Question

A 2014 headline reads "Sitting Is the New Smoking: Ways a Sedentary Lifestyle is Killing You," 12 and explains the mounting evidence for ways in which sitting is bad for you. A more recent large 2015 study \(^{53}\) contributed to this evidence by following 69,260 men and 77,462 women and finding that for women, those who spent more leisure time sitting were significantly more likely to get cancer. (a) What are the explanatory and response variables for the 2015 study? (b) Is the 2015 study an observational study or a randomized experiment? (c) Can we conclude from the 2015 study that spending more leisure time sitting causes cancer in women? Why or why not? (d) Can we conclude from the 2015 study that spending more leisure time sitting does not cause cancer in women?

Short answer

a) Explanatory variable: Amount of leisure time spent sitting; Response variable: Likelihood of getting cancer. b) The 2015 study is an Observational study. c) No, we cannot conclusively say that more sitting leads to cancer. It could be an association, not necessarily a causation due to possible confounding variables. d) Again, the study does not provide sufficient evidence to claim that more sitting does not cause cancer. More in-depth studies would be required to establish causation either way.

Step-by-step solution

Identify Explanatory and Response Variables

The explanatory variable (also known as the independent variable) is the one that is manipulated by the experimenter, which is the amount of leisure time sitting in this case. The response variable (the dependent variable) is the one that is being studied for its change or response to the explanatory variable, which in this case is the likelihood of getting cancer.

Identify the Type of Study

The 2015 study is an observational study because the researchers observed the participants without trying to manipulate or control the situation. In a randomized experiment, participants would be randomly assigned to different levels of the explanatory variable, which is not the case here.

Determine Implications for Causal Conclusions - Part 1

Causal relationships can be difficult to confirm in observational studies because they are often subject to confounding variables - unobserved differences between the groups that could also be contributing to the observed differences in outcomes. Because the researchers merely observed that women who spent more leisure time sitting were more likely to get cancer, it cannot provide conclusive evidence that sitting causes cancer.

Determine Implications for Causal Conclusions - Part 2

Similarly, the 2015 study does not provide sufficient reasoning to conclude that spending more leisure time sitting does not cause cancer in women. Lack of evidence for one direction does not substantiate the opposite. The study may not have been powerful enough, or there may be confounding variables at play, which makes reaching a firm conclusion difficult.

Key concepts

Explanatory and Response Variables

Understanding the relationship between variables is a core aspect of analyzing statistical data. In the context of the 2015 study mentioned above, the explanatory variable is the amount of leisure time spent sitting. This variable is what the researchers are focusing on as the potential influence or predictor of the outcome.

On the other hand, the response variable is the likelihood of developing cancer. It is referred to as the dependent variable because it is the outcome being observed to see if there is a response or change as the explanatory variable changes. In studies, identifying these variables correctly is crucial as they lay the groundwork for further analysis and understanding of potential relationships between them.

When conducting an analysis, researchers must ensure the explanatory variable is clearly defined so that they can accurately measure the response. In educational contexts, such clarity enhances student comprehension as it delineates the cause-and-effect framework within which the research is conducted.

Causal Relationship in Statistics

A causal relationship in statistics implies that a change in one variable is directly responsible for a change in another. However, establishing causality often requires careful experimentation or statistical control.

In the 2015 study, although there appears to be an association between 'more leisure time sitting' and 'higher likelihood of cancer', it is crucial to note that association does not equate to causation. This is because the study is observational and does not manipulate the explanatory variable. In an experimental study, the explanatory variable would be controlled by the researchers to observe the effect on the response variable, often through random assignment.

In order to affirm a causal relationship, one would need to be able to control for all other possible explanatory variables that could affect the outcome, which is often not possible in observational studies like the one in question. Educators are tasked with imparting the pivotal distinction between correlation and causation to prevent misconceptions and misinterpretations of statistical data.

Confounding Variables

Confounding variables are essentially the 'hidden' factors that may affect the outcome of a study without being directly studied themselves. These variables can create a false impression of the relationship between the explanatory and response variables.

For instance, in the study observing women's cancer rates and sitting habits, variables such as diet, genetic predispositions, or physical activity levels could potentially confound the results. These confounders could be independently associated with both the likelihood of developing cancer and the amount of time spent sitting, thus providing alternative explanations for the observed relationship.

Understanding and controlling for confounding variables are essential for reliable research outcomes. It is a critical concept for students to grasp, as controlling these variables in a study's design strengthens the validity of any causal inferences made. When designing studies or evaluating research, identifying potential confounders is integral to ensuring that conclusions drawn regarding cause-and-effect are trustworthy and credible.