Quantitative Analysis

Purpose

Students will be introduced to statistical techniques commonly used in the social sciences. Emphasis will be on both the proper use of the different statistical techniques in research situations and upon the proper interpretation and presentation of statistical analyses in research reports.

Calculation will be expected but not stressed. Our purpose is to enable social science students to understand the basic statistics they will encounter in the social sciences and to introduce them to the presentation of statistical results.

Structure

The course begins by discussing the nature of data and the concept of sampling and probability. Statistics describing one variable follow. Two variable statistics and techniques such as the t-test, analysis of variance, and measures of association are next presented.

Finally, one multivariable technique is introduced, regression analysis. However, multivariate procedures are studied more thoroughly in Psychology 305 (experimental methods). The instructor appreciates classroom discussion and encourages students to ask questions.

Computer Lab

Each week, students should work for about one hour in the computer lab in McCrary Hall. Lab times will be posted during the second week of class. The purpose of the lab is to help students learn how to analyze data on the computer using SPSS.

Lab exercises ask students to analyze real data sets using descriptive statistics, the t-test, one way analysis of variance, Pearson r, and regression. To support instruction in the lab, each student will be given handouts that include step by step instructions on how to use SPSS for Windows.

Educational Outcomes

1. Understand how to assign observations to appropriate scales of measurements.

2. Differentiate between descriptive and inferential statistics.

3. Relate appropriate descriptive statistics to each scale of measurement.

4. Create frequency distributions, histograms, and other graphs.

5. Calculate and interpret descriptive statistics and measures of variability.

6. Know how skewness effects measures of central tendency.

7. Understand the normal curve and Z scores.

8. Be able to state the law of large numbers.

9. Use Z scores to interpret test scores.

10. Define probability.

11. Combine probabilities for independent events.

12. Use probabilities to solve normal curve problems.

13. Define sample, population, parameter and statistic.

14. Calculate confidence intervals around a sample mean.

15. Understand random sampling.

16. Explain bias and sampling error.

17. Understand the sampling distribution of means and be able to calculate and interpret measures of central tendency and variability that are used to describe this distribution (the mean of the distribution of means and the standard deviation of the distribution of means.)

18. Be able to state the Central Limit Theorem.

19. Know how to accept or reject both the Null Hypothesis and the Alternative Hypothesis at a given alpha level.

20. Determine whether a sample is probably representative of the population.

21. Know how and when to use a single-sample t ratio.

22. Be able to define a Type I and a Type II error.

23. Define significance and alpha error.

24. Know how to use the t table to identify critical values of t.

25. Explain what is meant by degrees of freedom.

26. Illustrate what a sampling distribution of differences constitutes.

27. Understand why the mean of the distribution of differences should be about zero.

28. Calculate and interpret a t-ratio based on two independent samples.

29. Calculate and interpret both a one-tail and a two-tail t-test.

30. Be able to interpret the SPSS printout of T-Test results.

31. Know the requirements of the t-test.

32. Know when to use the hypothesis of association to observe co-variation.

33. Know how to interpret Pearson r in terms of direction, strength, and significance.

34. Know the assumptions of Pearson r.

35. Use the coefficient of determination to explain variance.

36. Be able to use the Pearson r table to identify critical values of r.

37. Be able to create a regression equation, plot the regression line, and determine the strength of the relationship between or among variables.

38. Be able to interpret an SPSS printout of correlation and regression findings.

39. Know when to use one-way analysis of variance instead of the t-test.

40. Understand how to partition variance into variance between, within, and total (explained, unexplained).

41. Be able to use the F table to identify critical values of F.

42. Know the requirements for using the F ratio.

43. Know how to determine the strength of the relationship among variables.

44. Be able to interpret the SPSS printout of ANOVA findings.

Quizzes

Quiz 1 covers objectives 1-6
Quiz 2 covers objectives 7-11
Quiz 3 covers objectives 12-20
Quiz 4 covers objectives 21-31
Quiz 5 covers objectives 32-37
Quiz 6 covers objectives 38-44

The Final Exam covers all 44 objectives

Quizzes include questions that ask students to use real data sets to answer questions about the data. Seventy-five percent of the cumulative final exam is based on questions similar to quiz questions. Twenty-five percent is composed of short answer questions similar to the questions at the end of the chapters in your text.

Grades

There will be seven examinations: six quizzes during the semester, a pre-final during the last week of the semester, and a final. Also, five computer exercises and five homework assignments must be completed. Computer exercises and homework assignments are worth ten points each. The entire course is worth 700 points.

Letter grades will be assigned at the end of the semester on the following scale:

Because this class tends to be large, more than 35 students, students are expected to take exams at the scheduled times. All students must take the final exam on the date indicated in the schedule of classes.

If an emergency arises and students miss a quiz, students are advised to obtain a note from the department chair explaining why a make-up test in necessary. Further, computer exercises and homework assignments must be submitted on time.

Students with test anxiety or other conditions that warrant individual accommodations are encouraged to let me know so that I can make arrangements for testing in an appropriate environment.

Honor Code

I support the honor code and expect students not to copy answers from one another on any graded assignments, including homework, computer exercises, and quizzes. Students are expected to state and sign the honor pledge on all work submitted for a grade.

Text

Required readings are from the following text:

Richard Sprinthall. Basic Statistical Analysis. Fourth edition (Allyn and Bacon, 1994).

Psychology 203 Weekly Schedule

Honor Code

All students are expected to follow the University of Virginia honor code.

Individual Accommodations and Study Groups

If there is any student who feels that she/he may need to make me aware of individual accommodations that need to be made, please make an appointment to see me during office hours.

Also, if students want to meet outside of class with their study or collaborative learning groups, I will be glad to meet with you.