Introduction to Measurement and Statistics
(Spring II 2019)
Course:PSYC/SOCI 2750: Introduction to Measurement and Statistics
Professor:Dr. Linda M. Woolf
- 318 Interdisciplinary Science Building
- 10:00 - 11:00, MWF or by appointment.
- Phone: 246-6970 or 246-7062
- Email through Canvas or firstname.lastname@example.org
- Woolf Web Page: http://faculty.webster.edu/woolflm/
Text:Pagano, R. R. (2012). Understanding statistics in the behavioral sciences (10th ed.). Belmont, CA: Wadsworth. ISBN-10: 1111837260 (Hardback: there are other options, such as loose-leaf and rental).
Catalog DescriptionDesigned to aid the student in learning how to "make sense" of a body of numbers; how to summarize and extract information from numbers; how to detect, measure, and use relationships between variables; and how to use statistical aids to the decision-making process. Course covers descriptive statistics, correlation and regression, and inferential statistics such as the t-test and analysis of variance.
Expanded 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:
- the contrast between descriptive and causal research
- types of measurement
- the use of descriptive statistics to summarize research results
- 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, homework, and examinations.
- To develop a practicing and theoretical understanding of descriptive and inferential statistics.
- 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.
- 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?).
- The student will have an understanding of basic research methodology and the impact of research design on data interpretation.
- The student will be able to differentiate between a descriptive and inferential statistic and will know when each is being interpreted appropriately.
- The student will know how to represent and interpret frequency and statistical data using basic graphing techniques.
- 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.
- The student will know when to appropriately use and how to interpret data from each of the above statistical techniques.
- The student will understand the underlying assumptions and theory of hypothesis testing.PSYC/SOCI 2750 has been coded for the Quantitative Literacy content area.
Quantitative Literacy: A habit of mind, competency and comfort in working with numerical data. Individuals with strong QL skills possess the ability to reason and solve quantitative problems from a wide array of authentic contexts and everyday life situations. They understand and can create sophisticated arguments supported by quantitative evidence, and they can clearly communicate those arguments in a variety of formats (using words, tables, graphs, mathematical equations, etc., as appropriate). Upon the successful completion of this course, students will be able to:
- Explain the crucial role of statistics in science.
- Describe the impact research design has on data interpretation.
- Differentiate between a descriptive and an inferential statistic.
- Describe how probability and hypothesis testing are related to statistical analyses, design, and data.
- Discuss how to select appropriate statistical analyses including why different statistical procedures are used for certain research designs.
- Perform descriptive and inferential statistics using a hand calculator and via statistical software (e.g., SPSS).
- Interpret statistical results and apply this information to contemporary problems.
- Evaluate published scientific research.
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
Prerequisite: All students should be capable of basic math and simple algebra.
The class will meet on Wednesdays 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.
A midterm exam, a final exam, and homework assignments (all homework assignments must be submitted through Canvas).
All grades will be assigned on a scale of 0 - 100 with:
90 - 100 A, A- Superior work 80 - 89 B+, B, B- Good work 70 - 79 C+, C, C- Satisfactory work 60 - 69 D+, D Passing, but less than Satisfactory (not passing if required for the major or general education) Less than 60 F 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. -
- Webster University's Plagiarism Info site
- Establishing Authorship by Paul C. Smith, Alverno College
- How to Avoid Plagiarism Tutorial
- The University of Indiana's Online Plagiarism Tutorial - You can print out a certificate of completion!
Students with disabilities who believe that they may need accommodations in this class are encouraged to contact me or the Director of the Academic Resource Center, as soon as possible to ensure that such accommodations can be implemented in a timely fashion.
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. No grade of Incomplete will be issued for this course.
March 19 Introduction to class
Introduction to statistics
Overview of methodology
Chapter 2 (particularly, pp. 30-38)
Introduction to Measurement and Statistics
March 26 Frequency Distributions and Graphing
Measures of Central Tendency
Chapter 4 (particularly, pp. 80-88)
April 2 Characteristics-distributions
Measures of relative standing
Chapter 4 (particularly, pp. 89-95)
April 9 Sampling distributions
Chapter 12 (particularly, pp. 299-312) April 16 Hypothesis testing
Tests of significance
Chapter 11 (particularly, pp. 278-288)
Chapter 13 (pp. 328-332)
April 23 ANOVA Chapters 15 April 30 Correlation
Chapter 7 (particularly, pp. 160-174)
May 7 FINAL EXAM