[代写范文]加拿大SFU 统计R语言代写

这是我们在2017年为加拿大温哥华SFU的学弟学妹们写的统计R语言代写范文,开头部分首先介绍文章的基本信息,本文节选R语言代写数据分析部分进行展示。在这一部分,选择了GEM数据集(全球创业和发展指数)来构建结构方程模型(SEM)。 在开始处理数据之前,需要解决几个问题。 首先,每列代表什么意思? 找出第三方专利等内在含义可以帮助我们理解这些因素之间的关系并建立合适的模型。 其次,如何区分潜在的内生变量和潜在的外生变量? 为了找出潜在因素,需要用探索性因素分析和主成分分析来重新分类这些变量。

As Part A has completed the data cleaning, data analysis will be followed. In this part, I choose GEM dataset (Global entrepreneurship and development index) to construct a structural equation model(SEM). Before I start to handle the data, several questions need to be settled. First of all, what is the meaning of each column represents for? Figure out the Intrinsic meaning, such as Tertiary, Patents, may help us understand the relationship among these factors and build appropriate model. Second, how to distinguish latent endogenous variables and latent exogenous variables? In order to find out latent factors, I would like to use exploratory factor analysis and principal component analysis to reclassify these variables. Once we solve above problems, the framework of model will be accomplished. Third, how to evaluate the model has been built? A proper model can withstand the test.

 

中间body部分开始进行论证,在过程中要用到文字和模型,结构方程模型(SEM)是一种基于统计分析技术的研究方法,主要用于解决一些社会科学研究的多元问题。 在研究中,它用于探索和分析复杂的多元研究数据。 与传统统计方法相比,SEM具有独特的优势。 在社会科学与经济,市场,管理等研究领域,前者无法处理多个结果之间的潜在变量和关系,但SEM消除了上述障碍。

Structural equation model(SEM) is a research methodology based on statistical analysis technology, which is mainly used to solve some multivariate problems of social science research. In the study, it is used to explore and analyze the complex multivariate research data. Compare to traditional statistical methods, SEM has unique advantages. In the social sciences and economy, Market, management and other research areas, the former can’t deal with the latent variables and relationship between multiple results, but SEM eliminates above obstacles. Also, SEM can be used to estimate and verify latent variables and complex independent variables / dependent variables at the same time. In this case I choose SEM to analysis GEM dataset and explore the potential connection.

 

最后分析数据处理结果,CFI是0.923,数字接近1,表明模型是可以接受的。 第二个指标RMSEA为0.214,远离1,接近于0.一些文献资料甚至认为该数量应该小于0.06,与此相比,该模型不适用。 其原因可能是内生潜变量仅由一个指标衡量。 原则上,结构方程模型不允许发生这种情况。 第三个指数AGFI是0.712,表示模型拟合是可接受的。

According to the correlation theory of the evaluation model in the structural equation model, the following indexes are used to evaluate the fitting effect of the model:

(1) Relative Fit Index(CFI): The value between 0-1, the closer to 1, the model as a whole fit better;

(2) Approximate root mean square error index(RMSEA): The smaller the value the better. It is generally believed that RMSEA below 0.1 indicates good fit, Less than 0.05 means very good fit.

(3) Adjusted Goodness Index(AGFI): The value of 0-1 between the closer to 1, the overall model of the better fit

I use fitMeasures() to estimate the evaluate the fit degree of the model. Here are the results.

From the above picture, CFI is 0.923, and the number is close to 1, indicates that the  model is acceptable. The second index RMSEA is 0.214, far from 1 and close to 0. Some literature even thought the number should smaller than 0.06, compare to this ,the model fit not good. The reason may be endogenous latent variables are measured by only one indicator, which is the index. In principle, the structural equation model does not allow this to happen. The third index AGFI is 0.712,means Model fit is acceptable. Consolidate these three indicators, the model is available.

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