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With the availability of software programs, such as LISREL, EQS, and AMOS, modelling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of the hypothesizing for a particular data set. However, the popularity of these techniques has often led to misunderstandings of them and even their misuse, particularly by students exposed to them for the first time. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and at the end of chapters.
List of contents
PART ONE: BACKGROUND
What Does It Mean to Model Hypothesized Causal Processes with Nonexperimental Data?
History and Logic of Structural Equation Modeling
PART TWO: BASIC APPROACHES TO MODELING WITH SINGLE OBSERVED MEASURES OF THEORETICAL VARIABLES
The Basics
Path Analysis and Partitioning of Variance
Effects of Collinearity on Regression and Path Analysis
Effects of Random and Nonrandom Error on Path Models
Recursive and Longitudinal Models
Where Causality Goes in More Than One Direction and Where Data Are Collected Over Time
PART THREE: FACTOR ANALYSIS AND PATH MODELING
Introducing the Logic of Factor Analysis and Multiple Indicators to Path Modeling
PART FOUR: LATENT VARIABLE STRUCTURAL EQUATION MODELS
Putting It All Together
Latent Variable Structural Equation Modeling
Using Latent Variable Structural Equation Modeling to Examine Plausability of Models
Logic of Alternative Models and Significance Tests
Variations on the Basic Latent Variable Structural Equation Model
Wrapping up
About the author
My interest in what happens in diverse urban schools began when I became involved in a study of school desegregation while in graduate school. Those interests have led me to study a range of issues in schools, including school schedules and structures, teaching approaches such as cooperative learning and conflict resolution, social influence processes, and student background characteristics including poverty, type of housing, language, ability, and race/ethnicity. This work has been facilitated by time I spent in the Saint Paul Public Schools as their director of research, evaluation and assessment. Recently, my work has moved beyond schools to look more broadly at how universities engage urban communities to build partnerships addressing key social issues. I have complemented my substantive interests with methodology interests in structural equation methods and program evaluation.
Finally, I have held administrative roles that have enriched and informed my research interests, including director of the Center for Applied Research and Educational Improvement (CAREI), assistant/associate vice president for multicultural and academic affairs, and now vice president for system academic administration . I am a past-president of the Society for the Psychological Study of Social Issues (SPSSI), and currently edit one of their journals, Analyses of Social Issues and Public Policy.
Summary
This text is designed to guide students through the logic underlying SEM approaches. It describes how SEM approaches relate to techniques like regression and factor analysis and analyzes the strengths and shortcomings of SEM as compared to alternative methodologies.
