Confidence interval for the slope of means in a one-factor experimental design with a quantitative between-subjects factor

Computes a test statistic and confidence interval for the slope of means in a one-factor experimental design with a quantitative between-subjects factor. This function computes both the unequal variance and equal variance confidence intervals and test statistics. A Satterthwaite adjustment to the degrees of freedom is used with the unequal variance method.

For more details, see Section 1.11 of Bonett (2021, Volume 2)

Usage

ci.slope.mean.bs(alpha, m, sd, n, x)

Arguments

alpha: alpha level for 1-alpha confidence
m: vector of sample means
sd: vector of sample standard deviations
n: vector of sample sizes
x: vector of quantiative factor values

Value

Returns a 2-row matrix. The columns are:

Estimate - estimated slope
SE - standard error
t - t test statistic
df - degrees of freedom
p - two-sided p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval

References

Bonett DG (2021). Statistical Methods for Psychologists https://dgbonett.sites.ucsc.edu/.

Examples

m <- c(33.5, 37.9, 38.0, 44.1)
sd <- c(3.84, 3.84, 3.65, 4.98)
n <- c(10,10,10,10)
x <- c(5, 10, 20, 30)
ci.slope.mean.bs(.05, m, sd, n, x)
#>                               Estimate         SE      t    df     p        LL
#> Equal Variances Assumed:     0.3664407 0.06770529 5.4123 36.00 0e+00 0.2291280
#> Equal Variances Not Assumed: 0.3664407 0.07336289 4.9949 18.66 8e-05 0.2127008
#>                                     UL
#> Equal Variances Assumed:     0.5037534
#> Equal Variances Not Assumed: 0.5201806

# Should return:
#                               Estimate         SE      t    df
# Equal Variances Assumed:     0.3664407 0.06770529 5.4123 36.00
# Equal Variances Not Assumed: 0.3664407 0.07336289 4.9949 18.66
#                                  p        LL        UL
# Equal Variances Assumed:     4e-06 0.2291280 0.5037534
# Equal Variances Not Assumed: 8e-05 0.2126998 0.5201815