2014. Vol.5, No.2, 91-98
Published Online February 2014 in SciRes (
Influences on the Marking of Examinations
Christina Bermeitinger 1, Benjamin Unger2
1Institute for Psychology, University of Hildesheim, Hildesheim, Germany
2Law firm Benjamin Unger, Hildesheim, Germany
Received December 6th, 2013; revised January 5th, 2014; accepted February 3rd, 2014
Copyright © 2014 Christina Ber meitinger, Benjamin Unger. Th is is an open access article d istributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited. In accordance of the Creative Commons Attribution Li-
cense all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property Christina Ber-
meitinger, Benjamin Unger. All Copyright © 2014 are guarded by law and by SCIRP as a guardian.
In the present work, we examined a phenomenon highly relevant in the educational field for assessing or
judging performance, that is, the question how the second examiner’s marking is influenced by the eva-
luation of the first examiner. This phenomenon is known as anchoring in cognitive psychology. In general,
in anchoring effects numeric information (i.e., the anchor) pulls estimations or judgments towards the
anchor. One domain which is highly important in real life has been investigated only occasionally, that is,
the marking of examinations. In three experiments, participants were asked to evaluate a written assign-
ment. The mark (either good or bad) of a ficticious first examiner was used as the anchor. We found clear
anchoring effects that were unaffected by feedback in a preceding task (positive, neutral, negative) or the
expert status of the presumed first examiner. We discussed the problems related to this effect.
Keywords: Anchoring Effect; Marking; Feedback; Mood; Written Tests; Performance Judgments
Our decisions and evaluations are influenced by many social
and cognitive things and even numbers. Often, we are not
aware of these influences, or we think we can shield our eval-
uations from such influences. However, there are a lot of
psychological phenomena that demonstrate inadvertent influ-
ences on our cognition, for example, priming, framing, cueing,
subliminal persuasion or advertising, and also anchoring.
The influence of numerical information on judgments is most
often studied with the anchoring paradigm. Anchoring refers to
the phenomenon that previously presented numerical informa-
tion (i.e., the anchor) biases numerical judgments or estimates
towards the anchor. Anchoring effects have been shown in a
variety of different tasks and domains (for reviews see Chap-
man & Johnson, 2002; Epley, 2004; Furnham & Boo, 2011;
Kudryavtsev & Cohen, 2010; Mussweiler, Englich, & Strack,
2004; Mussweiler & Strack, 1999), for example in probability
estimation (Tversky & Kahneman, 1974), legal judgments (e.g.,
Englich & Mussweiler, 2001; Englich & Soder, 2009), price
estimation (e.g., Englich, 2008), or general knowledge (e.g.,
Epley & Gilovich, 2001). Anchoring effects are present in a
broad range of conditions—they result from implausible as well
as plausible anchors (e.g., Mussweiler & Strack, 1999), from
subliminal presentations of anchors (e.g., Mussweiler & En-
glich, 2005), when participants are forewarned or especially
motivated not to be biased (e.g., Wilson, Houston, Etling, &
Brekke, 1996), in experts and novices (e.g., Englich & Muss-
weiler, 2001), and in the laboratory as well as in real-world
settings (e.g., Northcraft & Neale, 1987). Relevant but also
irrelevant information that is clearly uninformative for the re-
quired judgment can serve as an anchor. For example, North-
craft and Neale (1987) tested anchoring effects in real estate
agents. The authors provided a 10-page packet of information,
which included a large amount of information regarding a piece
of property currently for sale. The listing price for the property
was varied as a relevant anchor which had an influence on the
price the real estate agents had to state for the corresponding
property. In contrast, Englich (2008) used an irrelevant anchor.
She asked her participants to write down several numbers start-
ing either with 10,150 or 29,150 before judging the price of a
car. Such an irrelevant anchor had influences on the judgment
as well. In the classic study of Tversky and Kahneman (1974),
participants were required to estimate the percentage of African
countries in the United Nations after spinning a wheel of for-
tune which determined the irrelevant anchor nevertheless had
also influences on the estimation. Overall, anchoring effects are
one of the most robust cognitive effects and, more specifically,
cognitive heuristics.
Standard Anchoring vs. Basic Anchoring
Anchoring effects can be found with different approaches.
The approaches most often applied are standard anchoring and
basic anchoring. In standard anchoring (e.g., Tversky & Kahne-
man, 1974), participants are required to first make a compara-
tive judgment (e.g., is the percentage of African countries in the
United Nations higher or lower than 65%) and then an absolute
judgment (e.g., an estimation of the percentage of African
countries in the United Nations). In this approach, participants
must have a conscious representation of the numerical informa-
tion given by the anchor (otherwise they could not answer the
comparison task). In the basic anchoring approach, however,
this is not necessary; no direct comparison is required. Partici-
pants are confronted with numerical information (either rele-
vant or irrelevant, see above) and then they are asked to judge
or estimate the object of interest. Both approaches lead to anc-
horing effects, but possibly due to different underlying pro-
cesses (Englich, 2008; Mussweiler, 2002). In conclusion, the
anchoring effect represents a robust phenomenon present in a
broad range of diverse tasks and conditions.
Theories of Anchoring
There are different assumptions regarding the question why
anchoring effects occur (e.g., Chapman & Johnson, 1999; En-
glich, 2008; Mussweiler, 2002; Mussweiler & Strack, 1999;
Mussweiler et al., 2004). Originally, anchoring effects were
explained in terms of insufficient adjustment from a given
starting point (Tversky & Kahneman, 1974). In more recent
views, anchoring effects, and specifically standard anchoring
effects, are explained as the result of an active and elaborate
hypo thes is-testing process. It is assumed that persons hypo-
thesize that the to-be-estimated “target” value is close to the
anchor, and selectively activate information confirming this
hypothesis. Thus, judges search for information consistent with
their hypothesis via semantic associations between the target
and the anchor. Such knowledge is activated, which implies a
close relationship of target and anchor. Therefore, anchor-con-
sistent knowledge is easier accessible than knowledge not con-
sistent with the anchor. Furnham and Boo (2010) proposed that
confirmatory search and selective accessibility are the main
mechanisms contributing to the anchoring effect.
In contrast, basic anchoring effects are also explained by
numeric priming—the anchor number is used as a reference
point that is highly accessible simply due to the fact that it is
one of the last numbers the person had in mind (for further per-
spectives on anchoring effects see e.g., Wegener, Petty, Det-
weiler-Bedell, & Jarvis, 2001; for review see e.g., Furnham &
Boo, 2011).
Performance Judgments
Given the broad range of domains and conditions in which
anchoring effects are present and investigated, it seems note-
worthy that one domain has been subject to this research only
occasionally despite its real-world relevance for people’s ca-
reers—that is, performance judgment and specifically the
marking of exams and assignments in school or university. A
study by Dünnebier, Gräsel, and Krolak-Schwerdt (2009) is one
rare exception. The authors investigated anchoring effects in
teachers’ assessment of written assignments depending on their
expertise and the actual goal of assessment. The goal could be
either to build a first impression or to give an educational rec-
ommendation. Results revealed an anchoring effect overall, but
they were somewhat inconsistent with regard to the other fac-
tors. For example, only for a math test but not a German test
did experts show a substantial anchoring effect with the pro-
cessing goal of giving an educational recommendation.
Interestingly, in the domain of achievement judgments, there
is often a consensus (especially in non-psychological areas) that
examiners can make unbiased judgments. For example, in Ger-
man jurisdiction, courts feel certain that an examiner usually is
self-contained, self-dependent, free, unprejudiced, and com-
pletely unbiased by comments and marks of previous examiners
(e.g., BVerwG, 2002). However, this ideal examiner is most
likely far from reality (see also Brehm, 2003). Given this view
of the idealized examiner, the lack of conclusive evidence
available (cf. Dünnebier et al., 2009) is reason for concern.
Thus, it seems desirable to have more conclusive results re-
garding biases and anchor effects in the specific context of
The Present Experiments
In three experiments, we investigated anchoring effects on
the marking of a written assignment. The written assignment
comprised a question from the domain of motivational psy-
chology. Our participants were undergraduate students from the
University of Hildesheim, most of whom were enrolled in an
introductory psychology unit on motivation and emotion. The
written assignment was related to issues covered in the intro-
ductory unit. Thus, participants were more or less familiar with
the topic of the written assignment. They were complete novic-
es in the marking of examinations. However, these aspects also
apply to several situations in real life—many examiners are
rather inexperienced with examinations and sometimes even
student assistants are instructed to pre-assess or mark written
assignments. Additionally, in many cases—for example for
German legal state examinations, for some school-leaving ex-
aminations in Germany, or in the example above in which stu-
dent assistants mark the exams—examiners are confronted with
assignments they did not build themselves. Further, they do not
always teach the topics addressed in the written assignments. In
summary, we tested the influence of anchors on the marking of
examinations from student participants. Participants were non-
experts, at least in the marking of examinations.
There is some evidence that non-experts show larger anc-
horing effects than experts. Chapman and Johnson (1994), for
instance, found smaller anchoring effects for those participants
who showed high certainty about their judgment. However,
there are several studies that have demonstrated that evaluations
by experts (e.g., experienced legal professionals, car experts,
estate agents) are influenced by anchors as well (e.g., Englich
& Soder, 2009; Mussweiler, Strack, & Pfeiffer, 2000; North-
craft & Neale, 1987). For example, Englich, Mussweiler, and
Strack (2006) tested legal judges who were experts with exten-
sive experience in the particular domain of law they were asked
to judge during the study. These experts were influenced by
randomly determined and irrelevant anchors to the same extent
as judges who were experts in other domains (i.e., non-experts).
In conclusion, in most cases expertise does not reduce the in-
fluence of an anchor. Some studies even found that only experts
were influenced by an anchor (Englich & Soder, 2009). Thus, it
seems adequate to test non-experts.
Our experiments are designed to closely resemble real situa-
tions in which written assignments have to be marked. We used
the basic anchoring approach in which no comparison with the
anchor is required. For a lot of written assignments (at least at
German universities and for state examinations), it is typical
that the second examiner knows the marking and evaluation of
the first examiner1. Thus, our participants also saw this infor-
mation before individually marking the written assignments
(for a similar procedure, see, e.g., Northcraft & Neale, 1987,
who also provided a large amount of relevant information in
addition to the anchor). Further, we used marks as the anchor,
which clearly represents relevant information. Overall, it seems
rather likely that we would find anchoring effects (that is, in
general, we expected anchoring effects). However, it is impor-
tant to actually show that performance judgments are influ-
enced by the judgments of others, in particular given the myth
of the ideal and unswayable examiner. To broaden our focus
and to enlarge the innovative points of our study on anchoring
effects in the context of performance judgments, we tested
some further influences on this basic anchoring effect; first,
whether a qualitative difference (i.e., the first examiner marking
the assignment as “fail” vs. “pass”) between the high and low
anchor has an influence (Exp. 2 & 3); second, whether anchor-
ing effects change when the first examiner is introduced as an
expert vs. non-expert (Exp. 2); and third, whether positive,
negative, or neutral feedback regarding participants’ own per-
formance in a preceding test affects the basic anchoring effect
(Exp. 3).
Experiment 1
Participants. The sample consisted of 49 students (45 female,
4 male) who were recruited from the introductory psychology
unit on motivation and emotion at the University of Hildesheim.
The median age was 21 years (ranging from 18 to 33 years).
Subjects participated in several unrelated studies for course
credit. They were randomly assigned to the conditions; each
participant only marked one assignment.
Design. Experiment 1 was based on a one-factorial design.
The factor anchor (high [3,0] vs. low [2,0]) was varied between
Material. Essentially, the material consisted of the student’s
task (i.e. the exam question), the student’s response, the report
of the first examiner and his/her marking. The report included
positive as well as negative aspects of the student’s perfor-
mance. The mark of the first examiner was either 2.0 or 3.0,
which were both possible marks for the given performance.
Procedure. Participants were tested in groups of up to four
persons, but participants worked individually in sound-ate-
nuated chambers. All instructions were given on sheets of paper.
First, participants were informed that we wanted to test differ-
ent methods for evaluating student assignments and to find the
best and fairest way for marking assignments. These points
were emphasized to ensure that participants were motivated to
participate and that they took the task seriously. Then, they
were asked to work through the experimental materials in the
given order and to read instructions carefully. Participants were
informed that they had to judge the performance of a student in
a written assignment, specifically regarding a question on the
psychology of motivation. They were informed that they were
the second examiner, that is, that a first examiner had already
judged the performance and that they would see this judgment.
Then, participants read the alleged student’s task (i.e. the exam
question) and they received some additional information, for
example, they were given the marking scale (the standard
marking scale at German universities: 1.0 “very good” – 1.3
1.7 – 2.0 “good” – 2.3 – 2.7 – 3.0 “satisfying” – 3.3 – 3.7 – 4.0
“sufficient” – 4.3 “failed) and some information on the points
they should attend during their evaluation. Then, participants
read the alleged student’s response, which was presented on
two pages. On the next page, participants saw the report of the
alleged first examiner and his/her marking. Subsequently, par-
ticipants were asked: How do you as a second examiner mark
the written assignment?” Participants were additionally asked
to write down the main arguments why they gave this mark for
the given performance.
Results and Discussion
As Figure 1 reveals, participants’ judgments were influenced
by the given anchor. Participants who were confronted with the
higher anchor (i.e., 3.0) gave higher marks than those who were
confronted with the lower anchor (i.e., 2.0) producing a signifi-
cant main effect of anchor in a one-way ANOVA, F(1, 47) =
7.36, p = .009,
= .14.
The result showed that the general task, including materials,
procedure, and participants, produced clear anchoring effects.
The results also showed that overall, participants tended to
mark the written assignment worse than intended: With the
lower anchor of 2.0, participants mar ked the assignment with M =
2.91 which is close to 3.0 (i.e., the higher anchor which led to a
mark of M = 3.48) .
Experiment 2
Participants in Experiment 1 tended to evaluate the given as-
signment as worse than “good” (i.e. >2.0). Thus, in Experi-
ment 2, we used higher anchors (i.e. 3.7 vs. 4.3). The higher
anchor (4.3) indicates a mark that is associated with a “fail.
Note that in Experiment 1 only 3 out of 25 participants of the
Figure 1.
Mean mark by anchor (high [3,0] vs. low [2,0]). Er-
ror bars represent the standard error of the mean.
1One anonymous reviewer during the reviewing processes of this paper
wondered about the relevance of our study: “In actual scoring no one in their
right mind would allow the score from a different scorer to be shown.
Therefor e, this p aper has cr eated a completel y artifici al settin g that bear s no
relationship to applied reality.” Actually and unfortunately, that is not the
case. We created the situation for our participants as closely as possibly to
the applied reality as we found it in various exam situations, at least in Ger-
low anchor condition, and only 2 out of 24 participants of the
high anchor condition, evaluated the student’s assignment as
“fail”. This implies that a higher anchor is not associated with
higher “fail” rates per se. Thus, in Experiment 2 we used 3.7 as
the lower anchor and 4.3 as the higher anchor—there was hence
not only a quantitative difference between both anchors but also
a qualitative difference. We expected that participants who
were confronted with the higher anchor more often marked the
assignment as “fail” than participants who were confronted
with the lower anchor. Additionally, we varied whether the first
examiner was introduced as an expert with many years of expe-
rience in psychology and in evaluating psychological exams or
as a non-expert (i.e., a student of informatics without any expe-
rience in psychology). The question was whether participants
were more influenced by the anchor from the first examiner
who was introduced as an expert. Although previous research
(e.g., Englich & Mussweiler, 2001; Englich et al., 2006) could
not find differences in the anchoring effect dependent on anc-
hor relevance, it might be that in the present case the informa-
tional value of the anchor for the given task (which should be
higher if the first examiner is introduced as an expert rather
than a non-expert) does play a role.
Participants. The sample consisted of 76 undergraduate stu-
dents (63 female, 13 male), again recruited from the introduc-
tory psychology unit on motivation and emotion at the Univer-
sity of Hildesheim, none of whom had participated in Experi-
ment 1. The median age was 21 years (ranging from 18 to 36
years). Subjects participated in several unrelated studies for
course credit. They were randomly assigned to the conditions.
Design. Experiment 2 was based on a 2 (anchor: high [4,3]
vs. low [3,7]) × 2 (first examiner: expert vs. non-expert) design.
Both factors were varied between participants.
Material and Procedure. Materials and the procedure were
the same as in Experiment 1 with the following exceptions.
First, participants were additionally informed that the first ex-
aminer was either an expert with many years of experience in
psychology and in evaluating psychological exams or that the
first examiner was another student (i.e., a first-year informatics
student) with no experience in psychology or evaluating exams.
Second, while the report of the first examiner who was intro-
duced as an expert was the same as that used in Experiment 1,
with very few minor changes, the report of the first examiner
who was introduced as a non-expert was shorter and more col-
loquial. The report of the non-expert included positive as well
as negative aspects of the student’s performance, too. Third, the
mark of the first examiner was either 3.7 or 4.3 (i.e., a “fail”).
Results and Discussion
Mean marks (see Figure 2) were subjected to a 2 (anchor:
high vs. low) × 2 (first examiner: expert vs. non-expert) analy-
sis of variance (ANOVA). There was a significant main effect
of anchor, F(1, 72) = 8.43, p < .01,
= .11. On average,
participants who were confronted with the higher anchor (i.e.,
4.3) gave higher marks than those who were confronted with
the lower anchor (i.e., 3.7). In contrast, the main effect of first
examiner and the interaction of anchor and first examiner were
not significant, both Fs < 1, ps > .85, indicating that it did not
matter whether the first examiner was introduced as an expert
Figure 2.
Mean mark by anchor (high [4,3] vs. low [3,7]) and first
examiner (expert vs. non -expert). Erro r bars represen t th e
standard error of the mean.
or non-expert (see also Figure 2).
Overall, participants of the high anchor condition (4.3, i.e.,
“fail”) marked the assignment with M = 3.91 (i.e., on average,
the student would have passed). In this condition, there was no
significant difference (p = .14) between the number of partici-
pants who evaluated the assignment as failed (n = 14) and the
number of participants who evaluated the assignment as passed
(n = 24). That is, a substantial number of participants evaluated
the assignemt as “fail”. In contrast, participants of the low anc-
hor condition (3.7) marked the assignment on average with M =
3.56. In this condition, only 5 participants marked the assign-
ment as “fail”; there were significantly more participants who
marked the assignment as “pass” (n = 33, p < .001).
Again, we found clear anchoring effects. Additionally, we
found evidence that participants who were confronted with the
“fail” anchor more often marked the assignment as “fail” than
participants who were confronted with the lower anchor, as
there was no difference between “fail” and “pass” marks in the
high anchor condition but there were significantly more “pass”
marks in the low anchor condition. However, the “fail” anchor
did not lead to more “fail” than “pass” marks but only increased
the proportion of “fail” marks.
Additionally, we varied whether the first examiner was in-
troduced as a non-expert or as an expert with many years of
experience in psychology and in evaluating psychological ex-
ams. For this factor, we found no effect—it played no role
whether the first examiner was an expert or not. This finding
matches with previous research, in which even irrelevant in-
formation completely uninformative for the given task (e.g.,
Critcher & Gilovich, 2008; Englich, 2008; Englich et al., 2006;
Tversky & Kahneman, 1974) caused robust anchoring effects
of the same magnitude as anchoring effects from relevant anc-
hors (e.g., Englich & Mussweiler, 2001; Englich et al., 2006).
That is, in the domain of marking of examinations—highly
relevant for the individual’s career—robust anchoring effects
were found which were independent of whether the anchor
represented highly valuable (i.e., the anchor was given by an
expert) or less valuable (i.e., the anchor was given by a non-
expert) information. Typically, such findings are explained by
assuming that the accessibility of anchor-consistent information
biases judgments or estimations independent of the informa-
tional relevance of the anchor (e.g., Furnham & Boo, 2011).
Experiment 3
Experiment 1 and 2 revealed clear anchor effects on the
marking of a written assignment. In Experiment 3, we mainly
tested the influence of positive, negative, or neutral (fictitious)
feedback regarding participants’ own performance in a preced-
ing task on these anchoring effects. Such feedback could affect
the processing of following information by mechanisms also
operational in affective or semantic priming (e.g., Clore, Wyer,
Dienes, Gasper, Grohm, & Isbell, 2001; Klauer & Musch, 2003;
Neely, 1991). In the given situation, feedback may be able to
influence one’s mood. Such feedback is a very interesting fac-
tor in the context of marking, first, because some guides rec-
ommend examiners not to evaluate assignments or exams when
they are in a (too) bad or (too) good mood, and second, mood
can be influenced in everyday life by a lot of things that are
outside one’s control, for example by feedback in social or
achievement situations. Thus, in Experiment 3 we manipulated
whether participants got positive, negative, or neutral feedback
regarding their own performance in a computer test. Typically,
the own affective state is used as source of information (cf.,
“mood as information” hypothesis, for review see e.g., Schwarz
& Clore, 2003) when people had to evaluate something.
Schwarz and Clore assumed (and showed) that own states are
misread as a response to the object people had to judge. As a
result, they evaluate things more favorable under positive rather
than negative states (and vice versa). Thus, affective informa-
tion can affect decision making indirectly by influencing how
we process information (e.g., Clore et al., 2001; Englich &
Soder, 2009; Schwarz, 2001). While in a happy mood, a more
holistic and heuristic processing style is typical, whereas a more
elaborate and critical information processing style is often as-
sociated with negative/sad mood (for an overview see e.g.,
Huntsinger, Clore, & Bar-Anan, 2010).
However, results from anchoring studies seem to show a dif-
ferent pattern regarding their dependence on mood. Englich and
Soder (2009) tested the influence of mood on judgments (Study
1: in a legal shoplifting case; Study 2: in ordinary estimates).
For non-experts, the authors only found anchoring effects when
participants were in a sad mood but not when participants were
in a happy mood. In contrast, for experts, Englich and Soder
either found no anchoring effects at all (Study 2) or anchoring
effects occurred no matter what mood participants were in.
Based on these results, we expected anchoring effects at least
after negative feedback (see also Bodenhausen, Gabriel, &
Lineberger, 2000). Additionally, we added a condition in which
participants were first examiners, that is, without any anchor
from the mark of another examiner.
Participants. The sample consisted of 79 undergraduate stu-
dents (68 female, 11 male), again recruited from the introduc-
tory psychology unit on motivation and emotion at the Univer-
sity of Hildesheim or on campus. None of these had partici-
pated in one of the other experiments. The median age was 22
years (ranging from 18 to 42 years). Subjects participated in
several unrelated studies either for course credit or remunera-
tion. They were randomly assigned to the conditions.
Design. Experiment 3 was based on a 3 (anchor: high [4,3]
vs. low [2,7] vs. no anchor) × 3 (feedback: positive vs. negative
vs. neutral) design. Both factors were varied between partici-
Material and Procedure. Materials and the procedure were
identical to Experiment 1 with the following exceptions. First,
we introduced a control condition in which no anchor and no
report of another examiner was shown. In this condition, par-
ticipants were informed that they were the first examiner and
that the assignment would be subsequently judged by a second
examiner who would see their report and their mark. Second, in
the conditions in which participants were second examiners, the
mark (i.e., the anchor) of the first examiner was either 2.7 or
4.3 (i.e., “fail”), that is, there was again a qualitative difference
between both anchors (as in Experiment 2). Third, we manipu-
lated which (fake) feedback on their own performance in a
preceding computer task (in which we recorded reaction times
and error rates) was given to the participants.
This computer task was introduced as a reliable measure of
general mental efficiency (including intelligence, processing
speed, and so on). The computer task was run using E-Prime
software (version 1.3) with standard PCs and 17-in. CRT mon-
itors. Instructions were given on screen. One to three stimuli
occurred simultaneously at nine possible locations of an invisi-
ble 3 × 3 grid (with grid points located at 75%, 50%, and 25%
positions of the vertical and horizontal full screen span). The
stimuli were either squares or dots and either of blue, yellow,
green, or red color. The background color was white. The
computer task comprised 100 trials, each lasti ng 800 ms. How-
ever, single stimuli could be presented for one, two, three, or
four sequential trials. Thus, the duration of one stimulus could
be 800, 1600, 2400, or 3200 ms. The participants’ task was
very simple: They were instruct ed to press the space key as fast
as possible whenever a yellow square or a blue dot appeared
anywhere on the screen, which was the case in 22 out of the
100 trials. However, it was rather difficult to supervise the
whole field and stimuli appeared rather fast. Thus, it was not
possible to get a good appraisal of one’s own performance. The
whole computer task took approximately 2 minutes. Most im-
portantly, at the end, participants were informed that they had
achieved a mean reaction time for their correct responses of 547
ms. Participants of the positive feedback condition were addi-
tionally informed that they had achieved a very good result,
which only 10% of comparable participants had also managed
to achieve. Participants of the negative feedback condition were
informed that they had achieved a below-average result, and
that more than 70% of comparable participants had achieved a
better result. Participants of the neutral feedback condition
received no feedback2. Thereafter, participants proceeded to the
marking task, and were fully debriefed at the end of the expe-
In a pre-
test with 46 student participants, we tested whether the chosen
feedback was adequate to induce different mood states. In the pre
participants worked through the same reaction time task as used for the
main experiment and got the same fictitious feedb ack (negative, neu tral, or
positive) regarding their performance. Thereafter, they worked through the
Mehrdimensionaler Befindlichkeitsfragebogen (multidimensional mental
state questionnaire, Steyer, Schwenkmezger, Notz, & Eid, 1997). Therein,
mood was measured with eight adjectives (after recoding: the higher the
score, the better the mood). Results showed that participants’ mood was
indeed influenced by the given feedback,
F(1, 43) = 4.66, p = .02. Parti
cipants who got positive feedback had si g ni ficantly higher mood sco r es th an
participants who got neutral
(t(28) = 2.83, p = .01) or negative (t
(28) = 2.64,
= .01)
feedback. (Th ere wer e no sig nifican t dif feren ces between th e moo d
scores of participants who got negative vs. neutral feedback,
t < 1, p > .77.)
That is, t he tas k was i ndeed adequ ate to i nduce d iffer ences i n mood , at leas t
between posi tive and ne gative/ne ut ral feedback.
Results and Discussion
Mean marks (see Figure 3) were subjected to a 3 (anchor:
high vs. low vs. no) × 3 (feedback: positive vs. negative vs.
neutral) analysis of variance (ANOVA) . Th ere was a signific ant
main effect of anchor, F(2, 70) = 16.32, p < .001,
= .32. In
contrast, the main effect of feedback and the interaction of
anchor and feedback were not significant, both Fs ≤ 1, ps > .36,
indicating the feedback participants got had no effect (see also
Figure 3).
On average, participants who were confronted with the higher
anchor (i.e., 4.3) gave higher marks than those who were con-
fronted with the lower anchor (i.e., 2.7), t(50, 42.94) = 5.85, p
< .001 (t-test for unequal variances), Mhigh anchor = 4.00, SD =
0.46, Mlow anchor = 3.08, SD = 0.65. Additionally, participants
who were confronted with the higher anchor (i.e., 4.3) gave
higher marks than those who were confronted with no anc-
hor, t(52, 44.23) = 4.60, p < .001 (t-test for unequal va-
riances), Mno anchor = 3.24, SD = 0.73. In contrast, participants
who were confronted with the lower anchor did not differ sig-
nificantly from participants who were confronted with no anc-
hor, t(50) = 4.30, p = .43.
As before, in the high anchor condition (4.3, i.e., “fail”) there
was no significant difference (p = .70) between the number of
participants who evaluated the assignment as failed (n = 12)
and the number of participants who evaluated the assignment as
passed (n = 15). That is, a substantial number of participants
evaluated the assignment as “fail”. In contrast, only 2 partici-
pants of the low anchor condition (i.e., 2.7) and only 5 partici-
pants of the no anchor condition marked the assignment as
“fail”; there were significantly more participants who marked
the assignment as “pass” (n = 23, p < .001 and n = 23, p = .001,
for the low anchor condition and no anchor condition, respec-
Again, we were able to show an anchoring effect and we
could replicate the results from Experiment 2 regarding the
pattern of “fail” vs. “pass” marks depending on the presence of
a “fail” or “pass” anchor. Further, we found a mean mark of
3.24 for participants who were not confronted with any anchor.
Participants of the no anchor condition did not differ signifi-
cantly from participants of the low anchor condition. Combined
with results from Experiment 1 (especially the condition in
Figure 3.
Mean mark by anchor (high [4,3] vs. low [2,7] vs. no) and feedback
(positive vs. negative vs. neutral). Error bars represent the standard
error of the mean.
which we used an anchor of 2.0), we found evidence that the
higher anchor in the given task was able to bias evaluation to-
wards a higher (i.e., worse) mark. However, the lower anchor
did not bias the marking significantly towards a better mark
(i.e., in Exp. 1 an anchor of 2.0 led to a mean mark of 2.9; in
Exp. 3 an anchor of 2.7 lead to a mean mark of 3.08—both
results are close to the no anchor condition with a mean mark of
3.24; please note that the distance between the mean unbiased
mark and the lowest anchor in Exp. 1 was 1.24 and the distance
between the mean unbiased mark and the highest anchor was
1.06; however, there was no large bias towards the low anchor).
This result seems highly important for the given context as it
might be especially problematic that a second examiner is in-
fluenced by the bad mark of a first examiner, which might lead
to disproportionately high numbers of evaluations in which the
given mark would be worse than the appropriate mark.
In contrast, we found no influence of the (fictitious) feedback
in the preceding computer task, which was either negative,
positive, or neutral. Here, we can only speculate why we did
not find an effect of this manipulation. First, the manipulation
may have not been adequate to affect participants differently in
the main experiment. Either there was no influence or at least
no differential influence, that is, it might be that the computer
test per se was, for example, frustrating (comparable to the sad
mood condition with non-experts of Englich & Soder, 2009).
However, if we assume that mood or emotion, and hence also
the effects of our computer-task and feedback, was used as
source of information (Schwarz & Clore, 2003), we would
expect worse marking in general—compared to our previous
experiments. This was not the case. We found comparable re-
sults, especially for anchors that were identical across experi-
ments (i.e., 4.3 in Experiment 2 and Experiment 3). Second,
perhaps the manipulation was initially successful, however, it
may have lasted too short a time to influence the marking or the
bias at the end of the assignment. Third, there may actually be
no influence of feedback (or own affective state) on the mark-
ing of examinations—at least in the tested situation. Fourth, the
manipulation may have indeed been successful, however, as in
Englich and Soder (2009, Study 1) for experts, anchoring ef-
fects occurred no matter which feedback participants got or in
which mood they were. It has to be noted that we did not check
whether participants’ mood was influenced in the main experi-
General Discussion
In three experiments, we found conclusive evidence for anc-
horing effects in the marking of examinations. However, we
found no evidence for the impact of feedback on a preceding
task, or whether the anchor was introduced as an expert evalua-
tion or an evaluation of a novice. There are several implications
which we will discuss in the following.
As could be shown in Experiment 3 in comparison with Ex-
periment 1, a bad mark in particular was able to influence par-
ticipants’ marking (however, note that in Experiment 3 there
was also a qualitative difference between the anchors). This
seems to be unrelated to the specific number of the anchor: in
the study of Dünnebier et al. (2009), it was also the bad mark
that influenced participants’ judgments more (at least for the
German assignment) although “bad” in Dünnebier et al. was
associated with a lower anchor (they used a 0 - 15 grade scale
with 15 as the best grade). Participants of the no anchor condi-
tion did not differ significantly from participants of the low
anchor condition. That is, an anchor representing a bad mark
influences examiners more towards a bad mark. In contrast, an
anchor representing a good mark does not influence examiners
to the same extent towards a good mark. In real marking situa-
tions this seems rather alarming. If a first examiner gives an
assignment a bad mark, it seems rather hard for second ex-
aminers to evaluate the assignment in an unbiased fashion.
Especially in these cases, examinees may be evaluated more
harshly than their actual performance would warrant. Thus, it is
more likely overall that assignments marked by two examiners
are evaluated too harshly rather than too leniently.
Given the apparent robustness of this effect, we need to con-
sider different procedures for marking examinations. Critically,
even strategies or manipulations aiming to reduce anchoring
effects have typically failed to eliminate the effect entirely (e.g.,
Mussweiler et al., 2000). Mussweiler et al. (2000, Study 1)
asked car experts to estimate the price of a car. The experts
were given an anchor but then also some anchor-inconsistent
information (“[Someone] mentioned yesterday that he thought
this value is too high/low”). They were then confronted with
the question “What would you say argues against this price?”
By doing so, Mussweiler et al. were able to reduce the anchor-
ing effect significantly compared to a condition in which no
anchor-inconsistent information was provided. However, there
was still a trend of an anchoring effect.
In the same vein, Mussweiler (2002) found larger anchoring
effects with a similarity focus compared to a difference focus.
In this study, participants were first required to list either as
many similarities between two visual scenes as they could find
or as many differences as they could find. This task was intro-
duced as being completely independent of the following (anc-
horing) task on general knowledge. To transfer such effects to
the marking of examinations, perhaps it would be helpful if
second examiners should have the specific focus of finding
arguments against the marking of the first examiner. In contrast,
in the current practice, second examiners are virtually rewarded
when they agree with the marking of the first examiner—they
often have to justify a deviating evaluation but they can easily
write “I fully agree with the first examiner” (which, of course,
is much less effort) when they award the same mark.
In the context of achievement judgment by teachers, Dünne-
bier et al. (2009) showed that there was no significant anchor-
ing effect for experts with the processing goal of giving an
educational recommendation (at least in their German assign-
ment). For judgments of examinations at university or single
assignments in state examinations, it has to be assumed that
examiners do not necessarily have that goal, for example, be-
cause the single assignment represents just one amongst others.
Additionally, examiners cannot know how important the single
assignment will be for the future of the examinee. Thus, it
seems unlikely that they evaluate assignments with the specific
goal of giving an educational recommendation—or in a broader
sense with the goal to provide their most exact judgment on the
given assignment. Perhaps, it might be helpful to emphasize
that the single assignment may be highly relevant for a stu-
dent’s career, and an adequate assessment will provide highly
valuable information (and potentially serve as a recommenda-
tion) for potential employers.
Additionally, it might be discussed whether examinations
should be evaluated independently by two examiners without
knowledge of each other’s marking. Such a procedure would of
course require safeguards such that it is not undermined by
verbal agreements between examiners. Anchoring effects still
occur when persons are trained or even informed about the
influence of anchors. Thus, pure training of reviewers seems
insufficient to reduce anchoring effects. However, it might be
interesting to see results from future research directly con-
cerned with this question in the context of marking. Furthe r-
more, the use of automated scoring might be a method which
could circumvent the problems of biased marking (for an over-
view see Shermis & Burstein, 2003). However, potential ad-
vantages and disadvantages (e.g., greater time requirements in
the case of independent reviewing or the lack of human interac-
tion in the case of automated scoring) have to be balanced.
Last but not least, we want to point out some limitations of
our experiments which may stimulate some future studies. First
of all, we tested student participants. Anyway, further research
could investigate effects in expert examiners. Second, one
might ask whether other mood induction techniques might be
better suited to influence anchoring effects (or whether more
participants are needed to find differences). Additionally, it
could be helpful to test participants’ mood also before, during,
and after the main task (i.e., judging). Third, we only tested
relevant anchors. It might be interesting whether anchors com-
pletely unrelated to the judgment task (e.g., as given in the
classic study by Tversky & Kahneman, 1974) also influence the
judgment of examinations. Additionally and generally, it would
be interesting to relate the topic of anchoring effects and find-
ings of rater biases. Fourth, we used a rather unstandardized
task (which is used in a lot of exams, of course). However, it
might be interesting to test also more standardized tasks and
answers—are judgments of more standardized tasks influenced
comparably by the mark of a first examiner? Fifth, which in-
fluence has the relationship between first and second examiner?
Sixth, our participants had to judge only one answer. It would
be very interesting to have a situation in which one participant
has to judge a lot of answers which might create also reference
points across different answers and allow comparative judg-
ments. Eighth, it would be interesting to investigate the influ-
ence of rewards, for example for rapid judgments. In this con-
text, the report of the second examiner is most often rather short
and in applied reality it is very short in cases in which the
second examiner gives the same mark as the first examiner.
Generally, in reality, fast judgments are rewarded.
In conclusion, we have shown that anchoring effects are also
found in the domain of achievement judgments. We tested stu-
dents who were rather unfamiliar with marking situations, but
this situation is not uncommon at universities, where complete
novices are often given the task of evaluation of assignments.
From literature, we pointed out several ways to potentially re-
duce such anchoring effects—for example, a difference focus,
blind marking, emphasizing the importance of correct evalua-
tions, or automated scoring. It remains to be seen whether such
conditions could be implemented and whether they are instru-
mental for fairer and more objective evaluations of students’
achievements. In sum, we cannot ignore the discrepancy be-
tween our results, showing clear anchoring effects, and the
ideal of the unprejudiced examiner, which is actually far from
Acknowledgemen ts
We thank Nicolas Salzer, Luise Maier, Laura Flatau, David
Eckert, Lena Zepter, and Elke Förster-Fröhlich for their help in
data collection. We thank Ullrich Ecker for improving the
reada bi lity of this article.
Blankenship, K. L., Wegen er, D. T., Petty, R. E., Detw eiler-Bedell, B.,
& Macy, C. L. (2008). Elaboration and consequences of anchored es-
timates: An attitudinal perspective on numerical anchoring. Journal
of Experimental Social Psychology, 44, 1465-1476.
Bodenhausen, G. V., Gabriel, S., & Lineberger, M. (2000). Sadness and
susceptibility to judgmental bias: The case of anchoring. Psycho-
logical Science, 11, 320-323.
Brehm, R. (2003). The human is unique also as examiner. Neue Juris-
tische Wochenschrift, 56, 2808-2810.
BVerwG [Federal Administrative court of Germany] (2003). Urteil
vom 10.10.2002-6 C 7/02. Neue Juristische Wochenschrift, 56, 1063-
Chapman, G. B., & Johnson, E. J. (1994). Th e limits of anchoring. Journal
of Behavioral Decision Making, 7, 223-242.
Chapman, G. B., & Johnson, E. J. (1999). Anchoring, activation, and
the construction of values. Organizational Behavior and Human De-
cision Processes, 19, 115-153.
Chapman, G. B., & Johnson, E. J. (2002). Incorporating the irrelevant:
Anchors in judgments of belief and value. In T. Gilovich, D. Griffin,
& D. Kahneman (Eds.), Heuristics and biases: The psychology of
intuitive judgment (pp. 120-138). New York: Cambridge University
Clore, G. L., Wyer, R. S., Dienes, B., Ga sp er, K., Gohm, C ., & Is b ell , L.
(2001). Affective feelings as feedback: Some cognitive consequences.
In L. L. Martin, & G. L. Clore (Eds.), Theories of mood and cogni-
tion: A users guidebook. Mahwa, New Jersey: Lawrence Erlbaum.
Critcher, C. R., & Gilovich, T. (2008). Incidental environmental anc-
hors. Journal of Behavioral Decision Making, 21, 241-251.
Dünnebier, K., Gräsel, C., & Krolak -Schwerdt, S. (2009). Biases in tea-
chers’ assessments of studen t performance: An exp erimental stud y of
anchoring effects. Zeitsch rift fü r Päd ag o gis che Psycholo gie, 2 3 , 187-
Englich, B. (2008). When knowledge matters: Differential effects of
available knowledge in standard and basic anchoring tasks. European
Journal of Social Psychology, 38, 896-904.
Englich, B., & Mussweiler, T. (2001). Sentencing under uncertainty:
Anchoring effects in the court-room. Journal of Applied Social Psy-
chology, 31, 1535-1551.
Englich, B., & Soder, K. (2009). Moody experts: How mood and ex-
pertise influence judgmental anchoring. Judgment and Decision
Making, 4, 41-50.
Englich, B., Mussweiler, T., & Strack, F. (2006). Playing dice with cri-
minal sentences: The influence of irrelevant anchors on experts
judicial decision making. Perso nality and S ocial Psychology Bulletin,
32, 188-200.
Epley, N. (2004). A tale of tuned decks? Anchoring as accessibility and
anchoring as adjustment. In D. J. Koehler, & N. Harvey (Eds.), The
Blackwell handbook of judgment and decision making (pp. 240-256).
Oxford: Blackwell Publishers.
Epley, N., & Gilovich, T., (2001). Putting adjustment back in the anc-
horing and adjustment heuristic: Differential processing of self-gener-
ated and experimenter-provided anchors. Psychological Science, 12,
Furnham, A., & B oo , H. C. (201 1). A lit erat ure rev iew of the anch oring
effect. The Journal of Soci o-Economics, 40, 35-42.
Huntsinger, J. R., Clore, G. L., & Bar-Anan, Y. (2010). Mood and
global-local focus: Priming a local focus reverses the link between
mood and global -local processing. Emotion, 20, 722-726.
Klauer, K. C. & Musch, J. (2003). Affective priming: Findings and
theories. In J. Musch, & K. C. Klauer (Eds.), The psychology of
evaluation: Affective processes in cognition and emotion. Mahwah,
NJ: Lawrence Erlbaum.
Kudryavtsev, A., & Cohen, G. (2010). Illusion of relevance: Anchoring
in economic and financial knowledge. International Journal of Eco-
nomic Research, 1, 86-101.
Mussweiler, T. (2002). The malleability of anchoring effects. Experi-
mental Psychology, 49, 67-72.
Mussweiler, T., & En glich, B. (2005). Sub liminal anchoring: Judgmen-
tal consequences and underlying mechanisms. Organizational Beha-
vior and Human Decision Processes, 98, 133-143.
Mussweiler, T., & Strack, F. (1999). Comparing is believing: A selec-
tive accessibility model of judgmental anchoring. European Review
of Social Psychology, 10, 135-167.
Mussweiler, T., Englich, B., & Strack, F. (2004). Anchoring effect. In
R. Pohl (Ed.), Cognitive illus i ons : A handbook of fallacies and biases
in thinking, judgement, and memory (pp. 183-200). London, UK:
Psychology Press.
Mussweiler, T., Strack, F., & Pfeiffer, T. (2000). Overcoming the in-
evitable anchoring effect: Considering the opposite compensates for
selective accessibility. Personality and Social Psychology Bulletin,
26, 1142-1150.
Neely, J. H. (1991). Semantic priming effects in visual word recogni-
tion: A selective review of current findings and theories. In D. Besn-
er & G. W. Humphreys (Eds.), Basic processes in reading: Visual
word recognition (pp. 264-336). Hillsdale, NJ: Erlbaum.
Northcraft, G. B., & Neale, M. A. (1987). Experts, amateurs, and real
estate: An anchoring-and-adjustment perspective on property pricing
decisions. Organizational Behavior and Human Decisio n Processes,
39, 84-97.
Schwarz, N. (2001). Feelings as inf ormation: Implications for affective
influences on information proces sing. In L. L. Martin, & G . L. Clore
(Eds.), Theories of mood and cognition: A users guidebook (pp.
159-176). Mahwa, New Jersey: Lawrence Erlbaum.
Schwarz, N., & Clore, G. L. (2003). Mood as information: 20 years
later. Psychology Inquiry, 14, 296-303.
Shermis, M. D., & Burstein, J. C. (2003). Automated essay scoring: A
cross-disciplinary perspective. Mahwah: Lawrence Erlbaum.
Steyer, R., Schwenkmezger, P., Notz, P., & Eid, M. (1997). The multidi-
mensional mental state questionnaire: Manual. Göttingen: Hogrefe.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty:
Heuristics and biases. Science, 185, 1124-1130.
Wegener, D. T., Petty, R. E., Blankenship, K. L., & Detweiler-Bedell,
B. (2010). Elaboration and numerical anchoring: Implications of at-
titude theories for consu mer judgment and decision making. Journal
of Consumer Psychol ogy, 20, 5-16.
Wegener, D. T., P etty, R. E., Detwei ler-Bedell, B., & J arvis, W. B. G.
(2001). Implications of attitude change theories for numerical anc-
horing: Anchor plausibility and the limits of anchor effectiveness.
Journal of Experimental Social Psychology, 37, 62-69.
Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A
new look at anchoring effects: Basic anchoring and its antecedents.
Journal of Experimental Psychology: General, 125, 387-402.