"The primary purpose of the course is to educate students about basic [statistical] theories and techniques used in the behavioral sciences. The instructor will briefly review information typically covered in undergraduate statistics, and then introduce more advanced statistical techniques. Upon completion, the student is expected to understand the theoretical underpinnings for the various statistical techniques and the assumptions that data must meet to validly use these statistics. The student will also gain an introduction to computer-based statistical analysis.  30 hours.  3 credits."  (from Graduate Bulletin)

Many students enter the course several years after having taken an undergraduate statistics course.  Moreover, new material often precedes review material in order of logical development.  As a consequence, we will "briefly review" introductory material as if it were new material and do so for most of the semester.  Nonetheless, from the start, we will cover most of the material in more depth than a typical undergraduate course.

Computer based statistical analysis will largely be limited to the use of spreadsheets.  This is because we offer another course entirely devoted to the topic of computer based statistical analysis (PSY 737).  I highly recommend that course to both thesis and non-thesis students.

Course Objectives
1. Students will learn to view psychological phenomena from the perspective of quantitative stochastic processes.
2. Students will develop linguistic competence in interpreting, describing, and critically evaluating basic statistical data.
3. Students will gain experience reasoning from and about numerical data.

Note:  It is not possible for this course to cover all of the statistics that you might need for a thesis.  If you plan on doing a thesis, prepare yourself for the fact that your data analysis will likely involve at least some statistics not covered in this course.  See Blackboard for a document comparing what it covered in this course to other benchmarks.

Required Reading
Bachman, R & Paternoster, R. (2008). Statistical methods for criminology and criminal justice (3rd ed.).  New York: McGraw Hill.

Software
The course requires the use of either Excel (available as part of Microsoft Office in college computer labs) or Calc (freely available as part of Apache OpenOffice at the following URL: http://www.openoffice.org/).  The programs are very similar, but there are some important differences.  Such spreadsheet software remains a popular choice for data entry even among researchers who use more specialized statistical software for data analysis (the topic of PSY 737).  However, research shows that specialized statistical software can be more confusing than enlightening when learning fundamental statistical concepts.  Excel/Calc offer a simple computing environment for working with data ideal for learning and gaining confidence with fundamental statistical concepts.

Class Time
Do the reading before the corresponding class.  If you have questions about the reading, bring them to class and I will try to answer them there.  If you have trouble with the homework, we can also go over that in class.  I want to leave some flexibility to use the class time in the manner you will find most useful.

Homework
The homework assignment is the same for each assigned chapter:  (a) Provide a definition for each of the "Key Terms" listed at the end of the chapter and (b) provide a brief explanation of each of the "Key Formulas" listed at the end of the chapter (note that most of these are in fact equations rather than formulas).  A good explanation paraphrases the equation (or formula) in words, it tells the reader what the formula says.  Simply stating what the equation is used for does not offer a good explanation of the equation itself.  Note that you do not fully understand an equation unless you recognize each of the individual terms (e.g., n, s, x) used in the equation.

Homework assignments are due at the start of class for the day on which the reading was assigned.  See the course schedule at the end of the syllabus for details.  I recommend completing the homework as you read the chapter rather than reading first and saving the homework until afterward.

The examinations will not require calculations or software, but may include examples of spreadsheet formulas or results of spreadsheet calculations. The examinations will be multiple choice. Before the first examination, I will post a preparation guide to Blackboard.

At the final examination, you will have the option of taking alternate versions of Test A and Test B to possibly improve your grade on the earlier tests.

Grading
Your final grade comprises your three examination grades (75%, 25% each) and your homework assignment grade (25%, about 2% each).  I will return all grades in percent form, so to compute your final grade you multiply by the above percents and add them up:  Final Grade = .25(Exam A) + .25(Exam B) + .25(Exam C) + .25(Homework Total).  I will assign letter grades as indicated below. Note that I will do the above computation before rounding, so your results may differ by rounding error in the reporting of individual grades.

Grading on a curve normally refers to forcing grades onto a normal distribution to fix the proportion of students receiving each letter grade.  I do not grade on a curve because it makes it impossible for everyone to do well.  Anyone who meets the criteria can earn an "A" in this course (or any other grade) no matter how other students do.  In other words, grading on a curve means that your grade depends upon how well other students do in the course.  In this course, your grade only depends on how well you do.

However, I will determine your grades using a different kind of curve.  The reason for this is that multiple choice tests tend to be very effective assessment tools in the sense that that they distinguish effectively between different student's levels of understanding.  That is, they are very effective a spreading people out.  However, a variety of policies in the MA program were designed on the assumption that MA course grades would have a compressed distribution.  To accommodate this assumption, I will translate your proportion correct into a numeric course grade using a nonlinear transformation.  This transformation is based on the an arc connecting the point <0,0> to the point <1,1> along the edge of a circle with a center at <1.5,-0.5> (as shown in the graph).  Numeric Course Grade (NCG) can be computed from your proportion correct grade (PCG) using the following equation:  NCG = 100 * (-0.5 + sqrt(2.5 - ((PCG - 1.5)^2))).  This amounts to a diminishing returns curve such that each time Proportion Correct increases, Numeric Course Grade increases by a little less.  As Proportion Correct increases, the proportion of students with at least that Proportion Correct decreases, so the result is the desired compression of the grade distribution (squeezing students closer together).

I will use the following chart to convert Numeric Course Grades to Letter Course Grades.  I will round x.5 and above up and anything below x.5 down.

Contact Information: (It usually works best to email me.)

  Office Hours:  Tuesdays and Thursdays 4:15 PM to 5:15 PM.

  Office:  Room 10.63.11, 524 W59 Street.

  Phone:  212-237-8784 (I do not check voice mail when off campus.)

  Email:  KMarkus@aol.com