User:ASnieckus/Statistics/Statistical Methods I syllabus
Syllabus for Statistical Methods I, Fall 2012, offered at the Graduate School of Education, Rutgers University, USA
This course is the first part of a two-semester sequence in statistical methods designed to introduce students to the most commonly used methods in educational and social science research. This course focuses on exploratory data analysis, study design, probability, and sampling distributions.
Topics covered in this course include examining distributions, examining relationships, sampling, designing studies, finding probability of events, conditional probability and independence, random variables, sampling distributions, and an introduction to inference.
The best way to become proficient at the use of these analysis methods is to combine study of the statistics concepts with the design, creation, and interpretation of actual statistical analyses. Students will regularly analyze and interpret datasets provided for that purpose, as well as design and carry-out exploratory data analyses for a set of data.
Textbook and stat software
Text: Moore, D. S., McCabe, G. P., & Craig, B. A. (2009). Introduction to the practice of statistics (8th ed). New York: W. H. Freeman. Available used online for a tiny fraction of the cost of package at the bookstore.
Software: PASW Statistics (formerly SPSS) 18.0. Chicago: SPSS Inc. (packaged with texbook)
- IBM SPSS Statistics 20 is available as a rental (mac and windows downloads) at onthehub.com; 6-mo license for Statistics Base GradPack is $40.</nowiki>
- If you will not be taking any further statistics courses at the Graduate School of Education, you may want to consider using a spreadsheet for performing required data analyses.
Class participation is crucial to your understanding and application of course content. You are expected to come to class prepared to discuss assigned readings and to participate in class activities.
All homework assignments are listed on the Sakai course website, under "Resources", on the "Homework Assignments" webpage.
Reading/study: Students are expected to read the assigned pages in Introduction to the Practice of Statistics (IPS7e) prior to attending class. Students are encouraged to further study the topic with the optional "further study" materials listed on the homework page.
Check assessments: These assessments, available on Sakai, are designed to help a student assess how well he/she understands the statistical concepts presented in the reading and should be completed as part of the student's pre-class study. These are formative assessments, for which the feedback option is available during the "test". Be sure to use it, click on the button in the top left, to help you better understand the concepts. If you have trouble with a question, be sure to ask in class for a review.
Data analysis and interpretation: These exercises follow the discussion of a topic in class, encouraging students to practice the implementation and interpretation of the newly learned statistics method or concept. Many of the exercises will require the use of statistical software.
- Students who will be taking additional statistics courses at the Graduate School of Education should learn to use SPSS for these analyses.
- Students for whom this will be their only statistics course may choose to use spreadsheet software (e.g., Excel, Calc) to do the analyses.
Students will take chapter quizzes in class following discussion and practice of the concepts and methods presented in the chapter.
Exploratory data analysis project
Students will design and create analyses to explore the data in an existing dataset. After completing chapters 1 and 2 related to exploring distributions for variables and the relationships between variables, students will create a proposal to include: a description of the source of the data to be used, specification of the the variables to be analyzed, and a plan for the statistics and graphs to be produced. Using the proposal, students will implement their data analysis plan and write a report to describe the results. Students will do a short presentation of their exploratory data analysis in class.
Grading criteria will be developed in class. At the end of the course, each student will submit a self-assessment, including a suggested final grade along with evidence to support that decision.
Rutgers Academic Integrity Policy
From 1/25/2010 draft policy, academicintegrity.rutgers.edu
The principles of academic integrity require that:
- All work submitted in a course must be a student’s own and must have been produced without the aid of unsanctioned materials or collaboration.
- All use of the ideas, results, or words of others must be properly acknowledged and cited.
- All contributors to a given piece of work must be acknowledged properly.
- All data or results must be obtained by ethical means and reported accurately without suppressing any results inconsistent with the author’s interpretation or conclusions.
The following class schedule is subject to change. Ideally, reading assignments should be completed prior to each lecture. Additional study materials for many of the topics are specified on Sakai on the "Homework Assignments" page.
|Sep 5||Intro; Displaying distributions with graphs||
|Sep 12||Describing distributions with numbers||
|Sep 19||Density curves and normal distributions||
|Sep 26||Scatterplots and correlation||
|Oct 3||Chapt 1 quiz; Least-squares regression; Cautions||
|Oct 10||Two-way tables; The question of causation||
|Oct 17||Design of experiments; Sampling design||
|Oct 24||Chapt 2 quiz; Toward statistical inference; Ethics||
|Oct 31||Exploratory data analysis project due; Randomness; Probability models||
|Nov 7||Chapt 3 quiz; Exploratory data analysis presentations|
|Nov 14||Random variables; General probability rules||
|Nov 21||No class - Thanksgiving Recess|
|Nov 28||Sampling distributions for counts and propor'ns||
|Dec 5||Chapt 4 quiz; Sampling distribution of a sample mean||
|Dec 12||Estimating with confidence; Tests of significance; Self-assessment due||
|Dec 19||Chapt 5 quiz; Use and abuse of tests; Power and inference as a decision||