psychmeta (version 0.2.4)

ma_r_bb: Bare-bones meta-analysis of correlations

Description

This function computes bare-bones meta-analyses of correlations.

Usage

ma_r_bb(r, n, n_adj = NULL, sample_id = NULL, wt_type = "sample_size",
  error_type = "mean", correct_bias = TRUE, 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, ...)

Arguments

r

Vector or column name of observed correlations.

n

Vector or column name of sample sizes.

n_adj

Optional: Vector or column name of sample sizes adjusted for sporadic artifact corrections.

sample_id

Optional vector of identification labels for samples/studies in the meta-analysis. 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.

wt_type

Type of weight to use in the meta-analysis: native 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).

error_type

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.

correct_bias

Logical argument that determines whether to correct correlations for small-sample bias (TRUE) or not (FALSE).

conf_level

Confidence level to define the width of the confidence interval (default = .95).

cred_level

Credibility level to define the width of the credibility interval (default = .80).

conf_method

Distribution to be used to compute the width of confidence intervals. Available options are "t" for t distribution or "norm" for normal distribution.

cred_method

Distribution to be used to compute the width of credibility intervals. Available options are "t" for t distribution or "norm" for normal distribution.

var_unbiased

Logical scalar determining whether variances should be unbiased (TRUE) or maximum-likelihood (FALSE).

moderators

Matrix of moderator variables to be used in the meta-analysis (can be a vector in the case of one moderator).

cat_moderators

Logical scalar or vector identifying whether variables in the moderators argument are categorical variables (TRUE) or continuous variables (FALSE).

moderator_type

Type of moderator analysis ("none", "simple", or "hierarchical").

hs_override

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

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.

Value

A list object of the classes psychmeta, ma_r_as_r, and ma_bb.

References

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 3.

Examples

# NOT RUN {
## Example analysis using data from Gonzalez-Mule et al. (2014):

## Not correcting for bias and using normal distributions to compute uncertainty intervals
## allows for exact replication of the results reported in the text:
ma_r_bb(r = rxyi, n = n, correct_bias = FALSE, conf_method = "norm", cred_method = "norm",
               data = data_r_gonzalezmule_2014)

## Using hs_override = TRUE allows one to easily implement the traditional Hunter-Schmidt method:
ma_r_bb(r = rxyi, n = n, hs_override = TRUE, data = data_r_gonzalezmule_2014)

## With hs_override = FALSE, the program defaults will compute unbiased variances and use
## t-distributions to estimate confidence and credibility intervals - these settings make
## a noticeable difference for small studies like the textbook example:
ma_r_bb(r = rxyi, n = n, hs_override = FALSE, data = data_r_gonzalezmule_2014)
# }