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The history of an individual or group can always be characterized as a sequence of events. People finish school, enter the labor force, marry, give birth, get promoted, change employers, retire, and ultimately die. Formal organizations merge, adopt innovations, and go bankrupt. Nations experience wars, revolutions, and peaceful changes of government. It is surely the business of sociology to explain and predict the occurrence of such events. Why is it, for example, that some individuals try marijuana while others do not? Why do some people marry early while others marry late? Do educational For helpful suggestions, I am indebted to Charles Brown, Rachel Rosenfeld, Thomas Santner, Nancy Tuma, and several anonymous referees. 61 PAUL D. ALLISON enrichment programs reduce the likelihood of dropping out of school? What distinguishes firms that have adopted computerized accounting systems from those that have not? What are the causes of revolutions? Perhaps the best form of data for answering questions like these is an event history. Quite simply, an event history is a record of when events occurred to a sample of individuals (Tuma and Hannan, 1978). If the sample consists of women of childbearing age, for example, each woman's event history might consist of the birthdates of her children, if any. If one is interested in the causes of events, the event history should also include data on relevant explanatory variables. Some of these, like race, may be constant over time while others, like income, may vary. Although event histories are almost ideal for studying the causes of events, they also typically possess two featurescensoring and time-varying explanatory variables-that create major difficulties for standard statistical procedures. In fact, the attempt to apply standard methods to such data can lead to serious bias or loss of information. These difficulties are discussed in some detail in the following pages. In the last decade, however, several innovative methods for the analysis of event histories have been proposed. Sociologists will be most familiar with the maximum-likelihood methods of Tuma and her colleagues (Tuma, 1976; Tuma and Hannan, 1978; Tuma, Hannan, and Groeneveld, 1979). Similar procedures have been developed by biostatisticians interested in the analysis of survival data (Gross and Clark, 1975; Elandt-Johnson and Johnson, 1980; Kalbfleisch and Prentice, 1980). A related approach, known as partial likelihood, offers important advantages over maximum-likelihood methods and is now in widespread use in the biomedical sciences (Cox, 1972; Kalbfleisch and Prentice, 1980; Tuma, present volume, Chapter 1). Most methods for analyzing event histories assume that time is measured as a continuous variable-that is, it can take on any nonnegative value. Under some circumstances discrete-time models and methods may be more appropriate or, if less appropriate, highly useful.

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.

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In logit and probit regression analysis, a common practice is to estimate separate models for two or more groups and then compare coefficients across groups. An equivalent method is to test for interactions between particular predictors and dummy (indicator) variables representing the groups. Both methods may lead to invalid conclusions if residual variation differs across groups. New tests are proposed that adjust for unequal residual variation.