Lisrel 8

Simple examples - Mullti-sample examples - Path diagrams - Fitting and testing - Lisrel output - Simplis reference - Computer exercises.

Overview of the interface - Prelis examples - Lisrel examples - Multilevel examples - Firm examples - New statistical features - Syntax.

This book illustrates the ease with which various features of LISREL 8 and PRELIS 2 can be implemented in addressing research questions that lend themselves to SEM. Its purpose is threefold: (a) to present a nonmathmatical introduction to basic concepts associated with SEM, (b) to demonstrate basic applications of SEM using both the DOS and Windows versions of LISREL 8, as well as both the LISREL and SIMPLIS lexicons, and (c) to highlight particular features of the LISREL 8 and PRELIS 2 progams that address important caveats related to SEM analyses. This book is intended neither as a text on the topic of SEM, nor as a comprehensive review of the many statistical funcitons available in the LISREL 8 and PRELIS 2 programs. Rather, the intent is to provide a practical guide to SEM using the LISREL approach. As such, the reader is "walked through" a diversity of SEM applications that include both factor analytic and full latent variable models, as well as a variety of data management procedures.

Reviews some of the major issues facing researchers who wish to use structural equation modeling. This title includes individual chapters that present developments on specification, estimation and testing, statistical power, software comparisons and analyzing multitrait/multimethod data.

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples. Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research. This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.

Few would dispute the truth of the statement `People are Different', but there is much controversy over why. This book authoritatively explains the methods used to understand human variation, and extends them far beyond the primary `nature or nurture' question. After chapters on basic statistics, biometrical genetics, matrix algebra and path analysis, there is a state-of-the-art account of how to fit genetic models using the LISREL package. The authors explain not only the assumptions of the twin method, but how to test them. The elementary model is expanded to cover sex limitation, sibling interaction, multivariate and longitudinal data, observer ratings, and twin-family studies. Throughout, the methods are illustrated by applications to diverse areas such as obesity, major depression, alcohol comsumption, delinquency, allergies, and common fears.