Section: R12 Code: 16799 Location: online (zoom links provided on blackboard) Time: Thursdays 12:30pm-2:00pm Office Hours: T 12:15-1:15pm, TH 2-3pm (by appointment on zoom)
Instructor: Matthew Crump, email@example.com
This course is designed as a parallel lab component to PSYC 7706G: Statistical Methods in Psychology – II. Subject areas include: review of descriptive statistics (e.g., mean, variance, standard deviation), simple probability, distributions (e.g., binomial, normal, F), simple correlation and regression, multiple regression, inferential statistics (e.g., hypothesis testing, sources of error), analysis of variance designs (e.g., between subjects and repeated measures factorial designs, fixed, random, & mixed designs), comparisons (a priori, post hoc), and intensity of effect. Relationship between regression and analysis of variance is emphasized. New computational techniques such as Monte-Carlo, Bootstrap, and permutation tests are also presented. This lab course will introduce students to the open-source statistical software environment R and R-studio as a tool to reinforce statistical concepts through simulation techniques, and develop practical skills in data-analysis.
Prerequisites: PSYC 7705G: Statistical Methods in Psychology – I
Recommended Prerequisites: Undergraduate Statistics, College Algebra
Course objectives: develop the following skills in relation to content learning objectives in 7706.
All of the course materials will be available in a timely fashion on this course website and/or blackboard. This is a companion lab course for PSYC 7706G: Statistical Methods in Psychology – II, and the readings for each week are described in the syllabus for PSYC 7706G.
We are scheduled to have synchronous online meetings on a weekly basis. We will decide as a class how to best use this time to accomplish the course goals. In general, the lab for each week will focus on using R to reinforce statistical concepts that are discussed in the lecture portion of the course. Lab instruction will focus on the rudimentary R knowledge needed to demonstrate statistical phenomena of interest, example coding exercises, and a generalization exercise for students to demonstrate their independent knowledge. By completing the assignments, students will create a coding portfolio demonstrating a variety of data-analysis and communication skills.
There will be two forms of assessment, including 12 weekly lab assignments (72%) and a four part semester long project (28%), serving as the final exam for the course. If this class was run in person, we would be in a computer lab, and much of the time would be reserved for completing the lab assignment (after a lab tutorial) during the lab time. So, the lab assignments will be designed with similar expectations in terms of time needed to complete (and we may decide to use our online meeting time in a similar way).
The final project will involve an integrative data-analysis or statistical demonstration project. Multiple options will be given for the format of this exam, and students may request permission to have their own ideas approved for the final project. The main goal of the final project is to demonstrate mastery of the course concepts culminating in the production of a reproducible analysis. The schedule and assignment due dates are below:
|Week||Date||Lab Topic||Assignment Due|
|1||Th 4 Feb||Lab 1 Shaping Data||Th 11 Feb|
|2||Th 11 Feb||Orthogonal Multiple Regression||Th 18th Feb|
|3||Th 18th Feb||Nonorthogonal Multiple Regression||Th 25th Feb|
|4||Th 25th Feb||Semester Long Project 1||Th 4th Mar|
|5||Th 4th Mar||ANOVA||Th 11 Mar|
|6||Th 11 Mar||ANOVA regression approach||Th 18th Mar|
|7||Th 18th Mar||Comparisons||Th 8 Apr|
|8||Th 25th Mar||Semester Long Project 2|
|Th 1 Apr||SPRING BREAK|
|9||Th 8 Apr||2 Factor ANOVA||Th 15th Apr|
|10||Th 15th Apr||Contrast analyses||Th 22nd Apr|
|11||Th 22nd Apr||Semester Long Project 3||Th 29th Apr|
|12||Th 29th Apr||Repeated Measures ANOVA||Th 6 May|
|13||Th 6 May||Nested Designs||Th 13 May|
|14||Th 13 May||Kissing the cookie cutter goodbye|
|M 24 May||Final project DUE||Th 17th Dec|
There are 12 weekly assignments, and a final project. The grading rubric is below:
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Students are expected to attend all classes. In the event of an emergency, contact me as soon as possible. If you are missing a class for religious reasons refer to the state law regarding non-attendance because of religious beliefs noted in the front matter of the Undergraduate Bulletin and Graduate Bulletin. These may be found on the Academic Calendars, Course Schedules, and Bulletins page of the Registrar’s website. See also the student bereavement policy at http://www.brooklyn.cuny.edu/web/about/initiatives/policies/bereavement.php.
It is important to me that the course be accessible to all students. In order to receive disability-related academic accommodations students must first be registered with the Center for Student Disability Services. Students who have a documented disability or suspect they may have a disability are invited to set up an appointment with the Director of the Center for Student Disability Services, at 718-951-5538. If you have already registered with the Center for Student Disability Services please provide your professor with the course accommodation form and discuss your specific accommodation with him/her.
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The Library maintains a collection of links to sites that can assist you with proper citation format and paraphrasing and quoting other authors at Research & Writing Help. The Learning Center has writing tutors available to help you with your writing http://lc.brooklyn.cuny.edu/.
The best learning is done in conversation with others, whether they are people—classmates, teachers, friends—or texts—books, articles, essays, poems, films etc. It should not be a solitary process. However, the assignments that you hand in for this course must be done on your own, should represent your own thinking, and should be original work that you have done for this particular course. A good way to balance these two seemingly contradictory approaches (collaborative learning and original individually-produced work) without knowingly—or, even unwittingly—resorting to plagiarism or other forms of academic misconduct is to learn and meticulously observe the rules for citing the work of others (this could be the great point your roommate made that you used in your paper, it could be a well-turned phrase from an academic essay, or it could be anything in between). It is your responsibility to learn what constitutes plagiarism and the correct rules for citing sources—read the information on the following website carefully: http://www.brooklyn.cuny.edu/bc/policies/. The bottom line is: passing off anyone’s words or ideas as your own for any reason whatsoever is plagiarism.