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BIOL/STATS 2244 Assignment 3: Analysis and Conclusion
发布时间:2023-08-01
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BIOL/STATS 2244
Assignment 3: Analysis and Conclusion
Objectives
This Assignment is designed to demonstrate your current mastery of the following Learning Outcomes:
i. Analyse data using inference procedures to address a research question
a. Evaluate model diagnostics for common parametric inference procedures
ii. Use statistical software to explore, summarize, analyse, interpret, and communicate data;
a. Use R to conduct common parametric inference procedures, including model diagnostics.
b. Use R markdown to produce reproducible analyses and reports.
iii. Communicate statistical concepts, analyses, and arguments in an accurate and scholarly manner.
a. Use conventional and transparent formats for reporting results of statistical analyses in written/graphical form.
To achieve these objectives, students will need to draw on course material from the topics primarily from Week 5 (Lectures and Labs). However,answering some of the interpretation work may involve using ideas from earlier topics.
How this Assignment ‘works’
This Assignment is the last of three Assignments in the course; it continues our progression through the phases of the PPDAC Framework. The major focus of this Assignment are the Analysis and Conclusion stages of PPDAC.
In the first Assignment, you were introduced to some ‘Research Background’ related to aspects of cognitive offloading and using technology to complete tasks that require accuracy. We will continue to work with this background (you can refer back to it if necessary for this assignment). In the second Assignment, you were provided a dataset that is related to the Research Background; we will continue to use this dataset for this Assignment. Recall that the data is provided across TWO (2) datafiles (named trials.csv and overall.csv), and you have access to the research article that describes the way the data were collected (i.e. in the Materials and Methods section of the article) . You also have created a “Key to datasets” file that provides a brief description of the column headers in the datafiles, so you can link them to the information in the article.
As was the case with Assignment 2, you will not use every variable and/or every data point in the datafiles for this Assignment! Similarly, you likely will only need data from one of the datafiles.
Research Prompt (same as Assignments 1 and 2)
You area researcher with the Department of Computer Science at University “X” (whatever university you want — it’s not particularly important). You regularly collaborate with other researchers at your
university (and potentially elsewhere), including those with interests in psychology and human
behaviour. Your research interests and expertise are related to factors that influence human reliance on technology and artificial intelligence.
You have a research lab (with graduate students and research fellows who can help you with your research, e.g. as individuals helping to collect data if necessary) with funding that you are using to conduct research to further the following Research Objective:
Understand factors that influence willingness to use technology (like artificial intelligence) to perform job-related tasks.
Being successful on this Assignment
Remember, the Assignment is evaluating you on three things:
• Your ability to use R to conduct an inference procedure
• Your ability to apply and evaluate model conditions
• Your ability to work with an R markdown file
The knowledge to support these tasks is developed in lecture; the skills to use R are shown in labs; look back at what we did in lecture and lab to support you for this Assignment. Seriously. Your best approach would be the following (you don’t need to ‘answer’ these suggestions in your assignment; this is just a guide to how to approach the assignment):
1. Think about the Research Question (given below, in the Assignment Questions section) and the procedure being conducted. Which variables in the dataset(s) will you use to answer the
question? Use the article to understand what the variables represent and the way the data were collected (i.e. study design).
2. Review the lecture materials about the inference procedure; apply the concepts to the specific data and Research Question we are working with.
3. Use examples in the relevant Lab(s) to do the work with R.
Assignment Questions
Question 1.
What are the appropriate statistical hypotheses (null and alternative) that should be used to conduct
the hypothesis test to answer the Research Question described above? Write both hypotheses using
appropriate conventional symbolic format and write the null hypothesis in sentence format such that it makes it clear what is the interpretation of the symbolic format.
Question 2.
What are the conditions that must be met for the t-test to compare the difference in means between
men and women to be valid? Discuss whether each is met for our data, generating appropriate R output if/as necessary.
Question 3.
Use R to conduct the t-test to compare the difference in means between mean and women, consistent with the statistical hypotheses you stated in Question 1. You do NOT need to interpret the results.
Notes:
• You will need to use the LaTeX abilities of R markdown to write the symbolic (i.e. equation) format for Question 1.
• If you use any R code/output to answer a question, be sure both the code and output are showing in your knitted file.
• This is not meant to be along assignment. Question 1 is a sentence plus the two
symbolic/equation forms of the hypotheses. Question 2 will be — perhaps —about 4-8 sentences. Question 3 will be code and output.
Grading for Assignment 2
This Assignment is less ‘flexible’ that the previous two Assignments. Where the previous Assignments had many potential appropriate answers, this Assignment really has some ‘correct’ answers. Still, your Assignment will be graded using the same general grading scheme as the previous two Assignments.
General Overview of Grading
Your answers to the Assignment Questions will be graded based on a 4-level rubric (on page 6), which focuses on your demonstrated level of mastery of the course-level learning outcomes listed at the start of this instructions file. What that means is that, when the graded Assignment is returned to you, you will receive three letters that indicate which level on the rubric your submission received for the three Learning Outcomes being evaluated.
You will be able to see the three levels and some feedback about them through Gradescope when the graded Assignment is returned. These levels will also be communicated in a ‘comment’ for an OWL
Gradebook entry for Assignment 3. To be clear, there will be no numeric mark for the Assignment (e.g. like 75% or 8/10, etc.); the three levels are the only outcome of the evaluation.
Essential Requirements and Late/Accommodated Assignments
• Failure to submit the Assignment at all will result in a ‘0’ for the Assignment unless Academic Consideration from the Academic Counseling office of your home Faculty is received.
• Completion of all three (3) Assignments AND earning at least 5 level P and no more than 2 level N across all three Assignments (which evaluate you on three (3) learning outcomes each) is part of the ‘Essential Requirements’ to be eligible to earn credit (i.e. 50% or higher as a final course grade) for the course. Failing to meet the Essential Requirements with respect to Assignments will result in a final course grade recorded as 40% (or, your calculated course grade —whichever is lower). This is detailed in the course syllabus, page 7.
• Late Assignments (i.e. beyond the 48-hour grace period on the official deadline, and without
academic consideration from your home Faculty’s Academic Counseling unit) will be accepted
with a late penalty, equivalent to ONE (1) rubric level) for one learning outcome (taken from the learning outcome graded at the highest level, to minimize overall impact) per 24 hours or part thereof. To submit a late Assignment, upload your submission as you normally would. The date of upload will be used as the date to compute any late penalty. Note that I will not accept
submissions that are more than three (3) days after the end of the 48-h grace period without academic consideration.
• If academic consideration is received from your Faculty’s Academic Counseling office, with the consideration covering the deadline for the Assignment, arrangements should be made with
Jennifer Peter (through an OWL Message sent to “Jennifer Peter (Instructor)” group). In general, you are advised to continue to work on the Assignment (if you are physically/mentally able to) while you wait for Counseling to review your request for consideration. You can and should also submit your Assignment during that time if you are prepared to do so. In situations where
extended academic consideration is required, you and Jennifer will work together to find an appropriate method for completion of the Assignment.
Requests for clarification or review of grading
The graders work very hard with the rubric to grade your Assignment accurately and consistently.
Occasionally, mistakes can happen. If, after you’ve received your graded Assignment back, and have
carefully reviewed your lecture notes and feedback on the Assignment, you need clarification on some aspect of the grading, then the process to make that request is done through Gradescope (not email or in Student Hours, etc.). After the graded Assignment is returned, there will be a one-week opportunity to “Request Regrade” through Gradescope. This is achieved by clicking on the Assignment, clicking on “Entire submission” from the right side of the screen, and then choosing the button for Request
Regrade. This is the only method through which requests for regrades will be accepted. Jennifer and graders will not respond toregrade requests through any other form (e.g. email, OWL Message, OWL Forums, Student Hours, etc.).
When you make your request, you should be polite and clear about which aspect(s) of your Assignment you are seeking clarification/consideration for. Our grader will review your concern, and address it. Note that, in most situations, we will have to review your entire submission during a regrade. While it is
generally a rare outcome, it is possible for aregrade to result in a lower rubric level for some aspect of the assignment. Note that it will take sometime for the request to be addressed as the graders have multiple responsibilities to manage; please be patient.
Learning outcome |
Level M (Mastery) |
Level P (Proficiency) |
Level A (Approaching Proficiency) |
Level N (Not Met) |
Use R markdown to produce reproducible analyses and reports PLUS Use conventional and transparent formats for reporting results of statistical analyses in written/graphical form. |
LaTeX and/or R markdown formatting is used to successfully generate mathematical symbols in statistical hypotheses. Symbols used accurately reflect convention for test being conducted. Phrasing (symbolic and sentence format) accurately reflects the Research Question and structure of data being analysed (i.e. type of variables, structure of comparison groups). Sentence and symbolic format of null hypothesis are consistent. |
Attempt made at using LaTeX and/or R markdown formatting to generate mathematical symbols in statistical hypotheses but one of the following occur: • Inconsistency between sentence and symbolic format of null hypothesis, but one is accurate • Attempt at symbols clearly accurate (i.e. appropriate by convention) but symbols don’t render properly • Minor misunderstanding in general structure of null or alternative hypotheses |
Attempt made at using LaTeX and/or R markdown formatting to generate mathematical symbols in statistical hypotheses, but one or more of the following occur: • More than one error from Level P. • Symbols are not an accurate reflect of conventional symbols relevant to the test being conducted. • Evident misunderstanding of the general structure of null and/or alternative hypotheses • One of the requested hypotheses missing (e.g. no sentence format, or no alternative, or no null) |
No attempt at using LaTeX and/or R markdown formatting to generate symbols for the statistical hypotheses AND evident misunderstanding of statistical hypotheses in general. |
to Use R to conduct common parametric inference procedures, including model diagnostics. |
All data transformation, model diagnostics, and analyses are conducted in R. Application of R functions for diagnostics and test accurately reflect statistical hypotheses. All aspects of assignment are completed in R markdown file, which is successfully knit. All R code is successful in producing relevant output. All R code and resulting output is visible in submitted PDF (from knitting). |
All data transformation, diagnostics, and analyses are conducted in R, but 1 or 2 of the following occur: • RMD file is not successfully knit but all code would function otherwise • Some code and/or output is not showing in knitted file but is otherwise accurate • Minor mismatch between statistical hypotheses and application of hypothesis test with data |
R is used to perform model diagnostics and analyses but any of the following occur: • Some data transformation is not conducted in R • Some components of code are not/would not be successful at producing relevant output • Significant mismatch between statistical hypotheses and application of the test with |