Be especially cautious in weighting data when samples sizes are small. On the other hand, if you have fewer than 100 respondents, the cost in reduced accuracy might be very great. If you have thousands of respondents, you can weight the data as much as you please and the cost in reduced accuracy is very small. Remember that the cost of weighting data is greater (in terms of reduced accuracy) when the sample size is smaller.The sampling variance, standard deviation, and standard error increase. The cost of weighting data is reduced accuracy. If you do decide to weight survey data, remember there is a price to pay.This is almost always the best and most defensible solution. If possible, always perfectly balance the sample during the sampling and screening process so that you never have to weight any data.Here are some best practices to keep in mind when you are thinking about weighting survey data. That is, instead of a respondent counting as one (1) in the cross-tabulations, that respondent might count as 1.25 respondents, or. The term 'data weighting' in most survey-related instances refers to respondent weighting (which in turn weights the data or weights the answers). In these cases, data weighting might make sense, if you want totals that accurately reflect the whole population.

It often happens that a perfectly designed sampling plan ends up with too many women and not enough men completing the survey, or too many old people and not enough young people. Videos - Leadership Strategy Interviews.Predictive Analytics & Marketing Research.

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