阿莫斯為您提供了功能強大且易於使用的結構方程模型（SEM）軟體。
建立更加逼真的模型，如果你使用標準的多元統計分析或多元回歸模型，單獨。
使用AMOS，你指定，估計，評價，並提出您的模型在一個直觀的路徑圖，以顯示虛擬變量之間的關係。
這使您可以測試並確認索賠的有效性，如「價值驅動忠誠度」在幾分鐘內，而不是幾小時。
建立結構方程模型更準確地比標準的多元統計模型，使用直觀的拖放和拖放功能
獲得新的見解，觀察和潛變量
阿莫斯使您能夠構建模型，更真實地反映複雜的關係，有能力使用觀察到的變量，例如像「滿意度」的調查資料或潛變量來預測任何其他數值變量。
結構方程模型，有時也被稱為路徑分析，說明您獲得額外的洞察因果模型和變量關係的力量。
基於貝葉斯估計的擴大統計專案
與Amos，您可以進行有序的分類和刪失資料估計，使您能夠：
非數值資料的基礎上建立一個模型，而無需指定數值分數的資料
刪失資料，而無需作出假設，除標準的工作
您還可以歸咎於有序分類資料或刪失資料的數值，當一個人需要，這樣你就可以建立一個完整的數值資料。
或是，在新的資料集缺失值的插補值。您也可以選取缺失或部分缺失資料的潛變量模型的估計後驗預測分佈，以確定可能的值。
Amos provides you with powerful and easy-to-use structural equation modeling (SEM) software. Create more realistic models than if you used standard multivariate statistics or multiple regression models alone. Using Amos, you specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables. This enables you to test and confirm the validity of claims such as “value drives loyalty” in minutes, not hours.
Build structural equation models with more accuracy than standard multivariate statistics models using intuitive drag-and-drop functionality
Gain new insights using observed and latent variables
Amos enables you to build models that more realistically reflect complex relationships with the ability to use observed variables such as survey data or latent variables like “satisfaction” to predict any other numeric variable. Structural equation modeling, sometimes called path analysis, helps you gain additional insight into causal models and the strength of variable relationships.
Expanded statistical options based on Bayesian estimation
With Amos, you can perform estimation with ordered-categorical and censored data, enabling you to:
Create a model based on non-numerical data without having to assign numerical scores to the data
Work with censored data without having to make assumptions other than normality
You can also impute numerical values for ordered-categorical data or censored data, so you can create a complete numerical dataset when one is required. Or, impute values for missing values in the new dataset. You also have the option of estimating posterior predictive distributions to determine probable values for missing or partially missing data in a latent variable model.
結構方程模型（SEM）軟體 IBM SPSS Amos 21 Homepage:: https://www-01.ibm.com/software/analytics/spss/products/statistics/amos/
結構方程模型（SEM）軟體 IBM SPSS Amos 21 : 56.1 MB