Quantitative Methods in Psychology: Course Introduction

Size: px

Start display at page:

Download "Quantitative Methods in Psychology: Course Introduction"

Gilbert Marshall
6 years ago
Views:

1 Quantitative Methods in Psychology: Course Introduction

2 Quantitative Methods in Psychology (830:200) Instructor: Dr. Melchi M. Michel Lectures: T, Th 1:40-3:00pm in Hill Auditorium Office Hours: Th 10-11am in 125 Psych TAs: Kristina Howansky (sections 1 & 3) Lloyd Robotham (sections 4 & 5)

3 Recitation Sections Section Day & Time First Meeting Location TA 01 TH 10:20 11:40am 9/12 TIL 258 Howansky 03 W 10:20 11:40am 9/11 BE 253 Howansky 04 W 12:00 1:20pm 9/11 BE 253 Robotham 05 W 1:40 3:00pm 9/11 LSH B267 Robotham

operating on solar power is ideal (I recommend the Texas Instruments TI-30X IIS), but a simpler model will do.

4 Course Materials Textbook: Privitera, G. J. (2012). Statistics for the Behavioral Sciences. Sage Publications. ISBN: Calculator: A simple model capable, at minimum of computing square roots A scientific calculator, capable of operating on solar power is ideal (I recommend the Texas Instruments TI-30X IIS), but a simpler model will do. Clicker: ResponseCard RF LCD Turning Technology, ISBN: For earning extra credit points Access to a PC: a computer with internet access is needed to access Sakai and other course resources

5 Course Objectives Provide an introduction to the use of statistics in the behavioral sciences Become familiar with key concepts in descriptive and inferential statistics Basic rules of probability, measures of central tendency & dispersion, probability distributions Variability, sampling error, sampling distributions, and the relationship between sample statistics and population parameters Hypothesis testing and inference You should leave this course able to confidently read, understand, and evaluate statistical results presented both in scholarly journals and in the popular press. You should understand the concerns and relevant methods when preparing to test your own hypotheses

6 Course Requirements Lecture and recitation sections Conceptual material Sample computations Exams Outside of class Read the textbook Read assigned chapters before lecture Complete weekly homework assignments Complete or at least attempt these before recitation

7 Grading Exams Three exams (2 midterms + final exam) Final counts twice, lowest of 4 scores dropped Failure to take all exams will result in loss of whole-letter grade 85% of grade Homework/classwork Weekly (roughly) assignments Problems covered in recitation (before due date) 2 pts per problem (1 for effort, 1 for correctness) Must show work! 15% of grade

8 Grading: Extra Credit There are only two ways to earn extra credit in this course: 1. Answering clicker questions in class About 4 questions per class Full credit for correct answer Half credit for incorrect answer Earn up to 10 points on final grade 2. Extra credit questions on computational portion of exams Up to 10 exam points available

9 Grading: Exams Exams in two parts, administered together Conceptual multiple-choice and short-answer questions. Computational Numerical problems involving computation Must show work to receive credit Calculator required (no cell phones or computers or other electronic devices) One-sided 8.5 x 11 cheat sheet with formulas & notes (must be presented to exam proctors)

10 Grading: Scale The grading scale Will not be determined until after final exams are graded. However, it won t be any harsher than the standard Rutgers grading scale: A: % B+: 85-90% B: 80-85% C+: 75-80% C: 70-75% D: 60-70% F: <60%

11 Course Policies Attendance: While you will not be directly penalized for missing lectures or recitations, you are unlikely to do well if you don t come to class. Besides reducing your exposure to the course material, missed classes result in: Missing important material that may not appear in the textbook Missing the opportunity to correct homework errors (in recitation) to receive full credit Missing the opportunity to earn extra credit clicker points

12 Course Policies Academic Integrity: I have a zero-tolerance policy towards cheating. Collusion of any kind on exams is strictly prohibited. Students suspected of doing so will be brought up on charges before university s Office of Student Conduct, and penalties, up to and including expulsion, will be imposed for those found guilty.

13 Course Policies Academic Accommodations: If you require academic accommodations, it is your responsibility to self-identify and register with the Office of Disability Services You should do this as soon as possible (i.e., now) You must let me know and provide me with documentation from Disability Services at least one week prior to any request for specific accommodations (e.g., exam accommodations).

14 Resources Sakai site Announcements, changes, assignments, etc. will be posted here Textbook website: Chapter summaries Study guides SPSS in Focus screencasts SPSS & Excel (on lab computers), PSPP SPSS can also be accessed through apps.rutgers.edu R, Octave, & SciPy for those programming-inclined

15 Resources Tutoring Peer tutoring available in Livingston Learning Center (Tillett 111) Current Schedule is available on course Sakai site Khan Academy Screencast-style YouTube micro-lectures Includes several useful tutorials on probability & statistics Online statistics textbook (from Rice University)

State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210

1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210 Dr. Michelle Benson mbenson2@buffalo.edu Office: 513 Park Hall Office Hours: Mon & Fri 10:30-12:30