History 595 is a "how to" course. It teaches you how to use statistical analysis to answer historical questions. Although I do not assume that you have a knowledge of statistics or any math beyond algebra, you have to use some algebra to understand the statistics. The course also will make use of the SPSS data analysis program and a computer for computations. So you will need to understand how to use one of the programs and how to interpret the output.

While the questions, data, and applications we will examine will be historical, you will be able to use the skills you learn to analyze all types of quantitative questions. These skills will be important to you if you pursue graduate training in history and/or the social sciences, and be equally useful if you pursue a career in business, government, or teaching.

History 595 fulfills the methods requirement in the History major and the GER Quantitative Literacy (QLB) requirement.

  • Instructor: Dr. Margo Anderson
  • Required Text: Alan Agresti, Statistical Methods for the Social Sciences, 5th ed. Pearson Prentice Hall
  • Materials: data storageor flash drive; calculator (recommended).
  • Classrooms: BOL 293, MW 2-3:15 PM 
  • Campus Labs: BOL 225; Also Library, Union.
  • Telephones: Office: 414-229-3969, 229-4361. Make appointment by phone or via electronic mail: margo@uwm.edu
  • Instructor Office Location:  Holton 320
  • Office Hours: MW 3:30-4:30 PM and by appointment, Holton 320, occasionally in BOL293.
  • Grading: 

    Undergraduates:  There will be 10 home works, worth 50% of the total grade.  The first seven will be due on the following Monday. The last 3 are biweekly, due on April 10, April 24, and May 8.  More about this in class. The midterm will be on March 13 (worth 15%). The final exam, given on May 16, will be worth 25% of the grade. Attendance and class participation are worth 10%.
    Graduate Students:  There will be 10 home works, worth 40% of the total grade.  The first seven will be due on the following Monday.  The last 3 are biweekly, due on April 10, April 24, and May 8. More about this in class. The midterm will be on March 13 (worth 15%). The final exam, given on May 16, will be worth 25% of the grade. Attendance and class participation are worth 10%. You will also write a short research paper based upon your analysis of the data we are using (10% of the grade).

This is a hard class. The material is cumulative, so if you miss a week or two, you will likely be very confused.  The many assignments are designed to help you develop the routine to do well. Regular attendance in class also keeps you on track.  I also take attendance to monitor how everyone is doing.  So, my message is:  Do not fall behind. Each week, you should expect to spend 6 hours outside of class time working on the computer, reading, and writing. Take time now to set aside those hours each week so you can do the work for the course.

If you are a student with a disability, please feel free to contact me early in the semester for any help or accommodations which you may need. 

See www.uwm.edu/Dept/SecU/SyllabusLinks.pdf for UWM Academic policies.

The url for this syllabus is: margoanderson.org/595/595syl.htm.  The Annenberg Project has a video series, "Against All Odds:  Inside Statistics." The tapes are available in the Multimedia Library and on the web, http://www.learner.org/resources/series65.html.  These will supplement the materials in class.  We will also link data sets, examples, lecture notes, and supplemental websites to syllabus, so the syllabus will grow in links as the semester progresses.  

Discussion Topics, Assignments, and Due Dates






Jan 23

Introduction: (1) Quantitative History, Historical Methods, Research and Computers. (2) matrix, case, variable, code/value.  (3) algebra review.  (4) levels of measurement. (5) linear relationships, y = a + bx.   (6) Examples:  Variables and Codebook.  Accessing data:  Example from the City Building Process. Map of Milwaukee.  Read Agresti, chs 1-2.   First Short Assignment Answer Key


Jan 30

Univariate Statistic.  Frequency Distributions.  (1) Measures of central tendency:  mean, median, mode.  (2) Measures of dispersion:  ntiles, range, maximum, minimum, standard deviation, coefficient of variation.  Read:  Agresti, ch. 3.  Datasets.   Second Short Assignment.  Output for assignment.  Graph Paper Answer Key


Feb 6

Where does all this data come from anyway?  History of the Federal Statistical System. Lecture.   February 8: Special Class in Lubar S250, 12:30-3:10.  Probability.  Read Agresti, ch. 4.   Milwaukee 1880. Third Short Assignmen Answer Key.


Feb 13

Inferences and Estimation, Standard Deviations, Standard Errors, confidence intervals. Agresti, ch. 5. Handout and Fourth Short Assignment  Output for Fourth Short Assignment. Answer Key 


Feb 20

Inference and Significance Tests. Agresti, ch. 6. Fifth Short Assignment.  Answer Key


Feb 27

Comparing means and proportions, T-Test. Agresti, ch. 7. Cross Tabulation. Paper Assignment for Graduate Students or Extra Credit/Waiving Final Exam for Undergraduates. Sixth Short Assignment.Answer Key


Mar 6

Comparing Categorical Variables. Chi Square Test,  Agresti, ch. 8. Midterm Examples.


Mar 13

March 13: MIDTERM. Review of Univariate and Bivariate Methods.  Seventh Short Assignment UWM Graph  System Graphs Seventh Short Assignment Answer Key Midterm. Midterm AnswerKey


Mar 27

Bivariate regression.   Agresti, ch. 9. Regression Eighth Short Assignment Logarithms Scatterplot  Regression Output   Eighth Short Assignment Answer Key


Apr 3

Multivariate Relationships. Agresti, ch. 10.    Regression Models 1U.S. States DatasetNew World DatasetRegression ExerciseUsstates.sav variable list.  Newworld.sav variable list.  


Apr 10

Multiple Regression and Correlation. Agresti, ch. 11. Ninth Short Assignmen, Ninth Short Assignment Output. Ninth Assignment Answer Key, Analysis of Variance. Regression Models 2, Regression Examples


Apr 17

Analysis of Variance (ANOVA), Agresti, ch. 12.   Anova Output


Apr 24

Regression with Dummy Variables. Agresti, ch. 13. Tenth Short Assignment.  Regression Models 3. Residuals Analysis.  Time Series


May 1

Models and Regression Diagnostics. Regression Exercise.


May 8

Logistic Regression. LogisticRegression. Agresti, ch. 14-15 (parts) which correspond to lectures.


May 16

Final Examination: Tuesday, 12:30-2:30 PM.  Example of Final Exam. Final Exam. Extra Credit Exam

Graduate Students: Turn in Final Paper.