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AbstractCausal models often omit variables that should be included, use variables that are measured fallibly, and ignore time lags. Such practices can lead to severely biased estimates of effects. The discussion explains these biases and shows how to take them into account.

Multilevel data are structures that consist of multiple units of analysis, one nested within the other. Such data are becoming quite common in political science & provide numerous opportunities for theory testing & development. Unfortunately, this type of data typically generates a number of statistical problems, of which clustering is particularly important. To exploit the opportunities offered by multilevel data, & to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics & sociology -- multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, & determine how layers interact & impact a dependent variable of interest. Our objective in this article is to introduce the logic & statistical theory behind multilevel models, to illustrate how such models can be applied in political science, & to call attention to some of the pitfalls in multilevel analysis. 5 Tables, 96 References. Adapted from the source document.