BSAD/POLS/PSYC-239 Statistics for Social Science

Latest Course Offering: Fall 2011, Term 1
Course Time: Tuesday and Thursday, 8:10 – 10:25
Location: Krumm 24
Contact: tomschenkjr at gmail dot com

Instructional Resources

  1. Textbook: Understanding Statistics in Behavioral Sciences by Robert R. Pagano, 9th Edition, Wadsworth Publishing: 2008 ISBN-13: 978-0495596523
  2. Data: Census @ Schools, Data and Story Library, Classroom Survey of Basic Data, Historical Weather Data (via NASA), Iowa Community College Tuition & Fees Report, Median Earnings of College Graduates by Major & Gender
  3. Blogs: See Statistical Modeling, Social Science Statistics, Observational Epidemiology
  4. Other materials will be distributed via the course website:


All assignments will be scored on a 100 percent scale. The final grade, Y, will be derived using the following formula:

Y = 0.33 * A + 0.33 * E + 0.33 * F


F = Final
E = Exam
A = Average of assignments

Final/Exam: The final will explicitly be cumulative, although the emphasis will be slightly more on the latter third of the class.

Assignments: Assignments will be given throughout the semester through the course website. Assignments will vary in size from a few questions, to larger assignments that may resemble class projects.

Final Course Score

A = 90-100%
B = 80-89%
C = 70-79%
D = 60-69%
F = < 60%

Reading List

30 Aug: Review of the syllabus and introduction

Scientific Method

***Chp. 1 – Pagano

Basic Mathematical Concepts and Measurement Concepts

***Chp. 2 – Pagano
**KhanAcademy lecture on means (and notation)

1 Sep: Central Tendency and Variability (In-class Survey)

***Chp. 3 – Pagano
***Chp. 4 – Pagano
**Anatomy of a Standard Deviation
**KhanAcademy lecture on variance
**KhanAcademy lecture on standard deviation

2 Sep: Last Day to Drop without a “W”

6 Sep: Graphing

***The Basics of a Basic Graph,

8 Sep: Graphing (continued)

13 Sep: Normal and Standard Normal Curves (a.k.a. bell curve)

***Chp. 5 – Pagano
***KhanAcademy lecture on Normal Distribution Curve
***Normal distribution curve, Wikipedia

15 Sep: Correlation

***Chp. 6 – Pagano
***Data, Data everywhere, The Economist (2/15/2010)

20 Sep: Linear Regression

***Chp. 7 – Pagano
***Right-to-Work Law,
***Two major labor bills reappear at the Capitol, Jason Clayworth, Des Moines Register Blog, February 4, 2010.

22 Sep: Linear Regression / Multiple Regresson

***Chp. 7 – Pagano
**An Introduction to Regression, A. O. Sykes.
** Squared Error of a Regression Line via KhanAcademy
** Coefficient of Determination / R-Squared via KhanAcademy
* Proof: Regressions Minimize Error: Part 1, Part 2, Part 3, Part 4 via KhanAcademy

Take Home Exam

27 Sep: Random Sampling & Probability

***Chp. 8 – Pagano

29 Sep: Introduction to Hypothesis Testing Using the Sign Test

***Chp. 10 – Pagano

30 Sep: Last Day to Drop (grade of “W”)

4 OctRandom Sampling & Probability

***Chp. 8 – Pagano

6 OctIntroduction to Hypothesis Testing Using the Sign Test

***Chp. 10 – Pagano

11 Oct: Z-tests

***Chp. 12 – Pagano

13 Oct: Student’s t-Test

***Chp. 13 – Pagano

***Chp. 14 – Pagano

18 Oct: Final

Missed Exams and Assignments

Assignments will be due at the beginning of class every Tuesday and tests will be given on the days denoted below. Late assignments will be penalized 40 percent. Students must notify the professor of an upcoming absence. Students will be allowed to make up exams ONLY when the professor received prior notification for the inability to complete the exams. In extreme cases where prior notification is impossible, the student must provide written documentation—not by the student—explaining the absence. Students who miss a test for an unexcused absence will receive a zero.


Students will be expected to attend every class. Irregular attendance will be reflected in student performance. Those who already anticipate missing two or more classes are encouraged to enroll at another time.

Academic Integrity

Grand View University is dedicated to the development of the whole person and is committed to truth, excellence, and ethical values. Personal integrity and academic honesty in all aspects of the University experience are the responsibility of each faculty member, staff member, and student.

A student has an obligation to do work that is his or her own and reflects his or her learning and quest for academic knowledge. Dishonesty and cheating are not acceptable behaviors. Examples include helping others during exams, writing papers for others, falsifying data/records, copying other students’ work, taking work directly from the Internet or any printed source and claiming it as one’s own, and downloading/purchasing papers on-line. Students who cheat, could risk severe penalties, which may include failure of the assignment, failure of the course, or expulsion from the University.

“As a member of the Grand View University community, and in accordance with the mission of the University and its Lutheran identity, I agree to appreciate and respect the dignity and worth of each individual. I will honor and promote a community of open interaction, personal integrity, active and intellectual engagement, and academic honesty with students, faculty, and staff.”

Accelerated Courses

Grand View offers courses in accelerated or alternative delivery formats. They cover the same subject content and require the same or comparable assignments that are associated with a traditional fourteen week course.


Grand View University prohibits unlawful discrimination and encourages full participation by all students within the university community. When a student requires any instructional or other accommodation to optimize participation and/or performance in this course, it is the responsibility of the student to contact both the instructor and the Director of Academic Enrichment and Disability Coordinator and apply for any requested accommodation. The director is Dr. Kristine Owens and she can be reached at 515/263-2971.

Class Attendance

The Federal Government requires that students receiving financial aid attend classes. Students, who are identified by the instructor as not attending classes, will be reported to the Registrar’s Office. Students who fail to return to classes may lose all or a portion of their financial aid.

Classroom Conduct

Students should conduct themselves as responsible members of the University community respecting the rights of others. Any student behavior interfering with the professor’s ability to teach and/or the student’s ability to learn constitutes a violation of the Code of Student Conduct found in the Grand View Catalog. The professor may ask the student to leave the classroom and that student will be subject to disciplinary sanctions.

University E-Mail Account

It is essential that all students check their Grand View University e-mail account or set their account to forward to a preferred e-mail address.

Students may set-up an e-mail auto forward from the myView web site. Click on the “Manage and Update Personal Information” link and then select “set myView Mail Forwarding Address” under the “Links for You” section.

Appeal of Final Undergraduate Course Grade or Faculty Member’s Final Academic Disciplinary Action

Students who wish to appeal a final course grade or other academic disciplinary action of an instructor must complete at least section I.A. of the Academic Appeal Form on-line within fourteen calendar days after the published due date for the final grade submission of the academic term in which the issue of disagreement occurred. Visit site below to complete first part of the form. https://secure/

This form must be submitted electronically to the Office of the Provost. Nursing Students appealing a grade in a nursing course must follow the Nursing Division procedures.


  1. Homework #1 – Email me your preferred email address.
  2. Homework #2 – Due September 6 at start of class.
  3. Homework #3 – Due September 15 at start of class (Excel required).
  4. Homework #4 – Due September 24 by 11:59pm (Excel required).
  5. Midterm Exam – Due September 29 by 8p (Excel required).
  6. Homework #5 – Due October 18 by 8p (SPSS & Excel required).
  7. Final – Due October 23 by 10p (SPSS & Excel required).

27 thoughts on “BSAD/POLS/PSYC-239 Statistics for Social Science

  1. On the last question of the homework, I have a couple of questions: 1) It states there are 12 homeruns hit, but there are 15 scores. I assume we use the full 15 scores to calculate. 2) Can I use quintiles instead of quartiles to calculate, since 15 is divisible by 5 and not by 4? Thanks!

    1. Good question. First, it was my mistake to say “12”, clearly there are 15 observations. No, you don’t need to use quintiles, you can still use quartiles. Treat it similar to calculating a median with an even number of scores (recall: take the two middle numbers and divide). Here, just take the numbers on either side of the quartile and divide to find each quartile. Remember, the second quartile should be equal to the median.

  2. Are we supposed to email our homework in or should we print it off and bring it to class? This is in regards to homework #3.

  3. having some problems with estimating bin width with the histogram with cities. cant remember excel function for this

    1. Miranda, you’ll want to find the largest population using the MAX() function. Then you should divide each bin into equal widths using division.

  4. I am having a problem with the histogram. i did the add-on like in class but when I go to do it all it gives me is the frequency and bin range no histogram. Am i missing a step?


    1. Miranda-

      Vote-Exp is just a reference to the columns in the vote page. I used text boxes on that page instead of typing directly into the columns.

  5. I am getting an error in spss that says string variables are not allowed how can I fix that to do my regression

    1. Karen-

      Which data set are you trying to use (OECD or NSW)? When importing your data, remember to specifically choose the sheet with the data, don’t import the instructions.

  6. On the last question 3b) Are there 2 dependents variables? If yes, what menu tool is use to run the regression. Thank you

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