Online surveys provide a supple way for law departments and law firms to obtain upward evaluations or up-the-chain evaluations. An upward evaluation applies to someone’s manager whereas an up-the-chain evaluation also includes the manager of that manager. Bear in mind the following points about this potent kind of survey.
Time Period: When you ask employees to evaluate their managers, you should narrow the period over which they are to reflect back on their manager’s behavior.

Everyone who surveys in the legal industry should understand and correctly use the most common measures of central tendency. Each of the following statistics – called that by mathematicians because they are calculated from the responses collected by the survey – describes in a single figure a summary characteristic of the data about where it is “centered.”
Mean/Average: The mean (a statistician’s term for average) of a group of numbers is its arithmetic average.

If you are conducting an external survey, you want as many participants as you can get, so long as they are appropriate for your research. You have e-mail addresses of the starter set of invitee/potential participants, but you would like the participant circle to be as large as possible. Here are some techniques for spreading the word.
Invitees Forward It: In your e-mail invitation for people to take the survey, you can urge them to forward the invitation to others they know who are similarly situated.

Before relying on a regression model, it is sound practice to comb the data to detect unusual days of figures. Spotting and evaluating whether to retain abnormal data helps make sure that the loss figures are neither mistakes in measurement, collection, data entry, or calculation nor are they unjustifiably warping the regression model. A potential outlier would be a day where, according to the regression model, equipment losses very poorly predict the Soldier value (number of military casualties).

All very interesting, that a linear regression model shows which of various types of Russian equipment losses are most closely associated with deaths of Russian soldiers. Beyond those findings, can we also say something about how well those equipment losses in the aggregate account for Russian casualties? Yes, we can, with the Adjusted R-Squared value calculated for the linear regression model. We explain this value further below.
Here’s the takeaway: Despite having 288 days of data and the range of equipment losses, the model only explains a bit less than a quarter of the variance in the casualties.