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Chapter 2: Problem 40

To examine the effect of exercise on body composition, healthy women age 35 to 50 were classified as either active \((9\) hours or more of physical activity per week) or sedentary ("Effects of Habitual Physical Activity on the Resting Metabolic Rates and Body Composition of Women aged 35 to 50 Years." Journal of the American Dietetic Association [2001]: \(1181-1191\) ). Percent body fat was measured and the researchers found that percent body fat was significantly lower for women who were active than for sedentary women. a. Is the study described an experiment? If so, what are the explanatory variable and the response variable? If not, explain why it is not an experiment. b. From this study alone, is it reasonable to conclude that physical activity is the cause of the observed difference in body fat percentage? Justify your answer.

Short Answer

Expert verified
a. The study is not an experiment since treatments were not deliberately imposed on the women. The explanatory variable is the physical activity level and the response variable is the percent body fat. b. From this study alone, it is not reasonable to conclude that physical activity is the cause of the observed difference in body fat percentage. Correlation does not imply causation and other factors might be involved.

Step by step solution

01

Identify the experiment, explanatory variable and the response variable.

An experiment is a study in which a treatment is deliberately imposed on (or withheld from) the units of study in order to observe the possible changes in the responses or conditions being measured. Here, not any treatment is imposed on the women, they randomly fall into the active/sedentary category based on their lifestyle. Hence, it is not an experiment. The explanatory variable, also known as independent variable, is the variable that is manipulated to study its effects on the response variable. In this case, the explanatory variable would be the physical activity level (active or sedentary). The response variable, also called the dependent variable, is the variable that's value is suspected to change as a result of the changes in the explanatory variable. Here, the response variable is the percent body fat.
02

Analyze the cause-effect relationship

Reasoning about whether physical activity is the cause of the observed difference in body fat percentages involves understanding of a cause-effect relationship. A cause-effect relationship exists if changes in one variable lead to changes in another. From the study, it is observed that there exists a correlation between physical activity level and body fat percentages in women. But correlation does not imply causation. The study does not allow us to conclude that being more active causes a decrease in body fat percentage, since there may be other factors (e.g. diet, genetics) contributing to lower body fat.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Explanatory Variable
The explanatory variable in any study helps us understand what factors might be affecting the outcome. In the context of the study examining the effect of exercise on body composition, the explanatory variable is the level of physical activity. This is often referred to as the independent variable because it is assumed to influence or bring about a change in the response variable.

For this particular study, women were classified as either active or sedentary based on their weekly hours of physical activity. It is important to remember that in scientific studies, the terms active and sedentary are categories that can help researchers analyze data. Other examples of explanatory variables include temperature in a climate study or dosage of medication in a medical trial. Each serves to see how modifying or observing these factors might affect the study outcome.
Response Variable
The response variable is the focus of the study's outcome. It's what researchers are trying to observe some change in based on the explanatory variable. Often referred to as the dependent variable, its changes are being measured to see the effects of changes in the explanatory variable.

In this specific study, the response variable is the percent body fat of women aged 35 to 50 years. Researchers were interested in seeing if there was any difference in body fat percentage between women who were active versus those who were sedentary. The response variable provides the primary data that will be critically analyzed to answer the research question. This helps in forming conclusions, such as whether an increase in physical activity is associated with a decrease in body fat percentage.
Correlation vs Causation
Understanding the difference between correlation and causation is crucial in interpreting study results. Correlation refers to a relationship between two variables, where changes in one variable may accompany changes in another. However, this does not mean one causes the other.

In the study about body fat percentage and activity level, there is a clear correlation: active women tend to have different (lower) body fat percentages compared to sedentary women. However, it is vital to note that this does not establish causation. Other factors, like diet or genetics, could also influence body fat.

Causation implies that a change in one variable actually brings about a change in another. To determine causation, more controlled methods such as experiments where variables are manipulated under controlled conditions are typically used. As this study does not control for all other potential variables, it is classified as observational, meaning while it identifies correlation, it does not confirm causation.

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