PSYC 2002 Syllabus 2021 PDF

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Course Outline for Psyc 2002 Summer 2021...

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PSYC2002, Section B, Summer 2021

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Course Outline – PSYC 2002 B INTRODUCTION TO STATS IN PSYC

Introduction to Statistics in Psychology

Your Teaching Team Instructor: Lindsay Richardson “Extra” Hours: Fridays 10-11:30AM. Location: Zoom via Brightspace Email: please use Brightspace to email.

Class Time and Location This class will take place online. Lectures will be made available through Brightspace every Monday and Wednesday at 9AM. Students are responsible for all lecture material and are therefore expected to “attend” all classes. See Module 0 on class attendance and academic performance.

Student Support TA: TBA Please use Brightspace email to ask content-related questions.

TA Coordinator: TBA Please use Brightspace email to ask grade-related questions.

Office Hours Host: TBA Weekly office hours over Zoom via Brightspace.

Teaching Assistants TBA: all TAs will be announced in the first week of class.

Prerequisites and Preclusions Enrollment in this course means that you have successfully completed PSYC1001, PSYC1002, and PSYC2001. The material in these courses will assist in your ability to grasp statistical concepts more easily. However, the knowledge of material for those courses is not necessary to complete PSYC2002 successfully. Precludes additional credit for NEUR2002.

Future Directions This course is a prerequisite for PSYC3000. Moreover, the knowledge and skills gained from this course will assist you in successful completion of third-year seminar courses. Additionally, many transferrable skills are expected to be gained from this course. PSYC3000 (Advanced Design and Analysis): Techniques in data analysis, probability theory, sampling distribution theory and the ideas and procedures of estimation, classical and Bayesian approaches to hypothesis testing, linear regression and curve fitting, distribution free hypothesis testing, and the analysis of variance methods in experimental design. Third-Year Honours Seminar Courses: PSYC3100 (Social Psychology); PSYC3300 (Health and Illness); PSYC3400 (Forensic Psychology); PSYC3500 (Developmental Psychology); PSYC3600 (Personality); PSYC3700 (Cognitive Psychology); PSYC3805 (Organizational Psychology). Transferrable Skills: proficiency in Excel; improved problem-solving skills; critical thinking; increased understanding of empirical papers; interpretation and communication of statistical results.

Course Description This is a learner-centered lecture-style course, where successful completion of the course will provide you with .5 credits toward your degree. Description from Carleton Central: “A general introduction to statistical techniques employed within contemporary psychology. Topics include basic data analysis using descriptive and inferential statistics (t-tests, ANOVA, correlation, chi-square)”.

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INTRODUCTION TO STATS IN PSYC

PSYC2002, Section B, Summer 2021 Page 2 of 13 Welcome to Introduction to Statistics in Psychology! This course outline provides you with everything you will need to know in order to organize yourself for the course. Research in psychology has shown that students who spend more time organizing their semester earn higher final grades! So, this course outline should serve as a map for the entire semester. Please read it carefully, take notes of important dates and deadlines in your calendar, and ask questions if you are unsure of anything. Don’t forget about your other courses and plan your time in conjunction with them as well! This Table of Contents is here to help make it easier to find the information you might be looking for. The page numbers are clickable (you might need to hold down “Control” while you click). What are the general learning objectives for this course? ............................................................... 3 What skills are required for this course? ................................................................................................ 3 What material is required for this course? ............................................................................................. 3 Textbook ................................................................................................................................................................ 3 Lecture Notes ........................................................................................................................................................ 3 Hardware (basic requirements for online learning)......................................................................................... 3 Software ................................................................................................................................................................. 3 Course Web Site ................................................................................................................................................... 3 A Note About Intellectual Property .................................................................................................................... 4

What are the specific learning outcomes for this course? ................................................................ 4 How will I be evaluated? .......................................................................................................................... 4 Courtesy Grade Adjustments ............................................................................................................................. 4 Student Evaluation Breakdown........................................................................................................................... 5 More Information on Participating in Research Through Carleton’s SONA System ................................................................................... 7

Exam and Assignment Information .................................................................................................................... 8 Missed & Late Exam/Assignment Policy ................................................................................................................................................................ 10 Posting of Assignment and Exam Results .............................................................................................................................................................. 10

Regulations Common to All Psychology Courses .......................................................................................... 10 Requests for Academic Accommodations - The Paul Menton Centre for Students............................................................................... 10 Survivors of Sexual Violence .....................................................................................................................................................................................11 Accommodation for Student Activities................................................................................................................................................................. 11 Petitions to Defer .......................................................................................................................................................................................................... 11 Withdrawal Without Academic Penalty ............................................................................................................................................................... 11 Official Final Examination Period............................................................................................................................................................................. 12 Grading System ............................................................................................................................................................................................................ 12 Plagiarism & Academic Offenses ........................................................................................................................................................................... 12 Additional Resources .................................................................................................................................................................................................. 12

How will my teaching team help me? ................................................................................................. 13 Tips on How to Succeed in This Course (& Others…) ........................................................................ 13 Other Important Dates to Consider ...................................................................................................... 14 Course Calendar with Evaluation Dates & Deadlines ...................................................................... 16 Tentative Lecture Schedule with Assigned Readings ....................................................................... 18

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What are the general learning objectives for this course? This course is designed to build on students’ knowledge of descriptive statistics (from PSYC 2001). Thus, it will review descriptive statistics, but will be focused on inferential statistics. Inferential statistics are used to assert whether information from a sample can be generalized to a greater population of interest. So , students will learn what inferential statistics are, how they are employed, and how to think critically about them. Students will also learn how to use Excel for analytical purposes. Ultimately, this course will provide a basis on which students will be prepared to succeed in PSYC 3000.

What skills are required for this course? Simple mathematical knowledge is required (but may be supplemented with the use of electronics such as calculators). Otherwise, there are no perquisite skills that are compulsory for this course, per se. This course will cover all the necessary basic skills required to succeed. There is also a resource section on Brightspace with information on how to do well in this course. If you are worried about your success in the course, please email me as early as possible. The earlier you reach out for help, the more likely it is that you will benefit from the help our teaching team can provide!

What material is required for this course? Textbook

Software

The textbook required for this course is: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

Excel is required to complete assignments. No other spreadsheet software will be accepted. Microsoft has offered Carleton students a downloadable MS Office desktop software at no charge. See https://carleton.ca/its/ms-offerstudents/ for more details. SPSS is a software that will be lightly introduced in the course. Students do not need to download the software for any evaluations. However, to follow along with demonstrations during lectures, students might consider it. Again, Carleton offers SPSS for students at no charge. See https://carleton.ca/its/allservices/computers/site-licensedsoftware/spss/ for more details.

Lecture Notes Students are responsible for all class material presented during lectures. PowerPoint slides will be posted on Brightspace before the beginning of each class. However, slides may contain less information than what is shown in class. Students are responsible to fill in the blanks during the lectures. See Module 0 on note-taking and academic performance.

Hardware (basic requirements for online learning)

Course Web Site

This is a course that will be taught entirely online. Thus, students will require the use of a personal computer with a reliable internet connection.

All information pertaining to this course will be available on the course’s Brightspace webpage: carleton.brightspace.com/d2l/home.

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A Note About Intellectual Property All materials created for this course remain the intellectual property of the authors. They are intended for personal use and may not be reproduced or redistributed without prior written consent of the authors.

What are the specific learning outcomes for this course? By the end of the course, successful students will have demonstrated their ability to: 1: Interpret datasets using both descriptive and inferential statistical procedures. • Describe a sample using descriptive statistics from datasets. • Infer meaning from datasets and solved formulas. 2: Analyze datasets using appropriate formulas. • Solve statistical formulas provided. • Select appropriate formulas to answer research questions. 3: Recognize that statistics is conceptual math. • Explain basic statistical concepts (e.g., variability). • Constructively critique statistics presented in journal articles. 4: Embody a willingness to learn. 5: Execute functions in Microsoft Excel.

How will I be evaluated? Students’ final grades will be based on the following: 1. Best ten of twelve weekly online quizzes (2% each = 20%). 2. Asynchronous lesson participation (10%). 3. One Introduction Excel assignment (5%). 4. Best Three of four Excel assignments (10% each = 30%). 5. One cumulative final exam (Choose: 35% or 100%*). 6. Bonus Marks (up to 6% bonus) → any combination of : • Participation in Research – SONA (up to 4%) • CSAS Workshops (up to 3%) • Participating in understanding online (up to 6%).

Courtesy Grade Adjustments I believe in giving students more control over how their final grade is calculated! Here is how you can control your final grade: • Complete all the bonus mark work to gain up to 6% toward your final grade. • Drop your lowest two quiz grades (even if they are zero). • Drop one of your assignment grades (even if they are zero ; *not applicable to the introductory assignment). • The exam is cumulative. Thus, it measures how well you’ve met the learning outcomes for this course. So, if you receive a higher grade on your exam than your current overall grade in the course, then your exam grade will be your final grade. You’ve earned it! *IMPORTANT NOTE: to qualify for this grade adjustment, students must have demonstrated that they’ve completed the introductory assignment, three of the four Excel assignments, and ten of the twelve quizzes. This means that you cannot simply skip all the work and write an exam worth 100%.

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Students who qualify will receive the higher of the following two options: 1. Final Exam Grade or 2. Weighted Average + Bonus Marks

ALL MATERIAL PRESENTED DURING LECTURES AND WITHIN THE ASSIGNED READINGS IS TESTABLE MATERIAL. See Module 0 on how to prioritize testable material.

Student Evaluation Breakdown All course work is to be done individually. However, studying in groups is encouraged! Zoom on Brightspace will be made available for students to study together online (works best on Chrome browser). All evaluations will be released online at 9:00AM. Similarly, all evaluations are due on their respective due dates at 11:59PM. This is done to make it easier for students to remember when evaluations are due. 1. Online Quizzes – Twice Weekly (due Mondays & Wednesdays at 11:59PM) *Quiz 9 & 10 due Tues/Thurs because of a holiday. Twice per week, students will be given an online quiz to complete (i.e., 12 quizzes total). These are small formative assessments meant to build upon knowledge throughout the course. Incidentally, this will also gauge student understanding of the course and encourage them to stay on top of the material. Only the top ten for each student will be reflected in their grade. Quizzes will reflect the material presented in each Chapter of the text. Two quizzes per week will become available on Brightspace every Friday at 9AM. Each quiz will become unavailable the following Mondays and Wednesdays at 11:59PM (see the appended Course Calendar for clarification). Quizzes will comprise 10-15 multiple-choice questions (pulled randomly from a bank of 30 questions – no two students will receive identical quizzes). Once the quiz is submitted, students will not be able to change their answers. However, they will be allowed to review them once the quiz has closed for all students. 2. Asynchronous Lesson Participation – Recommended to be done Twice Weekly (due August 16th) Students in this course will be rewarded with marks for participating in the online lessons. This is done by answering the concept questions throughout the lessons. Students have an unlimited number of attempts and correctness is not graded. Only 10 of the 12 lectures will count toward the participation grade. Thus, should students miss 1- 2 lectures, they will not be penalized. 3. Introductory Excel Assignment – Due Friday, July 9th at 11:59PM. This Excel assignment will be done early in the course and have a lesser impact on students’ final grades in order to provide students with the experience and feedback necessary to succeed on subsequent Excel assignments. The assignment will cover the review of basic operations (e.g., BEDMAS), descriptive statistics, and introduction to inferential statistics. Students will learn how to use Excel in order to complete the assignment. Students will be given one week to complete the assignment. Collaboration with peers is encouraged. However, each student must submit their own assignment. Feedback will be provided in a timely manner to provide students with enough information to be able to begin their next assignment. ***only Excel-formatted assignments will be accepted (see page 3 for accessing Excel for free). 4. Best 3 of 4 Excel Assignments – Due Fridays 11:59PM: July 16th, 23rd, & 30th, & Aug. 6th. Three subsequent Excel assignments will be done throughout the course. Students will have one week to complete each assignment. Feedback will be provided for each assignment to assist students in preparing to succeed on the subsequent assignment.

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***only Excel-formatted assignments will be accepted (see page 3 for accessing Excel for free). 5. Final Exam – Opens Saturday, August 14th at 9AM & Closes Wednesday August 25th at 11:59PM. The final exam will comprise a take-home examination that will mirror what students have been doing in their assignments. The point is that the assignments will each help build toward the final exam. That being said, this exam is open book. Each student will receive a dataset and partially completed journal article. Students will have to complete the analysis, answering questions along the way. Additionally, students will write an APA-style Results section. Finally, students will be asked to interpret their results. More information about this Final Exam will be posted online before it is released Saturday, August 8th. 6. Bonus Mark Opportunities – due Friday August 16 th at 11:59PM There are plenty of different combinations of bonus marks to earn. Each of the following subsections has a maximum amount of bonus marks. You may choose any combination of bonus marks for a maximum of 6% total. *If students earn a Final Exam Grade as their final grade, bonus marks are not added to that total. a. Posting “difficult concepts” on Brightspace. To help each other understand and overcome difficult concepts, students can post in the online repository for bonus marks to go toward their final grade. Here’s how it works: • Each post must include the explanation of at least one concept that was well understood and the explanation of at least one concept that was difficult to understand. A ...