This function computes bare-bones meta-analyses of d values.
ma_d_bb(d, n1, n2 = rep(NA, length(d)), n_adj = NULL, sample_id = NULL,
citekey = NULL, wt_type = "sample_size", error_type = "mean",
correct_bias = FALSE, conf_level = 0.95, cred_level = 0.8,
conf_method = "t", cred_method = "t", var_unbiased = TRUE,
moderators = NULL, cat_moderators = TRUE, moderator_type = "simple",
hs_override = FALSE, data = NULL, ...)Vector of d values.
Vector or column name of primary sample sizes (if subgroup sample sizes are not known, these values are total sample sizes; if subgroup sample sizes are known, these values are sample sizes for the first of the two groups).
Optional: Vector or column name of secondary sample sizes. If subgroup sample sizes are known, these values are sample sizes for the second of the two groups. NULL by default.
Optional: Vector or column name of sample sizes adjusted for sporadic artifact corrections.
Optional vector of identification labels for samples/studies in the meta-analysis.
Optional vector of bibliographic citation keys for samples/studies in the meta-analysis (if multiple citekeys pertain to a given effect size, combine them into a single string entry with comma delimiters (e.g., "citkey1,citekey2").
When TRUE, program will use sample-size weights, error variances estimated from the mean effect size, maximum likelihood variances, and normal-distribution confidence and credibility intervals.
Type of weight to use in the meta-analysis: options are "sample_size", "inv_var_mean" (inverse variance computed using mean effect size), and "inv_var_sample" (inverse variance computed using sample-specific effect sizes). Supported options borrowed from metafor are "DL", "HE", "HS", "SJ", "ML", "REML", "EB", and "PM" (see metafor documentation for details about the metafor methods).
Method to be used to estimate error variances: "mean" uses the mean effect size to estimate error variances and "sample" uses the sample-specific effect sizes.
Logical argument that determines whether to correct effect sizes and error variances for small-sample bias (TRUE) or not (FALSE).
Width of confidence interval. Set to .95 by default.
Width of credibility interval. Set to .80 by default.
Distribution to be used to compute the width of confidence intervals. Available options are "t" for t distribution or "norm" for normal distribution.
Distribution to be used to compute the width of credibility intervals. Available options are "t" for t distribution or "norm" for normal distribution.
Logical scalar determining whether variances should be unbiased (TRUE) or maximum-likelihood (FALSE).
Matrix of moderator variables or column names of data to be used in the meta-analysis (can be a vector in the case of one moderator).
Logical scalar or vector identifying whether variables in the moderators argument are categorical variables (TRUE) or continuous variables (FALSE).
Type of moderator analysis ("none", "simple", or "hierarchical").
When TRUE, this will override settings for wt_type (will set to "sample_size"), error_type (will set to "mean"),
correct_bias (will set to TRUE), conf_method (will set to "norm"), cred_method (will set to "norm"), and var_unbiased (will set to FALSE).
Data frame containing columns whose names may be provided as arguments to vector arguments and/or moderators.
Further arguments to be passed to functions called within the meta-analysis.
A list object of the classes psychmeta, ma_d_as_d, and ma_bb.
Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Thousand Oaks, CA: Sage. https://doi.org/10/b6mg. Chapter 7.
# NOT RUN {
## Example meta-analyses using simulated data:
ma_d_bb(d = d, n1 = n1, n2 = n2,
data = data_d_meas_multi[data_d_meas_multi$construct == "Y",])
ma_d_bb(d = d, n1 = n1, n2 = n2,
data = data_d_meas_multi[data_d_meas_multi$construct == "Z",])
# }
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