Description Usage Arguments Details Value Note Author(s) References See Also Examples
The function RR performs Social Relation Model (SRM) analyses for single or multiple roundrobin groups.
1 2 
formula 
a formula (see details) consisting of a measure (e.g. a rating) obtained with a roundrobin group 
data 
A data frame in long format 
na.rm 
If missing values are present, you have to set this parameter to TRUE 
minData 
Sets the minimum of data points which have to be present in each row and column 
verbose 
Defines if warnings and additional information should be printed on execution 
g.id 
For internal use only; do not set this parameter 
index 
set 
exclude.ids 
For internal use only; do not set this parameter 
varname 
The name stem of the effects variables. By default, this is the first variable passed in the formula. In case of latent constructs, however, it might be preferable to set a new name for the latent construct. 
se 
This defines how significance tests are computed in the multigroup case. Either "LashleyBond" (= recommended significancetest by Lashley & Bond, 1997; default) or "SOREMO" (= between group significance test as implemented in SOREMO). In single groups, always the LashleyBond standard errors and significance tests are provided. 
minVar 
Actor and partners effects are only calculated if the respective relative variance component is greater than minVar. Set minVar to NA if this cleaning should not be performed. For small groups, Kenny (1994) suggests a minimum relative variance of 10% for the interpretation of SRM effects. In any case, actor/ partner effects and correlations with these variables should not be interpreted if these components have negative variance estimates. minVar defaults to zero; with RR.style this default can be changed for all subsequent analyses. 
... 
Further undocumented or internal arguments passed to the function 
Please note: detailed instructions on how to use the TripleR package are provided in the built in pdf document ‘How to use TripleR’. You can open this document either by on this link: ../doc/TripleR.pdf, or you can browse all included vignettes by opening the index of the package documentation (scroll down to the very end of this page and click on ‘Index’; than click on ‘Overview of user guides and package vignettes’). These help files are only for quick references.
The estimation of the parameters of the Social Relation Model (SRM) is based on formulae provided by Kenny (1994; p. 236244). If multiple groups are provided, SRMs are computed within each single group. Variance components then are calculated as the weighted average across groups (weighted with N <e2><80><93> 1). Please note that in case of largely varying group sizes a precision weighting based on inversevariance weights might be preferable but is not currently implemented in TripleR.
For tests of significance of a single group, Triple R computes standard errors by using formulae published by Bond and Lashley (1996) for the case of a univariate SRM analysis. The formulae for the standard errors of the bivariate SRM parameters were kindly provided to us by C.F. Bond in personal communication. If multiple groups are provided, by default the standard error is computed as the square root of the weighted mean of the squared groupspecific standard errors, as recommended by Lashley and Bond (1997). For se = <e2><80><9c>SOREMO<e2><80><9d>
a betweengroup ttest is employed to calculate the significance which was suggested by Kenny and La Voie (1984) and is used in the software SOREMO.
The BondLashley standard errors operate under the assumption that the only source of variance in the SRM parameter is sampling error, i.e. that variance estimates do not differ systematically between groups. Lashley and Kenny (1998) found that this assumption was basically true in 13 different round robin studies. Thus, we generally recommend using the BondLashley standard errors (se="LashleyBond"
), which are more powerful than the KennyLa Voie standard errors (se="SOREMO"
), see Lashley and Bond (1997) and Lashley and Kenny (1998). However, in case that you have reasons to assume that variances systematically vary across groups, the KennyLa Voie standard errors (se="SOREMO"
) might be preferable as they are likely more robust in the presence of group effects (Lashley & Kenny, 1998).
The formula to specify the round robin design has following notation:
DV ~ perceiver.id * target.id  group.id
(group.id is only provided if multiple groups are present). If two variables are used to describe one latent construct, both are connected with a slash on the left hand side: DV1a/DV1b ~ perceiver.id * target.id
. If two variables are used to describe two manifest constructs, both are connected with a +
on the left hand side: DV1+DV2 ~ perceiver.id * target.id
. A latent analysis of two constructs would be notated as following: DV1a/DV1b + DV2a/DV2b ~ perceiver.id * target.id
.
Data sets from the Mainz Freshman Study (see Back, Schmukle, & Egloff, in press) are included (liking_a, liking_b, metaliking_a, metaliking_b, likingLong), as well as an additional data set containing missing values (multiGroup)
The handling for missing data (na.rm=TRUE
) is performed in three steps:
Rows and columns which have less then minData
data points are removed from the matrix (i.e. both the ‘missing’ row or column and the corresponding column or row, even if they have data in them)
For the calculation of actor and partner variances, actorpartnercovariances and the respective effects, missing values are imputed as the average of the respective row and col means. The calculation of relationship variances and covariances as well as relationship effects is also based on the imputed data set; however, single relationship effects which were missing in the original data set are set to missing again.
In the case of multiple variables (i.e., latent or bivariate analyses), participants are excluded listwise to ensure that all analyses are based on the same data set.
Printed are both unstandardized and standardized SRM estimates with the corresponding standard errors and tvalues for actor variance, partner variance, relationship variance, error variance, actorpartner covariance, and relationship covariance. In case of a bivariate analysis values are additionally provided for actoractor covariance, partnerpartner covariance, actorpartner covariance, partneractor covariance, intrapersonal relationship covariance, and interpersonal relationship covariance.
Reliabilities of actor, partner, and relationship effects (the latter only in the case of latent analyses) are printed according to Bonito & Kenny (2010).
The returned values depend on the kind of analyses that is performed:
Univariate, single group:
effects 
actor and partner effects for all participants; if self ratings are provided they also are returned as group mean centered values 
effects.gm 
actor and partner effects for all participants, group mean added 
effectsRel 
relationship effects for all participants 
varComp 
variance components 
Bivariate, single group:
univariate 
List of results of univariate of SRM analyses of both variables specify variable in index: univariate[[1]] or univariate[[2]]. That means, each of the both 
bivariate 
Results of bivariate SRM analyses 
In the multiple group case, following values are returned:
univariate 
The weighted average of univariate SRM results 
bivariate 
The weighted average of bivariate SRM results 
groups 
SRM results for each group 
effects 
actor and partner effects for all participants 
effectsRel 
relationship effects for all participants 
varComp.group 
a list of variance components in all single groups 
group.var 
group variance estimate 
If self ratings are present in the data set, the function also prints the correlation between self ratings and actor/partner effects. In case of multiple groups, these are corrected for group membership (partial correlations). These correlations with selfratings can also directly be computed with the function selfCor
. Partial correlations to external (nonSRM) variables can be computed with the function parCor
In case that a behavior was measured, the variances of an SRM analysis are labeled as actor variance, partner variance and relationship variance (default output labels). In case that a perception was measured, perceiver is printed instead of actor and target is printed instead of partner. You can set appropriate output labels by using the function RR.style
.
These settings from RR.style, however, can be overwritten for each print
call: :
print(RRobject, measure1="behavior")
: prints output for a univariate analysis of behavior data.
print(RRobject, measure1="perception")
: prints output for a univariate analysis of perception data.
print(RRobject, measure1="behavior", measure2="behavior")
: prints output for a bivariate analysis of behavior data.
print(RRobject, measure1="perception", measure2="perception")
: prints output for a bivariate analysis of perception data.
print(RRobject, measure1="behavior", measure2="perception")
or
print(RRobject, measure1="perception", measure2="behavior")
: prints output for a bivariate analysis of behavior and perception data.
print(RRobject, measure1="perception", measure2="metaperception")
: is for the special case that a perception and a corresponding metaperception was measured. In this case, additionally the appropriate output labels for bivariate SRM analyses of other and metaperceptions (reciprocities, assumed reciprocities, metaaccuracies; see Kenny, 1994) are presented.
print(RRobject, digits=6)
: Provide the number of displayed digits.
You can plot any RR object using plot(RR)
. See plot.RRuni
for possible parameters.
Felix D. Sch<c3><b6>nbrodt, Mitja D. Back, Stefan C. Schmukle
The main reference for the TripleR package is:
Sch<c3><b6>nbrodt, F. D., Back, M. D., & Schmukle, S. C. (2012). TripleR: An R package for social relations analyses based on roundrobin designs. Behavior Research Methods, 44, 455470. doi:10.3758/s1342801101504
Please cite this paper if you use TripleR in your research.
Further information on SRM and its application:
Back, M. D., Schmukle, S. C. & Egloff, B. (2011). A closer look at first sight: Social relations lens model analyses of personality and interpersonal attraction at zero acquaintance. European Journal of Personality, 25, 225238. doi:10.1002/per.790
Bond, C. F., Jr., & Lashley, B. R. (1996). Roundrobin analysis of social interaction: Exact and estimated standard errors. Psychometrika, 61, 303311. doi:10.1007/BF02294341
Bonito, J. A., & Kenny, D. A. (2010). The measurement of reliability of social relations components from roundrobin designs. Personal Relationships, 17, 235  251. doi:10.1111/j.14756811.2010.01274.x
Kenny, D. A. (1994). Interpersonal perception: A social relations analysis. New York: Guilford Press.
Kenny, D. A., & La Voie, L. J. (1984). The social relations model. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 18, pp. 142182). San Diego, CA: Academic Press.
Kwan, V. S. Y., John, O. P., Kenny, D. A., Bond, M. H., & Robins, R. W. (2004). Reconceptualizing individual differences in selfenhancement bias: An interpersonal approach. Psychological Review, 111, 94<e2><80><93>110. doi:10.1037/0033295X.111.1.94
Lashley, B. R., & Bond, C. F., Jr. (1997). Significance testing for round robin data. Psychological Methods, 2, 278291. doi:10.1037/1082989X.2.3.278
Lashley, B. R. & Kenny, D. A. (1998). Power estimation in social relations analyses. Psychological Methods, 3, 328338. doi:10.1037/1082989X.3.3.328
RR.style
, getEffects
, plot_missings
, long2matrix
, matrix2long
, plot.RRuni
, RR.summary
, selfCor
, parCor
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108  # The example data are taken from the "Mainz Freshman Study" and consist
# of ratings of liking as well as ratings of the metaperception of
# liking at zeroacquaintance using a RoundRobin group of 54 participants
#
#  Single group 
#
# Load data frame in long format  it contains 4 variables:
#liking ratings indicator a (liking_a, "How likeable do you find this person?")
#liking ratings indicator b (liking_b, "Would you like to get to know this person?")
#metaliking ratings indicator a (metaliking_a, "How likeable does this person find you?")
#metaliking ratings indicator b (metaliking_b, "Would this person like to get to know you?")
data("likingLong")
#manifest univariate SRM analysis
RR1 < RR(liking_a ~ perceiver.id*target.id, data=likingLong)
## Not run:
#manifest bivariate SRM analysis
RR2 < RR(liking_a + metaliking_a ~ perceiver.id*target.id, data=likingLong)
#latent (constructlevel) univariate SRM analysis
RR3 < RR(liking_a / liking_b ~ perceiver.id*target.id, data=likingLong)
#latent (constructlevel) univariate SRM analysis, define new variable name for the latent construct
RR3b < RR(liking_a / liking_b ~ perceiver.id*target.id, data=likingLong, varname="liking")
# compare:
head(RR3$effects)
head(RR3b$effects)
#latent (constructlevel) bivariate SRM analysis
RR4 < RR(liking_a/liking_b + metaliking_a/metaliking_b ~ perceiver.id*target.id, data=likingLong)
# prints output of the manifest univariate analysis
# in terms of actor and partner variance (default output labels)
print(RR1, measure1="behavior")
# prints output of the manifest univariate analysis
# in terms of perceiver and target variance (appropriate for perception data)
print(RR1, measure1="perception")
# prints output of the manifest bivariate SRM analysis appropriate
# for perceptionmetaperception data
print(RR2, measure1="perception", measure2="metaperception")
#prints output of the latent univariate SRM analysis
# appropriate for perception data
print(RR3, measure1="perception")
#prints output of the latent bivariate SRM analysis
# appropriate for perceptionperception data
# Note: you can use abbreviations of the strings "behavior", "perception", and "metaperception"
print(RR4, measure1="p", measure2="p")
## End(Not run)
#
#  Multiple groups 
#
# data("multiLikingLong") is a variant of the liking data set (see above) with multiple groups
data("multiLikingLong")
# set RR.style to "perception" (affects subsequent printing of objects)
RR.style("perception")
#manifest univariate SRM analysis
RR1m < RR(liking_a ~ perceiver.id*target.idgroup.id, data=multiLikingLong)
## Not run:
#manifest bivariate SRM analysis
RR2m < RR(liking_a + metaliking_a ~ perceiver.id*target.idgroup.id, data=multiLikingLong)
#latent (constructlevel) univariate SRM analysis
RR3m < RR(liking_a / liking_b ~ perceiver.id*target.idgroup.id, data=multiLikingLong)
#latent (constructlevel) bivariate SRM analysis
RR4m < RR(liking_a/liking_b + metaliking_a/metaliking_b ~ perceiver.id*target.idgroup.id,
data=multiLikingLong)
# prints output of the manifest univariate analysis
# in terms of actor and partner variance (default output labels)
print(RR1m, measure1="behavior")
# prints output of the manifest univariate analysis
# in terms of perceiver and target variance (appropriate for perception data)
print(RR1m, measure1="perception")
#
#  Multiple groups with missing values 
#
# a multi group data set with two variables:
# ex = extraversion ratings, and ne = neurotizism ratings
data("multiGroup")
#manifest univariate SRM analysis, data set with missings
RR1miss < RR(ex~perceiver.id*target.idgroup.id, data=multiGroup, na.rm=TRUE)
#manifest univariate SRM analysis, data set with missings,
# minimum 10 data points are requested for each participant
RR1miss < RR(ex~perceiver.id*target.idgroup.id, data=multiGroup, na.rm=TRUE, minData=10)
## End(Not run)

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