RIM Weighting Without the Pain

Almost every survey needs weighting. Your achieved sample never perfectly matches the population on every dimension. RIM weighting (Random Iterative Method, also called raking or iterative proportional fitting) is the standard approach: you define your target marginals, the algorithm iterates until the weighted sample matches those targets on every dimension simultaneously.

The problem is that most researchers don't have easy access to a RIM weighting tool outside of expensive software. You can do it in R. You can do it in SPSS. You can set it up in Excel with enough patience and VBA. All of those require setup time and some degree of programming knowledge.

What the tool does

Define your weighting dimensions (age, gender, region, education, whatever you need). Enter the target proportions and your achieved sample proportions. The IPF algorithm iterates until convergence and outputs the individual weights, plus three metrics that matter: design effect (DEFF), effective sample size, and weight efficiency.

DEFF tells you how much precision you lost because of weighting. A DEFF of 1.3 means your n=1,000 sample has the statistical precision of an unweighted n=769 sample. That effective sample size is what you should use for significance testing and margin of error calculations, not the raw count.

Weight efficiency is the quick-glance metric. Above 80% is good. Below 60% means your sample was so skewed that the weighting is doing heavy lifting, and you should question whether the weighted results are reliable.

Tech stack

React 18.2 + Babel CDN. The IPF algorithm is implemented in JavaScript with configurable convergence tolerance and iteration limits. DEFF uses Kish's formula. Weight distribution visualization shows the spread so you can visually spot if any single respondent is carrying too much weight (anything above 4.0 is a red flag).