| Schedule Spring 2024 | ||
|---|---|---|
| This schedule may change during the semester | ||
| Date | Topic | Assignment |
| 01/26/24 | Statistical models, mantras, and connections with research designs | |
| 02/02/24 | Random variables and probability distributions | Practice 1 |
| 02/09/24 | Hypothesis Testing: differences in means | |
| 02/16/24 | Correlation | Practice 2 |
| 02/23/24 | Linear Regression I: Introduction | Assignment 1 |
| 03/01/24 | Linear Regression II: Introduction | |
| 03/08/24 | Linear Regression II: moderation and polynomials | |
| 03/15/24 | Analysis of Variance: one way, and factorial analysis | Assignment 2 |
| 03/22/24 | Longitudinal analysis: random effects models, and ANOVA | |
| 03/29/24 | Longitudinal analysis: random effects models, and ANOVA | Practice 3 |
| 04/05/24 | Spring Break: no class1 | |
| 04/12/24 | Meta-science: Power, effect size, and more simulations1 | Assignment 3 |
| 04/19/24 | Categorical analysis: probabilities and odds1 | |
| 04/26/24 | Categorical analysis: logistic, probit, and poisson regression1 | |
| 05/03/24 | Categorical analysis: logistic, probit, and poisson regression1 | Assignment 4 |
| 05/10/24 | Introduction to Structural Equation Modeling: Path analysis 1 | |
| 05/17/24 | Mediation models | Thesis plan due |
| 05/24/24 | Final Exam | Final exam |
| 1 These sessions might be asynchronous | ||
Advanced Statistical Analysis of Psychological Data, PSYC5020
Spring Semester 2025
Important
Course time: Fridays, 10:50AM to 1:30PM
Room: Dorothy & Bill Bizzini 234
Professor: Esteban Montenegro-Montenegro, PhD.
Office hours online: Schedule an appointment cliking here or send me an email.
Email: emontenegro1@csustan.edu Schedule an appointment: Warrior Connect
Course description
Course description (from course catalog): Teaches students how to perform advanced analyses of data from psychological studies.
Pre-requisites: PSYC 5010 and graduate standing in Psychology, or consent of instructor.
Course Introduction: This course is designed to give you a deeper understanding of the statistical analyses used in much of psychological research. You will learn about a number of statistical techniques, their assumptions, what they can and can’t tell you, when to use them, and perhaps most importantly how to run the analyses and read the output using statistical software. This class should be especially helpful in analyzing the data for your thesis. However, even if you aren’t using these analyses for your thesis, this information should be useful in better understanding the statistics reported in research articles.
Course Structure: This course will be in-person. We will meet every Friday at 10:00 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. I will assign only one cummulative exam at the end of the semester, this exam will be an applied guide to conduct data analysis.
I don’t expect that students memorize concepts or estimate models by hand using calculus or differential equations. I focus my course on understanding why and how you can analyze your quantitative information.
It is important to mention that I will post all lectures on Canvas, along with videos explaining how to solve the assignments and exam.
Course Learning Objectives
- Determine the appropriate test for a research question given the type of data.
- Conduct statistical analyses, interpret the output, and describe the results.
- Describe the importance of reporting effect sizes.
- Describe the limitations and assumptions of various statistical tests and procedures.
Course Materials:
This class will not require any text book, however I will borrow information from several sources. You will see the list of references in the bibliography section. You will also have access to my website where you’ll find all lectures, and additional information. My wepage is always under construction please let me know if something you need is missing.
In this class we will use R as the main tool to analyze quantitative information:
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.
Additionally, all software use in this class can be installed on Windows, MAC, Linux, or Chrome OS (Chromebooks).
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
Assignments (40%): 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 (25%): There will be one take home exams. This 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 analyze data, report the results, and/or find a possible theory to support your interpretation. The take home exam could be done in pairs but it is not mandatory.
R programming exercises (15%): 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!
Thesis Plan (20%): The goal is to create a draft for your methods section focusing on the analysis plan, and research design. You will need to estimate your tentative sample size based on statistical power. Don’t worry! We will study more about it. We might use the software G*Power to conduct this analysis, or statistical simulation (I do prefer this option) but the latter depends on time to learn it.
Late work: Late assignments will be accepted up to 48 hours after the deadline. Points will be deducted as follows: one day later will deduct 15% of your grade, two days late represents 25% less in your grade, three days delayed is 0% of your grade.
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.
Final Grades: Grades are based on all weighted evaluation categories (participation activities, graded assignments, and exams) total points. Letter grades will be assigned using the following percentages (rounded to the second decimal point):
| 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
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.
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.
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.