» Sifting Through Outcomes

Once data or information has been collected for a research project, effort needs to be put into understanding the outcomes. Certain research outcomes or findings are simple and straightforward, such as reporting on how many respondents answered “yes” to a particular question. Other outcomes or findings, however, are more complex and require additional, or deeper, analysis to fully understand.

» Handling The Data

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”.

» Digging Deeper

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:

• Regression/Logistic Regression
• HLM Modeling
• Discriminant Analysis
• Factor Analysis
• Cluster Analysis
• Perceptual Mapping
• Conjoint/Discrete Choice
• Max-Diff