Intermediate Statistics for the Behavioral Sciences appl 631. 085

Download 19.73 Kb.
Date conversion29.01.2017
Size19.73 Kb.
Intermediate Statistics for the Behavioral Sciences

APPL 631.085

University of Baltimore
Professor: Dr. Sally Farley Email:

Meeting details: Tuesdays, 2 – 4:30 in AC238 Office phone: 410.837.5279

Email: Office location: AC 209H

Office hours: Mondays and Thursdays: 4 – 5:30 p.m. and by appointment


  • The purpose of this course is to help you understand how statistics, as scientific tools, help researchers answer scientific questions. Please do not be overly concerned if you do not remember very much from your previous statistics course(s). This is actually quite typical. Because students arrive at graduate school with various degrees of exposure (and confidence) with statistics, we will be starting at the beginning. This course will be much like an advanced version of an introductory undergraduate statistics course, covering the same topics – descriptive statistics, hypothesis testing, t-tests, chi-square, correlation, and analysis of variance among them - but in somewhat greater depth.

  • One central purpose of this course is to prepare students to interpret data from real research. You, as students, will frequently provide the data for us to analyze in class. Research indicates that individuals process information more deeply when it is personally meaningful to them. Research also shows that nothing is more personally meaningful to people than themselves ;) so you will act as participants on a few occasions.

  • We will primarily tackle statistics at a conceptual level. We will utilize both hand-calculations and computer analyses (SPSS) for most of the computations in this course, but you will be in a stronger position to excel on the exams if you understand what various statistical techniques do and what they mean. I do not require students to memorize formulas in the class, but the exams will necessitate a sophisticated knowledge of the purpose and appropriate application of various statistical techniques.

Learning objectives:

  • Students will develop greater understanding of the various statistical techniques utilized in psychological research

  • Students will increase their competency for selecting appropriate statistical techniques (when, for example, an independent-samples t-test is used)

  • Students will learn how to conduct these statistical techniques through the use of a computer-software package (SPSS)

  • Students will increase their critical thinking about scientific research

  • Students will enhance their ability to write APA-style results sections

Required text:

Gravetter, F. J., & Wallnau, L. B. (2009). Statistics for the behavioral sciences (8th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Recommended supplement (if you have had little exposure to SPSS):

Green, S. B., & Salkind, N. J. (2008). Using SPSS for Windows and Macintosh (5th ed.). Upper Saddle River, NJ: Prentice Hall

Other recommendations/comments:

  • Although I view this course as an “advanced undergraduate course,” we will move quickly through the material. We have a tremendous amount to cover to adequately prepare you for Research Methods, in addition to your other courses. In addition, a basic understanding of SPSS is expected. To ensure that we complete the material in a timely manner, I cannot use class time to review SPSS. Please purchase the Green and Salkind book for assistance with SPSS.

  • I would also recommend the study guide for the textbook for those of you who think you will need it (due to struggling in a previous statistics course or weaker mathematical ability). Please complete the math review, if you have concerns about your math ability:

  • Please also obtain a scientific calculator and a network (NT) computer account. The calculator should have the square root function and the X2 buttons. We will use calculators in almost every class.

  • I created a WebTycho course for this course. I will typically post relevant announcements on the site rather than sending mass emails. Please check frequently.

Class Policies:

  • Please inform me as soon as possible regarding disability support services provided to you so that I can be as accommodating as possible.

  • Class attendance is expected. Students who miss classes for any reason are responsible for all of the material covered in class.

  • Please complete all reading assignments (other than today’s ) in advance. Your understanding of the material will be greatly enhanced if you come to class prepared.

  • Make-up exams are not allowed unless under reasonable and documented circumstances. Please contact me as quickly as possible if you are not able to come to class on exam day.

  • Cell phones and pagers must be turned off or on vibrate during class and exams. I reserve the right to answer your phone if it starts ringing in class. 

  • Any evidence of cheating will not be tolerated and will result in a 0 on the assignment or an F in the course, depending upon the severity (See Academic honesty section below).

  • All assignments are due at the beginning of class or as stated on the assignment. Assignments will be penalized 10% for every 24-hour period they are late. Please submit all late assignments to WebTycho

so that they are appropriately time-stamped. Late work may not be accepted after graded assignments

have been returned (typically the following class).

  • The following behaviors are disrespectful, and therefore, inappropriate:

  • Conversing with other students when the instructor or another student is speaking.

  • Use of cell-phones, pagers or computers for purposes other than taking notes.

  • Disrespectful actions such as obscene language, harassment of any kind, grumpy body language, or any other action that will interfere with student learning.

Academic honesty:

Academic honesty is part of the foundation of an academic community. Any violation of the standards of academic honesty threatens the trust upon which an academic community is built. Academic dishonesty can take many forms. In general, academic dishonesty is any behavior that results in the circumvention of the work required and expected to gain academic credit. For example, writing a paper without using your own thoughts and/or words or claiming participation in an academic requirement in which one did not participate.

Described simply, academic dishonesty consists of taking another person’s work and presenting it as your own. Plagiarism is a distinct form of academic dishonesty in which a person uses the words or ideas of another without proper acknowledgement. When anyone uses the work of others by copying their words or ideas without proper citation, it is plagiarism. Even paraphrasing material found in printed or online material (e.g. Wikapedia) requires proper citation. Unfortunately, most cases of plagiarism are not intentional, and they typically consist of using five or more of an author’s words in a row without quotation marks.

Academic dishonesty is a serious breach of the rules of proper academic conduct. The penalty for the first act of academic dishonesty will be a zero on the piece of work involved or an F in the course, at the discretion of the instructor. I will also consult with the Dean of Students regarding matters of academic dishonesty, which, for serious breaches, could result in dismissal from the University.

Course evaluation: Final grades will be weighted as follows: exams (70%) and assignments (30%).

  • Exams. Exams will comprise a mixture of computations, interpretation of computer outputs, and conceptual questions. The first two exams will be slightly less in value (20% each), with the final exam constituting 30% of the overall grade. The final exam is cumulative, but more heavily weighted on the final chapters.

  • Homework assignments will consist of problems from the text, SPSS assignments, and writing assignments. Periodically, I will collect your textbook homework problems to encourage you to keep up. The problems from text will be evaluated more for completeness and effort rather than accuracy (as they are practice for the exams). It will be important for you to show your work because the answers for the odd-numbered questions are in Appendix C in your book. If your answers look remarkably similar to the solutions in the book, you will not earn credit.  The SPSS assignments will require you to learn how to analyze and interpret statistical tests conducted on SPSS. In addition, your homework assignment may involve writing an APA-style results section to show that you understand the connection between various statistical tests and their application to research questions. All assignments are due at the beginning of class or as stated on the assignment. Assignments will be penalized 10% for each 24-hour period they are late and may not be accepted after graded assignments have been returned (typically the following class).

  • Extra credit. Students may earn up to 3 percentage points (added to their final grade) in extra credit for participation in research. Participation in one hour of research is equivalent to one percentage point, 30 minutes, .5 percentage point. Students should register on the sona system so that they are eligible for extra credit opportunities.

Grading Scale: Point Breakdown:

A = 93% and higher Exam One 100 points

A- = 90% to 92.9% Exam Two 100 points

B+ = 87% to 89.9% Final Exam 150 points

B = 83% to 86.9% Homework* 150 points

B- = 80% to 82.9% 500 points

C+ = 77% to 79.9% *There will be five larger assignments, each worth

C = 73% to 76.9% 20 points. The other 50 points come from spot-

C- = 70% to 72.9% checking your end of the chapter problems.

Below C- = failing gradeDate Topic Chapter(s)

September 1 (Week One) Review of Descriptive and Inferential Statistics, 1

Experimental Method, and Scales of Measurement
September 8 (Week Two) Frequency Distributions, Measures of Central Tendency 2/3

September 15 (Week Three) Variability and z-scores 4/5

September 22 (Week Four) Probability and the Normal Distribution 6

September 29 (Week Five) FIRST EXAM, Chapters 1-6
October 6 (Week Six) Probability and Samples, 7/8

Introduction to Hypothesis Testing

October 13 (Week Seven) Finish hypothesis testing 8
October 20 (Week Eight) The t-test for Two Independent Samples 9/10

The t-test for Related Samples

October 27 (Week Nine) Introduction to Analysis of Variance 13

November 3 (Week Ten) SECOND EXAM, Chapters 7-10, 13

November 10 (Week Eleven) Two-Factor Analysis of Variance 15
November 17 (Week Twelve) Correlation 16
November 24 (Week Thirteen) Introduction to Regression 17
December 1 (Week Fourteen) Chi-square and catch up/review 18
December 8 (Week Fifteen) Catch up and review
December 15 FINAL EXAM 2:30 p.m.

**The weekly topics may change (we may need more or less time to cover a topic appropriately), but the exams will not shift. The first exam will include material covered up to September 29th, the second exam on November 3rd will cover material since the first exam, and the final will be largely cumulative, but only include material covered during the semester.

The database is protected by copyright © 2016
send message

    Main page