Question

A study \(^{60}\) examined the impact of the color red on how attractive men perceive women to be. In the study, men were randomly divided into two groups and were asked to rate the attractiveness of women on a scale of 1 (not at all attractive) to 9 (extremely attractive). One group of men were shown pictures of women on a white background and the other group were shown the same pictures of women on a red background. The men who saw women on the red background rated them as more attractive. All participants and those showing the pictures and collecting the data were not aware of the purpose of the study. (a) Is this an experiment or an observational study? Explain. (b) What is the explanatory variable and what is the response variable? Identify each as categorical or quantitative. (c) How was randomization used in this experiment? How was blinding used? (d) Can we conclude that using a red background color instead of white increases men's attractiveness rating of women's pictures?

Short answer

(a) It's an experiment because the researchers manipulated a variable (background color) to observe its effect. (b) The explanatory variable is the background color (categorical), and the response variable is the attractiveness rating (quantitative). (c) Randomization was used in dividing the men into groups randomly and blinding was implemented as all participants involved were unaware of the purpose of the study. (d) Based on this experiment, we can infer that there's a correlation, but we cannot definitively say that red increases the attractiveness since there might be other variables at play.

Step-by-step solution

Identifying the Type of Study

To determine the type of the study, one needs to identify whether there was any intervention applied by the researchers to observe the effect of the intervention. In this case, yes, the researchers intervened by showing the men pictures of women on different background colors (red or white) and observed their attractiveness ratings. Therefore, this is an experiment.

Identifying the Explanatory and Response Variables

The explanatory variable, also known as the independent variable, is the variable that is changed or controlled in an experiment to test the effects on the dependent variable. In this experiment, the explanatory variable is the background color of the picture (red or white), and its nature is categorical. The response variable, also known as the dependent variable, is what you measure in the experiment and what is affected during the experiment. In this case, the response variable is the attractiveness rating of the women, and it is quantitative as it ranges on a scale from 1 to 9.

Understanding the Use of Randomization and Blinding

Randomization was used in dividing the men into two groups, it ensures that each participant has an equal chance of being placed into any group, reducing bias and increasing the reliability of the results. Blinding was used as the participants, people showing the pictures, and those collecting data were not aware of the purpose of the study. This reduces the chances of bias as the outcome of the experiment is not influenced by either the expectations of the scientist or the participant's knowledge about the experiment.

Evaluating the Conclusion

Based on the experiment, it was observed that men who saw women on the red background rated them more attractive. However, correlation does not imply causation. It means that even though an association has been found in this sample/experiment between the color red and increased attractiveness, it isn't definitive proof that a red background always increases attractiveness rating of women's pictures. There could be other variables affecting the results that haven't been accounted for. More research would be needed to establish a direct causation.

Key concepts

Explanatory Variable

When it comes to experimental study design, the explanatory variable is a fundamental component that helps researchers distinguish between cause and effect. Drawing from the example in the exercise above, the explanatory variable would be the color of the background against which the women's pictures were shown—red or white. This is a categorical variable because it describes distinct categories without any inherent numerical value.

The choice of an explanatory variable is crucial because it must be something that can be manipulated and controlled by the researcher. In an ideal experiment, the explanatory variable should be the only difference between two or more groups to ensure that any observed effects on the response variable can be attributed to changes in the explanatory variable alone.

Response Variable

In contrast to the explanatory variable, the response variable is what the researcher measures or observes in reaction to changes in the explanatory variable. In our study, the response variable is the level of attractiveness as rated by the men, which is clearly a quantitative variable since it involves numerical ratings from 1 to 9.

The importance of a well-defined response variable cannot be overstressed because it must accurately reflect the effects of the explanatory variable for the data to be meaningful. It should be measured in a reliable, consistent manner to allow for proper analysis and interpretation of the experiment’s results.

Randomization

A cornerstone of any well-designed experiment, randomization serves to ensure that each participant has an equal chance of being exposed to each condition of the explanatory variable. This tactic helps eliminate selection bias and compensates for other variables that the researchers may not have considered.

In our referenced study, randomization was effectively used to assign men to one of the two groups (red or white background), thus enhancing the validity of the findings. This process minimizes the impact of confounding variables as every participant is just as likely to be placed in either condition, thereby making the experiment's groups comparable.

Blinding

Blinding is employed in experiments to prevent the biases of the subjects or the researchers from affecting the results. There can be single-blind or double-blind studies. In the study of the men rating women's attractiveness, a form of blinding is clearly in use since neither the participants nor the individuals administering the experiment were aware of its purpose.

This method reduces the likelihood that preconceived notions or expectations will taint the data. Specifically, if those rating the attractiveness knew the hypothesis, they might unconsciously be influenced to rate the women on the red background as more attractive, which could skew the experiment’s outcome.

Causation versus Correlation

It's crucial to differentiate between causation and correlation in any study. Causation implies that one event is the result of the occurrence of the other event; i.e., there is a causal relationship between the explanatory and response variables. Correlation, on the other hand, simply indicates that there is a relationship or pattern between the variables but does not necessarily mean that one causes the other.

In the case of the study at hand, while a correlation has been found between the color red and higher attractiveness ratings, this does not mean red causes an increase in attractiveness. There might be other underlying factors or external variables influencing the results. To claim causation, further experiments or analytical methods would be required to rule out other possible explanations.