91Ó°ÊÓ

Identify the flaw(s) in reasoning in the following scenarios. Explain what the individuals in the study should have done differently if they wanted to make such strong conclusions. (a) Students at an elementary school are given a questionnaire that they are asked to return after their parents have completed it. One of the questions asked is, "Do you find that your work schedule makes it difficult for you to spend time with your kids after school?" Of the parents who replied, \(85 \%\) said "no". Based on these results, the school officials conclude that a great majority of the parents have no difficulty spending time with their kids after school. (b) A survey is conducted on a simple random sample of 1,000 women who recently gave birth, asking them about whether or not they smoked during pregnancy. A follow-up survey asking if the children have respiratory problems is conducted 3 years later, however, only 567 of these women are reached at the same address. The researcher reports that these 567 women are representative of all mothers. (c) An orthopedist administers a questionnaire to 30 of his patients who do not have any joint problems and finds that 20 of them regularly go running. He concludes that running decreases the risk of joint problems.

Step by step solution

01

Understand the Question and Context

In this step, we are tasked with identifying reasoning flaws in three different research scenarios. We need to pinpoint the issues in the process or conclusions and suggest what could be done differently.

02

Analyze Scenario (a)

In scenario (a), the flaw lies in the lack of consideration for response bias. Since the questionnaire was returned only by certain parents, it may not represent the views of all parents. The key issue is that non-respondents might have had different opinions. The researchers should have ensured a higher and more representative response rate before drawing conclusions.

03

Analyze Scenario (b)

In scenario (b), the error is assuming that the 567 reachable women are representative of all 1,000. This sample is self-selected, as only those who could still be contacted were surveyed, which might not be random. The researchers needed to either ensure they could follow up with all participants or acknowledge the potential bias and try to adjust for it.

04

Analyze Scenario (c)

Scenario (c) has a flaw in causal inference. The orthopedist concludes a decrease in joint problems from running based on an observational study without control groups. To support such a conclusion, a controlled experiment is needed to show causation rather than simple association.

05

Suggest Improvements

In all scenarios, improved methodologies are required: (a) ensure higher response rates and consider the possibility of bias in non-responses; (b) maintain contact with all initial survey participants or use a different sampling technique that allows for broader generalization; (c) use a controlled experimental design to properly test causation.

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Key Concepts

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

Response Bias

Response bias occurs when the answers provided by respondents differ from their true thoughts, feelings, or behaviors. In the context of scenario (a), the issue is that only the parents who chose to respond may not represent the entire group. They might be the ones who find it easier to communicate or have time to engage with the school. This can lead to misleading conclusions about the general parent population. To mitigate response bias, researchers should aim for a response rate close to 100% by providing incentives or follow-ups with non-respondents. Moreover, including a diverse group of respondents can help ensure that the survey results are more reflective of the entire target population.

Representative Samples

A representative sample accurately reflects the characteristics of the larger group it is drawn from. In scenario (b), the claim is made that 567 remaining participants represent all 1,000 originally sampled women. However, this might not be valid if the remaining participants share a specific characteristic that distinguishes them from those unreachable, such as more stable housing or availability. To declare a sample representative, researchers should ensure that the characteristics of the sample align with those of the broader population. This could involve using stratified random sampling or ensuring follow-ups can account for any differences between original and follow-up participants.

Causal Inference

Causal inference means determining whether one variable directly affects another. In scenario (c), a conclusion is drawn that running decreases joint problems based solely on an observational study. However, without controlling for other variables, such as overall health or lifestyle, it's challenging to conclude a cause-effect relationship. Observational studies can show correlation but cannot prove causation. To strengthen causal inference, studies should utilize controlled experiments where an independent variable is manipulated while keeping other factors constant. This way, results can more reliably point towards causation rather than mere association.

Controlled Experiments

Controlled experiments are crucial for establishing cause-and-effect relationships. They involve manipulating one or more variables while keeping others constant to observe the effect of these changes. In scenario (c), to correctly infer that running impacts joint health, a controlled experiment is recommended. This could involve a randomly assigned control group and a group instructed to run regularly. By comparing outcomes between these groups, while ensuring other factors like diet and lifestyle are uniform, researchers can ascertain if running truly affects joint health. Properly designed controlled experiments help avoid confounding variables and lead to more reliable conclusions.