ss_power_contrast: Sample Size and Statistical Power for a Contrast in a Fixed-Effects ANOVA

Description

Determine the necessary per-group sample size for a contrast in a one-way fixed-effects ANOVA so as to achieve a desired level of statistical power, or, alternatively, compute the achieved power for a given sample size. The contrast is specified by a vector of weights and the population means (or a population contrast value) along with the within-group variance.

Usage

ss_power_contrast(
  c_weights,
  mu = NULL,
  sigma_squared = NULL,
  psi = NULL,
  desired_power = 0.85,
  alpha_level = 0.05,
  directional = FALSE,
  n_per_group = NULL,
  print_progress = FALSE
)

Value

A data.frame with two columns, term and

value:

When solving for n (n_per_group = NULL): Rows are necessary_n_per_group, total_N, actual_power, noncentral_t_parm, and effect_size_f (Cohen's $f$ for a one-degree-of-freedom contrast).
When evaluating power (n_per_group supplied): Rows are specified_n_per_group (or NA for unequal n), total_N, actual_power, noncentral_t_parm, and effect_size_f.

The result carries the dmar_ss_power class, so

tidy and glance summarize it in broom convention; the reported size is the per-group n (or

NA when unequal group sizes are supplied).

Arguments

c_weights: Vector of contrast weights. Required to satisfy sum(c_weights) == 0, sum(c_weights[c_weights > 0]) == 1, and sum(c_weights[c_weights < 0]) == -1; that is, write the contrast in normalized fractional form so that the positive coefficients sum to 1 and the negative coefficients sum to -1. The contrast estimate is then directly interpretable as a (weighted) mean difference.
mu: Vector of population group means, of length length(c_weights). Either mu or psi must be supplied (but not both).
sigma_squared: Population within-group variance ($\sigma^2$); must be a positive number.
psi: Optional. Directly specify the population contrast value $\psi = \sum_j c_j \mu_j$ as a single number. Use this when the means themselves are not of interest, only the contrast value.
desired_power: Target statistical power for the test of the contrast (default 0.85). Used only when n_per_group is NULL.
alpha_level: Type I error rate (default 0.05).
directional: Logical. FALSE (default) yields a two-sided test. TRUE yields a one-sided test in the direction of the population contrast.
n_per_group: Optional per-group sample size at which to evaluate power. May be a scalar (the common per-group size, equal across groups) or a numeric vector of length length(c_weights) giving each group's size. When NULL (default), the function instead solves for the smallest per-group n achieving desired_power.
print_progress: If TRUE, print the trial $n$ and the corresponding power as the iterative search proceeds.

Author

Ken Kelley [email protected]

Details

Let $\psi = \sum_j c_j \mu_j$ be the population contrast. Under the usual fixed-effects ANOVA model with common within-group variance $\sigma^2$ and per-group sizes $n_j$, the standard error of the contrast estimate is $$\mathrm{SE}_{\hat\psi} = \sqrt{\,\sigma^2 \sum_j c_j^2 / n_j\,},$$ the test statistic $t = \hat\psi / \mathrm{SE}_{\hat\psi}$ follows a central t distribution with $N - a$ degrees of freedom under $H_0\!: \psi = 0$, and a noncentral t distribution with the same df and noncentrality parameter $\lambda = \psi / \mathrm{SE}_{\hat\psi}$ under the alternative.

For a two-sided test at level $\alpha$, $$\text{Power} = \Pr(t > t_{1-\alpha/2,\,df}) + \Pr(t < -t_{1-\alpha/2,\,df}),$$ computed exactly from the noncentral $t$ distribution; for a one-sided test, only the appropriate tail contributes.

Cohen's $f$ for a one-df contrast is reported as $f = |\lambda| / \sqrt{N}$, equivalently $f^2 = F_{\text{pop}} / N$ (Cohen, 1988, Ch. 8); this matches the value that pwr.f2.test expects when used with u = 1 and v = N - a.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Kelley, K., & Maxwell, S. E. (2008). Sample size planning with applications to multiple regression: Power and accuracy for omnibus and targeted effects. In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), The Sage handbook of social research methods (pp. 166--192). Sage.

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.

Examples

Run this code

# Four-group example from Maxwell & Delaney's textbook tradition: contrast
# the average of three treatment means with a fourth (control), under
# population means (90, 92, 88, 81), within-group variance 144.
#
# 1. Power achieved at n = 20 per group (total N = 80). Should be ~ .80.
ss_power_contrast(
  c_weights     = c(1/3, 1/3, 1/3, -1),
  mu            = c(90, 92, 88, 81),
  sigma_squared = 144,
  n_per_group   = 20
)

# 2. Per-group sample size needed for power = .90.
ss_power_contrast(
  c_weights     = c(1/3, 1/3, 1/3, -1),
  mu            = c(90, 92, 88, 81),
  sigma_squared = 144,
  desired_power = 0.90
)

# 3. Same effect size specification using a directly given psi.
ss_power_contrast(
  c_weights     = c(1/3, 1/3, 1/3, -1),
  psi           = 9,
  sigma_squared = 144,
  n_per_group   = 20
)

# 4. Unequal per-group sample sizes.
ss_power_contrast(
  c_weights     = c(0.5, 0.5, -0.5, -0.5),
  mu            = c(90, 92, 88, 81),
  sigma_squared = 144,
  n_per_group   = c(15, 25, 25, 15)
)

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