HISTORY 595: QUANTITATIVE ANALYSIS OF HISTORICAL DATA

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 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.

    Undergraduates:  There will be 10 home works, worth 50% of the total grade. The first seven will be due on the Monday following the distribution of the assignment. The last 3 are biweekly, due on April 9, April 23, and May 7. More about this in class. The midterm will be on March 14 (worth 15%). The final exam, given on May 12, 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 Monday following the distribution of the assignment. The last 3 are biweekly, due on April 9, April 23, and May 7. More about this in class. The midterm will be on March 14 (worth 15%). The final exam, given on May 12, 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: http://margoanderson.org/595/595syl2018.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 be required viewing for some topics and 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

1

Jan 22

Introduction.

(1) Quantitative History, Historical Methods, Research and Computers.

(2) matrix, case, variable, code/value. 

(3) algebra review. 

(4) levels of measurement: nominal, ordinal, interval variables.  

(5) linear relationships, y = a + bx; categorical relationships.

 

 Read: Simon, ch. 1. Look at the map on p. iv, and the linked map below.  View video 1 of Against All Odds: http://www.learner.org/courses/againstallodds/unitpages/unit01.html

 

If your algrebra is rusty, try the practice tests here:  http://www4.uwm.edu/Org/mmp/_activities/transition.html

 

 

Example:  Variables and Codebook.  Accessing dataExample from the City Building Process. Map of Milwaukee. 

 

Assignment 1

2

Jan 29

Univariate Statistics, Frequency Distributions.

 

Univariate Statistics

 

(1) Measures of central tendency:  mean, median, mode

(2) Measures of dispersion:  ntiles, range, maximum, minimum, standard deviation, coefficient of variation. 

 

 Read: Simon, ch. 2.  View video 4 of Against All Odds:  http://www.learner.org/courses/againstallodds/unitpages/unit04.html

 

 

Assignment2

3

Feb 5

Univariate Statistics, cont.

 

 

Read:  Simon, ch 3.  Recommended: View videos 2 and 3 of Against All Odds.  http://www.learner.org/resources/series65.html#program_descriptions and click on programs 2 and 3. 

 

Assignment 3:

 

4

Feb 12

Understanding Dispersion

Read: Simon, ch. 4. View video 6 of Against All Odds:  http://www.learner.org/resources/series65.html#program_descriptions and click on video 6.

 

Assignment 4:

5

Feb 19

The Normal Curve; Z Score.

 

Read Simon, ch. 5.  View video 7 and 8 of Against All Odds:  http://www.learner.org/resources/series65.html#program_descriptions and click on videos 7 and 8.

 

Sampling Error

Assignment 5

 

6

Feb 26

Probability and Sampling; The Concept of Statistical Significance.  Introduction to Bivariate Analysis

 

(1) Central Limit Theorem

(2) Confidence intervals

(3) Statistical Tests

 

Read:  Reread SimonÕs description of his research design, pp. 9-12.  View videos 16, 17, and 18 of Against All Odds, http://www.learner.org/resources/series65.html#program_descriptions and click on videos 16, 17 and 18.  Video 22 also has related material. 

 

Assignment 6:

7

Mar 5

Review of Univariate Analysis and Moving to Bivariate Analysis. Dependent and Independent Variables

 

(1) Comparing means and proportions, T-Test.

(2) Cross Tabulation, Chi Square Test.

(3) Bivariate Regression, y = a + bx. Correlation Coefficient (r); Regression Coefficient (b); Coefficient of Determination (R Square).

 

Read: View videos 13, 24, and 29 of Against All Odds, http://www.learner.org/resources/series65.html#program_descriptions and click on video 13, 24, 29.

 

Browse:  Here are websites with more tutorials, reference materials on basic statistics.  Use them as needed. 

 

VassarStats: http://vassarstats.net/

Online StatBook, couple versions:  http://onlinestatbook.com/ or http://onlinestatbook.com/2/

David LaneÕs Hyperstat page: http://davidmlane.com/hyperstat/index.html

 

Assignment 7:

8

Mar 12

Bivariate Analysis, Continued.

 

March 14: MIDTERM.

9

Mar 26

Bivariate Analysis, Cont.

 

Paper Assignment

 

Read:  View videos 10, 11, 12 of Against All Odds, http://www.learner.org/resources/series65.html#program_descriptions and click on     videos 10, 11, 12.


U.S. States DatasetNew World DatasetRegression ExerciseUsstates.sav variable list.  Newworld.sav variable list.    

Assignment 8:

10

Apr 2

Bivariate to Multivariate Relationships. Building Models.

 

Correlation; Logarithms; Analysis of Variance; Hypothesis Tests

 

Useful Tables

 

(1) Ordinary Least Squares Regression Models

(2) Dummy Variables

(3) Regression Diagnostics

(4) Advanced Topics as Time Permits:  Analysis of Variance (ANOVA); Time Series; Logistic Regression

 

Read:  Simon, ch. 6; View videos 26 and 30 of Against All Odds:  http://www.learner.org/resources/series65.html#program_descriptions and click on     videos 26 and 30.

11 

Apr 9

Bivariate to Multivariate Models, Cont.

 

Read: View videos 27 and 28 of Against All Odds:  http://www.learner.org/resources/series65.html#program_descriptions and click on     videos 27 and 28.

 

More Datasets.

Multiple Regression Tutorials
 

Assignment 9:

12

Apr 16

Multivariate Models, Cont.

13

Apr 23

Multivariate Models, Cont.  

Assignment 10:

14

Apr 30

Multivariate Models, Cont.

 

Time Series. Logistic Regression

15

May 7

Summing Up and Review.

 

Final Exam, Extra Credit Exam. Part V Chart. Part VI Tables. due May 12.

 

May 12

Final Examination: Saturday, 10:00-12:00.  Graduate Students: Turn in Final Paper.