Even more refs!

paper/paper.bib

@@ -149,4 +149,34 @@ @article{bollen1989structural
   author={Bollen, Kenneth A},
   journal={New York},
   year={1989}
+}
+
+@book{cramer1946mathematical,
+  title={Mathematical Methods of Statistics},
+  author={Cram{\'e}r, Harald},
+  year={1946},
+  publisher={Princeton: Princeton University Press}
+}
+
+@article{kelley1935unbiased,
+  title={An unbiased correlation ratio measure},
+  author={Kelley, Truman L},
+  journal={Proceedings of the National Academy of Sciences of the United States of America},
+  volume={21},
+  number={9},
+  pages={554},
+  year={1935},
+  publisher={National Academy of Sciences}
+}
+
+
+@article{friedman1982simplified,
+  title={Simplified determinations of statistical power, magnitude of effect and research sample sizes},
+  author={Friedman, Herbert},
+  journal={Educational and Psychological Measurement},
+  volume={42},
+  number={2},
+  pages={521--526},
+  year={1982},
+  publisher={Sage Publications Sage CA: Thousand Oaks, CA}
 }

paper/paper.md

@@ -42,7 +42,7 @@ In both theoretical and applied research, it is often of interest to assess the
 **effectsize**'s functionality is in part comparable to packages like **lm.beta** [@behrendt2014lmbeta], **MOTE** [@buchanan2019MOTE] or **MBESS** [@kelley2020MBESS]. Yet, there are some notable differences, e.g.:
 
 - **lm.beta** provides standardized regression coefficients for linear models, based on post-hoc model matrix standardization. However, the functionality is available only for a limited number of models (models inheriting from the `lm` class), whereas **effectsize** provides support for many types of models, including (generalized) linear mixed models, Bayesian models, and more. Additionally, in additional to post-hoc model matrix standardization, **effectsize** offers other methods of standardization (see below).  
-- Both **MOTE** and **MBESS** provide functions for computing effect sizes such as Cohen's *d* and effect sizes for ANOVAs, and their confidence intervals. However, both require manual input of *F*- or *t*-statistics, *degrees of freedom*, and *Sums of Squares* for the computation the effect sizes, whereas **effectsize** can automatically extract this information from the provided models, thus allowing for better ease-of-use as well as reducing any potential for error.  
+- Both **MOTE** and **MBESS** provide functions for computing effect sizes such as Cohen's *d* and effect sizes for ANOVAs [@cohen1988statistical], and their confidence intervals. However, both require manual input of *F*- or *t*-statistics, *degrees of freedom*, and *Sums of Squares* for the computation the effect sizes, whereas **effectsize** can automatically extract this information from the provided models, thus allowing for better ease-of-use as well as reducing any potential for error.  
 - Finally, in **base R**, the function `scale()` can be used to standardize vectors, matrices and data frame, which can be used to standardize data prior to model fitting. The coefficients of a linear model fit on such data are in effect standardized regression coefficients. **effectsize** expands an this, allowing for robust standardization (using the median and the MAD, instead of the mean and SD), post-hoc parameter standardization, and more.
 
 # Examples of Features
@@ -67,7 +67,7 @@ cohens_d(mpg ~ am, data = mtcars)
 
 ### Contingency Tables
 
-Pearson's $\phi$ (`phi()`) and Cramér's *V* (`cramers_v()`) can be used to estimate the strength of association between two categorical variables, while Cohen's *g* (`cohens_g()`) estimates the deviance between paired categorical variables.
+Pearson's $\phi$ (`phi()`) and Cramér's *V* (`cramers_v()`) can be used to estimate the strength of association between two categorical variables [@cramer1946mathematical], while Cohen's *g* (`cohens_g()`) estimates the deviance between paired categorical variables [@cohen1988statistical].
 
 ``` r
 M <- rbind(c(150, 130, 35, 55),
@@ -129,7 +129,7 @@ standardize_parameters(model, exponentiate = TRUE)
 
 ## Effect Sizes for ANOVAs
 
-Unlike standardized parameters, the effect sizes reported in the context of ANOVAs (analysis of variance) or ANOVA-like tables represent the amount of variance explained by each of the model's terms, where each term can be represented by one or more parameters. `eta_squared()` can produce such popular effect sizes as Eta-squared ($\eta^2$), its partial version ($\eta^2_p$), as well as the generalized $\eta^2_G$ [@olejnik2003generalized]:
+Unlike standardized parameters, the effect sizes reported in the context of ANOVAs (analysis of variance) or ANOVA-like tables represent the amount of variance explained by each of the model's terms, where each term can be represented by one or more parameters. `eta_squared()` can produce such popular effect sizes as Eta-squared ($\eta^2$), its partial version ($\eta^2_p$), as well as the generalized $\eta^2_G$ [@cohen1988statistical; @olejnik2003generalized]:
 
 
 ``` r
@@ -158,13 +158,13 @@ eta_squared(model, generalized = "Time")
 #> Chick:Time | Diet:Time |               0.03 | [0.00, 0.00]
 ```
 
-**effectsize** also offers the unbiased estimates of $\epsilon^2_p$ (`epsilon_squared()`) and $\omega^2_p$ (`omega_squared()`). For more details about the various effect size measures and their applications, see the [*Effect sizes for ANOVAs* vignette](https://easystats.github.io/effectsize/articles/anovaES.html).
+**effectsize** also offers $\epsilon^2_p$ (`epsilon_squared()`) and $\omega^2_p$ (`omega_squared()`), which are less biased estimates of the variance explained in the population [@kelley1935unbiased; @olejnik2003generalized]. For more details about the various effect size measures and their applications, see the [*Effect sizes for ANOVAs* vignette](https://easystats.github.io/effectsize/articles/anovaES.html).
 
 ## Effect Size Conversion
 
 ### From Test Statistics
 
-In many real world applications there are no straightforward ways of obtaining standardized effect sizes. However, it is possible to get approximations of most of the effect size indices (*d*, *r*, $\eta^2_p$...) with the use of test statistics. These conversions are based on the idea that test statistics are a function of effect size and sample size (or more often of degrees of freedom). Thus it is possible to reverse-engineer indices of effect size from test statistics (*F*, *t*, $\chi^2$, and *z*). 
+In many real world applications there are no straightforward ways of obtaining standardized effect sizes. However, it is possible to get approximations of most of the effect size indices (*d*, *r*, $\eta^2_p$...) with the use of test statistics [@friedman1982simplified]. These conversions are based on the idea that test statistics are a function of effect size and sample size (or more often of degrees of freedom). Thus it is possible to reverse-engineer indices of effect size from test statistics (*F*, *t*, $\chi^2$, and *z*). 
 
 ``` r
 F_to_eta2(f = c(40.72, 33.77),