In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Missing Data in Linear Modeling

Advance Organizer

Every study has the potential for incomplete or missing data. Missing data—the absence of an answer or response where one was expected—can occur for many reasons. For example, participants can fail to respond to questions (legitimately or illegitimately—more on that later), equipment and data collecting or recording mechanisms can malfunction, subjects can withdraw from studies before they are completed, and data entry errors can occur. Data cleaning can also create missingness, because data points that are deemed outside the bounds of reasonable range (or inappropriately influential) can be deleted.

The issue with missingness is that nearly all classic and modern statistical techniques (including the various types of linear modeling we are discussing in this book) require complete data, and ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles