Forensic Mental Health Professionals have entered the fray of child custody litigation in ways that could not have been predicted even a decade ago. Traditionally engaged as neutral court appointed evaluators or mediators, or as treatment providers for children, parents or families, FMHPs are assuming a range of consulting functions. Services span a wide range, including providing expert testimony on specific content areas; reviewing and critiquing colleague 's work product; providing behind the scenes consultation to attorneys, and even help attorneys manage difficult cases and clients. These more recent services raise questions about sound professional practice. This volume tackles these thorny issues head on, and discusses questions how consultants can work creatively and ethically to make a positive contribution in the challenging world of family law.

This book was originally published as a special issue of Journal of Child Custody.

"This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results." -Nicole Kalaf-Hughes, Bowling Green State University Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata, and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The author's website provides a downloadable toolkit of Stata (R) routines to produce the calculations, tables, and graphics for each interpretive tool discussed. Also available are the Stata (R) dataset files to run the examples in the book.

This volume covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction. Emphasizing how to apply diagnostic tests and corrections for heteroskedasticity in actual data analyses, the book offers three approaches for dealing with heteroskedasticity: variance-stabilizing transformations of the dependent variable; calculating robust standard errors, or heteroskedasticity- consistent standard errors; and generalized least squares estimation coefficients and standard errors. The detection and correction of heteroskedasticity is illustrated with three examples that vary in terms of sample size and the types of units analyzed (individuals, households, U.S. states). Intended as a supplementary text for graduate-level courses and a primer for quantitative researchers, the book fills the gap between the limited coverage of heteroskedasticity provided in applied regression textbooks and the more theoretical statistical treatment in advanced econometrics textbooks.