Package 'srm'

Title: 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>).
Authors: Steffen Nestler [aut], Alexander Robitzsch [aut, cre], Oliver Luedtke [aut]
Maintainer: Alexander Robitzsch <[email protected]>
License: GPL (>= 2)
Version: 0.5-1
Built: 2024-03-29 06:56:11 UTC
Source: https://github.com/alexanderrobitzsch/srm

Help Index


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 <[email protected]>

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.


Dataset Back et al. (2011)

Description

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

Usage

data(data.back)

Format

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

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


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


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

model.syntax

Syntax similar to lavaan language, see Examples.

data

Data frame containing round robin identifier variables and variables in the round robin design

group.var

Name of grouping variable

rrgroup_name

Name of variable indicating round robin group

person_names

Names for identifier variables for actors and partners

fixed.groups

Logical indicating whether groups should be handled with fixed effects

var_positive

Nonnegative value if variances are constrained to be positive

optimizer

Optimizer to be used: "srm" for internal optimization using Fisher scoring and "nlminb" for L-FBGS optimization.

maxiter

Maximum number of iterations

conv_dev

Convergence criterion for change relative deviance

conv_par

Convergence criterion for change in parameters

do_line_search

Logical indicating whether line search should be performed

line_search_iter_max

Number of iterations during line search algorithm

verbose

Logical indicating whether convergence progress should be displayed

use_rcpp

Logical indicating whether Rcpp package should be used

shortcut

Logical indicating whether shortcuts for round robin groups with same structure should be used

use_woodbury

Logical indicating whether matrix inversion should be simplified by Woodbury identity

object

Object of class srm

file

Optional file name for summary output

digits

Number of digits after decimal in summary output

layout

Different layouts (1 or 2) for layout of summary

...

Further arguments to be passed

Value

List with following entries (selection)

parm.table

Parameter table with estimated values

coef

Vector of parameter estimates

vcov

Covariance matrix of parameter estimates

parm_list

List of model matrices

sigma

Model implied covariance matrices

...

Further values

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

data

Rectangular dataset currently containing only one round robin group. Bivariate observations are stacked one below the other (see example dataset data.bm1).

serror

Logical indicating whether standard errors should be calculated.

use_srm

Logical indicating whether the rewritten code (TRUE) or the original code of Bond and Malloy (2018b) should be used.

object

Object of class srm_arbsrm

file

Optional file name for summary output

digits

Number of digits after decimal in summary output

...

Further arguments to be passed

Value

List containing entries

par_summary

Parameter summary table

est

Estimated parameters (as in Bond & Malloy, 2018b)

se

Estimated standard errors (as in Bond & Malloy, 2018b)

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)

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