Quantitative Methods and Computer Applications in the Historical and Social Sciences Roman Studer Nuffield College

Aim of the Course Provide an introduction to elementary quantitative methods and statistics, covering some of the techniques most widely used in research in the historical and social sciences Introduce the statistical software package STATA and show how the relevant statistical applications can be performed with this software

Motivation of the Course Understanding quantitative research  Quantitative methods are widely used both in History as well as in (other) Social Sciences  Being able to make use of quantitative research, but also being able to assess its limits Using quantitative methods in your own research  How representative or reliable are observed patterns?  If many factors determine an outcome, the relative importance of the factors can be determined  Graphs and tables can be very persuasive tools, they can also give valuable hints and raise new questions General usage  We live in a “data world”, hence being able to handle data and quantitative studies properly is a very useful qualification both in the job market as well as for everyday life

Lecture Plan Week 1: Introduction Week 2: Descriptive Statistics Week 3: Correlation Week 4: Simple Linear Regression Week 5: Statistical Significance I Week 6: Statistical Significance II Week 7: Multiple Linear Regression Week 8: Dummy Variables

Course Arrangements Lectures/computer classes  Normally the first hour will consist of a lecture introducing one of the topics  The second hour will be devoted to a computer class where we look at applications of the new topics, using STATA Textbook  The principal text will be Feinstein, C. H. and M. Thomas (2002). Making History Count: A Primer in Quantitative Methods for Historians. Cambridge: Cambridge University Press. Weekly homework  Readings (relevant chapters in the textbook and articles containing applications of the topics  Problem sets  To be handed in by noon on Mondays

Course Arrangements (II) All information is available on the course’s website: including slides, problem sets and data sets Mock exam  At the end of the course there will be a simple take-away examination to test your understanding of the various concepts and procedures covered during the course

Your Input Any questions so far? Any comments? Short introduction of participants  What’s your background and how much do you know already about statistics and quantitative methods?  Why are you taking the course?  Are you planning to use quantitative methods in your research?  Is there anything specific you want to learn that is not on the program?

Lecture 1: Definitions & Concepts Cases, variables, and values  A data set consists of a series of cases each of which has one or more characteristics, known as variables. For each variable there is a sequence of varying observations, each with its own particular value  Cases are the basic unit of measurement, and they can be individuals, households, firms, towns, countries, etc.  Example: English Poor Law data set 311 English parishes:Cases?Variables? Values? Relief expenditure of each parish:Cases?Variables? Values? 20.4, 16.3:Cases?Variables? Values?

Cross-section and time-series variables Time-series variables  A set of measurements that applies to a single case at different periods of time is referred to as a time series  Example: Real wages in London, Source: RC Allen (2001), “The Great Divergence”

Cross-section and time-series variables (II) Cross-section variables  A set of measurement that applies to different cases at a single point in time is referred to as a cross-section  Example: Real wages across Europe in 1750 Source: RC Allen (2001), “The Great Divergence”

Cross-section and time-series variables (III) Panel data  Time series are used to study changes over time and cross-sections to study differences between cases  It is also possible to combine cross-section and time series data to create a panel or longitudinal data set  Example: Real wages across Europe, Source: RC Allen (2001), “The Great Divergence”

Levels of Measurement Nominal measurement  Each value defines a distinct category but gives no information other than the label or name (hence nominal level)  This is the lowest level of measurement  Example: Cases:Migrants Variables:Birthplace of migrants Values:London, Oxford, London Ordinal measurement  This applies when it is possible to order or rank all the categories according to some criterion without being able to specify the exact size of the interval between any two categories  Example: Cases:Migrants Variables:Skill level of migrants Values:unskilled, unskilled, semi-skilled

Levels of Measurement Interval or ratio measurement  These measurements have all the properties of an ordinal scale. In addition, it is now possible to measure the exact distance between any pair of values  This level of measurement is truly quantitative and any statistical procedure can be applied to values on an interval or ratio scale  Values are either continuous or discrete  Example 1: Cases:Migrants Variables:Number of children Values:0, 2, 7  Discrete variable! Values can take only a number of pre-determined values  Example 2: Cases:Migrants Variables:Height of migrants Values:1.55m, 1.62m, 1,58m  Continuous variable; is measured in units that can (theoretically) be reduced in size to an infinite degree

Populations and Samples The population  Refers to all possible observations. In the Poor Law example that would be all parishes in the whole country The sample  Is a subset of the population, e.g. all the parishes starting with O, like Oxford.  Normally we only have samples, either because not all cases are available or because it would take far too much time to study all cases  This is not a big problem, as we have tools to make valid inferences from a sample  A crucial feature of any sample is whether it is random or not  In Social Science there are clear procedures how to construct and deal with samples  As historical data is a lot harder to obtain, you sometimes just have to use what you are able to get

Dummy variables  Variables that cannot be measured can still be used for quantitative work by assigning values (mostly 0 and 1) representing the (two or more) categories  Example 1: Cases:Migrants Variable:Sex Values:0 (man), 0, 1 (woman)

Homework  Readings Feinstein & Thomas, Ch. 1-2, Appendix A.1 (describing the data set we use next time)  Get a textbook, if you still don’t have one  Also, register with IT if you haven’t done so already  No problem set this week