Spring 2000                     PSYCHOLOGY 2113, RESEARCH METHODS I: STATISTICS

Class Slides Review Sheet 1 Review Sheet 2

Purpose of this course Statistics plays an integral role in interpreting the results of research. You should exit this course knowing how and when to compute descriptive statistics (e.g. mean, variance, z-score, correlation), and, how and when to compute inferential statistics (e.g. t-tests, ANOVA, chi-square) to test hypotheses.

Where does it fit? It is a required course for all Psychology majors. Students from other majors take it to learn statistics for their undergraduate research projects. In the next semester after this course, Psychology majors should take Psy 3114, Research Methods II: Experimental. Planning to attend graduate school? Consider taking Advanced Undergraduate Statistics, Psy 3003.

Teaching philosophy The philosophy used in this course is that you will learn best by direct involvement. This means that you will learn best if you dig out concepts on your own, if you perform computations of statistical methods, if you wrestle with the learning process. This is in direct contrast to a "spoon-fed, lecture-only" approach. I will lecture over a lot of the material. But you will be responsible for reading and learning large chunks of content in short periods of time on your own. Often class time will be spent in question/answer, discussion, quizzes, explanations, interaction, etc. In the time schedule below, you are to have the reading assignment done by class time on the date indicated so you can answer questions in class. In this class, it is very important that you make every effort not to fall behind in your work: stay on schedule! Note that all quizzes and the midterm are on Tuesdays.

Week  Topic Chapter Section)
1 Statistics in research, Research and science

Pictorial description, Summary statistics

1-2

1-4

2 Q1 on Jan 18, Summary descriptive statistics

z-scores and normal distribution

4

5(1-3)

3 Q2 on Jan 25, summary descriptive measures for two variables

Correlation and regression

6

7(1-5)

4 Probability 8(1-2)
5 Q3 on Feb. 8, sampling distributions and estimation 9(1-8)
6 Sampling distributions and estimation, hypothesis testing 10
7 More on hypothesis testing 10
8 Midterm on Feb 29, power, one-sample r and t 11and 12(1,2,3)
9 Two-sample t methods 13
10 Spring Break (March 13-18) Study for Quiz
11 Q4 on March 21, One-way ANOVA 14
12 Multiple comparison procedures 15(1-3)
13 Q5 April 4, Two-way ANOVA 16
14 Nonparametric methods 18(1,2,4)&19(1)
15 Choice of statistics 20
16 Review, final will on May 1 at 8 a. m.

 Performance and Grades Performance in the course will be evaluated by the following: five  QUIZZES (10 pts ea.,  total 50 pts.), MIDTERM EXAM (100 pts.), FINAL EXAM (100 pts.), various HOMEWORK assignments (total 120 pts.), various CLASS APPLICATION ASSIGNMENTS (30 pts.), for a total of 400 pts. APPLICATION ASSIGNMENTS will be handed out and returned in class; they will involve evaluation of actual published scientific articles and/or Internet projects. EXAMS AND QUIZZES: 1. will have many types of items including multiple-choice with four responses and rank-order scoring, and short answer; 2. will be cumulative; 3. are in the test files. MAKE-UP QUIZZES will be given to students missing a quiz for a valid reason (please come to talk with me about absences and let me decide if your reason is valid). I give one makeup quiz for Q1-Q3 (the week before the midterm) and one makeup quiz for Q4-Q5 (during the last week of class). GRADES are based on total points earned (out of 400) and will be assigned on the basis of percentage of points possible. A will be 89.5 to 100%, B will be 79.5 to 89.49%, C will be 69.5 to 79.49%, D will be 59.5 to 69.49%, and F will be 59.49% and below. This same percentage scale will be used for quizzes and major exams. Note that the cut points already take into account rounding for borderline grades.

Lab and Homework Your computer lab (in Dale Hall TOWER, 105) is mandatory. Note that there are no labs the week of Sept. 6. Instruction will be given on SAS, on use of the MAC's, etc. Homework will be handed out, questions over homework answered, etc. during this time. Some material will be discussed only in lab sessions, so you must be prepared for items over this material on the quizzes and major exams. HOMEWORK is due IN LAB ONE WEEK after it is handed out. Any work not returned to the graduate assistant on time will lose one point for each class day it is late.

Academic Misconduct Academic misconduct (cheating) will be dealt with according to University policy. The following are examples of cheating: looking on another person's quiz or exam, copying homework (or application assignment) or having another person do your homework (or application assignment), or helping someone else to cheat. To be safe, do your own work. Please see the other side of this sheet for an elaboration on academic misconduct in this course.

Disabilities If you have a disability that may prevent you from fully demonstrating your abilities, contact me personally as soon as possible so we can discuss reasonable accommodations necessary to ensure full participation and facilitate your educational opportunity.