% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/es_from_ANOVA_statistics.R
\name{es_from_anova_pval}
\alias{es_from_anova_pval}
\title{Convert a p-value from a one-way independent ANOVA to several effect size measures}
\usage{
es_from_anova_pval(
  anova_f_pval,
  n_exp,
  n_nexp,
  smd_to_cor = "viechtbauer",
  smd_var = "borenstein",
  reverse_anova_f_pval
)
}
\arguments{
\item{anova_f_pval}{p-value (two-tailed) from an ANOVA (binary predictor). If your p-value is one-tailed, simply multiply it by two.}

\item{n_exp}{number of participants in the experimental/exposed group.}

\item{n_nexp}{number of participants in the non-experimental/non-exposed group.}

\item{smd_to_cor}{formula used to convert the \code{anova_f_pval} value into a coefficient correlation (see details).}

\item{smd_var}{name of the sampling-variance formula for the standardized mean difference: "borenstein" (default) or "hedges_olkin" (alias "viechtbauer"). The two differ by a squared small-sample-correction factor (J^2); "hedges_olkin" is a few percent larger at small samples.}

\item{reverse_anova_f_pval}{a logical value indicating whether the direction of generated effect sizes should be flipped.}
}
\value{
This function estimates and converts between several effect size measures.

\tabular{ll}{
\code{natural effect size measure} \tab D + G\cr
\tab \cr
\code{converted effect size measure} \tab OR + R + Z\cr
\tab \cr
\code{required input data} \tab See 'Section 11. ANOVA statistics, Student's t-test, or point-bis correlation'\cr
\tab https://metaconvert.org/html/input.html\cr
\tab \cr
}
}
\description{
Convert a p-value from a one-way independent ANOVA to several effect size measures
}
\details{
This function converts the p-value from the F-value of an ANOVA (one-way, binary predictor) into a t-value,
and then relies on the calculations of the \code{\link{es_from_student_t}()} function.

\strong{To convert the p-value into a t-value,} the following formula is used (table 12.1 in Cooper):
\deqn{student\_t = qt(\frac{anova\_f\_pval}{2}, df = n\_exp + n\_nexp - 2)}
Then, calculations of the \code{\link{es_from_student_t}()} are applied.

As for \code{\link{es_from_anova_f}}, this conversion is valid only for an F-test with a
single numerator degree of freedom (a two-group comparison): the p-value of a
multi-level (numerator df > 1) omnibus F is inverted here as if it were a two-sided
two-group t p-value, which is incorrect. The two-sided p-value also carries no
direction, so the generated effect sizes are always non-negative; use
\code{reverse_anova_f_pval} to encode the correct sign.
}
\examples{
es_from_anova_pval(anova_f_pval = 0.0012, n_exp = 20, n_nexp = 22)
}
\references{
Cooper, H., Hedges, L.V., & Valentine, J.C. (Eds.). (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation.
}
