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Chapter 9: Problem 25

Does exposure to aircraft noise increase the risk of hospitalization for cardiovascular disease in older people ( 265 years) residing near airports? Selecting a random sample of approximately 650,000 Medicare claims, it was found that about 75,000 people had zip codes near airports and the remaining 575,000 did not. The proportions of hospital admissions related to cardiovascular diseases were computed for those with zip codes near airports and those who did not have zip codes near airports. A larger proportion of admissions for cardiovascular disease was found for older people living in zip codes near airports. Which of the following statements is correct? (a) Because this is an observational study, living in a zip code near an airport may or may not be causing the increase in the proportions of admissions for cardiovascular disease. (b) Because of the large sample sizes from each group, we can claim that living in a zip code near an airport is causing the increase in the proportion of admissions for cardiovascular disease. (c) Because this is an experiment, but not a randomized experiment, we can still conclude that living in a zip code near an airport is causing the increase in the proportions of admissions for cardiovascular disease.

Short Answer

Expert verified
(a) is correct. The study is observational and correlation does not imply causation.

Step by step solution

01

Identify Study Type

The problem mentions analyzing a sample of Medicare claims, dividing people into groups based on their proximity to airports. This setup involves observing existing data without manipulation, characteristics typical of an observational study.
02

Evaluate Causation vs. Correlation

Since the study is observational, it can show correlation but not causation. Observational studies can reveal that two variables are related, but they cannot conclude that one variable causes another due to potential confounding factors.
03

Analyze Sample Size

The large sample size of 650,000 helps in making reliable estimates and conclusions about the population. However, the size of the samples alone does not provide the ability to imply causation in observational studies.
04

Compare Statements

(a) acknowledges the limitations of observational studies in inferring causality, which matches our evaluation in steps 1 and 2. (b) incorrectly claims causation from sample size, which ignores the nature of observational studies. (c) incorrectly refers to the study as an experiment and claims causation, contrasted by our identification in step 1.

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Key Concepts

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

Correlation vs. Causation
In research, understanding the difference between correlation and causation is crucial. Correlation refers to a relationship or connection between two variables where they may move together in a pattern. However, this observed pattern does not imply that one variable is causing the changes in the other. Many factors could be responsible for this association. This is a common scenario in observational studies, where data is gathered without influencing the environment or the subjects directly.

Causation, on the other hand, means that one variable directly affects another. Establishing causation requires more rigorous testing, often through controlled experiments where other influencing factors can be isolated and managed.
  • For example, finding a correlation between living near airports and increased cardiovascular disease hospital admissions does not mean that the noise or air pollution causes health issues. Other variables could be impacting health outcomes.
  • Observational studies, like in the exercise, are limited to highlighting correlations, and they can't definitively prove causation due to the presence of other interfering variables.
Sample Size in Research
The sample size plays an essential role in research, determining how reliably the results reflect the wider population. With larger sample sizes, like the 650,000 Medicare claims in the original study, researchers can feel more confident in the accuracy and reliability of the study's findings. This is because larger samples reduce the margin of error and increase statistical power, making it less likely for the study to support incorrect conclusions due to random chance.

However, it's important to note that while a large sample size can enhance the reliability of observed correlations, it does not inherently enable researchers to draw causal conclusions.
  • A significant sample size can mean more precise estimates but should not be confused with causation, especially in observational studies.
  • The key focus of evaluating sample size is understanding the limits of what those numbers can tell us, rather than overstating the evidence they provide for causality.
Confounding Factors
Confounding factors are variables that can interfere with the relationship between the independent and dependent variables in a study. In observational studies, these factors can create a false sense of causation, leading researchers to incorrect conclusions.

For example, in the exercise about aircraft noise and cardiovascular risk, confounding factors could include:
  • Socioeconomic status: People living near airports might have different socioeconomic backgrounds, affecting health care access and lifestyle choices.
  • Pre-existing health conditions: Those with existing cardiovascular problems might be more likely or less likely to live near airports for various reasons.
  • Environmental pollution: Beyond noise, higher general pollution levels near airports could influence health outcomes independently.
To mitigate the impact of confounding factors, researchers can use statistical methods or conduct experimental designs where confounders are controlled. This is why observational studies are good for raising questions about relationships but rarely provide definitive causal answers without further controlled investigations.

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Most popular questions from this chapter

The effect of product density and ambient scent on consumer anxiety. Although retail stores overflowing with merchandise can make consumers anxious, minimally stocked spaces can have the same effect. Researchers investigated whether the use of ambient scents can reduce anxiety by creating feelings of openness in a crowded environment or coziness in a minimally stocked environment. Participants were invited to a lab that simulated a retail environment that was either jam-packed or nearly empty. For each of these two product densities, the lab was infused with one of three scents: (1) a scent associated with spaciousness such as the seashore, (2) a scent associated with an enclosed space like the smell of firewood, and (3) no scent at all. Consumers evaluated several products and their level of anxiety was measured. 28 (a) Use a diagram like Figure 92 (page 233) to display the treatments in a design with two factors: "product density" and "ambient scent." Then outline the design of a completely randomized experiment to compare these treatments. (b) There are 30 subjects avallable for the experiment, and they are to be randomly assigned to the treatments, an equal number of subjects in each treatment. Explain how you would number subjects and then randomly assign the subjects to the treatments. If you use the Simple Random Sample applet or other software, assign all the subjects. If you use Table \(B\), start at line 130 and assign subjects to only the first treatment group.

Observation versus experiment. Researchers at the University of Pennsylvania found that patients who are divorced, separated, or widowed had approximately a \(40 \%\) greater chance of dying or developing a new functional disability in the first two years following cardiac surgery than their married peers. The data included 1576 subjects who underwent cardiac surgery, of which \(65 \%\) were married; 33\% were divorced, separated, or widowed; and \(2 \%\) had never been married. The findings were reported to be statistically significant. 25 (a) Without reading any further details of this study, how do you know that this was an observational study? (b) Suggest some variables that might differ between the subjects in the study who were married versus those who are divorced, separated, or widowed. Are any of these possible confounding variables? Explain. (c) Summarize briefly the limitations of this study. Despite these limitations, explain why this study still fumishes useful information in formulating a recovery plan for those undergoing cardiac surgery.

Prayer and Meditation. You read in a magazine that "nonphysical treatments such as meditation and prayer have been shown to be effective in controlled scientific studies for such ailments as high blood pressure, insomnia, ulcers, and asthma." Explain in simple language what the article means by "controlled scientific studies." Why can such studies in principle provide good evidence that, for example, meditation is an effective treatment for high blood pressure?

Can low-fat food labels lead to obesity? What are the effects of low-fat food labels on food consumption? Do people eat more of a snack food when the food is labeled as low fat? The answer may depend both on whether the snack food is labeled low fat and whether the label includes serving-size information. An experiment investigated this question using university staff, graduate students, and undergraduate students at a large university as subjects. Subjects were asked to evaluate a pilot episode for an upcoming TV show in a theater on campus and were given a cold 24-ounce bottle of water and a bag of granola from a respected campus restaurant called The Spice Box. They were told to enjoy as much or as little of the granola as they wanted. Depending on the condition randomly assigned to the subjects, the granola was labeled as either "Regular Rocky Mountain Granola" or "Low-Fat Rocky Mountain Granola." Below this, the label indicated "Contains 1 Serving" or "Contains 2 Servings" or it provided no serving-size information. \({ }^{31}\) Twenty subjects were assigned to each treatment, and their granola bags were weighed at the end of the session to determine how much granola was eaten. (a) What are the factors and the treatments? How many subjects does the experiment require? (b) Outline a completely randomized design for this experiment. (You need not actually do the randomization.)

Price Promotions-Amount versus Percentage Off. Price promotions are commonly used by retailers to motivate consumers to make a purchase. Both the amount off and percentage off are in widespread use, and research on when to wse each method has been mixed. The current study evaluated intent to purchase for a lower- and higher-priced item using either a fixed amount or a percentage off. For the lower-priced item, participants saw a promotion for balloons regularly selling for 48 pesos, but on sale with either a 12-peso or a \(25 \%\) discount. The higher-priced item was a 480-peso sweater on sale with either a 120 -peso discount or a \(25 \%\) discount. One hundred and fifty-one students were randomly assigned to the treatments and responded to two questions measuring value perceptions: "I would be saving a lot of money if I made my purchase at this store," and "This store is selling the advertised product at a considerable discount." Participants answered each question on the scale 1 ("strongly disagree") to 5 ("strongly agree").
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