nPropMoe | R Documentation |

Calculates a simple random sample size based on a specified margin of error.

nPropMoe(moe.sw, e, alpha = 0.05, pU, N = Inf)

`moe.sw` |
switch for setting desired margin of error (1 = CI half-width on the proportion;
2 = CI half-width on a proportion divided by |

`e` |
desired margin of error; either |

`alpha` |
1 - (confidence level) |

`pU` |
population proportion |

`N` |
number of units in finite population |

The margin of error can be set as the half-width of a normal approximation
confidence interval, *e=z_{1-α/2}√{V(p_s)}*, or as the half-width
of a normal approximation confidence interval divided by the population proportion,
*e=z_{1-α/2}CV(p_s)*. The type of margin of error is selected by the
parameter `moe.sw`

where `moe.sw=1`

sets *e=z_{1-α/2}√{V(p_s)}* and `moe.sw=2`

sets i.e., *e=\frac{z_{1-α/2}√{V(p_s)}}{p_U}*.

numeric sample size

Richard Valliant, Jill A. Dever, Frauke Kreuter

Valliant, R., Dever, J., Kreuter, F. (2018, chap. 3). *Practical Tools for Designing and Weighting Survey Samples, 2nd edition*. New York: Springer.

`nCont`

, `nLogOdds`

, `nProp`

, `nWilson`

# srs sample size so that half-width of a 95% CI is 0.01 # population is large and population proportion is 0.04 nPropMoe(moe.sw=1, e=0.01, alpha=0.05, pU=0.04, N=Inf) # srswor sample size for a range of margins of error defined as # half-width of a 95% CI nPropMoe(moe.sw=1, e=seq(0.01,0.08,0.01), alpha=0.05, pU=0.5) # srswor sample size for a range of margins of error defined as # the proportion that the half-width of a 95% CI is of pU nPropMoe(moe.sw=2, e=seq(0.05,0.1,0.2), alpha=0.05, pU=0.5)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.