CoDa and the methods

Learn about how you can use CoDa and the methods underlying the platform.


Search the literature

  • Select studies that have measured or manipulated a specific variable (e.g., a personality variable)
  • Select studies that have used a specific paradigm to measure cooperation (e.g., public goods dilemma)
  • Select studies that have specific parameters in the paradigm (e.g., group size, number of iterations)
  • Select studies with a specific kind of sample (e.g., gender, nationality)
  • For all selected studies, download the formatted references

Analyze the literature

  • Decide which studies to include in a meta-analysis
  • Perform on-demand meta-analyses
  • Perform meta-regressions predicting the effect size
  • Estimate publication bias in the literature
  • Compute a priori statistical power analyses for future studies
  • Download data to perform meta-analysis using other platforms

Visualize trends in the literature

  • Plot studies according to nationality of samples, year of publication, and sample size
  • Visualize literature on a topic using forest plots and density plots
  • Use a funnel plot to detect publication bias
  • Construct tables of the data that display each study’s characteristics and data

Linked data to discover even more

  • Retrieve existing data from the web and embed it into the dataset
  • Use linked data to identify cross-societal indicators to predict effect sizes
  • Retrieve online information about authors, labs, and other study characteristics
  • Use meta-study data to map citation networks

Explore the concepts used in cooperation research

  • Identify common parameters of cooperation experiments
  • Discover the variables that most strongly influence cooperation
  • Learn which variables are frequently studied
  • Identify gaps in the literature



Effect Size Calculations

Effects sizes describe the direction and strength of the relation between two variables, which can be used to describe the data in a sample, but also estimate the effect size at the level of the population. There are many possible different effect sizes, and common examples include the correlation coefficient, Cohens d, and Odds Ratio. Here we link to further descriptions of how we calculate effect sizes from the data reported in papers included in CoDa, and some references for further reading about effect sizes.

R script and resource Recommended readings about effect size
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Meta-analysis is a statistical method of aggregating results of several individual studies. For example, several studies may report an effect size (e.g., the correlation between gender and cooperation), the effect sizes may vary across studies, and each study may have a limited sample size to estimate the effect size in the population. Meta-analytic techniques give a weighted average of the effect size that can provide a more precise estimate of the population-level effect size. Below we share the R code we use for conducting meta-analysis and some recommended reading to learn more about the method.

R script and resource Recommended readings about meta-analysis
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Publication Bias

Researchers often fail to publish results that are not statistically significant, which can lead to biased estimates in meta-analysis. Researchers also can make several decisions when conducting and reporting studies that can bias the direction and magnitude of effect sizes. Scientists have developed several methods to estimate the direction and degree of bias in meta-analyses, which are possible to use in CoDa. Below you can find the R code we use to analyze publication bias and further reading to learn more about these methods.

R script and resource Recommended readings about publication bias


Meta-regression represents an extension of the regression models to the meta-analysis setting. As such, it allows to examine the relationships between one or more continuous or categorical variables (i.e., covariates) and a dependent variable (i.e., the effect sizes). In CoDa, the use of meta-regression allows to investigate whether certain covariates (e.g., study characteristics) can account for the variation in effect sizes and cooperative behavior observed across the studies.

R script and resource Recommended readings about meta-regression

Statistical Power Analysis

Power is the probability of correctly rejecting the null hypothesis, when the alternative hypothesis it true. Low statistical power is a severe problem with studies in the social and biological sciences. One solution to this problem is increasing sample sizes of studies. Estimates of effect sizes can be used in power analysis to determine the number of participants to be included in future studies. Here we provide the R code for power analyses we use in CoDa and some additional reading about statistical power analyses.

R script and resource Recommended readings about statistical power analysis

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