Georgesen & Harris (1998) - denigration power and performance

Personality
and
Social
Psychology
Review
1998,
Vol.
2,
No.
3,
184-195
Copyright
C)
1998
by
Lawrence
Erlbaum
Associates,
Inc.
Why's
My
Boss
Always
Holding
Me
Down?
A
Meta-Analysis
of
Power
Effects
on
Performance
Evaluations
John
C.
Georgesen
and
Monica
J.
Harris
Department
of Psychology
University
of
Kentucky
One factor
with potential
links
to performance
evaluation
is
evaluator
power.
In
a
meta-analytic
review of
the available
literature,
the
relation
between
power
and
performance
evaluation
was examined.
Results
indicate
that
as
power
levels
increase,
evaluations
of others
become
increasingly
negative
and
evaluations
of
the
self
become
increasingly
positive.
We examined
moderators
of
these
relations,
and
methodological
variables
caused
the
most
differences
in
effect
sizes
across
studies.
The
article
addresses
implications
of
these
findings
for
businesses
and
social
psychological
theories
of power.
From
being
threatened
with additions
to one's
"per-
manent
record"
in
grade
school
to
the review
process
for a
tenured position,
individuals
encounter
evaluative
situations
throughout
their
lives. Performance
evalu-
ations
are omnipresent
in
many
careers
and show
no
signs
of
lessening
in either
frequency
or
popularity
(Budman
&
Rice,
1994).
Periodic
evaluations
occur
throughout
the
employment
of most
individuals
in
pro-
fessional
jobs,
and
the
topic
has
been of
interest
to
psychologists
for
more than
50
years
(Landy
&
Farr,
1980).
As one might
expect
of
such
a well-established
practice,
performance
evaluations
offer
tangible
bene-
fits:
They
may
yield
concrete
measures
of
employee
performance,
often
provide
useful
information
for
im-
proving
problem
weak
areas,
and
may
facilitate
careers
(Queen,
1995;
White,
1979).
Although
the
previous
factors
illustrate
only
a
few of
the
total
benefits,
why
organizations
rely
on
the
wealth of
information
stem-
ming
from
evaluations
is
obvious.
Some
members
of
organizations,
however,
perceive
evaluative
rating
as biased
and
unreflective
of
their
true
abilities
(Gioia
&
Longenecker,
1994;
Landy
&
Farr,
1980).
A variety
of factors
may
bias
the
performance
evaluation
process.
Executives
often
complain
about
politics
pervading
others'
evaluations
of
them
(Gioia
&
Longenecker,
1994).
As their
position
in an
organiza-
tion
rises,
executives increasingly
feel
evaluations
of
their
performance
are
driven
by
factors
such
as
issues
of executive
control
and
ensuring
the continuance
of
the
current
structure
in
the
organization
(Gioia
&
Longenecker,
1994).
This
dissatisfaction
with
the
evaluation
process
is
not
limited
to
executives,
how-
ever.
Individuals
lower
in
the
organizational
hierarchy
also may
view
performance
evaluations
as
inaccurate
and
biased (Kleiman,
Biderman,
&
Faley,
1987).
Given
that
many
employees
feel their
evaluations
are overly
negative
and
inaccurate,
it is interesting
to
note
that substantial
differences
in evaluation
results
exist between
differing
types
of
evaluators
such
as
the
self,
supervisors,
and peers
(M.
M. Harris &
Schaub-
roeck,
1988).
Performance
evaluations
traditionally
have
been
conducted
by
one's
supervisor,
although
increasing
numbers
of
organizations
use
multiple
raters
in conducting
evaluations
(Budman
& Rice,
1994).
Self-evaluations
and
peer-employee
evaluations
are
becoming
increasingly
common.
Frequently,
however,
evaluations
of
the same
individual
differ significantly
between
raters
(M.
M. Harris
& Schaubroeck,
1988;
Landy
&
Farr,
1980).
Furthermore,
only
moderate
agreement
exists between
self-evaluations
and
supervi-
sor evaluations,
although
the relation
between
peer
and
supervisor
evaluations
is somewhat
stronger
(M.
M.
Harris
& Schaubroeck,
1988).
What
causes
these
dis-
crepancies
in evaluation?
Egocentric
bias has been suggested
as
one
explana-
tion of
the
discrepancies
between
supervisor,
self-,
and
peer
evaluations
(M.
M. Harris & Schaubroeck,
1988).
This
theory
suggests
that
individuals
inflate
self-ratings
to improve
their evaluation
and secure
their
employ-
ment.
Within
this
theory,
discrepancies
are
also
be-
lieved
to result
from
attributional differences;
peers
and
supervisors,
serving
as
observers,
attribute
poor
per-
formance
to
internal
personality
factors whereas self-
evaluators,
or
actors,
attribute
poor
performance
to their
environment (M.
M.
Harris
&
Schaubroeck,
1988).
Furthermore,
peers
and supervisors
attribute good
per-
formance
to the environment
whereas self-evaluators
184
Requests
for
reprints
should
be
sent
to John C.
Georgesen,
Depart-
ment of
Psychology,
125
Kastle
Hall,
University
of
Kentucky,
Lex-
ington,
KY 40506-0044.
E-mail:
jcgeorl
@pop.uky.edu.
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POWER
EFFECTS ON
PERFORMANCE
EVALUATION
attribute
good
performance
to
internal
factors.
Other
variables,
however,
also
affect
performance
evaluation,
cause
discrepancies
between
raters,
or
both
(M.
M.
Harris
& Schaubroeck,
1988;
Landy
&
Farr,
1980).
For
example,
the
general
job
performance
of
the rater
af-
fects
the
quality
of that rater's evaluations
(Landy
&
Farr,
1980)
as
does the rater's
opportunity
to
observe
the target's
performance
(M.
M. Harris
& Schaubroeck,
1988;
Landy
& Farr,
1980).
Another
factor potentially
biasing
performance
evaluation
is the
power
level
of the
evaluator.
Although
addressed
in
one
research
program
(Kipnis,
1972;
Kip-
nis,
Castell,
Gergen,
& Mauch,
1976;
Kipnis,
Schmidt,
Price,
&
Stitt,
1981;
Wilkinson
&
Kipnis,
1978)
and
other
isolated studies,
the effects
of
power
on evaluation
have not
been systematically
explored.
In
addition,
except
for
a recent resurgence
of
interest, power
phe-
nomena
have
been irregular
targets
of
social
psycho-
logical
inquiry
and
theorizing (Fiske
&
Morling,
1996;
Ng,
1980).
Therefore,
a
general
definition
of
power
may
be useful
before addressing
its
potential
effects
on
performance
evaluation.
In the
past,
power
has
been
defined
as the amount
of
influence
that
one
person
can
exercise
over
another
person
(Dahl,
1957;
Huston,
1983;
Kipnis
et
al.,
1976).
More
recent definitions
of
power
(Fiske,
1993;
Fiske
&
Morling,
1996),
however,
have
focused
on actual con-
trol rather
than
influence
as
the
key
component
of
power.
The
reason for
this
change
in
focus
has been
the
realization
that
one
may
possess
influence
without
pos-
sessing
actual
control
over
the
outcomes
of others.
For
example,
one
might
be a
moral
leader
in
the
community
and
influence
others'
decisions
through persuasive
rhetoric
but
possess
no actual
control over others'
out-
comes.
Thus,
in current
conceptualizations,
power
is the
difference
in the
amount
of control
possessed
by
mem-
bers
of a
dyad
(Fiske,
1993;
Fiske
&
Morling,
1996).
The
member
of
a
dyad
with
the
most control over
the
other's
outcomes
has
more
power.
Defining
power
in
this
way
is
also
congruent
with
other
traditional
conceptions
of
power.
For
example,
in
the classic
French
and
Raven
(1959;
Raven,
1992)
typology,
a distinction
is made
between
power
that
involves
control
of
others'
outcomes
(e.g.,
reward
and
coercive
power)
and
bases
of
power
that do
not
(e.g.,
expert,
informational,
referent).
The latter
forms
of
power
are
more indirect
and
involve the
willing
coop-
eration and
deference
of
the
target.
These
power
bases
involve
influence
over
others,
but
whether
they
address
the actual
ability
of
the
power
holder to
control
the
outcomes
of
the other
is
not
clear.
For
example,
an
individual high
in referent
power
may
not
be
able to
actually
control
another
by
reward,
coercion,
or both.
Therefore,
we
prefer
to
adopt
a definition of
power
that
encompasses
the idea
of
unilateral
control because
we
feel
that
making
this
distinction
allows the
creation
of
theoretically
unambiguous
operational
definitions
of
power.
Defining power
in other
ways
requires
the as-
sumption
that
the
target
actively
participates
in the
influence
attempt.
With
its relation
to the
control
of others,
power
is
an obvious
candidate
for
consideration
of its
potential
effects
on evaluation.
Two
models
address
the potential
relation
between
power
and
perform-
ance
evaluation.
The
first
model
of
how
power
may
affect
evaluations
stems
from
the research
of
Kipnis
and
his
colleagues
(Kipnis,
1972;
Kipnis
et al.,
1976,
1981;
Wilkinson
& Kipnis,
1978).
They
ar-
gued
that
as
an evaluator's
power
increases,
that
evaluator will
make
more
attempts
to
influence
others.
As
more
attempts
to
influence
others
are
made, the
evaluator
comes
to
believe
that
he
or she
controls
the
behavior(s)
of other
people.
This
por-
tion of Kipnis'
model
is similar
to
Kelley
and
Thibaut's (1978)
conception
of fate
control.
This
belief of responsibility
for others'
behaviors
conse-
quently
causes
the
individual
to
devalue
the
per-
formance of others;
that is, the
higher
power
person
comes
to
believe
that he or
she is
the causal
agent
in producing
relevant
outcomes.
Furthermore,
this
bias conceivably
may
cause
an evaluator
to
take
responsibility
for
any successes
associated
with
the
work
of others.
Applied
to
a performance
evalu-
ation situation,
the
model
suggests
that
as the
power
level of an evaluator
increases,
the positivity
of
their
evaluations
of
subordinates
will
decrease.
Another model
less
directly
addressing
the
power-evaluation
relation but
theoretically promising
in its
implications
concerns
the association between
power
and
stereotyping
(Fiske,
1993;
Fiske
&
Morling,
1996).
The
general
premise
of this
model is
that
persons
in
positions
of
power
are
especially
vulnerable
to
stereotyping
subordinate
others. Fiske
and Morling
(1996)
argued
that individuals
in
powerful
positions
attended less
to subordinates
for three
reasons:
they
lacked
cognitive
capacity,
their outcomes
were
not
controlled
by
their
subordinates,
and
they
may
not have
wanted to
attend because
of their
dominance
level
and
associated
beliefs. Individuals
in
powerful
positions
may
suffer a lack
of
cognitive
capacity
due
to increased
attentional
demands
(Fiske,
1993).
For example,
a pow-
erful individual's position
may
involve
the
supervision
of
20 subordinates
whereas
an
individual
lower
in
the
organizational
hierarchy
may
supervise
only
1
or
2
employees.
Also,
because
their
outcomes
often
do not
depend
on their subordinates,
individuals
with
power
may
focus
the brunt of their attention
elsewhere.
Fi-
nally,
dominance
orientation
may
lead
to alack
of
attention in that individuals
with
a dominant
personality
may
attempt
to control
their
interactions
with others
and
consequently
ignore
the
actions
and motivations
of
others
during
the
interaction
(Fiske
&
Morling,
1996).
Regardless
of
its cause,
decreases in
attention
make
powerful
individuals
more
likely
to
depend
on stereo-
185
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GEORGESEN
&
HARRIS
types
in
interacting
with
subordinates
(Fiske
&
Mor-
ling,
1996).
This
model
has
not
been
directly
tested
on
a
range
of performance
evaluation
situations
but
is
intuitively
appealing
in
its
implications.
If
a
powerful
individual
in an
evaluator
role
does
not attend
to
a
subordinate
and
holds
negative
stereotypes
about subordinates
gener-
ally,
then
an
evaluation
of that subordinate
might
be
negatively
affected
by
stereotyping.
This
reasoning
as-
sumes
that individuals
in
positions
of
power
hold
nega-
tive
stereotypes
about
subordinates,
an
argument
that
may
be supported
in
part
by
Western
beliefs
that
power
is
given
to
people
on
the basis
of
their
talents
and skills
(Goodwin,
Fiske,
&
Yzerbyt,
1997).
Thus,
on the
basis
of
their
belief
that
power
is
earned,
powerful
individuals
may
be
more
likely
to
stereotype
negatively
those
indi-
viduals
with
lower
power
than themselves.
Both
of
the
previous
models
hold
promise
for
ex-
plaining
potential
relations
between
power
and
per-
formance
evaluation.
However,
the
basic
question
of
what
exactly
the effect
of
power
is on
performance
evaluation
remains
as
yet
unanswered.
Some
re-
searchers
have
found negative
effects
of
power
on
evaluation
(Kipnis,
1972;
Kipnis
et
al.,
1976,
1981;
Wilkinson
&
Kipnis,
1978),
whereas
other
researchers
have
found
power
effects
to be
negligible
(Lightner,
Burke,
&
Harris,
1997;
Pandey
&
Singh,
1987;
Wexley
&
Snell,
1987).
The
range
of
findings
in this
area
suggest
that
a
synthesis
is necessary,
both
to ascertain
if
power
actually
affects
performance
evaluation
and
to
lay
a foundation
for
later
testing
of
the
mechanism(s)
by
which
it affects
evaluation.
Our
purpose
here
was
to
conduct
the
first
quantita-
tive
review
of
research
on
the
effects of
power
on
performance
evaluation.
We
addressed
three
areas
of
primary
interest
in
this
meta-analysis:
1.
Does
the
power
position
of
the
evaluator
affect
performance
evaluation?
We asked
this
question
from
two
perspectives:
(a)
What is the
effect
of
power
on
evaluations
of
a lower
ranked
other?,
and
(b)
What
is
the effect
of
power
on self-evaluations?
What
is the
size
and
direction
of the
effect?
Based on
both
of
the
power
theories
mentioned
earlier,
one
might
expect
that
as
power
increases,
evaluations
of
lower
power
others
become
increasingly
negative.
Available
literature,
however,
provides
no
clear
predictions
concerning
the
effects
of
power
on self-evaluation.
2.
What
theoretical
variables
moderate
the
effect
of
power
on
performance
evaluation?
For
the
purposes
of
this
meta-analysis,
a variable
was considered
a
theoreti-
cal
moderator
if
that
variable
would
add
to
a
more
refined
theoretical
understanding
of
the
nature
of
power.
Theoretical
moderators
examined
in this
study
include
location
of
study,
participant
gender,
and
par-
ticipant age.
For
example,
location
of
study
is consid-
ered
a
theoretical
variable
in that
various cultural
factors
associated
with
different
geographic
regions
may
affect
patterns
of power
usage.
3.
Can
differences
in effect
sizes
between
studies
be
explained
by
methodological
variables?
Methodologi-
cal
moderators
studied
were
the
laboratory
responsible
for
research,
quality
of
study,
strength
of power
ma-
nipulation,
type
of
research,
type
of
study,
and
year
of
study.
Method
Sample
The
literature
search
began
with
a
computer-based
strategy.
Searches
were
conducted
on
the
PsycLIT
(American
Psychological
Association,
1974-present)
database
for
articles
published
since
1974.
Also,
com-
puter
searches
were
conducted
on
ABI
Inform
(Univer-
sity
Microfilms
International,
1975-present),
a
busi-
ness
database,
for
articles
published
since
1975.
After
these
searches,
the
first author
manually
inspected
the
Journal
of
Applied
Psychology
(1960-1996)
to
ensure
both
that
articles
were
not
missed on
the
computer
searches
due
to
improper
keyword
usage
and
that
earlier
articles
in the
area
had been
uncovered.
Following
the
collection
of
articles
elicited
in
the
previous
searches,
an ancestry
approach
of
exploring
their
references
was
used
to
collect
any
previously
undiscovered
articles.
The ancestry
approach
involves
using
the references
of
relevant
articles
to retrieve
more
articles.
Then,
the
references
of
the
relevant
articles
are
used
to
retrieve
yet
more
articles
in
a continuing
process
until
no further
useful
references
are
discovered.
As
a
final step,
the
Social
Sciences
Citation
Index
(Institute
for Scientific
Information,
1981-1998)
was used
in
a
descendancy
search
involving
several
often-cited
arti-
cles
(Kipnis
et al.,
1976;
Wexley
& Snell,
1987;
Wilkin-
son &
Kipnis,
1978)
in the
already
obtained
literature.
The descendancy
approach
involves
perusing
the
Social
Sciences
Citation
Indexes to
retrieve
a list
of
all
the
articles
that cite
a
particular
well-known
study.
The
retrieval
of
articles discovered
in
the
ancestry
and
descendancy
searches
was
not
limited
by
year
of
pub-
lication.
Criteria
for
inclusion
and
exclusion.
Articles
col-
lected
in the
previous
searches
were
chosen
for
inclu-
sion
or exclusion
on
the basis
of both
publication
and
content
criteria. Studies
included in this
meta-analysis
were
published
journal
articles
and
available
unpub-
lished experiments.
The
content of a study
also
affected
its
inclusion
or
exclusion.
To be included
in this
analysis,
a
study
must
have conceptualized
power
in a
manner
similar
to
the
following
definition:
Power
is
the ability
of
an individ-
186
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POWER
EFFECTS ON
PERFORMANCE
EVALUATION
ual
to exert
more control
or influence
over
another's
outcomes
than
the
other
can
exert
over
the
controlling
individual's outcomes
(Fiske,
1993;
Fiske
&
Morling,
1996).
The
study also
had
to
address
the effects
of
power
on
evaluations
explicitly
and either
manipulate
posi-
tional
power
differences
or
use
preexisting
power
dif-
ferences. Studies
were
included
whether
they
measured
real
or
perceived
power
differences.
Investigations
ex-
ploring
status without
mention
of
power
were
excluded
from this review
because
individuals
may
possess
dif-
fering
status
without the
ability
to
exercise
unequal
amounts of
influence
over one
another
(Fiske,
1993).
Finally,
the
study had to
contain an
actual
evaluation
of
a
subordinate,
the
self,
or
both
by
an
individual(s)
possessing
a
power
advantage
over
the
outcomes
of
others.
Coding
The first
author
Georgesen
coded
studies
selected
for inclusion
along
the
following
dimensions to
inves-
tigate
the
moderating
effects of both
theoretical and
methodological
variables on
the
relation
between
evaluator
power
and
performance evaluation.
In
addi-
tion,
a
second
rater
also coded
the
subjective
variables
included
in
this
study:
overall
quality,
strength
of
ma-
nipulation,
and
type
of
study.
Initial
interrater
reliabili-
ties,
as
measured
by
interrater
correlations,
were varied:
.40 for the
strength
of
manipulation
variable,
.68 for
overall
quality,
and
1.00 for
the
type
of
study
variable.
Due to the
somewhat
low
reliabilities
associated
with
the
strength
of
manipulation
and
quality
variables,
these
rating
discrepancies
were
explored
and
resolved
by
conference.
The
coding
process
began
with each
study
being
coded as to whether it measured
self-
or
other
perform-
ance evaluation. Studies were
also
coded
by
location,
percentage
of male
participants,
and
participants'
age.
Location,
defined as
whether
the
study
was
conducted
in
the United States or
elsewhere,
may
moderate the
effects of
power
on
evaluation
via
unique
cultural
fac-
tors. For
example,
power
may
have
larger
effects on
evaluation in an
individualistic
rather than a
collectivis-
tic
cultures due
to
emphasis
placed
on
individual
achievement in
individualistic cultures.
Coding
for
the
percentage
of
male
participants
allows for
examining
potential
gender
effects on
power
roles.' The
considera-
tion of
age
is
important
in
that one
may
become
increas-
Studies
were
not coded
for
gender
more
specifically
(i.e., gender
of
supervisor)
because the vast
majority
of the
retrieved studies did
not contain
the
necessary
information. Available
evidence from
several
studies in
our
own
laboratory,
which
did
code
and
analyze
for
supervisor
gender,
suggests
that the
supervisor's
gender
does not
affect self- and
other-evaluation.
ingly likely
to hold
more
powerful
positions
with
in-
creasing
age,
which
may strengthen
any
effects of
power
on
other-evaluation.
When
possible,
median
age
of
study participants
was
coded.
Age
was
dropped
as a
potential
moderator after
initial
coding,
however,
due
to
its
complete
redundancy
with
the
type
of
study
(in all
cases, experimental
investigations
of
power
took
place
on
college campuses).
Also
redundant
with
type
of
study,
the
type
of
power
measured
may moderate
the
effects of
power
on
other-evaluation.
Participants
who
actually
possess
power
in
a
workplace
setting
may be
more
likely
to
derogate
others in
their
evaluations
than
participants
experiencing
a contrived
power
difference
stemming
from
an
experimental
manipulation.
Studies
were
also
coded
for
several
methodological
variables that
may
moderate the
relation
between
evalu-
ator
power
and
evaluation.
Each
study
was
coded
by
the
raters
on
a
4-point scale
ranging from
1
(very
poor
overall)
to
4
(very good
overall)
for
both
overall
study
quality
and
strength
of
experimental
manipulation
(if
applicable).
The
overall
quality
of
the
study was
rated
by
considering
issues
such
as
power,
sample
size,
ade-
quate
controls, randomization,
and
other
methodologi-
cal
factors
that
can
affect
study
outcomes
(Rosenthal,
1991).
For
example,
a
study
that
had
a
large
enough
sample
to
investigate
the
question
of
interest
with at
least a
moderate
degree
of
power,
randomly
assigned
participants
to
groups,
conceptualized
measures
clearly,
and
performed
the
appropriate
analyses
would
receive
a
quality
rating
of 4.
A
study
that
met all
of
the
previous
requirements
except
for
one
criterion
would
receive
a
quality
rating
of
3,
and
so
forth
down
the
rating
scale
until
a
study
that
met
only
one
of
the
previous
criteria
would
receive
a
quality
rating
of 1.
The
strength
of an
experimental
manipulation
was
rated
by
considering
the
realism,
believability,
and
participant
involvement
associated
with
the
power
ma-
nipulation.
For
example,
a
manipulation
that
led
the
participants
to
believe
they
were
part
of
a
supervi-
sor-subordinate
dyad
in
which
the
supervisor
could
negatively
affect
the
subordinate's
outcomes,
directly
involved
both
participants
in
their
respective
roles,
and
made it
seem
as if
the
power
difference
was
affecting
the
results
of
the
interactions
would
receive
a
strength
manipulation
of 4.
A
study
that
contained
two
of
these
elements
but
not
three
would
be
down
rated
to a
strength
of
3,
a
trend
that
could
continue
downward until
a
manipulation
was
rated
as
having
a
strength
of
1 if
it
contained
none
of
the
previous
elements.
The
studies
were
also
coded
for
author
(laboratory
responsible
for
research),
overall
number
of
partici-
pants, nature
of
research
(psychology
or
business
pub-
lication),
type of
study
(correlational,
quasi-experimen-
tal, or
experimental),
and
the
year in
which
the
article
was
published.
These variables
were
chosen
for
inclu-
sion
because
of their demonstrated
effects
on
study
outcomes
in
other
meta-analyses
(Rosenthal,
1991).
187
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GEORGESEN
& HARRIS
In addition
to
being
coded for theoretical and meth-
odological
variables,
each
study
was coded to
yield
one
index of
effect size
and
significance
level.
This meta-
analysis
used the Pearson
correlation coefficient
r as
the
estimate
of effect size.
In studies
with
multiple
outcome
measures
of
other-evaluation,
self-evaluation,
or
both,
the effect
size for
each measure was transformed
to its
associated Fisher Zr
and
combined with the other Fisher
Zr's
to
yield
the
mean Fisher
Zr.
This was then trans-
formed
back
to.
r
to
yield
the mean effect
size for
the
study.
In the case of
one of the other-evaluation
studies,
not
enough
statistical
information
was included to cal-
culate
an effect
size,
so
in line
with
Rosenthal's
(1995)
recommendations,
that
study
was
assigned
an
r
of .00
and
a
p
of .50.
Results
The
literature search yielded
a total of 25 codable
studies;
7 studies examined
the
effects of
power
on
self-evaluation,
and 18 studies examined
the
effects
of
power
on other-evaluation.
Tables
1
and
2
list
the
studies
included
in
each
category,
their scores on the
coded
methodological
and theoretical
variables,
and the
effect
size
and
significance
level extracted
from each
study.
As
mentioned,
we
analyzed
these two
groups
of
studies
separately
due to
the
differing predictions
asso-
ciated
with
their relation
to
power.
For both
groups
of
studies,
all
effects were
in the
predicted
direction with
the
exception
of the other-
evaluation
study
coded
as
having
an effect size
of
r
=
.00 and
p
=
.50
due to insufficient
statistical
informa-
tion.
Following
the calculation
and
coding
of individual
effect
sizes and
significance
levels
(see
Table
1
and
Table
2),
these results
were combined
to
yield
a
mean
effect size and
mean
significance
level for
each of the
two
groups
of studies.
General
Analyses
The
overall effect
size
for
each
set of studies
was
calculated
by
obtaining
the
weighted
mean
Fisher
Zr
and
transforming
it
back to the
weighted
mean
r
(Rosenthal,
1991).
Due to the
disparate
sample
sizes
of articles
included
in this
meta-analysis,
each
study
was
weighted
by
its total
degrees
of
freedom as a
means of
taking
study
size
and the
reliability
associated
with
larger
samples
into
account.
Where
possible,
this
weighting
occurred
throughout
the
analyses.
The
weighted
mean
effect size
associated
with the
combined
self-evaluation studies
was r=
.45,
indicating
a medium to
large
effect
of
power
on
self-evaluation.
As
power
levels
increase,
self-evaluations
become
in-
creasingly positive
in
tone.
When
unweighted,
the mean
effect size for
this
set of
studies
was
somewhat
smaller,
r
=
.38
(see
Table
3).
The
median
effect
size,
minimum
and maximum
effect size,
quartile scores, and
95%
confidence intervals for this
set of studies were
also
computed,
and are reported
in Table 3. Throughout
the
analyses,
confidence intervals
were calculated
accord-
ing to the
procedures recommended
by Rosenthal
(1991),
using weighted mean
effect sizes. A stem
and
leaf plot (see
Table 3) was constructed,
using the
effect
sizes of the self-evaluation studies,
to check for
possible
outliers affecting the results,
but none were found.
The
weighted mean effect
size associated
with the
combined
other-evaluation studies
was r
=
.29,
indicat-
ing
a roughly medium effect
of power on other-evalu-
ation.2 As
power
levels increase, one's evaluations
of
others become more derogatory.
When unweighted,
the
mean effect size did not vary,
r
=
.29 (see Table
4). The
median effect
size, minimum
and maximum effect
size,
quartile scores,
and
95% confidence
intervals
were also
calculated for this set of studies (see Table 4).
Confi-
dence intervals were calculated using the
weighted
mean effect size.
A
stem
and
leaf plot did not reveal
any
outlying
effect sizes.
The combined
significance
levels for each of
the two
sets of studies
were
calculated with the Stouffer
method
(Rosenthal,
1991), again
weighted by degrees
of free-
dom. The combined
significance
level
for
the self-
evaluation studies
was
significant,
Z
=
12.88,
p
<
.001.
The combined
significance
level for the other-evalu-
ation studies
was
significant
as
well,
Z=
1
1.07,p
< .001.
Thus,
that no relation exists
between power and
evalu-
ation
for
either set of
studies
is extremely unlikely.
Following
the
previous
computations,
a
chi-square
testing
heterogeneity
of
variance
was
computed
for
each set of studies
to examine the
consistency
of results
across studies (Rosenthal,
1991).
The
chi-square
test
for
self-evaluation studies indicated
significant
differences
in
effect
sizes across
studies, X2(6,
N
=
7)
=
21.26, p
<
.05.
Significant
differences
in
effect
sizes were
also
found for the other-evaluation
studies,
x2(
17,
N
=
18)
=
62.62, p
<
.01. These results
suggested
that various
factors
may
moderate the effects of
power
on
evaluation
and
prompt
closer
examination of
the data.
Effects of
Moderating
Variables
The
moderating
effects of coded methodological
and
theoretical variables were assessed
using
both the con-
trast
Z
and
correlational
approaches
(Rosenthal,
1991).
Recall that one of
the
other-evaluation
studies was assigned
an
effect
size
of zero due to insufficient
statistical
information.
A
set of
analyses
was also
conducted
excluding
this
study,
thus treating
it as
missing
data. The
weighted
mean effect
size
r
for
other-evaluation
studies
was
nearly identical,
r
=
.30, unweighted mean effect
size
r
=
.30,
and
median
r
=
.31.
Therefore,
leaving
out the
effect size
r
of .00
did
not
alter
our
pattern
of
results,
and
we
decided
to continue its
inclusion
in
subsequent analyses,
a
more
conservative
approach
recommended
by
Rosenthal
(1991).
188
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