This function computes individual-correction meta-analyses of correlations.
ma_r_ic(rxyi, n, n_adj = NULL, sample_id = NULL, wt_type = "sample_size",
error_type = "mean", correct_bias = TRUE, correct_rxx = TRUE,
correct_ryy = TRUE, correct_rr_x = TRUE, correct_rr_y = TRUE,
indirect_rr_x = TRUE, indirect_rr_y = TRUE, rxx = NULL,
rxx_restricted = TRUE, rxx_type = "alpha", ryy = NULL,
ryy_restricted = TRUE, ryy_type = "alpha", ux = NULL,
ux_observed = TRUE, uy = NULL, uy_observed = TRUE, sign_rxz = 1,
sign_ryz = 1, 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",
impute_method = "bootstrap_mod", hs_override = FALSE,
use_all_arts = FALSE, supplemental_ads_x = NULL,
supplemental_ads_y = NULL, data = NULL, ...)Vector or column name of observed correlations.
Vector or column name of sample sizes.
Optional: Vector or column name of sample sizes adjusted for sporadic artifact corrections.
Optional vector of identification labels for studies in the meta-analysis.
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 scalar that determines whether to correct correlations for small-sample bias (TRUE) or not (FALSE).
Logical scalar or vector that determines whether to correct the X variable for measurement error (TRUE) or not (FALSE).
Logical scalar or vector that determines whether to correct the Y variable for measurement error (TRUE) or not (FALSE).
Logical scalar or vector or column name determining whether each correlation in rxyi should be corrected for range restriction in X (TRUE) or not (FALSE).
Logical scalar or vector or column name determining whether each correlation in rxyi should be corrected for range restriction in Y (TRUE) or not (FALSE).
Logical vector or column name determining whether each correlation in rxyi should be corrected for indirect range restriction in X (TRUE) or not (FALSE).
Superceded in evaluation by correct_rr_x (i.e., if correct_rr_x == FALSE, the value supplied for indirect_rr_x is disregarded).
Logical vector or column name determining whether each correlation in rxyi should be corrected for indirect range restriction in Y (TRUE) or not (FALSE).
Superceded in evaluation by correct_rr_y (i.e., if correct_rr_y == FALSE, the value supplied for indirect_rr_y is disregarded).
Vector or column name of reliability estimates for X.
Logical vector or column name determining whether each element of rxx is an incumbent reliability (TRUE) or an applicant reliability (FALSE).
String vector identifying the types of reliability estimates supplied (e.g., "alpha", "retest", "interrater_r", "splithalf"). See the documentation for ma_r for a full list of acceptable reliability types.
Vector or column name of reliability estimates for Y.
Logical vector or column name determining whether each element of ryy is an incumbent reliability (TRUE) or an applicant reliability (FALSE).
Vector or column name of u ratios for X.
Logical vector or column name determining whether each element of ux is an observed-score u ratio (TRUE) or a true-score u ratio (FALSE).
Vector or column name of u ratios for Y.
Logical vector or column name determining whether each element of uy is an observed-score u ratio (TRUE) or a true-score u ratio (FALSE).
Sign of the relationship between X and the selection mechanism (for use with bvirr corrections only).
Sign of the relationship between Y and the selection mechanism (for use with bvirr corrections only).
Confidence level to define the width of the confidence interval (default = .95).
Credibility level to define the width of the credibility interval (default = .80).
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 or column names of moderator variables 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" means that no moderators are to be used, "simple" means that moderators are to be examined one at a time, and "hierarchical" means that all possible combinations and subsets of moderators are to be examined.
Method to use for imputing artifacts. See the documentation for ma_r for a list of available imputation methods.
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).
Logical scalar that determines whether artifact values from studies without valid effect sizes should be used in artifact distributions (TRUE) or not (FALSE).
List supplemental artifact distribution information from studies not included in the meta-analysis. The elements of this list are named like the arguments of the create_ad() function.
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 (e.g., create_ad_int and create_ad_tsa).
A list object of the classes psychmeta, ma_r_as_r, ma_bb, and ma_ic.
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.
Dahlke, J. A., & Wiernik, B. M. (2017). One of these artifacts is not like the others: New methods to account for the unique implications of indirect range-restriction corrections in organizational research. Unpublished manuscript.
# NOT RUN {
## Simulated example satisfying the assumptions of the Case IV range-
## restriction correction (parameter values: mean_rho = .3, sd_rho = .15):
ma_r_ic(rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi, ux = ux, data = data_r_uvirr)
## Published example from Gonzalez-Mule et al. (2014)
ma_r_ic(rxyi = rxyi, n = n, hs_override = TRUE, data = data_r_gonzalezmule_2014,
rxx = rxxi, ryy = ryyi, ux = ux, indirect_rr_x = TRUE, moderators = Complexity)
# }
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