Jack L. Vevea (psyc105ucm@gmail.com)
(Please note that this is a special email address for this class; we will not monitor it after the conclusion of the class. My regular email is jvevea@ucmerced.edu.)
Social Science and Management Building (SSM) 306 A
Office hours: Wednesdays 2:00-4:00, or by appointment.
Telephone: (209) 658-1706 (although email is usually a more effective way to contact me)
Kyle Hamilton (psyc105ucm@gmail.com)
Office hours: Thursdays after class.
No text is required for this class. It will be useful for you to have access to an introductory statistics text. If you no longer have one, contact me. You will need a simple calculator with basic mathematical functions like logs and square roots. We will be holding class on line, so I assume that you will have access to a computer during class.
We will be making heavy use of a statistical program called R in this class. Here is a link to a guide that covers some important R basics.
We will meet Tuesdays and Thursdays from 11:00 A.M. to 12:15 A.M. at a Zoom link that has been distributed via Catcourses.
Psychology 105 will focus on description and inference in the context of the general linear model. We will approach this subject from both the frequentist and the Bayesian perspectives.
In the class, you will:
By the end of the class, you will be able to:
While you should not think of this class as a class in statistical computing, we will use statistical software (specifically, R, OpenBugs, and G*Power) frequently throughout the quarter. Ordinarily, students can learn R comfortably from classroom work and posted transcripts. However, some of you may find this introduction useful.
The overall goal of this course is not to offer a sequential presentation of all the basic statistical techniques you might need for simple analyses of psychological data. Rather, it is to teach the skill of thinking statistically, and to foster a deeper understanding that will enable you to learn and apply new analytic techniques independently.
Completion of Psychology 10 and Psychology 15 (or equivalent). The Psychology 15 prerequisite may be waived under some circumstances.Although the course does not emphasize mathematics, you should know something about the basics of algebra (the ideas of equations and manipulation of variables) and geometry (plotting points on a plane, the equation of a line). If you feel ill-prepared in any of those areas, a quick review might be in order.
Ordinarily for this class, grading is based on a combination of attendance and class participation, written homework, a midterm exam, and a comprehensive final exam. Attendance and participation normally count for 20% of your final grade; homework normally count for 50% of your final grade, and each exam normally counts for 15%. However, because of the logistical difficulties of administering exams remotely, this semester grading will be based entirely on homework and attendance and participation.The attendance and participation component will be based on your participation when called on to ask a question. Each student should come to every class with well-defined questions about recent class content. We will periodically choose a random student to ask a question. This will happen at least four times for each student during the semester; your participation grade will be A if you are present with a question prepared on all four occasions, B for three, C for two, D for one, and F if you were never present on a day you were selected. This does not imply that questions from anyone are not welcome at any time; rather, you are encouraged to ask as many questions as you find useful. But you must be present and prepared on the days you are called to get participation credit. Your attendance and participation score will count for 25% of your final grade.
There will be five homework assignments. Your lowest score will be dropped before grades are calculated. Further, when a homework assignment is returned to you, you may resubmit it for up to 10 days following the date it was returned. All homework scores improved in that way will replace the original score. Your homework score will count for 75% of your final grade.
Those components make up the final grade in the following manner. First, each component (attendance and participation, homework) gets a grade point value: A+ = 4.3, A = 4.0, A- = 3.7, B+ = 3.3, B = 3.0, B- = 2.7, and so on. The weighted average of the grade points from the two components determines your final grade. The following table shows the mapping of grade point averages to letter grades:
Grade Point Range Letter Grade GPA = 4.15 A+ 3.75 < GPA < 4.15 A 3.50 < GPA < 3.75 A- 3.25 < GPA < 3.50 B+ 2.75 < GPA < 3.25 B 2.50 < GPA < 2.75 B- 2.25 < GPA < 2.50 C+ 1.75 < GPA < 2.25 C 1.50 < GPA < 1.75 C- 0.75 < GPA < 1.50 D GPA < 0.75 F
Students should be familiar with University policies on academic honesty. You will find relevant information here. In the overall context of that policy, the following information is specific to this class:
UC Merced has a variety of services available to accommodate students with disabilities. Information is available here.
You should submit homework assignments through CatCourses. After you navigate to the CatCourses page for this course, click on the "Assignments" button in the list on the left. Then, click on the link to the specific homework assignment you are submitting. Make sure to combine everything into one document because CatCourses will allow only one file. That file must have a .doc, .docx, or .pdf file extension (no .odt, .zip, .pages file extensions). All deadlines are at midnight on the due date, and the CatCourses system will not accept submissions after that time.
Initial class meeting: introduction, using the class web page. Obtaining and using R. R basics: reading in data, simple functions. Graphical methods. Some technical vocabulary. Understanding empirical distributions.
Measures of central tendency. Measures of variability. Some subtleties of graphing. R as a tool for describing distributions.
Other aspects of shape. Putting it all together: the use of graphics and descriptive statistics to describe distributions.
Homework One is available (due February 11). You can find a document that contains an example of how you might approach the homework here.
Review of probability. Random variables and probability distributions. The frequentist approach to understanding probability. The distinction between discrete and continuous random variables. Computer simulation as a tool for understanding probability distributions.
Bayes' theorem. Statistics: a special kind of random variable. Sampling distributions. Empirical approximations to sampling distributions.
Models and conditional distributions. Introducing simple linear regression.
Simple linear regression: inference and assumptions. The decomposition of the sum of squares.
Homework Two is available (due March 7).
Regression diagnostics. Wrapping up simple linear regression.
Review of the class so far; catching up.
Homework Three is available (due March 18). You can find a document that contains an example of how you might approach the homework here.
Principles of estimation. The likelihood. Maximum likelihood estimation.
No class: Spring break. Please stay safe!
Bayesian estimation and inference. Markov chain Monte Carlo; OpenBugs.
Conditional means with a simple binary predictor. Effect sizes and confidence intervals. The t test as a linear model: dummy coding, effects coding, nonsense coding. Comparing means, a Bayesian approach.
Homework Four is available (due April 20). Note that this deadline is five days later than normal; that's because this is a somewhat longer than normal assignment. Get an early start.
An introduction to multiple linear regression.
Analysis of variance (ANOVA): conceptual approach. ANOVA and the linear model.
Contrasts and comparisons.
Homework Five is available (due May 6).
Power analysis for one- and two-sample tests. Power analysis for ANOVA.