rep_consensus_id_mods_builder: Individual level Moderators of Hearsay Consensus Model...

In Coryc3133/ReputationAnalyses: Functions to model and analyze triadic (T-P1-P2) reputational data

Description

This takes the variables needed to assess an individual-level moderator on hearsay consensus, and builds a model for lavaan estimating the corresponding parameters. At a minimum, it requires P1-reports, P2-reports, an individual-level moderator, and the interaction term. Note that the P2-reports and moderator variable should be mean-centered.

Usage

rep_consensus_id_mods_builder(p1_reports, p2_reports, id_mod_variable,
  interaction_term, n_triads = length(p1_reports), n_p1s_per_p2s = 1,
  n_p2s_per_p1s = 1)

Arguments

p1_reports	Quoted column names that contain P1 reports, or ratings made by the person that knows the target directly. If more than one is supplied, the target-wise 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.
n_triads	The number of exchangeable triads in each group. By default, this is determined by counting the number of P1 reports. This parameter rarely needs to be changed.
n_p1s_per_p2s	The number of P1s for every P2. This defaults to 1. Currently, only values of 1 are supported.
n_p2s_per_p1s	The number of P2s for every P1;. This defaults to 1. Currently, only values of 1 are supported.

Details

The parameters for the individual-level moderator analyses are:

hc_me

hearsay consensus main effect; this should correspond to hearsay consensus at average level of moderator variable (if data were properly mean-centered).

mod_me

The meain effect of the moderator variable; it can be interpreted as the difference in P1-reports related to differences in the individual-level moderator variable.

interaction

This is the interaction term. It indicates the extent to which hearsay consensus, depends on the moderator variable

v_p1

variance for P1(T)

v_p2

variance for P2(T)

v_mod

variance for moderator variable

v_interaction

variance for interaction term

int_p1

intercept for P1(T)

int_p2

intercept for P2(T)

int_mod

intercept for moderator variable

int_interaction

intercept for interaction term

The function can handle up to n exchangeable triads.

Value

The function returns a list containing an object of class tbl_df and a string object of the model in lavaan syntax. Model information includes the type of model, the number of exchangeable triads, and the number of p1s per p2s, and the number of p2s per p1s.

Examples

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 consensus model
agree_pt_mod_consensus_model <- rep_consensus_id_mods_builder (p1_reports = c("A_C_agreeableness", "C_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"))

 # view model
   cat(agree_pt_mod_consensus_model$model)

 # view model Information
   agree_pt_mod_consensus_model$rep_model_info

Coryc3133/ReputationAnalyses documentation built on July 31, 2019, 8:35 a.m.