Description Usage Arguments Details Value Examples
This function fits a model for individual-level moderator on hearsay consensus, and builds a model for lavaan estimating the corresponding parameters. It requires a dataframe and either a model from the relevant model builder function or names of columns for p1_reports, p2_reports, and id_mod_variable. The estimated parameters are:
1 2 3 | rep_consensus_id_mods(data, model = NULL, p1_reports, p2_reports,
id_mod_variable, interaction_term, n_triads = length(p1_reports),
n_p1s_per_p2s = 1, n_p2s_per_p1s = 1)
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data |
The dataframe that contains the ratings, moderator variable, and the interaction term. Data should be wide, with a row for every group of participants. At a minimum, it must contain four columns: one for P1 reports, one for mean-centered P2 reports, one for the mean-centered moderator variable, and one for the interaction term. |
model |
Optional. A model from the corresponding ReputationModelR model builder function. If this is supplied, no additional arguments need to be specified. |
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. |
hearsay consensus main effect; this should correspond to hearsay consensus 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 P1-reports related to differences in the individual-level moderator variable.
This is the interaction term. It indicates the extent to which hearsay consensus, depends on the moderator variable
variance for P1(T)
variance for P2(T)
variance for moderator variable
variance for interaction term
intercept for P1(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 17 18 19 20 21 22 23 24 25 | 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)
# Fitting by supplying variable/column names
agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
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"))
# alternatively, build the model frst
agree_pt_mod_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"))
# Then fit the model you just built
agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
model = agree_pt_mod_model)
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