Honors Stats

                         PSYCHOLOGY 3101 (Honors)
Introduction to Statistics in Psychological Research


Table of Contents:

Administrative Matters
Course Goals
Plan of Course
First Part of Course
Making the Mastery Exam Work For You
Second Part of Course
Grading Table
Class Calendar

Administrative Matters:

Main Class Website:

Class Meeting:
Section: 880
Time: 9:30-10:45 Tue,Thu and Lab 3:30-5:20 Thu
Room: ATLAS 104

Instructor: Matthew Keller
D347B Muenzinger, 303-735-5376
Office Hours: 11-noon & 1:30-3, Thu
email: matthew.c.keller@gmail.com
WWW URL: www.matthewckeller.com

Teaching Assistant: Laura Hink
TBA Muenzinger D314D
email: Laura.Hink@colorado.edu
Office Hours: 1-4 Fri

Textbook: McClelland, G.H. (1999). Seeing Statistics.
Duxbury Press.

Goals of the Course

1. To prepare you for the required, upper-division laboratory courses (Cognitive, Learning, Perception, or Social). We will cover thoroughly all the statistical procedures you will be expected to know in those courses.
2. To acquaint you with the role of statistics and methodology in scientific research.
3. To educate you to be an informed citizen who can critically evaluate statistical arguments that are presented in the news or used in political discourse. You will learn how to evaluate arguments based, for example, on surveys (e.g., political polls) and experiments (e.g., medical research).
4. To prepare you to conduct a research project for a Senior Honors Thesis.
5. To prepare you for possible graduate work in psychology or other statistically demanding fields.

The course will acquaint you with many aspects of the research process in psychology: why empirical research is necessary, posing questions, designing experiments or surveys, collecting data, doing statistical analyses, and finally, interpreting the results to answer the original question and to pose new ones. In short, this is not a math course, but is instead about doing research and using statistics to understand the natural world. The lay public often believes that science is about finding deterministic relationships in the world (e.g., that scientists ‘prove’ things), but this is very rarely the way science works. Due to our imperfect state of knowledge, we almost always can only discern probabilistic trends. Statistics is a way to systematically describe these probabilistic trends and to make conclusions about them. I hope that throughout the course you will become acquainted with the scientific method in two ways: by reading, hearing, and thinking about it from the textbooks and lectures and, more importantly, by participating in the research process yourself.

Plan of the Course

Statistics and the research process are best learned by doing it, so the course is organized to give you lots of experience doing statistics and research. In Part I of the course, approximately the first half of the semester, we will cover basic concepts and statistical techniques. You will master the techniques by using them in laboratory exercises and homework problems. There will be a Mastery Exam at the end of Part I. Part II will then provide you with experience in all phases of the research process and it will allow you the opportunity to follow your own topical interests.

NOTE: All important dates are provided in the schedule available elsewhere on the main course website. You are responsible for knowing all due dates.

First Part of Course

There will generally be a reading assignment from the textbooks for each class and assigned exercises for each lab meeting. Homework assignments will be distributed Tuesday in class and are due in lab on the following Tuesday. Each of the eight homework assignments will be worth five points.

We will review the reading material, consider additional examples and applications, and answer questions you might have. You should read carefully the text assignment before coming to class. The lectures will be interactive, because you will be using your own computer trying the procedures as I talk about them.

Reading the assignments beforehand and using the book interactively are key to learning statistics in this course. For each reading, make sure you complete each Discovery, Equations, and Checkup box. The Help and Application boxes are optional. You do not need to complete the Survey, Computer, or My Data boxes.

You will learn how to do statistical analyses using R, how to interpret the output, how to make graphs, and how to integrate the computer output into a report.

There will be two short quizzes worth ten points each on the dates indicated on the schedule. The primary purpose of these quizzes is to give you feedback on your performance so that any serious problems you may have can be corrected before the Mastery Exam.

Mastery Exam:
At the end of Part I (see schedule for dates), there will be a two-day Mastery Exam. The first day will test knowledge of basic concepts and will be closed-book. The second day will test your ability to perform and interpret analyses; it will be done using the computer and is open-book. Each part of the Mastery Exam is worth 60 points. The exam will cover all text assignments, lecture, and lab material.

You must earn 135 or more points to pass Part I, and you must pass Part I to pass the course. The points come from the following sources:






Eight HW scores



Two quizzes

Mastery Exam


Closed- and open-book parts



possible points









Making the Mastery Exam System Work for You

You may take the Mastery Exam again if you fail it in order to pass Part I. However, you only have one shot at each Quiz and Homework assignment. This system is designed to put less pressure on you and to let you spend your time on learning rather than on worrying about your grades. Your letter grade is determined by the amount of work you do in Part II and is not affected by your score on the Mastery Exam, so long as you pass it.

However, don't treat the Mastery Exam like a typical midterm or final. Cramming the night before by studying your notes and the text book will not work. The focus of this course is on learning by doing. The Exam will evaluate your ability to do statistics. The homework assignments and the quizzes are designed to give you feedback on your progress and to give you practice on the kinds of questions you will be expected to answer on the Mastery Exam.

If you are getting low quiz or homework scores, or are not understanding the lab or class material, or are feeling lost in any way, see your instructors IMMEDIATELY!! The material we cover in Part I is usually covered in a full semester in an old-style statistics course. You will also need to understand and be able to use this material in order to complete the second part of the course. Don't let yourself fall behind.

Second Part of Course

The purpose of Part II is to give you experience in applying the statistical concepts and tools you learned in Part I. You will be able to get practical experience in all phases of the research process: posing questions, designing a statistical analysis to answer these questions, performing the statistical analyses, interpreting results, reviewing other's research papers, and revising your manuscripts to produce an excellent final product. Below is a brief description of the various activities; more details will be provided in a subsequent syllabus for Part II.

I will present the details of each of the activities described below, discuss other aspects of the research process such as publication, and hold workshops on the various activities and specific statistical techniques that might be of interest to certain students.

Research Manuscript and Article:
You will need to write your own APA-style research manuscript and article. To do this, you can explore a number of survey datasets that have already been collected and that I will provide for you. Alternatively, you can use data that is being collected in a lab you are working in or that you yourself have collected. You will gain practical experience in design, analysis, and interpretation of a series of research studies. For a C, you need to answer only one research question and use only one type of statistical analysis. To get an A, you must answer at least two research questions and use at least two different statistical analyses. These research questions should be on the same topic. This is often how research articles are written  - a particular result inspires a new set of questions, which are then investigated in turn.

Your analyses, results, and background research must be written up in an APA-style manuscript, complete with title page, abstract, Introduction, Methods, Results, Discussion, and References sections. Your manuscript will be peer-reviewed by two of your colleagues as well as the TA for the course. They will provide detailed feedback to you so that you can revise your manuscript and turn it in as a finished product, the Research Article. Please note: the manuscript SHOULD NOT be considered a rough-draft. It should be close to a final product. If you turn in a rough draft that is incomplete, poorly organized, etc., the feedback you get will suffer, which will translate into a poor finished product.

The Research Article that you turn in will be a polished and quality piece of work, meaning that: a) your statistical analyses are appropriate, as are the conclusions you draw from them; b) the meaning of each sentence in your paper is clear; c) you say what you need to say but no more; d) your logic and conclusions make sense and are easy to follow; and e) you follow APA style exactly.

Peer Review:
A central aspect of scientific research is peer review. Other scientists evaluate the quality of articles submitted for publication. We will simulate the publication process by having you be a journal reviewer of two other students' Research Articles. You should endeavor to provide detailed feedback to the author, letting them know precisely (e.g., page number, paragraph, and sentence) what is unclear, needs to be reworked, is confusing, or wrong. All aspects of the manuscript, from APA style to clarity of presentation to statistical analyses to conclusions drawn from them are fair game. Your job isn’t to provide a grade for the author; rather, it is to provide detailed feedback that will help the author to revise their paper into an excellent, clear, articulate article.

Scientific Presentation:
An important aspect of the scientific process is disseminating what you have learned to the wider scientific community. It is not enough to have discovered something interesting in your research; equally important is explaining it to others through presentations and publications. In this activity, you will give a short (~ 10 min.), in-class presentation about your project in the style of presentations at scientific conventions.


For the most part, you determine your own grade in this course by choosing how much work that you decide to do. The table below gives the activities required for a given grade. This does not mean however that you can turn in sub-par work and receive a certain grade. If you are going for an “A,” it is expected that you do “A” quality work in your presentation, peer review, and (especially) your research article (your research manuscript does not count towards your grade unless you are going for a “C”; however, it is very much in your self-interest to turn in as good of a manuscript as possible in order to get back useful feedback). If any of the three activities do not meet this expectation, your final grade will be lowered by a half step, with research article counting double. For example, if you are going for an A, your base grade starts at an A and I fully expect that this will be your final grade. If however your peer review is only B quality, your final grade will be lowered a step to an A-; if C quality, your final grade will be a B+. Research articles are doubly important, so if it is B quality, your final grade would be a B+; if C quality, it would be a B-, and so forth. Truly fantastic work will merit similar increases in grades.


Required Elements


Pass Part I
Evaluation Activity


Pass Part I
Evaluation Activity
Research Manuscript (1 research question/analysis)


Pass Part I
Evaluation Activity
Research Manuscript (1 research question/analysis)
Research Article (a revised, polished manuscript that includes response to comments)
Peer Review


Pass Part I
Evaluation Activity
Research Manuscript (2 research questions/analyses)
Research Article (a revised, polished manuscript that includes response to comments)
Peer Review
Scientific Presentation












Disabilities, Learning Difficulties, and Related Problems

The flexible structure and small size of this course makes it easy to accommodate a wide range of learning-related difficulties. Please send me an email or talk to me in office hours about any special problems you may have.

Religious and Other Absences

Similarly, the flexible structure and small size of this course makes it easy to accommodate absences for religious observances or similar needs. Please send me email or talk to me in office hours about any absences you will need and we will make arrangements.

Students are expected to know and comply with University policies described by these links:
Responsible Computer Use
Classroom and Course-related Behavior
Honor Code
Index of all University Policies

Class Calendar

Aug 24 - CLASS 1 - Introductions, syllabus, and overview
Aug 27 - CLASS 2 - Basic concepts. Have read SS Ch. 0 & 1
Aug 27 - LAB 1 - Basics of R.

Sep 1 - CLASS 3 - Descriptive statistics. Have read SS Ch. 2-5. HW #1 (basic concepts) due.
Sep 3 - CLASS 4 - Sampling distributions. Have read SS Ch. 6-8.
Sep 3 - LAB 2 - Descriptive statistics in R.

Sep 8 - CLASS 5- Sampling distributions part II & statistical decisions. HW #2 (descriptive stats) due
Sep 10 - CLASS 6 - 1-sample, indep., and paired t-tests. Have read SS Ch. 9 & 10.
Sep 10 - LAB 3 - t-tests and graphs in R.

Sep 15 - CLASS 7 - Research design. HW #3 (means and t-tests) due. Quiz 1.
Sep 17 - CLASS 8 - Correlation and regression. Have read SS Ch. 12.
Sep 17 - LAB 4 - Correlation and regression with graphs in R.

Sep 22 - CLASS 9 - Regression. HW #4 (correlation and regression) due.
Sep 24 - CLASS 10 - Regression practicals.
Sep 24 - LAB 5 - Correlation and regression with graphs in R

Sep 29 - CLASS 11 - Multiple regression part I. HW #5 (Regression) due.
Oct 1 - CLASS 12 - Multiple regression part II in R.
Oct 1 - LAB 6 - Multiple regression in R.

Oct 6 - CLASS 13 - Multiple regression: interactions. HW #6 (multiple regression II) due. Quiz 2.
Optional reading: http://www.jerrydallal.com/LHSP/reginter.htm

Oct 8 - CLASS 14 - ANOVA from a regression standpoint.
Oct 8 - LAB 7 - ANOVA and interactions in R.

Oct 13 - CLASS 15 - Factorial ANOVA using regression. HW #7 (interactions & ANOVA) due.
Oct 15 - CLASS 16 - Factorial ANOVA part II.
Oct 15 - LAB 8 - Factorial ANOVA in R.

Oct 20 - CLASS 17 - Factorial ANOVA part III. Have read SS Ch. 8-10. HW #8(ANOVA) due.
Oct 22 - CLASS 18 - Logistic regression.
Oct 22 -  LAB 9 - Find articles online. APA bibliographic format.

Oct 27 - CLASS 19 - Review for Mastery Exam
Oct 29 - CLASS 20 - Mastery Exam - Closed-book portion.
Oct 29 - LAB 10 - Mastery Exam - Open book portion.

Nov 3 - CLASS 21 - Mastery Exam results. Overview of Part II
Nov 5 - CLASS 22 - Work on Research Project (Instructor absent)
Nov 5 - LAB 11 - Coding data in R. Work on Research Project (Instructor absent)

Nov 10 - CLASS 23 - Work on Research Project
Nov 12 - CLASS 24 - Work on Research Project
Nov 12 - LAB 12 - Work on Research Project

Nov 17 - CLASS 25 - Work on Research Project
Nov 19 - CLASS 26 - Work on Research Project
Nov 19 - LAB 13 - Work on Research Project

Nov 24 - Fall Break - no classes
Nov 26 - Fall Break - no classes
Nov 26 - Fall Break - no classes

Dec 1 - CLASS 27 - Work on Research Project
Dec 3 - CLASS 28 - Research Project Presentations. Last possible day to turn in Research Manuscript.
Dec 3 - LAB 14 - Research Project Presentations.

Dec 8 - CLASS 29 - Research Project Presentations.
Dec 10 - CLASS 30 - Research Project Presentations. Last possible day to turn in Peer Reviews.
Dec 10 - LAB 15 - Research Project Presentations.




NOTE: A heartfelt thanks to Gary McClelland for sharing with me his successful formula for teaching this course, which he has developed over the years.

Comments to: matthew.c.keller@gmail.com
Last Modified: Mon, Aug 24, 2009

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