Significance of Model testing
Model testing, as defined in Environmental Sciences, involves using AMOS 28.0 to validate a proposed theoretical model. This process focuses on examining path coefficients and testing hypotheses related to different variables that influence purchase intention. The goal of model testing is to confirm the relationships and strengths of these variables within the established theoretical framework using AMOS 28.0.
Synonyms: Assessment, Evaluation, Validation, Verification, Examination, Trial, Experimentation, Analysis, Model validation, Model evaluation, Model assessment, Model verification, Testing
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
The concept of Model testing in scientific sources
Model testing, using AMOS 28.0, validates a theoretical model by assessing path coefficients and hypotheses. This process verifies relationships between variables and purchase intention.
From: Sustainability Journal (MDPI)
(1) Model testing is one of the methodologically statistical analysis that the research draws upon, which also employs Pearson’s correlation, and multivariate regression executed through specialized statistical software.[1] (2) Model testing used the data from 2009 to 2017 to test the model to ensure that the model, parameters, and codes are correct.[2] (3) The model testing in this article consists of two steps, namely testing the measurement model and testing the structural model, ensuring a comprehensive evaluation of the research model.[3] (4) It was used for structural model testing, to estimate the hypothetical relationship of the proposed model, and the maximum likelihood method was used.[4] (5) A research method involving scaled-down physical experiments to simulate and analyze the behavior of pile foundations in expansive soil.[5]
From: International Journal of Environmental Research and Public Health (MDPI)
(1) Model testing involves the use of statistical techniques to evaluate the relationships between variables and determine whether a proposed theoretical model is supported by empirical data.[6] (2) This is part of a software system for model development and simulation in agricultural systems research, called APSIM.[7]