Scientific Methods II: Correlational Research EXAMPLES "MARRIAGE SLOWS CANCER DEATHS Evidence that married people have a better chance of surviving cancer than do singles means that the unmarried might be good targets for cancer-prevention programs. Married people with cancer had a 23% higher overall survival rate than the unmarried. Example 2 Isolation increases with Internet use Psychologist Robert Kraut and his colleagues report that greater use of the Internet leads to shrinking social support and happiness, and increases in depression and loneliness And the findings were unexpected, Kraut says, given that most people use the Internet for chat lines and e-mail, e not just to isolate themselves in mounds of electronic information. Example 3 Revenge of the Beaker Bunch..Researchers Find that Scientists Live Longer If it were a movie, it would probably be called "Nerds:The" Ultimate Revenge. A study spanning nearly 70 years suggests that, all else being equal, scientists live longer than non-scientists According to Friedman's study, nonscientists are 26 percent more likely to die at any given age than scientists. In a sample of 600 men born around 1912, Friedman's group found that only 67% of non scientists were still alive by age 70, compared to 72% of the scientists... 1
Correlational Research Correlational research examines whether 2 factors are naturally associated. [Co-relation: the degree to which one variable is related to another.] Examples focus on naturally occurring relationships marriage & cancer recovery Internet use & depression occupation & longevity Correlations: The Basics Numerical range: -1.00 to +1.00 The Correlational Method Correlation Coefficient Positive correlations indicate that an increase in one variable is associated with an increase in the other. Negative correlations indicate that an increase in one variable is associated with a decrease in the other. 2
The Correlational Method The Correlational Method Surveys The correlational method often relies on surveys, as well as on observational data. Surveys are used when the variable of interest is not easily observable. How do we evaluate correlational findings? Amount use Internet is associated with greater depression. Why? 3 possible explanations Using Internet depression Depression greater Internet use (cannot determine direction of causality) A 3 rd variable might be related to both using the Internet and becoming depressed 3
3 rd Variables People. People. Evaluating Correlational Research Correlational research cannot determine causality. Problems knowing direction of causality Problems knowing whether some 3 rd variable (confounding variable) might explain the observed correlation How do we evaluate correlational findings? Consider in context of research on TV exposure and aggression Start w/theoretical idea and to test specific hypotheses. Hypothesis: Children who watch more violent television shows behave more aggressively. 4
TV & Violent Content 8 out of 10 Sat. morning programs contain violence Sat. morning children s shows: 20 violent acts per hour Average American watches 4 hrs of TV per day, or over 1000 hrs. per year. What is relationship between watching TV and children s aggressive behavior? TV and Aggression Many studies Sample correlational paradigm: Parents record how much TV their kids watch Teachers record aggressive behaviors (hitting, pushing, shoving, insulting, etc.) IV AND DV in Correlational Research Quasi-Independent Variable: how much TV kids watch (*not* manipulated, but acts like IV) Dependent Variable: amount of aggressive behavior at school 5
TV and Aggression: Correlational Studies Conclusion: Children who watch TV that includes violent content are more likely to behave aggressively. Why? Theory: Watching TV causes aggression But: Maybe more aggressive kids are attracted to watching violence on TV (direction of causality) Or: A 3 rd variable might account for the relationship Third Variables What are some possible third variables that could explain the correlation between watching TV and aggressive behavior? 6
Marriage and Surviving Cancer Direction of causality is not as much of a problem (unlikely that surviving cancer caused marriage!) What are some 3 rd variables that could account for the greater survival rate of married individuals? Disadvantage Correlation cannot determine causality Even if we measure possible third variables (e.g., measure eating habits, measure health care, etc.) and use statistical techniques to control for them, we still cannot be sure about the cause- and-effect relationship. Then Why do correlational research? Allows for the study of problems that would be difficult to study in the lab Can t manipulate who is married or not, who has a stressful job or not, gender, race/ethnicity Useful for generating hypotheses that can be tested later in a controlled experiment 7
Video on correlational method Retrospective Study Observe some outcome (lung cancer) and then gather information from people with and without the outcome (e.g., about smoking, eating habits, exercise, parenting style) Prospective Study A sample of individuals (preferably a representative sample) is followed over time to see whether certain experiences predict a later outcome (e.g., whether smoking predicts later lung cancer) 8
Common Cause Kids who eat more sugar might be more aggressive (during sugar high) and watch more TV when their blood sugar drops and they become lethargic. Common cause would explain why we see an association between TV and aggression Confounding Factor Something that occurs (accidentally) that provides an alternative explanation for the correlation. (Video: speed limit lowered, but confound = gas crisis and less driving) Coincidence Two variables are coincidentally associated, but doesn t happen again. 9
Simpson s Paradox Lurking variables can change the direction of an association. In video, more men than women applied to law school, and the law school had easier admission standards than the business school. Thus, the overall numbers (across both programs) appeared to show that more men were accepted to the professional programs. Professional Schools Applied Accepted Men 360 198 55% Women 200 88 44% *Looks like few women admitted Business School Men 120 18 15% Women 120 24 20% *Greater % women admitted than men Law School Men 240 180 75% Women 80 64 80 *Greater % women admitted than men 10