Everyday Research Methods

Can ties with teachers prevent drug abuse in kids?

Some adolescents experiment early with alcohol and drugs, a behavior that puts them at later risk for substance abuse and other health problems. A recent study looked at this risky behavior. Journalistic coverage by a Yahoo! news reporter started with this encouraging headline:

Caring Teachers May Help Keep Kids From Trying Alcohol, Drugs

The full story appears here. The basic details of the study were summarized by Yahoo! news as follows:

The researchers found that students in middle school who felt more emotional support from teachers had a lower risk of early alcohol and illicit drug use. The students defined teacher support as feeling close to a teacher or being able to discuss problems with a teacher.

a. What are the two main variables in this study, as described above? Can you tell how they were operationalized? Do you think these two variables were measured or manipulated?

b. Take a careful look. I bet you'll notice a difference in how the researcher, Carolyn McCarty, describes her own study, when she said:

"this is the first data-driven study which shows that teacher support is associated with lower levels of early alcohol use."

...compared to how the Yahoo! journalist describes it in the headline:

"teachers may help keep kids from trying alcohol, drugs."

b. What kind of claim is each person making?

c. Is the journalist's claim justified by the study?

Suggested answers.

a. One variable in this study is "early alcohol and drug use." It's hard to tell how this was operationalized---probably through self-report. Since early alcohol and drug use is almost impossible to manipulate ethically, this is almost certainly a measured variable. The other variable is "emotional support from teachers." This variable appears to be operationalized through a self-report of 'feeling close to a teacher or being able to discuss problems with them'. This is a measured variable, too. They simply asked kids about their relationships with teachers.

b. The researcher is making an association claim (she clearly says "is associated with"). But the journalist's verb choice, "keep from," is a causal verb--making it a causal claim.

c. The journalist's causal claim is not justified by the study. Only an experiment (with one manipulated and one measured variable) can potentially support causation. This study, in contrast, is a corelational study because all of the variables were measured.

Chocolate consumption and BMI

More good diet news from the New York Times, along with a chance to apply lessons from Chapter 8 in the text.

A recent study showed that people who eat chocolate more frequently tend to weigh 5 to 7 pounds less than people who don't.

The people who ate chocolate the most frequently, despite eating more calories and exercising no differently from those who ate the least chocolate, tended to have lower B.M.I.’s.

Importantly, the variable concerning chocolate consumption was about frequency, not quantity. It was the frequency of eating chocoloate, not how much people ate, that was associated with lower body weight.

 

“It’s not the case that eating the largest amount of chocolate is beneficial; it’s that eating it more often was favorable,” Dr. Golomb said. “If you eat 10 pounds of chocolate a day, that’s not going to be a favorable thing.”

Can we make a causal statement here? Can eating chocoloate more frequently cause people to lose weight? Here's what the article says:

Dietary studies can be unreliable, since so many complicating factors can influence results, and it is difficult to pinpoint cause and effect. But the researchers adjusted their results for a number of variables, including age, gender, depression, vegetable consumption, and fat and calorie intake. “It didn’t matter which of those you added, the relationship remained very stably significant,” said Dr. Golomb.

a. In the quoted text you should recognize the telltale signs of multiple regression. As an exercise, create a regression table with a dependent variable indicated up top, and then a list of independent variables that corresponds to what you read above. Then estimate beta weights that might correspond to what the researcher is describing above.

b. Imagine that a friend says, "It's probably not the chocolate. It's just that the frequent chocolate eaters somehow consume fewer calories." What should you say in reply?

Suggested answers:

a. The dependent variable in this analysis is BMI. The independent variable of interest is frequency of chocolate consumption. The other independent variables include age, gender, depression, vegetable consumption, and fat and calorie intake. Your invented beta for the frequency of chocolate consumption variable should be negative and significant (indicating that as frequency of chocolate consumption goes up, BMI goes down, controlling for the other variables in the equation).

(For more fun, you can even check your invented values against the original article, linked here!)

b. Your friend isn't right, because even when the authors controlled for fat and calorie intake, the beta for chocolate is still significant. Chocolate consumption is still related to lower BMI, even taking that variable into account.