Basics of Structural Equation Modeling

1. Auflage 1998
ISBN: 978-0-8039-7409-8
Verlag: Sage Publications, Inc.

Buch, Englisch, 328 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 534 g

With the availability of software programs such as LISREL, EQS, and AMOS modeling techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and for testing the plausibility of hypothesizing for a particular data set. The popularity of these techniques, however, has often led to misunderstandings of them, particularly by students being exposed to them for the first time. Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (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.

Autoren/Hrsg.

Maruyama, Geoffrey M.

Maruyama

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologie: Allgemeines
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Demographie, Demoskopie
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

Inhaltsverzeichnis

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

Über Autor(innen)

Maruyama, Geoffrey M.
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.