ROCHESTER INSTITUTE OF TECHNOLOGY
School Psychology Program
Course 0527-728-01 Fall 2007
Vincent Pandolfi, Ph.D. Course Outline
Inferential Statistics I
Class Meetings: Wednesday 12:00-1:50pm
Building 6 Room 3232
Instructor: Vincent Pandolfi, Ph.D., Ext. 475-2875
vxpgla@rit.edu
Office Hours: Tuesday & Wednesday 2:00-4:00 pm
Or by appointment
Course Textbooks:
Hinkle, D.E., Wiersma, W., & Jura, S.G. (2003). Applied statistics for the behavioral sciences (5th Ed.). Boston: Houghton Mifflin Co.
Recommended:
Norusis, M.J. (2006). SPSS 14.0 guide to data analysis. Upper Saddle River,
N.J.: Prentice-Hall.
SPSS, Inc. (2005). SPSS Base 14.0 user’s guide. Chicago: Author.
Course Description:
This course reviews descriptive and inferential statistics. Basic and advanced conceptual material will be presented to assist students in their understanding of diverse data analytic methods, their appropriate application, and how to interpret statistical analyses. Course content will be taught through lectures, discussion, and applied data analysis exercises. Student mastery of the material will be evaluated through small group discussion of data set analyses and two exams.
Course Objectives:
This course aims to develop student competency in the application of descriptive and inferential statistics to school-related problems and original research. This competency will help students: (a) critically evaluate findings reported in scientific papers; (b) evaluate the impact of primary, secondary, and tertiary interventions; and (c) present their own research findings or program evaluation data to relevant stakeholders. Students will develop their understanding of:
- Key concepts and methods in descriptive and inferential statistics;
- Hypothesis testing for multiple independent and dependent samples;
- Statistical software applications;
- How to select appropriate statistical procedures for specific research problems;
- Best practices in interpreting statistical results.
Course Requirements:
Data Sets: Two take-home data sets will be given during the quarter. Teams of students will select appropriate statistical methods to analyze data. Special sessions will be arranged with the instructor during class time to review the data sets. Data set answers will be orally defended by the student teams. Grades will be assigned to each individual student based on the quality of the team performance.
Exams: Two 40-item multiple-choice exam exams will be given. The final exam is not cumulative, but will require knowledge of concepts from the first half of the course.
Grading:
Each student’s overall grade will be based on a composite of all work during the quarter. Course requirements are weighted as follows:
Data Set I 15%
Data Set II 15%
Exam I 35%
Exam II 35%
Notes:
Unexcused absences from the data set meeting and missed exams will result in a “0”. No make-up exams will be given. Exceptions to these rules are considered on a case-by-case basis, and final decisions pertaining to grades are at the discretion of the instructor.
Individuals with Disabilities:
Persons with disabilities requiring specific accommodations should talk privately with the instructor for discussion and planning for individualized needs.
Academic Honesty:
All students will abide by the RIT Academic Honesty Policy (see attached) and the guidelines set forth in the RIT School Psychology Program Student Handbook.
Course Outline- Fall Quarter, 2007
Week Topics Hinkle
9/5 Introduction & Basic Concepts 1-2
9/12 Central Tendency & Variability 3
9/19 Normal Curve 4
9/26 Sampling Distributions 7
10/3 Hypothesis Testing: One-Sample Case 8
10/10 EXAM I
Hypothesis Testing: One-Sample Case
Interval Estimation 9
10/17 Hypothesis Testing: Two-Sample Case 11
Data Set #1
10/24 Hypothesis Testing: Two-Sample Case
10/31 Nonparametric Tests 21-22
Data Set #2
11/7 Correlation 5
11/14 EXAM II
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