Our approach to data analysis is to glean everything we can from the data that has just been collected. First, data are “cleaned”, removing incomplete, corrupt, inaccurate, or even fraudulent responses. Once cleaning and editing the data is complete, we perform descriptive statistic tests (frequencies, crosstabulation) which provide a sense of the general “story”.
At times, more in-depth analyses are required. An example is when it’s important to identify group differences or to determine variables that have an impact on certain outcomes. Then, we employ one or more of the following multi-variate techniques:
Organizations need accurate information in order to be successful. We help our clients succeed by developing robust and efficient ways to collect the necessary information
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