Applying Structural Equation Modeling (SEM) in Educational Research

Teo, Timothy; Tsai, Liang Ting; Yang, Chih-Chien

doi:10.1007/978-94-6209-332-4_1

Cited by 85 publications

(76 citation statements)

References 20 publications

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“…The unstandardised value of parameters, together with their statistical significance and standard errors, can be found in Table 6. Factor loadings from the factors of the observed variables are, on average, 3 A brief description of the most commonly reported model fit indicators can be found in Byrne (2016) and Teo et al (2013). the highest for the dynamic capabilities (from β = 0.75 to β = 0.65).…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…The obtained value was lower than the cut-off point of 0.08, implying the well-fitting characteristics of the model. GFI is an absolute index of model fit and is analogous to R 2 in the regression analysis (Teo et al 2013). For a good fit, a recommended value of 0.90 or higher should be obtained, which was achieved for our research model.…”

Section: Model Evaluation (Model Fit)mentioning

confidence: 97%

“…More recent research papers with the application of SEM in these areas are those of Cunningham et al (2017), Putri et al (2017), Khongmalai and Distanont (2017), Smaliukiene et al (2017), and many others. Regardless of the area of the application of SEM, practitioners and theorists agree on five steps involved in testing SEM: model specification, identification, estimation, evaluation and (if needed) modification (Teo et al 2013). …”

Section: Application Of Sem Approach On Firm Performance Research Modelmentioning

confidence: 99%

“…One of the measures to look at is the overall chi-square (x 2 ), which indicates whether the observed and implied variance-covariance matrices differ (Teo et al 2013) or not. A statistically nonsignificant value of this measure is an indicator of a good model.…”

Section: Model Evaluation (Model Fit)mentioning

confidence: 99%

“…Koropp et al (2014) recommend a value of 0.90 or higher as an adequate model-fit indicator. TLI index is used to compare a research model to the null model (Teo et al 2013). A model with a good fit will have a value of TLI index that approaches 1.…”

Section: Model Evaluation (Model Fit)mentioning

confidence: 99%

See 4 more Smart Citations

The Influence of Industry Characteristics and Dynamic Capabilities on Firms’ Profitability

Pervan

Ćurak

Kramarić

2017

IJFS

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Abstract:The aim of this research was to explore one of the most intriguing dimensions of every firm's business-its performance. Aside from analysing the influence of industry's characteristics on the firm's performance, the authors addressed the characteristics of dynamic capabilities and their role in contribution to the firm's ultimate success. The analysis was conducted on a sample of 118 small Croatian manufacturing companies. The application of the Structural Equation Modelling (SEM) approach revealed a statistically significant influence of both the industry's characteristics (represented by Porter's five forces framework) and dynamic capabilities (based on Teece's theory) on the firm's performance, where the influence of dynamic capabilities is proven to be larger than that of the industry.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Model Evaluation (Model Fit)mentioning

confidence: 97%

Section: Application Of Sem Approach On Firm Performance Research Modelmentioning

confidence: 99%

Section: Model Evaluation (Model Fit)mentioning

confidence: 99%

Section: Model Evaluation (Model Fit)mentioning

confidence: 99%

See 3 more Smart Citations

The Influence of Industry Characteristics and Dynamic Capabilities on Firms’ Profitability

Pervan

Ćurak

Kramarić

2017

IJFS

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A structural equation model relating unemployment stress, spiritual intelligence, and mental health components: Mediators of coping mechanism

Arnout

2019

Journal of Public Affairs

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This study aimed to evaluate a hypothesized structural model that reflects the relationship between unemployment stress, spiritual intelligence and mental health components. The sample of the study consisted of (761) unemployed adults (412 males and 349 females), their ages ranged between 30 to 48 year. They were divided into two samples: the first consisted of (352) unemployed adults to test the validity and reliability of the scales used in this study. And the second sample consisted of (409) unemployed adults to evaluate the hypothesized structural model. Researcher prepared the unemployment stress scale, and used the spiritual intelligence scale‐27, as well as prepared the short version of the mental health components scale based on the mental health scale‐50. In order to find how unemployment stress and spiritual intelligence predict the mental health components, the hypothesized model was examined using structural equation modeling. The results of test the proposed structural model concluded that the independent variable: unemployment stress contributed statistically significant directly to predict of the five dimensions of mental health components: mental mindfulness, flexibility, self‐efficiency, social support and happiness as dependent variables. At the same time, the independent variable: unemployment stress contributed statistically significant indirectly and proportionally to the prediction of the five dimensions of mental health as dependent variables through the dimensions of spiritual intelligence: spiritual mindfulness, spiritual abilities, spiritual presence as median variables. The proposed model of unemployment stress, spiritual intelligence and mental health components has a good fitness with the data were collected, and able to predict mental health among unemployed adults.

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Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance

2016

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Recent research on achievement in science asserts that motivation, emotion, and metacognition are important driving forces for learning. This study sought to examine the relationships between two physics class emotions (hope and anxiety), their motivational predictors (instrumentality and self‐efficacy), and their effects on metacognitive problem solving strategies (planning, monitoring, and evaluation) and performance. Data were collected from 520 grade 11 Spanish students (54.7% girls). Structural equation models (SEM), followed by a bootstrap procedure, were used to examine direct and mediated relationships. The results supported the model, suggesting that instrumentality and self‐efficacy negatively predicted anxiety, and enhanced hope, planning, monitoring, evaluation, and performance; metacognitive strategies and performance were negatively predicted by anxiety, and were positively predicted by hope; metacognitive strategies positively predicted performance. Furthermore, the hypothesized mediated relations were also statistically significant. The interpretation of these findings, their implications for physics teaching and learning, and future lines of research are discussed. © 2016 Wiley Periodicals, Inc. J Res Sci Teach 54:558–585, 2017

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Applying Structural Equation Modeling (SEM) in Educational Research

Cited by 85 publications

References 20 publications

The Influence of Industry Characteristics and Dynamic Capabilities on Firms’ Profitability

The Influence of Industry Characteristics and Dynamic Capabilities on Firms’ Profitability

A structural equation model relating unemployment stress, spiritual intelligence, and mental health components: Mediators of coping mechanism

Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance

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