An Introduction to Path Analysis Modeling Using LISREL

2011 ◽  
pp. 57-81
Author(s):  
Sean B. Eom
Keyword(s):  
Path Analysis ◽  
Statistical Models ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Confirmatory Factor ◽  
E Learning ◽  
Research Stream

Over the past decades, there has been a wide range of empirical research in the e-learning literature. The use of multivariate statistical tools has been a staple of the research stream throughout the decade. Path analysis modeling is part of four related multivariate statistical models, including regression, path analysis, confirmatory factor analysis, and structural equation models. This chapter focuses on path analysis modeling for beginners using LISREL 8.70. Several topics covered in this chapter include foundational concepts, assumptions, and steps of path analysis modeling. The major steps in path analysis modeling explained in this chapter consist of specification, identification, estimation, testing, and modification of models.

Educational Services in Second Life

2011 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Joana Ancila Pessoa Forte ◽  
Danielle Miranda de Oliveira Arruda Gomes ◽  
Cláudio André Gondim Nogueira ◽  
Carlos Felipe Cavalcante de Almeida
Keyword(s):  
Learning Environment ◽  
Second Life ◽  
Social Life ◽  
Structural Equation ◽  
Equation Modeling ◽  
Online Learning Environment ◽  
Online Environment ◽  
Multivariate Statistical ◽  
Confirmatory Factor ◽  
E Learning

Among many changes influenced by the Internet, interactivity in spaces that promote relationships, entertainment, and businesses can be highlighted. Considering this, Second Life stands out because it is a tridimensional online environment which imitates human real social life. Despite social and commercial influences, Second Life suggests a new format for e-learning. Then, the question of how to explore the facets of an online learning environment may be answered by Flow Theory. Hence, the main objective of this paper is to analyze the most significant antecedent and subsequent relations of the Flow Experience in the Second Life’s educational environment, based on Novak, Hoffman, and Yung (2000). This research used tools from multivariate statistical analysis such as confirmatory factor analysis and structural equation modeling. The results confirmed the hypotheses, indicating that there is flow in Second Life’s e-learning environment, with interactive speed, exploratory behavior and telepresence as the most significant constructs detected.

Educational Services in Second Life

2012 ◽  
pp. 180-196
Author(s):  
Joana Ancila Pessoa Forte ◽  
Danielle Miranda de Oliveira Arruda Gomes ◽  
Cláudio André Gondim Nogueira ◽  
Carlos Felipe Cavalcante de Almeida
Keyword(s):  
Learning Environment ◽  
Second Life ◽  
Social Life ◽  
Structural Equation ◽  
Educational Environment ◽  
Equation Modeling ◽  
Educational Services ◽  
Multivariate Statistical ◽  
Confirmatory Factor ◽  
E Learning

Among many changes influenced by the Internet, interactivity in spaces that promote relationships, entertainment, and businesses can be highlighted. Considering this, Second Life stands out because it is a tridimensional online environment which imitates human real social life. Despite social and commercial influences, Second Life suggests a new format for e-learning. Then, the question of how to explore the facets of an online learning environment may be answered by Flow Theory. Hence, the main objective of this paper is to analyze the most significant antecedent and subsequent relations of the Flow Experience in the Second Life’s educational environment, based on Novak, Hoffman, and Yung (2000). This research used tools from multivariate statistical analysis such as confirmatory factor analysis and structural equation modeling. The results confirmed the hypotheses, indicating that there is flow in Second Life’s e-learning environment, with interactive speed, exploratory behavior and telepresence as the most significant constructs detected.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Volition to transfer: mastering obstacles in training transfer

2017 ◽  
Vol 46 (4) ◽  
pp. 809-823 ◽  
Author(s):  
Christian Seiberling ◽  
Simone Kauffeld
Keyword(s):  
Structural Equation ◽  
Transfer Of Training ◽  
Structural Equation Models ◽  
Training Transfer ◽  
Bootstrap Analysis ◽  
Content Type ◽  
Confirmatory Factor ◽  
Motivation To Transfer ◽  
Practical Implications

Purpose The purpose of this paper is to seek a better understanding of the role of volition in the learning transfer system beyond the well-established concept of motivation to transfer. Design/methodology/approach Participants of a two-day leadership training were asked to complete two online questionnaires (t1 directly after training, t2 eight weeks after training). In total, 891 managers answered the first questionnaire, 465 the second. Findings Confirmatory factor analysis suggests that motivation and volition to transfer are perceived as two different constructs. Hierarchical linear regression shows that additional variance in training transfer can be explained when volition to transfer is taken into account. Structural equation models and bootstrap analysis suggest that both motivation and volition to transfer mediate effects of supervisor support and trainer performance on training transfer. Research limitations/implications The results imply that besides motivation to transfer, volition to transfer may be a relevant construct in the transfer of training. It remains to be tested how far these findings can be generalized to other training settings beside leadership trainings. Practical implications Organizations aiming at improving training transfer should focus on enhancing the participants’ motivation and volition to transfer. Both trainers and supervisors seem to promote transfer of training by influencing a trainee’s motivation to transfer and volition to transfer. Originality/value To the authors’ knowledge, this is the first study to systematically examine the role of volition in training transfer.

scholarly journals Examining the ParticipACTION brand using the brand equity pyramid

2018 ◽  
Vol 8 (4) ◽  
pp. 378-396 ◽  
Author(s):  
Alexander Lithopoulos ◽  
Peter A. Dacin ◽  
Tanya R. Berry ◽  
Guy Faulkner ◽  
Norm O’Reilly ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Brand Equity ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Equation Modeling ◽  
Content Type ◽  
Activity Promotion ◽  
Confirmatory Factor

Purpose The brand equity pyramid is a theory that explains how people develop loyalty and an attachment to a brand. The purpose of this study is to test whether the predictions made by the theory hold when applied to the brand of ParticipACTION, a Canadian non-profit organization that promotes active living. A secondary objective was to test whether this theory predicted intentions to be more physically active. Design/methodology/approach A research agency conducted a cross-sectional, online brand health survey on behalf of ParticipACTION. Exploratory factor analysis and confirmatory factor analysis established the factor structure. Structural equation modeling was used to test the hypothesized model. Findings A nationally representative sample of Canadian adults (N = 1,191) completed the survey. Exploratory factor analysis and confirmatory factor analysis supported a hypothesized five-factor brand equity framework (i.e. brand identity, brand meaning, brand responses, brand resonance and intentions). A series of structural equation models also provided support for the hypothesized relationships between the variables. Practical implications Though preliminary, the results provide a guide for understanding the branding process in the activity-promotion context. The constructs identified as being influential in this process can be targeted by activity-promotion organizations to improve brand strength. A strong organizational brand could augment activity-promotion interventions. A strong brand may also help the organization better compete against other brands promoting messages that are antithetical to their own. Originality/value This is the first study to test the brand equity pyramid using an activity-promotion brand. Results demonstrate that the brand equity pyramid may be useful in this context.

scholarly journals ANALISIS STRUKTUR GAJI TERHADAP KEPUASAN KERJA, KOMITMEN ORGANISASIONAL DAN TURNOVER INTENTION (STUDI KASUS PADA RUMAH SAKIT UMUM DAERAH AL-MULK KOTA SUKABUMI)

2020 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Yathestha Yoga Dwi Rahestha ◽  
Budi Hartono ◽  
Gofur Ahmad
Keyword(s):  
Path Analysis ◽  
Turnover Intention ◽  
Structural Equation ◽  
Structural Equation Models

Dampak dari struktur gaji merupakan fenomena yang sering terjadi pada sebuah perusahaan dimana karyawan memiliki keinginan keluar dari perusahaan karena rendahmya kegairahan kerja karyawan serta ketidakpuasan karyawan saat bekerja yang dapat mengakibatkan kondisi pekerjaan yang tidak nyaman sehingga akan terjadi turnover intention yang tinggi. Penelitian ini bertujuan untuk menganalisis pengaruh stuktur gaji teradap kepuasan kerja, komitmen organisasional dan turnover intention. Metode penelitian ini adalah penelitian kuantitatif dengan model SEM (Structural Equation Models) dengan tehnik analisis jalur (path analysis). Penelitian ini dilakukan pada Agustus-Oktober 2019 pada 118 sampel karyawan RSUD Al-Mulk Kota Sukabumi dengan teknik sampling jenuh. Hasil penelitian didapatkan bahwa struktur gaji berpengaruh sebesar 0,58 terhadap kepuasan kerja, sebesar 0,11 terhadap komitmen oganisasional dan sebesar 0,11 terhadap turnover intention. Kepuasan kerja berpengaruh sebesar 0,51 terhadap komitmen organisasional dan berpengaruh sebesar -0,34 terhadap turnover intention. Komitmen organisasional berpengaruh sebesar -0,23 terhadap turnover intention. Kepuasan kerja, komitmen organisasional, dan turnover intention secara simultan berpengaruh signifikan terhadap struktur gaji. Pihak rumah sakit agar memperhatikan permasalahan terkait struktur gaji yang ada untuk meminimalisir terjadinya turnover intention pada karyawan.

scholarly journals ShareSEM: Example and Template R Scripts for SEM, Bayesian, and More

2019 ◽  
Author(s):  
Daniel John Phipps
Keyword(s):  
Path Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Bayesian Modelling ◽  
Meta Analyses

The ShareSEM project is a simple depository of R scripts for running structural equation models and Bayesian modelling in R. The scripts are designed such that, if measurement items are named consistently, models can be ran with no extra scripting. If models or items differ from the template file, the scripts are designed to be easily adaptable. Bayesian path analysis scripts are also available, with pre-specified priors from meta-analyses of the models.

Confirmatory Factor Analysis and Structural Equation Models to dissect the relationship between Gait and Morphology in Campolina horses

2021 ◽  
pp. 104779
Author(s):  
Fernando de Oliveira Bussiman ◽  
Fabyano Fonseca e Silva ◽  
Rachel Santos Bueno Carvalho ◽  
Ricardo Vieira Ventura ◽  
Elisângela Chicaroni Mattos ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Confirmatory Factor ◽  
The Relationship
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