psychmeta (version 2.3.4)

create_ad_tibble: Create a tibble of artifact distributions by construct

Description

Create a tibble of artifact distributions by construct

Usage

create_ad_tibble(ad_type = c("tsa", "int"), n = NULL,
  sample_id = NULL, construct_x = NULL, facet_x = NULL,
  measure_x = NULL, construct_y = NULL, facet_y = NULL,
  measure_y = NULL, rxx = NULL, rxx_restricted = TRUE,
  rxx_type = "alpha", k_items_x = NA, ryy = NULL,
  ryy_restricted = TRUE, ryy_type = "alpha", k_items_y = NA,
  ux = NULL, ux_observed = TRUE, uy = NULL, uy_observed = TRUE,
  estimate_rxxa = TRUE, estimate_rxxi = TRUE, estimate_ux = TRUE,
  estimate_ut = TRUE, moderators = NULL, cat_moderators = TRUE,
  moderator_type = c("simple", "hierarchical", "none"),
  construct_order = NULL, supplemental_ads = NULL, data = NULL,
  control = control_psychmeta(), ...)
create_ad_list(ad_type = c("tsa", "int"), n = NULL, sample_id = NULL,
  construct_x = NULL, facet_x = NULL, measure_x = NULL,
  construct_y = NULL, facet_y = NULL, measure_y = NULL, rxx = NULL,
  rxx_restricted = TRUE, rxx_type = "alpha", k_items_x = NA,
  ryy = NULL, ryy_restricted = TRUE, ryy_type = "alpha",
  k_items_y = NA, ux = NULL, ux_observed = TRUE, uy = NULL,
  uy_observed = TRUE, estimate_rxxa = TRUE, estimate_rxxi = TRUE,
  estimate_ux = TRUE, estimate_ut = TRUE, moderators = NULL,
  cat_moderators = TRUE, moderator_type = c("simple", "hierarchical",
  "none"), construct_order = NULL, supplemental_ads = NULL,
  data = NULL, control = control_psychmeta(), ...)

Arguments

ad_type

Type of artifact distributions to be computed: Either "tsa" for Taylor series approximation or "int" for interactive.

n

Vector or column name of sample sizes.

sample_id

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

construct_x, construct_y

Vector of construct names for constructs initially designated as "X" or "Y".

facet_x, facet_y

Vector of facet names for constructs initially designated as "X" or "Y". Facet names "global", "overall", and "total" are reserved to indicate observations that represent effect sizes that have already been composited or that represent construct-level measurements rather than facet-level measurements. To avoid double-compositing, any observation with one of these reserved names will only be eligible for auto-compositing with other such observations and will not be combined with narrow facets.

measure_x, measure_y

Vector of names for measures associated with constructs initially designated as "X" or "Y".

rxx

Vector or column name of reliability estimates for X.

rxx_restricted

Logical vector or column name determining whether each element of rxx is an incumbent reliability (TRUE) or an applicant reliability (FALSE).

rxx_type, ryy_type

String vector identifying the types of reliability estimates supplied. See documentation of ma_r for a full list of acceptable values.

k_items_x, k_items_y

Numeric vector identifying the number of items in each scale.

ryy

Vector or column name of reliability estimates for Y.

ryy_restricted

Logical vector or column name determining whether each element of ryy is an incumbent reliability (TRUE) or an applicant reliability (FALSE).

ux

Vector or column name of u ratios for X.

ux_observed

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).

uy

Vector or column name of u ratios for Y.

uy_observed

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).

estimate_rxxa

Logical argument to estimate rxxa values from other artifacts (TRUE) or to only used supplied rxxa values (FALSE). TRUE by default.

estimate_rxxi

Logical argument to estimate rxxi values from other artifacts (TRUE) or to only used supplied rxxi values (FALSE). TRUE by default.

estimate_ux

Logical argument to estimate ux values from other artifacts (TRUE) or to only used supplied ux values (FALSE). TRUE by default.

estimate_ut

Logical argument to estimate ut values from other artifacts (TRUE) or to only used supplied ut values (FALSE). TRUE by default.

moderators

Matrix or column names 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" 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.

construct_order

Vector indicating the order in which variables should be arranged, with variables listed earlier in the vector being preferred for designation as X.

supplemental_ads

Named list (named according to the constructs included in the meta-analysis) of supplemental artifact distribution information from studies not included in the meta-analysis. This is a list of lists, where the elements of a list associated with a construct are named like the arguments of the create_ad() function.

data

Data frame containing columns whose names may be provided as arguments to vector arguments.

control

Output from the control_psychmeta() function or a list of arguments controlled by the control_psychmeta() function. Ellipsis arguments will be screened for internal inclusion in control.

...

Additional arguments

Value

A tibble of artifact distributions

Examples

# NOT RUN {
## Examples to create Taylor series artifact distributions:
# Overall artifact distributions (not pairwise, not moderated)
create_ad_tibble(ad_type = "tsa",
                 n = n, rxx = rxxi, ryy = ryyi,
                 construct_x = x_name, construct_y = y_name,
                 sample_id = sample_id, moderators = moderator,
                 data = data_r_meas_multi,
                 control = control_psychmeta(pairwise_ads = FALSE,
                                             moderated_ads = FALSE))

# Overall artifact distributions by moderator combination
create_ad_tibble(ad_type = "tsa",
                 n = n, rxx = rxxi, ryy = ryyi,
                 construct_x = x_name, construct_y = y_name,
                 sample_id = sample_id, moderators = moderator,
                 data = data_r_meas_multi,
                 control = control_psychmeta(pairwise_ads = FALSE,
                                             moderated_ads = TRUE))

# Pairwise artifact distributions (not moderated)
create_ad_tibble(ad_type = "tsa",
                 n = n, rxx = rxxi, ryy = ryyi,
                 construct_x = x_name, construct_y = y_name,
                 sample_id = sample_id, moderators = moderator,
                 data = data_r_meas_multi,
                 control = control_psychmeta(pairwise_ads = TRUE,
                                               moderated_ads = FALSE))

# Pairwise artifact distributions by moderator combination
create_ad_tibble(ad_type = "tsa",
                 n = n, rxx = rxxi, ryy = ryyi,
                 construct_x = x_name, construct_y = y_name,
                 sample_id = sample_id, moderators = moderator,
                 data = data_r_meas_multi,
                 control = control_psychmeta(pairwise_ads = TRUE,
                                             moderated_ads = TRUE))
# }