Statistical Analysis Criterias for Structural Equation Modeling (Sem) (original) (raw)
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The Basics of Structural Equation Modeling
Structural equation modeling (SEM) is a methodology for representing, estimating, and testing a network of relationships between variables (measured variables and latent constructs). This tutorial provides an introduction to SEM including comparisons between " traditional statistical " and SEM analyses. Examples include path analysis/ regression, repeated measures analysis/latent growth curve modeling, and confirmatory factor analysis. Participants will learn basic skills to analyze data with structural equation modeling. Rationale Analyzing research data and interpreting results can be complex and confusing. Traditional statistical approaches to data analysis specify default models, assume measurement occurs without error, and are somewhat inflexible. However, structural equation modeling requires specification of a model based on theory and research, is a multivariate technique incorporating measured variables and latent constructs, and explicitly specifies measurement error. A model (diagram) allows for specification of relationships between variables. Purpose The purpose of this tutorial is to provide participants with basic knowledge of structural equation modeling methodology. The goals are to present a powerful, flexible and comprehensive technique for investigating relationships between measured variables and latent constructs and to challenge participants to design and plan research where SEM is an appropriate analysis tool. Structural equation modeling (SEM) • is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables (Hoyle, 1995). • is a methodology for representing, estimating, and testing a theoretical network of (mostly) linear relations between variables (Rigdon, 1998). • tests hypothesized patterns of directional and nondirectional relationships among a set of observed (measured) and unobserved (latent) variables (MacCallum & Austin, 2000). Two goals in SEM are 1) to understand the patterns of correlation/covariance among a set of variables and 2) to explain as much of their variance as possible with the model specified (Kline, 1998). The purpose of the model, in the most common form of SEM, is to account for variation and covariation of the measured variables (MVs). Path analysis (e.g., regression) tests models and relationships among MVs. Confirmatory factor analysis tests models of relationships between latent variables (LVs or common factors) and MVs which are indicators of common factors. Latent growth curve models (LGM) estimate initial level (intercept), rate of change (slope), structural slopes, and variance. Special cases of SEM are regression, canonical correlation, confirmatory factor analysis, and repeated measures analysis of variance (Kline, 1998). Similarities between Traditional Statistical Methods and SEM SEM is similar to traditional methods like correlation, regression and analysis of variance in many ways. First, both traditional methods and SEM are based on linear statistical models. Second, statistical tests associated with both methods are valid if certain assumptions are met. Traditional methods assume a normal distribution and SEM assumes multivariate normality. Third, neither approach offers a test of causality. Differences Between Traditional and SEM Methods Traditional approaches differ from the SEM approach in several areas. First, SEM is a highly flexible and comprehensive methodology. This methodology is appropriate for investigating achievement, economic trends, health issues, family and peer dynamics, self-concept, exercise, self-efficacy, depression, psychotherapy, and other phenomenon. Second, traditional methods specify a default model whereas SEM requires formal specification of a model to be estimated and tested. SEM offers no default model and places few limitations on what types of relations can be specified. SEM model specification requires researchers to support hypothesis with theory or research and specify relations a priori. Third, SEM is a multivariate technique incorporating observed (measured) and unobserved variables (latent constructs) while traditional techniques analyze only measured variables. Multiple, related equations are solved simultaneously to determine parameter estimates with SEM methodology. Fourth, SEM allows researchers to recognize the imperfect nature of their measures. SEM explicitly specifies error while traditional methods assume measurement occurs without error. Fifth, traditional analysis provides straightforward significance tests to determine group differences, relationships between variables, or the amount of variance explained. SEM provides no straightforward tests to determine model fit. Instead, the best strategy for
A Handbook on SEM Overview of Structural Equation Modeling (SEM
Academicians, researchers, as well as postgraduate students are developing theories concerning the relationships among certain hypothetical constructs. They are modeling their theorized relationships with the intention to test their theoretical model with the empirical data from the field. The example of a Theoretical Framework is given in Figure A. Figure A: The Schematic Diagram Showing the Theoretical Framework of a Study. The schematic diagram in Figure A is converted into Amos Graphic and analyzed using empirical data. In Amos Graphic, the rectangles represent the directly observed variables while the ellipses represent the unobserved variables or latent constructs. The schematic diagram of theoretical framework in Figure A is converted into Amos Graphic as shown in Figure B. The schematic diagram of the model for the study is developed based on debates in theory and literature. One needs to come out with a theoretical framework for the study.
(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling
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 pro- vides 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 should be inte- grated into their research tools. Commented findings of an internet survey give a "State of the Union Address" on SEM users and usage. Which kinds of models are preferred? Which software is favoured in current psychological research? In order to assist the reader on his first steps, a SEM first-aid kit is included. Typical problems and possible solutions are addressed, helping the reader to get the support he needs. Hence, the paper may assist the novice on the first st...
Journal of Organizational Behavior Review (JOB Review), 2024
Structural equation modeling (SEM) is one of the multivariate analyses that is used to test complicated research models, which include several constructs that have a group of independent and dependent variables with a series of hypothesized relations and associations between them. It starts with examining the collected data by conducting a data screening analysis and descriptive statistics. The scale used to measure the variables should be examined by conducting factor analysis (EFA & CFA) to make sure the data fits the research measurement model and to assess the scale's reliability, validity, and its level of fit to the collected data. The analysis of multivariate assumption should be reviewed then path analysis can be done for hypotheses testing and getting the final results. The final results have to be explained and interpreted based on the research's theoretical background and its literature foundation. This review paper explains these steps in detail for quantitative analysis students and other researchers who have basic knowledge of statistics, using simple words without diving deeply into statistics' details and its related formulas.
STRUCTURAL EQUATION MODEL (SEM
This paper critically examined a broad view of Structural Equation Model (SEM) with a view of pointing out direction on how researchers can employ this model to future researches, with specific focus on several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics, Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing new insights to researchers for conducting longitudinal investigations. .
JOURNAL OF SOCIAL SCIENCE RESEARCH
Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limita...
An Application of Structural Equation Modelling – A Tutorial
covariance structure analysis remains a niche area in statistics. As such SEM is not very well known to most statisticians. This is somewhat surprising because the primary building blocks of SEM-common factor analysis (common factors are by definition latent variables) and linear regression-are all too well known to the statisticians! The primary use of SEM is to test
Essentials of Structural Equation Modeling
Structural Equation Modeling is a statistical method increasingly used in scientific studies in the fields of Social Sciences. It is currently a preferred analysis method, especially in doctoral dissertations and academic researches. However, since many universities do not include this method in the curriculum of undergraduate and graduate courses, students and scholars try to solve the problems they encounter by using various books and internet resources. This book aims to guide the researcher who wants to use this method in a way that is free from math expressions. It teaches the steps of a research program using structured equality modeling practically. For students writing theses and scholars preparing academic articles, this book aims to analyze systematically the methodology of scientific studies conducted using structural equation modeling methods in the social sciences. This book is prepared in as simple language as possible so as to convey basic information. It consists of two parts: the first gives basic concepts of structural equation modeling, and the second gives examples of applications.
A SYSTEMATIC REVIEW OF STRUCTURAL EQUATION MODEL (SEM
Open Journal Nigeria, 2020
Structural Equation Model (SEM) is a multivariate statistical technique that has been explored to test relationships between variables. The use of SEM to analyze relationship between variables is premised on the weak assumption of path analysis, regression analysis and so on; that variables are measured without error. This review thus sheds light on the meaning of SEM, its assumptions, steps and some of the terms used in SEM. The importance of item parcelling to SEM and its methods were briefly examined. It also dealt on the stages involved in SEM, similarities and differences between SEM and conventional statistical methods, software packages that can be used for SEM. This article employed systematic literature review method because it critically synthesized research studies and findings on structural equation modeling (SEM). It could be concluded that SEM is useful in analyzing a set of relationships between variables using diagrams. SEM can also be useful in minimizing measurement errors and in enhancing reliability of constructs. Based on this, it is recommended that SEM should be employed to test relationship between variables since it can explore complex relationships among variables such as direct, indirect, spurious, hierarchical and non-hierarchical.
The SAGE Dictionary of Statistics
Structural Equation Modelling (SEM) is a relatively recently developed statistical technique based upon factor analysis and multiple regression. This review will cover four of the most widely used (in psychology departments) packages, looking in particular at their suitability for use in a teaching environment, rather than an in depth look at their technical capabilities: L ISREL 8.20, EQS 5.6, AMOS 3.6, SEPath. The four programs reviewed were all tested running Windows 95, on a 166 MHz Pentium, with 32 MB RAM.