本篇文章的主要内容是SEM的介绍及应用，SEM无疑是社会科学定量领域最著名的方法论之一。它的流行归功于复杂的社会理论基础。关键的潜力在于解决重大的实质性问题，以及致力于SEM的软件的简单性和可用性。然而，尽管该方法如此流行和有用，关键的论点是在传统实践中嵌入了第一代方法的使用(Bro & Smilde, 2014)。这些倾向于排除额外的实质性进展以及统计上的进展。第一代扫描电镜的关键问题是如何得到一个拟合良好的模型。此外，通常会发现对不合适的模型发起的许多修改，以确保数据的对齐。本篇美国论文代写文章由美国论文人EducationRen教育网整理，供大家参考阅读。
Without any doubt, SEM is among the most famous methodologies in the quantitative field of social science. There is attribution of its popularity with the sophisticated underlying social theory. The key potential is to address significant substantive doubts, and the simplicity and availability of software dedicating towards SEM. However, irrespective of the method being so popular and useful, the key argument is that there is embedding of first generation utilization of method within the conventional practice (Bro & Smilde, 2014). These tend to be precluding additional substantive as well as statistical advancements. The key issue in practicing first generation SEM is the attempt of attaining a well- fitted model. In addition, it is common for finding a number of modifications initiated to an ill fitted model ensuring alignment of data.
Mostly, there is supplementing of justification as post hoc for how there is alignment of the modification with the actual theoretical framework. Further ahead, the issue lies in obsessing the testing of null hypothesis, surely an issue receiving significant focus (Bandalos, 2002). There is embedment of practicing first generation SEM perceiving the view that there is worthy interpretation of a well-fitting model. Therefore, it can be stated that this method has both, advantages and disadvantages.There are a number of reasons for using SEM. SEM is inclusive of latent growth modeling, LISREL, partial least squares path analysis, path analysis and confirmatory factory analysis. These are used more often for the assessment of latent unobservable constructs. More often, they tend to be invoking a model of measurement defining latent variables by the use of several observed variables (Bowen & Guo, 2011).
It is a structural model imputing the relationships between variables that are latent. The links in constructing SEM can be considered with independent equations of regression or by approaches involved more, like the ones employing LISREL. The most common justification for using SEM in the field of social science is its ability for imputing relations between observable variables and unobserved constructs. A number of methods in SEM have been applied in the fields of education, business, sciences and other field (O’Rourke & Hatcher, 2013). The utilization of SEM method with respect to analysis does face some criticism. This is because there is lack of goodness of fit statistic with wider acceptance, and majority of the respective software tend to be offering less latitude for the analysis of error. This creates a disadvantage for SEM in context with systems for methods of regression equation, though there is limitation of the latter within the ability of fitting latent, unobserved constructs.