Normative and Distinctive Situational Accuracy
1
Running Head: NORMATIVE AND DISTINCTIVE SITUATION PERCEPTION ACCURACY
Normative and Distinctive Accuracy in Situation Perceptions:
Magnitude and Personality Correlates
John F. Rauthmann1 & Ryne A. Sherman2
1
Humboldt-Universität zu Berlin (Germany)
2
Florida Atlantic University (USA)
Accepted for publication in
Social Psychological and Personality Science
– Version before copy-editing from 11/02/2016 –
Author Notes
We thank Jeremy Biesanz for valuable advice on data-analytical issues concerning SAM.
All findings and R codes as well as additional analyses can be found openly at osf.io/qgu6h
as well as in the online supplemental materials.
Correspondence: John Rauthmann, Humboldt-Universität zu Berlin, Unter den Linden 6, D10099 Berlin, Germany. Phone: 0049-30-2093-1836. Fax: 0049-30-2093-9342. E-mail:
[email protected].
Normative and Distinctive Situational Accuracy
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Abstract
To what extent do people achieve accuracy in judging others’ situations? Based on interpersonal
perception models, we propose that ex-situ raters may attain accuracy by judging the
psychological characteristics of a situation that in-situ raters have experienced according to a
normative and distinctive characteristics profile. Biesanz’ Social Accuracy Model (SAM)
provides a flexible crossed-effects random coefficient modeling framework that can be applied to
situation perception data. By targeting characteristics profiles with the analytical unit of the exsitu rater-situation dyad, the extent of and variation in normative and distinctive accuracy of exsitu raters can be estimated and explained by personality correlates to quantify “the good ex-situ
rater.” We demonstrate a SAM approach to situational accuracy with real in-situ and ex-situ data
(402 ex-situ raters judged 10 situations on 8 characteristics) and sketch future research.
Key words: situations, situation perception, accuracy, normative and distinctive accuracy, Social
Accuracy Model
Normative and Distinctive Situational Accuracy
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Normative and Distinctive Accuracy in Situation Perceptions:
Magnitude and Personality Correlates
People form impressions of situations as if they were real, coherent entities (Cantor et al.,
1982; Edwards & Templeton, 2005; Forgas, 1976; Magnusson, 1981; Rauthmann, 2012;
Rauthmann et al., 2015a,b; Serfass & Sherman, 2013; Sherman et al., 2012, 2013). Thus,
situation perception may follow principles similar to person perception (Nystedt, 1972a,b, 1981;
Rauthmann, 2012; Rauthmann et al., 2014). Attending to situation perception is important
because knowing others’ situations can help understand and predict their behavior better. Thus, a
crucial question is how good people actually are at deciphering others’ situations. Further, most
research interested in the situation as the unit of analysis or as a moderating variable will need
explicit measurements of situations (Rauthmann et al., 2015a), which will most often be ratings
of the psychological characteristics of target-situations from different sources (e.g., in-situ raters:
people in and affected by the situation; ex-situ raters: neither present nor affected) and thus
essentially tap perceptions. This opens up the door to investigate the agreement between different
kinds of situation raters (Rauthmann & Sherman, in revision).
This work serves to deepen our understanding of situational accuracy as the extent to
which ex-situ raters can accurately judge the characteristics of situations from in-situ raters with
only minimal information available. Specifically, we aim to address three questions within a
flexible multi-level modeling (MLM) approach:
(1) How accurately can people generally judge others’ situations?
(2) How strong is normative and distinctive situational accuracy?
(3) Which broad personality traits are associated with individual differences in being
normatively and distinctively accurate?
Normative and Distinctive Situational Accuracy
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Situation Perception
Person and Situation Perception
Concepts and methods of interpersonal perception and person(ality) judgment literature
can also be applied to situation perception (Nystedt, 1981; Rauthmann, 2012; Rauthmann &
Sherman, in revision; Rauthmann et al., 2015a). Nonetheless, situation perception is in key
respects different from person perception. First, perceptions are only unidirectional (i.e., no
reciprocity as in interpersonal perceptions; Kenny, 1994). Second, situation perceptions could
fluctuate more as situations are ever-changing, dynamic, and fleeting (in contrast to persons as
lasting, physical beings). Third, situations cannot rate themselves, so raters are required to assess
their psychological characteristics. This latter point invites the question of reality: To what extent
is a situation a “real thing?” To cope with this question, Rauthmann et al. (2015a) proposed three
principles of psychological situation research: A psychologically relevant situation (1) only
“exists” if at least one person processes it, (2) is grounded in three types of reality (physical,
social, and personal), and (3) should be measured from different perspectives (i.e., in-situ and exsitu raters). These principles require a better understanding of how in-situ and ex-situ raters agree
in their perceptions of the same situation.
Situation Characteristics
People pervasively process environmental cues and form psychological situation
representations imbued with meaning and interpretations (Argyle et al., 1981; Block & Block,
1981; Magnusson, 1981; Rauthmann et al., 2015a,b). Psychological situations can be described
with situation characteristics similarly to how persons can be described with traits (de Raad,
2004; Edwards & Templeton, 2005; Rauthmann et al., 2014). Rauthmann et al. (2015a,b) argued
that situation research should proceed in a variable-oriented way by using continuous dimensions
of characteristics. For example, situations are assessed by asking participants to rate the extent
Normative and Distinctive Situational Accuracy
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that a certain characteristic applied (e.g., work has to be done) rather than just list cues (e.g.,
books lying on the desk) or classify the situation (e.g., work situation). Thus, we obtain data on
what situations mean to people. This work concerns to what extent people agree in their
assessments of situations’ characteristics.
Recently, Rauthmann et al. (2014) proposed to taxonomize situation characteristics into
eight major domains, the Situational Eight DIAMONDS (Duty: Does work need to be done?
Intellect: Is deep thinking required? Adversity: Is someone threatened? Mating: Is the situation
sexually/romantically charged? pOsitivity: Is the situation enjoyable? Negativity: Could negative
feelings ensue? Deception: Is mistrust an issue? Sociality: Can meaningful social interaction and
relationships develop?). These eight domains, integrating most previously identified
characteristic dimensions into a common framework and language, have been shown to be useful
in understanding how people’s everyday situations look like (Brown & Rauthmann, 2016;
Serfass & Sherman, 2015) and how personality, situations, and behavior work together
(Rauthmann, 2016; Rauthmann et al., 2015c; Rauthmann et al., 2016; Rauthmann & Sherman,
2016a; Sherman et al., 2015). Thus, we deem the Situational Eight a good starting point to
examine situational accuracy.
Accuracy in Judging Situation Characteristics
When judging others’ situations, there can be two accuracy criteria: capturing what the
typical situation is like (= normative profile) and what makes the judged situation unique (=
distinctive profile as the deviation from the norm). People may be accurate with regard to none,
both, or only one of these. This means we should distinguish between normative and distinctive
accuracy (Biesanz, 2010; Furr, 2008), though achieving any form of situational accuracy is
important in daily life. To understand others, predict their behavior, and coordinate own behavior
Normative and Distinctive Situational Accuracy
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with others, it is paramount to accurately judge others’ situations – both in terms of what
situations are generally like (normative accuracy) and what makes a specific situation stand out
from the average situation (distinctive accuracy). Being normatively accurate may aid navigating
typical or recurrent social situations, while being distinctively accurate could aid understanding
specific situations. Having distinctive situation knowledge (Sherman et al., 2012; Serfass &
Sherman, 2013) may, in turn, translate into better perspective-taking skills and more empathy or
at least underlie them. Thus, understanding situational accuracy is an important endeavor.
To date, however, there is only little direct research on situational accuracy. First,
Rauthmann et al. (2014) had ex-situ raters read brief vignettes describing the situations of in-situ
raters (e.g., “Going shopping with my boyfriend”), while both in-situ and ex-situ raters rated the
situations on the DIAMONDS. The average agreement hovered around r=.50, which is quite
sizable given that ex-situ raters had only limited written information on in-situ raters’ situations
available. However, this study cannot tell us whether ex-situ raters were normatively and/or
distinctively accurate.
Second, Rauthmann and Sherman (in revision) outlined how situational accuracy could
be studied using different variance decompositions (Biesanz, 2010; Cronbach, 1955; Jussim,
2005; Kenny, 1994; Kenny et al., 2006). Despite their appeal, the showcased decomposition
techniques cannot clearly distinguish between normative versus distinctive accuracy (Biesanz,
2010) and extract individual differences therein. For example, most techniques require piecemeal
procedures to examine personality correlates of situational accuracy (i.e., effect scores need to be
extracted first, which are then correlated with personality variables). This work employs an
MLM-approach by Biesanz (2010) to disentangle normative from distinctive situational accuracy
and flexibly incorporate personality moderators.
Normative and Distinctive Situational Accuracy
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A Social Accuracy Model Approach to Situation Perceptions
Common ANOVA-inspired decomposition techniques usually require multi-step
approaches (e.g., extracting effects, correlating them, etc.). As Biesanz (2010, p. 858) noted,
such a “two-stage modeling approach is inelegant, inefficient, requires a complete and balanced
design, and is restrictive in the questions that it allows to be asked,” while “through the use of
[MLM], the entire analysis can be placed within a single model that will allow a richer set of
substantively important questions to be addressed.” MLM thus offers several advantages. First, it
can be used to analyze the entire data with robust estimations, handling missing values
effectively. Second, ex-situ raters’ personality traits may be introduced simultaneously into one
model in higher-order levels. Third, common decomposition techniques can be readily translated
into MLM (e.g., Gelman, 2006; Hoffman & Rovine, 2007; Kenny et al., 2006). Lastly, variations
in stimuli – here situations – can also be explicitly modeled by treating situations as random
factors (Judd et al., 2012).
To date, the most sophisticated MLM-approach to address accuracy questions is Biesanz’
(2010) Social Accuracy Model (SAM). SAM is particularly elegant because it disentangles
normative and distinctive accuracy in a straightforward manner. It embraces and reconciles the
Cronbachian tradition, concerned with distinguishing normative from distinctive accuracy, and
Kenny’s Social Relations Model tradition, concerned with relations between perceivers and
targets. Applying SAM to situation perception data, let the index p represent the pth perceiver, s
the sth situation, and c the cth characteristic:
Lower level:
expsc = 0ps + 1psinsc + 2psc + psc
(1)
Normative and Distinctive Situational Accuracy
Upper level:
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(1.1)
0ps = 00 + 01Traitp + u0p + u0s + u0(ps)
1ps = 10 + 11Traitp + u1p + u1s + u1(ps)
2ps = 20 + 21Traitp + u2p + u2s + u2(ps)
– Table 1 –
The variables as well as fixed and random effects in this crossed-random effects MLM
are summarized in Table 1 (see also Biesanz, 2010, pp. 866-868). The basic rationale is that for
each rater-situation dyad across all characteristics, ex-situ ratings are predicted from the in-situ
criterion scores as well as the norm values of all characteristics (= typical/average situation: how
situations are generally rated on all characteristics sampled). Thus, profile relationships across
characteristics are focused on, and for these there are two kinds of accuracy (Equation 1). The
parameter 1ps provides an estimate of distinctive accuracy: how ex-situ ratings uniquely capture
in-situ ratings, controlling for the normative profile. The parameter 2ps provides an estimate of
normative accuracy: how ex-situ ratings capture the normative profile (controlled for in-situ
ratings).
The random effects of normative and distinctive accuracy for ex-situ raters and situations,
respectively, point towards differences in “the good ex-situ rater” and “the good situation” (for
personality judgment analogs, see Funder, 1995, 1999): ex-situ raters may attain more or less
normative or distinctive accuracy, respectively (i.e., perceptive accuracy; Biesanz, 2010), and
situations may be judged with more or less normative or distinctive accuracy, respectively (i.e.,
expressive accuracy; Biesanz, 2010). These relations are summarized in Table 2.
– Table 2 –
Normative and Distinctive Situational Accuracy
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Going even further (Equation 1.1), we can attempt to explain the individual differences in
perceptive accuracy.1 Specifically, we can model to what extent personality traits of ex-situ raters
moderate distinctive (11) and normative perceptive accuracy (21). Such analyses address the
traits of who is a “good” (= more accurate) or “bad” (= less accurate) ex-situ rater.
Current Work
Aims
This work serves three main aims in demonstrating how an MLM-implementation can be
fruitfully applied to situation perception data. In doing so, we focus on Biesanz’ SAM approach
because it readily grants examining the magnitude of normative and distinctive situational
accuracy. First, we aim to uncover how well ex-situ raters can generally judge in-situ raters’
situations (impressionistic accuracy). Second, we aim to disentangle normative from distinctive
situational accuracy. Third, we seek to identify trait correlates in being normatively and
distinctively accurate as, within SAM, personality moderators can be added to explain individual
differences in perceptive accuracy.
Hypotheses
First, we expected both normative and distinctive accuracy to be sizable, though the
former should be higher than the latter (e.g., Biesanz, 2010). Second, we expected sizable
individual differences in perceptive accuracy: ex-situ raters should vary in their accuracies.
Substantial (and meaningful) variance in perceptive accuracy is a prerequisite to introducing
personality traits as predictors (or cross-level moderators).1 Third, we expected that broad
personality traits, such as the Big Five, would explain perceiver slope variances. However, we
did not form any clear a priori hypotheses on which Big Five traits would be positively or
1
Differences between situations in their normative and distinctive expressive accuracy could also be explained (e.g.,
by situation cues, other characteristics, or class memberships). However, this is not of primary concern here because
we do not introduce any situation variables to explain or moderate expressive accuracy and the number of targetsituations is relatively small.
Normative and Distinctive Situational Accuracy
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negatively associated with normative and distinctive accuracy, respectively. The lone exception
was Neuroticism, a trait associated with higher vigilance towards potentially negative (or
ambiguous) stimuli and negative biases (e.g., Hirsh & Inzlicht, 2008; Robinson et al., 2007) so
that situations may be processed more negatively than they are which may entail less accuracy in
judging situations.
Methods
Participants
The data used here were detailed in Rauthmann and Sherman (2015a,b). Further, Rauthmann
and Sherman (in revision) performed various variance decomposition techniques on the data to
tease apart different forms of accuracy. However, the current SAM analyses are novel and
provide unique insights into situational accuracy and possible personality correlates. The online
supplemental materials contain all data and R codes to reproduce findings (see also osf.io/qgu6h).
In-situ Raters. In-situ criterion data were gathered online from a German sample of
N=547 participants (407 women, 140 men; age: M=28.01, SD=10.47, range:15-77 years).
Participants were first asked to think about the situation they were in 24 hours earlier and then
answer five questions in an open-text field: What was happening? Who was with you? What
were you doing? Where were you? What time was it (approximately)? Next, participants rated
their situation on the S8-I (as well as some other measures not of relevance here). From the total
pool of 547 situations, we selected 10 target-situations (based on the criterion to have different
DIAMONDS profiles and reflect situations people could commonly experience) to be presented
to a second sample, from which we obtained ex-situ ratings. Table 3 shows the 10 criterion
situations, along with their raw in-situ ratings on the S8-I items.
– Table 3 –
Normative and Distinctive Situational Accuracy
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Ex-situ Raters. Ex-situ ratings came from N=404 participants (300 women, 104 men;
age: M=25.2, SD=5.51, range:17-51 years) who rated each of the 10 situations in Table 3 on the
same S8-I items as in the in-situ data. Additionally, these participants completed a Big Five
questionnaire.
Measures
Situation Characteristics. Situations were rated in-situ and ex-situ on the S8-I with one
item per DIAMONDS characteristic (Rauthmann & Sherman, 2016b), using a seven-point Likert
type scale (1=not at all, 7=totally). The items can be found under Table 3. The S8-I has favorable
psychometric properties (Rauthmann & Sherman, 2016b) and is useful for answering substantive
research questions (Sherman et al., 2015).
Personality Traits. Ex-situ raters’ Big Five traits were assessed with the 16-item BFIS16 (Gerlitz & Schupp, 2005, Lang, 2005), using a seven-point Likert-type scale (1=does not
apply at all to me, 7=applies totally to me). We obtained trait-scores for Openness (M=5.50,
SD=0.95, α=.62), Conscientiousness (M=4.96, SD=1.12, α=.68), Extraversion (M=4.65,
SD=1.32, α=.81), Agreeableness (M=5.21, SD=1.01, α=.46), and Neuroticism (M=4.31,
SD=1.31, α=.74).
Results
SAM analyses were conducted on 32,320 observations consisting of 404 perceivers’ exsitu ratings of 10 situations on 8 characteristics. The ex-situ ratings were made on a 7-point
Likert-type scale from 1 to 7, with M=3.58 (SD=2.38). We within-characteristic centered the insitu criterion ratings on the 8 characteristics by subtracting out the characteristics norm ratings on
those characteristics.2 These normative ratings stem from the total in-situ sample (Rauthmann &
2
J. Biesanz (personal communication)
Normative and Distinctive Situational Accuracy
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Sherman, 2016b,c) with the following means (also on a 1-7 Likert-type scale): Duty=3.60,
Intellect=3.48, Adversity=1.58, Mating=2.56, pOsitivity=4.91, Negativity=2.71,
Deception=1.47, and Sociality=4.85. The norm values and personality scores were grand mean
centered.3
In total, we estimated three models: Model 1 predicted ex-situ ratings from in-situ ratings
(Aim 1), Model 2 predicted ex-situ ratings from in-situ ratings and normative values (Aim 2),
and Model 3 added perceivers’ personality traits to Model 2 as moderators (Aim 3). Findings
from Model 3 are summarized in Table 4 and random effects of Models 1-3 in Table 5. 4
– Table 4, 5 –
Model 1: Impressionistic Accuracy
Model 1 examined the overall effect of accuracy (i.e., impressionistic accuracy) by
predicting ex-situ ratings only from the in-situ data. Intercepts and slopes were allowed to vary
across perceivers and across situations (i.e., different perceivers and different situations could
vary in their accuracy levels). There was a statistically significant fixed effect (b=0.52, 95%
CI=[0.23, 0.80], t=3.40) indicating that, on average, ex-situ raters accurately judged the pattern
of characteristics of the situations.
The SDs of perceiver effects were 0.30 [0.27, 0.33] for intercepts and 0.05 [0.04, 0.07]
for slopes. The SDs of situation effects were 0.72 [0.42, 1.06] for intercepts and 0.48 [0.27, 0.74]
for slopes. The residual SD was 1.91. Overall, this indicates that there was sizable variation in
accuracies between situations, while the variation in perceiver accuracies was not as large (see
Figure 1). This indicates that there may be more differences in being a “good situation” (i.e.,
Random effects of perceiver  situation dyads were examined, but showed no unique variance beyond random
effects of perceiver and situation. Thus, analyses here did not estimate random intercepts and slopes at the dyad
level. Additionally, n=2 participants were missing personality data and therefore the analyses for Model 3 are based
on N=402 or 32,160 observations.
4
To ensure convergence, models were estimated using lme4.0, but 95% CIs were calculated using lme4 (Bates,
Maechler, Bolker, & Walker, 2015) with k=500 bootstrapped simulations.
3
Normative and Distinctive Situational Accuracy
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situations that are generally judged with more accuracy) than being a “good ex-situ rater” (i.e.,
ex-situ raters that generally judge situations with more accuracy).
– Figure 1 –
Model 2: Normative and Distinctive Accuracy
Model 2 added the normative characteristics profile as both a fixed and random effects
predictor of ex-situ ratings. Including the normative profile allows simultaneously estimating
normative and distinctive accuracy (Biesanz, 2010; Cronbach, 1955; Furr, 2009). By estimating
them as random effects, we allowed both normative and distinctive accuracy slopes to vary
across ex-situ raters and across situations. The results showed statistically significant effects of
both normative (b=0.88 [0.61, 1.15], t=6.56) and distinctive (b=0.49 [0.31, 0.68], t=5.82)
accuracy. Thus, on average, perceivers were both normatively and distinctively accurate in
judging the characteristics profiles of situations.
The SDs of the perceiver effects were 0.33 [0.30, 0.36] for intercepts, 0.19 [0.17, 0.21]
for normative accuracy slopes, and 0.08 [0.07, 0.09] for distinctive accuracy slopes. The SDs of
situation effects were 0.51 [0.29, 0.75] for intercepts, 0.42 [0.24, 0.57] for normative accuracy
slopes, and 0.27 [0.15, 0.41] for distinctive accuracy slopes. Thus, there was substantial variation
in both normative and distinctive accuracy between situations, with smaller but non-trivial
variation in normative and distinctive accuracy between ex-situ raters (see Figure 2).
– Figure 2 –
Model 3: Personality Correlates
Model 3 examined Big Five traits as moderators of normative and distinctive accuracy.
Centered scores of the personality variables and their interaction terms were added to Model 2 as
fixed effects. The results from this analysis are summarized in Table 4 (with random effects in
Table 5). As can be seen, the effects of normative accuracy were indeed associated with ex-situ
Normative and Distinctive Situational Accuracy
14
raters’ personality: those higher on Conscientiousness, Extraversion, and Agreeableness and
lower on Neuroticism were more normatively accurate, and those higher on Neuroticism more
distinctively accurate.
Discussion
This work used SAM analyses to disentangle normative from distinctive situational
accuracy and additionally examine personality moderators. First, in line with our expectations,
we found both substantial normative and distinctive accuracy, and the former was stronger in all
three estimated models. Second, the variances in normative and distinctive perceptive accuracy
were not as sizable as those of normative and distinctive expressive accuracy. Thus, on average,
there were only small differences between ex-situ raters in their accuracy levels (Figures 1 and
2). Because of the relatively large sample size of ex-situ raters, we could nonetheless associate
those small differences with the Big Five. Conscientiousness, Extraversion, Agreeableness, and
Neuroticism emerged as significant predictors, but not Openness. Additionally, these traits
predicted mostly only normative perceptive accuracy.
Magnitude of Accuracy
How strong was accuracy? This question can be answered by comparing the
unstandardized effect estimates found here to those from the person perception literature. First,
the level of normative accuracy (0.88) was similar to those found in person perception literature,
while the level of distinctive accuracy (0.49) was higher (i.e., often around 0.10-0.30; e.g.,
Biesanz & Human, 2010; Biesanz et al., 2011; Chan et al., 2011; Human & Biesanz, 2011, 2012;
Lorenzo et al., 2010). Second, because standardized effect sizes are somewhat problematic for
level 1 effects in MLM, we only report unstandardized regression coefficients for effects at this
level (as is common in SAM analyses). The following is intended to aid in interpretation of these
effects. The intercept is the average rating made by all raters across all situations and across all
characteristics. The distinctive coefficient means that a 1-point increase in the in-situ ratings
Normative and Distinctive Situational Accuracy
15
yielded a 0.49 increase in the ex-situ ratings (across all characteristics), controlling for all other
fixed effects in the model (which includes the normative profile). In other words, the 0.49 value
is an index of the average ex-situ rater’s sensitivity to changes in the criterion (in-situ), where
1.00 would be perfect correspondence. Again, this effect is across all situations and all
characteristics. The normative coefficient means that a 1-point increase in the average situation
(in-situ ratings) yielded a 0.88 increase in ex-situ ratings (again, across all situations and
characteristics). This is close to perfect correspondence.5 Taken together, the accuracy levels
found are actually quite high, especially considering that ex-situ raters had only limited
information available (Table 3).
Inter-individual and Inter-situational Differences
Differences between Persons. We did not find strong individual differences in
perceptive accuracy despite a relatively large sample that should have created sufficient
variation. This may be the case because people are, on average, accurate in judging others’
situations – and there is little (but still non-negligible) room for variation. After all, it is adaptive
to perceive situations as most other people do because this enables joint communication and
coordination in a socially shared reality (Miller, 2007; Rauthmann et al., 2015a). Indeed,
person(ality) perception literature also finds small variation in perceptive accuracy (Biesanz,
2010; Kenny, 1994), explaining why it has been so difficult to identify “the good judge.” Small
perceptive accuracy differences may thus generalize across judging persons and situations.
Nonetheless, some differences between persons still emerged. For example, interindividual differences in normative (but not distinctive) perceptive accuracy could be explained
by the Big Five (except for Openness), with small to medium effect sizes (see d in Table 4). On
5
The moderator effects can be interpreted accordingly. For example, the unstandardized .04 estimate for
Conscientiousness means that a 1-point increase in Conscientiousness yields an increase of .04 to the average
correspondence between the normative in-situ and ex-situ profile (which was .88).
Normative and Distinctive Situational Accuracy
16
the other hand, only (high) Neuroticism emerged as a predictor of distinctive perceptive
accuracy. One interpretation of this pattern of findings could be that those with a more normative
personality profile (i.e., high Conscientiousness, Extraversion, and Agreeableness, with low
Neuroticism) tend to use situational normativeness to achieve accuracy. This is similar to
findings in the person(ality) perception literature indicating that well-adjusted individuals tend to
be accurate perceivers of what others are generally like (Human & Biesanz, 2011). In contrast,
those with more non-normative personalities (i.e., high Neuroticism) may tend to be more
distinctively accurate, probably because they deviate more from the normative profile.
Interestingly, this improvement in distinctive accuracy for those high on Neuroticism is more
than negated by the losses in normative accuracy (i.e., .01 vs. -.02). However, individuals high in
Neuroticism may also be better in distinctive cue detection due to their hyper-sensitivity and
vigilance (Allen & Badcock, 2003). Guillaume et al. (2015) found that situations around the
world were typically social and mildly pleasant. Thus, as negative situations seem less
normative, distinctive accuracy could be achieved by a stronger focus on negative aspects.
Together, our data suggest that personality differences may matter most when using
normative situation knowledge. This is intriguing because traditionally unique and distinctive
patterns of perception have been associated with personality (Sherman et al., 2012). However,
such research only concerned how people (uniquely) perceive situations and not whether those
perceptions align with any criterion variables. Thus, our findings provide novel insights into
situational accuracy.
Differences between Situations. An interesting finding is the relatively large variance in
situations’ expressive accuracy: there were wide inter-situational differences in being judged
normatively and distinctively accurately. This finding, again, corresponds to person(ality)
Normative and Distinctive Situational Accuracy
17
perception literature which also find levels of expressive accuracy larger than perceptive
accuracy (Human & Biesanz, 2013). We insert the caveat, however, that we only had a limited
set of situations that were not selected to be homogeneous because we wanted meaningful
variance. Nonetheless, it is striking how much more situation variance there was than rater
variance. If we had sampled more situations and had other information available (e.g., physicobiological cues of situations; “style” characteristics such as base-rate or situation strength;
situation class membership), we could also attempt to explain inter-situational differences in
accuracy slopes the same way as inter-individual accuracy differences were explained here by
traits. Examining explanatory variables of “the good (or bad) situation” as moderators of
expressive accuracy may be a fruitful direction for future research.
A Note on Statistical Modeling
We have demonstrated how SAM can be applied to situation perception data. It may not
have escaped the notice of those initiated in MLM that other data-analytical choices could have
been made. The accuracies uncovered reside at the ex-situ rater and situation level. However, one
could also quantify accuracies at other levels. First, running a model where we estimate random
intercepts and slopes for characteristics and for situations (i.e., not for raters) yields accuracies at
the level of situations and characteristics. In other words, for every situation separately the exsitu profile is predicted from the in-situ profile. So for each situation, this regression would be
based on 404 raters × 8 characteristics = 3,232 pairs of scores (across all raters and
characteristics). Additionally, because we have a cross-classified MLM, for each characteristic
we get a regression based on 404 raters × 10 situations = 4,040 pairs of scores across all
situations and perceivers.
Normative and Distinctive Situational Accuracy
18
Second, as another alternative, we could estimate random intercepts and slopes for
perceivers and characteristics, yielding accuracies at the level of raters and characteristics. Thus,
for each rater, we get a regression based on 10 situations × 8 characteristics = 80 pairs of scores,
and for each characteristic a regression based on 404 raters × 10 situations = 4,400 pairs of
scores. Findings of these models are compiled in the online supplemental materials (together
with R code) for interested readers.
Though these models can make interesting sense of the data (e.g., looking at normative
and distinctive characteristic accuracy), they are conceptually and statistically different from
SAM.6 In SAM, characteristics are fixed, while in these other analyses they are random. These
different sets of analyses create the problem that we cannot include moderators of normative
accuracies, though distinctive accuracies are not affected. Additionally, cross-random effects
need to be orthogonal, so we are not able to model the effect of characteristics changing by
situation and/or rater. Hence, SAM represents a more flexible modeling approach. For the sake
of completeness and to stimulate future research on MLM-implementations in situational
accuracy studies, we wanted to alert to different but non-equivalent modeling procedures here.
Limitations and Future Directions
The limitations of this work point towards future research that is needed to overcome
them. First, we have employed a relatively limited set of target-situations and characteristics
which may suggest lack of stimulus sampling (Judd et al., 2012; Wells & Windschitl, 1999; cf.
Biesanz, 2010). This was mainly the case to reduce participant burden. However, given that exsitu raters’ variance in accuracy estimates was not too high, future research could use fewer exsitu raters, but have them rate more situations on more characteristics (e.g., Situational Eight on
32 items: Rauthmann et al., 2014; on 24 items: Rauthmann & Sherman, 2016c; on 89 items from
6
J. Biesanz (personal communication)
Normative and Distinctive Situational Accuracy
19
the Riverside Situational Q-sort: Sherman et al., 2010; Wagerman & Funder, 2009). If more
situations are available, moderators of expressive situational accuracy can be examined.
Second, we used only one normative profile for the “average situation.” However, the
average DIAMONDS profile may be different for different classes of situations (e.g., van
Heck’s, 1984, 1989 ten types of situations). It did not make sense to distinguish different classes
within our sampled situations, but future research may seek to sample different situations within
different classes (and possibly derive normative profiles for each class). It will be interesting to
examine accuracy slopes for different classes and whether some people show high perceptive
accuracy across all or only within specific classes.
Third, future research could sample several situations per in-situ rater (we used just one
per person), preferably a representative set of situations in daily life. This allows examining
idiosyncrasies in in-situ ratings and to what extent ex-situ raters might pick up on those.
Lastly, future research could use traits other than the Big Five, such as empathy,
perspective-taking ability, and socio-emotional competencies to explain inter-individual
accuracy differences. These may be more closely tied to accurately judging distinctive situation
profiles and thus increase the chance of predicting distinctive perceptive accuracy.
Conclusion
We demonstrated that person perception models, such as SAM, may be fruitfully applied
to situation perception data, making it possible to examine normative and distinctive accuracies
concerning ex-situ raters (perceptive accuracy) and situations (expressive accuracy) – a
distinction that has so far not been made in situations literature. This work thus provides a first
window into how strong normative and distinctive situational accuracy are and which person
variables may explain them. These findings may be important for research seeking to understand
the basis of good perspective-taking skills.
Normative and Distinctive Situational Accuracy
20
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Table 1. Overview of multi-level variables and parameters for situation perception data
Variable/Parameter
Variables in the model
expsc
Insc
c
Traitp
Lower level estimates
0ps
1ps
2ps
psc
Upper level estimates
Fixed effects
00
10
20
Random effects
u1p
u1s
u1(ps)
u2p
u2s
u2(ps)
Moderator effects
01
11
21
Meaning
ex-situ ratings by perceiver p of situation s on characteristic c
in-situ criterion-rating of situation s on characteristic c
Norm value of characteristic c based on a(nother) large, preferably representative sample
Trait-score of perceiver p (to be treated as a moderator of accuracy)
Intercept
Level of distinctive accuracy (as the correspondence between perceiver p’s ratings and situation s’ ratings while controlling for the
average characteristic profile by holding constant c)
Level of normative accuracy (as the correspondence between perceiver p’s ratings and the average characteristic profile after
partialling out situation s’ ratings)
Error
Average intercept across perceivers and situations
Average distinctive accuracy slope across perceivers and situations
Average normative accuracy slope across perceivers and situations
Perceiver p’s unique distinctive accuracy slope averaged across all situations
Situation s’ unique distinctive accuracy slope averaged across all perceivers
Dyadic (plus residual) component as the specific distinctive accuracy of Perceiver p for Situation s after the grand mean (10), the
perceiver main effect (u1p), and the situation main effect (u1s) of distinctive accuracy have been removed
Perceiver p’s unique normative accuracy slope averaged across all situations
Situation s’ unique normative accuracy slope averaged across all perceivers
Dyadic (plus residual) component as the specific normative accuracy of Perceiver p for Situation s after the grand mean (20), the
perceiver main effect (u2p), and the situation main effect (u2s) of normative accuracy have been removed
Slope of Perceiver p’s trait associated with his/her ex-situ ratings
Slope of Perceiver p’s trait associated with his/her distinctive accuracy level
Slope of Perceiver p’s trait associated with his/her normative accuracy level
Note. See more information in Biesanz (2010). Perceiver = ex-situ rater.
Normative and Distinctive Situational Accuracy
27
Table 2. The “good ex-situ rater” and the “good situation”
Accuracy types
Normative accuracy
Distinctive accuracy
Ex-situ rater:
Perceptive accuracy
u2p:
Individual differences in
the extent to which a perceiver’s ratings of
a situation’s characteristics capture the
characteristics of the
normative/average/general situation
u1p:
Individual differences in
the extent to which a perceiver’s ratings of
a situation’s characteristics capture the
situation’s distinct, unique characteristics
Note. Adapted from Biesanz (2010, Figure 4, p. 864).
Situation:
Expressive accuracy
u2s:
Situational differences in
the extent to which a situation’s
characteristics are perceived similar to the
characteristics of the
normative/average/general situation
u1s:
Situational differences in
the extent to which a situation’s distinct,
unique characteristics are perceived
Normative and Distinctive Situational Accuracy
28
Table 3. Criterion data: Target-situations and their in-situ ratings
Situation vignette (from in-situ raters)
1
2
3
4
5
6
7
8
9
10
At home with my three children: Did the housework, homework help and packed for the kids their sports stuff for the evening
Sat in the library and read, researched, took notes
My spouse and I work together and he criticized me in front of our employee
Conversation with partner in bed while we cuddled
Watched a movie lying in bed and drank a glass of wine
My partner and I were arguing in the kitchen. Tried to make him realize that he hurt me
Had to introduce myself and present in English in a role play in front of everyone (1 teacher and 9 course-participants) in a course room of a
language center. I managed pretty well
Had breakfast and conversations with female roommate, male roommate + his girlfriend and former female roommate
Drinking coffee with a female friend and chatting
Meeting at a friend's apartment with 4 friends (from the university) before a party; drinking cocktails together, chatting, and listening to music
Criterion ratings in-situ
D
I
A
M O
7
2
1
1
4
4
7
1
5
5
7
5
7
1
1
2
5
1
7
7
1
1
1
1
7
1
1
7
1
1
6
4
5
2
5
N
6
5
7
2
1
7
6
D
1
1
6
1
1
1
7
S
7
4
7
7
1
7
5
1
1
2
1
6
2
1
1
1
7
4
7
1
3
4
1
1
1
1
4
5
7
7
7
Note. Situations 1-8 were selected specifically because of their high ratings on one Situational Eight DIAMONDS dimension (see gray-shaded
cells). Situations 9 and 10 were not selected on any specific basis except for being mundane, everyday situations.
English translations of the German vignettes are given.
The Situational Eight DIAMONDS dimensions were measured with the S8-I (Rauthmann& Sherman, 2015b).
D = Duty: Work has to be done.
I = Intellect: Deep thinking is required.
A = Adversity: Somebody is being threatened, accused, or criticized.
M = Mating: Potential romantic partners are present.
O = pOsitivity: The situation is pleasant.
N = Negativity: The situation contains negative feelings (e.g., stress, anxiety, guilt, etc.)
D = Deception: Somebody is being deceived.
S = Sociality: Social interactions are possible or required.
Normative and Distinctive Situational Accuracy
29
Table 4. Multi-level regression estimates for Model 3
Effects
Estimate
SE
95% CI
d
Fixed effects
Intercept (b0)
3.3928*** 0.1614 [3.0475, 3.7052]
In-situ rating (distinctive accuracy; b1)
0.4936*** 0.0851 [0.3282, 0.6776]
Normative profile (normative accuracy; b2) 0.8789*** 0.1340 [0.637, 1.1366]
Ex-situ raters’ personality effects
Ex-situ rating (b01)a
Openness
0.0721*** 0.0195 [0.0534, 0.0924]
0.43
Conscientiousness
-0.0360*
0.0165 [-0.053, -0.0185]
-0.26
Extraversion
-0.0109
0.0143 [-0.0237, 0.0036] -0.09
Agreeableness
-0.0561** 0.0184 [-0.0751, -0.0388] -0.36
Neuroticism
0.0350*
0.0141 [0.0215, 0.0475]
0.29
In-situ rating (distinctive accuracy, b11)b
Openness
-0.0040
0.0059 [-0.0139, 0.0064] -0.09
Conscientiousness
0.0042
0.0050 [-0.0054, 0.0126] 0.12
Extraversion
0.0031
0.0043 [-0.0048, 0.0114] 0.10
Agreeableness
0.0004
0.0056 [-0.0104, 0.0108] 0.01
Neuroticism
0.0134** 0.0043 [0.0061, 0.0221]
0.44
Normative profile (normative accuracy, b21)b
Openness
0.0140
0.0119 [-0.0101, 0.0363] 0.15
Conscientiousness
0.0416*** 0.0101 [0.0238, 0.0628]
0.55
Extraversion
0.0252**
0.0088 [0.0099, 0.0427]
0.39
Agreeableness
0.0397*** 0.0113 [0.0185, 0.0605]
0.47
Neuroticism
-0.0201*
0.0086 [-0.0352, -0.0034] -0.31
Note. N = 402, Nobs = 32,160. Estimate = effect sizes of slopes. SE = Standard Error.
*** p < .001, ** p < .01, * p < .05 using Estimate/SE as t-statistic on 384 degrees of freedom.
95% confidence intervals (CIs) are based on 500 bootstrapped simulations.
𝑆𝐷
d = standardized effect size estimates, computed as 2 ∙ (𝑏 ∙ (𝑆𝐷𝑥 )), where b is the regression coefficient,
𝑦
SDx the standard deviation for the moderator variable, and SDy the random effect standard deviation for
the slope of interest. The multiplication by 2 makes yields an effect size equivalent to a Cohen’s d, as
suggested by Gelman (2008).
a
These estimates correspond to main effects. Because the intercept is the person’s average rating across
all situations and all characteristics, personality predictors of individual differences in scale usage are
captured. More open and neurotic ex-situ raters had higher than average ratings and those more
conscientious and agreeable had lower than average ratings.
b
These estimates correspond to cross-level interaction effects.
Normative and Distinctive Situational Accuracy
Table 5. Summary of random effects
Models
SDs
Intercept
In-situ rating
Normative profile
Model 1
Perceivers
0.31
0.05
Situations
0.72
0.48
Residual
1.91
Model 2
Perceivers
0.33
0.08
0.19
Situations
0.51
0.27
0.42
Residual
1.54
Model 3
Perceivers
0.31
0.08
0.17
Situations
0.51
0.27
0.42
Residual
1.54
Note. N = 402, Nobs = 32,160. SD = standard deviation. See text for details on Models 1-3.
Perceivers = ex-situ raters.
30
Normative and Distinctive Situational Accuracy
Figure 1. Slope density distributions for Model 1
31
Normative and Distinctive Situational Accuracy
Figure 2. Slope density distributions for Model 2
32