Statistical Analysis: It is important that you know how you want to analyze the data before constructing the survey. This way statistical assumptions (properties of the data that have an important influence on the validity of a statistic) can be met. Two important types of analyses are Descriptive Analysis and Inferential Analysis

Descriptive Analysis involves tabulating or compiling the data and summarizing them both visually and numerically. At the most basic level this means generating percentages and averages. This type of analysis may reveal the difference between two groups (Group "X" = 78%, while Group "Y" = 82%), but does not explain whether those differences are different enough to warrant impacting your business decisions.

Inferential Analysis provides that extra information, revealing if relationships are different enough (or similar enough) to warrant impacting your business decisions. Inferential Analysis involves applying statistics to the data to draw more general conclusions about the overall population. These conclusions may be based on the relationship between two topics.

For example, a customer satisfaction survey may center around two "satisfaction factors"- which can be derived using a statistical procedure known as factor analysis. These factors may be the core of what satisfies your customers. There are variables that predict satisfaction - which can be identified with another statistical modeling procedure known as multiple regression. Understanding which factors contribute to overall customer satisfaction and identifying the variables that predict satisfaction will reveal what is and is not working in your business.

Let us analyze your survey data for you. We will compile the results and mine the data using the appropriate statistical procedures and provide you with a practical, straightforward report with tables and graphs that you can use for your presentations.