Resource Library

The best way to predict the future is to invent it. This is a quote by American computer scientist Alan C. Kay (1940 - ). The quote was said at a 1971 meeting of Xerox Corporation's Palo Alto Research Center.

The same set of statistics can produce opposite conclusions at different levels of aggregation. is a quote useful in teaching about Simpson's Paradox from American Economist Thomas Sowell (1930 - ). The quote may be found on page 102 of his 1996 book "The vision of the Anointed: Self-Congratulation As a Basis for Social Policy". The quote may also be found at the science history website www.todayinsci.com.

This applet shows a scatterplot of height versus foot length. Users can add or delete points and then guess the regression line by clicking "Your Line" and moving the blue regression line. By clicking "Regression Line" users can see the actual regression line. The applet also shows the correlation and R-square for the data as well as the residuals and squared residuals for the guessed regression line and the actual regression line.

This applet generates a histogram for two provided datasets, or by clicking "Edit Data", users can input their own data. Users can also manipulate the axes and bin width.

Research has shown that marital status and employment are correlates of desistance. That is, adolescents involved with crime were more likely to discontinue offending in adulthood if they were married and had a good job. Most of what criminologists know about the process of desistance from crime is based on a sample of adult males in the 1950's. There is no question that life in America has changed drastically in the past fifty years. Given the importance of examining historical change inherent in the life course perspective, it is important to determine how changes in the social structure over time impact individuals. Therefore, the goals of this data analysis exercise are to examine changes in marriage and employment over the last fifty years. The purposes are to identify the changes that have taken place, and to hypothesize how these changes may affect the process of desistance from crime today.

This module provides an activity were students will attempt to explain how each of the following variables is related to child poverty within the United States: Race, Age, Family Type, Family Size, and Immigrant Status.

This collection of datasets covers many application areas, but are all for time series analysis. The data are in text format.

This module is designed to illustrate the effects of selection bias on the observed relationship between premarital cohabitation and later divorce. It also serves as a review of key methodological concepts introduced in the first part of the course.

How are earnings determined? Why do some people earn more than others? Does a better job necessarily mean a better salary? In this module, students will attempt to answer these questions and many others by examining factors such as education and occupation in terms of the role they play in determining earnings. Students will also look at the earnings of whites and compare them to the earnings of blacks, Latinos, and Asians. Another consideration will center on the effect of gender. Finally, students will turn their attention to the age of workers in terms what role it plays in determing earnings. Aside from earnings, students will also take a brief look at poverty with respect to the effect race-ethnicity and family structure has on creating and sustaining it.

In this module you will have the opportunity to explore the frequency of different types of residential moves carried out by Americans. You will examine some of the basic determinants of residential mobility by looking at variations in different types of mobility by age, marital status, education, and housing tenure. Finally, you will have an opportunity to test hypotheses, drawn from a popular theoretical perspective, about racial differences in residential mobility.