maturation: Changes in respondents over the time period of the experiment (maturing, getting hungry, getting tired)

25. Marketing Research Seminar Threats to Internal Validity Testing Effect: A before treatment measurement sensitizes subjects to the treatment Example: Colon Cancer PSA (phoning subjects for pre-test measurements may have sensitized subjects to ads that appeared on TV)

26. Marketing Research Seminar Threats to Internal Validity Instrumentation Effects: The measuring instrument may change, different interviewers may be used, or an interviewer or confederate gets tired A common case: order of presentation produces an effect Example: consumers may prefer first product tasted if they can’t tell the difference

27. Marketing Research Seminar Threats to Internal Validity Mortality (or attrition): Some subjects drop out of the experiment between measurements. Those subjects who drop out may differ from those who stay Example: testing a weight-loss program

28. Marketing Research Seminar Threats to Internal Validity Selection Bias: An experimental group is different from control groups For convenience, many experimental studies have self-selected subjects random assignment to treatments will solve this Example: Latin students

29. External validity

30. Experimentation: Pros and Cons Best method to evaluate causation Costs Security Implementation Issues

31. Steps for starting a good design 1. Select problem 2. Determining dependent variables 3. Determining independent variables 4. Determining the number of levels of independent variables 5. Determining the possible combinations 6. Determining the number of observations 8. Randomization