20 Sep Describe the variables SOUND, ENVIRONMENT, and TESTSCORE

RSM801

Week 3 Assignment

The purpose of this exercise is to give you the opportunity to determine appropriate tests to run, carry out the analyses, and interpret the results of the statistics. Be sure to open this document and SAVE IT as yourlastname_lab_Week3.doc to the desktop. Once complete, upload it to the Week 3 Blackboard dropbox.

General Instructions:

· Read each question carefully before answering.

· Plan your time effectively: Review all questions before starting and develop a strategy. If you get stuck on one question, move on and return to it later.

· Clear documentation matters: Clearly indicate your steps to maximize the potential for partial credit.

· Refer to course materials: You may consult your textbook or other course materials for assistance with statistical tests.

· Label all figures: Figures should have numbers and titles per APA guidelines.

· Ask for clarification if needed: I may provide general guidance, but not answers. Please don’t hesitate to ask if you have questions!

Use JAMOVI OR SPSS data file

QUESTION 1 (14 points)

Describe the variables SOUND, ENVIRONMENT, and TESTSCORE:

· Generate frequency tables and bar charts for all categorical (nominal or ordinal) variables.

· For continuous (scale) variables, generate and interpret descriptive statistics.

· Include histograms with a normal curve superimposed for each scale variable.

· Summarize the data in a paragraph, highlighting key findings (e.g., distribution shape, central tendency, variability).

QUESTION 2 (6 points)

Analyze whether the TESTSCORE variable has any significant outliers:

· Identify potential outliers, including relevant JAMOVI OR SPSS output and writing a brief justification for whether or not these are true outliers. [4 points]

· Justify whether any potential outliers should be kept, removed, or transformed. Be sure not to remove or transform outliers, simply report what you would do based on this analysis. [2 points]

QUESTION 3 (11 points)

You want to determine whether participants in your sample performed significantly differently from a benchmark average score (80) on the math exam (variable TESTSCORE in the data set).

· What statistical test would you conduct? Explain your reasoning. [3 points]

· Run the test in JAMOVI OR SPSS. Paste your output into the document. [3 points]

· Write up your findings as if reporting them in a journal article, following APA guidelines. [5 points]

QUESTION 4 (16 point s)

Is test taking environment (variable ENVIRONMENT) associated with math test scores (variable TESTSCORE)?

· Identify the appropriate statistical test for this analysis. Explain your choice. [3 points]

· Identify the independent variable (IV) and dependent variable (DV). [2 points]

· Conduct the test in JAMOVI OR SPSS. Paste your output into the document. [2 points]

· Write up your findings as if reporting them in a journal article, following APA guidelines. [9 points]

QUESTION 5 (13 points)

Participants rated the attractiveness of a potential romantic partner while under the influence of alcohol (ATTRAC_ALC) and again when completely sober (ATTRAC_SOB). Ratings were on a scale from 1 to 14 (higher values indicate greater attractiveness). These variables should be treated as scale (continuous) variables. Analyze whether alcohol influenced ratings of attractiveness:

· What test would you conduct? Why is this test appropriate? [3 points]

· Conduct the test in JAMOVI OR SPSS. Paste your output into the document. [2 points]

· Write a results section as if reporting for a journal article, following APA guidelines. [6 points]

QUESTION 6 (10 points)

A researcher examined how free time affects creativity:

· One group of children had 30 minutes of unstructured play time (Unstructured), while another had 30 minutes of an observer directing play time (Structured).

· All children were then asked to draw a picture of something they had imagined.

· Independent observers rated the creativity of drawings (higher scores = more creativity).

The researcher conducted a t-test for independent means on the data. The SPSS output is below.

1. Explain what Levene's Test for Equality of Variances tells us, why it matters, and how it affects interpretation. Use the information provided under “Levene’s Test for Equality of Variances.”[4 points]

2. Describe the results of the t-test in a way that would be clear to a reader unfamiliar with the data set. Write up the results thoroughly, following APA guidelines. [6 points]

QUESTION 7 (14 points)

A psychologist studied the effects of location and noise level impacted math exam scores. The results are displayed in the figure below.

What statistical test was performed? You should base your answer on the study description as well as the figure above. Explain your reasoning. [3 points]

· Label and number the figure according to APA guidelines. [3 points]

· Write the hypotheses for the impact of location and noise sound level on exam scores. [4 points]

· Based on the figure, describe the results of the study. Follow APA guidelines. [4 points]

QUESTION 8 (8 points)

A psychologist is studying the effect of sleep deprivation on reaction times. Participants were asked to complete a reaction time task after three conditions: (1) a full night's sleep, (2) 24 hours of sleep deprivation, and (3) 48 hours of sleep deprivation. Reaction times (in milliseconds) for each condition were recorded as continuous variables. The researcher hypothesizes that sleep deprivation will significantly impact reaction times.

What statistical test would you conduct to analyze the data? Explain why this test is appropriate. [2 points]

Write the hypotheses for the test you selected. Clearly state the null and alternative hypotheses. [2 points]

If you were to run this test in JAMOVI OR SPSS, how would you check assumptions (e.g., sphericity) and what would you do if they are violated. [4 points]

** End of Week 3 Application **

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there we go all righty all right you all so welcome to week three as always once i start screen sharing i can't see the chat or hear or i'm sorry see any comments or anything like that any hands raised so please feel free to just unmute yourself if you have questions comments you want me to pause for a minute i i want you to to stop me and ask your question please Okay, so let's start off a little silly today. How are we feeling? Beginning of week three, we came into this course hot and heavy with a lot of big data work. So some of my favorite staff memes, me in the top left corner, my staff teacher explaining to me P less than 0.05 and P, me still confused with which one is significant and which is not significant. Not sure if I should use quantitative research or qualitative research. I love the Patrick Starr one, critically evaluating someone else's research article versus writing a paper explaining your own research. It feels like that. I'm sure so many of you are like, why am I studying numbers so I can understand feelings? And all of us just smile to hide completely how overwhelmed we are half the time. So a little silliness to start. You're doing great. Message going into this week is don't miss the forest for the trees. Those of you that were at residency heard me give a version of this before, but you all are headed towards the end. So the whole last year of your program is going to be like, wake up and finish your PhD. so don't get lost okay i want to take a few minutes to remind you what you already know so let's do that you've at least had some previous research methods courses so you've learned essentially about the scientific method the design of research fundamentals different sampling methods different types of variables like independent and dependent variables basic measurement scales like nominal ordinal interval ratio, how we're measuring the things we're interested in. So this gives you a good foundation. And then we need to build from that onto hypothesis testing and choosing the appropriate statistical test. Okay. So many folks stay confused about null hypothesis testing. And this is the time where I hope if you haven't gotten it, that you're really going to get it now. So the way that things work in the scientific method and using null hypothesis significance testing is you first formulate a research question in which you operationalize both your independent and dependent variables saying how they will be measured or manipulated. You state both your null and your alternative hypothesis. So the null hypothesis always says there is no effect. There's nothing to see here. and the alternative hypothesis says there is an effect. Now, we are statistically testing the null hypothesis that says there's no effect. So, we're looking for the probability of no effect, okay? So, step three, based on your research question, your variables, and your hypotheses, then you choose the appropriate statistical test. So as I said, it depends on your question and the types of variables. You set your significance level or your alpha level. So the conventional in behavioral science is an alpha level of 0.05, meaning that we're looking at a 0.05 or 5% probability as our cutoff range. Then we would go out and collect the data and input the data into whatever software we choose. So SPSS, R, Jmovi, Excel, whatever you prefer. And then you'll run the analysis and interpret the output. So there are many, many kinds of statistical tests, you all, but anytime we are looking at the output, once you've run the test, whatever it is, the first thing you're going to go do is look at the p-value. So you're going to look at the probability and then the test statistic and the confidence intervals and effect size. So the p-value is going to tell you whether or not you can reject the null hypothesis of no effect. It just simply tells us, is the probability that we would get that test statistic greater than or less than 0 .05? We just say it's either significant or it's not. And then you look at the test statistic and the confidence intervals and the effect size to say if it's a meaningful effect. Because you can get significant results that don't have any practical meaning. So we look at all of those things. Step seven, you make a decision, so you either reject or fail to reject the null hypothesis, and then draw conclusions back to the context of your research question. Ultimately, you will report your results and limitations, and then make suggestions for future research or to address limitations, and all of those things. All right, so then I want to go through some of the most common types of statistical tests and kind of walk you through when you would want to use them with examples and structure. So let's start with a one-sample t-test. You want to use a one -sample t-test when you are comparing one group to a known benchmark value. So, for example, do professional musicians practice more than 20 hours per week? There's evidence out there that gives us an idea of how many hours per week professional musicians practice, and we could take a sample of professional musicians and see how they compare to that known benchmark. So, the structure here is there's no actual independent variable. It's just that benchmark value. So, what the average or the benchmark is. And then there's one continuous. So, on a numerical scale, one continuous dependent variable. So in this case, it would be practice hours. And it's only one single group of subjects. So you look at your single group of subjects and you compare their results with the known benchmark. That's a one sample t-test. Now, there are other kinds of t-tests. So, the next I want to talk about is the independent samples t-test, independent, independent samples. So, this is what we use when we want to compare two unrelated groups. So, for example, if we wanted to compare grip strength between rut climbers and swimmers. Those are two completely unrelated separate groups and we're going to measure them on the same outcome. So for an independent samples t-test, that structure is a categorical IV with two groups. So in this case, it's climbers and swimmers. The continuous dependent variable would be grip strength. And the observations need to be independent of one another. So, the groups are separate. They're not related. Okay. So, we've done one sample t-test, independent samples t-test, and now there's the paired or, well, we'll just call it a paired samples t-test and have multiple names or repeated measures t-test. So you want to use a paired samples t-test when you are measuring the same subjects twice. So anytime I hear of a pre-post type design, I know they're most likely using a paired samples t-test to measure scores before and after some type of intervention. So, for example, memory scores before meditation training versus memory scores after meditation training. So, the structure, it's a within-subjects design. In this case, time or condition, you know, before or after, before and after meditation training is your independent variable. the continuous dependent variable would be the memory scores, and the the structure here involves matched observations. So we need to compare the same individuals pre and post to see if that group changed. Okay, kind of stepping it up here. So if you have two groups, like one or two groups, you're most likely going to use a t -test to make comparisons. But if you have three or more independent groups, so a categorical independent variable with three groups and a continuous outcome variable, you're going to want to use a one-way ANOVA. So you all have dug into a little ANOVA work already. But for example, if you wanted to compare reaction times between folks who worked morning shift, afternoon shift, and night shift. So, you can understand that structure. It's one categorical independent variable. So, shift worked with three levels, morning, afternoon, and night, and one continuous dependent variable. So, reaction times. And those would be independent observations across all three groups. now a two-way ANOVA is when we're looking at two independent variables okay so two factors affecting one outcome so for example looking at the effects of lighting so natural versus artificial lighting and music classical versus jazz versus none on productivity levels See how we just get a little more complex and layered as we move along here? So the structure of a two -way ANOVA is two categorical independent variables, one continuous dependent variable, and independent observations across categories. Okay, the repeated measures ANOVA, you want to use this. Um, so it's, it's essentially like the ANOVA version of a paired samples t-test, but we've got three or more measurements per subject rather than a simple pre -post. We have three or more measurements per subject. So for example, blood pressure checkups across, uh, for quarterly visits. So, if we were trying to look at differences in the same people across four time points, we would need to consider a repeated measures and OPA. So, here the time or condition was the independent variable with three levels. I'm sorry, it was actually four levels. And a continuous dependent variable. Importantly, these are the same subjects throughout. Mixed ANOVA, we got a little familiar here, when combining between subjects and within subjects factors. So, for example, if you wanted to compare language learning progress with using weekly tests between intensive and standard courses. So, the structure of a mixed ANOVA, you must have at least one between subjects factor where you're measuring two separate groups and you must have at least one within subject factor where you're measuring within groups as well and then you need a continuous outcome measure and this involves a mix of both independent and repeated measurements All right, you all. So there's your little quick and dirty review for you. I want to make sure that you're prepared for the week three quiz. So you're going to need to be able to identify the structure of studies. So is it between groups? Are they two completely separate groups? or is it within subjects? Are we looking at changes over time within the same subjects? How many groups were there? How many measurements? What's being compared? These are the questions to ask yourself to identify the study structure. Some of the key phrases and tests that you need to be familiar with are t-tests of course so one sample you're comparing um to a standard or benchmark if you've got between two groups think independent samples if you see before and after or pre -post think paired samples if you've got more than two groups you need or a variety of categorical, multiple independent variables, you're going to need to consider something from the ANOVA family. So, three or more groups, one-way ANOVA. Multiple measurements over time would be repeated measures, ANOVA. Groups measured multiple times would be considered a mixed ANOVA because we'd be looking at differences between and within groups. And then if we're looking at two or more factors affecting an outcome you could have a two-way where all the there are very complicated and open designs for each factor that you add okay then i want to make sure that you're prepared for the assignment this week so for your descriptive statistics you all please pay careful attention to what you do with categorical and continuous variables. So remember, it doesn't make sense to calculate the mean or the average of a categorical variable. So think of what city you were born in for me. I was born in a little place called Chaffee, Missouri. We all have different cities of birth and there is no average city of birth. You can't add together and divide categories meaningfully. So the mean and standard deviation should never be reported for a categorical variable. For those, you want to give frequencies and percentages. So however many people belonged in that category and what percentage of the overall sample they made up. Now, continuous variables that are numerical in nature and indicate an actual scale value, that's where we want those descriptive statistics like means and standard deviations and those things. So, please be careful attention to that. Practice writing integrated summaries combining multiple data points. You'll need to review You'll need to create and review and interpret the histogram overlaying and normal bell curve onto it. And please continue working to master proper APA table and figure formatting, just all the APA stuff. For your outlier analysis, I want to make sure that you understand multiple detection methods. And I just want you to practice justifying outlier decisions and know how to discuss the characteristics of your distribution and to explain any associated practical limitations based on any flaws in the data. So for statistical tests, you need to know how to select the appropriate test type. Practice writing complete hypotheses, review the checking of your assumptions, make sure you check all of the associated assumptions and report that, and more APA stuff. Work on mastering reporting of your APA results. all right so now i'm just going to quickly walk you through the assignment and i want to point out how many points these are worth you all so pay attention to the point value of questions um and and make sure that your answers are uh appropriate for those number of points so for question one you're going to describe the variables sound environment and test score You will generate frequency tables and bar charts for all categorical, so nominal or ordinal variables. For continuous scale variables, you need to generate and interpret those descriptive statistics. Make sure you include your histograms with the normal curve, the density curve, superimposed over each variable. and then summarize the data in a paragraph, highlighting the key findings, so the shape of the distribution, central tendency, variability, all of that stuff. For question two, you will analyze whether the test score variable has any significant outliers. So, you will identify potential outliers, including your output, and writing a brief justification for whether or not these are true outliers. So think Z scores and box plots are the way that we identify outliers. And then you need to justify whether any potential outliers should be kept, removed, or transformed. So I want you to tell me what you would do, but do not remove or transform any of the outliers. Just simply report what you would do based on your analysis. Okay, question three. You want to determine whether participants in your sample perform significantly differently from a benchmark average score of 80 on the math exam. So that's the variable test score in the data set. So you'll answer these questions. What statistical test would you conduct and why? Run the test in your software of choice and paste your output into the document. Then write up your findings as if you were reporting them in a journal article perfectly following APA guidelines. Moving on, the next question is test -taking environment, so the variable environment, associated with math test scores. So are they associated? You will need to identify the appropriate statistical test for this analysis and explain your choice. You need to identify the independent variable, if there is one, and dependent variable. It might not be a true independent variable in this situation where we're just looking for an association. Conduct the test, paste your output in, and then write up the findings as if you're reporting them in a journal. Question five. The scenario is that participants rated the attractiveness of a potential romantic partner while under the influence of alcohol. So there's the variable name, attract underscore ALK. And again, when completely sober, so underscore SOB. Ratings were on a scale from 1 to 14 where higher values indicate greater attractiveness. These variables should be treated as scale or continuous variables. And you are asked to analyze whether alcohol influenced ratings of attractiveness. So, what test would you conduct and why? Conduct the test, paste in your output, and then give me a little formal write-up of the results. Alrighty, so then question six, you will see this output here. One group of children had 30 minutes of unstructured playtime, which is labeled unstructured, while another had 30 minutes of an observer directing playtime. So, that was structured. So, all children were then asked to draw a picture of something they had imagined, and then independent observers rated the creativity of drawings. So, higher scores meant more creativity. The researcher conducted a t-test for independent means on the data the spss output is given here and what i'm as