(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling
@inproceedings{Nachtigall2003WhySW, title={(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling}, author={C. Nachtigall and Ulf Kroehne and Friedrich Funke and Rolf Steyer and Friedrich von Schiller}, year={2003} }
During the last two decades, Structural Equation Modeling (SEM) has evolved from a statistical technique for insiders to an established valuable tool for a broad scientific public. This class of analyses has much to offer, but at what price? This paper provides an overview on SEM, its underlying ideas, potential applications and current software. Furthermore, it discusses avoidable pitfalls as well as built-in drawbacks in order to lend support to researchers in deciding whether or not SEM…
210 Citations
How to Use Structural Equation Modeling in Medical Education Research: A Brief Guide
- MedicineTeaching and learning in medicine
- 2007
This article outlines the basic tenets of SEM, principles of model creation, identification, estimation, and model fit to data, and the use of SEM in medical education research and summarizes an example of SEM to test a hypothetical model.
Likelihood and PLS Estimators for Structural Equation Modeling: An Assessment of Sample Size, Skewness and Model Misspecification Effects
- Business
- 2013
This chapter aims to contribute to a better understanding of partial least squares (PLS) and maximum likelihood (ML) estimators’ properties, through the comparison and evaluation of these estimation…
Analysis of partial least squares on split path models: underpinning delone & maclean theory
- Mathematics
- 2017
The analysis of partial least squares utilizing Structural Equation Modeling is well known as a second generation technique .Its true that , this technique has not been fully utilized in the field of…
Analyzing the relationship between dependent and independent variables in marketing: a comparison of multiple regression with path analysis
- Business
- 2010
This paper argues the superiority of path analysis by reanalyzing data from selected marketing studies that have used multiple regression models, and provides a technical appendix depicting use of the EQS software for re-estimating several regression models.
Structural equation modeling: a primer for health behavior researchers.
- MedicineAmerican journal of health behavior
- 2007
SEM must be thrust into the daily vocabulary and routine practice of health behavior researchers by allowing for the specification of complex, theory-driven models that can be tested with empirical data.
Mind the Gap: Identifying Latent Objective and Subjective Multi-Dimensional Indices of Well-Being
- Economics
- 2016
Within the OECD Better Life Initiative, the Better Life Index (BLI) represents a major attempt to measure well-being and societal progress beyond GDP, following up the recommendations outlined in the…
Reliability and Stability on the Numerical Analysis in Structural Equation Modeling
- Engineering
- 2013
The study will introduce an application of the optimized calculation of genetic algorithms (GA) in structural equation modeling in order to see elaborately what is going on with these issues and also to examine the goodness-of-fit, validity, stability and reliability of structural model.
The ABCs of Math Attitudes: Reliability and Validity of the Three Factor Model
- Psychology
- 2019
Although the Attitudes toward Mathematics Inventory (ATMI; four-factor model with 40 items) has been well-established as a viable tool to test the multidimensionality of math attitudes, studies have…
The Reading Maturity Survey: Steps Toward Instrument and Construct Validation with College-Level Readers
- EducationReading Psychology
- 2018
There is a need to promote growth past basic reading proficiency toward the more substantial idea of reading maturity. The reading maturity construct has a history of being valued, at least in…
STRUCTURAL EQUATION MODELS EXAMINING THE RELATIONSHIPS BETWEEN THE BIG FIVE PERSONALITY FACTORS AND THE MUSIC MODEL OF ACADEMIC MOTIVATION COMPONENTS
- Education, Psychology
- 2015
Scholars have long been interested in the complex relationships between personality and motivation. However, much of their understanding has been limited to The Big Five personality factors (namely,…
References
SHOWING 1-10 OF 76 REFERENCES
Principles and Practice of Structural Equation Modeling
- Economics
- 1998
The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Bootstrapping Goodness-of-Fit Measures in Structural Equation Models
- Mathematics
- 1992
Assessing overall fit is a topic of keen interest to structural equation modelers, yet measuring goodness of fit has been hampered by several factors. First, the assumptions that underlie the…
Can There Be Infinitely Many Models Equivalent to a Given Covariance Structure Model?
- Psychology
- 2001
The problem of equivalent models has plagued researchers since the earliest developmental stages of structural equation modeling (SEM; Marcoulides & Schumacker, 1996). Equivalent models are those…
Reporting Analyses of Covariance Structures
- Psychology
- 2000
This contribution is focused on how to write a research paper when structural equation models are being used in empirical work. The main question to be answered is what information should be reported…
How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
- Education
- 2002
A common question asked by researchers is, "What sample size do I need for my study?" Over the years, several rules of thumb have been proposed. In reality there is no rule of thumb that applies to…
Interactions of latent variables in structural equation models
- Mathematics
- 1998
Interactions of variables occur in a variety of statistical analyses. The best known procedures for models with interactions of latent variables are technically demanding. Not only does the potential…
Analysis of Covariance Structures
- Computer Science
- 1988
Analysis of covariance structures is the common term for a number of techniques for analyzing multivariate data in order to detect and assess latent (unobserved) sources of variation and covariation…
Bootstrap-corrected ADF test statistics in covariance structure analysis.
- MathematicsThe British journal of mathematical and statistical psychology
- 1994
It is shown that the bootstrap correction of additive bias on the ADF test statistic yields the desired tail behaviour as the sample size reaches 500 for a 15-variable-3-factor confirmatory factor-analytic model, even if the distribution of the observed variables is not multivariate normal and the latent factors are dependent.
A comparison of some methodologies for the factor analysis of non‐normal Likert variables: A note on the size of the model
- Mathematics
- 1992
This paper expands on a recent study by Muthen & Kaplan (1985) by examining the impact of non-normal Likert variables on testing and estimation in factor analysis for models of various size. Normal…
Comment: Which Ifs Have Causal Answers
- Mathematics
- 1986
I congratulate my friend Paul Holland on his lucidly clear description of the basic perspective for causal inference referred to as Rubin's model. I have been advocating this general perspective for…