Experimental Methods and Design, PSYC3000

Spring Semester 2025

Important

Course time: Tuesdays and Thursdays, 10:50AM to 11:50AM
Room: No room, the course is synchronous online on Zoom every week.
Professor: Esteban Montenegro-Montenegro, PhD.
Email: emontenegro1@csustan.edu
Schedule an appointment: CLICK HERE

Course description

Course description (from course catalog): Provides a working knowledge of fundamental scientific methods in psychology, stressing the integration of laboratory methods, measurement theory, and inferential statistical techniques, including introduction to analysis of variance.

Pre-requisites: You must have already passed an intro to psychological research methods course (PSYC 2020 at Stan State, PSYC 102 at MJC, PSYC 4 at Delta, or PSYC 1B at Merced College) and a statistics course (MATH 1600, MATH 1610 or MATH 1620 at Stan State, MATH 134 at MJC, MATH 12 at Delta, MATH 10 at Merced College, or MATH 2 at Columbia).

Course Introduction: In this class, we are going to take what you learned in your intro to psychological research methods class and your stats class, combine the information, and kick it up a notch. We are going to review the basics of psychological research and dig deep into several statistical models. You will leave this class ready (and maybe even eager) to conduct your own research.

Course Structure: This course will be synchronous online. We will meet on Tuesdays and Thursdays on Zoom at 10:50 a.m. Students will be exposed to R language to analyze data, and understand statistical concepts. I will use statistical simulations to explain basic concepts, misconceptions and models in statistics. But, don’t panic! I will teach how we will use simulations. Students will have to submit several take home assignments, and applied practices. This course has a lab which will be asynchronous online. We will not have meetings online, instead you will solve hands on excercises at home with the help of videos recorded by the instructor.

Course Learning Objectives

1. Identify strengths and weaknesses associated with various methodologies.
2. Recognize the most appropriate way to describe and visualize data for different types of variables.
3. Identify which statistical test would be most suitable given the research question(s) and data.
4. Execute analytical techniques (descriptive and inferential statistics) using statistical software.
5. Test hypotheses using inferential statistics.
6. Interpret results and report them in APA style.

Program Learning Outcome	Covered?
1) Demonstrate psychological literacy	X
2) Be able to identify strengths and weaknesses in psychology studies	X
3) Apply psychology concepts to address real-world problems	X
4) Communicate effectively in formal and informal written and oral modes	X
5) Be able to identify the commonalities and differences between different theoretical approaches
6) Describe and act in accordance with the scientist-practitioner model	X
7) Act according to the ethical principles adopted by the profession	X

Course Materials:

We will learn R language! I will provide R exercises and also videos where I explain more details about R language. I always encourage students to look for more materials online, the R community is a big open resource. I will be doing gently introductions to R and JAMOVI, Don’t panic! In addition, you’ll need to download the R IDE named “RStudio”. All software used in this class will be free and open source, and they work on Windows, Mac, Linux or Chrome OS.

R is a programming language. It is an open source language, free and very famous around the world. We will learn how we can use it to analyze diverse type of statistical models. You can download the installer and read more about it from: https://www.r-project.org/

We will also complement R with the software named RStudio:

RStudio is an Integrated Development Environment (IDE) it works as a friendly interface to use R language. You can read and download RStudio from here: https://www.rstudio.com/products/rstudio/download/

We might use Jamovi or JASP depending on time during the semester.

Jamovi is a friendly software based on R language. It runs R behind scenes, many users believe that Jamovi is a friendly approach to learn R. You can download JAMOVI from here: https://www.jamovi.org/. JASP is also another implementation of R, it is more friendly for the type of user who likes an appealing user interface.

All software used in this course will be open source this means we will use free software that is supported by a large scientific community, on top of that, you’ll have access to the source code. I don’t expect you to understand the source code, we won’t study computer coding in this class but you will use some principles of computer coding. You may read more about open source software on this link CLICK HERE.

Computer/Laptop: Your device should be good enough to get access to internet, office and conduct basic data analysis models. Most of the modern computer will work for this class, however depending on your final projects you might need a computer with at least 8 GB of ram memory. IPads are not able to run all the software mentioned out of the box. You may tray to jailbreak your IPad but I don’t recommend to jailbreak you device because it can damage your IPad if you don’t know what you are doing. If you need a computer you may borrow one from the IT department.

Microsoft Office programs (PowerPoint, Word, Excel): You may use Word or any other text processor to submit your assignments. Google doc will also work for this class. However, I will be explaining a new format to create documents in R. It is a software called Quarto and it is integrated in R. If you use Quarto I’ll give you 20 extra points in each assignment, exam or practice. It is not mandatory to use Quarto. You may read more about Quarto by clicking here.

Evaluation

Graded Assignments (40% of course grade): There will be frequent graded assignments. Some of these will be short writing assignments, worksheets, and/or involve using software to analyze data. Homework assignments will require online submission via the course Canvas site.

Take home exam (30%): There will be two take home exams. These exams will consist of an application of the topics learned in class. It is a hand-on exercise where you will have the opportunity to formulate your own research design, analyze data, report the results, and/or find a possible theory to support your interpretation. The take home exams could be done in pairs but it is not mandatory.

R programming exercises (30%): This fall I will try to teach you more content related to programming in R. Don’t panic! I know it can be overwhelming but I’m here to help you. You will complete at least four exercises at home. I will create videos to guide your learning process. The goal is to help you to learn more basics in R language. It will be fun!

SONA. One point will be given for each credit earned via the Sona-systems website, I will accept up to 5 extra points from SONA. Make sure that you sign up using a participant (not a researcher) account, and that you assign the credits to this course. I will get a report of the credits assigned to this course during the exam’s week.

Extra points (3%): You will have the chance to earn extra points during class or after each session. Some students might earn extra points by going an extra mile in assignments or exams. For example, detail graphs, tidy code, or implementing an alternative model that explains the data better. Extra assignments might also apply. Additionally, I will try to teach a new method to create reports in R with the help of a software called Quarto. If you use Quarto to write your assignments and practices, I’ll give you 5 extra points in each submission. It is not mandatory to use Quarto.

Late work: Late assignments will be accepted up to 48 hours after the deadline. Points will be deducted as follows: one day after the deadline reduces 15% of your grade, two days late represents 25% less in your grade, three days late is 0% of your grade. For instance, if the assignment is worth 50 credit points, and you submit your assignment 23 hours after the deadline you can earn maximum 42.5 points.

Disputing a Score: Students are welcome to dispute a score if they believe they were incorrectly deducted for their work. For this, students must provide a written explanation, specifying why they were incorrectly graded. For non-final assignments or exams, this must be done within seven (7) days from the date the score was posted on Canvas. For final assignments or exams, this must be done before final course grades are submitted; your instructor will post an announcement on Canvas with this date.

Grading Criteria
A = 93 - 100 \(A^-\)= 90-93 \(B^+\) = 87-90 B = 83-87 \(B^-\) = 80-83	C = 73-77 \(C^-\) = 70-73 D = 63-67 \(D^-\) = 60 - 63

Expectations and Policies

Communication skills: You are expected to exercise strong academic verbal and writing skills for expressing yourself to complete class assignments. Failing to clearly communicate your ideas on class assignments may result in you losing points on that assignment. You also need strong analytical and critical thinking skills for completing weekly tasks. I will do my best to respond to emails within 1 - 2 business days (Monday through Friday). If you do not hear back from me within this time interval, send me a follow-up email that includes your original email message. Please keep in mind that if your email question is sent at the last minute it may not be possible to send you a response right before the submission of an assignment or exam. Before emailing me, please see if the answer to your question can be found on the course syllabus, schedule, or Canvas webpage.

Attendance: If you want a good grade in this class, you need to keep up with the course material. However, attendance is not contemplated in the final grade.

Diversity: I will always embrace diversity as the most important human value. It is expected that students understand the importance of creating diverse and safe places free of discrimination by gender, age, race, ethnicity, nationality, sexual orientation, gender identity or disability. I am an ally to the lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) community, and I am available to listen and support you in an affirming manner. I can assist in connecting you with resources on campus to address problems you may face pertaining to sexual orientation and/or gender identity that could interfere with your success at Stan State.

Academic misconduct: Academic dishonesty will not be tolerated. Instances of academic misconduct (e.g., plagiarism, cheating) will result in a grade of zero on the exam/assignment in question. Additionally, you may also receive a lower letter grade or “F” in the class, be reported to Judicial Affairs for academic misconduct activity tracking or disciplinary action, suspended or expelled from the university. It is your responsibility to know the rules. Always paraphrase and cite the source properly according to APA style, avoid copying sentences unless they are necessary, and you cite the author in APA style. Always cite your source! In detail, pay attention to the California Code of Regulations:

Warning

“Title 5, California Code of Regulations, Section 41301 notes that students may be”expelled, suspended, placed on probation, or given a lesser sanction for one or more of the following causes which must be campus related: 1. Cheating or plagiarism in connection with an academic program at a campus. . . .” (see “Student Rights & Responsibilities” section of the current Stanislaus State catalog).”

APA Style: Unfortunately, I have to enforce the use of APA style, this is important to generate clean and tidy documentation while you follow scientific formatting. You have to follow the APA style 7th edition, I would recommend to buy the manual or just use this website, it has plenty of information about it, it also provides tools to generate references and citations: https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_style_introduction.html

**Artificial Intelligence policy:* I know students use AI tools to answer assignments faster. I believe AI is not totally bad if the student uses AI as another learning tool. You may use AI to find how to code in R but if I detect that all the assignment was submitted to an AI, I will ask the student to resubmit the assignment. It is simple to detect when the student allow the AI to do all the work.

Students with disabilities: If you are a student with a documented disability at Stan State, please see me immediately to discuss appropriate accommodations. You must email me your letter of accommodation from Stan State’s DRS department as soon as possible. For exam accommodations, you must email me your accommodation letter at least seven days (7) before a scheduled exam to receive your accommodation (see schedule for exam dates). Contact me via email if you wish to discuss your accommodation or if you are in the process of registering for DRS services. Note, that accommodations are not provided retroactively.

Student Resources

Here are some of the resources available to you here at Stan State. All these services are available to you, as a Stan State student, free of charge (except certain medical appointments and procedures). Please visit their web pages to learn more about the services they provide.

Basic Need Support: 209-667-3108. Resources are available to help with securing food and emergency finances.

Student Health Center: 209-667-3396. Medical care, health education, disease prevention, laboratory testing, physicals, women’s and reproductive health, flu shots, immunizations.

Disability Resource Services: 209-667-3159. Supports students and arranges accommodations for students with disabilities, including disabilities related to learning, vision, mobility, hearing, autism, or chronic or temporary health factors.

Psychological Counseling Services: 209-667-3381. Confidential individual personal counseling and group/wellness workshops to help students deal with stress, anxiety, depression, grief, relationships.

Diversity Resources: Workshops, student space, reading nook, complimentary coffee and tea, social justice library, conference room space.

Undocumented Student Services: 209-667-3519. Walk-in advising, workshops, legal services, DACA renewal, scholarships, peer support, family and community engagement.

Academic Success Center: 209-667-3700. Drop-in advising for general education, university requirements, undeclared majors, academic probation, and California Promise.

Learning Commons: 209-667-3642, Tutoring (walk-in and regular appointments), supplemental instruction, WPST, writing center.

Career and Professional Development: 209-667-3661. Career coaching, workshops, resume building, business attire.

Warrior Food Pantry: 209-667-3561. Non-perishable food items and toiletries, at no cost. Collect up to 10 items per week.

Student Affairs: 209-667-3177. General hub for all student academic and support services on campus.

Note

In case you are impacted by U.S. Immigration and Customs Enforcement (ICE) you may contact Dr. Heather Dunn Carlton, AVP for Student Affairs and the Dean of Students at dos@csustan.edu.

Class Schedule

The following class schedule is always under construction, which might change every week. You will receive a notification if there are changes. If not, you will not be penalized because of any unannounced change.

Warning

You should always check Canvas, I might add additional readings such as scientific articles, press articles or videos.

Schedule Spring 2025
This schedule may change during the semester
Date	Topic	Assignment
01/28/25	Introductions and syllabus
01/30/25	Research Designs	Practice 1
02/04/25	Statistical models, mantras, and connections with research designs
02/06/25	Statistical models, mantras, and connections with research designs	Practice 2
02/11/25	Random variables and probability distributions
02/13/25	Random variables and probability distributions	Assignment 1
02/18/25	Descriptive Statistics, and Variability
02/20/25	Descriptive Statistics, and Variability
02/25/25	Data Visualization	Assignment 2
02/27/25	Data Visualization
03/04/25	Introduction to Hypothesis Testing	Practice 3
03/0625	Introduction to Hypothesis Testing
03/11/25	Mean comparison
03/13/25	Mean comparison
03/18/25	Bi-variate analysis and intro to linear regression
03/20/25	Bi-variate analysis and intro to linear regression
03/25/25	Multiple linear regression	Assignment 3
03/27/25	Multiple linear regression II
04/01/25	Spring Break
04/03/25	Spring Break
04/08/25	Multiple linear regression III
04/10/25	Model selection
04/15/25	Model selection II
04/17/25	Psychological Measurement; Reliability; Validity1	Assignment 4
04/22/25	Psychological Measurement; Reliability; Validity II1
04/24/25	Psychological Measurement; Reliability; Validity III1
04/29/25	Special topics1
05/01/25	Special topics1	Practice 4
05/06/25	Special topics1
05/08/25	Special topics1
05/13/25	Special topics1
05/15/25	Special topics1
08/19/25	Final EXAM	Final EXAM
1 These sessions might be asynchronous

References

Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Academic Press.

Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications.

Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.

Salkind, N. J., & Shaw, L. A. (2020). Statistics for people who (think they) hate statistics: Using R. Sage publications.

Westfall, P. H., & Arias, A. L. (2020). Understanding regression analysis: A conditional distribution approach. Chapman; Hall/CRC.