userfriendlyscience (version 0.7.2)

prevalencePower: Power analysis for establishing a prevalence

Description

This function can be used to establish how many participants are required to establish a prevalence rate with a given margin of error.

Usage

prevalencePower(expectedPrevalence,
                marginOfError = 0.05,
                conf.level = 0.95)

Arguments

expectedPrevalence

The expected prevalence.

marginOfError

The desired precision.

conf.level

The confidence of the confidence interval.

Value

The required number of participants.

Details

Note that when uncertain as to the expected prevalence, it's better to assume a prevalence closer to 50%. Prevalences closer to 0% or 100% are easier to detect and therefore have more power.

See Also

convert.percentage.to.se

Examples

# NOT RUN {
### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 10% wide:
prevalencePower(.1);

### Required participants for detecting a prevalence of 10%
### with a 95% confidence interval of 4% wide:
prevalencePower(.1, .02);

### Required participants for detecting a prevalence of 60%
### with a 95% confidence interval of 10% wide:
prevalencePower(.6);
# }