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Chapter 4: Problem 103

Multiple choice: Effect of response categories \(\quad\) A study (N. Schwarz et al., Public Opinion Quarterly, vol. \(49,1985,\) p. 388 ) asked German adults how many hours a day they spend watching TV on a typical day. When the possible responses were the six categories (up to \(\frac{1}{2}\) hour, \(\frac{1}{2}\) to 1 hour, 1 to \(1 \frac{1}{2}\) hours, \(\ldots,\) more than \(2 \frac{1}{2}\) hours \(), 16 \%\) of respondents said they watched more than \(2 \frac{1}{2}\) hours per day. When the six categories were (up to \(2 \frac{1}{2}\) hours, \(2 \frac{1}{2}\) to 3 hours, ..., more than 4 hours \(), 38 \%\) said they watched more than \(2 \frac{1}{2}\) hours per day. a. The samples could not have been random, or this would not have happened. b. This shows the importance of question design, especially when people may be uncertain what the answer to the question really is. c. This study was an experiment, not an observational study.

Short Answer

Expert verified
b. This shows the importance of question design.

Step by step solution

01

Understand the Problem

We have two different ways of categorizing television watching time, and we want to analyze why the percentage of respondents who watch more than 2.5 hours per day differs under these categories: 16% for one set and 38% for the other.
02

Analyze the Options

Review each option provided in the multiple-choice section. Option (a) suggests that the samples could not have been random due to the differing percentages. Option (b) suggests that the design of the question influences the responses, especially in cases where people are uncertain. Option (c) indicates that the study may not have been an observational study but rather an experiment.
03

Assess Option A

Consider the claim in option (a) that the samples could not have been random. Random samples can still yield different outcomes when other factors, such as response options, influence participants' answers. Thus, the claim in option (a) is likely incorrect because the difference is due to response category design, not the randomness of sampling.
04

Assess Option B

Consider the claim in option (b) that question design affects responses. Changing response categories can lead to different perceptions and understanding, especially when people may not remember their exact television watching habits. Therefore, the difference in percentages can be attributed to the variation in question design.
05

Assess Option C

Evaluate the statement in option (c) which suggests that the study was an experiment, not an observational study. The study involves asking questions to participants without manipulating variables; therefore, it is not an experiment. The nature of the study remains observational since the varying designs in questioning don't convert it into an experiment.
06

Final Step: Select the Correct Answer

Given the analysis, Option (b) is the most plausible choice. The difference in the survey results is due to how the question categories were designed, not because of the sample being non-random or the study type being experimental.

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Key Concepts

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

Question Design
The art of question design in surveys can significantly influence the responses you get. Through carefully crafted questions, survey designers can direct the participants to think and respond in specific ways. The study in question asked participants about their television-watching habits but changed how they categorized the time. This small change can lead to large variations in responses not because the participants are inconsistent, but because the way questions are designed tweaks their memory and perception.
For example, splitting time categories into smaller segments might make a participant aware of their behavior in increments they hadn't considered before, or conversely, more extensive categories might cause them to overestimate or underestimate. This demonstrates how subtle differences in wording or structure of survey questions can sway responses. Question design needs careful consideration to ensure that it gathers accurate and meaningful data. Even a minor adjustment can affect outcomes significantly.
Response Categories
In any survey, the response categories provided are crucial. They define the frame through which participants interpret and report their behaviors. In the mentioned study, the differing response options produced drastically different outcomes.
When respondents were given broader categories, more reported watching over 2.5 hours of TV. This might prompt them to reconsider their habits in the context of fewer, but broader timescale ranges. Alternatively, when narrower response categories were provided, fewer people reported television-watching times exceeding 2.5 hours.
It's important to recognize that differing response categories don't change facts, but they do reshape perception. Survey designers must choose response categories that closely align with the truths they wish to uncover without directing or influencing the results inadvertently.
Observational Study
Understanding whether a study is observational is vital for interpreting its findings accurately. An observational study is one where the researcher observes subjects without manipulating any variables. This is exactly what happened in the TV watching study. Participants provided responses based on their current habits, without any interventions from researchers, making it observational rather than experimental.
Observational studies are distinct because they lack the controlled environment typical of experiments. They offer a snapshot of real-world behaviors and opinions as they naturally occur. However, because there is no manipulation of variables, causations cannot be definitively established.
Despite these limitations, observational studies provide invaluable insights, especially in fields where experimental manipulation would be impractical or unethical. They are fundamental in gathering large-scale data that reflect what's truly happening in everyday life.

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Most popular questions from this chapter

A pharmaceutical company has developed a new drug for treating high blood pressure. The company would like to compare the drug's effects to those of the most popular drug currently on the market. Two hundred volunteers with a history of high blood pressure and who are currently not on medication are recruited to participate in a study. a. Explain how the researchers could conduct a randomized experiment. Indicate the experimental units, the response and explanatory variables, and the treatments. b. Explain what would have to be done to make this study double-blind.

A newspaper designs a survey to estimate the proportion of the population willing to invest in the stock market. It takes a list of the 1000 people who have subscribed to the paper the longest and sends each of them a questionnaire that asks, "Given the extremely volatile performance of the stock market as of late, are you willing to invest in stocks to save for retirement?" After analyzing result from the 50 people who reply, they report that only \(10 \%\) of the local citizens are willing to invest in stocks for retirement. Identify the bias that results from the following: a. Sampling bias due to undercoverage b. Sampling bias due to the sampling design c. Nonresponse bias d. Response bias due to the way the question was asked

Spinal fluid proteins and Alzheimer's \(\quad\) A research study published in 2010 in the Archives of Neurology investigated the relationship between the results of a spinal fluid test and the presence of Alzheimer's disease. The study included 114 patients with normal memories, 200 with memory problems, and 102 with Alzheimer's disease. Each individual's spinal fluid was analyzed to detect the presence of two types of proteins. Almost everyone with Alzheimer's had the proteins in their spinal fluid. Nearly three quarters of the group with memory problems had the proteins, and each such member developed Alzheimer's within five years. About one third of those with normal memories had the proteins, and the researchers suspect that those individuals will develop memory problems and eventually Alzheimer's. a. Identify the explanatory and response variable(s). b. Was this an experimental or nonexperimental study? Why? c. Would it be possible to design this study as an experiment? Explain why or why not.

Since 1976 the Nurses' Health Study has followed more than 100,000 nurses. Every two years, the nurses fill out a questionnaire about their habits and their health. Results from this study indicated that postmenopausal women have a reduced risk of heart disease if they take a hormone replacement drug. a. Suppose the hormone-replacement drug actually has no effect. Identify a potential lurking variable that could explain the results of the observational study. (Hint: Suppose that the women who took the drug tended to be more conscientious about their personal health than those who did not take it.) b. Recently a randomized experiment called the Women's Health Initiative was conducted by the National Institutes of Health to see whether hormone therapy is truly helpful. The study, planned to last for eight years, was stopped after five years when analyses showed that women who took hormones had \(30 \%\) more heart attacks. This study suggested that rather than reducing the risk of heart attacks, hormone replacement drugs actually increase the risk. \({ }^{3}\) How is it that two studies could reach such different conclusions? (For attempts to reconcile the studies, see a story by Gina Kolata in The New York Times, April 21, 2003.) c. Explain why randomized experiments, when feasible, are preferable to observational studies.

Activity: Sampling the states This activity illustrates how sampling bias can result when you use a nonrandom sample, even if you attempt to make it representative: You are in a geography class, discussing center and variability for several characteristics of the states in the contiguous United States. A particular value of center is the mean area of the states. A map and a list of the states with their areas (in square miles) are shown in the figure and table that follow. Area for a state includes dry land and permanent inland water surface. Although we could use these data to calculate the actual mean area, let's explore how well sampling perfoms in estimating the mean area by sampling five states and finding the sample mean. a. The most convenient sampling design is to use our eyes to pick five states from the map that we think have areas representative of all the states. Do this, picking five states that you believe have areas representative of the actual mean area of the states. Compute their sample mean area. b. Collect the sample means for all class members. Construct a dot plot of these means. Describe the distribution of sample means. Note the shape, center, and variability of the distribution. c. Another possible sampling design is simple random sampling. Randomly select five states (using an app or computer program) and compute the sample mean area. d. Collect the sample means from part c of all class members. Construct a dot plot of the sample means using the same horizontal scale as in part b. Describe this distribution of sample means. Note the shape, center, and variability of the distribution. e. The true mean total land area for the 48 states can be calculated from the accompanying table by dividing the total at the bottom of the table by \(48 .\) Which sampling method, using your eyes or using random selection, tended to be better at estimating the true population mean? Which method seems to be less biased? Explain. f. Write a short summary comparing the two distributions of sample means. $$ \begin{array}{lr} \hline \ {\text { Areas of the } 48 \text { States in the Continental U.S. }} \\\ \hline \text { State } & \text { Area (square miles) } \\ \hline \text { Alabama } & 52,419 \\ \text { Arizona } & 113,998 \\ \text { Arkansas } & 53,179 \\ \text { California } & 163,696 \\ \text { Colorado } & 104,094 \\ \text { Connecticut } & 5,543 \\ \text { Delaware } & 2,489 \\ \text { Florida } & 65,755 \\ \text { Georgia } & 59,425 \\ \text { Idaho } & 83,570 \\ \text { Illinois } & 57,914 \\ \text { Indiana } & 36,418 \\ \text { Iowa } & 56,272 \\ \text { Kansas } & 82,277 \\ \text { Kentucky } & 40,409 \\ \text { Louisiana } & 51,840 \\ \text { Maine } & 35,385 \\ \text { Maryland } & 12,407 \\ \text { Massachusetts } & 10,555 \\ \text { Michigan } & 96,716 \\ \text { Minnesota } & 86,939 \\ \text { Mississippi } & 48,430 \\ \text { Missouri } & 69,704 \\ \text { Montana } & 147,042 \\ \text { Nebraska } & 77,354 \\ \text { Nevada } & 110,561 \\ \text { New Hampshire } & 9,350 \\ \text { New Jersey } & 8,721 \\ \text { New Mexico } & 121,589 \\ \text { New York } & 54,556 \\ \text { North Carolina } & 53,819 \\ \text { North Dakota } & 70,700 \\ \text { Ohio } & 44,825 \\ \text { Oklahoma } & 69,898 \\ \text { Oregon } & 98,381 \\ \text { Pennsylvania } & 46,055 \\ \text { Rhode Island } & 1,545 \\ \text { South Carolina } & 32,020 \\ \text { South Dakota } & 77,116 \\ \text { Tennessee } & 42,143 \\ \text { Texas } & 268,581 \\ \text { Utah } & 84,899 \\ \text { Vermont } & 9,614 \\ \text { Virginia } & 42,774 \\ \text { Washington } & 71,300 \\ \text { West Virginia } & 24,230 \\ \text { Wisconsin } & 65,498 \\ \text { Wyoming } & 97,814 \\ \text { U.S. TOTAL } & \mathbf{3 , 1 1 9 , 8 1 9} \\ \hline \end{array} $$
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