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- Colorado
- University of Colorado Boulder
- Psychology
- Psychology 3101
- Manik
- PSY3101 Statistics CU Syllabus SP10.pdf

Alexis B.

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1 PSYC 3101-300 Statistics and Research Methods in Psychology SP10 University of Colorado at Boulder Class Location: Muen E0046 Time: Mon, Wed, Fri 10:00 – 10:50am Lab Location: Muen D346 Instructor: Martin Lanik, M.S. Office: Hrs: Mon & Wed 11:00 – 12:00pm Phone: Email: martin.lanik@colostate.edu GTAs: Leif D. Oines Email: Leif.Oines@Colorado.edu Jake Westfall Jake.Westfall@Colorado.edu Keith R. Lohse Keith.Lohse@Colorado.edu Labs: Lab 310: Mon 8:00 – 9:50am Leif D. Oines Lab 311: Mon 11:00 – 12:50pm Leif D. Oines Lab 312: Tue 9:00 – 10:50am Jake Westfall Lab 313: Thu 9:00 – 10:50am Jake Westfall Lab 314: Mon 3:00 – 4:50pm Keith R. Lohse Course website: Required Text: Gravetter, F. J., & Wallnau, L. B. (2009). Statistics for the behavioral sciences, 8th Ed. Please bring your textbook to class and lab. Required Calculator: You must bring to class and labs a calculator with a square root and memory capability. A statistical calculator is preferred. Course Description: Statistics and Research Methods in Psychology is designed to introduce students to the application of basic statistics to the measurement, analysis, and interpretation of psychological phenomena. This course will survey basic concepts in statistics, including frequency distributions, probability, central tendency, variability, sampling distributions, hypothesis testing, correlation, regression, analysis of variance, and others. Students will also learn how to use the R statistical program to analyze data. Course Objectives: There are four main objectives in this course: (1) graphing and describing distributions; (2) understanding the principles of probability; (3) using the correct inferential statistics to test hypotheses; (4) using the R statistical program to analyze data. Learning Methods: 1. Readings. Readings are assigned for most class meetings. Please check the course schedule to find out which chapter from the textbook you are expected to read BEFORE each class meeting. 2. Lectures. Lectures are designed to explain and elaborate on the class readings. The instructor will provide alternate explanations and examples to help you understand the material. Lecture notes – all lecture notes will be posted on the course website. 3. Labs. Labs are designed to provide guided opportunities for practice. Your lab instructor will briefly review the concepts presented in lecture and help you with the lab assignments. A large portion of lab time is dedicated to working with the R statistical program. 4. Homework assignments. Homework will be assigned for most class sessions. You will be required to turn in your homework to the instructor at the beginning of the next class. 2 Evaluation Methods: 1. Homework. There will be 12 homework assignments during the semester. Each homework assignment is due that week in lab (see the course schedule) and each is worth 10 points. Your final grade will be based, in part, on your ten highest homework scores (I will drop your two lowest homework scores). Students cannot make up late or missed homework. 2. Lab Assignments. There will be 14 lab assignments during the semester. Lab assignments must be completed in lab and turned in to the lab instructor at the end of the lab. Each lab assignment is worth 10 points. Your final grade will be based, in part, on your twelve highest lab assignment scores (I will drop your two lowest lab assignment scores). Students cannot make up late or missed lab assignments. 3. Exams. There will be four exams (each worth 60 points) plus a comprehensive final exam (worth 100 points) administered during the finals week. Each of the four exams will be a combination of multiple-choice and short answer questions and will NOT be comprehensive. The comprehensive final will be a combination of multiple- choice and short answer questions from the previous four exams. No make-up exams will be offered. Your final grade will be based, in part, on the 3 highest exam scores (I will drop your lowest exam score) and the comprehensive final. Students may use one letter page of notes during exams. Grading: Students can earn up to 500 points – 100 for homework assignments, 120 for lab assignments, 180 for exams, and 100 for the comprehensive final. Final grades will be based solely on the total points earned during the semester. No extra credit or make-up exams will be allowed. Grade Points A 500 – 450 B 449 – 400 C 399 – 350 D 349 – 300 F 299 or less Attendance: Regular class attendance is crucial to your success in this course and therefore I expect that you attend every class and lab unless you have a personal or medical emergency. I will not take or grade attendance. Please note that students who attend class regularly generally do much better than students who do not come to class. If you miss a class, it is solely your responsibility to contact a fellow student and obtain lecture notes from that day. Special Needs: If you need specific accommodations due to disability or other circumstances, please notify the instructor or the GTA right away so that accommodations can be made for you in the most efficient manner. Last but not least, WELCOME to the exciting field of statistics! Let’s have fun learning!!! 3 Course Schedule Date Lecture Reading Homework (due in lab that week) Lab 1-11 Introductions 1-13 Introduction to stats Ch 1 1-15 Introductions and levels of measurement Lab Assgmnt 1: pg. 32/problems 12, 17, 18, 20 1-18 No class – MLK Holiday * attend another lab that fits your schedule 1-20 Frequency distributions Ch 2 1-22 Central tendency Ch 3 HW1: pg.32/problems 1, 2, 7, 15, 19 Frequency distributions and central tendency Lab Assgmnt 2: pg.66/problems 5, 8 pg.101/problems 5, 28 1-25 Variation Ch 4 1-27 1-29 EXAM 1 HW2: pg.66/problems 1, 17, 19 pg.102/problems 25, 26 Variation Lab Assgmnt 3: pg.134/problems 7, 28 2-1 z scores Ch 5 2-3 2-5 Probability Ch 6 HW3: pg.134/problems 1, 5, 11, 19, 23 z scores and areas under the normal curve Lab Assgmnt 4: pg.160/problems 5, 11 pg.195/problems 11, 15 2-8 Sampling distribution Ch 7 2-10 2-12 Hypothesis testing Ch 8 HW4: pg.160/problems 3, 7, 27 pg.195/problems 5, 9 Standard error and areas under the normal curve Lab Assgmnt 5: pg.226/problems 11, 21, 22 2-15 2-17 Effect size 2-19 Statistical power HW5: pg.226/problems 1, 3, 5, 13, 17 Hypothesis testing, effect size, and power Lab Assgmnt 6: pg.275/problems 5, 6, 25 2-22 EXAM 2 2-24 t statistic Ch 9 2-26 HW6: pg.275/problems 2, 3, 4, 9, 21, 22, 23 Review hypothesis testing, effect size, and power Lab Assgmnt 7: Problems distributed in lab 3-1 t test for independent samples Ch 10 3-3 3-5 t test for dependent samples Ch 11 t test for independent and dependent samples Lab Assgmnt 8: pg. 335/problems 22, 23 pg.360/problems 15, 21 3-8 Confidence intervals Ch 12 3-10 3-12 EXAM 3 HW7: pg. 303/problems 1, 5, 15 pg. 335/problems 5, 15, 21 pg. 360/problem 1, 11 Confidence intervals Lab Assgmnt 9: pg.388/problems 7, 11, 25 3-15 Analysis of Variance (ANOVA) Ch 13 3-17 3-19 ANOVA Lab Assgmnt 10: pg.442/problems 26, 27 3-22 Spring Break 3-24 3-26 3-29 ANOVA Review 3-31 Factorial ANOVA Ch 15 4-2 HW 8: pg.438/problems 3, 4, 11, 19, 23 Factorial ANOVA Lab Assgmnt 11: pg. 517/problems 24, 25 4-5 Repeated measures ANOVA Ch 14 HW 9: pg.513/problems 1, 5, 9, 15, 19 Repeated measures ANOVA 4 4-7 4-9 Correlation Ch 16 Lab Assgmnt 12: pg. 475/problems 24, 25 4-12 4-14 Regression Ch 17 4-16 HW10: pg. 471/problems 1, 3, 5, 7, 15 Correlation Lab Assgmnt 13: pg.559/problems 15, 22 4-19 4-21 Non-parametric tests Ch 18 4-23 HW11: pg. 557/problems 1, 2, 5, 8, 9 Regression Lab Assgmnt 14: pg.602/problems 22, 24 4-26 EXAM 4 4-28 Review 4-30 Review HW12: pg. 600/problems 1, 2, 5, 7, 15 Review for final Finals COMPREHENSIVE FINAL EXAM Please note that topics and assignments are subject to change without prior notice. Martin Lanik Microsoft Word - Statistics CU Syllabus SP10.doc

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