PSYC 5104- Foundations of Research in the Psychological Sciences I (Fall 2023)

Instructor: Ian Stevenson (ian.stevenson@uconn.edu)
Office: BOUS 112
Office hours by appointment

Lecture meets Mon and Wed 10:10am - 11:40am in Bous 160

Course Description

This course is designed to introduce you to graduate-level behavioral research with a focus on data analysis. We will examine the core concepts in inferential statistics, as well as issues of replication and reproducibility. The course also provides a survey of methods that serves as a foundation for other quantitative courses, both within and outside the department.

Prerequisites: This course assumes that students have taken a previous undergraduate-level research methods course (e.g. PSYC 2100WQ) and have a strong understanding of descriptive statistics, t-tests, and correlations. If you are concerned about your readiness for the course you may want to discuss how to approach the course with Professor Stevenson or your advisor.

Problem sets can use your choice of programming language (e.g. R or SPSS). R is available here (or Rstudio). SPSS is available here (or AnyWare).

Textbooks:
Rosenthal and Rosnow (2008) Essentials of Behavioral Research: Methods and Data Analysis. 3rd ed. McGraw-Hill.
Howell (2012) Statistical Methods for Psychology. 8th ed. Cengage.
Field (2017) Discovering Statistics Using IBM SPSS Statistics. 5th ed.
Field, Miles, Field (2012) Discovering Statistics Using R 3rd ed.

Refreshers on undergraduate statistics and methods:
Cote, Gordon, Randell, Schmitt, and Marvin (2021) Introduction to Statistics in the Psychological Sciences
Diez, Cetinkaya-Rundel, and Barr (2019). OpenIntro Statistics, 4th ed.

Other Resources:
R for Psychological Science
Learning Statistics with R
DataCamp Introduction to R
SPSS Tutorials
Nature Methods – Points of Significance
Tutorials in Quantitative Methods for Psychology
Fundamentals of Data Visualization
Data Visualization: A Practical Introduction
Open Stats Lab

Schedule

TopicAssignments
08.28
08.30		Course Intro
Exploratory Data Analysis
Problem Set 1 due 9/1
09.04
09.06		Labor Day (no class)
Visualization

09.11
09.13		Null Hypothesis Significance Testing
...and its interpretation (focus on t-tests)		 Problem Set 2 due 9/12
09.18
09.20		Validity and Reliability
Measurement
Problem Set 3 due 9/22
09.25
09.27		Experimental Design
Effect size and power

10.02
10.04		One-way ANOVA
General Linear Model		 Problem Set 4 due 10/03

10.09
10.11		Contrasts and trend analysis
ANOVA in practice (contrasts con't)
Problem Set 5 due 10/13
10.16
10.18		Factorial ANOVA
Factorial ANOVA con't

10.23
10.25		Multiple Comparisons
ANCOVA		 Problem Set 6 due 10/24

10.30
11.01		Repeated Measures ANOVA
Repeated-measures ANOVA con't
Problem Set 7 due 11/03
11.06
11.08		Mixed-effects Models
Outliers and Transformations

11.13
11.15		Tests for distributions (Chi-squared)
Nonparametric tests		 Problem Set 8 due 11/14

11.20
11.22		Fall Break (no class)
Fall Break (no class)

11.27
11.29		Intro to Advanced Topics 1
Intro to Advanced Topics 2

12.04
12.06		Project Presentations
Project Presentations		 Project Reports due 12/15

Guidelines

Problem Sets

Working on your problem sets in groups is encouraged, but you must turn in your own work. Problems involve programming in your choice of SPSS, R, Matlab, or Python (discuss with your advisor/program, which would make the most sense for you). If your advisor does not have a strong preference, I recommend using R. TAs will host computer-lab sessions to go through programming exercises, and can answer any questions by email.

Projects

Find a paper with publicly shared data and attempt to reproduce (at least some of) their results. Find an aspect of the published analysis that could be improved and implement this improvement. At the end of the semester, you should turn in a short report (5-10 pages) that describes what you did, why you did it, what you found, and why it matters.

Additionally, during the last week of classes students will give a short oral presentation on their project. The format should be similar to a "data blitz" conference talk. Allow 5-10 min for the presentation.

Resources

Open Data

Further Reading

Course Logistics

Grading

Based on problem sets (60%) and project (40%).

Accessibility

Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Center for Students with Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Academic Student Code

Academic dishonesty of any type will not be tolerated. Students should refer to the Student Code for specific guidelines (see section on Academic Integrity).

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