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Chapter 9: Problem 47

Better Sleep? Is the number of times you awaken during the night affected by whet her you have a glass of wine before bed and whether you have a snack before you go to bed? Describe briefly the design of an experiment with two explanatory variables-whether or not you have a glass of wine and whether or not you have a snack before going to bed -to investigate this question. Be sure to specify what the response variable will be. Also tell how you will handle lurking variables such as amount of sleep the previous night.

Short Answer

Expert verified
Design an experiment with four groups based on wine and snack consumption, measure awakenings, and control for sleep amount the previous night in the analysis.

Step by step solution

01

Define Variables and Groups

Identify the two explanatory variables: 1) Consumption of a glass of wine before bed (Yes or No) and 2) Consumption of a snack before bed (Yes or No). Establish the four treatment groups: (a) Wine and Snack, (b) Wine and No Snack, (c) No Wine and Snack, (d) No Wine and No Snack.
02

Select Participants

Randomly select a group of participants to ensure the experiment is not biased. Make sure to recruit a sufficient number of participants to obtain statistically significant results. Ensure that participants represent a variety of demographics.
03

Random Assignment

Randomly assign participants to one of the four treatment groups. This is crucial to control for individual variability and ensure that the effects observed are due to the experimental treatments.
04

Conduct the Experiment

Over a predetermined period, have participants follow their group assignments (e.g., consume wine and/or a snack as assigned) before sleep. Instruct participants to document the number of times they awaken during the night.
05

Measure the Response Variable

The response variable is the number of times a participant awakens during the night. Collect and record this data accurately for each participant.
06

Analyze Data

Conduct statistical analysis to compare the mean number of awakenings across the four groups. Use ANOVA or a similar method suitable for comparing more than two groups.
07

Address Lurking Variables

Control for lurking variables by including them as covariates in the analysis. For example, ask participants to report their sleep amount from the previous night and consider factors such as stress level and use of electronics before bed as potential contributors.

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Key Concepts

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

Explanatory Variables
In experimental design, explanatory variables are crucial. They are the factors manipulated to observe their effect on a response variable. In our sleep study, we focus on two: whether the person had a glass of wine before bed, and whether they had a snack. These are categorical variables, meaning they have defined categories or groups. Here, they each have two categories: 'Yes' or 'No'.
The experiment involves assigning participants to one of four groups:
  • Wine and Snack
  • Wine but No Snack
  • No Wine but a Snack
  • No Wine and No Snack

This allows us to examine how combinations of these activities influence sleep. By clearly defining the explanatory variables, we can attribute differences in the response variable directly to these factors.
Response Variable
The response variable in an experiment is what researchers measure to see if it is affected by changes in the explanatory variables. In this study on sleep, the response variable is the number of times the participant awakens at night. This is a quantitative variable, providing a clear, numeric measurement.
The choice of the response variable is central to the experiment's success. Accurate tracking of awakenings ensures that the data reflects true changes due to the wine and snack variables. By focusing on awakenings, we gain insights into the quality of sleep, not just quantity. This can help determine the real-world implications of having a pre-sleep drink or snack.
Statistical Analysis
Statistical analysis is the backbone of drawing conclusions from experiment data. Once data collection on awakenings is complete, comparisons across groups can be conducted. For this purpose, using statistical tests like ANOVA (Analysis of Variance) is helpful. ANOVA examines if there are significant differences in sleep disruption among the four groups influenced by our explanatory variables.

Choosing the right statistical method ensures valid and reliable conclusions. It allows us to discern patterns or differences that are not due to random variation. Additional analysis can include addressing potential influencing factors, like previous night's sleep or stress levels. This ensures that such lurking variables do not bias the results.
Random Assignment
Random assignment is a fundamental step in experimental research. This process allocates participants to various groups purely by chance, ensuring each person has an equal probability of being assigned to any of the four groups. Random assignment is crucial to control for individual differences that may affect sleep outcomes, like baseline sleep quality or lifestyle choices.
By doing so, any observed effect on the response variable, such as variation in awakenings, can be confidently attributed to the explanatory variables. It eliminates biases and ensures that results are due to the experimental interventions rather than random or extraneous factors. This step, therefore, is vital in bolstering the experiment's internal validity.

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Most popular questions from this chapter

Algal Blooms. Algal blooms have become a recurring problem on many American lakes. Among other things, they can cause damage to a person's liver, kidneys, and nervous system. Phosphorus runoff from farms is one factor that contributes to algal blooms. Will inserting fertilizer into soil rather than spreading it across the surface help reduce runoff? To study this, researchers compare the effects of these two methods of fertilizing fields on the amount of phosphorus in runoff. Specific features of a field, such as slope of the ground and nature of the soil, can affect runoff, so the researchers divide each of four fields into two plots of equal size in such a way that the runoff from each plot can be measured separately. They use a matched pairs design, with the two plots in the same field as the matched pairs. a. Draw a sketch of the four fields, displaying each as a rectangle. Divide each field (rectangle) in half, each half representing one of the two plots. Label the two plots for each field as Plot 1 and Plot \(2 .\) b. Do the randomization required by the matched pairs design. That is, randomly assign the two treatments to the two plots in each field. Mark on your sketch which treatment is used in each plot.

The Font Matters! In general, when trying to change your behavior, if the effort required is perceived as high, this will be an impediment to change, whether it is modifying your diet or your study habits. Researchers divided 40 students into two groups of 20 . The first group reads instructions for an exercise program printed in an easy-to-read font (Arial, 12 point), and the second group reads identical instructions in a difficult-to-read font (Brush, 12 point). Each subject estimates how many minutes the program will take and also uses a seven-point rating scale to report whether they are likely to include the exercise program as part of their daily routine ( 7 = very likely). The researchers hypothesized that those reading about the exercise program in the more difficult-toread font would estimate that the program would take longer and, they would be less likely to make the exercise program part of their regular routine. 3 Is this an experiment? Why or why not? What are the explanatory and response variables?

Sounds Big. Does the lower pitch of a voice in an ad lead consumers to envision a bigger product? To test this, researchers had students listen to a radio advertisement for the new Southwest Turkey Club Sandwich at a fictitious sandwich chain, Cosmo. Half the students were randomly assigned to hear the ad spoken at a high pitch and the other half at a low pitch. In all other respects, the ads were identical, and no clues were given as to the size of the sandwich. After hearing the ad, students were asked to rate the perceived size of the sandwich on a 7-point scale, ranging from \(-3\) (much smaller than average) to \(+3\) (much larger than average). \(.\)

Quitting Smoking and Risk for Type 2 Diabetes. Researchers studied a group of 10,892 middle-aged adults over a period of nine years. They found that smokers who quit had a higher risk of diabetes within three years of quitting than either nonsmokers or continuing smokers. \({ }^{4}\) Does this show that stopping smoking causes the short-term risk for Type 2 diabetes to increase? (Weight gain has been shown to be a major risk factor for developing Type 2 diabetes and is often a side effect of quitting smoking. Smokers also often quit due to health reasons.) Based on this research, should you tell a middle-aged adult who smokes that stopping smoking can cause diabetes and advise him or her to continue smoking? Carefully explain your answers to both questions.

Observation Versus Experiment. Researchers at the University of Pennsylvania found that patients who were 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 whom \(65 \%\) were married; \(33 \%\) were divorced, separated, or widowed; and \(2 \%\) had never been married. The findings were reported to be statistically significant.26 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 were 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 furnishes useful information in formulating a recovery plan for those undergoing cardiac surgery.
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