Introduction

The purpose of this course is to equip students with the skills and confidence to count things creatively. It will introduce the methods used by economic and demographic historians to answer questions about the past with quantitative data. In addition to statistical methods and data management, we will cover research design and measurement issues. By the end of the course, you should be able to:

 

·         Develop testable research questions

·         Design an analysis that will answer your questions

·         Apply appropriate measurement strategies and statistical techniques 

·         Critically read and understand quantitative historical research

 

The course meets the Liberal Education Mathematical Thinking requirement, which means that it goes beyond rote computational skills.  Students will gain broad understanding of mathematical thinking and quantitative analysis as a body of knowledge. In addition, students will learn specific analytic tools that have broad application for understanding the quantitative dimensions of historical change.

 

The course emphasizes the logic of quantitative measurement rather than cookbook recipes for statistical analysis. Students will be expected to manipulate equations both to understand the probabilistic basis of statistical analysis and to apply demographic and economic methods to historical data.

 

We will focus on four general areas:

 

1. Methods and statistics

We will move quickly through 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).

 

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 a vital skill, and the most advanced statistics in the world won't help you if you haven't got it right. In fact, very often clever measurement strategies can actually save you from having to use fancy statistics. Presentation is also critically 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 read and discuss quantitative historical articles. The readings for most of the quarter are not set in stone; we will tailor them to the substantive interests of the class. 

 

Prerequisites

Although there are no prerequisites, HIST 3011 is an advanced undergraduate course. Students are expected to have a solid background in intermediate algebra. Basic computer skills are also assumed.

 

Organization

The class will meet in 440 Blegen Hall. 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. HIST 3011 and HIST 5011 students will meet together from 11:15 to 12:30. From 12:30 to 1:10, we will have lab work, in the same room. The basic lab (intended mainly for students in HIST 3011) will be on Tuesdays, and the advanced lab (intended mainly for students in HIST 5011) will be on Thursdays.

 

Software

Most of the course will focus on two software applications: SPSS and Excel. SPSS once stood for Statistical Package of the Social Sciences, but after IBM bought it and decided to market it more broadly, it no longer stands for anything. SPSS will be available in our labs in Blegen 440, but you will also need to use SPSS outside of class time. SPSS is available in multiple computer labs on campus, including Coffman Memorial Union B060 (open Monday-Thursday 8 a.m. - 9 p.m., Friday 8 a.m. - 6 p.m. and Saturday 10 a.m. - 6 p.m.), Walter Library 103 (open Monday-Thursday 8 a.m. – 9 p.m., Friday 8 a.m. - 6 p.m., and Saturday 12 p.m. - 6 p.m. and Magrath B50 on the St. Paul Campus (Monday-Thursday 8 a.m. - 8 p.m. and Friday 8 a.m. - 5:30 p.m.). Visit http://www.oit.umn.edu/computerlabs/ for more locations and hours. If you want your own copy, you can buy the SPSS Statistics Grad Pack; the best price I have seen is $79.99 from StudentDiscounts.Com (and you get two installs, so you could team up with someone else and get it for $40). You need proof of enrollment.

 

Excel is available in all labs. For most of the exercises you can probably get away with the Google spreadsheet, but there will be several cases in which you will need the real thing.