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Commercial weighting tends to be different to that used in academic studies which is in-turn, different to that used in official statistics and other government statistics. This book focuses on the commercial applications of weighting.^{1}

A key aspect of commercial weighting is that typically one or a small number of weight variables are created for use in many subsequent analyses. Where there is only a single analysis of primary interest, such as a single regression model or an opinion poll, alternative approaches may be preferable, including:

- Performing a regression with the adjustment variables that would have been used to create the weights (see the next chapter) included as predictors.
- Using MrP (“Mister P”). See: http://www.misterp.org/papers.html.

**Weights, adjustment variables, and targets**

Worked example Adjustment variables and targets Categorical adjustment variables

Composite categorical adjustment variables

Numeric adjustment variables

Using market share as a numeric adjustment variable

Using dates (waves) in composite variables

Selecting adjustment variables

The weighting variable(s)

**Creating sampling weights **

Design weights

Propensity weights (propensity score adjustment)

Cell weights

Rim weights/Raking

Calibration weights

Calibrated weight

Volumetric weights

Weights for different purposes

Combining weights

More exotic type of weights

Frequency weights

Variance weights

Replicate weights

Replication weights

Expansion weights

Analytic weights

Importance weights

Self-weighting samples

**Common mistakes **

No sample for a target group

Targets for categorical adjustment variables that don’t add up to 1

Categorical targets that add up to 0.99999 or 1.00001

Impossible numeric targets

Inconsistent categorical targets

Inconsistent numeric targets

Ignoring measurement error

Using weight factors instead of targets

**Checking weights **

Case study

Weighted tables of adjustment variables

The average of the weight variable

The range of the weight variable

The distribution of the weight variable

Effective sample size

**Improving a weight **

The bias versus error tradeoff

Changing the targets

Trimming (bounds on weights)

**Analysis of weighted data **

Bias and statistical inference

Approaches to incorporating weights in statistical analyses

Variance estimation

Resampling

Weight calibration

Using the observed sample size

Modifying the weight to have an average weight of 1

Use the weight as is

Does choice of approach make a big difference? Yes

Approaches available by stat package

Q and Displayr

SPSS Statistics

R

When weighting does and does not work

**Weighting in Displayr**

Applying a weight to an analysis

Weighted versus unweighted data

Effective sample size

Using weights created in other software

Cell weighting with a single variable

Creating a composite categorical adjustment variable

More complex weights

**Weighting in Q**

Applying a weight to an analysis

Weighted versus unweighted data

Effective sample size

Using weights created in other software

Cell weights

Rim weights

More complex weights

Weighting in R

Applying a weight to an analysis

Creating weights