Term
| 1. Two TAs independently scored student’s writing assignments. Prof. Cody wants to know how closely the TAs scored each paper (because if it is close enough, he can just assign the mean score for each). He found a correlation coefficient of .91. Is there a relationship? If so, is it positive or negative and how strong? Explain the relationship in plain English. |
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Definition
| Yes, positive & strong. The higher one TA rated a particular paper, the higher the other TA tended to rate it. (As with all relationships, you could also state this in reverse, of course: The lower one TA rated a particular paper, the lower the other TA tended to rate it.) |
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Term
| 2. Shirley investigates if there is a relationship between employees’ number of vacation days and their overall job satisfaction. She finds a correlation of .29. Is there a relationship? If so, is it positive or negative and how strong? Explain the relationship in plain English. |
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Definition
| Yes, positive & weak. The more vacation days people get, the more they tend to like their jobs. (This is a weak relationship though, so although people’s vacation days affect their job satisfaction, other factors probably affect it too.) |
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Term
| 3. Jose wants to know if people’s religious beliefs are associated with their levels of moral reasoning. He finds a correlation coeffiecient of .11. Is there a relationship? If so, is it positive or negative and how strong? Explain the relationship in plain English. |
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Definition
| He found no relationship between religious beliefs and levels of moral reasoning. |
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Term
| 4. Ramesh investigates if the number of sport activities students are involved in is related to the number of academic credit hours they are taking. He finds a correlation coefficient of -.44. Is there a relationship? If so, is it positive or negative and how strong? Explain the relationship in plain English. |
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Definition
| Yes, negative & moderately strong. The more sports events a student is involved in, the lower number of credit hours they tend to pursue. |
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Term
| 5. Grisel tests her theory that people will perform better when an audience is present (compared to when an audience is not). What will be her IV? DV? Experimental group? Control group? |
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Definition
IV = audience presence
DV = performance
Exp group: Those with whom she provides an audience
Control group: Those with whom she does not provide an audience |
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Term
| 6. Shambrika tests her theory that smokers will smoke less if hard candy is available. What will be her IV? DV? Experimental group? Control group? |
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Definition
IV = candy availability
DV = smoking
Exp group: Those to whom she makes candy available
Control group: Those to whom she does not make candy available |
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Term
| 7. Brittany explores her theory that sleeping with your eyes partially open can cause nightmares. What is her IV? DV? Experimental group? Control group? |
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Definition
IV = eyelid position
DV = nightmares
Exp group: Those she has sleep with their eyes partially open
Control: Those she has sleep with their eyes fully closed |
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Term
| 8. You want to investigate smokers’ tendencies to properly dispose of their cigarette butts instead of littering outside of our campus buildings. What kind of study should you begin with? Why? |
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Definition
| An observational study. It is public behavior (and you are not looking for cause or a relationship). |
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Term
| 9. You want to investigate if spreading studying over many days increases learning (compared to cramming over one day). What kind of study should you begin with? Why? |
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Definition
| An experiment. You want to know if one variable affects another. Thus you are looking for cause & effect, and only an experiment can investigate cause. |
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Term
| 10. You want to investigate whether bigotry is linked to homophobia. What kind of study should you begin with? Why? |
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Definition
| A correlational study. You are looking for a relationship between two variables but not which one affects or causes the other. |
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Term
| 11. You want to study if attractiveness of cashiers increases how politely customers treat them. What kind of study should you begin with? Why? |
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Definition
| An experiment. You want to know if one variable (cashier attractiveness) affects another (customer politeness). Thus you are looking for cause & effect, and only an experiment can investigate cause. |
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Term
| Suppose we do a study looking at the effects of weather on impressions we form of people we meet outdoors and get a p-value of .079. What does this mean? |
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Definition
| A case study because it is difficult to find students who survived such odds at birth. |
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Term
| 15. Suppose we do our study looking at the effects of weather on impressions of we form of people we meet outdoors and get a p-value of .007. What does this mean? |
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Definition
| The weather did not affect impressions. |
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Term
| 16. In number 15 above, suppose the means for the sunny weather and rainy weather were 6.5 and 6.1, respectively. According to the p-value given in that question, what is the probability (percentage chance) that these two means are different by random chance only – NOT because of the weather? |
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Definition
| Less than 1%! (A p-value of .01 would be one percent probability that the difference is due to chance, so .007 is less than one percent probability that the difference is due to chance.) |
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Term
| 17. Suppose we do our study looking at the effects of weather on impressions we form of people we meet outdoors and get a p-value of .802. What does this mean? |
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Definition
| The weather did not affect impressions. |
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Term
| 18. In number 17 above, suppose the means for the sunny weather and rainy weather were 6.5 and 6.1, respectively. There is no way of knowing if the mean in the first group is higher because of the sunny weather or just chance. (After all, what are the odds that the means would be exactly the same, even if the weather had no effect?) You must look to the p-value for this answer. According to the p-value given in that question (.802), what is the probability (percentage chance) that these two means are different by random chance only – NOT because of the weather? |
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Definition
| A little more than 80%! See how a p-value like this would clearly indicate no effect of the weather? |
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Term
| 19. A wrinkle cream company claims that their studies show that the women cream users, themselves, and the company’s judges, rated the women as looking younger when wearing this cream. You ask for more details on their studies and find they were not blind, let along double blind, studies. Their reported effects are contaminated with what? (Hint two contaminants) |
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Definition
| Demand effects (also known as expectancy effects and placebo effects) on the part of the participant women, and experimenter bias (on the part of the judges). |
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Term
| 20. Why do you suppose there is no such thing as a p-value of zero? |
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Definition
| There is no such thing as being 100% sure of anything. Even though the sun has risen each day of your life so far and you are pretty darn sure it will rise again tomorrow, you can’t be 100% certain. So a p-value can be extremely low (.001 = one-tenth of a percent), but there is no such thing as a p-value of .000. |
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