rep_full_w_3pmeta: Reputation Consensus, Accuracy, and 3rd-Person...

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

Description

This fits a model estimating the possible hearsay reputation parameters for a design with P1-, P2-, target-self-reports, and P1- and P2-Meta-perception reports. It requires a dataset with those ratings, and either a model from the relevant model builder function or the names of the columns with each rating type. The estimated parameters are:

hc

hearsay consensus; the correlation between P1(T) & P2(T)

ha

hearsay accuracy; the correation between P2(T) & T(T)

da

direct accuracy; the correlation between P1(T) & T(T)

p1ma

P1 Meta-Accuracy; the correlation between P1(P2(T)) & P2(T)

p2ma

P2 Meta-Accuracy; the correlation between P2(P1(T)) & P1(T)

as_ac1

P1 Assumed Accuracy; the correlation between P1(P2(T)) & T(T)

as_con1

P1 Assumed Consensus; the correlation between P1(P2(T)) & P1(T)

mp_rec

Meta-Perception Reciprocity; the correlation between P1(P2(T)) & P2(P1(T))

as_ac2

P2 Assumed Accuracy; the correlation between P2(P1(T)) & T(T)

as_con2

P2 Assumed Consensus; the correlation between P2(P1(T)) & P2(T)

int_p1

Intercept for P1(T)

int_p2

Intercept for P2(T)

int_self

Intercept for T(T)

int_mp1

Intercept for P1(P2(T))

int_mp2

Intercept for P2(P1(T))

v_p1

variance for P1(T)

v_p2

variance for P2(T)

v_self

variance for T(T)

v_mp1

variance for P1(P2(T))

v_mp2

variance for P2(P1(T))

p1_p2_rel_el

P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))

self_p2_rel_el

Self-P2 Relative Elevation (i.e., Mean T(T) - Mean P2(T))

self_p1_rel_el

Self-P1 Relative Elevation (i.e., Mean T(T) - Mean P1(T))

p1_meta_rel_el

P1 Meta Relative Elevation (i.e., mean P2(T) - Mean P1(P2(T)))

p2_meta_rel_el

P2 Meta Relative Elevation (i.e., mean P1(T) - Mean P2(P1(T)))

If n exchangeable triads > 1:

rec

direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))

h

hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))

m

unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))

tru_sim

True Similarity; the correlation between targets' self-reports. (e.g., A(A) <-> C(C))

as_sim_3p

Third-person assumed similarity; correlation between P2(T) and P1's self-report (e.g., B(C) <- A(A))

as_sim_1p

First-person assumed similarity (i.e., interpersonal assumed similarity); correlation between P1(T) and P1's self-report (e.g., A(C) <-> A(A))

as_sim_p1m

P1 Meta-assumed similarity (e.g., A(B(C)) <-> A(A))

ukp1m1

unknown p1-meta 1

P1 meta-perception with opposite P1-report (e.g., A(B(C)) <-> C(A))).

p1meta_sim

P1 Meta-Similarity

correlation between exchangeable P1 meta-perceptions (e.g., A(B(C)) <-> C(D(A))).

ukp2m1

unknown P2-meta 1

P2 Meta-perception with exchangeable P2-reports (e.g., B(A(C)) <-> D(A))

ukp2m2

unknown P2-meta 2

P2 Meta-perception with exchangeable target self-report (e.g., B(A(C) <-> A(A)))

ukp2m3

unknown P2-meta 3

P2 Meta-perception with exchangeable P1-reports (e.g., B(A(C)) <-> C(A))

p2meta_sim

P2 Meta-Similarity

correlation between exchangeable P2 meta-perceptions (e.g., B(A(C)) <-> D(C(A))).

ukm1

unknown Meta-perception

P1 Meta-perception with exchangeable P2 Meta-Perception (e.g., A(B(C)) <-> D(C(A))) The function can handle up to n exchangeable triads.

Usage

rep_full_w_3pmeta(data, model = NULL, p1_reports, p2_reports,
  target_self, p1_meta, p2_meta, n_triads = length(p1_reports),
  n_p1s_per_p2s = 1, n_p2s_per_p1s = 1, n_p1s_per_ts = 1,
  n_p2s_per_ts = 1, n_ts_per_p1s = 1, n_ts_per_p2s = 1)

Arguments

data	The dataframe that contains ratings (P1, P2, target self-report, P1, and P2 meta-perceptions). Data should be wide, with a row for every group of participants. At a minimum, it must contain 5 columns: 1 for each of the five rating types (P1-, P2-, self-, P1-meta-, P2-meta-perceptions).
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. If more than one is supplied, the target-wise order must match the other rating types.
target_self	Quoted column names that contain target self-reports. If more than one is supplied, the target-wise order must match the other rating types.
p1_meta	Quoted column names that contain P1 3rd person Meta-perceptions, or P1's ratings of how they think P2 sees the target. If more than one is supplied, the target-wise order must match the other rating types.
p2_meta	Quoted column names that contain P2 3rd person Meta-perceptions, or P2's ratings of how they think P1 sees the target. If more than one is supplied, the target-wise order must match the other rating types.
n_triads	The number of exchangeable triads in each group. By default, this is determined by counting the number of P1 reports. It is rare that this parameter would need 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.
n_p1s_per_ts	The number of P1s for every target;. This defaults to 1. Currently, only values of 1 are supported.
n_p2s_per_ts	The number of P2s for every target;. This defaults to 1. Currently, only values of 1 are supported.
n_ts_per_p1s	The number of targets for every P1;. This defaults to 1. Currently, only values of 1 are supported.
n_ts_per_p2s	The number of targets for every P2;. This defaults to 1. Currently, only values of 1 are supported.

Value

The function returns an object of class lavaan.

Examples

data("rep_sim_data")
          agree_full <- rep_full_w_3pmeta(data = rep_sim_data,
                            p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
                            p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
                            target_self = c("C_C_agreeableness", "A_A_agreeableness"),
                            p1_meta = c("A_B_C_agree_meta", "C_D_A_agree_meta"),
                            p2_meta = c("B_A_C_agree_meta", "D_C_A_agree_meta"))
         # alternatively:

         agree_full_model <- rep_full_w_3pmeta_builder(p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
                                                           p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
                                                           target_self = c("C_C_agreeableness", "A_A_agreeableness"),
                                                           p1_meta = c("A_B_C_agree_meta", "C_D_A_agree_meta"),
                                                           p2_meta = c("B_A_C_agree_meta", "D_C_A_agree_meta"))
         agree_full <- rep_full_w_3pmeta(data = rep_sim_data,
                                         model = agree_full_model)

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