Application of Structural Equation Modeling in Educational Research and Practice

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Model estimation determines how the tested model fits the generated data based on the extent to which the observed covariance matrix data generated is equivalent to the model-implied covariance matrix e. In the attempt to find the perfect fitting glove i. This method consists of repeating calculations until the best-fitting estimations for the parameters are obtained.

However, the most frequently employed method is ML, often the default estimation procedure on many SEM programmes. The GOF is critical to conducting SEM, as it allows the adequacy of the tested model to be evaluated and permits comparison of the efficacy of multiple competing models. Specifically, GOF reflects the extent to which the model fits the data.

In order to find a statistically significant theoretical model with practical and substantive meaning, multiple goodness-of-fit indices to assess model fit have been put forward. Although there are no concrete rules about which fit statistics to use to evaluate models, a combination of fit statistics are employed when comparing and contrasting models. The AIC is used to compare a number of competing models and, in these instances, the model which generates the lowest AIC values is regarded as the best fitting model.

The actual AIC value is not relevant, although AIC values which are close to zero are considered to be more favourable. However, the respecification needs to grounded in theoretical relevance, as opposed to empirical relevance. Specifically, the respecification of causal relationships needs to be theoretically meaningful and supported by empirical evidence.

It should not be empirically guided, as this can result in a good-fitting model in the absence of any theoretical value. Respecification can be conducted in a number of ways. Firstly, non-significant pathways can be deleted or trimmed. Secondly, parameters can be added or deleted in the model to improve the fit. SEM contains modification indices such as the Lagrange Multiplier tests and Wald tests, which provide suggestions for this; however, proceeding with such suggestions should be driven by theory and consistent with the research hypotheses.

As the research questions being tested have become more complex, there has been a concomitant rise in the demand by reviewers and journal editors for authors to undertake more sophisticated modes of analyses. However, caution must be exercised here as SEM may not be suitable for all research questions. Reviewers and journal editors often want an author to use SEM but do not always understand that it is inappropriate in some cases. In this respect, it is important to clearly understand the nature of the research question being examined, as well as the answers that one would like to generate.

Therefore, prior to applying SEM, it is important to consider the strengths and weaknesses of SEM over other multivariate analyses. Results generated by SEM can provide evidence for causal relationships between variables. However, as SEM is a priori dependent on theory and previous empirical evidence, researchers must be aware and confident of a relationship between the variables observed and measured as well as the direction of that relationship.


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Moreover, such relationship should occur in isolation and not be influenced by other variables Kline, However, it is important to note that SEM does not prove causality: rather, it only highlights whether the hypothesized relations or model are consistent with the empirical data. SEM can test models with multiple dependent and independent variables, as well as mediating and interactive effects. SEM is able to manage with difficult data e.

For example, SEM programs have procedures that are robust against violations of normality e. It can also work with experimental and non-experimental data, as well as continuous, dichotomous, and interval data. SEM is unable to compensate for inadequate psychometric properties of measures Byrne, ; Kline, , in particular measures that are underpinned by poor reliability.

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The employment of unreliable measures or the use of a single measure to reflect the latent variable is likely to reduce the amount of variability in the latent variable, thus increasing measurement error. Similarly, it cannot compensate for the limitations of the research design nor its methodology. SEM requires a large sample size.

Still, it should be noted that in populations where this is not always feasible, there are ways to overcome the shortage of participants see the section on parceling above. SEM rejects theory and models on the basis of the global fit statistics. It is possible for the relations between variables to be significant although the model yields a poor fit, thus indicating that the model does not fit the data. Before rejecting the model, researchers should consider checking for errors in data or violations of SEM assumptions.

Another proposed method to improve fit indices is to estimate as many parameters as there are data-points just identified model ; however, this renders the data meaningless, explains nothing more about the tested model and, as such, should be avoided Mulaik et al.

8 Best SEM images in | Structural equation modeling, Research, Author

In this step researchers should develop and formulate a research question that is grounded in theory and underpinned by empirical evidence. Moreover, as SEM functions using a priori hypotheses, it is critical that the measurements used to capture and reflect the chosen construct are valid and reliable for use within the given population. Accordingly, based on theory and evidence, researchers should formulate a testable model or a number of competing testable models.

This testable model is then specified. Specifically, the relationships between variables at both the measurement and structural model should be noted. In the current example, the research problem was aimed at examining the applicability of the components underlining the transdiagnostic cognitive-behavioural theory of eating disorders within an athletic population. The transdiagnostic cognitive-behavioural theory of eating proposes the mechanisms that cause and maintain eating disorders be it Anorexia Nervosa, Bulimia Nervosa, or Eating Disorder Not Otherwise Specified are the same Fairburn et al.

Specifically, Fairburn et al. This gap needs to be addressed by utilizing appropriate research and statistical applications that can help extension professionals to explain technical findings in a simple and straightforward manner to their clients. Through evidence-based extension work practices, extension work professionals can connect clients with research-based information, which will eventually improve their overall well-being.


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Current and future trends in extension work practices call for more participatory knowledge and technology transfer approaches as compared to the old 'top down' model. Extension professionals should thus equip themselves with knowledge on adult education and extension education research, as well as substantial technical knowledge in planning, implementation and evaluation of extension programs.

Given the need for a combination of research-driven, participatory and demand-driven extension work practices, this inaugural lecture will focus on how extension education research and organizational extension work practices can be enhanced through the utilization of appropriate statistical applications, specifically Structural Equation Modeling SEM. Based on my personal and professional knowledge and experience, the first part of this lecture will be an elaboration on how extension education stakeholders can take advantage of the powerful statistical analyses provided by Structural Equation Modeling SEM in their research and development projects.

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Secondly, I will highlight the importance of equipping extension professionals with adequate knowledge and skill in the application and interpretation of SEM outputs. Finally, I will address how the incorporation of SEM analysis can lead to better development and enrichment of evidence-based extension work practices. Malaysia and the world are faced with the task of providing more evidence-based extension education work practices.

Positive symptoms were measured by hallucinations, disorganized thoughts, and unusual thought content. Dichotic listening was measured by accuracy of sounds identified in each ear according to the condition in which the patients heard the sounds. In terms of the relationship between dichotic listening and schizophrenia, duration of schizophrenia and number of positive symptoms were related to accuracy of sound detection. That is, patients who have had schizophrenia for a longer duration and experience more positive symptoms, the poorer their identification of vowel-consonant blends.

These results support findings from previous research suggesting impaired language processing and structural abnormalities in the left superior temporal gyrus for patients with schizophrenia.

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Application of Structural Equation Modeling in Educational Research and Practice

The advantage of this research over other studies is that it examines three types of positive symptoms and duration of schizophrenia simultaneously, rather than separately, in relation to dichotic listening. In other words, the model also suggests that patients with many positive symptoms are likely to have difficulty identifying sounds accurately, especially if the duration of the illness is long. Greater confidence can be placed in these results than other regression models because more than one indicator of the constructs of interest was used in the model.

Identifying basic underlying latent variables positive symptoms and dichotic listening is another advantage over interpreting simple correlations among measured variables. Because this is a cross-sectional model, it is unknown whether the language processing deficit existed before, at the same time, or after the onset of schizophrenia. Direction of cause in the model is, thus, unknown. Given that time was an important variable in this model, we can explore the advantages of longitudinal modeling, or measuring variables at more than one point in time.

That is, using the same measures of positive symptoms of schizophrenia and language processing taken at Time 1 and Time 2, path coefficients between the two latent variables at both points in time can be simultaneously examined to determine those that are significant. Previously a cross-lagged design would have been used whereby positive symptoms at Time 1 are correlated with language processing at Time 2. This correlation is then compared to the correlation between language processing at Time 1 and positive symptoms at Time 2.

This comparison does not account for autoregression, does not include latent variables, and cannot be easily applied to multiple time points or multiple variables. An alternate method is multiple regression analysis whereby the positive symptoms of schizophrenia and language processing measured at Time 1 are used to predict language processing at Time 2. The magnitude of the regression weights would indicate the strength of the relationship between schizophrenia and language processing while controlling for initial language processing. Although this takes autoregression into account and includes multiple measured variables, latent variables cannot be used, and reciprocal patterns impact of language processing on positive symptoms cannot be examined.

Epub Application Of Structural Equation Modeling In Educational Research And Practice

A second example of SEM is of a model in population health that depicts the relationship between childhood victimization and school achievement. Beran and Lupart postulated that children who are targeted by acts of aggression from their peers may be at risk for poor achievement [ 31 ].

This argument is supported by Eccles' Expectancy-Value theory [ 32 ]. Accordingly, achievement involves the culture, socialization, and the environmental "fit" of schools for students.

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When children are exposed to positive experiences within this environment they are likely to gain academic and social competence [ 33 ]. Exposure to aggressive initiations from peers, however, may reduce a child's sense of competence for interpersonal interactions. Given that learning at school takes place in a social environment these harmful interactions may reduce learning behaviors such as volunteering answers and asking questions.