Description Usage Arguments Details Value Examples
This function builds a lavaan model for asssessing individual-level moderators of hearsay accuracy across multiple groups. It requires names of columns for Target Self-Reports, P2 reports, an Individual Difference Moderator Variable, an interaction term, and a set of group labels. The baseline model (built by this function) builds a model that allows groups to differ for all parameters.
1 2 3 | rep_accuracy_group_id_mods_builder(target_self, p2_reports,
id_mod_variable, interaction_term, groups = NULL, use_labs = TRUE,
n_triads = length(target_self), n_ts_per_p2s = 1, n_p2s_per_ts = 1)
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target_self |
Quoted column names that contain target self-reports. If more than one is supplied, the order must match the other rating types. |
p2_reports |
Quoted column names that contain P2 reports, or ratings made by the person that knows the target indirectly through the corresponding P1. Ratings should be grand-mean-centered to increase the interpretibility of the model parameters. If more than one is supplied, the target-wise order must match the other rating types. |
id_mod_variable |
Quoted column names that contain the individual-level moderator of interest. If more than one is supplied from multiple exchangeable triads, the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered to facilitate interpretability. |
interaction_term |
Quoted column names that contain the interaction term, or the product of the mean-centered P2-report and the mean-centered moderator variable. If more than one is supplied from multiple exchangeable triads, the target-wise order must match the order of the ratings. |
groups |
Vector of quoted group labels (from group-level categorical moderator). |
use_labs |
Logical indicating whether or not to use the group labels to create the parameter labels. If FALSE, generic labels (grp1 to grpk, where k is the number of groups) are used. |
n_triads |
The number of exchangeable triads in each group. By default, this is determined by counting the number of Target self-reports. This parameter rarely needs to be changed. |
n_ts_per_p2s |
The number of targets that each P2 rated. This defaults to 1. Currently, only values of 1 are supported. |
n_p2s_per_ts |
The number of P2s that rated each target;. This defaults to 1. Currently, only values of 1 are supported. |
The parameters for this model are:
hearsay accuracy main effect; this should correspond to hearsay accuracy at average level of moderator variable (if data were properly mean-centered).
The meain effect of the moderator variable; it can be interpreted as the difference in target self-reports related to differences in the individual-level moderator variable.
This is the interaction term. It indicates the extent to which hearsay accuracy, depends on the moderator variable
variance for T(T)
variance for P2(T)
variance for moderator variable
variance for interaction term
intercept for T(T)
intercept for P2(T)
intercept for moderator variable
intercept for interaction term
The function can handle up to n exchangeable triads.
The function returns an object of class lavaan
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data("rep_sim_data")
# Prepare data
moderator_data <- rep_sim_data %>%
dplyr::mutate(B_C_agreeableness_cent = scale(B_C_agreeableness, scale = FALSE),
D_A_agreeableness_cent = scale(D_A_agreeableness, scale = FALSE),
B_iri_perspective_cent = scale(B_iri_perspective, scale = FALSE),
D_iri_perspective_cent = scale(D_iri_perspective, scale = FALSE),
B_ptXagree_interaction = B_C_agreeableness_cent*B_iri_perspective_cent,
D_ptXagree_interaction = D_A_agreeableness_cent*D_iri_perspective_cent)
# build a model examining perspective taking moderating hearsay consensus across two studies
agree_pt_mod_model <- rep_accuracy_group_id_mods_builder (target_self = c("C_C_agreeableness", "A_A_agreeableness"),
p2_reports = c("B_C_agreeableness_cent", "D_A_agreeableness_cent"),
id_mod_variable = c("B_iri_perspective_cent", "D_iri_perspective_cent"),
interaction_term = c("B_ptXagree_interaction", "D_ptXagree_interaction"),
groups = c("study1", "study2"))
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