The document provides an outline for a thesis on defining the state of the art in structural equation modeling (SEM). It discusses the goals of introducing SEM methods, describing state-of-the-art techniques, classifying them, comparing techniques, and establishing an SEM selection guideline. An introduction to SEM is provided, outlining that it is a multivariate analysis combining regression and factor analysis. The document also describes some state-of-the-art SEM methods like covariance-based SEM and partial least squares path modeling.
Introduction to Structural Equation Modeling Partial Least Sqaures (SEM-PLS)Ali Asgari
Partial least squares structural equation modelling (PLS-SEM) has recently received considerable attention in a variety of disciplines.The goal of PLS-SEM is the explanation of variances (prediction-oriented approach of the methodology) rather than explaining covariances (theory testing via covariance-based SEM).
This document discusses structural equation modeling (SEM) and partial least squares SEM (PLS-SEM). It provides an overview of the key differences between covariance-based SEM and PLS-SEM, including their objectives, assumptions, strengths, and evaluation. It also discusses important considerations for using SEM such as data characteristics, model specification, and the systematic process of applying PLS-SEM. Guidelines are provided for determining whether PLS-SEM or CB-SEM is best suited for a given research question and study.
SEM is not a single statistical technique but rather integrates multiple multivariate techniques like factor analysis, path analysis, and regression into a unified framework. It allows modeling of complex latent constructs that are measured with error through multiple observed variables. Path analysis using latent variables can partition variance between true scores on latent variables and measurement error, and examine direct, indirect, and total effects in a system of relationships between variables.
This document provides an overview of structural equation modeling (SEM) using AMOS. It defines key SEM concepts like latent variables, observed variables, path analysis, and model identification. It also explains how to specify and estimate a SEM model in AMOS, including how to draw path diagrams, name variables, set regression weights, and view output. Model fit is discussed along with potential issues like sample size. Confirmatory factor analysis and other SEM models like path analysis and latent growth models are also introduced.
This document provides an introduction and overview of SmartPLS software for structural equation modeling. It discusses key concepts in SEM like latent and manifest variables, reflective and formative measurement models, and the structural and measurement models. It then demonstrates how to use SmartPLS by opening a project file, evaluating a sample research model and hypotheses, and interpreting the output metrics to assess model fit and quality.
This document provides an overview of the important steps in conducting a Confirmatory Factor Analysis (CFA) using Structural Equation Modeling (SEM). It outlines the 5 key steps as model specification, identification, estimation, assessment of model fit, and potential model re-specification. For model specification, the document emphasizes that SEM is a confirmatory technique that requires a theoretically-driven model delineating relationships among variables. It also defines exogenous and endogenous variables. For model identification, it notes that a unique parameter solution is required. The document discusses maximum likelihood estimation and several absolute and relative fit indices for assessing model fit. It concludes by noting guidelines for potential model modification.
This document provides an overview of key concepts in structural equation modeling (SEM) including:
1) Path diagrams are used to represent structural equations and the relationships between latent and observed variables.
2) SEM analyzes the variance/covariance matrix of observed variables rather than raw data. Maximum likelihood estimation is used to estimate model parameters by maximizing the likelihood of the sample data.
3) Model identification, fit, and constraints are important concepts. A model must be over-identified to yield a likelihood value for assessing fit. Parameter constraints can be used to make a just-identified model over-identified.
Introduction to Structural Equation Modeling Partial Least Sqaures (SEM-PLS)Ali Asgari
Partial least squares structural equation modelling (PLS-SEM) has recently received considerable attention in a variety of disciplines.The goal of PLS-SEM is the explanation of variances (prediction-oriented approach of the methodology) rather than explaining covariances (theory testing via covariance-based SEM).
This document discusses structural equation modeling (SEM) and partial least squares SEM (PLS-SEM). It provides an overview of the key differences between covariance-based SEM and PLS-SEM, including their objectives, assumptions, strengths, and evaluation. It also discusses important considerations for using SEM such as data characteristics, model specification, and the systematic process of applying PLS-SEM. Guidelines are provided for determining whether PLS-SEM or CB-SEM is best suited for a given research question and study.
SEM is not a single statistical technique but rather integrates multiple multivariate techniques like factor analysis, path analysis, and regression into a unified framework. It allows modeling of complex latent constructs that are measured with error through multiple observed variables. Path analysis using latent variables can partition variance between true scores on latent variables and measurement error, and examine direct, indirect, and total effects in a system of relationships between variables.
This document provides an overview of structural equation modeling (SEM) using AMOS. It defines key SEM concepts like latent variables, observed variables, path analysis, and model identification. It also explains how to specify and estimate a SEM model in AMOS, including how to draw path diagrams, name variables, set regression weights, and view output. Model fit is discussed along with potential issues like sample size. Confirmatory factor analysis and other SEM models like path analysis and latent growth models are also introduced.
This document provides an introduction and overview of SmartPLS software for structural equation modeling. It discusses key concepts in SEM like latent and manifest variables, reflective and formative measurement models, and the structural and measurement models. It then demonstrates how to use SmartPLS by opening a project file, evaluating a sample research model and hypotheses, and interpreting the output metrics to assess model fit and quality.
This document provides an overview of the important steps in conducting a Confirmatory Factor Analysis (CFA) using Structural Equation Modeling (SEM). It outlines the 5 key steps as model specification, identification, estimation, assessment of model fit, and potential model re-specification. For model specification, the document emphasizes that SEM is a confirmatory technique that requires a theoretically-driven model delineating relationships among variables. It also defines exogenous and endogenous variables. For model identification, it notes that a unique parameter solution is required. The document discusses maximum likelihood estimation and several absolute and relative fit indices for assessing model fit. It concludes by noting guidelines for potential model modification.
This document provides an overview of key concepts in structural equation modeling (SEM) including:
1) Path diagrams are used to represent structural equations and the relationships between latent and observed variables.
2) SEM analyzes the variance/covariance matrix of observed variables rather than raw data. Maximum likelihood estimation is used to estimate model parameters by maximizing the likelihood of the sample data.
3) Model identification, fit, and constraints are important concepts. A model must be over-identified to yield a likelihood value for assessing fit. Parameter constraints can be used to make a just-identified model over-identified.
This document discusses various techniques for measuring latent variables using factor analysis, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), fixing the scale of latent variables, modeling mean structures, the use of formative indicators, item parcelling, and higher-order factor models. EFA is described as an initial, inductive approach to uncover the underlying factor structure in observed variables, while CFA provides a confirmatory, theory-driven approach to explicitly test a hypothesized measurement model. Additional topics covered include identifying latent variables, modeling means of observed and latent variables, and distinguishing between reflective and formative indicator specifications.
This document provides an introduction to structural equation modeling (SEM) concepts. It defines key terms like measured variable, indicator variable, latent variable, measurement model and structural model. It explains that measured variables are directly observed, while latent variables are abstract constructs measured indirectly through multiple indicators. It describes measurement models as showing the relationships between latent variables and their measured items, and structural models as specifying relationships between latent and observed variables with arrows. It provides examples of confirmatory factor analysis (CFA), path analysis with observed and latent variables, and discusses data sample size considerations for SEM.
The document discusses partial least squares structural equation modeling (PLS-SEM). It provides an overview of key concepts in PLS-SEM, including the differences between PLS-SEM and covariance-based SEM, the objectives and assumptions of each method, and guidelines for when each method is most appropriate. The document also outlines the stages of applying PLS-SEM, including specifying measurement and structural models, model estimation, and evaluating results. Examples are provided to illustrate reflective versus formative measurement models.
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
This document provides an introduction to structural equation modeling (SEM) through a series of definitions and explanations. It discusses key concepts in SEM including latent versus measured variables, covariance versus correlation, and the history and development of SEM. Sample size requirements and software for conducting SEM are also covered. The document is intended as introductory material for postgraduate students learning about SEM.
The document provides an overview of confirmatory factor analysis (CFA). It defines CFA and explains that CFA requires specifying the number of factors and which variables load on which factors before analysis. The document outlines the 6 stages of CFA: 1) defining constructs, 2) developing the measurement model, 3) designing a study, 4) assessing the measurement model, 5) specifying the structural model, and 6) assessing the structural model. It emphasizes that CFA confirms or rejects preconceived theories about relationships between observed and latent variables.
Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:2 – Basic concepts of Latent, Manifest and Observed Variables using in SEM analysis.
SEM consists of two parts:
1. Measurement model(s) for each latent variable
2.2. Path analysis between the latent and observed variables.
Latent Variables:
These phenomena are termed as latent variables, or common factors.
Examples of latent variables:
·Psychology: Self concept and Motivation.
·Education:Verbal ability& Teacher Expectancy.
Manifest and Observed Variables:
The unobserved variable is linked to one that is observable, thereby measurement is possible.
Examples:
·Self-report to an attitudinal scale.
·Scores on achievement test.
Watch more.
Contact Us:
UK NO:+44-1143520021
India No: +91-8754446690
US NO:+1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Exploratory factor analysis (EFA) is a statistical technique used to identify the underlying relationships between measured variables. EFA can group variables into a smaller number of factors and reduce complexity in the data. The document discusses EFA methodology, including conducting EFA in SPSS, determining the number of factors, rotating factors, and interpreting results. Assumptions of EFA and different extraction and rotation methods are also covered.
1. The document introduces structural equation modeling (SEM) as a statistical technique for testing theoretical models using empirical data.
2. SEM involves evaluating both a measurement model to assess the relationships between observed and latent variables, as well as a path model to examine the structural relationships between latent variables.
3. The document provides an example conceptual model to illustrate the basic components of an SEM, including observed and latent variables, paths of influence, and residuals.
This document provides an overview of econometric modeling techniques. It discusses objectives of econometric modeling including empirical verification of economic theories and policy analysis. It also describes types of econometric models such as single-equation regression models, simultaneous-equation models, and time series models. Model building criteria and assumptions of single-equation regression models are outlined along with methods for dealing with violations of assumptions like multicollinearity and autocorrelation.
CB-SEM assumes normally distributed data which is rarely the case in social sciences research, while PLS-SEM is non-parametric and works well with non-normal distributions. The example showed that CB-SEM resulted in losing many indicator variables to achieve good model fit, whereas PLS-SEM retained more indicators to support both measuring and developing the structural theory. PLS-SEM is preferable when data is non-normal, though CB-SEM can work if the theory and measurement are well established.
This document provides an agenda for a workshop on structural equation modeling using SmartPLS2.0. The morning session will cover basic concepts, components of structural and measurement models, and the PLS algorithm. After lunch, the analysis method for reflective models will be discussed. The afternoon will conclude with examining the analysis method for formative models. Hugo Watanuki is the PhD student leading the workshop at Napier University.
Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
Here are the steps I would take to conduct an exploratory factor analysis on SERVQUAL data:
1. Examine the correlation matrix to identify highly correlated variables and check for suitability of factor analysis using KMO and Bartlett's test.
2. Extract initial factors using principal axis factoring or maximum likelihood.
3. Examine scree plot and eigenvalues to determine number of factors to retain.
4. Rotate the factors using varimax to achieve a simple structure and more interpretable factors.
5. Interpret the rotated factor solution by examining which variables load highly on each factor. Name the factors based on the groupings of variables.
Previous research on SERVQUAL has found a 5 factor solution
This document discusses factor analysis, a statistical technique used to reduce the dimensionality of correlated variables into a smaller number of underlying factors. It begins by motivating factor analysis through an example involving measuring frailty. It then provides an overview of factor analysis, including key concepts like observed and latent variables, assumptions of the factor model, and common applications. The document also covers the mathematical underpinnings of one-factor and multiple-factor models, and explains important outputs of factor analysis like factor loadings and communalities.
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MANCOSA aims at building relationships with the BRICS nations. We aim to establish educational relations with various business schools within the BRICS nations who are willing to offer our MBA programme. Novosibirsk State University of Economic and Management (NSUEM) has partnered up with MANCOSA to offer our MBA programme at their institution. It was during 2015 that three of our professors obtained their professorship at NSUEM and were asked to award the MBA graduates at their graduation ceremony and exposed to various academic seminars, tourist attractions and company visits.
This document appears to be part of an examination for a business research methods course. It includes multiple choice and essay questions covering various topics in business research including: defining business research, research design types, sampling concepts, data collection sources, variables for analysis, hypothesis testing, and research reports. It also includes case study questions and questions involving calculating financial metrics such as weighted average cost of capital, break-even analysis, and capital budgeting techniques.
This document discusses various techniques for measuring latent variables using factor analysis, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), fixing the scale of latent variables, modeling mean structures, the use of formative indicators, item parcelling, and higher-order factor models. EFA is described as an initial, inductive approach to uncover the underlying factor structure in observed variables, while CFA provides a confirmatory, theory-driven approach to explicitly test a hypothesized measurement model. Additional topics covered include identifying latent variables, modeling means of observed and latent variables, and distinguishing between reflective and formative indicator specifications.
This document provides an introduction to structural equation modeling (SEM) concepts. It defines key terms like measured variable, indicator variable, latent variable, measurement model and structural model. It explains that measured variables are directly observed, while latent variables are abstract constructs measured indirectly through multiple indicators. It describes measurement models as showing the relationships between latent variables and their measured items, and structural models as specifying relationships between latent and observed variables with arrows. It provides examples of confirmatory factor analysis (CFA), path analysis with observed and latent variables, and discusses data sample size considerations for SEM.
The document discusses partial least squares structural equation modeling (PLS-SEM). It provides an overview of key concepts in PLS-SEM, including the differences between PLS-SEM and covariance-based SEM, the objectives and assumptions of each method, and guidelines for when each method is most appropriate. The document also outlines the stages of applying PLS-SEM, including specifying measurement and structural models, model estimation, and evaluating results. Examples are provided to illustrate reflective versus formative measurement models.
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
This document provides an introduction to structural equation modeling (SEM) through a series of definitions and explanations. It discusses key concepts in SEM including latent versus measured variables, covariance versus correlation, and the history and development of SEM. Sample size requirements and software for conducting SEM are also covered. The document is intended as introductory material for postgraduate students learning about SEM.
The document provides an overview of confirmatory factor analysis (CFA). It defines CFA and explains that CFA requires specifying the number of factors and which variables load on which factors before analysis. The document outlines the 6 stages of CFA: 1) defining constructs, 2) developing the measurement model, 3) designing a study, 4) assessing the measurement model, 5) specifying the structural model, and 6) assessing the structural model. It emphasizes that CFA confirms or rejects preconceived theories about relationships between observed and latent variables.
Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.stat...Stats Statswork
Statswork (Statswork.com), Lecture:2 – Basic concepts of Latent, Manifest and Observed Variables using in SEM analysis.
SEM consists of two parts:
1. Measurement model(s) for each latent variable
2.2. Path analysis between the latent and observed variables.
Latent Variables:
These phenomena are termed as latent variables, or common factors.
Examples of latent variables:
·Psychology: Self concept and Motivation.
·Education:Verbal ability& Teacher Expectancy.
Manifest and Observed Variables:
The unobserved variable is linked to one that is observable, thereby measurement is possible.
Examples:
·Self-report to an attitudinal scale.
·Scores on achievement test.
Watch more.
Contact Us:
UK NO:+44-1143520021
India No: +91-8754446690
US NO:+1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Exploratory factor analysis (EFA) is a statistical technique used to identify the underlying relationships between measured variables. EFA can group variables into a smaller number of factors and reduce complexity in the data. The document discusses EFA methodology, including conducting EFA in SPSS, determining the number of factors, rotating factors, and interpreting results. Assumptions of EFA and different extraction and rotation methods are also covered.
1. The document introduces structural equation modeling (SEM) as a statistical technique for testing theoretical models using empirical data.
2. SEM involves evaluating both a measurement model to assess the relationships between observed and latent variables, as well as a path model to examine the structural relationships between latent variables.
3. The document provides an example conceptual model to illustrate the basic components of an SEM, including observed and latent variables, paths of influence, and residuals.
This document provides an overview of econometric modeling techniques. It discusses objectives of econometric modeling including empirical verification of economic theories and policy analysis. It also describes types of econometric models such as single-equation regression models, simultaneous-equation models, and time series models. Model building criteria and assumptions of single-equation regression models are outlined along with methods for dealing with violations of assumptions like multicollinearity and autocorrelation.
CB-SEM assumes normally distributed data which is rarely the case in social sciences research, while PLS-SEM is non-parametric and works well with non-normal distributions. The example showed that CB-SEM resulted in losing many indicator variables to achieve good model fit, whereas PLS-SEM retained more indicators to support both measuring and developing the structural theory. PLS-SEM is preferable when data is non-normal, though CB-SEM can work if the theory and measurement are well established.
This document provides an agenda for a workshop on structural equation modeling using SmartPLS2.0. The morning session will cover basic concepts, components of structural and measurement models, and the PLS algorithm. After lunch, the analysis method for reflective models will be discussed. The afternoon will conclude with examining the analysis method for formative models. Hugo Watanuki is the PhD student leading the workshop at Napier University.
Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
Here are the steps I would take to conduct an exploratory factor analysis on SERVQUAL data:
1. Examine the correlation matrix to identify highly correlated variables and check for suitability of factor analysis using KMO and Bartlett's test.
2. Extract initial factors using principal axis factoring or maximum likelihood.
3. Examine scree plot and eigenvalues to determine number of factors to retain.
4. Rotate the factors using varimax to achieve a simple structure and more interpretable factors.
5. Interpret the rotated factor solution by examining which variables load highly on each factor. Name the factors based on the groupings of variables.
Previous research on SERVQUAL has found a 5 factor solution
This document discusses factor analysis, a statistical technique used to reduce the dimensionality of correlated variables into a smaller number of underlying factors. It begins by motivating factor analysis through an example involving measuring frailty. It then provides an overview of factor analysis, including key concepts like observed and latent variables, assumptions of the factor model, and common applications. The document also covers the mathematical underpinnings of one-factor and multiple-factor models, and explains important outputs of factor analysis like factor loadings and communalities.
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MANCOSA aims at building relationships with the BRICS nations. We aim to establish educational relations with various business schools within the BRICS nations who are willing to offer our MBA programme. Novosibirsk State University of Economic and Management (NSUEM) has partnered up with MANCOSA to offer our MBA programme at their institution. It was during 2015 that three of our professors obtained their professorship at NSUEM and were asked to award the MBA graduates at their graduation ceremony and exposed to various academic seminars, tourist attractions and company visits.
This document appears to be part of an examination for a business research methods course. It includes multiple choice and essay questions covering various topics in business research including: defining business research, research design types, sampling concepts, data collection sources, variables for analysis, hypothesis testing, and research reports. It also includes case study questions and questions involving calculating financial metrics such as weighted average cost of capital, break-even analysis, and capital budgeting techniques.
1) Unilever ran an integrated online and traditional advertising campaign for a new Dove hair conditioner product in the Netherlands to understand the impact of online marketing.
2) Research found that online advertising boosted key metrics like brand awareness and purchase intent more than print or TV alone, and that combining media channels had an even greater effect.
3) The study concluded that online advertising deserves a larger percentage of the marketing budget than it typically receives, and that Unilever plans to significantly increase its online spending based on the success of this campaign.
The new machine will cost '2,000,000 and has an expected life of 10 years. It will
be depreciated at 20 percent per year under straight line method. The annual operating cost
of the old machine is '100,000 whereas that of the new machine is '150,000. The annual
production capacity of the old machine is 50,000 units whereas that of the new machine is
80,000 units. The selling price per unit is '20. The tax rate is 40 percent.
You are required to:
(i) Calculate the cash flows for the next 10 years if the old machine is retained.
(ii) Calculate the cash flows for the next 10 years if the new
A study on perfoemance management of mahindra and mahindra in bidarProjects Kart
This document outlines a management thesis submitted by Mahananda Baburao for their MBA program. The thesis focuses on performance management at Mahindra and Mahindra in Bidar, India. It includes an introduction outlining the need for and objectives of the study. It also provides the table of contents which shows it will cover a company profile, research design and analysis, findings, suggestions, and conclusions. Various tools and aspects of performance management are defined. The thesis will collect primary data using a questionnaire and analyze performance management practices at Mahindra and Mahindra in Bidar.
This document provides background information on Unilever acquiring a major Russian ice cream producer called Inmarko. It discusses the tasks and challenges facing Anna, the HR Director of Unilever, in integrating the two companies, including developing a new organizational model, optimizing staffing, integrating corporate cultures, and achieving synergy targets. Anna must address differences in the companies' cultures, structures, and skills to successfully integrate Inmarko into Unilever.
This document provides an introduction to structural equation modeling (SEM). It defines key SEM concepts like latent and measured variables, path and measurement models, and direct and indirect effects. Diagrams are used to represent relationships between variables with different symbols. SEM allows modeling of complex relationships between observed and latent variables and assessing how well the modeled patterns fit the data. It has advantages over other methods but also limitations like sample size requirements and assumptions of multivariate normality.
The state of the art in integrating machine learning into visual analyticsCagatay Turkay
Slides for my talk on our paper at EuroVis 2017 on the STAR track:
Endert, A., Ribarsky, W., Turkay, C., Wong, B.L., Nabney, I., Blanco, I.D. and Rossi, F., 2017, March. The state of the art in integrating machine learning into visual analytics. In Computer Graphics Forum.
http://openaccess.city.ac.uk/16739/
This document discusses partial least squares structural equation modeling (PLS-SEM). It begins by introducing the fundamentals of structural equation modeling, including path models with latent variables, structural theory, and measurement theory. It then explains how to specify and estimate path models using the PLS-SEM method and how to evaluate the results. The document concludes by providing an application of PLS-SEM to a corporate reputation model using SmartPLS software.
ABSTRACT : 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.
.
Penalized Regressions with Different Tuning Parameter Choosing Criteria and t...CSCJournals
Recently a great deal of attention has been paid to modern regression methods such as penalized regressions which perform variable selection and coefficient estimation simultaneously, thereby providing new approaches to analyze complex data of high dimension. The choice of the tuning parameter is vital in penalized regression. In this paper, we studied the effect of different tuning parameter choosing criteria on the performances of some well-known penalization methods including ridge, lasso, and elastic net regressions. Specifically, we investigated the widely used information criteria in regression models such as Bayesian information criterion (BIC), Akaike’s information criterion (AIC), and AIC correction (AICc) in various simulation scenarios and a real data example in economic modeling. We found that predictive performance of models selected by different information criteria is heavily dependent on the properties of a data set. It is hard to find a universal best tuning parameter choosing criterion and a best penalty function for all cases. The results in this research provide reference for the choices of different criteria for tuning parameter in penalized regressions for practitioners, which also expands the nascent field of applications of penalized regressions.
Abud F. week 3 discussion #2I’m starting off with this ad .docxdaniahendric
Abud F. week 3 discussion
#2
I’m starting off with this ad by loreal for two reasons. The first being that in our last class I wrote my paper on the feminist movement. The second being that I come from a family of extremely strong, confident, exceptional women, so ads like this really speak to me. This ad has several ways that it draws you in. The obvious thing: The title says it’s an ad for men, yet it’s an ad from a makeup company that traditionally sells makeup for women. That in itself is very catchy. The ad claims that profitability will be higher the more women there are in leadership roles. I don’t know if this is real fact, but it can be researched. I love this ad. I think its powerful.
#1
Next up is this advertisement that at first glance you think might be for Marlboro cigarettes. Then it grabs you. Why are there children’s crayons in a box of cigarettes? This one is black and white except for the crayons themselves. Really catches the eyes that way. The statement, “DO YOU WANT THEM TO TAKE AFTER EVERYTHING YOU DO?” plays to your emotions. Obviously, you wouldn’t want your children to pick up your unhealthy habits. This is an extremely powerful usage of emotion and color.
#3
This ad uses a very powerful visual. It poses a question and the visual poses as the possible answer to the stated question. The posed question is: “DO YOU KNOW HOW MUCH YOU REALLY SPEND ON CIGARETTES?”. The question is also small compared to the visual of the car in the shape of a cigarette butt being “put out”. This in and of itself is a powerful image as well. This is food for thought. How much money could you really save if you didn’t spend it on cigarettes? A car? Maybe. Quit and save. You’ll find out. It’s very convincing.
Editor’s Comments
EDITOR’S COMMENTS
A Critical Look at the Use of PLS-SEM in MIS Quarterly
By: Christian M. Ringle
Professor of Management
Hamburg University of Technology (TUHH) and
University of Newcastle (Australia)
[email protected]
Marko Sarstedt
Assistant Professor of Quantitative Methods in Marketing and Management
Ludwig-Maximilians-University Munich and
University of Newcastle (Australia)
[email protected]
Detmar W. Straub
Editor-in-Chief, MIS Quarterly
Professor of CIS
Georgia State University
[email protected]
Introduction
Wold’s (1974, 1982) partial least squares structural equation modeling (PLS-SEM) approach and the advanced PLS-SEM algorithms
by Lohmöller (1989) have enjoyed steady popularity as a key multivariate analysis method in management information systems (MIS)
research (Gefen et al. 2011). Chin’s (1998b) scholarly work and technology acceptance model (TAM) applications (e.g., Gefen and
Straub 1997) are milestones that helped to reify PLS-SEM in MIS research. In light of the proliferation of SEM techniques, Gefen et
al. (2011), updating Gefen et al. (2000), presented a comprehensive, organized, and contemporary summary of the minimum reporting
requirements for SEM applications.
Such guidelines ...
Implementation of SEM Partial Least Square in Analyzing the UTAUT ModelAJHSSR Journal
ABSTRACT:Partial Least Squares (PLS) Structural Equation Modeling (PLS-SEM) is a statistical technique
used to analyze the expected connections between constructs by evaluating the existence of correlations or
impacts among these constructs. The objective of this work is to employ the Structural Equation Modeling
(SEM) technique, specifically Partial Least Squares (PLS), to investigate the Unified Theory of Acceptance and
Use of Technology (UTAUT) model in the specific domain of payment technology acceptance and utilization.
The UTAUT model encompasses latent variables classified into independent, mediator, moderator, and
dependent categories. Hence, the appropriate approach, the partial least squares structural equation modeling
(PLS-SEM) method, was chosen. The rationale behind this decision is the capability of PLS-SEM to assess
models with a relatively limited dataset, as demonstrated in this study, which included a sample of 50
participants. This study employs a quantitative methodology utilizing a survey-based approach to gather data via
questionnaires. The UTAUT model in the technology acceptance and use domain was accurately assessed by
PLS-SEM, as evidenced by the findings. The findings have substantial implications for comprehending the
factors that influence the adoption of payment technology, specifically focusing on the linkages between
constructs in the UTAUT model. This research validates the model and establishes a foundation for a more
profound comprehension of user behavior in accepting and utilizing payment technologies. Ultimately, using
PLS-SEM demonstrated its efficacy in examining the UTAUT model.
KEYWORDS :Structural Equation Model, Partial Least Square, UTAUT
This document discusses an automatic method for selecting non-linear econometric models. It begins by outlining a general strategy for testing for non-linearity, specifying a general non-linear model, and then simplifying it using encompassing tests. It then identifies five specific problems that arise when selecting non-linear models: testing for non-linearity, collinearity between non-linear transformations, non-normal errors, excess variables when approximating non-linearity, and retaining irrelevant variables. Solutions to address each of these problems are proposed. The document concludes by applying this non-linear automatic selection method to an empirical example on returns to education.
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ECO 301: Decision-Making Analysis Paper Guidelines and Rubric
Overview
Your final project for this course is a detailed analysis of a specific problem statement. How economic themes, such as demand, production, cost, and market
structure relate to a particular company will be a focus of this analysis. You will analyze these components with quantitative techniques,
A Novel Latent Factor Model For Recommender SystemAndrew Parish
The document proposes a novel latent factor model for recommender systems that is less complex than Funk SVD while maintaining comparable accuracy. It introduces the proposed model, which transforms users into a one-dimensional latent space and items into a multidimensional latent space, with ratings predicted as the product of the user's latent factor and the sum of the item's latent factors. An evaluation on movie and product rating datasets found the proposed model has comparable accuracy to Funk SVD but lower complexity due to requiring fewer latent features.
1. The document discusses terms and concepts related to structural equation modeling (SEM) using SmartPLS software.
2. It provides details on normality assessment, internal consistency, convergent validity, and discriminant validity tests that were performed on survey data to evaluate the measurement model in SmartPLS.
3. Assessment of the structural model included calculating p-values and confidence intervals to test hypotheses and determine the significance and relevance of relationships between latent variables.
This document provides an overview of the steps involved in quantitative data analysis and applying Partial Least Square Structural Equation Modeling (PLS-SEM). It discusses pretesting questionnaires, preparing raw data through editing and coding, assessing validity through measures like content validity and unidimensionality, and establishing construct validity through convergent and discriminant validity techniques. The goal is to review all the necessary steps for quantitative data analysis using SPSS and applying SEM, from preparing the data to reporting the results.
In this research, we broaden the advantages of nonnegative garrote as a feature selection method and empirically show it provides comparable results to panel models. We compare nonnegative garrote to other variable selection methods like ridge, lasso and adaptive lasso and analyze their performance on a dataset, which we have previously analyzed in another research. We conclude by showing that the results from nonnegative garrote are comparable to the robustness checks applied to the panel models to validate statistically significant variables. We conclude that nonnegative garrote is a robust variable selection method for panel orthonormal data as it accounts for the fixed and random effects, which are present in panel datasets.
Pharmacokinetic-pharmacodynamic modeling involves creating mathematical models to represent biological systems. These models use experimentally derived data and can be classified as either models of data or models of systems. Models of data require few assumptions, while models of systems are based on physical principles. The model development process involves analyzing the problem, collecting data, formulating the model, fitting the model to data, validating the model, and communicating results. Model validation assesses how well a model serves its intended purpose, though models can never be fully proven and are disproven through validity testing.
Statistical modelling is of prime importance in each and every sphere of data analysis. This paper reviews the justification of fitting linear model to the collected data. Inappropriateness of the fitted model may be due two reasons 1.wrong choice of the analytical form, 2. Suffers from the adverse effects of outliers and/or influential observations. The aim is to identify outliers using the deletion technique. In I extend the result of deletion diagnostics to the ex- changeable model and reviews some results of model analytical form checking and the technique illustrated through an example.
In der vorliegenden Tabelle sind die Basisklassen von Klassifikationsschemen zur Usability-Evaluation hinsichtlich ihrer inhaltlichen Verwandtschaft gegenübergestellt.
Similar to 2015 Vadim Genin NIT MBA Thesis SEM Defining the state of the art 14122015 (20)
2015 Vadim Genin NIT MBA Thesis SEM Defining the state of the art 14122015
1. Structural Equations Modeling (SEM)
Defining the State of the Art
by
Dipl. Eng. VADIM GENIN
Supervisor: M.Sc. Kai G. Mertens
Hamburg, 15th of December 2015
MBA THESIS DEFENSE
Northern Institute of Technology Management
2. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Outline
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 215.12.2015
3. Many software and authors treat SEM as a “black-box”
Structural Equation Modeling. Defining the state of the art. 315.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Before 20th century only qualitative research methods in social sciences, biology and
genetics
In the 20th century new quantitative methods (e.g. Structural Equations Modeling -
SEM) plus use of mainframes and PC
-> it makes complicated for new business users like me, especially with limited
knowledge in statistics, to get general understanding and to start properly apply
SEM in their research work and evaluate results
What is it
SEM?
How? Can I use it for
my research?
Which SEM
method
should I use?
4. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 415.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
5. Goals
Structural Equation Modeling. Defining the state of the art. 515.12.2015
Provide introduction into SEM
Describe the state of the art SEM methods
Classify the state of the art SEM methods
Elaborate on possible comparison criteria
Establish simplified SEM selection guideline
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
6. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 615.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
7. SEM is more, than just a one method
Structural Equation Modeling. Defining the state of the art. 715.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
“Structural equation modeling is a growing family of statistical methods for
modeling relations between variables” (Hoyle, 2012)
SEM “is a multivariate technique combining aspects of multiple regression and
factor analysis to estimate a series of interrelated dependence relationship
simultaneously” (Gefen et al., 2000, p.72)
In general SEM relates to the combination of so called measurement model and
structural model (Henseler et al., 2009)
Measurement model shows relationship between observed indicators and
latent variables
Structural model shows relationship between latent variables
Observed indicators measure latent (or unobserved) variables (LV)
Latent variables (or factors) represent abstract phenomena or perception, for
example, social or emotional experiences, behavior patterns that are not
possible to measure or observe directly (Henseler et al., 2009)
8. 3 main alternative depictions of an SEM model
Structural Equation Modeling. Defining the state of the art. 815.12.2015
Path diagram
Source: Hoyle (2012)
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Measurement
models
Structural
model
Source:
adopted from Hoyle (2012)
Equations notation
Source: Hoyle (2012)
Matrix notation
9. SEM implementation framework
Structural Equation Modeling. Defining the state of the art. 915.12.2015
Source: Hoyle (2012)
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Steps in SEM implementation
10. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 1015.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
11. State of the art SEM methods
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1115.12.2015
Name of SEM method Main contributors
Covariance-based SEM – CBSEM Wright; Jöreskog;
Partial Least Squares Path Analysis - PLS-PA Wright; Wold, H.; Lohmöller; Henseler;
Ringle;
Consistent Partial Least Squares Path Modeling Dijkstra; Henseler;
Generalized structured component analysis - GeSCA Hwang; Takane;
Systems of Regression Equations Cowles; Koopmans; Zellner;
12. Purpose and/or motivation of use: Type of measurement model:
Hypothesized model validation vis-à-vis observed data
(Tenenhaus, 2008); “theory oriented, and emphasizes the
transition from exploratory to confirmatory analysis”
(Henseler et al., 2009)
Both formative (but identification problems often may
occur) and reflective (Jarvis et al., 2003; Henseler et al.,
2009)
Latent variables: Path coefficients:
“True latent variables (i.e. hypothetically existing entities or
constructs” (Marcoulides et al., 2009). Common factors
(therefore random) (Hwang et al., 2010)
Covariance and variances (Henseler et al., 2009; Bowen &
Guo, 2011)
Type of estimators: Popular estimation methods:
Full information methods (Tenenhaus, 2008; Westland,
2015) – “Full information methods estimate the full
network model equations jointly using the restrictions on
the parameters of all the equations as well as the variances
and covariances of the residuals” (Westland, 2015, p.20)
Maximum Likelihood (ML), generalized least squares (GLS),
Unweighted least squares (ULS), Weighted least squares
(WLS), Asymptotically distribution-free (ADF) (Dijkstra &
Henseler, 2015; Bowen & Guo, 2011; Westland, 2015)
Model fit statistical measures: Sampling characteristics:
Overall and local model fit (Hwang et al., 2010; Gefen et al.,
2000); Many statistical measures, for examples see
(Westland, 2015)
Usually needs a large sample (Tenenhaus, 2008); non-
convergence issues in relatively small samples (less than
200) (Boomsma & Hoogland, 2001)
Main disadvantage:
Usually requires larger sample size and normally distributed data (Gefen et al., 2000). But nowadays problems with non-
normal distribution can be mitigated by special estimation options (Muthen & Muthen, 2010; Gefen et al., 2011)
Covariance-based SEM - CBSEM
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1215.12.2015
13. Purpose and/or motivation of use: Type of measurement model:
Exploration and prediction (oriented research) - creation of
causal models and theory building (Gefen et al., 2000,
Gefen et al., 2011, Ringle et al., 2012; ); Score computation
(Tenenhaus, 2008); Rare - confirmatory research (Gefen et
al., 2000);
Both formative and reflective (Henseler et al., 2009; Ringle
et al., 2012)
Latent variables: Path coefficients:
Components or weighted sums of observed variables
(Hwang et al., 2010); possible both linear (usually) and not
linear (seldom) combination of observed indicators (Bollen,
1989) – composite variables (Henseler et al., 2009)
Loadings, inner regression weights (Hwang et al., 2010,
Henseler et al., 2009); Pearson correlations (Wright, 1921);
Regression coefficients (Wold, 1975)
Type of estimators: Popular estimation methods:
It is a „limited information method“ (Westland, 2015;
Tenenhaus, 2008). “Limited information methods estimate
individual node pairs or paths in a network separately using
only the information about the restrictions on the
coefficients of the particular equation. The other equations’
coefficients may be used to check for identifiability, but are
not used for estimation purposes” (Westland, 2015, p.19)
Combination of methods OLS for path estimates and PLS
for weights (Goodhue et al., 2012a). OLS (Westland, 2015;
Hwang et al., 2010) with goal “to maximize the explanation
of variance in a structural model’s dependent constructs”
(Henseler et al., 2009) and PLS – for estimation of weights
(in a measurement model).
Partial Least Squares Path Analysis - PLS-PA (1 of 2)
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1315.12.2015
14. Model fit statistical measures: Two sets of equations:
Local (Hwang et al., 2010) First, for structural model – scores are computed via OLS
regressions (Tenenhaus, 2008); second, for measurement
model (Lohmöller, 1989) - “computed using the PLS
algorithm” (Tenenhaus, 2008)
Sampling characteristics: Sample distribution characteristics:
Possible to use relatively small sample size for
complex models (Reinartz et al., 2009; Hair et al.,
2011; Ringle et al., 2012), but this point isn’t
without critic and frequently debated, (e.g.,
Goodhue et al., 2006; Marcoulides et al., 2009;
Gefen et al., 2011)
Possible to work with both standard and non-standard
distribution (Reinartz et al., 2009); For rules of thumb see
(Barclay et al., 1995)
Convergence: Main disadvantage:
Calculations usually converge, because algorithm is
relatively simple (Tenenhaus, 2008)
Absence of global optimization function and measures of
GOF (Henseler et al., 2009)
Partial Least Squares Path Analysis - PLS-PA (2 of 2)
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1415.12.2015
15. Consistent Partial Least Squares Path Modeling – PLSc
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1515.12.2015
PLSc is a Refined PLS-PA algorithm, which overcomes the problem in of adverse
consequences for hypothesis testing, because of inconsistency of PLS path
coefficient estimates in case of reflective measurement.
PLSc provides a correction for estimates when PLS is applied to reflective constructs
In Classification of PLSc is defined as PLS sub-method, and inherits all main
distinctive features of its ancestor
16. Purpose and/or motivation of use: Type of measurement model:
Mainly explanation and conformation of hypothesis
(Tenenhaus, 2008);
Formative and reflective (Hwang & Takane, 2004)
Latent variables: Path coefficients:
“Components or weights assigned to” manifest indicators
(Hwang et al., 2010)
Component weights (Hwang & Takane, 2004)
Type of estimators: Popular estimation methods:
Full information method (Tenenhaus, 2008; Hwang &
Takane, 2004; Hwang et al., 2010)
Alternating least squares (ALS) algorithm (Hwang &
Takane, 2004); for ALS see (Leeuw et al., 1976)
Model fit statistical measures:
Global (e.g., FIT and AFIT indices) and local because GeSCA “can handle the relationships among components and
observed variables in a unified algebraic framework” which distinct it from PLS-PA methods (Hwang & Takane, 2004;
Hwang et al., 2010)
Generalized structured component analysis - GeSCA
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1615.12.2015
17. Purpose and/or motivation of use: Type of measurement model:
Data description, parameter estimation, prediction and
estimation, and control of models (Montgomery et al.,
2012)
Reflective (Westland, 2015)
Latent variables: Path coefficients:
Response variables - linear functions of indicators
(predictor variables) (Westland, 2015)
Regression coefficients (often used symbol– b) (Tukey,
1954)
Type of estimators: Popular estimation methods:
Both full (e.g., 3SLS) and limited (e.g., 2SLS) information
methods are possible
Two-stage least squares (2SLS) described by (e.g.,Theil,
1953); Three-stage least squares (3SLS) described by
(Zellner, 1962; Zellner & Theil, 1962); Limited information
maximum likelihood developed by (Anderson, 1983)
Model fit statistical measures: Special features:
Performance fit statistics and well defined hypothesis tests
(Westland, 2015)
Multi-equation regression model with not diagonal
covariance matrix (Westland, 2015)
Systems of Regression Equations
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Structural Equation Modeling. Defining the state of the art. 1715.12.2015
18. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 1815.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
19. Classification of the SEM state of the art methods - CSEMSAM
Structural Equation Modeling. Defining the state of the art. 1915.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Name of SEM
method:
CBSEM GSCA PLS-PA Systems of Regression Equations
Type: Covariance based SEM Component based SEM Systems of regression approaches
Latent Variables: Common factors (therefore
random)
Components or weighted sums of observed
variables
Variables - linear functions of
indicators
Purpose and/or
motivation of use:
Hypothesis model
validation and
conformation of theory
Exploration and prediction - creation of new
hypothesis.
And Confirmatory research (Rare use)
Data description, parameter
estimation, prediction and
estimation, and control of models
Sub-Methods: Software based e.g.:
LISREL, AMOS, TETRAD,
Mplus, etc.
N/A PLSC Software based e.g.:
SAS, STATA, system fit (R), SPSS,
MATLAB, etc.
Measurement
model:
Reflective
and Formative (rare use)
Formative and reflective
indicators
Formative and reflective Reflective
Path coefficients: Covariance's and variances Parameters or correlations, Regression coefficients Regression coefficients
Type of Estimators: Full Information Methods Full Information
Methods
Limited Information
Methods
Limited
Information
Methods
Full Information
Methods
Popular estimation
methods:
ML (most popular), ULS,
GLS, WLS, ADF
Alternating least
squares (ALS)
Fixed point OLS
regressions and PLS
algorithm
2SLS, OLS 3SLS
Model fit statistical
measures:
Overall and local Overall and local Local Local Overall and
local
Founders/Main
contributors:
Wright; Jöreskog; Hwang; Takane; Wright; Wold, H.;
Lohmöller; Dijkstra;
Henseler; Ringle;
Cowles; Koopmans; Zellner;
20. Structure of the Classification of the SEM state of the art methods
Structural Equation Modeling. Defining the state of the art. 2015.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
7 criteria are used as a basis to classify SEM methods and create rows of this
classification
5 SEM methods are clustered and placed in 4 main columns and 1 sub-column
SEM methods are divided into 3 main types:
1) Covariance based SEM - most frequently used methods are
LISREL and AMOS
2) Component based SEM – PLS-PA, PLSc, GeSCA
3) Systems of regression approaches
These 3 types are coming from the type of latent variables
21. Criteria used in the Classification of the SEM
Structural Equation Modeling. Defining the state of the art. 2115.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
The type of LV is a “cornerstone” of the whole Classification of the SEM state of
the art methods
Purpose and/or motivation of use - there is ambiguity and redundancy between
methods
Type of “Measurement model”:
1) reflective models are primarily used in CBSEM
2) formative model, can help to derive and depict new phenomena out of
observed indicators’ scores – mainly used for PLS-PA
The rest of criteria: path coefficients, type of estimators, estimation methods and
model of fit measures, are more technical and are more in focus of interest of
professional statistical and mathematical specialists, rather than social or business
science researches
Modern software often allows users to choose between several estimation
methods
22. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 2215.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
23. 3 things that are important to know before comparing SEM methods
Structural Equation Modeling. Defining the state of the art. 2315.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Therefore, before comparing SEM methods important:
1) to understand, when in research process should a researcher to choose which
SEM technique to apply
2) to understand how to compare performance of SEM methods and is it indeed
possible to compare different SEM methods
3) what can be the conditions during the application of SEM and what are criteria
to compare efficiency
In order to establish a set of rules of thumb or a guideline (e.g., Gefen et al., 2011;
Hair et al., 2011), many scholars compare different SEM methods in different
sample size and distributional conditions
(e.g. Hwang et al.,2010; Goodhue et al. ,2012b; Dijkstra & Henseler ,2015)
24. CBSEM
type
Component
based SEM
type
Regression
Equations
type
(SEM technique)
Note: Research Model:
Measurement + Structural
Models
The selection of SEM method in social and business researches
Structural Equation Modeling. Defining the state of the art. 2415.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Source: Adopted from: Goodhue et al. (2012a)
InputOutput
25. Comparative simulation study: Feasibility, Conditions, Comparison criteria
Structural Equation Modeling. Defining the state of the art. 2515.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Is it indeed possible to compare different SEM types with each other?
It’s possible to perform comparative simulation study, the best way -
Using Monte Carlo simulation.
Feasibility
Normal or non-normal distribution
Sample size
Model Complexity
Conditions
Convergence
Raw bias
Statistical power
Criteria to compare efficiency
26. Analysis of recent comparative studies
Structural Equation Modeling. Defining the state of the art. 2615.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
1
(Authors, Year) (Hwang et al., 2010)
SEM types / methods CBSEM; PLS-PA; GeSCA.
Sample size N= 100, 200, 300, 400 and 500.
Data Distributions Normal and nonnormal.
Criteria of compared
efficiency
Loading and path coefficient estimates: 1) relative bias; 2) standard
deviation; 3) mean square error.
2
(Authors, Year) (Goodhue et al., 2012b)
SEM types / methods PLS, multiple regression, CBSEM (LISREL).
Sample size N= 20, 40, 90, 150 and 200, generation 500 data sets for each sample size.
Data Distributions Normal and nonnormal.
Criteria of compared
efficiency
1) arriving at a solution (convergence), 2) producing accurate path
estimates,
3) avoiding false positives (Type 1 errors), 4) avoiding false negatives (Type 2
errors, related to statistical power).
3
Authors (Year) (Dijkstra & Henseler, 2015)
SEM types / methods PLS-PA, PLSc, regression equations and CBSEM (FIML, GLS, WLS, DWLS, ULS).
Sample size N= 100, 200 and 500.
Data Distributions Normal and nonnormal.
Criteria of compared
efficiency
1) convergence - confidence of getting the solution by particular algorithm
2) raw bias - consistency between coefficients in a model
3) statistical power - occurrence Type-I and Type-II errors .
27. The result of the analysis of recent comparative studies
Structural Equation Modeling. Defining the state of the art. 2715.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Simplified SEM selection guideline
28. Background and Motivation
Goals of the Thesis
Introduction into SEM
Description of the state of art SEM methods
Classification of the state of art SEM methods
Comparison and selection of SEM technique
Conclusion, contribution, limitations & further research
Outline
Structural Equation Modeling. Defining the state of the art. 2815.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
29. Conclusions and contributions
Structural Equation Modeling. Defining the state of the art. 2915.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Description of the theoretical bases of SEM & Description of SEM methods;
Contribution:
1) Creation of Classification of the SEM state of the art methods;
2) Analysis of : Feasibility, Conditions, criteria to compare efficiency; of comparative
studies;
3) Newly established simplified SEM guideline;
The analysis of 3 comparative studies showed that there is no “magical silver bullet”,
and selection of the method should be based on many factors, for example :
1) research goal; 2) data, sample, distribution and model specification;
Conclusion: The initial goals of this thesis have been achieved
30. Limitations and opportunities for further research
Structural Equation Modeling. Defining the state of the art. 3015.12.2015
Background
and Motivation
Introduction
into SEM
Goals
SEM
methods
Classification
Comparison and
selection of SEM
Conclusions &
Future Research
Based on the existing literature and studies performed by other researchers
Limited the number of SEM methods, complexity of the models and variety of
sample sizes
Focused on amateur SEM users with limited knowledge in statistics
Only existing state of the art SEM techniques
Limitations
Extensive Monte Carlo simulation studies with all existing SEM types with all
possible estimators in models of different complexity and with different sample size.
To establish of more sophisticated SEM selection guidelines, focused on both
amateur and experts in this field
Creation of new SEM techniques or a refinement of existing SEM methods
Focus on how to use a bundle of SEM methods in research
Opportunity for further research
31. List of References (1 of 3)
Structural Equation Modeling. Defining the state of the art. 3115.12.2015
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SEM is well mathematically developed since 1960-1970, but there is no formal and widely accepted classification and their pros and cons are often misinterpreted
The reasons can be statistical complexity, big variety of definitions, which are often ambiguous and misleading, misalignment in procedures and evaluation criteria between scientists
Business users like me
In order to support not professional (not statisticians) SEM users in their selection of SEM method, goals are:
Structure of the Classification of the SEM state of the art methods
7 criteria are used as a basis to classify SEM methods and create rows of this classification
5 SEM methods are clustered and placed in 4 main columns and 1 sub-column
SEM methods are divided into 3 main types:
1) Covariance based SEM - most frequently used methods are
LISREL and AMOS
2) Component based SEM – PLS-PA, PLSc, GeSCA
3) Systems of regression approaches
Criteria used in the Classification of the SEM
These 3 types are coming from the type of latent variables
The type of LV is a “cornerstone” of the whole Classification of the SEM state of the art methods
Purpose and/or motivation of use - there is ambiguity and redundancy between methods
Type of “Measurement model”:
reflective models are primarily used in CBSEM
formative model, can help to derive and depict new phenomena out of observed indicators’ scores – mainly used for PLS-PA
The rest of criteria: path coefficients, type of estimators, estimation methods and model of fit measures, are more technical and are more in focus of interest of professional statistical and mathematical specialists, rather than social or business science researches
Modern software often allows users to choose between several estimation methods
To have but not really to show this slide.
To tell all this text, during the presentation of the Classification
As the result of the analysis of 3 recent comparative studies: Hwang et al. (2010); Goodhue et al. (2012b); Dijkstra & Henseler (2015). The following SEM selection guideline was created:
As the result of the analysis of 3 recent comparative studies: Hwang et al. (2010); Goodhue et al. (2012b); Dijkstra & Henseler (2015). The following SEM selection guideline was created:
Description of: theoretical introduction into SEM & Description of SEM methods; can help to new SEM users to understand the general concepts of SEM
Contribution: 1) Creation of Classification of the SEM state of the art methods; 2) Analysis of : Feasibility, Conditions, Comparison criteria; of comparative studies; 3) Newly established simplified SEM guideline; Can guide amateur business researchers (not professional statisticians) in their first steps in implementation of SEM and can facilitate a selection of a proper SEM type
Limitations:
This study based on the existing literature and studies performed by other researchers Hence, the nomenclature of SEM methods, complexity of the models and variety of sample sizes considered in the study is constrained
The main focus group of this thesis is SEM practitioners, who are new in SEM field and have limited knowledge in statistics
This study tried to examine only existing state of the art SEM techniques
Opportunity for further research
To perform extensive Monte Carlo simulation studies where all existing SEM types with all possible estimators in models of different complexity and with different sample sizes would be tested.
To establish of more sophisticated SEM selection guidelines, which can be used by both amateur and experts in this field
Creation of new SEM techniques or a refinement of existing SEM methods
Focus on how to use a bundle of SEM methods in research