## 2 Literature Review
### Shortcomings of Existing Survey Analysis Methods  
In addition to the literature on analysis of the HCAHPS dataset; it is also worth reviewing the methods typically used for general survey analysis. Commonly used exploratory data analysis methods such as ANOVA, regression, discriminant analysis, and factor analysis are not applicable to HCAHPS data because of its unique characteristics.  
Analysis of variance (ANOVA) is a collection of statistical methods that form an exploratory tool for explaining observations. ANOVA provides a statistical test of whether or not the means of several groups are equal [2]. For finding a specific correlation in the HCAHPS dataset, different levels of ANOVA should be combined. To this end, all possible combinations of satisfaction questions and demographic data should be exhaustively tested, which could be time prohibitive. In addition, ANOVA assumes a normal distribution of the sample observation, continuous dependent variables, and at least one categorical independent variable with two or more levels. The sample data collected for HCAHPS is not guaranteed to be normally distributed. Moreover, the demographic data which form the dependent variables are not always categorical. Given the violation of these assumptions, ANOVA may not produce reliable results for HCAHPS dataset.  
Regression analysis is a statistical tool for investigation of relationship between multiple continuous or categorical independent variables and a continuous dependent variable [10]. Regression models can be used for validating correlations, although they are usually used for predicting and forecasting. Variations of this model can be used for the HCAHPS categorical dependent variables and lead to nonlinear models. Using these interpretation, different hypothesis on the data set can be tested by using forward and backward selection of combinations of satisfaction questions and patients' demographic data. However, forward and backward selection is not efficient in the context of HCAHPS data as the search space contains a very large number of combinations of dependent and independent variables. Moreover, fitting a separate model for each satisfaction identifier cannot capture the relationships between different satisfaction questions.  
Discriminant analysis is another technique that allows for studying the difference between two or more groups of objects with respect to several variables simultaneously [11]. This model can be fitted to HCAHPS data set in terms of categorical dependent variables as the satisfaction questions and continuous and categorical independent variables such as patient's demographical data. Although this method works better than regression in terms of interpreting categorical variables, it suffers from the same deficiencies when it comes to analyzing the HCAHPS dataset.  
Factor analysis is another method that is typically applied to survey data. The main application of this method is to reduce the number of variables and to detect structure in the relationship between variables. In particular, factor analysis can be used to explore the data for patterns, confirm hypotheses, or reduce a large number of variables to a more manageable number [12]. Compared to regression and discriminant analysis, factor analysis is more suitable for an exploratory analysis of the HCAHPS dataset as it does not require a priori hypothesis; however, it has two limitations: (1) the naming of the factors can be problematic and may not accurately reflect the variables within that factor. In particular, it may not be possible to directly compile factors into a set of recommendations for the hospitals. (2) factor analysis is based on the assumption that there is a linear relationship between factors and the variables when computing correlations. The features in the HCAHPS survey data may not necessarily be linearly correlated and factor analysis cannot capture non-linear relations.