1.   Jan 18-20

2.   Jan 25-27

3.   Feb 1-3

4.   Feb 8-10

5.   Feb 15-17

6.   Feb 22-24

7.   Mar 1-3

8.   Mar 8-10

9.   Mar 15-17

10.   Mar 22-24

11.   Mar 29-31

12.   Apr 5-7

13.   Apr 12-14

14.   Apr 19-21

15.   Apr 26-28

16.   May 3-5





Department of History

History 5011

Quantitative Methods


Historical Research


Spring 2010

Steven Ruggles


945 Heller Hall

Office Hours:

Tuesday-Thursday after class
(Click on the figure to enlarge)












The purpose of this course is to equip students with the skills and confidence to count things creatively. We will focus on three general areas:


1. Methods and statistics

We will move quickly through a gloss of the usual topics of elementary statistics (percentages, means, basic probability and tests of statistical significance, bivariate regression and correlation) and then turn to more advanced topics of special significance for historians (e.g., sample designs, family reconstitution, multiple standardization and demographic decomposition, indexes, measures of inequality, and logistic regression). You will not end up an expert on any of these topics, but you should at least get an intuitive sense of what they are about and how to find out more.


2. Data management, software, and computers

We will focus on the use of SPSS for Windows and Microsoft Excel. We will also briefly cover several other programs for quantitative analysis. The topics covered will include design of data collections, data entry, analysis of microdata, management of hierarchical data, making graphs, mapping, and techniques of aggregate data analysis.


3. Principles of measurement and presentation of quantitative information

These often-neglected topics are the heart of the course. They include the principles and philosophy of measurement, research designs and data sources, and aesthetic considerations in the presentation of quantitative findings. Choosing what to measure and how to measure it is an art, and the most advanced statistics in the world won't help you if you haven't got it right. Frequently, clever measurement strategies can actually save you from having to use fancy statistics. Presentation is just as important, especially for historians because our audience is often innumerate. Measurement and presentation issues will permeate all aspects of the course, and will be the sole focus of several classes.


4. Literature of quantitative history

We will occasionally have a discussion of several quantitative historical articles. I will distribute these articles in advance. The readings for most of the quarter are not set in stone; we will tailor them to the substantive interests of the class. 




The class will meet in 628 Social Sciences, which was once the History Department Computer Lab. I have scheduled the room for an extra hour each day for lab time to carry out your assignments and so we will have time to pursue extra topics of interest to particular students. On Tuesdays, the extra hour is before class (8:45-9:45) and on Thursdays, the extra hour is after class (11:00-12:00). My office hours will be held during these extra hours.




The readings are listed below. All readings are available online at no cost. The hyperlinks following each article will get you the pdf if you are logged in on campus. If you want to get access from home, you will have to either save the article while on campus or authenticate through the library website and search for the journal or use the citation linker.


There will be three types of written assignments:


1. Statistics and/or computer exercise. You should submit it via e-mail to Unless otherwise specified, the assignments are die on Thursday of the week in which they appear.


2. Table of the Week. After the first few weeks, the homework will be a table or graph of your choosing. You will prepare a table or graph with substantive historical findings.


3. Research project. In the latter half of the course, we will focus on substantive historical research projects. The main product of your research will be a poster. We will display the major findings in the famous Quantitative History Poster Session at the end of the semester, which is widely attended by faculty and students from several departments. Prizes will be awarded, including the coveted "People's Choice Award," determined by popular vote.


Most of the quantitative assignments will be based on the Integrated Public Use Microdata Series (IPUMS), the fabulous general-purpose historical database covering the United States from 1850 through 1990. The IPUMS was created right here at the Minnesota Historical Census Projects. Students who wish to use other data for their research projects should contact me right away. 










Week 1: Jan 18-20


1.     What is a Number?

Comparisons by Subtraction and by Division 

Denominators: "At Risk" Populations and Levels of Measurement

General Strategies of Quantitative Research


2.     Introduction to IPUMS website

Historical Census Data from the University of Minnesota


3.     Software: SPSS for Windows

Defining Data

Frequency Distributions

SPSS Tutorial



Introduction to SPSS and IPUMS (skip if you don’t need these)

SPSS Brief Guide, Chapter 1

SPSS Step-by-Step Part I, Chapter 1

SPSS tutorial introduction

IPUMS Introduction (skim), variable availability (browse), and Instructions for Extract System


Margo Anderson, “Quantitative History,” The Sage Handbook of Social Science Methodology, edited by William Outhwaite and Stephen Turner (London: Sage Publications, 2007), 246-63.



Assignment 1

1.     Click here to download a dataset called MN1880.sav

2.     Open the dataset by double-clicking on it.

3.     Scroll around to look at the variables and cases available.

4.     Choose three variables, run frequencies, interpret results. 



Week 2: Jan 25-27


1.     Computers and Data.

History of Data Processing

Varieties of Historical Data

Aggregate vs. Microdata: Levels of Analysis

Ecological Analysis


2.     Statistics: Basic Descriptive Measures.

3.     Software: SPSS for Windows.

Descriptives, Means

Crosstabs; row and column percents

Weighted Samples


Readings: Working with syntax files.

SPSS Step-by-Step Part I, Chapter 2


Lawrence Stone. 1979. “The Revival of Narrative: Reflections on a New Old History” Past & Present 85: 3-24.



1.   Register and make an extract from IPUMS

·         Select census years 1900 and 1910

·         Choose some variables

2.  Download data file and syntax file and put it in the directory c:\hist5011

3.  Decompress data file [pre-decompressed file: data codebook spss]

4.  Open syntax file in SPSS and edit Data List command to add the correct path.

The first line of the command should look like this:


data list file ='c:\hist5011\usa_00047.dat'/


 5. Select Run All to run the syntax file and read the data into SPSS.

Week 3: Feb 1-3


1.     The Decline of Quantitative History

2.     The Aesthetics of Table Design

3.     Statistics: Sampling, Sample Distributions, Basic Probability, Significance Testing. 

Reese’s Pieces

Sampling pennies





Daniel Scott Smith, Parental Power and Marriage Patterns: An Analysis of Historical Trends in Hingham, Massachusetts. Journal of Marriage and Family, Vol. 35, No. 3.  (Aug., 1973), pp. 419-428.



Create a new IPUMS extract.  Use crosstabs to make an interesting table using IPUMS data for multiple census years, using row percents or column percents.  Use weights as appropriate.  Email the .spo file to me before class.


Week 4: Feb 8-10


1.     Review of Beautiful Tables

2.     Software: SPSS (data)

·     Recoding Values and Selecting Cases

·     Missing Values.

3.     Excel

4.     Alternative Machine-Readable Data Sources.




Lutz K. Berkner, The Stem Family and the Developmental Cycle of the Peasant Household: An Eighteenth-Century Austrian Example. American Historical Review 77 (1975), 398-418.

Steven Ruggles, The Transformation of American Family Structure. American Historical Review 99 (1994), 103-128.



Table of the week.



Week 5: Feb 15-17


1. Significance Testing

Margin of error

T-Tests, Chi-Square


2. Data Management I

·       Creating New Variables (compute, if, do if).

·         Splitting and Merging

·         Linking spouses and parents

Tiny 1880 file    example.sps    pointers



IPUMS chapter 5, “Family Interrelationships”

The Bellesiles Affair



Make a table that uses a new variable constructed by combining two variables.



Week 6: Feb 22-24


1.     Life-Course and Cohort Measures. PowerPoint

·         Longitudinal Data.

·         Cohorts in Successive Cross-Section.

·         election Biases and Censoring.

·         Family Reconstitution.

·          Cohorts graph


2. Methods:

·         Synthetic Cohorts and Life-Course Analysis.

·         Period and Cohort Measures of Fertility and Mortality.

·         Indirect Period Measures of Age at Marriage, Age at Leaving Home, Years of Schooling.


3. Data Management II

·         Lagging

·         Aggregate




Patricia Kelly Hall and Steven Ruggles. 2004.“'Restless in the Midst of Their Prosperity': New Evidence on the Internal Migration of Americans, 1850-2000," Journal of American History 91: 829-846.



Week 7: Mar 1-3


Making graphs PowerPoint




Make a nice graph



Week 8: Mar 8-10


Software: SPSS for Windows

Correlate, Regression

Statistics: Correlation, Regression



Preliminary project plan


Week 9: Mar 15-17  (spring break)

Week 10: Mar 22-24


Standardization and Indexes

·         Index Numbers

·         Direct Standardization


NTS example

Phelps-Brown and Hopkins

CPS components

EH-Net price indexes

Standardization spreadsheet

Inequality and dissimilarity

Inequality and dissimilarity


Command file


Week 11: Mar 29-31


Making maps: Kelsey McDonald, MPC Spatial Analysis Core


Week 12: Apr 5-7


Making Posters

Poster Template


Week 13: Apr 12-14

Week 14: Apr 19-21

Week 15: Apr 26-28

Week 16: May 3-5

Poster Exhibition/Reception May 6, 3:00-5:00 pm




Maintained by: Steven Ruggles,
Revised: January 2010
Copyright © 1996-2010 Regents of the University of Minnesota

The University of Minnesota is an equal opportunity educator and employer

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