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New research \(^{62}\) supports the idea that people who get a good night's sleep look more attractive. In the study, 23 subjects ages 18 to 31 were photographed twice, once after a good night's sleep and once after being kept awake for 31 hours. Hair, make-up, clothing, and lighting were the same for both photographs. Observers then rated the photographs for attractiveness, and the average rating under the two conditions was compared. The researchers report in the British Medical Journal that "Our findings show that sleep- deprived people appear less attractive compared with when they are well rested." (a) What is the explanatory variable? What is the response variable? (b) Is this an experiment or an observational study? If it is an experiment, is it a randomized comparative design or a matched pairs design? (c) Can we conclude that sleep deprivation causes people to look less attractive? Why or why not?

Step by step solution

01

Identify the Variables

The explanatory variable is sleep-deprivation or a good night's sleep. It is the independent variable that is manipulated. The response variable, or the dependent variable, is attractiveness. This is what is being measured in response to the changes in the explanatory variable.

02

Classify the Study Type

This study is an experiment, not an observational study, because the researchers actively manipulated a variable. That's why the subjects were deliberately kept awake for a period of 31 hours while a comparative group was rested.

03

Determine the Design Type

The study design is a matched pairs design. This is because each subject was treated under both conditions: good night's sleep and sleep-deprivation. The same subjects were used for direct comparison, hence this is a matched pairs design as opposed to a completely randomized design.

04

Evaluate Cause-Effect Relationship

The experiment suggests that sleep-deprivation causes people to look less attractive. This statement comes from the controlled setup where other factors such as hair, make-up, clothing, and lighting were kept constant which allows us to focus on the impact of sleep only. However, caution should be taken as there might be other unknown variables that could have affected the outcome i.e. individual's health, mood etc.

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Key Concepts

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

Explanatory Variable

In research studies, an explanatory variable is what you might think of as the "cause" or factor being manipulated or changed by the researchers. It helps to understand what potential influence this variable has on another outcome. In our sleep study, the explanatory variable is "sleep condition", which comprises two specific states: a good night's sleep and being sleep-deprived for 31 hours. This is the variable that the researchers deliberately changed to assess its effect on the participants' attractiveness.

• This variable is also known as the independent variable because its variation does not depend on other variables within the context of the study.
• The goal of manipulating the explanatory variable is to observe any resulting changes in the response variable, giving insights into possible causal relationships.

Understanding the role of the explanatory variable is crucial in designing experiments that aim to test hypotheses about cause and effect.

Response Variable

The response variable is the outcome researchers measure in an experiment or study. It is also commonly referred to as the dependent variable. The value of this variable is believed to depend on changes in the explanatory variable. In the study about sleep and attractiveness, the response variable is "attractiveness". This is what researchers measured by having observers rate photographs of the subjects under different sleep conditions.

• The focus here is on how the explanatory variable (sleep condition) affects the response variable (attractiveness).
• By comparing the attractiveness ratings under different sleep conditions, researchers can potentially identify correlations and discuss possible cause-effect relationships.

Evaluating the response variable is fundamental because it provides evidence that supports or refutes the initial hypothesis of the study.

Experimental Design

An experimental design is a structured plan for conducting research to ensure that the data collected can help answer the research questions effectively. In our scenario, the study used a matched pairs design.
The matched pairs design is when each subject in the study is exposed to both conditions of the explanatory variable. Here, each of the 23 subjects was photographed once after a good night's sleep and once after staying awake for 31 hours.

• This design allows researchers to compare the effects of sleep and sleep deprivation on the same individual, minimizing the variability due to individual differences.
• Since all other factors like hair, makeup, clothing, and lighting were kept constant, any change in attractiveness can be more confidently attributed to the sleep condition.

This matched pairs design helps ensure that any observed effects are due to the manipulated explanatory variable rather than other extraneous factors.

Observational Study

An observational study is different from an experimental study because, in observational studies, researchers do not manipulate the variables. Instead, they observe and measure the variables as they naturally occur, trying to find correlations or associations without influencing them.
In the context of the sleep study described earlier, it is important to note why it is not an observational study. Instead, it is an experiment because:

• Researchers actively manipulated the sleep conditions (good sleep vs. sleep deprivation), which is not something that occurs naturally without intervention.
• The goal was to analyze the effect (attractiveness) under controlled conditions, rather than simply observing what naturally happens without intervention.

In summary, an observational study would typically involve observing subjects in their natural settings without any imposed conditions. The classification between observational studies and experimental studies is key to understanding the extent to which researchers can draw causal conclusions.