Simulate the margin of error for an overall survey or a single question

For the overall survey: given weights and/or a sample size, the maximum margin of error gives us the upper bound on the margin of error for all y/n questions across the entire survey – by estimating it for a hypothetical y/n question with 50/50 (the inherently most uncertain population estimand).

Usage

simu_moe(
  .data = NULL,
  weights = NULL,
  n = NULL,
  x = NULL,
  conf = 0.95,
  sims = 5000,
  random = FALSE
)

Arguments

.data

data frame to optionally pipe in dplyr-style (see example).

weights

vector of sample weights used to adjust the sample (optional if n or x is specified).

n

the sample size of the suvrey (not needed if weights or x is specified).

x

the particular question to estimate margin of error for (if not specified, estimate the survey's MOE instead).

conf

the level of statistical confidence to estimate the error.

sims

number of bootstrap re-samples to perform in order to simulate the sampling distribution.

random

whether or not to return different, random estimates every time.

Value

The survey margin of error (MOE) or "modeled error" for the overall survey or question of interest.

Details

For a single question: given a vector of sample responses and/or weights, the margin of error estimates the margin of error by simulating the sampling distribution.

Estimates via simulation tend to be more conservative (larger) than using the formulaic margin of error (est_modeled_error) as it incorporates more variance from the weights.

This method is recommended for non-probability surveys with unknown sampling mechanisms (e.g. river samples).

Examples

data(ev22)
if (FALSE) { ## not run

# Unweighted maximum margin of error
simu_moe(n = nrow(ev22))
simu_moe(weights = rep(1, nrow(ev22)))

# Weighted maximum margin of error
simu_moe(weights = ev22$weight_genpop)

# Unweighted margin of error for specific question
simu_moe(x = ev22$ev_heard_1)

# Weighted margin of error for specific question
ev22 %>%
  filter(!is.na(weight_genpop)) %>%
  simu_moe(x = ev_heard_1, weights = weight_genpop)
}