psychmeta (version 0.2.4)

ma_r_ad: Artifact-distribution meta-analysis of correlations

Description

This function computes artifact distribution meta-analyses of correlations. It supports interactive methods as well as Taylor series methods for all available corrections.

Usage

ma_r_ad(ma_obj, ad_obj_x = NULL, ad_obj_y = NULL,
  correction_method = "auto", use_ic_ads = "tsa", correct_rxx = TRUE,
  correct_ryy = TRUE, correct_rr_x = TRUE, correct_rr_y = TRUE,
  indirect_rr_x = TRUE, indirect_rr_y = TRUE, residual_ads = TRUE,
  sign_rxz = 1, sign_ryz = 1, decimals = 2, ...)

Arguments

ma_obj

Meta-analysis object of correlations or d values (regardless of input metric, output metric will be r).

ad_obj_x

Artifact-distribution object for the X variable (output of the create_ad function). If ma_obj is of the class ma_master (i.e,. the output of ma_r or ma_d), the object supplied for ad_obj_x must be a named list of artifact distributions with names corresponding to the "X" constructs in the meta-analyses contained within ma_obj.

ad_obj_y

Artifact-distribution object for the Y variable (output of the create_ad function). If ma_obj is of the class ma_master, the object supplied for ad_obj_y must be a named list of artifact distributions with names corresponding to the "Y" constructs in the meta-analyses contained within ma_obj.

correction_method

One of the following methods for correcting artifacts: "auto", "meas", "uvdrr", "uvirr", "bvdrr", "bvirr", "rbOrig", "rb1Orig", "rb2Orig", "rbAdj", "rb1Adj", and "rb2Adj". (note: "rb1Orig", "rb2Orig", "rb1Adj", and "rb2Adj" can only be used when Taylor series artifact distributions are provided and "rbOrig" and "rbAdj" can only be used when interative artifact distributions are provided). See "Details" for descriptions of the available methods.

use_ic_ads

Determines whether artifact distributions should be extracted from the individual correction results in ma_obj. Only evaluated when ad_obj_x or ad_obj_y is NULL and ma_obj does not contain individual correction results. Use one of the following commands: tsa to use the Taylor series method or int to use the interactive method.

correct_rxx

Logical argument that determines whether to correct the X variable for measurement error (TRUE) or not (FALSE).

correct_ryy

Logical argument that determines whether to correct the Y variable for measurement error (TRUE) or not (FALSE).

correct_rr_x

Logical argument that determines whether to correct the X variable for range restriction (TRUE) or not (FALSE).

correct_rr_y

Logical argument that determines whether to correct the Y variable for range restriction (TRUE) or not (FALSE).

indirect_rr_x

If correct_rr_x = TRUE: Logical argument that determines whether to correct for indirect range restriction in X (TRUE) or not (FALSE).

indirect_rr_y

If correct_rr_y = TRUE: Logical argument that determines whether to correct for indirect range restriction in Y (TRUE) or not (FALSE).

residual_ads

Logical argument that determines whether to use residualized variances (TRUE) or observed variances (FALSE) of artifact distributions to estimate sd_rho.

sign_rxz

Sign of the relationship between X and the selection mechanism (for use with the bvirr correction_method only).

sign_ryz

Sign of the relationship between Y and the selection mechanism (for use with the bvirr correction_method only).

decimals

Number of decimal places to which interactive artifact distributions should be rounded (default is 2 decimal places). Rounding artifact distributions can help to consolidate trivially different values and speed up the computation of meta-analyses (especially in simulations).

...

Additional arguments.

Value

A list object of the classes psychmeta, ma_r_as_r or ma_d_as_r, ma_bb, and ma_ad (and that inherits class ma_ic from ma_obj)

Details

The options for correction_method are:

• "auto" Automatic selection of the most appropriate correction procedure, based on the available artifacts and the logical arguments provided to the function. (default)

• "meas" Correction for measurement error only.

• "uvdrr" Correction for univariate direct range restriction (i.e., Case II). The choice of which variable to correct for range restriction is made using the correct_rr_x and correct_rr_y arguments.

• "uvirr" Correction for univariate indirect range restriction (i.e., Case IV). The choice of which variable to correct for range restriction is made using the correct_rr_x and correct_rr_y arguments.

• "bvdrr" Correction for bivariate direct range restriction. Use with caution: This correction is an approximation only and is known to have a positive bias.

• "bvirr" Correction for bivariate indirect range restriction (i.e., Case V).

• "rbOrig" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied interactively. We recommend using "uvdrr" instead.

• "rbAdj" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied interactively. Adjusted to account for range restriction in the reliability of the Y variable. We recommend using "uvdrr" instead.

• "rb1Orig" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied using their TSA1 method. We recommend using "uvdrr" instead.

• "rb1Adj" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied using their TSA1 method. Adjusted to account for range restriction in the reliability of the Y variable. We recommend using "uvdrr" instead.

• "rb2Orig" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied using their TSA2 method. We recommend using "uvdrr" instead.

• "rb2Adj" Not recommended: Raju and Burke's version of the correction for direct range restriction, applied using their TSA2 method. Adjusted to account for range restriction in the reliability of the Y variable. We recommend using "uvdrr" instead.

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 4.

Law, K. S., Schmidt, F. L., & Hunter, J. E. (1994). Nonlinearity of range corrections in meta-analysis: Test of an improved procedure. Journal of Applied Psychology, 79(3), 425<U+2013>438. https://doi.org/10.1037/0021-9010.79.3.425

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.

Raju, N. S., & Burke, M. J. (1983). Two new procedures for studying validity generalization. Journal of Applied Psychology, 68(3), 382<U+2013>395. https://doi.org/10.1037/0021-9010.68.3.382

Examples

# NOT RUN {
## Compute barebones meta-analysis
ma_obj <- ma_r_bb(r = rxyi, n = n, correct_bias = FALSE,
                           conf_method = "norm", cred_method = "norm", data = data_r_mcdaniel_1994)

## Construct artifact distribution for X
ad_obj_x <- create_ad_tsa(mean_rxxi = data_r_mcdaniel_1994$Mrxxi[1],
                          var_rxxi = data_r_mcdaniel_1994$SDrxxi[1]^.5,
                          ux = data_r_mcdaniel_1994$ux,
                          wt_ux = data_r_mcdaniel_1994$`ux frequency`)

## Construct artifact distribution for Y
ad_obj_y <- create_ad_tsa(rxxi = data_r_mcdaniel_1994$ryyi,
                          wt_rxxi = data_r_mcdaniel_1994$`ryyi frequency`)

## Compute artifact-distribution meta-analysis, correcting for measurement error only
ma_r_ad(ma_obj = ma_obj, ad_obj_x = ad_obj_x, ad_obj_y = ad_obj_y, correction_method = "meas")

## Compute artifact-distribution meta-analysis, correcting for univariate direct range restriction
ma_r_ad(ma_obj = ma_obj, ad_obj_x = ad_obj_x, ad_obj_y = ad_obj_y, correction_method = "uvdrr",
        correct_rr_y = FALSE, indirect_rr_x = FALSE)
# }