01_18_2023---PSY 101_ Section #1 Meeting Assignment (1)

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Jan 9, 2024

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1. Propose a descriptive study that focuses on ONE of these variables. • What descriptive technique would you use (e.g., case study, survey, naturalistic observation) and why? Briefly, how would your study work? What could we learn from such a study? - The study would be built around a survey on the average quality of sleep each individual gets. The reason for using a survey instead of per-say a case study or naturalistic observation is due to the fact that we can get a larger pool of applicants through accessing a basic questionnaire. The questions can include, “what time did you go to bed last night”, “how did you feel after throughout the day, based on those hours of sleep you got”. The answers to these questions will help us learn how the varying hours of sleep translates from person to person as well as their productivity, alertness, attention to detail, and much more. 2. Propose a correlational study that focuses on TWO of these variables. • Would you expect a positive, negative, or zero correlation between these two variables? Why? - Two of the topics that I will be focusing on are quality of sleep and coffee consumption. In this case, if there was a correlation between the two variables, if I were to conduct an experiment on the quality of sleep one gets on an average week or day to day basis, based on the amount of caffeine that person or group, groups will be randomly distributed on the two variables, drinks possibly show be a negative correlation of caffeine to average sleep altogether, because there is scientific evidence from sources such as Banner, Cleveland, Harvard, and more that indicates a negative correlation between caffeinated drinks and sleeping patterns. • I don’t expect you to calculate r, but what do you think that number might look like? State a specific value that you predict for r and explain why. - The specific value of “r” in this case would be represented in our results, which is self explanatory. Nevertheless, in this case we would be calculating the data we would gain from all of the people involved in the experiment based on the overall range, mode, and median preferably. We would then use those numbers to develop a correlation graph, in order to see the average amount of sleep the average caffeine drinking person gets, based on coffee consumption. Therefore, it's to be expected we will see a strong negative correlation, where the more caffeine consumed on the x-axis, results in the less sleep the individual will get and the less its quality will be as well, on the y-axis. • If your correlation prediction is correct, what does that tell you about the two variables? (Hint: Think about prediction.) - If my prediction is correct, then this shows that two variables are inversely related in the context that the more one is used the less the other will be affected. In this case, if the average caffeine drinking person were to drink less caffeine a day, then it's to be expected that they will get more sleep due to not drinking as much caffeine on a daily basis. Therefore, if my prediction of a strong negative correlation were to exist, then it’s easy to say that there is strong correlation between sleep and caffeine consumption.

• If your correlation prediction is correct, what does it NOT tell you? (Hint: Think about causation.) - Well if it's correct then first off, there are already flaws with the experiment as based on not only how certain people react to caffeinated products but also the people in general. For instance, if we were to run an experiment on a bunch of college students we can already make an assumption, if the correlation was correct, that these college students were staying up incredibly late and getting little to no sleep, even if they drink little to no caffeine, based on their day-to-day school life. This concept also applies to people who drink caffeine based on taste alone, yet aren’t impacted by its influence. This can be based on the type of caffeinated drink to the total amount but there is still the possibility that these wouldn’t be impacted at all. Therefore, if the correlation was correct these are two of many things that the experiment would NOT tell us as caffeine to sleep impacts each and every person differently. 3. Propose an experiment that focuses on TWO of these variables. • What is the independent variable in your study? How specifically would you manipulate it in your study to create groups? How would you employ random assignment in your experiment? - In this case, the independent variable of our experiment would easily have to be caffeine consumption. In order to get specific results, I would manipulate the study based on two groups: a caffeine group and a control group(non-caffeine drinkers). This case study would analyze a control group's sleeping patterns with that of caffeine drinkers and their sleep patterns. The coffee group would people varying in all levels of caffeine consumption and sleeping habits whether its little to no caffeine to red bull is running through their bloodstream. Not to mention, in order to achieve random assignment we would be mixing up the people in this case study experiment and their caffeine consumption patterns, while maintaining a control group, so that we can gain varying insight, instead of displacing 100 people who drink 1 gallon of caffeine a day in the same testing group. Therefore, in this case, obviously we wouldn’t be forcing anyone to be drinking an atrocious amount of coffee, but simply keeping track of people's basic caffeine consumption, however it may be, we can measure by a certain average mL’s to displace groups and in turn get results. • What is the dependent variable in your study? How specifically would you manipulate it in your study to create groups? How would you employ random assignment in your experiment? - The dependent variable in this case study would be quality of sleep. Similarly to caffeine consumption, the manipulation of the dependent variable will be based on the relative mL’s of coffee or any other caffeinated products that our users consume on a daily basis. The correlation between the two, primarily from the changing between participant to participant in terms of caffeine consumed, will be what's manipulated in this study. Lastly, in terms of random assignment the same will apply in terms of not having people with similar sleeping patterns in the same group of people, whether the groups will be randomized on coffee and sleep production in order to keep bias and any sort of means of faltered data out of the equation.

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