Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben

2015 | Buch

An Introduction to Structural Equation Models Kapitel lesen Erstes Kapitel lesen

Structural Equation Models

From Paths to Networks

verfasst von: J. Christopher Westland

Verlag: Springer International Publishing

Buchreihe : Studies in Systems, Decision and Control

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten
insite
SUCHEN

Über dieses Buch

This compact reference surveys the full range of available structural equation modeling (SEM) methodologies. It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable. This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow importance in the near future. SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained and methods are presented for extending their power, including guidelines for data preparation, sample size calculation and the special treatment of Likert scale data. Tables of software, methodologies and fit statistics provide a concise reference for any research program, helping assure that its conclusions are defensible and publishable.

Inhaltsverzeichnis

Frontmatter
Chapter 1. An Introduction to Structural Equation Models
Abstract
The past two decades have witnessed a remarkable acceleration of interest in structural equation modeling (SEM) methods in many areas of research. In the social sciences, researchers often distinguish SEM approaches from more powerful systems of regression equation approaches by the inclusion of unobservable constructs (called latent variables in the SEM vernacular), and by the use of computationally intensive iterative searches for coefficients that fit the data. The expansion of statistical analysis to encompass unmeasurable constructs using SEM, canonical correlation, Likert scale quantification, principal components, and factor analysis has vastly extended the scope and relevance of the social sciences over the past century. Subjects that were previously the realm of abstract argumentation have been transported into the mainstream of scientific research.
J. Christopher Westland
Chapter 2. A Brief History of Structural Equation Models
Abstract
Though structural equation models today are usually associated with soft problems in the social sciences, they had their origin in the natural sciences—specifically biology. Europe’s nineteenth-century scholars were challenged to make sense of the diverse morphologies observed during an age of explorations, in Asia, Africa, and the Americas, as well as at home. In this period, new species of plants and animals were transplanted, domesticated, eaten, and bred at an unprecedented rate. An American ultimately provided one statistical tool that allowed scholars to build a science out of their diverse observations.
J. Christopher Westland
Chapter 3. Partial Least Squares Path Analysis
Abstract
We begin our review of modern path analysis tools with partial least squares path analysis software. PLS-PA has achieved near-cult-like stature within its circle of practitioners, but is not without its critics. Many issues arise from PLS-PA not being a proper statistical “methodology”—it has failed to accumulate a body of statistical research on assumptions, the role of data, objectives of inference, statistics, or performance metrics. Rather, PLS consists of a half dozen or so software packages that though only lightly documented seem to be able to conjure path estimates out of datasets that other methodologies reject as inadequate. This chapter explores whether PLS-PA software really possesses some “secret sauce” that makes it possible to generate estimates from weak data, or conversely, whether such imputed path structures may indeed be illusory.
J. Christopher Westland
Chapter 4. LISREL and Its Progeny
Abstract
Sewall Wright’s path coefficients were conceived as dimensionless binary indicators of whether a genetic trait was passed to an offspring, or not. Correlations in Wright’s context were almost overkill, though their magnitude might have been considered to suggest varying degrees of confidence in heritability of a trait. As path analysis began to find application in analyzing relationships that were multivalued or continuous, limitations in the ability to resolve effects began to reveal themselves. It was in this context that Tukey (1954) advocated systematic replacement in path analysis of the dimensionless path coefficients by the corresponding concrete path regressions. Geneticists Turner and Stevens (1959) published their seminal paper presenting the mathematics of path regression and inviting extensions that would apply other developments in statistics. In the early days of data processing, both Herman Wold and his student Karl Jöreskog developed software, building on Turner and Stevens’ mathematics, that took advantage of computer-intensive techniques becoming available to universities in the 1970s and 1980s.
J. Christopher Westland
Chapter 5. Systems of Regression Equations
Abstract
Alfred Cowles III hailed from an established Chicago publishing family, his father and uncle having founded the Chicago Tribune and Cleveland Leader, respectively (Grier, 2013). For a short time after WWI Cowles successfully ran a Chicago investment firm that acquired and restructured small railroads. His firm also published a stock market newsletter providing fundamental analysis and recommendations on railroad stock issues as well as other investments, and for a time there was even an Alfred Cowles Railroad.
J. Christopher Westland
Chapter 6. Data Collection, Control, and Sample Size
Abstract
Many questions in social sciences can only be addressed through individual perceptions, impressions, and judgments. A consumer’s willingness to pay for a product or service is a noisy signal, and the consumer has no obligation to follow through on a purchase intent, no matter how much the researcher might like to infer that “intention” is “action.” Such inherently unobservable constructs need to be modeled as a latent variable. Personal statements of intent, whether they are for purchases, good deeds, or other promises, can only be considered rough indicators; researchers like them because they are cheap and easy to collect by questioning the individual. But like confessions and New Year’s resolutions, intentions are pliable and yielding, and often mendacious. Psychologists have created improved polygraph protocols involving such questions over nearly a century; yet polygraph evidence is still not admissible in court. Obtaining truthful and accurate data from surveys and questionnaires is challenging and the quality of information is invariably lacking. Latent constructs that are of actual interest—ones that help us build theory—are often unobservable. The only way to understand them is through objective measurement of related constructs—the indicator variables.
J. Christopher Westland
Chapter 7. Survey and Questionnaire DataLikert Scale data
Abstract
Surveys study various characteristics of individuals from a population. Modern surveys grew out of census procedures dating back to the Romans. Today, they are more often directed towards assessing the sentiment of a large population through marketing research, public opinion polls, epidemiological surveys, and various national economic, tax, and consumption surveys. Surveys are an essential part of managing complex bureaucracies of business, government, and public health.
J. Christopher Westland
Chapter 8. Research Structure and Paradigms
Abstract
John Ioannidis, a highly respected medical researcher, has a serious bone to pick—with the modern practitioners of the Galilean hypothetico-deductive model-data duality discussed in Chap. 6. Ioannidis’ 2005 paper, provocatively titled “Why Most Published Research Findings Are False,” has been the most downloaded technical paper in PLoS Medicine and one of the single most cited and downloaded papers in the past decade (J. P. A. Ioannidis, 2005; Ioannidis et al., 2001). In it, Ioannidis analyzed “49 of the most highly regarded research findings in medicine over the previous 13 years” comparing them with data from subsequent studies with larger sample sizes. His findings included the following: 7 (16 %) of the original studies were contradicted, 7 (16 %) of the effects were smaller than in the initial study, 20 (44 %) were replicated, and 11 (24 %) of the studies remained largely unchallenged (D. H. Freedman, 2010; J. P. A. Ioannidis, 2005; Liberati et al., 2009; McCarthy et al., 2008).
J. Christopher Westland
Chapter 9. From Paths to Networks: The Evolving Science of Networks
Abstract
Path models were always a kludge; a hodgepodge of available technologies cobbled together, as best as possible, to make sense of naturally occurring networks. Scientists in the past simply did not possess the analytical power to map more than a few links at a time. PLS-PA, LISREL, and systems of regressions were designed for calculation on paper and with adding machines; they were disappointingly inadequate, but the best we had at the time. Statistical power has always lagged the size and complexity of the networks under analysis, and as a result generated unreliable, simplistic, and inapplicable results. This is doubly unfortunate when we consider how important network models have reigned throughout mankind’s history. For example:
J. Christopher Westland
Backmatter
Metadaten
Titel
Structural Equation Models
verfasst von
J. Christopher Westland
Copyright-Jahr
2015
Electronic ISBN
978-3-319-16507-3
Print ISBN
978-3-319-16506-6
DOI
https://doi.org/10.1007/978-3-319-16507-3

Premium Partner

    Bildnachweise