
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: https://culearn.colorado.edu/webct/entryPageIns.dowebct
Class Meeting: Section: 880 Time: 9:3010:45 Tue,Thu and Lab 3:305:20 Thu Room: ATLAS 104
Instructor: Matthew Keller D347B Muenzinger, 3037355376 Office Hours: 11noon & 1:303, 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: 14 Fri
Textbook: McClelland, G.H. (1999). Seeing Statistics. Duxbury Press.
Goals of the Course
1. To prepare you for the required, upperdivision 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
Assignments: 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.
Lecture: 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.
Readings: 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.
Lab: 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.
Quizzes: 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 twoday Mastery Exam. The first day will test knowledge of basic concepts and will be closedbook. The second day will test your ability to perform and interpret analyses; it will be done using the computer and is openbook. Each part of the Mastery Exam is worth 60 points. The exam will cover all text assignments, lecture, and lab material.
Grading: 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:
Source

Points

Notes

HW

40

Eight HW scores

Quizzes

20

Two quizzes

Mastery Exam

120

Closed and openbook parts

Total

180

possible points

Passing

135

(75%)


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 oldstyle 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.
Lectures: 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 APAstyle 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 APAstyle manuscript, complete with title page, abstract, Introduction, Methods, Results, Discussion, and References sections. Your manuscript will be peerreviewed 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 roughdraft. 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.), inclass presentation about your project in the style of presentations at scientific conventions.
Grading
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 subpar 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 selfinterest 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.
Grade

Required Elements

D

Pass Part I Evaluation Activity

C

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

B

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

A

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 learningrelated 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 Courserelated 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. 25. HW #1 (basic concepts) due. Sep 3  CLASS 4  Sampling distributions. Have read SS Ch. 68. 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  1sample, indep., and paired ttests. Have read SS Ch. 9 & 10. Sep 10  LAB 3  ttests and graphs in R.
Sep 15  CLASS 7  Research design. HW #3 (means and ttests) 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. 810. 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  Closedbook 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.
Dec 14  REVISED RESEARCH ARTICLE & RESPONSE TO COMMENTS DUE BY 5PM
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
