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Effects of TV Advertising on Consumer Behavior

This chapter explores the use of experiments to study the effects of TV advertising on consumer behavior. It discusses the different variables and factors involved, as well as the importance of randomization in ensuring unbiased results.

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Effects of TV Advertising on Consumer Behavior

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  1. Chapter 8 Producing Data: Experiments Chapter 8

  2. Explanatory and Response Variables • Response variable measures what happens to the individuals in the study • In an experiment, the investigator controls the values of the explanatory variable in individuals to see if he or she can influences the response variable Chapter 8

  3. Experiments: Vocabulary • Subjects = people studied in an experiment • Factors = explanatory variables (specific experimental condition applied to subjects) • Treatment = a combination of a specific set of factors Chapter 8

  4. Case Study Effects ofTV Advertising p. 200 in text Chapter 8

  5. Case Study Effects ofTV Advertising Objective: To determine the effects of length of message and how often message is repeated Chapter 8

  6. Case Study • Subjects: a sample of undergraduate students • Subjects viewed a 40-minute television program that included ads for a digital camera Chapter 8

  7. Case Study • Factors • Some subjects saw a 30-second commercial; others saw a 90-second version • Commercial were shown either 1, 3, or 5 times during the program • Thus: two factors: • length of the commercial (2 levels) • number of repetitions (3 levels) Chapter 8

  8. Subjects assigned to Treatment 3 see a 30-second ad five times during the program Case Study • The 6 combinations of factors, i.e., 6 treatments Chapter 8

  9. Case Study • After viewing, subjects answered questions about • recall of the ad • their attitude toward the camera • their intention to purchase • Three response variables Chapter 8

  10. Comparative Experiments • Comparison is the leading principle • Effects of treatment can be judged only in relation to what happens in a similar group • This sorts out changes that are unrelated to treatment • You cannot assess the effect of a treatment in isolation because: • Many factors contribute to a response • Conditions change on their own • People are open to suggestion (Placebo effect) • Observations changes things (Hawthorne effect) Chapter 8

  11. Randomized Experiments • Randomization is the second leading principle • Randomization = use impersonal chance mechanisms to assign treatments • Randomization balances lurking variables across treatments groups Chapter 8

  12. Example: Quitting Smoking with Nicotine Patches (JAMA, Feb. 23, 1994, pp. 595-600) • Subjects: 60 people • Explanatory variable: Treatment assignment • Nicotine patch • Control (placebo) patch Random assignment of treatment! • Response variable: Cessation of smoking (yes/no) Chapter 8

  13. Outline of Experiment Group 130 smokers Treatment 1 Nicotine Patch Random Assignment CompareCessation rates Treatment 2 Placebo Patch Group 230 smokers Chapter 8

  14. Randomizing the Treatment • Assign labels 01,…,60 to subjects • Use random digits (TABLE B) • Select line at random (say 102)73|67|6 4|71|50| 99|40|0 0|19|27 • First four subjects are 50, 40, 19, and 27 • Keep using table until you get 30 subjects in Group 1 • Remaining subjects are assigned to Group 2 Chapter 8

  15. Mozart, Relaxation and Performance on Spatial Tasks (Nature, Oct. 14, 1993, p. 611) • Subjects (30 undergraduate students) • Variables: • Explanatory: 3 treatments (see below) • Response: Change in Stanford-Binet IQ score • Treatment: • Group 1: Listen to Mozart • Group 2: Listen to relaxation tapes • Group 3: Silence • Random assignment of treatments Chapter 8

  16. Outline of Experiment Group 110 students Treatment 1 Mozart Random Assignment Group 210 students Treatment 2Relaxation CompareChange in IQ score Group 310 students Treatment 3Silence Chapter 8

  17. Logic of Randomized Comparative Experiments • Random assignment ensure  difference in response due to either • Treatment • Chance in the assignment of treatments • If an experiment finds a difference among groups, we ask whether this difference is (“real”) or due to the chance assignment • If the observed difference is larger than what would be expected just by chance, then it is statistically significant Chapter 8

  18. Illustrative example • Consider an experiment of weight gain in laboratory rats • There would be differences in weight gain even if both groups received the same diet • Just by luck, some faster-growing rats would end in one group or the other • If we assign many rats to each group, the effects of chance will balance out • Key concept: use enough controls to balance out chance differences Chapter 8

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