2. Lecturer’s desk INTEGRATED LEARNING CENTER ILC 120 Screen 11 10 2 1 98 7 6 5 13 12 15 14 17 16 19 18 4 3 Row A Row B Row C Row D Row E Row F Row G Row H Row I Row J Row K Row L Computer Storage Cabinet Cabinet Table 20 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 29 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 28 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 27 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 26 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 25 24 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 24 4 3 13 12 14 16 15 17 18 19 11 10 2 1 9 8 7 6 5 21 20 23 22 4 3 13 12 14 16 15 17 18 19 11 10 9 8 7 6 5 4 3 13 12 14 16 15 17 18 19 broken desk

3. BNAD 276: Statistical Inference in Management Spring 2016 Green sheets

4. Exam 1 – This Thursday, February 18 th Study guide is online Bring 2 calculators (remember only simple calculators, we can’t use calculators with programming functions) Bring 2 pencils (with good erasers) Bring ID Stats Review by Nick and Jonathon When: Wednesday evening February 17 th - 6:30 – 8:30pm Where: Modern Languages 350 Cost: $5.00 Stats Review by Nick and Jonathon When: Wednesday evening February 17 th - 6:30 – 8:30pm Where: Modern Languages 350 Cost: $5.00

5. By the end of lecture today 2/16/16 7 Most Common Analyses (Confidence Intervals, t-tests, ANOVA, 2-way ANOVA, Correlation, Regression, Chi Square) Review for Exam 1

6. No homework Assignment Just study for Exam 1 Homework Assignment

8. Schedule of readings Before next exam: February 18 th Please read Chapters 1 - 4 in OpenStax Supplemental reading (Appendix D) Supplemental reading (Appendix E) Supplemental reading (Appendix F) Please read Chapters 1, 5, 6 and 13 in Plous Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

9. Connecting intentions of studies with Experimental Methodologies Appropriate statistical analyses Appropriate graphs We’ll come back to these distinctions over and over again, and build on them for the rest of the semester. Let’s get this overview well! Not worry about calculation details for now

10. Comparing Two Means? Use a t-test Study Type 2: t-test Study Type 1: Confidence Intervals We are looking to compare two means http://www.youtube.com/watch?v=n4WQhJHGQB4

11. Yes Vacation No Vacation Productivity Independent Variable Dependent Variable Between or within Quasi or true Causal relationship? Andrea was interested in the effect of vacation time on productivity of the workers in her department. She randomly assigned workers into two groups, she allowed one group to go on vacation while the other group had no vacation. After the vacation she measured productivity for the two groups. Single Independent Variable (categorical) comparing two groups Study Type 2: t-test analysis Single Dependent Variable (numeric) Used to test the effect of the IV on the DV

12. Andrea was interested in the effect of vacation time on productivity of the workers in her department. She randomly assigned workers into two groups, she allowed one group to go on vacation while the other group had no vacation. After the vacation she measured productivity for the two groups. This is an example of a true experiment. If “true” experiment (randomly assigned to groups) we can conclude that vacation had an effect - it increased productivity Dependent variable is always quantitative In t-test, independent variable is qualitative (with two groups) If “quasi” experiment (not randomly assigned to groups), we can conclude only that data suggest that vacation may have had an effect; productivity increased for those who went on vacation, but we can’t rule out other explanations.

13. Single Independent Variable (categorical) comparing two groups Study Type 2: t-test analysis Single Dependent Variable (numerical/continuous) Used to test the effect of the IV on the DV Please note: a t-test allows us to compare two means If the means are statistically different - we say that there is “real” difference that is not just due to chance - we say there is a statistically significant difference p < 0.05 p < 0.05 is most common value – the “p value” can vary (p < 0.01, or p < 0.001) Comparing two means (2 bars on graph)

14. Study Type 2: t-test Study Type 1: Confidence Intervals Study Type 3: One-way Analysis of Variance (ANOVA) Comparing more than two means

15. Single Independent Variable comparing more than two groups Study Type 3: One-way ANOVA Single Dependent Variable (numerical/continuous) Independent Variable: Type of incentive Levels of Independent Variable: None, Bike, Trip to Hawaii Dependent Variable: Number of cookies sold Levels of Dependent Variable: 1, 2, 3 up to max sold Between participant design Causal relationship: Incentive had an effect – it increased sales Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment Used to test the effect of the IV on the DV How could we make this a quasi-experiment?

16. Single Independent Variable comparing more than two groups Study Type 3: One-way ANOVA Single Dependent Variable (numerical/continuous) Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and looked to see who sold more cookies. The 3 incentives were 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a true experiment Used to test the effect of the IV on the DV None New Bike Sales Trip Hawaii Sales None New Bike Trip Hawaii Dependent variable is always quantitative In an ANOVA, independent variable is qualitative (& more than two groups)

17. Study Type 2: t-test Study Type 1: Confidence Intervals Study Type 3: One-way Analysis of Variance (ANOVA) Comparing two independent variables Study Type 4: Two-way Analysis of Variance (ANOVA) Each one has multiple levels

18. Study Type 4: Two-way ANOVA Ian was interested in the effect of incentives (and age) for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and he looked to see who sold more cookies. The 3 incentives were: 1) Trip to Hawaii, 2) New Bike or 3) Nothing. He also measured the scouts’ ages. Independent Variable #1 Independent Variable #2 Dependent Variable “Two-way” = “Two IVs”

19. Multiple Independent Variables (categorical), each variable comparing two or more groups Study Type 4: Two-way ANOVA Single Dependent Variable (numerical/continuous) Independent Variable #1: Type of incentive Levels of Independent Variable: None, Bike, Trip to Hawaii Independent Variable #2: Age Levels of Independent Variable: Elementary girls versus college Dependent Variable: Number of cookies sold Levels of Dependent Variable: 1, 2, 3 up to max sold Between participant design Results: Incentive had an effect – it increased sales Data suggest age had an effect – older girls sold more Used to test the effect of two IV on the DV

20. Study Type 4: Two-way ANOVA None New Bike Sales Trip Hawaii Sales None New Bike Trip Hawaii College Elementary College Elementary Two Independent Variables (categorical) Single Dependent Variable (numerical/continuous) Used to test the effect of two IV on the DV Dependent variable is always quantitative In an ANOVA, both independent variables are qualitative (with more than two groups)

21. Study Type 5: Correlation Study Type 2: t-test Study Type 3: One-way Analysis of Variance (ANOVA) Study Type 4: Two-way Analysis of Variance (ANOVA) Study Type 1: Confidence Intervals

22. Study Type 5: Correlation plots relationship between two continuous / quantitative variables Neutral relative to causality – but especially useful for predictions Relationship between amount of money spent on advertising and amount of money made in sales Positive Correlation Dollars in Sales Dollars spent on Advertising Describe strength and direction of correlation – in this case positive/strong Graphing correlations use scatterplots (not bar graphs) Dependent variable is always quantitative In correlation, both variables are quantitative Pretty much all correlations are “quasi-experimental”

23. Study Type 5: Correlation Study Type 2: t-test Study Type 3: One-way Analysis of Variance (ANOVA) Study Type 4: Two-way Analysis of Variance (ANOVA) Study Type 1: Confidence Intervals Study Type 6: Simple and Multiple regression

24. Expenses per year Yearly Income If you spend this much You probably make this much The predicted variable goes on the “Y” axis and is called the dependent variable. The predictor variable goes on the “X” axis and is called the independent variable Study Type 6: Regression: Using the correlation to predict the value of one variable based on its relationship with the other variable

25. Angelina Jolie Buys Brad Pitt a $24 million Heart-Shaped Island for his 50th Birthday Expenses per year Yearly Income If you spend this much You probably make this much Dustin spends $12 for his Birthday If you spend this much You probably make this much

26. Multiple regression will use multiple independent variables to predict the dependent variable Expenses per year Yearly Income If you spend this much You probably make this much The predicted variable goes on the “Y” axis and is called the dependent variable. The predictor variable goes on the “X” axis and is called the independent variable Study Type 6: Regression: Using the correlation to predict the value of one variable based on its relationship with the other variable Dependent Variable (Predicted) Independent Variable 1 (Predictor) Independent Variable 2 (Predictor) If you spend this much If you save this much You probably make this much

27. Study Type 5: Correlation Study Type 2: t-test Study Type 3: One-way Analysis of Variance (ANOVA) Study Type 4: Two-way Analysis of Variance (ANOVA) Study Type 1: Confidence Intervals Study Type 6: Simple and Multiple regression Study Type 7: Chi Square

28. Study Type 7: Chi-squared is used to evaluate whether the differences found in your sample match what you would expect to find. It is used with nominal or ordinal data when we simply count how many participants fall into each category. We are comparing frequencies, not means. What is your favorite type of restaurant? (Do university students show the same results as the general population?) What is your political affiliation? (Do the proportions change when the country is at war or otherwise stressed? What is the most popular ride at Disneyland? (Are all the rides at Disneyland equally popular?) Do more children, teens or adults play video games? or objects or events What is the most popular ride at Disneyland? Just count how many people ride each one. a. Dumbo b. Small World c. Space Mountain d. Splash Mountain We could gather this data using clickers

29. Study Type 5: Correlation Study Type 2: t-test Study Type 3: One-way ANOVA Study Type 4: Two-way ANOVA Study Type 1: Confidence Intervals Study Type 6: Regression Remember when p < 0.05 we say: - results are statistically significant -there is “real” difference (not just due to chance) Connecting intentions of studies with Experimental Methodologies Appropriate statistical analyses Appropriate graphs Study Type 7: Chi-squared

30. Scores, standard deviations, and probabilities The normal curve always has the same shape. They differ only by having different means and standard deviation

31. If score is within 2 standard deviations (z < 2) “not unusual score” If score is beyond 2 standard deviations (z = 2 or up to 3) “is unusual score” If score is beyond 3 standard deviations (z = 3 or up to 4) “is an outlier” If score is beyond 4 standard deviations (z = 4 or beyond) “is an extreme outlier”

32. Raw Scores Area & Probability Z Scores Formula z table Have raw score Find z Have z Find raw score Have area Find z Have z Find area Normal distribution Raw scores z-scores probabilities

33. Always draw a picture! Homework worksheet

34. 1.6800 z =-1 z = 1

35. Homework worksheet 2.9500 z =-2 z = 2

36. Homework worksheet 3.9970 z =-3 z = 3

37. Homework worksheet 4.5000 z = 0

38. Homework worksheet 5 2 z = 33-30 z = 1.5 Go to table.4332 z = 1.5

39. 6 z = 33-30 2 z = 1.5 Go to table.4332 Add area Lower half.4332 +.5000 =.9332 z = 1.5

40. Homework worksheet 7 2 z = 33-30 = 1.5 Go to table.4332 Subtract from.5000.5000 -.4332 =.0668 z = 1.5

41. 8 z = 29-30 2 = -.5 Go to table.1915 Add to upper Half of curve.5000 +.1915 =.6915 z = -.5

42. 9.4938 +.1915 =.6853 = 25-30 2 = -2.5.4938 Go to table = 31-30 2 =.5.1915 Go to table z =.5 z =-2.5

43. 10 z = 27-30 2 = -1.5 Go to table.4332 Subtract From.5000.5000 -.4332 =.0668 z =-1.5

44. 11 z = 25-30 2 = -2.5 Go to table.4938 Add lower Half of curve.5000 +.4938 =.9938 z =-2.5

45. 12 z = 32-30 2 = 1.0 Go to table.3413 Subtract from.5000.5000 -.3413 =.1587 z =1

46. 13 50 th percentile = median 30 z =0

47. 14 28 32 z =-1 z = 1

48. 15 x = mean + z σ = 30 + (.74)(2) = 31.48 77 th percentile Find area of interest.7700 -.5000 =.2700 Find nearest z =.74 z =.74

49. 16 13 th percentile Find area of interest.5000 -.1300 =.3700 Find nearest z = -1.13 x = mean + z σ = 30 + (-1.13)(2) = 27.74 z =-1.13

50. Please use the following distribution with a mean of 200 and a standard deviation of 40.

51. 17.6800 z =-1 z = 1

52. .9500 18 z =-2 z = 2

53. .9970 19 z =-3 z = 3

54. 20 = 230-200 40 =.75 Go to table.2734 z =.75

55. 21 Go to table Subtract from.5000 z = 190-200 40 = -.25.0987.5000 -.0987 =.4013 z =-.25

56. 22 Go to table Add to upper Half of curve z = 180-200 40 = -.5.1915.5000 +.1915 =.6915 z =-.5

57. 23 z = 236-200 40 = 0.9 Go to table.3159 Subtract from.5000.5000 -.3159 =.1841 z =.9

58. 24.0793 +.2088 =.2881 z = 192 - 200 40 = -.2.0793 Go to table z = 222 - 200 40 =.55.2088 Go to table z =-.2 z =.55

59. 25 40 z = 275-200 = 1.875 Go to table.4693 or.4699 Add area Lower half.4693 +.5000 =.9693.4699 +.5000 =.9699 z =1.875

60. 26 z = 295-200 40 z = 2.375 Go to table.4911 or.4913 Add area Lower half.5000 -.4911 =.0089.5000 -.4913 =.0087 z =2.375

61. 27 z = 130-200 40 = -1.75.4599 Add to upper Half of curve Go to table.5000 +.4599 =.9599 z =-1.75

62. 28 40 z = 130-200 = -1.75.4599 Subtract from.5000.5000 -.4599 =.0401 Go to table z =-1.75

63. 29 x = mean + z σ = 200 + (2.33)(40) = 293.2 99 th percentile Find area of interest.9900 -.5000 =.4900 Find nearest z = 2.33 z =2.33

64. 30 33 rd percentile Find area of interest.5000 -.3300 =.1700 Find nearest z = -.44 x = mean + z σ = 200 + (-.44)(40) = 182.4 z =-.44

65. 31 40 th percentile Find area of interest.5000 -.4000 =.1000 Find nearest z = -.25 x = mean + z σ = 200 + (-.25)(40) = 190 z =-.25

66. 32 67 th percentile Find area of interest.6700 -.5000 =.1700 Find nearest z =.44 x = mean + z σ = 200 + (.44)(40) = 217.6 z =.44

67. Exam 1 Review

68. Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for afternoon (matinee) movies vs. evening movies. She took a random sample of 25 purchases from the matinee movie (mean of $7.50) and 25 purchases from the evening show (mean of $10.50). She compared these two means. This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA What type of analysis is this? Let’s try one This is an example of a a. between participant design b. within participant design c. mixed participant design Let’s try another one t-test Between

69. Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for people of all ages. She simply measured their age and how much they spent on treats. This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA What type of analysis is this? Let’s try one Correlation

70. Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for afternoon (matinee) movies and evening movies. She took a random sample of 25 purchases from the matinee movie (mean of $7.50) and 25 purchases from the evening show (mean of $10.50). Which of the following would be the appropriate graph for these data Let’s try one Matinee Evening Concession purchase a. Movie Time Concession b. Movie Times Concession purchase d. c. Concession purchase Movie Times Two means What type of analysis is this? t-test

71. Gabriella is a manager of a movie theater. She wanted to know whether there is a difference in concession sales between teenage couples and middle-aged couples. She also wanted to know whether time of day makes a difference (matinee versus evening shows). She gathered the data for a sample of 25 purchases from each pairing. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA Let’s try one What are the two IV? What are the levels of each? Two-way ANOVA What type of analysis is this?

72. Gabriella is a manager of a movie theater. She wanted to know whether there is a difference in concession sales between teenage couples and middle-aged couples. She also wanted to know whether time of day makes a difference (matinee versus evening shows). She gathered the means for a sample of 25 purchases from each pairing. Let’s try one Matinee Older couples Evening Teenagers Concession purchase a. Movie Time Old / young b. c. Concession purchase Matinee Older couples Evening Teenagers Evening Older couples Movie Times Concession purchase d. Older couples Teenagers Matinee Evening Four means What type of analysis is this?

73. Pharmaceutical firm tested whether fish-oil capsules taken daily decrease cholesterol. They measured cholesterol levels for 30 male subjects and then had them take the fish-oil daily for 2 months and tested their cholesterol levels again. Then they compared the mean cholesterol before and after taking the capsules. This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA Let’s try one This is an example of a a. between participant design b. within participant design c. mixed participant design Let’s try another one t-test What type of analysis is this? Within

74. Elaina was interested in the relationship between the grade point average and starting salary. She recorded for GPA and starting salary for 100 students and looked to see if there was a relationship. This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA Let’s try one Relationship between GPA and Starting salary GPA Starting Salary correlation What type of analysis is this?

75. An automotive firm tested whether driving styles can affect gas efficiency in their cars. They observed 100 drivers and found there were four general driving styles. They recruited a sample of 100 drivers all of whom drove with one of these 4 driving styles. Then they asked all 100 drivers to use the same model car for a month and recorded their gas mileage. Then they compared the mean mpg for each driving style. This is an example of a _____. a. correlation b. t-test c. one-way ANOVA d. two-way ANOVA Let’s try one This is an example of a a. between participant design b. within participant design c. mixed participant design Let’s try another one This is an example of a a. true experimental design b. quasi-experimental design c. mixed design Let’s try another one Quasi-experiment Between One-way ANOVA What type of analysis is this?

76. Let’s try one Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true? a. The IV is gender while the DV is time to finish a race b. The IV is time to finish a race while the DV is gender

77. Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____ Let’s try one a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

78. Let’s try one Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____ a. Nominal level of measurement b. Ordinal level of measurement c. Interval level of measurement d. Ratio level of measurement

79. Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The independent variable is a(n) _____ Let’s try one a. Discrete b. Continuous

80. Let’s try one Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. The dependent variable is a(n) _____ a. Discrete b. Continuous

81. Let’s try one Albert compared the time required to finish the race for 20 female jockeys and 20 male jockeys riding race horses. He wanted to know who averaged faster rides. Which of the following is true? a. This is a quasi, between participant design b. This is a quasi, within participant design c. This is a true, between participant design d. This is a true, within participant design

82. Let’s try one Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. In her experiment she rewarded the employees in her Los Angeles stores with bonuses and fun prizes whenever they sold more than 5 items to any one customer. However, the employees in Houston were treated like they always have been treated and were not given any rewards for those 2 months. Judy then compared the number of items sold by each employee in the Los Angeles (rewarded) versus Houston (not rewarded) stores. In this study, a _____________ design was used. a. between-participant, true experimental b. between-participant, quasi experimental c. within-participant, true experimental d. within-participant, quasi experimental

83. Let’s try one Judy is running an experiment in which she wants to see whether a reward program will improve the number of sales in her retail shops. (As described in previous question). She wants to use her findings with these two samples to make generalizations about the population, specifically whether rewarding employees will affect sales to all of her stores. She wants to generalize from her samples to a population, this is called a. random assignment b. stratified sampling c. random sampling d. inferential statistics

84. Let’s try one Naomi is interested in surveying mothers of newborn infants, so she uses the following sampling technique. She found a new mom and asked her to identify other mothers of infants as potential research participants. Then asked those women to identify other potential participants, and continued this process until she found a suitable sample. What is this sampling technique called? a. Snowball sampling b. Systematic sampling c. Convenience sampling d. Judgment sampling

85. Let’s try one Steve who teaches in the Economics Department wants to use a simple random sample of students to measure average income. Which technique would work best to create a simple random sample? a. Choosing volunteers from her introductory economics class to participate b. Listing the individuals by major and choosing a proportion from within each major at random c. Numbering all the students at the university and then using a random number table pick cases from the sampling frame. d. Randomly selecting different universities, and then sampling everyone within the school.

86. Let’s try one Marcella wanted to know about the educational background of the employees of the University of Arizona. She was able to get a list of all of the employees, and then she asked every employee how far they got in school. Which of the following best describes this situation? a. census b. stratified sample c. systematic sample d. quasi-experimental study

87. Let’s try one Mr. Chu who runs a national company, wants to know how his Information Technology (IT) employees from the West Coast compare to his IT employees on the East Coast. He asks each office to report the average number of sick days each employee used in the previous 6 months, and then compared the number of sick days reported for the West Coast and East Coast employees. His methodology would best be described as: a. time-series comparison b. cross-sectional comparison c. true experimental comparison d. both a and b

88. Let’s try one A researcher wrote the following item stem for a five point rating scale. "Don't you agree that the University needs a football team.” What is the problem with this item? a. It uses unfamiliar language. b. It uses double negatives. c. It is a double-barreled question. d. It is a "leading" question.

89. Let’s try one A researcher wrote the following item for a survey on school financing (they were to agree or disagree with the statement), "Parents should support the schools and taxes should be increased." What is the problem with this item? a. It uses unfamiliar language. b. It uses double negatives. c. It is a double-barreled question. d. It is a "leading" item.

90. Let’s try one When several items on a questionnaire are rated on a five point scale, and then the responses to all of the questions are added up for a total score (like in a miniquiz), it is called a: a. Checklist b. Likert scale c. Open-ended scale d. Ranking

91. Let’s try one Which of the following is a measurement of a construct (and not just the construct itself) a. sadness b. customer satisfaction c. laughing d. love

92. What if we were looking to see if our new management program provides different results in employee happiness than the old program. What is the independent variable? a. The employees’ happiness b. Whether the new program works better c. The type of management program (new vs old) d. Comparing the null and alternative hypothesis How many levels of the IV are there?

93. What if we were looking to see if our new management program provides different results in employee happiness than the old program. What is the dependent variable? a. The employees’ happiness b. Whether the new program works better c. The type of management program (new vs old) d. Comparing the null and alternative hypothesis

94. Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for afternoon (matinee) movies vs. evening movies. She took a random sample of 25 purchases from the matinee movie (mean of $7.50) and 25 purchases from the evening show (mean of $10.50). She compared these two means. This is an example of a _____. a. between participant design b. within participant design c. mixed participant design

95. Marietta is a manager of a movie theater. She wanted to know whether there is a difference in concession sales for afternoon (matinee) movies vs. evening movies. She took a random sample of 25 purchases from the matinee movie (mean of $7.50) and 25 purchases from the evening show (mean of $10.50). She compared these two means. This is an example of a _____. a. quasi experimental design b. true experimental design c. mixed participant design quasi

96. Victoria was also interested in the effect of vacation time on productivity of the workers in her department. In her department some workers took vacations and some did not. She measured the productivity of those workers who did not take vacations and the productivity of those workers who did (after they returned from their vacations). This is an example of a _____. a. quasi-experiment b. true experiment c. correlational study Let’s try one quasi

97. Ian was interested in the effect of incentives for girl scouts on the number of cookies sold. He randomly assigned girl scouts into one of three groups. The three groups were given one of three incentives and he looked to see who sold more cookies. The 3 incentives were: 1) Trip to Hawaii, 2) New Bike or 3) Nothing. This is an example of a ___. a. quasi-experiment b. true experiment c. correlational study Let’s try one true