samplesize4surveys (version 4.1.1)

ss4ddp: The required sample size for estimating a double difference of proportions

Description

This function returns the minimum sample size required for estimating a double difference of proportion subjecto to predefined errors.

Usage

ss4ddp(
  N,
  P1,
  P2,
  P3,
  P4,
  DEFF = 1,
  conf = 0.95,
  cve = 0.05,
  me = 0.03,
  T = 0,
  R = 1,
  plot = FALSE
)

Arguments

N

The population size.

P1

The value of the first estimated proportion at first wave.

P2

The value of the second estimated proportion at first wave.

P3

The value of the first estimated proportion at second wave.

P4

The value of the second estimated proportion at second wave.

DEFF

The design effect of the sample design. By default DEFF = 1, which corresponds to a simple random sampling design.

conf

The statistical confidence. By default conf = 0.95. By default conf = 0.95.

cve

The maximun coeficient of variation that can be allowed for the estimation.

me

The maximun margin of error that can be allowed for the estimation.

T

The overlap between waves. By default T = 0.

R

The correlation between waves. By default R = 1.

plot

Optionally plot the errors (cve and margin of error) against the sample size.

Details

Note that the minimun sample size (for each group at each wave) to achieve a particular margin of error \(\varepsilon\) is defined by: $$n = \frac{n_0}{1+\frac{n_0}{N}}$$ Where $$n_0=\frac{z^2_{1-\frac{\alpha}{2}}S^2}{\varepsilon^2}$$ and $$S^2 = (P1 * Q1 + P2 * Q2 + P3 * Q3 + P4 * Q4) * (1 - (T * R)) * DEFF$$ Also note that the minimun sample size to achieve a particular coefficient of variation \(cve\) is defined by: $$n = \frac{S^2}{(ddp)^2cve^2+\frac{S^2}{N}}$$ And \(ddp\) is the expected estimate of the double difference of proportions.

References

Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas

See Also

ss4dp

Examples

# NOT RUN {
ss4ddp(N=100000, P1=0.05, P2=0.55, P3= 0.5, P4= 0.6, cve=0.05, me=0.03)
ss4ddp(N=100000, P1=0.05, P2=0.55, P3= 0.5, P4= 0.6, cve=0.05, me=0.03, plot=TRUE)
ss4ddp(N=100000, P1=0.05, P2=0.55, P3= 0.5, P4= 0.6, DEFF=3.45, conf=0.99, 
cve=0.03, me=0.03, plot=TRUE)
ss4ddp(N=100000, P1=0.05, P2=0.55, P3= 0.5, P4= 0.6, DEFF=3.45, conf=0.99,
 cve=0.03, me=0.03, T = 0.5, R = 0.9, plot=TRUE)

#################################
# Example with BigLucyT0T1 data #
#################################
data(BigLucyT0T1)
attach(BigLucyT0T1)

BigLucyT0 <- BigLucyT0T1[Time == 0,]
BigLucyT1 <- BigLucyT0T1[Time == 1,]
N1 <- table(BigLucyT0$SPAM)[1]
N2 <- table(BigLucyT1$SPAM)[1]
N <- max(N1,N2)
P1 <- prop.table(table(BigLucyT0$ISO))[1]
P2 <- prop.table(table(BigLucyT1$ISO))[1]
P3 <- prop.table(table(BigLucyT0$ISO))[2]
P4 <- prop.table(table(BigLucyT1$ISO))[2]
# The minimum sample size for simple random sampling
ss4ddp(N, P1, P2, P3, P4, conf=0.95, cve=0.05, me=0.03, plot=TRUE)
# The minimum sample size for a complex sampling design
ss4ddp(N, P1, P2, P3, P4, T = 0.5, R = 0.5, conf=0.95, cve=0.05, me=0.03, plot=TRUE)
# }