Chapter 1

C-1

  • 1.1 Descriptive statistics organize, summarize, and communicate a group of numerical observations. Inferential statistics use sample data to make general estimates about the larger population.
  • 1.3 The four types of variables are nominal, ordinal, interval, and ratio. A nominal variable is used for observations that have categories, or names, as their values. An ordinal variable is used for observations that have rankings (i.e., 1st, 2nd, 3rd) as their values. An interval variable has numbers as its values; the distance (or interval) between pairs of consecutive numbers is assumed to be equal. Finally, a ratio variable meets the criteria for interval variables but also has a meaningful zero point. Interval and ratio variables are both often referred to as scale variables.
  • 1.5 Discrete variables can only be represented by specific numbers, usually whole numbers; continuous variables can take on any values, including those with great decimal precision (e.g., 1.597).
  • 1.7 A confounding variable (also called a confound) is any variable that systematically varies with the independent variable so that we cannot logically determine which variable affects the dependent variable. Researchers attempt to control confounding variables in experiments by randomly assigning participants to conditions. The hope with random assignment is that the confounding variable will be spread equally across the different conditions of the study, thus neutralizing its effects.
  • 1.9 An operational definition specifies the operations or procedures used to measure or manipulate an independent or dependent variable.
  • 1.11 When conducting experiments, the researcher randomly assigns participants to conditions or levels of the independent variable. When random assignment is not possible, such as when studying something like gender or marital status, correlational research is used. Correlational research allows us to examine how variables are related to each other; experimental research allows us to make assertions about how an independent variable causes an effect in a dependent variable.
  • 1.13
    • a. “This was an experiment.” (not “This was a correlational study.”)
    • b. “…the independent variable of caffeine …” (not “…the dependent variable of caffeine…”)
    • c. “A university assessed the validity …” (not “A university assessed the reliability…”)
    • d. “In a between-groups experiment …” (not “In a within groups experiment…”)
  • 1.15
    • a. An outlier is a participant or observation that is very different from other observations in the study.
    • b. When identifying why a particular observation is so different from the other observations in the study (i.e., outlier analysis), the researcher may gain insight into other factors that influence the dependent variable.
  • 1.17 The sample is the 100 customers who completed the survey. The population is all of the customers at the grocery store.
  • 1.19
    • a. 73 people
    • b. All people who shop in grocery stores similar to the one where data were collected
    • c. Inferential statistic
    • d. Answers may vary, but people could be labeled as having a “healthy diet” or an “unhealthy diet.”
    • e. Answers may vary, but there could be groupings such as “no items,” “a minimal number of items,” “some items,” and “many items.”
    • f. Answers may vary, but the number of items could be counted or weighed.
  • 1.21
    • a. The independent variables are physical distance and emotional distance. The dependent variable is accuracy of memory.
    • b. There are two levels of physical distance (within 100 miles and 100 miles or farther) and three levels of emotional distance (knowing no one who was affected, knowing people who were affected but lived, and knowing someone who died).
    • c. Answers may vary, but accuracy of memory could be operationalized as the number of facts correctly recalled.
  • 1.23 Both Miguel Induráin and Lance Armstrong could be considered outliers because their scores (number of wins) are extreme compared to the typical number of wins by Tour de France winners.
  • 1.25
    • a. The average weight for a 10-year-old girl was 77.4 pounds in 1963 and nearly 88 pounds in 2002.
    • b. No; the CDC would not be able to weigh every single girl in the United States because it would be too expensive and time consuming.
    • c. It is a descriptive statistic because it is a numerical summary of a sample. It is an inferential statistic because the researchers drew conclusions about the population’s average weight based on this information from a sample.

    C-2

  • 1.27
    • a. Ordinal
    • b. Scale
    • c. Nominal
  • 1.29
    • a. Discrete
    • b. Continuous
    • c. Discrete
    • d. Discrete
    • e. Continuous
  • 1.31
    • a. The independent variables are temperature and rainfall. Both are continuous scale variables.
    • b. The dependent variable is experts’ ratings. This is a discrete scale variable.
    • c. The researchers wanted to know if the wine experts are consistent in their ratings—that is, if they’re reliable.
    • d. This observation would suggest that Robert Parker’s judgments are valid. His ratings seem to be measuring what they intend to measure—wine quality.
  • 1.33
    • a. Age: teenagers and adults in their 30s; video game performance: final score on a video game or average reaction time on a video game task
    • b. Spanking: spanking and not spanking; violent behavior: parental measure of child aggression or number of aggressive acts observed in an hour of play.
    • c. Meetings: go to meetings and participate online; weight loss: measured in pounds or kilograms, or by change in waist size
    • d. Studying: with others and alone; statistics performance: average test score for the semester or overall grade for the semester.
    • e. Beverage: caffeinated and decaffeinated; time to fall asleep: minutes to fall asleep from when the participant goes to bed, or the actual time at which the participant falls asleep.
  • 1.35
    • a. An experiment requires random assignment to conditions. It would not be ethical to randomly assign some people to smoke and some people not to smoke, so this research had to be correlational.
    • b. Other unhealthy behaviors have been associated with smoking, such as poor diet and infrequent exercise. These other unhealthy behaviors might be confounded with smoking.
    • c. The tobacco industry could claim it was not the smoking that was harming people, but rather the other activities in which smokers tend to engage or fail to engage.
    • d. You could randomly assign people to either a smoking group or a nonsmoking group, and assess their health over time.
  • 1.37
    • a. This is experimental because students are randomly assigned to one of the incentive conditions for recycling.
    • b. Answers may vary, but one hypothesis could be “Students fined for not recycling will report lower concerns for the environment, on average, than those rewarded for recycling.”
  • 1.39
    • a. The person who took 3 minutes would be considered an outlier because the person’s response time was much more extreme than any of the response times exhibited by the other participants.
    • b. In this case, the researcher might look to see if the participant was slow on other experimental tasks as well or if there was some other independent evidence that the participant did not take the experimental task seriously.
  • 1.41
    • a. Researchers could have randomly assigned some people who are HIV-positive to take the oral vaccine and other people who are HIV-positive not to take the oral vaccine. The second group would likely take a placebo.
    • b. This would have been a between-groups experiment because the people who are HIV-positive would have been in only one group: either vaccine or no vaccine.
    • c. This limits the researchers’ ability to draw causal conclusions because the participants who received the vaccine may have been different in some way from those who did not receive the vaccine. There may have been a confounding variable that led to these findings. For example, those who received the vaccine might have had better access to health care and better sanitary conditions to begin with, making them less likely to contract cholera regardless of the vaccine’s effectiveness.
    • d. The researchers might not have used random assignment because it would have meant recruiting participants, likely immunizing half, then following up with all of them. The researchers likely did not want to deny the vaccine to people who were HIV-positive because they might have contracted cholera and died without it.
  • 1.43
    • a. A “good charity” is operationally defined as one that spends more of its money for the cause it is supporting and less for fundraising or administration.
    • b. The rating is a scale variable, as it has a meaningful zero point, has equal distance between intervals, and is continuous.
    • c. The tier is an ordinal variable, as it involves ranking the organizations into categories (1st, 2nd, 3rd, 4th, or 5th tier) and it is discrete.
    • d. The type of charity is a nominal variable, as it uses names or categories to classify the values (e.g., health and medical needs) and it is discrete.
    • e. Measuring finances is more objective and easier to measure than some of the criteria mentioned by Ord, such as importance of the problem and competency and honesty.
    • f. Charity Navigator’s ratings are more likely to be reliable than GiveWell’s ratings because they are based on an objective measure. It is more likely that different assessors would come up with the same rating for Charity Navigator than for GiveWell.
    • g. GiveWell’s ratings are likely to be more valid than Charity Navigator’s, provided that they can attain some level of reliability. GiveWell’s more comprehensive rating system incorporates a better-rounded assessment of a charity.
    • h. This would be a correlational study because donation funds, the independent variable, would not be randomly assigned based on country but measured as they naturally occur.
    • i. This would be an experiment because the levels of donation funds, the independent variable, are randomly assigned to different regions to determine the effect on death rate.