Tiffany A. Whittaker – författare

James Stevens’ best-selling text, Intermediate Statistics, is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. SAS and SPSS are an integral part of each chapter. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured.

The assumptions underlying each analysis are emphasized and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises.

The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include:

A new chapter on hierarchical linear modeling using HLM6Downloadable resources containing all of the book’s data setsNew coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Chapter 6)More exercises in each chapter.

Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. An Instructor's Resource is available upon adoption. See www.researchmethodsarena.com .

The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research.This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.

The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research.This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.

The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research.This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.

The sixth edition of A Beginner’s Guide to Structural Equation Modeling has been redesigned to consider the medium-term needs of a beginner in structural equation modeling (SEM) to guide them through their research.This new update includes thorough insights on theory testing, data analysis, and results interpretation; a focus on using LISREL, Mplus, and R programs; and an increased focus on SEM terminology, output, model types, and analyses. It also includes two new chapters on reproducing results in SEM journal articles and conducting a Monte Carlo analysis. Examples with real data make theory easier to understand and allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models. Exercises at the end of each chapter strengthen the utility of the book for beginners.This book is intended for beginners in SEM and designed for introductory graduate courses in SEM taught in psychology, education, business, and the social and health care sciences. It also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.