scholarly journals Improved Structural Equation Models Using Factor Analysis

2018 ◽  
pp. 995-1012
Author(s):  
Busanga Jerome Kanyama ◽  
Peter Njuho ◽  
Jean-Claude Malela-Majika
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Food Production ◽  
Structural Equation ◽  
Energy Transport ◽  
Structural Equation Models ◽  
Real Life ◽  
Principal Component ◽  
Analysis Of Covariance ◽  
Positive Contribution

We develop an agricultural adaptive structural equation model (SEM) that incorporates a large number of factors. These factors simultaneously account for food production while uncompromising food quality and safety. Using the principal component analysis (PCA), we obtain provisional factors, which we rotate using factor analysis, thus leading to reduced number of variables. To decide on the form of the covariance structure in the estimation of the parameters of the regression model, we conduct analysis of covariance. The generated principal components are incorporated into the SEMs where testing of different inter-associations among latent variables (LV) is conducted. For simplicity of the model, we utilise J reskog linear structural equation (LSE) system throughout the investigation process. Using a comprehensive real-life example, we illustrate the concepts and effects of the outcomes. The results show that factors such as energy, transport, labour and fertilizer make a positive contribution in the increase of the quantity and quality food. In addition, we demonstrate how to determine the key factors that influence food production where some factors are not directly measured.

scholarly journals A systematic review of the main factors that determine agility in sport using structural equation modeling

2016 ◽  
Vol 52 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Vladimir Hojka ◽  
Petr Stastny ◽  
Tomas Rehak ◽  
Artur Gołas ◽  
Aleksandra Mostowik ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Elite Athletes ◽  
Principal Component ◽  
Maximum Strength ◽  
Equation Modeling ◽  
Factorial Model ◽  
Main Factors

Abstract While tests of basic motor abilities such as speed, maximum strength or endurance are well recognized, testing of complex motor functions such as agility remains unresolved in current literature. Therefore, the aim of this review was to evaluate which main factor or factor structures quantitatively determine agility. In methodological detail, this review focused on research that explained or described the relationships between latent variables in a factorial model of agility using approaches such as principal component analysis, factor analysis and structural equation modeling. Four research studies met the defined inclusion criteria. No quantitative empirical research was found that tried to verify the quality of the whole suggested model of the main factors determining agility through the use of a structural equation modeling (SEM) approach or a confirmatory factor analysis. From the whole structure of agility, only change of direction speed (CODS) and some of its subtests were appropriately analyzed. The combination of common CODS tests is reliable and useful to estimate performance in sub-elite athletes; however, for elite athletes, CODS tests must be specific to the needs of a particular sport discipline. Sprinting and jumping tests are stronger factors for CODS than explosive strength and maximum strength tests. The authors suggest the need to verify the agility factorial model by a second generation data analysis technique such as SEM.

Simulating the Effects of Common and Specific Abilities on Test Performance: An Evaluation of Factor Analysis

2014 ◽  
Vol 57 (5) ◽  
pp. 1919-1928 ◽  
Author(s):  
Dennis J. McFarland
Keyword(s):  
Factor Analysis ◽  
Test Performance ◽  
Test Scores ◽  
Latent Variables ◽  
Structural Equation ◽  
Linear Models ◽  
Principal Component ◽  
Equation Modeling ◽  
Multiple Sources ◽  
Simulated Test

Purpose Factor analysis is a useful technique to aid in organizing multivariate data characterizing speech, language, and auditory abilities. However, knowledge of the limitations of factor analysis is essential for proper interpretation of results. The present study used simulated test scores to illustrate some characteristics of factor analysis. Method Linear models were used to simulate test scores that were determined by multiple latent variables. These simulated test scores were evaluated with principal components analysis and, in certain cases, structural equation modeling. In addition, a subset of simulated individuals characterized by poor test performance was examined. Results The number of factors recovered and their identity do not necessarily correspond to the structure of the latent variables that generated the test scores. The first principal component may represent variance from multiple uncorrelated sources. Practices such as correction or control for general cognitive ability may produce misleading results. Conclusions Inferences from the results of factor analysis should be primarily about the structure of test batteries rather than the structure of human mental abilities. Researchers and clinicians should consider multiple sources of evidence to evaluate hypotheses about the processes generating test results.

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.

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.

Who are sustainably minded apparel shoppers? An investigation to the influencing factors of sustainable apparel consumption

2018 ◽  
Vol 46 (2) ◽  
pp. 148-162 ◽  
Author(s):  
Hyo Jung Chang ◽  
Kittichai (Tu) Watchravesringkan
Keyword(s):  
Factor Analysis ◽  
Structural Equation ◽  
Sustainable Consumption ◽  
Principal Component ◽  
Control Beliefs ◽  
Student Sample ◽  
Content Type ◽  
Factors Affecting ◽  
College Student Sample ◽  
Buying Behaviour

Purpose Consumers’ environmental behaviours are not only the result of their positive attitudes towards environments, but also different reasons and motivations exist. Thus, the purpose of this paper is to find out important factors affecting sustainable apparel buying behaviour. Applying the theory of planned behaviour (TPB), this study further examines how knowledge about sustainable apparel, perceived money availability, and perceived accessibility to the store influence sustainable apparel consumption. Design/methodology/approach Using a purposive college student sample, 235 usable responses were collected to answer the questions. An exploratory factor analysis with principal component analysis was first performed followed by confirmatory factor analysis, and a structural equation modelling analysis. Findings Results revealed that the TPB was successfully applied in the context of sustainable apparel buying behaviour. Furthermore, it was found that consumers’ perceived money availability and perceived store accessibility are important factors that affect control beliefs and sustainable consumption. Research limitations/implications This study found the needs of educating college students for contexts of environmental apparel and textiles issues. Originality/value Even though previous literature often found the gap between the behavioural intentions and the actual behaviour, this study found the respondents of this study walk their talk. This study successfully applied the TPB to explain consumers’ sustainable apparel buying behaviour.

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