Creating a categorical response field
The response field should be categorical, with one value representing all positive responses. Any other non-missing value is assumed to be a negative response. If the response field represents a continuous (scale) value, such as number of purchases or monetary amount of purchases, you need to create a new field that assigns a single positive response value to all non-zero response values.
- If negative responses are recorded as 0 (not blank, which is treated
as missing), this can be computed with the following formula:
where NewName is the name of the new field and OldName is the name of the original field. This is a logical expression that assigns a value of 1 to all non-missing values greater than 0, and 0 to all non-missing values less than or equal to 0.
- If no value is recorded for negative responses, then these values
are treated as missing, and the formula is a little more complicated:
In this logical expression, all non-missing response values are assigned a value of 1 and all missing response values are assigned a value of 0.
- If you cannot distinguish between negative (0) response values and missing values, then an accurate response value cannot be computed. If there are relatively few truly missing values, this may not have a significant effect on the computed response rates. If, however, there are many missing values -- such as when response information is recorded for only a small test sample of the total dataset -- then the computed response rates will be meaningless, since they will be significantly lower than the true response rates.
To Create a Categorical Response Field
- From the menus choose:
- For Target Variable, enter the new field (variable) name.
- If negative responses are recorded as 0, for the Numeric Expression enter OldName>0, where OldName is the original field name.
- If negative responses are recorded as missing (blank), for the Numeric Expression enter NOT(MISSING(OldName)), where OldName is the original field name.