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Written in non-technical language, this popular and practical volume has been completely updated to bring readers the latest advice on major issues involved in longitudinal research.
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This text begins by showing how logistic regression combines aspects of multiple linear regression and loglinear analysis to overcome problems both techniques have with the analysis of dichotomous dependent variables with continuous predictors. It then examines what can go wrong with the model and how to detect and correct it.
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The focus in this second edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail.
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