Edit data directly in MarketSight

Using the Data View feature, cleaning or editing data in MarketSight is a snap. Each response is in one row and each question or variable is in each column. Simply click on the cell containing the data point you wish to edit, and select the response from the drop-down or enter a new one.

Data Views also allows for entire record deletion, an excellent way to remove outliers from the dataset or normalize a set of responses for easier analysis.

This is a powerful, time-saving feature that can be turned on or off for certain users or groups, so you never have to worry about unauthorized changes to your data.

Create new variables in MarketSight

Using simple point-and click tools, it's easy to create new variables based on the original questions - or variables - in your uploaded dataset. For example, if you know each respondent's age and you'd like to create certain age groups: Under 18, 18-25, 25-35, etc. It's easy to do that in MarketSight by creating a Regrouping Variable.

You can also combine several different variables in a Conditional Variable, using Boolean logic to combine age, zip code, gender, and income, for example, to create precise market segments for analysis and reporting.

MarketSight also enables you to create and save Filters and work with Multiple Response questions, where respondents "check all that apply".

Edit Codes and Labels

Once a dataset is uploaded, you have full control over how each variable, and its corresponding values, are displayed. Add or edit a variable's name for easier identification, and include the original question text in the variable's Description field.

The Edit Variable window lets you specify the appropriate category and default calculation method used for each variable. Organizing your variable list helps improve your workflow when building crosstabs, as full groups of variables can be added to a crosstab with one gesture.

MarketSight allows you to rename, or recode the values of your variables. If your dataset contains similarly structured variables, you can apply the same value name or value code changes to multiple variables at once. Specifying a Missing Value for a variable is very easy, and allows you to denote values that should be omitted from calculations.

Read more about Crosstabs.