這是我們在2017年為加拿大溫哥華SFU的學弟學妹們寫的統計R語言代寫範文，開頭部分首先介紹文章的基本信息，本文節選R語言代寫數據分析部分進行展示。在這一部分，選擇了GEM數據集（全球創業和發展指數）來構建結構方程模型（SEM）。 在開始處理數據之前，需要解決幾個問題。 首先，每列代表什麼意思？ 找出第三方專利等內在含義可以幫助我們理解這些因素之間的關係並建立合適的模型。 其次，如何區分潛在的內生變量和潛在的外生變量？ 為了找出潛在因素，需要用探索性因素分析和主成分分析來重新分類這些變量。
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.
中間body部分開始進行論證，在過程中要用到文字和模型，結構方程模型（SEM）是一種基於統計分析技術的研究方法，主要用於解決一些社會科學研究的多元問題。 在研究中，它用於探索和分析複雜的多元研究數據。 與傳統統計方法相比，SEM具有獨特的優勢。 在社會科學與經濟，市場，管理等研究領域，前者無法處理多個結果之間的潛在變量和關係，但SEM消除了上述障礙。
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.
最後分析數據處理結果，CFI是0.923，數字接近1，表明模型是可以接受的。 第二個指標RMSEA為0.214，遠離1，接近於0.一些文獻資料甚至認為該數量應該小於0.06，與此相比，該模型不適用。 其原因可能是內生潛變量僅由一個指標衡量。 原則上，結構方程模型不允許發生這種情況。 第三個指數AGFI是0.712，表示模型擬合是可接受的。
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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