Data Collection and Bias

• Part A: Observational Study: The researchers surveyed respondents and recorded their opinions without imposing any changes or treatments. No variables were manipulated.
• Part B: Experiment: The researchers imposed a treatment by implementing an intensive program to lower systolic blood pressure. They then measured the participants' responses (reduction in the risk of death).

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A single study does not prove a result because of the following issues.

• Any single study is subject to random variation due to chance, as a sample may not perfectly represent the population. This error can cause the results to differ from the true population characteristics purely by chance.
• Without replication, it is difficult to determine whether the observed result is reliable or simply due to random variation. A single study cannot confirm the consistency of findings across repeated experiments.

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The psychologists’ concern about the lack of blindness refers to the fact that the experimenter, who rated the subjects’ anxiety levels both before and after the experiment, knew which group each subject belonged to (meditation or relaxation). This lack of blindness could introduce bias in the following ways:

• The experimenter might have unconsciously expected the meditation group to show greater improvement, leading them to rate that group’s anxiety levels more favorably.
• Since the experimenter knew the treatment assignments, their personal beliefs about meditation's effectiveness could have influenced their ratings, even if unintentionally.

To reduce bias, the experimenter could have implemented a double-blind procedure. In a double-blind experiment,  the experimenter conducting the interviews would not know which group each subject was in. This would ensure that the ratings are based solely on the subjects’ behavior and responses, without being influenced by the experimenter’s expectations.

By using proper blinding, the anxiety ratings would be less likely to reflect the experimenter’s or subjects’ biases, leading to more reliable and unbiased results.

Many people lie in response to this question because voting is socially desirable. People may feel pressure to give an answer that aligns with societal expectations, even if it is not truthful. This is an example of response bias because the respondent’s behavior (lying to conform to expectations) influences the data, making it inaccurate.

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People are more likely to agree with Question A because the phrase "assistance to the poor" evokes a sense of helping those in need. In contrast, the term "welfare" may carry negative connotations for some people, as it is often associated with misuse or dependency. This demonstrates how subtle changes in wording can influence how respondents perceive and answer questions, a key example of wording effects.

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The total number of households contacted was 2000, and the number of responses was 831. To calculate the rate of nonresponse:

\[ \text{Nonresponse Rate} = \dfrac{\text{{Nonresponses}}}{\text{Total Households}} = \dfrac{2000 - 831}{{2000}} = \dfrac{{1169}}{{2000}} = 0.5845 \text{ (or 58.45%)}. \]

The nonresponse rate is 58.45%, which is quite high. The likely reasons include:

• People may not answer calls from unknown numbers or may be busy when called.
• Some people may not feel comfortable sharing personal information, such as how often they go to the movies.
• The question may not seem relevant to everyone, leading to disengagement.

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