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Psych 241_Syllabus_S22.docx 

Psychology 241 – CN 23029

Introduction to Psychological Statistics

Syllabus

Spring 2022

Hybrid Instruction

 

Course and Instructor Information

Instructor:	Brandi Goodspeed, M.A.	Lecture:		Asynchronous with weekly schedule
Email:	blg200@humboldt.edu	Lab: (only attend the lab section that appears on your Student Center schedule)		M 3-4:50 pm via Zoom W 3-4:50 in BSS 317
Office:	BSS 517	Office Hours:		T TR 12-12:50 pm, W 2-2:50 pm, & by appt. via Zoom or in office.
GA Name:	Mayra Mendez	Email:	msm172@humboldt.edu
GA Name:	Kaitlin Duskin	Email:	krd321@humboldt.edu

 

Course Description

This course covers descriptive statistics, normal distributions, probability, correlation, regression, hypothesis tests for comparing means, ANOVA, and chi-square. We make extensive use of R and R Studio software for data analysis.

Course Objectives

• Acquire the concepts, terms, and symbols used in data analysis.
• Understand descriptive and inferential statistical techniques.
• Be able to correctly apply statistical techniques to psychological data.
• Be able to correctly interpret the results of statistical analyses.
• Be able to convey, in plain English terms, the details of statistical analyses and results.
• Be able to critically evaluate scientific research and real-world examples of statistical application.

Department Objectives

• Students will demonstrate knowledge of methodological, analytical, and research skills appropriate to the field of psychology. 
• Students will demonstrate knowledge of ethics involved in conducting research and working in the field of psychology. 
• Students will demonstrate the skills needed for postbaccalaureate employment, graduate, or professional school.

Course Expectations 

The major expectation for this course is student investment and participation with a positive and curious attitude. Your active participation will be key ingredients to the success of your learning and the class as a whole. I have high expectations for student performance, and I expect all students to try their best.

While time-management and organization are important skills to develop for academic and professional success, these skills will be even more critical while learning remotely. Students should plan to dedicate approximately the same amount of time to this course each week as you would for a face-to-face class—this includes reviewing lecture content, listening to recorded material and/or exploring other provided content, and completing assignments.

Mathematics

This course assumes mastery of basic mathematical concepts such as percentages, decimals, fractions, solving equations for unknown values, and order of mathematical operations (PEMDAS).

Computers

This course assumes knowledge of word processing programs (e.g., MS Word), internet applications (navigating web pages, using email), and the ability to perform file management tasks (e.g., copy a file, save a file to a USB drive).

Textbook & Technology

Textbook

I do not assign readings from the textbook, nor do I rely on it in class. I do provide references to useful chapters that may supplement your learning. Some students find the textbook useful; others can extract from lecture and other materials all the information they need for the class. The textbook is free.

 

Text: Navarro, D. (2020). Learning Statistics with R. https://learningstatisticswithr.com/book/

Materials

The primary materials for this course are packaged as tutorials. These tutorials include a series of short videos (i.e., what would normally be lectures but broken up into several short videos), knowledge check questions, and exercises. Each tutorial is set up as an assignment, you will get a completion code to upload to Canvas when you complete them. This approach will undoubtedly be different from the structure of your other courses but is likely far better than sitting through a 50-minute video all at once.

 

Copies of PowerPoint slides, HW, data files, and handouts are accessible via Canvas. These handouts include a detailed “manual” for running analyses.

 

Other materials: USB drive, scientific calculator (something that does square roots, exponents, etc.).

 

Technology

PSYC 241 is lecture is fully online this semester. Additionally, one lab section is also meeting online. The other lab section will be meeting in person. You will need a computer with a WIFI connection. If you use a tablet or Chromebook, you will need to familiarize yourself with HSU’s virtual lab (vlab.humboldt.edu) environment to complete analyses. A computer microphone and camera are recommended.

Canvas

You will need to be able to log on to Canvas several times per week to access content, participate in discussion forums, complete quizzes, upload assignments, etc. Be sure to set your email notifications as “ON” for ‘Announcements’ so you do not miss any important information. You can view your progress in the gradebook. You can download the Canvas app on your cell phone, which will notify you of upcoming due dates, posted grades, and announcements.

 

Questions about Canvas? Use the Help button bottom left of the Canvas page, 24/7. 

 

It looks like this 

 

Zoom Video Conferencing

Additionally, for those of you enrolled in the Monday lab section, we will use Zoom to meet for lab weekly. You will find the Zoom meeting link on Canvas. I have also included the links below. You may also download the Zoom mobile app and use the ID code provided to access the meeting. If you are new to Zoom or need assistance with a problem, you will find more information and a troubleshooting guide on Canvas.

 

Monday Lab Zoom Link

 

Zoom Meeting Link: https://humboldtstate.zoom.us/j/88662073918?pwd=cy9JSDBla2EwSGhIYzErN0JLc1BoQT09

 

Meeting ID: 886 6207 3918

 

The R Computing Environment

Lab sessions focus on learning the widely used (and free) R platform for data analysis. Tutorial assignments include detailed examples of analyses similar to those you need to run for HW assignments. Students must do all required R problems with code rather than RCommander or other add-ins.

Resources for Students

 

Office Hours (Virtual or In Office)

Below you will find the link to access my virtual office hours for which I will be in my office on campus (BSS 517). Please use whichever method with which you are most comfortable. If you are on a computer, use the weblink; if you are using the Zoom mobile app you will need the meeting ID. Please note that I have the waiting room function enabled so that my meetings with students are private. If you are in the waiting room it is because I am with another student, please be patient and I will allow you to enter the meeting as soon as I can. Also remember that if these times do not work for you, I can always schedule an appointment outside of the posted hours. Send me an email with several times that you are available, and I will schedule a Zoom meeting with you.

 

Tuesdays and Thursdays from 12 p.m. to 12:50 p.m. via Zoom (or BSS 517)

 

Zoom Link: https://humboldtstate.zoom.us/j/86271453262?pwd=WW55dktkNjRIWkpZM2tIak05L1dyZz09

 

Zoom Meeting ID: 862 7145 3262

 

Wednesdays from 2 p.m. to 2:50 p.m. via Zoom (or BSS 517)

 

Zoom Link:

https://humboldtstate.zoom.us/j/86379123245?pwd=bzBOVTNSTHRna3l3RTdrVzBUNTRXdz09  

 

Zoom Meeting ID: 863 7912 3245

Graduate Assistants

Given the class size, I rely on graduate teaching assistants for help with grading and laboratory sessions. Each of our assistants has a deep understanding of statistics. They are excellent resources to help you learn the material. Please treat them respectfully.

Tutoring & Supplemental Instruction

Some students may need a tutor for additional help, you can find information on Canvas regarding the tutors available for you. In addition, there is a drop-in supplemental instruction course for PSCY 241 this semester, on MW from 5-5:50 p.m. Please contact the Learning Center: www.humboldt.edu/learning/tutorial_services.php

Keep Learning

Visit the Center for Teaching and Learning’s Keep Learning webpage for many helpful links. They can: help you access technology and equipment, provide troubleshooting, offer support for getting set up for the online semester, and direct you to other services you may need:

https://ctl.humboldt.edu/content/keep-learning

Tech Help Desk

Visit the link provided for the most common problems submitted to the Technology Help Desk. If you are experiencing a problem related to technology, the folks at the help desk will likely have the answer, or direct you to someone who does:

https://its.humboldt.edu/help/top-help-desk-issues

Student Support Services

The link provided highlights a variety of support services offered to students at Humboldt State University:

https://acac.humboldt.edu/students/campus-student-support-services

Class & University Policies

Academic Integrity/Disruptive Behavior

Academic dishonesty, including plagiarism, will not be tolerated and may result in a failing letter grade at the discretion of the instructor or the university. Additionally, students are expected to act with respect by not engaging in any disruptive behaviors including, but not limited to, arriving late, cell phone use, side conversations, inappropriate technology use (i.e. Facebook, Amazon, etc.), and leaving early. These policies apply to our virtual classroom as well. Students who engage in disruptive behavior may be asked to leave class. Students are responsible for knowing the policy regarding academic dishonesty and disruptive behavior:

http://www2.humboldt.edu/academicprograms/syllabus-addendum-campus-resources-policies

Doing Your Own Work

This is a course where students naturally work together. I encourage students to do so. There is, however, a huge difference between helping each other understand material and simply copying someone else’s work. If I find that students turn in identical work, I will give those assignments no credit and may refer students for discipline as noted in HSU’s Academic Dishonesty Policy. Please do not make me do this.

Email & Discussion Etiquette

Please use appropriate language when sending emails and include your name and the course in which you are enrolled. Please send me your questions: blg200@humboldt.edu. You may also use the message feature in Canvas to reach out to me. I try my best to respond within 24 to 48 hours. If you do not get a response within that time frame, please resend your email.

 

When interacting with each other in discussion and collaborative activities, please be respectful. Also, note that ideas and thoughts that are shared remain in the safe space of this online course and are not to be shared outside of our collective work.

Students with Disabilities

Persons interested in disability related accommodations, please contact the Student Disability Resource Center (SDRC):  826-4678 (voice) or 826-5392 (TDD). Please be aware that accommodations may take a few weeks to arrange. See more details on the SDRC website: http://www2.humboldt.edu/disability/

 

This course was intentionally developed with accessibility in mind. However, if you discover something in the course that is not as accessible, please let me know right away. It is very important to me that everyone has access to all information.

Add/Drop Policy

Students are responsible for knowing University policy and deadlines regarding dropping and adding classes. Be advised students who are absent from class during the first week may be dropped automatically. Incompletes may be granted to students in situations where they cannot reasonably complete the course. The last day to drop/add courses without a serious and compelling reason is: 01/31/21

http://pine.humboldt.edu/registrar/students/regulations/schedadjust.html

Other University Policies & Procedures

See for a full review of campus policies, procedures, and resources:

http://www2.humboldt.edu/academicprograms/syllabus-addendum-campus-resources-policies

Course Requirements

Tutorials (100 points)

There are ten tutorials, completion of each is worth 10 points. (There will be more, but I’ll give extra credit for those). All tutorials are due by the end of the day prior to the exam but it will benefit you the most if you complete these as early as possible as the homework requires knowledge gained from the tutorials.

Lab Attendance (100 points)

Attendance in lab is essential to performing well in this class. I cannot stress this enough. On 10 days in lab, I will take attendance. Students who are present earn 10 points.

Homework Assignments (400 points)

Homework involves computations of statistics, independent use of R, and detailed interpretations. There are eight homework assignments, each worth 50 points. All homework assignments are due by the end of the day Fridays on the week they are listed on the course schedule. To earn full points on homework assignments you must include all hand calculations (where requested) and R output, along with your interpretations and conclusions.

 

Late Work

Assignments build on material from earlier assignments. Completing work late makes it difficult to perform well in this class. For that reason, I rarely grant extensions. HW is penalized 20% of the total possible points (e.g., 10 points). I will not accept any work after the final day of class (5/6/22).

Exams (400 points)

Three midterms and a semi-cumulative final, each worth 100 points. Exams cover techniques discussed in lecture and lab. Please see Canvas for exam reviews. Exams will open the whole week prior to the due date and will be due by the end of the day Fridays on the week listed in the course schedule.

 

*Exams must be taken by the deadline. If you fail to take an exam by the scheduled deadline, you must provide documentation for a serious and compelling reason to schedule an exam make-up.

Extra Credit

There is a variety of extra credit opportunities. I strongly encourage you to take advantage of these chances to earn extra point. In many cases, extra credit helped students earn higher grades.

 

Extra credit quizzes/lab assignments. Near the end of the semester, I provide extra credit quizzes and one EC assignment (20 points EC possible).

 

Participation in departmental research studies. To participate you need an account on our web-based participation scheduling system – see https://hsupool.sona-systems.com/ for instructions. Choose Psych 241 as your course (you can choose multiple courses) and assign your participation to 241. You may NOT count participation completed for other courses toward this extra credit (30 points total allowed for the semester for research participation, 1 point per unit).

Grading (1000 total points)

Final course grades will be scaled in the fashion highlighted below.

 

A = 100-90%	A- = 89-87%	B+ = 86-84%	B = 83-80%	B- = 79-77%	C+ = 76-74%
C = 73-68%	C- = 67-65%	D+ = 64-62%	D = 61-51%	F = 50-0%

 

Course Schedule

Week	Lecture Topic(s)	Readings	Assignments
1	Introduction to Statistics – Basics (Tutorial 1)	Ch. 1-3
2	Displaying Data (Tutorial 2); Central Tendency & Dispersion (Tutorial 3)	Ch. 5-6
3	Standard Normal Distribution (Tutorial 4)		HW 1
4	Basic Concepts of Probability	Ch. 9, 5.7	HW 2
5	Correlation (Tutorial 5)	Ch. 5.7, 15	Tutorials 1, 2, & 3 Exam 1
6	Regression (Tutorial 6)	Ch. 15	HW 3
7	Sampling Distributions & Hypothesis Testing (HT) (Tutorial 7)	Ch. 10	HW 4
8	HT Applied to Means: One Sample	Ch. 11	Tutorials 4 & 5 Exam 2
9	Spring Break
10	HT: Independent and Related Means (Tutorial 8)	Ch. 13	HW 5
11	HT: Statistical Decisions & Power		HW 6
12	One Factor ANOVA (Tutorial 9)	Ch. 14
13	One Factor ANOVA	Ch. 14	Tutorials 6 & 7 Exam 3
14	Factorial ANOVA (Tutorial 10)	Ch. 16
15	Factorial ANOVA; HT with Correlation & Regression	Ch. 16	HW 7
16	Chi-Square; Selecting the Correct Test	Ch. 12	HW 8 Tutorials 8, 9, & 10
Final	Final Exam Due May 13th, 2022, by 11:59 p.m.