When creating a weight it is possible for there to be a mis-match between the targets that have been specified and the actual sample sizes in the data set which prevents the weight from being calculated. This page describes the kinds of mis-match that can occur and the steps that can be taken to allow the weight to be calculated. When creating a weight you can view diagnostic information about the weight in the bottom right of the weight window.
Problems with weights often occur when the samples in the categories used by the weight are small or empty. Errors that are encountered when constructing a weight can indicate that there are problems with the sample, or that the weight scheme is too complicated. The general approach to solving problems with weighting is to simplify the weighting scheme by either reducing the number of questions that are being used, or by consolidating categories within the input questions, see How to Make Merged Categories Appear in Targets when Weighting. For information on things to consider when weighting your survey see How to Weight a Survey, and when you are ready to construct your weight in Q see How to Configure a Weight from Variable(s).
You may run a survey that does not have any respondents in a particular target weighting group. In this case, it is impossible to create the weight because there is no respondent to apply the target weight to. If a category or combination of categories is empty in the data file, then it is logically impossible for that category to represent more than 0% of the data when weighted. As a result, if you assign a target value to that category which is not zero then the weight cannot be calculated.
In this situation, you will see a message like:
This message tells us that there are two categories in the data which are empty, Males, Less than 18 and Females, Less than 18, but that these categories have targets assigned to them. Approaches to solving this problem include:
- Enter target values of 0 for the empty categories. This will require the other targets to be adjusted appropriately according to the design of the research.
- Merge the empty categories with other categories. Merging is done by dragging-and-dropping categories in the table where weight targets are entered.
For example, let's say you attempt to create a weight with the following variable. Notice that the first category has zero cases.
Now, suppose you attempt to create the following weight which includes the empty categories:
Notice the error message at the bottom along with a suggestion about how to correct it, which in this case is to set the targets for the empty categories to 0.
Another solution is to merge the empty categories with other categories that have sample. Merging is done by dragging-and-dropping categories in the table where weight targets are entered.
Rim Weighting does not Converge
When the weighting involves multiple adjustment variables Q uses an algorithm called Rim Weighting or raking to estimate the weight for each respondent so that each of the different sets of targets is achieved. Because each set of targets is independent of the others, the situation can arise where one set of weight targets contradicts one of the other sets of targets by calculating two different sample sizes for the same group of respondents. When a contradiction like this occurs, it is logically impossible to calculate an appropriate weight.
In cases of this kind you will see the following message:
Diagnosing and Solving the Problem
If you have used more than two adjustment variables then the first step to solving the problem is to identify which pair of questions is in conflict. It is possible that three or more questions can be in conflict with one another. The following process will allow you to identify which adjustment variables are contributing to the conflict:
- Use Insert Variables > Weight to create your weight, include as many weight sets in the weight as possible without getting an error in the Diagnostics Report at the lower right.
- Save the variable by clicking the New Weight at the lower right.
- For each remaining adjustment variable:
- Right-click the weight variable in the Variables and Questions tab and select Edit Weight.
- Complete the weight, ensuring that all targets are entered for all adjustment variables.
- Remove the current adjustment variable.
- Check the Diagnostics Report at the bottom right. If an error is still generated then you know that the adjustment variable that you removed is not the source of the conflict in the weight. If an error is not generated then the adjustment variable that has been removed is contributing to the conflict.
- Repeat these steps as needed to identify the source of the conflict.
The final step is to try and identify the reason why the questions that have been identified above are in conflict with the weight targets. There is no general solution, and the problem can be tricky to identify. Some trial-and-error is required. One approach is to:
- Create a cross-tab between each pair of questions identified above, and show the n in the Statistics - Cells.
- Examine the sample sizes in the tables and try to identify those which are particularly small (e.g. n = 1 or n = 2) as these are most likely to present problems.
- Create your weight again.
- In the Edit weight window, merge the category with the small sample size with another category, enter a target for the combined category and then check the Diagnostics Report. If the same error still occurs then a different combination of categories is required, and the process should be repeated. The choice of category to merge will depend both on what makes the most sense from the point of view, and which combination of categories solves the issue.
The following example illustrates a mis-match of weight sets which is particularly extreme. Here, a weight is constructed from three weight sets, one for each of Age, Gender, and Income. The weight generates the error message shown above.
The first step is to try to determine which questions are at fault by creating the weight with each of the weight sets left out. Doing this, we find that leaving Gender out of the weight set still results in the same error, but removing either Age or Income does not. From this we can conclude that Age and Income are in conflict with one another.
The next step is to inspect Age and Income to determine where the fault lies. Creating a cross-tab and showing Statistics - Cells > n shows us that there is a single respondent who is in the 50 or more age bracket and $45,001 to $60,000 income bracket. This is the source of the contradiction in the targets.
The targets for age predicts that this person should account for 20% of the weighted sample, whereas the targets for income predict that this person should account for 30% of the weighted sample. Since these two conditions cannot be true at the same time, there is no way to calculate a weight for these targets. The solution is to merge one of the trouble categories with another, so that this respondent is no longer the sole occupant of two categories with different targets. In this case it might be appropriate to merge $45,001 to $60,000 with Less than $40,000, or merge 50 or more with 35 to 49. Both of these options will allow the weight to be computed.