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Chapter 7

Chapter 7. Primary Data Collection: Experimentation. 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

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Chapter 7

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  1. Chapter 7 Primary Data Collection: Experimentation

  2. 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

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

  4. 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.

  5. 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

  6. Observing an association • If X, then Y • and • If not X, then not Y

  7. 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?

  8. 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

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

  10. Basic Issues • Control Factors • Randomization • Statistical Control • Experimental Validity • Internal Validity • External Validity

  11. Basic Symbols and Notations • Odenotes a formal observation or measurement • Xdenotes 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

  12. Experimental Designs One Group, After-only Design EG X O1 Two Group, After-only Design EG X O1 - - - - - - - - - - - - - - CG O2

  13. Experimental Designs (Contd.) • One-group Before-After Design EG O1 X O2 Two-group, Before-after Design EG O1 X O2 • - - - - - - - - - - - - - • CG O3 O4

  14. True-experimental Designs • Two-group After-only Design EG R X O1 • - - - - - - - - - - - - - • CG R O2

  15. True-experimental Designs • Two-group Before-After Design EG R O1 X O2 • - - - - - - - - - - - - - • CG R O3 O4

  16. 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? Marketing Research Seminar

  17. Internal validity

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

  19. 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

  20. 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 Marketing Research Seminar

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

  22. 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) Marketing Research Seminar

  23. 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 Marketing Research Seminar

  24. 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 Marketing Research Seminar

  25. 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 Marketing Research Seminar

  26. External validity

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

  28. 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

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