Weighting - Create Weight

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This is the R-based approach to computing weights, that can be applied to tables and other analyses to adjust over- and under-representation of groups in the sample relative to the population. It is recommended to read How to Weight Survey Data for more information. The min/max/iterations fields in the OPTIONS section are using alongside the "repeated raking and trimming" method of trimming weights.

Applying weights using this method involves three steps:

  1. Configuring the weight (an R Output, accessed via Insert > More > Weighting > Create Weight). Key Options for this are discussed below.
  2. Generating the weighting variable (R Variable, created by selecting the output at (1) above and then going to Insert > More > Weighting > Save Variable)
  3. Applying the variable made at (2) above, to whatever outputs (using the Object Inspector of the relevant target outputs: pages, tables, charts, visualizations)

Example

Options

Filter(s) If a filter is selected, the weight that is created will only apply to the filtered cases. Other cases will have missing values (NA).
Weight An (optional) existing weight. If supplied, the new weight will be as close as possible[1] to this existing weight.

CATEGORICAL ADJUSTMENT VARIABLES

Adjustment variables Categorical or text variables that are to be used in creating weights. See How to Weight Survey Data for more information.
Add variable targets One such button will appear for each adjustment variable. When clicked, the targets are added into a spreadsheet (by typing or pasting). They are entered in the following format.

AgeTargets.PNG

NUMERIC ADJUSTMENT VARIABLES

Adjustment variables Numeric variables that are to be used in creating weights. See How to Weight Survey Data for more information.
Add variable targets One such button will appear for each adjustment variable. When clicked, the targets are added into a spreadsheet (by typing or pasting).

OPTIONS

Minimum Weight The lowest value that a weight will, ideally, have for any case in the data. It is not guaranteed that a weight will be greater than this value, as after application of this minimum, the weight is than divided by its average, so that it takes an average value of 1.
Maximum Weight The maximum value that a weight will, ideally, have for any case in the data. It is not guaranteed that a weight will be less than this value, as after application of this maximum, the weight is than divided by its average, so that it takes an average value of 1.
Iterations The number of iterations of the algorithm applied after trimming the weight to reflect the minimum and maximum.

SAVE VARIABLE(S)

Save weighting variable Saves a new variable to the data set that contains the weights.

Code

form.group({label: "Categorical adjustment variables", expanded: true})
var cat_adj_vars = form.dropBox({name: "formCategorical",
                                 label: "Adjustment variables:", 
                                 types: ["Variable"],
                                 multi: true,
                                 required: false}).getValues();
var target_controls = [];
for (var i = target_controls.length; i <  cat_adj_vars.length; i++)
    target_controls.push(form.dataEntry({name: "formCategoricalTarget" + (i + 1),
                label: "Add variable targets " + (i + 1), 
                edit_label: "Edit variable targets " + (i + 1), 
                default_value: [["Target label 1",".5"],["Target label 2",".5"]],
                required: true,
                prompt: "Target percentages, proportions, or values. Needs to match the input Adjustment Variable."}))
form.group({label:"Numeric adjustment variables", expanded:true})
var num_adj_vars = form.dropBox({name: "formNumeric",
                                 label: "Adjustment variables:", 
                                 types: ["Variable: numeric"],
                                 multi: true,
                                 required: false}).getValues();
var num_target_controls = [];
for (var i = num_target_controls.length; i <  num_adj_vars.length; i++)
    target_controls.push(form.textBox({name: "formNumericTarget" + (i + 1),
                label: "Target average variable " + (i + 1), 
                required: true,
                prompt: "Target average"}))
form.group({label:"Options", expanded: true})
form.textBox({name: "formMinimumWeight",
              label: "Minimum weight",
              required: false,
              default_value: 0,
              prompt: "A weight will be created with a minimum of the specified value (if possible)"})
form.textBox({name: "formMaximumWeight",
              label: "Maximum weight",
              required: false,
              default_value: 1000,
              prompt: "A weight will be created with a minimum of the specified value (if possible)"})
form.numericUpDown({name: "formTrimIterations",
              label: "Iterations",
              increment: 1,
              minimum: 0,
              maximum: 1000,
              default_value: 20,
              prompt: "High values can, in some circumstance, improve the extent to which the lower and upper bounds are satisfied."})


# Reading targets
numeric.targets = rep(NA, length(formNumeric))
for(i in seq_along(numeric.targets))
    numeric.targets[i] = as.numeric(get0(paste0("formNumericTarget", i)))
categorical.targets = vector("list", length(formCategorical))
for(i in seq_along(categorical.targets))
    categorical.targets[[i]] =  get0(paste0("formCategoricalTarget", i))

sampling.weight = flipData::Calibrate(formCategorical,
                            categorical.targets, 
                            formNumeric,
                            numeric.targets, 
                            lower = as.numeric(get0("formMinimumWeight")),
                            upper = as.numeric(get0("formMaximumWeight")),
                            input.weight = QPopulationWeight,
                            subset = QFilter,
                            always.calibrate = FALSE,
                            trim.iterations = formTrimIterations)

References

  1. Deville, J.-C. and Särndal, C.-E. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87, 376-382