Introduction to Measurement and Statistics

(Fall 2011)

Course:

PSYC/SOCI 2750: Introduction to Measurement and Statistics - (SCI, MTH)

Instructor:

Dr. Linda M. Woolf

Office Hours:

Text:

McCall, R. B. (2000). Fundamental statistics for behavioral sciences (8th ed.). New York: Wadsworth. ISBN: 978-0534577803

Course Description:

Introduction to Measurement and Statistics (PSYC/ANSO 2750) is for the university student who wishes to gain an understanding of basic statistical concepts. Knowledge of these concepts is essential for the reading of technical journals in one's field and basic research design. In other words, no matter whether you are sitting by the fireplace catching up on your reading about depression or working on a new treatment method, knowing when and how to use measurements and statistics is fundamental. The basic concepts to be covered are:

  1. the contrast between descriptive and causal research
  2. types of measurement
  3. the use of descriptive statistics to summarize research results
  4. the use of inferential statistics to draw conclusions based on a sample(s) drawn from a population.

No prior statistical knowledge is required for this class. Classroom techniques that will be used to achieve the course objectives will include lecture, active problem solving sessions, online sample problems, homework, and examinations.

This course is coded for the Scientific Understanding goal in the General Education program. Scientific Understanding is defined as the analysis of the concepts of a scientific discipline and its methods, limitations, and impact in the modern world.

This course is also coded for the Mathematics goal in the General Education program. Mathematics is defined as the recognition of the value and beauty of mathematics as well as the ability to appraise and use quantitative data.

Course Objectives:

  1. To develop a practicing and theoretical understanding of descriptive and inferential statistics.

  2. To develop an understanding of how to choose a statistic or determine if the one used is appropriate based on the type or source of quantitative data.

  3. To develop an appreciation for the use of statistics and an ability to recognize the misuse of statistics. In other words, to make the student an active consumer of statistics (i.e. do people really lie with statistics?).

Course Outcomes:

  1. The student will have an understanding of basic research methodology and the impact of research design on data interpretation.

  2. The student will be able to differentiate between a descriptive and inferential statistic and will know when each is being interpreted appropriately.

  3. The student will know how to represent and interpret frequency and statistical data using basic graphing techniques.

  4. The student will know how to compute and evaluate measures of central tendency, measures of variability, standard scores, correlation coefficients, linear regression, standard error, confidence intervals, t-tests (independent and correlated), and one/two factor ANOVA.

  5. The student will know when to appropriately use and how to interpret data from each of the above statistical techniques.

  6. The student will understand the underlying assumptions and theory of hypothesis testing.

NOTE:

Statistics can be fun or at least they don't need to be feared. The logical and mathematical concepts required to do well in this class are not prohibitive for any university student. The key to doing well in this class can be summarized by two words, "KEEP UP!". If you don't understand a concept in class, ask. I'm more than willing to re-explain. If you start to fall behind, contact me as soon as possible. We'll make some arrangements. This is important because the material discussed four weeks from today will be based on material discussed today. For more survival tips, see Survival Tips

Incoming Competencies/Prerequisites:

Prerequisite: All students should be capable of basic math and simple algebra.

Class Meetings:

The class will meet on Thursdays from 5:30 - 9:30. Attendance is strongly recommended as this material is difficult and conceptually complex. Classroom attendance will greatly enhance your understanding of the information.

Course Requirements:

A midterm exam, a final exam, and homework assignments.

All grades will be assigned on a scale of 0 - 100 with:

90 - 100A,A-Superior work
80 - 89B+,B,B-Good work
70 - 79C+,C,C- Satisfactory work
60 - 69D+,D Passing, but less than Satisfactory
Less than 60F Unsatisfactory

Percent of Grade:

Midterm Exam 40%
Final Exam 40%
Homework 20%

Examination format will include short answer and problem solving. The midterm will be take-home. The final will be in-class. The final will be open-book and open-note. Each exam will constitute 40% of your final grade. All exams must be taken on the date scheduled except in case of emergency. In case of the above, the instructor must be notified. No make-up exams will be provided if you fail to notify and discuss your situation with the instructor. Please note that no extra credit work will be made available to make-up for a poor test grade.

Homework will be assigned for the material covered during lecture. This will provide you with the opportunity to review and reinforce the material covered in class. It also serves as a diagnostic tool for me to see where people might be having problems. No late assignments will be accepted except in cases of emergency. Homework will constitute 20% of your final grade. Note: Not turning in homework assignments can result in a one to two letter grade drop in your final grade.

Plagiarism (attempting to pass off the work of another as one's own) is not acceptable. Plagiarism includes copying all or part of another's writings (even a single sentence), inappropriate paraphrasing, using another student's paper as your own, submitting a paper for more than one class. All papers will be submitted to the university's plagiarism database for review. Plagiarism, either intentional or unintentional, will result in a grade of 0 for that assignment but also may be turned over to the appropriate university source for disciplinary action and a grade of F for the course. In addition, cheating on exams will also result in the same fate.

Here are some Web sites that will help you avoid the problem of plagiarism particularly plagiarism resulting from paraphrasing too closely to the original source. -

Late withdraws from this class will not be approved by the instructor except in cases of emergency discussed with the instructor. No late withdraws will be approved on the basis of poor class performance.

This syllabus is subject to change at the instructor's discretion. All changes concerning course requirements will be provided in writing. Changes concerning exam dates may be made at the instructor's discretion and communicated verbally to the class.

It is understood that remaining in this course (not dropping or withdrawing from this course) constitutes an agreement to abide by the terms outlined in this syllabus and an acceptance of the requirements outlined in this document.


COURSE OUTLINE


Week


Topic


Reading

August25 Introduction to class
Introduction to statistics
Overview of methodology
Chapter 1
Chapter 12
Introduction to Measurement and Statistics
Research Methods

September1Frequency Distributions and Graphing
Measure of Central Tendancy
Chapter 2
September8Characteristics-distributions
Measures of relative standing
Chapter 3
Chapter 5
September15 Sampling distributions
Hypothesis Testing

Receive Midterm

Chapter 8
Chapter 9
September22Midterm Due

Hypothesis testing
Tests of significance



Chapter 9
Chapter 10 - 11
September29 ANOVA Chapters 14 - 15
October6Correlation
Linear Regression

Practice Session!

Chapter 7
Chapter 6
October13FINAL EXAM




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