Structural Equation Modeling for the Social Relations Model

Description

Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).

Author(s)

Steffen Nestler [aut], Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]

Maintainer: Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>

References

Bond, C. F., & Malloy, T. E. (2018a). Social relations analysis of dyadic data structure: The general case. In T. E. Malloy. Social relations modeling of behavior in dyads and groups (Ch. 14). Academic Press. doi: 10.1016/B978-0-12-811967-9.00014-X

Gill, P. S., & Swartz, T. B. (2001). Statistical analyses for round robin interaction data. Canadian Journal of Statistics, 29(2), 321-331. doi: 10.2307/3316080

Kenny, D. A., & La Voie, L. J. (1984). The social relations model. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 18, pp. 142-182). Orlando, FL: Academic. doi: 10.1016/S0065-2601(08)60144-6

Nestler, S. (2018). Likelihood estimation of the multivariate social relations model. Journal of Educational and Behavioral Statistics, 43(4), 387-406. doi: 10.3102/1076998617741106

Warner, R. M., Kenny, D. A., & Soto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37(10), 1742-1757. doi: 10.1037/0022-3514.37.10.1742

See Also

See also the R packages amen and TripleR for estimating the social relations model.

Hallmark and Kenny Round Robin Data

Description

Data from Kenny et al. (1994)

Usage

data(HallmarkKenny)

Format

A data frame with 802 measurements of 30 round-robin groups on the following 7 round-robin variables (taken on unnumbered 7-point rating scales with higher numbers indicating a higher value of the trait):

calm: rating of dimension calm-anxious
sociable rating of dimension sociable-withdrawn
liking rating of dimension like-do not like
careful rating of dimension careful-careless
relaxed rating of dimension relaxed-tense
talkative rating of dimension talkative-quiet
responsible rating of dimension responsible-undependable

The data frame also contains participants gender (actor.sex; 1 = F, 2 = M) and their age in years (actor.age). Note that the data was assessed in two conditions: odd round robin group numbers indicate groups in which participants rated all traits for a person at a time whereas even numbers refer to groups in which participants rated all the people for each trait.

Source

http://davidakenny.net/srm/srmdata.htm

References

Kenny, D. A., Albright, L., Malloy, T. E., & Kashy, D. A. (1994). Consensus in interpersonal perception: Acquaintance and the big five. Psychological Bulletin, 116(2), 245-258. doi: 10.1037/0033-2909.116.2.245

Zero Acquaintance Round Robin Data from Kenny

Description

Data from Albright et al. (1988) Study 2

Usage

data(Kenzer)

Format

A data frame with 124 measurements from 7 round-robin groups on the following 5 round-robin variables (taken on unnumbered 7-point rating scales with higher numbers indicating a higher value of the trait):

sociable: rating of dimension sociable
irritable: rating of dimension good-natured
responsible: rating of dimension responsible
anxious: rating of dimension calm
intellectual: rating of dimension intellectual

The data frame also contains the gender (actor.sex; 1 = F, 2 = M) of the participants and their self-ratings on the five assessed traits (actor.sociable and so on).

Source

http://davidakenny.net/srm/srmdata.htm

References

Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgments at zero acquaintance. Journal of Personality and Social Psychology, 55(3), 387-395. doi: 10.1037/0022-3514.55.3.387

Zero Acquaintance Round Robin Data from Malloy

Description

Data from Albright et al. (1988) Study 1

Usage

data(Malzer)

Format

A data frame with 216 measurements from 12 round-robin groups on the following 5 round-robin variables (assessed on numbered 7-point rating scales with higher numbers indicating a higher value of the trait with the exception for good and calm):

sociable: rating of dimension sociable
irritable: rating of dimension good-natured
responsible: rating of dimension responsible
anxious: rating of dimension calm
intellectual: rating of dimension intellectual

The data frame also contains the gender (actor.sex; 1 = F, 2 = M) of the participants and their self-ratings on the five assessed traits (actor.sociable and so on).

Source

http://davidakenny.net/srm/srmdata.htm

References

Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgments at zero acquaintance. Journal of Personality and Social Psychology, 55(3), 387-395. doi: 10.1037/0022-3514.55.3.387

Round Robin Data Reported in Warner et al.

Description

Data from Warner et al. (1979)

Usage

data(Warner)

Format

A data frame with 56 measurements of a single round-robin group on a single round-robin variable that was measured at three consecutive time points. The variable reflects the proportion of time an actor spent when speaking to a partner.

prop.T1: proportion of time spent in the first interaction
prop.T2: proportion of time spent in the second interaction
prop.T3: proportion of time spent in the third interaction

Source

See Table 7 (p. 1752) of the Warner et al. (1979).

References

Warner, R. M., Kenny, D. A., & Soto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37(10), 1742-1757. doi: 10.1037/0022-3514.37.10.1742

Zero Acquaintance Round Robin Data From Albirght, Kenny, and Malloy

Description

Data from Study 3 of Albright et al. (1988)

Usage

data(Zero)

Format

A data frame with 636 measurements of 36 round robin groups on the following 15 round-robin variables (taken on 7-point rating scales with higher values indicating more of the trait):

sociable: rating of dimension sociable-reclusive
good: rating of dimension good-natured-irritable
responsible: rating of dimension responsible-undependable
calm: rating of dimension calm-anxious
intellectual: rating of dimension intellectual-unintellectual
imaginative: rating of dimension imaginative-unimaginative
talkative: rating of dimension talkative-silent
fussy: rating of dimension fussy-careless
composed: rating of dimension composed-excitable
cooperative: rating of dimension cooperative-negativistic
physically_attractive: rating of dimension physically attractive-unattractive
formal_dress: rating of dimension formal dress-casual dress
neatly_dressed: rating of dimension neatly dressed-sloppy dress
athletic: rating of dimension athletic-not athletic
young: rating of dimension young-old

The data frame also contains the gender (actor.sex; 1 = F, 2 = M) of the participants and their self-ratings on the five assessed traits (actor.sociable and so on).

Source

http://davidakenny.net/srm/srmdata.htm

References

Albright, L., Kenny, D. A., & Malloy, T. E. (1988). Consensus in personality judgments at zero acquaintance. Journal of Personality and Social Psychology, 55(3), 387-395. doi: 10.1037/0022-3514.55.3.387

Dataset Back et al. (2011)

Description

Dataset used in Back, Schmukle and Egloff (2011).

Usage

data(data.back)

Format

• The dataset data.back is a round-robin desiogn with 54 units and has the following structure

  'data.frame': 2862 obs. of 8 variables:
$ Group : num 1 1 1 1 1 1 1 1 1 1 ...
$ Actor : int 1 1 1 1 1 1 1 1 1 1 ...
$ Partner: int 2 3 4 5 6 7 8 9 10 11 ...
$ Dyad : int 1 2 3 4 5 6 7 8 9 10 ...
$ y : int 3 3 2 2 4 3 3 2 3 3 ...
$ sex : int 1 1 1 1 1 1 1 1 1 1 ...
$ age : int 22 22 22 22 22 22 22 22 22 22 ...
$ n : num -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 -1.17 ...
  

Source

https://osf.io/zd67x/

References

Back, M. D., Schmukle, S. C., & Egloff, B. (2011). A closer look at first sight: Social relations lens model analysis of personality and interpersonal attraction at zero acquaintance. European Journal of Personality, 25(3), 225-238. doi: 10.1002/per.790

Dataset Bond and Malloy (2018)

Description

This is the illustration dataset of Bond and Malloy (2018) for a bivariate social relations model. The round robin design contains 16 persons and some missing values for one person.

Usage

data(data.bm1)
data(data.bm2)

Format

• The dataset data.bm1 contains all ratings in a wide format. The two outcomes are arranged one below the other.

  'data.frame': 32 obs. of 16 variables:
$ a: int NA 12 13 14 15 15 14 14 13 13 ...
$ b: int 10 NA 10 18 7 15 14 8 12 12 ...
$ c: int 13 12 NA 14 13 14 13 13 11 12 ...
[...]
$ p: int 11 13 14 14 9 8 17 13 11 12 ...
  

• The dataset data.bm2 is a subdataset of data.bm1 which contains observations 9 to 16.

Source

http://thomasemalloy.org/arbsrm-the-general-social-relations-model/

References

Bond, C. F., & Malloy, T. E. (2018a). Social relations analysis of dyadic data structure: The general case. In T. E. Malloy. Social relations modeling of behavior in dyads and groups (Ch. 14). Academic Press. doi: 10.1016/B978-0-12-811967-9.00014-X

Example Datasets for the srm Package

Description

Some simulated example datasets for the srm package.

Usage

data(data.srm01)

Format

• The dataset data.srm01 contains three variables, 10 round robin groups with 10 members each.

  'data.frame': 900 obs. of 7 variables:
$ Group : num 1 1 1 1 1 1 1 1 1 1 ...
$ dyad : num 1 2 3 4 5 6 7 8 9 10 ...
$ Actor : num 1 1 1 1 1 1 1 1 1 2 ...
$ Partner: num 2 3 4 5 6 7 8 9 10 3 ...
$ Wert1 : num -0.15 -0.95 0.82 1.15 -1.79 1.17 1.79 -0.57 -0.46 1.19 ...
$ Wert2 : num -0.77 0.17 0.42 0.16 -0.44 0.89 1.67 -1.9 -0.74 2.67 ...
$ Wert3 : num -0.49 0.08 -0.12 1.16 -2.78 -0.74 2.66 -1.28 -0.45 1.93 ...
  

Structural Equation Model for the Social Relations Model

Description

Provides an estimation routine for a multiple group structural equation model for the social relations model (SRM; Kenny & La Voie, 1984; Warner, Kenny, & Soto, 1979). The model is estimated by maximum likelihood (Gill & Swartz, 2001; Nestler, 2018).

Usage

srm(model.syntax = NULL, data = NULL, group.var = NULL, rrgroup_name = NULL,
  person_names = c("Actor", "Partner"), fixed.groups = FALSE, var_positive = -1,
  optimizer = "srm", maxiter = 300, conv_dev = 1e-08, conv_par = 1e-06,
  do_line_search = TRUE, line_search_iter_max = 6, verbose = TRUE, use_rcpp = TRUE,
  shortcut = TRUE, use_woodbury = TRUE)

## S3 method for class 'srm'
coef(object, ...)
## S3 method for class 'srm'
vcov(object, ...)
## S3 method for class 'srm'
summary(object, digits=3, file=NULL, layout=1, ...)
## S3 method for class 'srm'
logLik(object, ...)

Arguments

Value

List with following entries (selection)

References

Gill, P. S., & Swartz, T. B. (2001). Statistical analyses for round robin interaction data. Canadian Journal of Statistics, 29(2), 321-331. doi: 10.2307/3316080

Kenny, D. A., & La Voie, L. J. (1984). The social relations model. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 18, pp. 142-182). Orlando, FL: Academic. doi: 10.1016/S0065-2601(08)60144-6

Nestler, S. (2018). Likelihood estimation of the multivariate social relations model. Journal of Educational and Behavioral Statistics, 43(4), 387-406. doi: 10.3102/1076998617741106

Warner, R. M., Kenny, D. A., & Soto, M. (1979). A new round robin analysis of variance for social interaction data. Journal of Personality and Social Psychology, 37(10), 1742-1757. doi: 10.1037/0022-3514.37.10.1742

See Also

See also TripleR and amen packages for alternative estimation routines for the SRM.

Examples

#############################################################################
# EXAMPLE 1: Univariate SRM
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
%Person
F1@A =~ 1*Wert1@A
F1@P =~ 1*Wert1@P
Wert1@A ~~ 0*Wert1@A + 0*Wert1@P
Wert1@P ~~ 0*Wert1@P

%Dyad
F1@AP =~ 1*Wert1@AP
F1@PA =~ 1*Wert1@PA
Wert1@AP ~~ 0*Wert1@AP + 0*Wert1@PA
Wert1@PA ~~ 0*Wert1@PA
'

#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4, maxiter=20)
summary(mod1)
round(coef(mod1),3)


#############################################################################
# EXAMPLE 2: Bivariate SRM
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
%Person
F1@A =~ 1*Wert1@A
F1@P =~ 1*Wert1@P
F2@A =~ 1*Wert2@A
F2@P =~ 1*Wert2@P
Wert1@A ~~ 0*Wert1@A + 0*Wert1@P
Wert1@P ~~ 0*Wert1@P
Wert2@A ~~ 0*Wert2@A + 0*Wert2@P
Wert2@P ~~ 0*Wert2@P

%Dyad
F1@AP =~ 1*Wert1@AP
F1@PA =~ 1*Wert1@PA
F2@AP =~ 1*Wert2@AP
F2@PA =~ 1*Wert2@PA
Wert1@AP ~~ 0*Wert1@AP + 0*Wert1@PA
Wert1@PA ~~ 0*Wert1@PA
Wert2@AP ~~ 0*Wert2@AP + 0*Wert2@PA
Wert2@PA ~~ 0*Wert2@PA
'

#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4, maxiter=20)
summary(mod1)

#############################################################################
# EXAMPLE 3: One-factor model
#############################################################################

data(data.srm01, package="srm")
dat <- data.srm01

#-- define model
mf <- '
# definition of factor for persons and dyad
%Person
f1@A=~Wert1@A+Wert2@A+Wert3@A
f1@P=~Wert1@P+Wert2@P+Wert3@P

%Dyad
f1@AP=~Wert1@AP+Wert2@AP+Wert3@AP

# define some constraints
Wert1@AP ~~ 0*Wert1@PA
Wert3@AP ~~ 0*Wert3@PA
'
#-- estimate model
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", conv_par=1e-4)
summary(mod1)
coef(mod1)

#- use stats::nlminb() optimizer
mod1 <- srm::srm(mf, data = dat, rrgroup_name="Group", optimizer="nlminb", conv_par=1e-4)
summary(mod1)

Least Squares Estimation of the Social Relations Model (Bond & Malloy, 2018)

Description

Provides least squares estimation of the bivariate social relations model with missing completely at random data (Bond & Malloy, 2018a). The code is basically taken from Bond and Malloy (2018b) and rewritten for reasons of computation time reduction.

Usage

srm_arbsrm(data, serror = TRUE, use_srm = TRUE)

## S3 method for class 'srm_arbsrm'
coef(object, ...)
## S3 method for class 'srm_arbsrm'
summary(object, digits=3, file=NULL, ...)

Arguments

Value

List containing entries

Note

If you use this function, please also cite Bond and Malloy (2018a).

Author(s)

Rewritten code of Bond and Malloy (2018b). See http://thomasemalloy.org/arbsrm-the-general-social-relations-model/ and http://thomasemalloy.org/wp-content/uploads/2017/09/arbcodeR.pdf.

References

Bond, C. F., & Malloy, T. E. (2018a). Social relations analysis of dyadic data structure: The general case. In T. E. Malloy. Social relations modeling of behavior in dyads and groups (Ch. 14). Academic Press. doi: 10.1016/B978-0-12-811967-9.00014-X

Bond, C. F., & Malloy, T. E. (2018b). ARBSRM - The general social relations model. http://thomasemalloy.org/arbsrm-the-general-social-relations-model/.

See Also

Without missing data, ANOVA estimation can be conducted with the TripleR package.

Examples

#############################################################################
# EXAMPLE 1: Bond and Malloy (2018) illustration dataset
#############################################################################

data(data.bm2, package="srm")
dat <- data.bm2

#- estimation
mod1 <- srm::srm_arbsrm(dat)
mod1$par_summary
coef(mod1)
summary(mod1)


#-- estimation with original Bond and Malloy code
mod1a <- srm::srm_arbsrm(dat, use_srm=FALSE)
summary(mod1a)