View source: R/create_ad_wrappers.R

create_ad | R Documentation |

This function generates artifact-distribution objects containing either interactive or Taylor series artifact distributions.
Use this to create objects that can be supplied to the `ma_r_ad`

and `ma_r_ad`

functions to apply psychometric corrections to barebones meta-analysis objects via artifact distribution methods.

Allows consolidation of observed and estimated artifact information by cross-correcting artifact distributions and forming weighted artifact summaries.

For u ratios, error variances can be computed for independent samples (i.e., settings in which the unrestricted standard deviation comes from an external study) or dependent samples (i.e., settings in which the range-restricted standard deviation comes from a sample that represents a subset of the applicant sample that provided the unrestricted standard deviation). The former circumstance is presumed to be more common, so error variances are computed for independent samples by default.

create_ad( ad_type = c("tsa", "int"), rxxi = NULL, n_rxxi = NULL, wt_rxxi = n_rxxi, rxxi_type = rep("alpha", length(rxxi)), k_items_rxxi = rep(NA, length(rxxi)), rxxa = NULL, n_rxxa = NULL, wt_rxxa = n_rxxa, rxxa_type = rep("alpha", length(rxxa)), k_items_rxxa = rep(NA, length(rxxa)), ux = NULL, ni_ux = NULL, na_ux = NULL, wt_ux = ni_ux, dep_sds_ux_obs = rep(FALSE, length(ux)), ut = NULL, ni_ut = NULL, na_ut = NULL, wt_ut = ni_ut, dep_sds_ut_obs = rep(FALSE, length(ut)), mean_qxi = NULL, var_qxi = NULL, k_qxi = NULL, mean_n_qxi = NULL, qxi_dist_type = rep("alpha", length(mean_qxi)), mean_k_items_qxi = rep(NA, length(mean_qxi)), mean_rxxi = NULL, var_rxxi = NULL, k_rxxi = NULL, mean_n_rxxi = NULL, rxxi_dist_type = rep("alpha", length(mean_rxxi)), mean_k_items_rxxi = rep(NA, length(mean_rxxi)), mean_qxa = NULL, var_qxa = NULL, k_qxa = NULL, mean_n_qxa = NULL, qxa_dist_type = rep("alpha", length(mean_qxa)), mean_k_items_qxa = rep(NA, length(mean_qxa)), mean_rxxa = NULL, var_rxxa = NULL, k_rxxa = NULL, mean_n_rxxa = NULL, rxxa_dist_type = rep("alpha", length(mean_rxxa)), mean_k_items_rxxa = rep(NA, length(mean_rxxa)), mean_ux = NULL, var_ux = NULL, k_ux = NULL, mean_ni_ux = NULL, mean_na_ux = rep(NA, length(mean_ux)), dep_sds_ux_spec = rep(FALSE, length(mean_ux)), mean_ut = NULL, var_ut = NULL, k_ut = NULL, mean_ni_ut = NULL, mean_na_ut = rep(NA, length(mean_ut)), dep_sds_ut_spec = rep(FALSE, length(mean_ut)), estimate_rxxa = TRUE, estimate_rxxi = TRUE, estimate_ux = TRUE, estimate_ut = TRUE, var_unbiased = TRUE, ... )

`ad_type` |
Type of artifact distribution to be computed: Either "tsa" for Taylor series approximation or "int" for interactive. |

`rxxi` |
Vector of incumbent reliability estimates. |

`n_rxxi` |
Vector of sample sizes associated with the elements of |

`wt_rxxi` |
Vector of weights associated with the elements of |

`rxxi_type, rxxa_type, qxi_dist_type, rxxi_dist_type, qxa_dist_type, rxxa_dist_type` |
String vector identifying the types of reliability estimates supplied (e.g., "alpha", "retest", "interrater_r", "splithalf"). See the documentation for |

`k_items_rxxi, mean_k_items_qxi, mean_k_items_rxxi, k_items_rxxa, mean_k_items_qxa, mean_k_items_rxxa` |
Numeric vector of the number of items in each scale (or mean number of items, for pre-specified distributions). |

`rxxa` |
Vector of applicant reliability estimates. |

`n_rxxa` |
Vector of sample sizes associated with the elements of |

`wt_rxxa` |
Vector of weights associated with the elements of |

`ux` |
Vector of observed-score u ratios. |

`ni_ux` |
Vector of incumbent sample sizes associated with the elements of |

`na_ux` |
Vector of applicant sample sizes that can be used in estimating the sampling error of supplied ux values. |

`wt_ux` |
Vector of weights associated with the elements of |

`dep_sds_ux_obs` |
Logical scalar or vector determining whether supplied ux values were computed using dependent samples ( |

`ut` |
Vector of true-score u ratios. |

`ni_ut` |
Vector of incumbent sample sizes associated with the elements of |

`na_ut` |
Vector of applicant sample sizes that can be used in estimating the sampling error of supplied ut values. |

`wt_ut` |
Vector of weights associated with the elements of |

`dep_sds_ut_obs` |
Logical scalar or vector determining whether supplied ut values were computed using dependent samples ( |

`mean_qxi` |
Vector that can be used to supply the means of externally computed distributions of incumbent square-root reliabilities. |

`var_qxi` |
Vector that can be used to supply the variances of externally computed distributions of incumbent square-root reliabilities. |

`k_qxi` |
Vector that can be used to supply the number of studies included in externally computed distributions of incumbent square-root reliabilities. |

`mean_n_qxi` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of incumbent square-root reliabilities. |

`mean_rxxi` |
Vector that can be used to supply the means of externally computed distributions of incumbent reliabilities. |

`var_rxxi` |
Vector that can be used to supply the variances of externally computed distributions of incumbent reliabilities. |

`k_rxxi` |
Vector that can be used to supply the number of studies included in externally computed distributions of incumbent reliabilities. |

`mean_n_rxxi` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of incumbent reliabilities. |

`mean_qxa` |
Vector that can be used to supply the means of externally computed distributions of applicant square-root reliabilities. |

`var_qxa` |
Vector that can be used to supply the variances of externally computed distributions of applicant square-root reliabilities. |

`k_qxa` |
Vector that can be used to supply the number of studies included in externally computed distributions of applicant square-root reliabilities. |

`mean_n_qxa` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of applicant square-root reliabilities. |

`mean_rxxa` |
Vector that can be used to supply the means of externally computed distributions of applicant reliabilities. |

`var_rxxa` |
Vector that can be used to supply the variances of externally computed distributions of applicant reliabilities. |

`k_rxxa` |
Vector that can be used to supply the number of studies included in externally computed distributions of applicant reliabilities. |

`mean_n_rxxa` |
Vector that can be used to supply the mean sample sizes of externally computed distributions of applicant reliabilities. |

`mean_ux` |
Vector that can be used to supply the means of externally computed distributions of observed-score u ratios. |

`var_ux` |
Vector that can be used to supply the variances of externally computed distributions of observed-score u ratios. |

`k_ux` |
Vector that can be used to supply the number of studies included in externally computed distributions of observed-score u ratios. |

`mean_ni_ux` |
Vector that can be used to supply the mean incumbent sample sizes of externally computed distributions of observed-score u ratios. |

`mean_na_ux` |
Vector or scalar that can be used to supply the mean applicant sample size(s) of externally computed distributions of observed-score u ratios. |

`dep_sds_ux_spec` |
Logical scalar or vector determining whether externally computed ux distributions were computed using dependent samples ( |

`mean_ut` |
Vector that can be used to supply the means of externally computed distributions of true-score u ratios. |

`var_ut` |
Vector that can be used to supply the variances of externally computed distributions of true-score u ratios. |

`k_ut` |
Vector that can be used to supply the number of studies included in externally computed distributions of true-score u ratios. |

`mean_ni_ut` |
Vector that can be used to supply the mean sample sizes for of externally computed distributions of true-score u ratios. |

`mean_na_ut` |
Vector or scalar that can be used to supply the mean applicant sample size(s) of externally computed distributions of true-score u ratios. |

`dep_sds_ut_spec` |
Logical scalar or vector determining whether externally computed ut distributions were computed using dependent samples ( |

`estimate_rxxa` |
Logical argument to estimate rxxa values from other artifacts ( |

`estimate_rxxi` |
Logical argument to estimate rxxi values from other artifacts ( |

`estimate_ux` |
Logical argument to estimate ux values from other artifacts ( |

`estimate_ut` |
Logical argument to estimate ut values from other artifacts ( |

`var_unbiased` |
Logical scalar determining whether variance should be unbiased ( |

`...` |
Further arguments. |

Artifact distribution object (matrix of artifact-distribution means and variances) for use artifact-distribution meta-analyses.

## Example computed using observed values only: create_ad(ad_type = "tsa", rxxa = c(.9, .8), n_rxxa = c(50, 150), rxxi = c(.8, .7), n_rxxi = c(50, 150), ux = c(.9, .8), ni_ux = c(50, 150)) create_ad(ad_type = "int", rxxa = c(.9, .8), n_rxxa = c(50, 150), rxxi = c(.8, .7), n_rxxi = c(50, 150), ux = c(.9, .8), ni_ux = c(50, 150)) ## Example computed using all possible input arguments (arbitrary values): rxxa <- rxxi <- ux <- ut <- c(.7, .8) n_rxxa <- n_rxxi <- ni_ux <- ni_ut <- c(50, 100) na_ux <- na_ut <- c(200, 200) mean_qxa <- mean_qxi <- mean_ux <- mean_ut <- mean_rxxi <- mean_rxxa <- c(.7, .8) var_qxa <- var_qxi <- var_ux <- var_ut <- var_rxxi <- var_rxxa <- c(.1, .05) k_qxa <- k_qxi <- k_ux <- k_ut <- k_rxxa <- k_rxxi <- 2 mean_n_qxa <- mean_n_qxi <- mean_ni_ux <- mean_ni_ut <- mean_n_rxxa <- mean_n_rxxi <- c(100, 100) dep_sds_ux_obs <- dep_sds_ux_spec <- dep_sds_ut_obs <- dep_sds_ut_spec <- FALSE mean_na_ux <- mean_na_ut <- c(200, 200) wt_rxxa <- n_rxxa wt_rxxi <- n_rxxi wt_ux <- ni_ux wt_ut <- ni_ut estimate_rxxa <- TRUE estimate_rxxi <- TRUE estimate_ux <- TRUE estimate_ut <- TRUE var_unbiased <- TRUE create_ad(rxxa = rxxa, n_rxxa = n_rxxa, wt_rxxa = wt_rxxa, mean_qxa = mean_qxa, var_qxa = var_qxa, k_qxa = k_qxa, mean_n_qxa = mean_n_qxa, mean_rxxa = mean_rxxa, var_rxxa = var_rxxa, k_rxxa = k_rxxa, mean_n_rxxa = mean_n_rxxa, rxxi = rxxi, n_rxxi = n_rxxi, wt_rxxi = wt_rxxi, mean_qxi = mean_qxi, var_qxi = var_qxi, k_qxi = k_qxi, mean_n_qxi = mean_n_qxi, mean_rxxi = mean_rxxi, var_rxxi = var_rxxi, k_rxxi = k_rxxi, mean_n_rxxi = mean_n_rxxi, ux = ux, ni_ux = ni_ux, na_ux = na_ux, wt_ux = wt_ux, dep_sds_ux_obs = dep_sds_ux_obs, mean_ux = mean_ux, var_ux = var_ux, k_ux = k_ux, mean_ni_ux = mean_ni_ux, mean_na_ux = mean_na_ux, dep_sds_ux_spec = dep_sds_ux_spec, ut = ut, ni_ut = ni_ut, na_ut = na_ut, wt_ut = wt_ut, dep_sds_ut_obs = dep_sds_ut_obs, mean_ut = mean_ut, var_ut = var_ut, k_ut = k_ut, mean_ni_ut = mean_ni_ut, mean_na_ut = mean_na_ut, dep_sds_ut_spec = dep_sds_ut_spec, estimate_rxxa = estimate_rxxa, estimate_rxxi = estimate_rxxi, estimate_ux = estimate_ux, estimate_ut = estimate_ut, var_unbiased = var_unbiased)

psychmeta documentation built on Aug. 26, 2022, 5:14 p.m.

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