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Design Effect

Source: R/design_effect.R
design_effect.Rd

Estimate the design effect using various methods. This is an S3 generic that dispatches on the class of x.

Usage

design_effect(x = NULL, ...)

# S3 method for class 'numeric'
design_effect(
  x,
  ...,
  y = NULL,
  x_cal = NULL,
  prob = NULL,
  strata_id = NULL,
  cluster_id = NULL,
  stages = NULL,
  method = "kish"
)

# Default S3 method
design_effect(x = NULL, ..., delta = NULL, psu_size = NULL, method = "cluster")

Arguments

x

A numeric vector of survey weights (for diagnostic methods), or NULL (for the "cluster" planning method).

...

Additional arguments passed to methods.

y

Outcome variable (required for "henry", "spencer", "cr").

x_cal

Calibration covariate (required for "henry").

prob

1-draw selection probabilities (required for "spencer").

strata_id

Stratum IDs (required for "cr").

cluster_id

Cluster IDs (required for "cr").

stages

Integer vector of stages per stratum (required for "cr").

method

For numeric weights: one of "kish" (default), "henry", "spencer", or "cr". For planning (no weights): "cluster" (default and only option).

delta

ICC / homogeneity measure, scalar or svyplan_varcomp (extracts delta[1]).

psu_size

Mean cluster size (scalar).

Value

For "kish", "cluster", "henry", "spencer": a numeric scalar. For "cr": a list with $strata (data frame) and $overall (numeric scalar).

Details

The "kish" method uses only weights and produces a single survey-wide DEFF. The "henry", "spencer", and "cr" methods are outcome-dependent: they require y, and the resulting DEFF varies by outcome variable.

Methods (by class)

  • design_effect(numeric): Method for numeric weights vector.

  • design_effect(default): Planning method (no weights needed).

References

Kish, L. (1965). Survey Sampling. Wiley.

Henry, K. A. and Valliant, R. (2015). A design effect measure for calibration weighting in single-stage samples. Survey Methodology, 41(2), 315–331.

Spencer, B. D. (2000). An approximate design effect for unequal weighting when measurements may correlate with selection probabilities. Survey Methodology, 26(2), 137–138.

Chen, S. and Rust, K. (2017). An extension of Kish's formula for design effects to two- and three-stage designs with stratification. Journal of Survey Statistics and Methodology, 5(2), 111–130.

See also

effective_n() for effective sample size, varcomp() for estimating inputs to the "cluster" method.

Examples

# Kish design effect from weights
set.seed(2)
w <- runif(100, 1, 5)
design_effect(w, method = "kish")
#> [1] 1.160423

# Planning: cluster design effect
design_effect(delta = 0.05, psu_size = 25, method = "cluster")
#> [1] 2.2