NORTH CAROLINA STATE UNIVERSITY GRADUATE COURSE ACTION FORM

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1 NORTH CAROLINA STATE UNIVERSITY GRADUATE COURSE ACTION FORM NOTE: Click once on shaded fields to type data. To check boxes, right click at box, click Properties, and click Checked under Default Values. DEPARTMENT/PROGRAM Psychology COURSE PREFIX/NUMBER PSY 781 PREVIOUS PREFIX/NUMBER DATE OF LAST ACTION COURSE TITLE ABBREVIATED TITLE None Applied Multiple Regression Techniques in Psychology REGRESSION IN PSY SCHEDULING Fall Spring Summer Every Year Alt. Year Odd Alt. Year Even Other COURSE OFFERED BY DISTANCE EDUCATION ONLY ON CAMPUS ONLY CREDIT HOURS 3 BOTH ON CAMPUS AND BY DISTANCE EDUCATION CONTACT HOURS Lecture/Recitation 3 Seminar Laboratory Problem Studio Independent Study/Research Internship/Practicum/Field Work TYPE OF PROPOSAL New Course Drop Course Course Revision Dual-Level Course REVISION Content Prefix/Number Title Abbreviated Title Credit Hours Contact Hours Grading Method Pre-Corequisites Restrictive Statement Description Scheduling GRADING ABCDF S/U INSTRUCTOR (NAME/RANK) Jason C. Allaire, Assistant Professor Graduate Faculty Status Associate Full ANTICIPATED ENROLLMENT Per semester 24 Max.Section 1 Multiple sections Yes No PREREQUISITE(S) PSY 780 COREQUISITE(S) None PRE/COREQUISITE FOR PSY 782, PSY 783 RESTRICTIVE STATEMENT CURRICULA/MINORS Required Qualified Elective Course is limited to graduate students in Psychology All Psychology graduate students None PROPOSED EFFECTIVE DATE 01/08 APPROVED EFFECTIVE DATE CATALOG DESCRIPTION (limit to 80 words): Logic and application of regression models and related techniques in psychology. Emphasis on application of regression techniques and communication of results for psychological research. DOCUMENTATION AS REQUIRED Please number all document pages Course Justification Proposed Revision(s) with Justification Student Learning Objectives Enrollment for Last 5 Years New Resources Statement Consultation with other Departments Syllabus (Old and New) Explanation of differences in requirements of dual-level courses RECOMMENDED BY: Department Head/Director of Graduate Programs ENDORSED BY: Chair, College Graduate Studies Committee College Dean(s) APPROVED: Dean of the Graduate School

2 Quantitative Sequence Justification (PSY 780, PSY 781, PSY 782, PSY 783) In an attempt to consolidate the quantitative training for our doctoral students, the Department of Psychology has developed a sequence of applied quantitative courses specifically geared toward the needs of our graduate students. This sequence consists of four progressive courses, of which two are required for the department as a whole, and three are required for two of the five graduate concentrations. The overarching pedagogical approach of these four courses is to provide students with a theoretical and practical foundation in quantitative methods germane to psychology. PSY 781 Course Justification This course represents the second in a sequence of four quantitative courses. It is intended for all first year graduate students in the Psychology Department and focuses on multiple regression and other regression techniques. In keeping with the Scientist-Practitioner model of the Psychology Department, this course will balance the presentation of basic theories of quantitative methods for psychology with the application of these methods to the field of psychology and to the students own research areas. Lectures, assignments, readings, exams, and papers will all be geared toward the five areas within the Psychology Department (Developmental, Ergonomics/Experimental, Industrial/Organizational, Psychology in the Public Interest, and School). Student Learning Objectives By completion of this course, a student should be able to: 1) Understand the assumptions and conditions governing the appropriateness of the regression techniques considered in this class. 2) Be an informed user of statistical software, able to implement each of the major analytic techniques on a computer, and able to interpret the results. 3) Identify which procedures are best suited for particular research questions in their own area of psychological research. 4) Communicate and explain the results of statistical analyses using APA (American Psychological Association) style. Previous Enrollment (PSY 710-Q) The content for this proposed course has been taught previously as a Special Topics course. The enrollments in that course are as follows: Semester Enrollment Spring Spring Spring New Resources Required None. The proposed course will utilize existing resources. PSY 710-Q has already been incorporated into the instructor s normal load for Spring semesters. Consultation with Other Departments A copy of a memo from the DGP of the Statistics Department follows.

3 Syllabus A syllabus based on that used for PSY 710K during Spring 2006 follows. Details here have been changed to indicate the proposed course number and specific class dates have been changed to a simple identification by week of the semester.

4 Psychology 781: Applied Multiple Regression Techniques in Psychology Thursday 6:00 8:50 Poe Rm 417 Instructor: Jason C. Allaire, Ph.D. Office: Poe Hall Room 750 Phone: Office hours: Tuesday (3:00-5:00), Thursday (4:00-5:30) Teaching Assistant: Seungah Ryu Office: Poe Hall Room 706 Office Hours: Tuesday/Thursday 12:30-2:00 Course Description Course Webpage: This is an applied regression course for graduate students interested in learning how to use regression techniques to answer theoretical or applied questions within their domains of research. As an applied course, less emphasis will be placed on formulae and their derivation, and more emphasis will be placed on the following: (1) major assumptions of regression techniques, (2) the conditions under which the analysis might be appropriate, (3) implementation of the analysis in major statistical packages, (4) interpretation of analyses, and (5) effective communication of results. The content and pedagogical approach of each class will be similar to a workshop, with didactic lecture followed by a more hands on practice with real data. Course Goals By completion of this course, a student should be able to: 5) Understand the assumptions and conditions governing the appropriateness of the regression techniques considered in this class. 6) Be an informed user of statistical software, able to implement each of the major analytic techniques on a computer, and able to interpret the results. 7) Identify which procedures are best suited for particular research questions in their own area of psychological research. 8) Communicate and explain the results of statistical analyses using APA (American Psychological Association) style. Grading procedure and scales: Overall course grades will conform to the following: A+ (97-100%), A (93-96%), A- (90-92%), B+ (87-89%), B (83-86%), B- (80-82%), C+ (77-79%), C (73-76%), C- (70-72%), D+ (67-69%), D (63-66%), D- (60-62%), F (anything below 60%). Grades will be weighted according to the number of points available for each component. Evaluation in the course will be based on three major components: (1) Weekly Quizzes (35%). At the beginning of each class, there will be a quiz either on the material from the previous class or on the reading assignment for that class meeting. These quizzes will be closed book and will be timed unless otherwise specified. You will be able to drop your lowest quiz score.

5 (2) Homework assignments (35%). Homework assignments ask students to practice and interpret selected statistical procedures discussed in class. It is expected that each homework assignment will be typed/printed (unless otherwise specified) and well-organized. This means that if I can t easily identify what your answer to a question is, appropriate deductions will be taken. Homework assignments are due at the beginning of class on the day specified on the class schedule. There will be no dropping of any homework assignments. (3) Final Project (30%). There will be a final project due during finals week. At the last regular class meeting, you will be provided with a data set and a list of research hypotheses. Your task will be to utilize the techniques learned during the semester to write a full research report, presenting your analyses, results, and conclusions. Incomplete Grades An incomplete grade may be assigned at the discretion of the instructor as an interim grade for a course in which the student has: (1) completed a major portion of the course with a passing grade, (2) been unable to complete course requirements prior to the end of the term because of extenuating circumstances, and (3) obtained agreement from the instructor and arranged for resolution of the incomplete grade. Attendance Policy In the event of an excused absence that is accepted by the instructor (see REG for definitions of an excused absence), the student will have one week after returning to class to make up the missed work or quiz. Such make-up work shall be at a comparable level of difficulty with the original assignment or examination. Make-up quizzes shall be at a time and place mutually agreeable to the instructor and student For Extra Help I will make every effort to support students in understanding course content and reading materials. The TA or myself can meet with students as needed by appointment. Students are also welcome to questions at any time. Students are strongly encouraged to read their required readings before class, and to try to work out problems themselves first, to fully capitalize on the benefits of self-discovery. Data Acquisition & Statistical Software Given the applied nature of this course, we will be working a number of different data sets both in class and in homework assignments. These data sets will be available on the class website for you to download as SPSS files. You are solely responsible for obtaining these data sets, so if for some reason you can t download them, you must contact me immediately. In other words, don t come to me and say, I didn t do the homework cause I couldn t download the data. This class will use SPSS, SAS, and AMOS. NCSU has an agreement with SAS and SPSS such that all students are entitled to install a copy of the program on their personal computer, with versions available for Windows, Macintosh and Linux operating systems. See for further information and to obtain a CD copy of SAS. Visit for information about getting SPSS and AMOS.

6 Academic Integrity Students will adhere to the University s Code of Student Conduct ( Consistent with the provisions of this Code, academic dishonesty is defined as cheating, plagiarism, and aiding and abetting others to cheat or plagiarize. It is understood that the student s signature on any quiz or assignment means that the student neither gave nor received unauthorized aid. Students who are accused of violations of the Code will be referred to the Coordinator, Office of Student Conduct. Required Readings Cohen, J., Cohen, P, West, S.G., Aiken, L.S. (2003). Applied Multiple Regression/ Correlation Analysis for the Behavioral Sciences, 3 rd Edition. Mahwah, NJ: Lawrence Erlbaum Associates. There will also be supplemental readings that will either hand out in class or make available via the library s electronic reserve. I reserve the right to add or take away any readings, as I believe to be pedagogically appropriate. Students with Disability Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students at 1900 Student Health Center, Campus Box 7509, Written confirmation of disability from this office is mandatory before accommodations can be made. For more information on NC State's policy on working with students with disabilities, please see

7 COURSE SCHEDULE Week Topic Reading Homework Week 1 Introduction (SPSS and SAS Review) Chapter 1 Week 2 Bivariate Correlations and Regression Chapter 2 Homework 1 Week 3 Multiple Regression (2 or more IV s) Chapter 3 Week 4 Multiple Regression Diagnostics Chapter 4 & 10 Homework 2 Week 5 Week 6 Week 7 No CLASS Multiple Regression/Categorical Predictors Hierarchical Regression: Testing Mediation Chapter 8 Chapter 5 Homework 3 Week 8 Interaction Effects: Testing Moderation Chapter 7 Week 9 SPRING BREAK Week 10 Interaction Effects: Testing Moderation Chapter 9 Homework 4 Week 12 SPRING Holiday Week 13 Logistic Regression Chapter 13 and supplemental Week 14 Logistic Regression and Survival Analysis Chapter 13 and supplemental Week 15 Causal Modeling/Path Analysis Supplemental Week 16 Introduction to Multilevel Models Chapter 14 and Supplemental Week 17 Flex Class (Final project handed out) Homework 5 Homework 6 Week 18 FINAL PROJECT DUE (5:00pm)

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