Relating Covariation Information to
Causal Dimensions through Principles
of Contrast and Invariance
Frank J. Van Overwalle and Francis P. Heylighen
Vrije Universiteit Brussel, Belgium
We are grateful to Vivianne De Schaepdrijver and Nils Burckhard for serving diligently
as experimenters. We also sincerely thank Dennis Hilton for his very valuable
suggestions and Daan Van Knippenberg for his statistical advice. For correspondence
write to : Frank Van Overwalle, Vrije Universiteit Brussel, Department of Psychology,
Pleinlaan 2, B-1050 Brussels, Belgium.
Abstract
This paper examines the proposition that covariation information guides judgments about
the dimensionality of attributions on the basis of causal principles of contrast and
invariance, which are derived from Mill's methods of difference and agreement
respectively. It is argued that the standard attribution categories specified in earlier
research (e.g., person, occasion and stimulus) represent just one extreme of the
attributional dimensions and require the principle of contrast, whereas additional
attributional categories reflecting the opposite extreme of the dimensions (e.g., external,
stable, general) require the principle of invariance. In three studies, subjects were given
covariation information, and were asked to rate the properties of the likely cause along
the dimensions of locus, stability, globality and control. In line with the predictions,
consensus with others, consistency in time, distinctiveness between stimuli and
contingency of one's actions showed the strongest effects on judgments of locus, stability,
globality and control respectively. Similar results were obtained in a fourth study, where
subjects had to judge the influence of eight causes with varying dimensional properties.
Moreover, these judgments were rated somewhat higher given causes requiring the
principle of invariance rather than the principle of contrast.
Introduction
How do people infer a causal explanation[1 for some event ? Theory and research in the
attribution literature that people make use of covariation information about the event to
solve an attributional question. Drawing on Mill's (1872/ 1973) ]joint method of
agreement and difference, Kelley (1967) postulated that an effect will be attributed to the
condition with which it covaries : "the effect is attributed to that condition which is
present when the effect is present and which is absent when the effect is absent" (Kelley,
1967, p. 194). Although Kelley's model received some support (McArthur, 1972, 1976;
Ruble & Feldman, 1976), the original covariation notion has subsequently been refined
and complemented by several alternatives such as the template-matching model (Orvis,
Cunningham & Kelley, 1975), the logical model (Hewstone & Jaspars, 1987; Hilton &
Jaspars, 1987; Jaspars, 1983), the abnormal conditions focus model (Hilton & Slugoski,
1986) and, most recently, the probabilistic contrast model (Cheng & Novick, 1990) and
the dispositional model (Hilton, Smith & Kim, 1994).
Although the models that developed from Kelley's (1967) original model differ strongly
with respect to how covariation information is used and processed, virtually all of them
agree in one important respect : they all assume that people's causal attributions can be
captured in three responses, including something about the person, something about the
occasion, something about the stimulus, and a combination of these three factors. These
three responses, which we have termed standard responses, have been widely used and
considered sufficient in the literature to represent people's entire attributional universe.
This untested assumption stands in sharp contrast with findings from research on the
dimensionality of attributions. Research on every-day causal attributions has revealed that
they can be categorized along four fundamental dimensions of locus (internal vs.
external), stability (stable vs. variable over time), globality (general vs. situationspecific), and control (changeable versus unchangeable by one's actions; see Abramson,
Seligman & Teasdale, 1978; de Jong, Koomen & Mellenbergh, 1988; Lunt, 1988; Van
Overwalle, 1989; Weiner, 1986). In the most simple case, these dimensions are viewed as
dichotomies and thus reflect eight possible causal categories. Clearly, three of these
categories are also represented by the standard responses specified in earlier attribution
models : Attributions to the "person" reflect internal explanations, attributions to the
"occasion" reflect variable explanations, and attributions to a "stimulus" represent
specific causes.
However, as noted before, earlier theory and research that developed from Kelley's
(1967) approach was limited to these three standard causal categories and did not specify
responses to represent the opposite causal categories of external, stable and general
attributions. Moreover, none of the responses reflected the dimension of causal control.
We argue that these additional causal categories are an inherent part of a person's causal
thinking and psychology, and that these responses should therefore be included in any
general theory of the attribution process. For instance, failure to arrange a date with a girl
can be explained not only by the fact that the actor did not call the girl sufficiently in
advance (a joint attribution to the actor, occasion and the stimulus), but also by strict
religious rules (an external, stable and general attribution). Most theories in applied
attributional domains typically specify a larger variety of attributions, including the nonstandard categories discussed above, for example in theories of motivation and emotion
(e.g., Weiner, 1986), learned helplessness (e.g., Peterson & Seligman, 1984; consumer
behavior (Folkes, 1984), marriage (e.g., Bradbury & Fincham, 1990) and others. Below,
we will present a model of causal inference that specifies principles of contrast and
invariance, through which people can make attributions to both standard and nonstandard (opposite) causal factors. As these principles are derived from Mill's
(1872/1973) joint method of agreement and difference, we have likewise termed our
model a joint model of causal attribution.
Principles of Contrast and Invariance
The proposed joint model represents a more comprehensive integration of Mill's
(1872/1973) methods of both difference and agreement than earlier attribution models.
Kelley's (1967) covariation model and its later theoretical extensions mainly developed
on the method of difference (e.g., Jaspars, 1973; Hilton & Slugoski, 1986, Cheng &
Novick, 1990), and left relatively unspecified the complementary method of agreement
(for a similar argument see Hilton, Smith & Kim, 1994). However, Mill (1872/1973)
proposed both methods as "potent instruments" for causal inquiry. We will illustrate that
these two methods are required to make attributions to standard and non-standard causes.
Mill's method of difference compares the target event where the effect is present to other
events in which the same effect is absent, and specifies the cause as the one condition that
differs between these two events.[2 Cheng and Novick's (1990) probabilistic contrast
model is an instantiation of this method, and is based on their ]contrast principle which
specifies that "a factor is designated a cause if the proportion of times the effect occurs
when that factor is present is greater (by some criterion) than the proportion of times the
effect occurs when the factor is absent" (Cheng & Novick, 1990, p. 561). Thus, if the
contrast between those proportions is sufficiently large, causality is attributed to that
factor. For example, the information that Mary always plays with children while most
other persons do not, indicates that Mary is "different" and designates something special
about Mary as the cause.
Borrowing from Kelley (1973), Cheng and Novick (1990) presented three information
variables which reflect a high or low contrast between a target event and comparison
events : Consensus reflects the extent to which a target person's outcomes resemble those
of other persons, consistency denotes the frequency with which similar effects occurred in
the past, and distinctiveness indicates the degree to which the effect of the target stimulus
differs from that of other stimuli. For example, imagine that John failed a maths exam.
The low consensus information that nobody else failed reveals that the cause is something
about the person, John; the low consistency information that John never failed before
indicates that the cause is something about the current occasion; and the high
distinctiveness information that John passed the other exams reveals that the cause is
something about the stimulus, the maths exam. In sum, the condition that reflects the
"difference" is designated as the cause. Conversely, Cheng and Novick (1990, 1991)
predicted that high consensus, high consistency and low distinctiveness refer to a lack of
contrasts and therefore these factors are seen as causally irrelevant or as mere enabling
conditions.
We argue, however, that these latter information variables can result in causal attributions
by applying Mills' method of agreement. The method of agreement compares the target
event to other events in which the same effect is invariably present, and specifies the
cause as the one condition that is also invariably present among a number of otherwise
varying conditions : "If two or more instances of the phenomenon under investigation
have only one circumstance in common, the circumstance in which alone all the instances
agree, is the cause of the given phenomenon" (Mill, 1872/1973, p. 390). For example, if
Mary always plays a particular game, causality is attributed to this game because that
factor is invariably present together with her playing behavior. The method of agreement
thus enables attributions to factors that contain no contrasts, in contradiction to the
predictions of Cheng and Novick (1990, 1991). In particular, we propose that when
contrasts are absent, people will infer that the cause does not fall on the standard extreme
of the dimension, and that it must therefore necessarily fall at the opposite (i.e., nonstandard) extreme. For example, high consensus information that all students failed the
exam reveals that the cause is external to the target person (as opposed to something
internal); the high consistency information that John always failed in the past reveals that
the cause is stable (as opposed to some temporary occasion); and the low distinctiveness
information that John failed on all courses reveals that the cause is something general (as
opposed to something specific about the stimulus). We define this instantiation of the
method of agreement as the invariance principle, because we predict that non-standard
attributions to the opposite extremes of the causal dimensions require invariant, as
opposed to contrastive information variables.
Table 1 presents the predictions of our joint model which incorporates both principles of
contrast and invariance. The information variables are listed in the first column in their
conventional labels, while the causal categories are depicted in the last column. The
information patterns of contrast and invariance that establish the inferential linkages
between information variables and causal categories are shown in the middle column.
In the bottom panel of Table 1, a less familiar information variable, contingency, is
introduced. It is tentatively proposed that this variable is diagnostic for perceived control
over causes. The notion of contingency stems from the learned helplessness literature
where it is often used synonymously with the notion of objective control (Abramson,
Seligman & Teasdale, 1978). In the present context, we use the term contingency only to
reflect the stimulus characteristics of an event, and we use the term control to refer to
perceived properties of a cause. Following Abramson et al. (1978), an outcome is said to
be noncontingent if the occurrence of the outcome is not related to a person's behavior,
that is, the outcome remains invariant whether or not a particular action is made by the
actor. For example, a child may not recuperate from an illness regardless of what the
doctor prescribes; and our student, John, may continue to do poorly no matter how hard
he tries. As these examples illustrate, low contingency reveals that the agent has little
control over the cause of the event. Conversely, an outcome is contingent when after
some action it differs from the outcome when the action was not made. In such instances,
people may infer that the cause was the action under the control of the agent. Thus, the
joint model predicts that contingency information determines inferences of causal
(un)controllability.
Table 1
Predictions of the Joint Model
Information Variables
Factor
Pattern
Causal
Categories
Consensus
low
high
Persons
contrast
invariant
Internal (Person)
External
Consistency
low
high
Time
contrast
invariant
Variable (Occasion)
Stable
Distinctiveness
high
low
Stimuli
contrast
invariant
Specific (Stimulus)
Global
Contingency
high
low
Actions
contrast
invariant
Controllable
Uncontrollable
Note. Between parentheses on the right are the standard attribution responses predicted by
earlier theory and research (e.g., Cheng & Novick, 1990)
The linkages between information variables and dimensional categories shown in Table 1
have been pointed out in earlier attribution research (e.g., Kammer, 1984; Meyer, 1980;
Read, 1987; Read & Stephan, 1979), although an explicit theoretical rationale of the
underlying attribution process was never provided. As mentioned earlier, many earlier
models based on the method of difference specify only standard attributions (shown
between parentheses) and their combinations (e.g., Cheng & Novick, 1990; Försterling,
1989; Hewstone & Jaspars, 1987; Jaspars, 1983; Hilton & Slugoski, 1986). Most of these
models predict that no attributions are possible following the other information variables
listed in Table 1, although Cheng and Novick's (1990) contrast model allows for
(relatively weak) attributions under invariant conditions depending on the default
assumptions perceivers make about unspecified information (e.g., the behavior of other
people in other situations; see also footnote 5). In contrast, the joint model predicts that
when there is invariance over one or more information variables, the perceiver may still
infer strong causality in terms of external, stable, global and/or uncontrollable
attributions. For example, the fact that all living creatures (high consensus) in the whole
universe (low distinctiveness) always die sooner or later (high consistency) whatever
their attempts to avoid this (low contingency), can be sufficiently explained by an
external, global, stable and uncontrollable factor such as God or Laws of Nature.
In the following sections, four empirical studies will be presented to illustrate that
subjects infer causal attributions in a manner consistent with the proposed informationdimension linkages through the application of both principles of contrast and invariance
of the joint model. Before turning to our empirical data, however, we will first review
prior work that bears upon the joint model.
Evidence from Previous Research
Several studies in the attribution literature provide support for our hypothesis that people
make attributions in accordance with the proposed information-dimension linkages. Most
empirical work comes from prior studies by Weiner and others, relating Kelley's
information patterns with single attributions of ability, effort, task difficulty, and luck
(see Table 2). In two exemplary experiments by Frieze and Weiner (1971), subjects were
presented with information patterns involving achievement outcomes and then had to
indicate how much of an actor's outcome was due to skill, effort, task difficulty and luck.
Consensus was manipulated by giving the percentage of others who succeeded or failed
at the given task, consistency was indicated by the percentage of the actor's past
successes or failures at the same task, and distinctiveness was provided by the percentage
of outcomes for the actor in other tasks. In other studies, information about the event was
manipulated experimentally. For example, Feather and Simon (1971a, 1971b) varied
consistency information by letting the subjects first fail or succeed on "practice" items
before beginning the "test" items. The subjects who failed or succeeded on both practice
and test items were thus given high consistency experiences, whereas subjects who
attained different outcomes on practice and test items were exposed to low consistency
experiences. Unfortunately, none of these studies manipulated the contingency variable.
Table 2 summarizes the findings of single-attribution investigations that used similar informative or
experimental procedures and reported relevant significance tests.[3 All studies involved an achievement
task. Subjects ranged from fourth grade elementary school children to college students, and attributions
were given from the perspective of the self or others. To analyze these findings, however, it is first
necessary to make some reasonable assumptions with regard to the dimensional location of ability, effort,
task and luck. A possible categorization, based on Weiner (1986), is shown in Table 2 for each relevant
information variable. The entries in Table 2 further denote whether consensus, consistency and
distinctiveness is ]high or low. Attributions following this information are shown on top of the table. For
example, the first row shows that low consensus is followed by ability, effort and luck attributions, whereas
high consensus is associated with attributions to task difficulty.
Studies relating Information Variables to Single
Attributions
Attributions
Study
Ability
Effort
Task
Luck
Consensus information
Frieze & Weiner
(1971, study 1)
Frieze & Weiner
(1971, study 2)
Weiner & Kukla
(1970, study 6)
Fontaine (1975,
study 1)
intern[a]
Low
intern
Low
extern
High
extern
Low
ns
Low
High
ns
Low
Low
----
----
Low
Low
High
Low
Fontaine (1975,
study 2)
Read & Stephan
(1979)
Frieze & Bar-Tal
(1980)
ns
ns
ns
ns
Low
Low
High
Low
ns
ns
High
Low
Consistency information
Frieze & Weiner
(1971, study 1)
Frieze & Weiner
(1971, study 2)
Feather & Simon
(1971a)
Feather & Simon
(1971b)
Nicholls (1975)
Read & Stephan
(1979)
Frieze & Bar-Tal
(1980)
stable[a]
High
unstable
ns
stable
High
unstable
Low
High
Low
High
Low
High
----
----
Low
High
ns
ns
Low
High
High
ns
ns
ns
ns
Low
Low
High
Low
High
Low
Distinctiveness information
Frieze & Weiner
(1971, study 1)
global[a]
Low
global
ns
global
ns
specific
High
Note. Entries denote whether consensus, consistency and distinctiveness is high or low.
The attributions given following this information are shown on top of the table.
Horizontal lines (---) indicate that the attribution was not measured. Insignificant results
are denoted by ns.
[a
] Presumed causal position.
As can be seen from Table 2, the findings are generally in agreement with the assumed
linkages between information and dimensions. Low consensus between people was
followed by internal attributions to ability and effort, whereas high consensus increased
external attributions to task difficulty. High consistency with past performance increased
stable attributions to ability and task difficulty, whereas low consistency led to unstable
attributions of effort and luck. Low distinctiveness increased attributions to global causes
such as ability, whereas high distinctiveness led to specific attributions of luck. Some
attributions, however, provided rather weak support for the hypotheses, particularly in the
case of effort and task difficulty following consistency and distinctiveness information.
Presumably, this is due to the fact that the properties of stability and globality are less
explicit for these causes (Weiner, 1983). Indeed, variable effort is sometimes perceived as
stable, whereas difficulty is at times seen as moderately variable. Similarly, general effort
may be confounded with specific preparation for a particular exam.
However, the predictions were strongly contradicted for luck attributions, as this external
cause followed after low instead of high consensus. Read and Stephan (1979, p. 199)
suggested several reasons for this unexpected finding. First, people may view luck as
belonging to the individual and in that sense perceive it as internal. If people view luck as
internal, then the fact that low consensus leads to greater attributions to luck becomes
consistent with predictions. A second explanation provided by Read and Stephan is that
because of its random (i.e., unstable and specific) nature, luck cannot reasonably be
invoked to explain the independent outcomes of a large number of people at one time.
Attribution to an extremely unstable and specific factor precludes its use to explain
outcomes for which there is high consensus. According to this explanation, properties on
one dimension can sometimes override properties on other dimensions. This may follow
from the fact that causal dimensions correlate with one another (rs from .19 to .68,
Anderson, 1983).
An important limitation of single-attribution studies is that dimensions have to be inferred
by the researcher from the given causes. Therefore, we tested the information-dimension
linkages predicted by the joint model more directly by analyzing the effect of the
information variables on perceived causal dimensionality; that is, the subjects themselves
rated the properties of the most likely cause. To our knowledge, only two studies have
been published which measured perceived dimensionality, although only indirectly.
Meyer (1980) measured dimensionality through a factor-analysis of nine attributions and
found the predicted linkages between consensus and locus, and between consistency and
stability. Kammer (1984) asked her subjects to rate a number of causes varying in
globality, and showed an effect of distinctiveness on specific versus global causes.
The effects of covariation information on dimensional judgments were tested in three
studies. The first study involved a single event, set in an achievement context (i.e., losing
a computer game), while the second study took place in a social context (i.e., being
complemented for a dress at a party). These events were deliberately set in an unfamiliar
context (i.e., a newly developed computer game, a party in a foreign culture) so as to
avoid preconceived attributions that might interfere with the information manipulated.
The third study involved sixteen different achievement and social events, taking place in
more familiar contexts. Our prediction is that information on consensus, consistency,
distinctiveness and contingency determines causal properties of respectively locus,
stability, globality and controllability.
Study 1
Method
Subjects
Subjects in the first study were 42 freshmen from the communications department at the
Vrije Universiteit Brussel (Belgium). During a regular class hour, they were invited to
participate in a study on "how people make attributions". After receiving a booklet with
written instructions on the first three pages, the subjects completed the questions
individually. The whole procedure took about 30 minutes, and subjects were thanked for
their participation.
Material
A scenario was introduced in a small paragraph on the first page of the booklet. It
described "a boy, Peter, who lost the XB03 game during a test session when trying out a
Gambit tactic". The subjects had to imagine themselves as researchers who had to infer
the reasons for winning or losing in order to improve the initial test version of this newly
developed computer game. After this introduction, a "situation" was depicted on each
subsequent page which involved a set of four sentences providing additional information
on consensus, consistency, distinctiveness and contingency. The wording of these
information variables is illustrated below (the sentence parts that alter given invariant
versus contrastive information variables are shown in italic) :
- Everyone / Nobody except Peter lost the XB03 game (high/low consensus);
- On prior test sessions, Peter always lost / sometimes won on the XB03 game (high/low
consistency);
- Peter lost / won on every other computer game (low/high distinctiveness);
- Peter lost / won on the XB03 game no matter what or how much he did / when he tried
some other tactics (low/high contingency).
In total, there were sixteen situations (pages) involving all possible combinations of
information variables. Following each situation, the subjects were asked to analyze
carefully the information and to rate the properties of the cause on four 7-point scales (the
dimensions are indicated between square brackets) : resides within Peter -- resides
outside of Peter [locus]; influences only this game (specific) -- influences many different
games (global) [globality]; is not controllable by Peter -- is controllable by Peter
[control]; is temporary (variable) -- is permanent (stable) [stability]. Each dimension was
fully defined with the aid of an example in the introductory pages of the booklet to
preclude any lack of understanding. The situations (pages) in the booklet were presented
in one random order and its reverse. Within each situation, the information variables were
counterbalanced between subjects in four different (Latin square) orders.
Results and Discussion
The data were analyzed with a 2 (high vs. low consensus) x 2 (high vs. low consistency)
x 2 (high vs. low distinctiveness) x 2 (high vs. low contingency) within-subjects ANOVA
with the four attribution measures as dependent variables. As noted before, the
attributional dimensions may not be entirely independent and, to control for such
interactions, the ANOVAs were conducted for each attribution dimension with the other
three attribution measures as covariates. The means and F-values are shown in the top
panel of Table 3. The findings were generally in line with the joint model as the causal
dimensions were most strongly determined by the predicted information variables. These
predicted effects are shown by the significant F-values on the main diagonal of the top
panel in Table 3. As can be seen, the first three predicted effects relating the information
variables of consensus, consistency and distinctiveness with the dimensions of locus,
stability and globality are highly significant, p<.0001, whereas the predicted effect of
contingency on control only reaches the .05 level.
Table 3
Mean Causal Judgments and
Variables : Studies 1 - 3.
F-values
following
the
Information
Locus
Stability
Globality
Control
High
4.47
3.87
3.70
3.57
Low
2.64
3.94
3.84
4.17
F
25.06****
----
----
----
High
3.63
4.52
3.74
3.68
Low
3.48
3.28
3.80
4.07
F
----
26.81****
----
----
Low
3.46
4.11
5.23
3.90
High
3.64
3.69
2.31
3.85
F
----
----
63.73****
----
Low
4.02
4.41
3.75
3.09
High
3.09
3.39
3.80
4.65
F
----
----
----
5.13*
Study 1
Consensus
Consistency
Distinctiveness
Contingency
Study 2
Consensus
Consistency
Distinctiveness
Contingency
High
4.36
3.74
3.92
3.57
Low
3.28
3.76
4.05
4.20
F
23.03****
----
----
6.04*
High
3.89
4.32
3.84
3.95
Low
3.92
3.17
4.12
3.82
F
----
26.58****
----
----
Low
3.81
4.09
5.39
4.03
High
4.01
3.40
2.58
3.74
F
----
----
53.81****
----
Low
4.11
4.05
4.08
3.12
High
3.70
3.44
3.88
4.66
F
----
----
----
25.40****
High
3.40
3.48
3.56
4.23
Low
2.48
3.97
4.00
4.54
F
14.18***
6.74*
----
----
High
2.94
4.42
3.76
4.31
Low
2.93
3.03
3.80
4.45
F
----
67.92****
----
----
Low
2.81
4.15
4.76
4.35
High
3.06
3.30
2.80
4.41
F
----
----
27.62****
----
Low
2.87
3.85
3.98
4.21
High
3.01
3.60
3.57
4.55
F
----
----
4.90*
4.38*
Study 3
Consensus
Consistency
Distinctiveness
Contingency
Note. The entries reflect attributions to external, stable, global and controllable causes
respectively. Degrees of freedom for F = 1 and 38 for Study 1; 1 and 36 for Study 2; 1
and 60 for Study 3. F-values that are not significant are denoted by a horizontal bar.
Expected tests are in italic.
* p < .05. ** p < .01. *** p < .001. **** p < .0001.
The ANOVA also revealed a number of interactions. However, given that there were 44
possible interactions over the three studies, we limit the discussion in this and the next
two studies only to those interactions that are significant beyond the .01 level to avoid
undue type II errors. The ANOVA showed a significant interaction between consensus
and consistency on locus attributions, F(1,38) = 16.26, p<.001. The means indicated that
the predicted discrepancy between internal and external attributions given consensus
information is increased when consistency is high (M = 2.45 vs. 4.80) as opposed to low
(M = 2.83 vs. 4.14). A possible explanation is that repeated failure on the computer game
increased the subjects' confidence in their locus ratings. The ANOVA also revealed an
interaction between consistency and contingency on perceived stability, F(1,38) = 12.18,
p<.01. The means showed that the predicted stable attributions following high
consistency were increased after low contingency (M = 5.30) as opposed to high
contingency (M = 3.74). This seems to point to a confound between contingency and
consistency. Indeed, as low contingency indicates that outcomes remain identical even
after multiple actions by the actor, this necessarily implies that the outcome was
consistent over some time as well, because different behaviors by one person are
typically performed one after another. While developing the scenarios for these studies,
extreme care was taken to avoid such confounds by taking larger time frames for the
consistency information (e.g., several test sessions) and shorter ones for the contingency
variable (e.g., several tactics within one test session). Apparently, though, it was not
possible to exclude these confounds entirely.
Study 2
Method
Subjects in the second study were 40 freshmen from the psychology department at the
same university. The procedure and materials were similar in format to those of Study 1.
The present study described "a girl, Nawal, who was complimented on her outfit at a
party when she wore her pink dress". Subjects had to imagine themselves as
anthropologists who studied the habits and rites of a non-Western culture. The
information variables provided were phrased as follows :
- Everyone / Nobody except Nawal was complimented on her outfit (high/low consensus);
- At prior parties, Nawal was always / never complimented on her outfit (high/low
consistency);
- Nawal was complimented on her outfit everywhere she went / nowhere else (low/high
distinctiveness);
- Nawal was / was not complimented on her outfit no matter what or how much she did /
when she wore one of her other dresses (low/high contingency).
Next, subjects rated the properties of the cause on the following 7-point scales : resides
within Nawal -- resides outside of Nawal; influences only this party (specific) -influences many parties (global); is not controllable by Nawal -- is controllable by
Nawal; is temporary (variable) -- is permanent (stable).
Results and Discussion
The data were analyzed as before, that is, with a 2 (high vs. low consensus) x 2 (high vs.
low consistency) x 2 (high vs. low distinctiveness) x 2 (high vs. low contingency) withinsubjects ANOVA and with the four attribution measures as dependent variables. The
results are shown in the middle panel of Table 3. Consistent with our predictions, the Fvalues for the predicted information-dimension linkages (see main diagonal) were all
very strong, p<.0001. There was, however, a small unexpected main effect of low
consensus on perceived causal control, F(1,36) = 6.04, p<.05. A possible explanation is
that subjects may have brought with them some preconceived notion that social events
caused by an internal agent (as indicated by low consensus) also imply that the cause was
under the actor's control to some degree. The ANOVA further revealed that none of the
interactions reached the .01 level.
Study 3
Method
Subjects in the third study were 64 freshmen from the economics department at the same
university. The procedure and materials were similar in format to those of Study 1 and 2,
although this study involved 16 scenarios rather than just one. Eight scenarios described
an achievement-related event (e.g., "John attained a low mark this week on a math test
when he studied the material from book A") and eight scenarios involved a social event
(e.g., "Mark was able to convince his friends to go to a Woody Allen movie when he
showed them a good review"). Each scenario was provided in a single sentence at the top
of a page of the booklet, followed by four information variables and attribution scales
phrased in a similar manner as in the first two studies. The scenarios were combined with
the information variables in four random orders.
Results and Discussion
The data were analyzed as before. The means and F-values of the third study are depicted
in the bottom panel of Table 3. As can be seen, the predicted main effects of the first
three information variables on the related causal dimensions are highly significant,
p<.0001, whereas the effect of contingency on controllable attributions is quite weak,
p<.05. Moreover, there are two small unexpected main effects. The ANOVA revealed an
effect of consensus on stability attributions, F(1,60) = 6.74, p<.05. The means show that
the cause is judged to be more stable when consensus is low (M = 3.97) as opposed to
high (M = 3.48). A possible explanation is that low consensus tends to be attributed to
dispositional traits, which also contain stable properties and therefore result in stable
judgments as well (see Hilton et al., 1994). There was also a small unexpected effect of
contingency on globality attributions, F(1,60) = 4.90, p<.05, showing that low
contingency leads to more global attributions (M = 3.98) than high contingency (M =
3.57). Perhaps, this is again due a confound between two information variables, in this
case between contingency and distinctiveness. As low contingency denotes that different
behaviors by an actor did not change the outcome, it may imply that the outcome under
different material circumstances or stimuli (brought about by the action) would not
change either, resulting in more global attributions.
The ANOVA also revealed three significant interactions. There was an interaction
between consistency and contingency on stability judgments, F(1,60) = 20.75, p<.0001.
This is a very robust interaction because it also appeared in Study 1 (p<.01) and Study 2
(p<.05). The means indicated that stable attributions following high consistency are
further increased when contingency is low (M = 4.78) as opposed to high (M = 4.06). As
intimated before, this result is probably due to an inherent confound of the contingency
variable with time-related consistency information, because behaviors of one person
cannot be performed in a time-less vacuum and thus necessarily involve some lapse of
time. That is, low contingency implies that the actor's outcomes (following several
behavioral attempts) remained identical over some period of time, and thus may increase
the predicted effect of high consistency on stable attributions. There was also a
significant interaction between distinctiveness and contingency on globality judgments,
F(1,60) = 16.82, p<.001. The means indicated that global judgments following low
distinctiveness are increased when contingency is low (M = 5.18) as opposed to high (M
= 4.33). This interaction extends the unexpected main effect of contingency on globality
attributions discussed above, and is probably due to a confound between low contingency
and low distinctiveness. Finally, there was also a three-way interaction between
consistency, consensus and distinctiveness on stability attributions, F(1,60) = 11.05,
p<.01. The means indicate that low consensus and low distinctiveness combine to
increase stability attributions after high consistency information is given. This interaction
is difficult to interpret, and is perhaps a result of the unexpected main and interaction
effects discussed above.
Discussion of Studies 1 - 3
In all three studies, the predicted main effects of information variables on causal
dimensions were found to be significant. However, there were also a number of
unexpected main and interaction effects. Most of these unexpected effects were found in
Study 3, which used scenarios involving familiar events, as opposed to the scenarios in
Studies 1 and 2, which took place in contexts that were less familiar to the subjects.
Hence, a possible explanation is that preconceived beliefs about the events (i.e., scripts;
Read, 1987) and their likely causes distorted the effect of the information variables. An
alternative explanation may lie in the source of the effects. Indeed, the majority of the
unexpected effects (i.e., 5 out of 7) involve contingency information. Moreover, the
predicted main effect of this variable was the weakest overall.
This gives some cause for concern. Is contingency not the predecessor of causal control
as hypothesized in the joint model ? Or is contingency to some extent confounded with
the other information variables so that its predicted main effect stands out less ? In an
attempt to answer this question, we conducted an additional study in which we
manipulated only contingency information over the same 16 scenarios, and we asked
another 44 subjects to rate the perceived control of the cause. (We also provided another
phrasing for the contingency variable, but this manipulation had no effect.) The results
revealed a strong effect of contingency on controllability attributions, F(4,40) = 21.63,
p<.0001. Consistent with the joint model, the cause was seen to be substantially more
controllable given high contingency (M = 5.65) as opposed to low contingency (M =
3.66). This finding indicates that contingency determines attributions of control to a large
extent when manipulated in isolation, and suggests that the weak main effect of this
variable in Studies 1 and 3 is perhaps due to confounds with the other information
variables.
Study 4
The previous studies demonstrate that inferences about causal dimensionality are strongly
determined by the predicted information variables. However, this research is far from
conclusive. Showing that subjects appear to be sensitive to both extremes of the causal
dimensions does not necessarily imply that they applied principles of both contrast and
invariance. One might argue, for example, that subjects based their judgments only on
contrasts in the information, and that the apparent use of the invariance principle is an
artifact of the bipolar dimension scales used. For example, indicating a decreased
influence of an internal (person) cause necessarily results in greater external judgments,
because internal and external lie at the opposite extremes on the same judgment scale.
Although earlier research on single attributions reviewed above indicated that subjects do
make attributions to single causes in accordance with the proposed joint model, the
crucial properties of these causes were assigned post-hoc by experimenters rather than by
the subjects themselves.
Hence, this study was designed to provide a more stringent test of our hypotheses. Subjects directly
indicated the causal likelihood of a number of single causal factors, the dimensional properties of which
were assigned on the basis of prior pilot testing on the same population. We predicted that attributions to
single causes would be determined by the information variables as proposed by the joint model. Moreover,
we also predicted that these attributions would be based on both principles of contrast and invariance. This
can be tested directly by comparing attributional judgments following contrastive as opposed to invariant
information patterns. Some earlier models, such as the covariation models of Kelley (1973) and Försterling
(1989), the natural logic model of Hewstone and Jaspars (1987), and the default abnormal conditions focus
model of Hilton and Slugoski (1986) predict that subjects will infer no attributions when the stimulus
information involving that factor is invariant.[4 The probabilistic contrast model of Cheng and Novick
(1990) predicts that causal inferences are possible depending on the default assumptions people make about
other information that is not given by the experimenter (the ]non-configurational cells, e.g., what other
people do in other situations). However, the contrast model also predicts that regardless of these
assumptions, attributions should be less strong given invariant rather than contrastive information.[5 In
contrast, we predicted that subjects would infer an attribution given invariant information and we expected
these attributions to be equally as strong as inferences following contrastive information.
]
Method
Subjects
Subjects were 40 freshmen taking an introductory psychology course, who participated
for a partial course requirement. During the last part of a regular class hour, they were
invited to participate in a study on "how people make attributions". The subjects
completed the questions individually. The whole procedure took about 30 minutes, after
which subjects were thanked for their participation.
Material
The subjects received two booklets with brief scenarios depicting an actor who attained a
negative outcome at a job interview or in an exam. The application scenario ran as
follows : "Annie is unemployed. One day she applies for a job as a secretary in a firm.
After an interview with the personnel manager, she has been told that she will not be
hired". The exam scenario read as follows : "John is a student. During the first exam
session, he has been told that he failed the math oral exam".
After this introduction, one information variable was provided on each subsequent page.
This involved a sentence describing one of eight information variables (i.e., high or low
consensus, consistency, distinctiveness or contingency) phrased in a manner similar to
that used in Studies 1 - 3. Subjects were instructed to analyze carefully this information.
Next, eight possible causes were listed on the same page. Subjects had to indicate the
degree to which each cause had influenced the outcome on a 7-point scale ranging from
no influence (1) to strong influence (7). Within each scenario, the causes were presented
in one random order or its reverse. The information variables (pages in the booklet) were
also presented in one random order and its reverse. The order in which the two scenarios
were presented was also randomized.
Causes
The causes for each scenario were selected on the basis of a pilot study with 175 students
from the same university who participated voluntarily. In this pilot study, subjects
received a description of four scenarios and then indicated for each scenario the property
of about 60 causes for one of the four dimensions on a 6-point scale. The cause "luck"
was not included since this factor does not conform to the predictions of joint model as
shown in earlier research (see earlier section). Two target causes were then selected for
each dimension on the criteria that they received very high (above 4.5) or very low
(below 2.5) means on that dimension, while the means for the remaining dimensions were
relatively moderate (between 2.5 and 4.5). It should be noted that it was not always
possible to satisfy this latter criterion as most causes received high or low ratings on more
than one dimension. One scenario was not selected for the main study because too many
causes failed to satisfy the latter criterion (as they showed loadings on more than one
dimension). Another scenario was eliminated because the causes failed to show extreme
ratings on the globality dimension.
The target causes in the application scenario are listed below (the dimensional
assignments and their mean ratings from the pilot study are given in parentheses) : Annie
has no interest in a secretarial job (internal : 5.42), the personnel manager set high
standards (external : 1.52), Annie lacks necessary aptitude for this job (stable : 5.04),
Annie was ill during the interview (unstable : 1.42), Annie has low general intelligence
(global : 5.10), the personnel manager is unfriendly (specific : 2.14), Annie was not well
prepared for the interview (controllable : 5.12), Annie is physically handicapped
(uncontrollable : 1.80). The means of the target causes on the remaining dimensions were
on average 3.53.
For the exam scenario, the target causes were : John has no interest in maths (internal :
5.36), the maths examinator set high standards (external : 1.60), John has low general
intelligence (stable : 5.26), John was tired (unstable : 1.62), John has a bad memory
(global : 5.10), the maths exam took place at an inconvenient moment (specific : 2.10),
John did not study regularly (controllable : 5.56), John was ill for a long time
(uncontrollable : 1.80). On the other dimensions, the means of the target causes were on
average 2.90.
Results and Discussion
To compute an index for each dimension, the ratings of the target cause with low
loadings on that dimension were reversed (1 was changed to 7, 2 to 6, and so on) and then
averaged with the ratings of the target cause with high loadings. For instance, for the
locus dimension, the internal target cause and the (reversed) external target cause were
averaged. Thus, each dimension index represents the aggregated ratings of two target
causes. These dimension indices were then averaged over the two scenarios (see Table 4).
The indices of each contrastive information condition were then subtracted from those of
the corresponding invariant conditions, resulting in a difference score (see also Table 4).
These difference scores indicate to what extent subjects differentiate causes with opposite
properties (e.g., internal vs. external causes) given opposite conditions of the information
variables (e.g., low vs. high consensus). In line with the joint model, the predicted
information-dimension linkages showed the highest (absolute) difference scores (see
main diagonal on Table 4).
Table 4:
Causal Attributions Weighted along their Dimensional
function of the Information Variables : Study 4
Consensus
Consistency
Distinctiveness
Contingency
Properties
in
High
3.05
4.03
3.28
4.13
Low
4.88
3.93
4.65
4.02
Difference
-1.83
.11
-1.37
.11
High
4.62
4.62
4.71
4.19
Low
3.01
2.67
2.70
3.73
Difference
1.61
1.94
2.01
.46
Low
4.41
4.84
5.06
3.86
High
3.67
3.06
2.94
4.21
Difference
.74
1.79
2.12
-.35
Low
3.30
4.08
4.07
3.58
High
3.79
3.49
3.67
4.53
Difference
-.49
.59
.40
-.96
Contrast F
64.31*
39.14*
76.78*
31.05*
Residual F
16.83*
21.37*
44.19*
9.39*
Note. The entries reflect judgments to attributions with internal,
stable, global and controllable properties respectively. Degrees of
freedom for Contrast F = 1 and 39; and for Residual F = 2 and 38.
Highest
difference
scores
in
each
column
are
in
italic.
* p < .0001.
Causal Explanations and Dimensional Properties : Experiment 4
Causal Explanation
Property
Application Scenario
Annie has no interest in a secretarial job
The personnel manager set high standards
Annie was ill during the interview
Annie lacks necessary aptitude for this job
The personnel manager is unfriendly
Annie has low general intelligence
Annie was not prepared for the interview
Annie is physically handicapped
i--e---v--f---s--g---c
---u
Exam scenario
John has no interest in math
The math examinator set high standards
John was tired
John has low general intelligence
The math exam was held at an inconvenient moment
John has a bad memory
John did not study regularly
John was ill for a long time
i--e---v--f---s--g---c
---u
Note. Dimensional Properties: i=intern, e=extern, v=variable, f=fixed,
s=specific, g=general, c=controllable, u=uncontrollable.
The difference scores were analyzed with a 4 (dimension) by 4 (information variable)
within-subjects ANOVA. The results showed significant mean effects for dimension, F
(3,37) = 31.35, p<.0001, for information variable, F (3,37) = 25.58, p<.0001, and as
expected, a significant interaction between dimension and information variable, F (3,37)
= 31.35, p<.0001. To test our specific hypotheses, we conducted a series of planned
comparisons. For example, we compared the difference scores for locus given consensus
information and compared them with the difference scores of the other information
variables. If these comparisons were significant then our hypotheses would be supported.
Moreover, if no residual variance remained to be explained then the hypothesized
variables were unique determinants of the attribution judgments. The F-values resulting
from these comparisons are depicted in the bottom panel of Table 4. As can be seen, all
contrast Fs testing our hypotheses are highly significant, p<.0001. As in the previous
studies, the effect of contingency on controllable attributions was weakest, as shown by
the smaller difference scores and the lower F-values. Unexpectedly, the residual Fs were
also significant for all dimensions, p<.0001, although they attained lower values than the
hypothesized contrasts.
There can be many reasons why the predicted effects were not unique. First, as the
subjects who indicated the dimensional properties in the pilot study were not the same
ones that provided attributional judgments, some noise may have entered the data due to
individual differences in the interpretation of causal properties. As Weiner (1985, p. 555)
cautioned, "the interpretation of specific causal inferences might vary over time and
between people and situations". Thus, what might have been a clear instance of an
internal cause for most subjects, may nonetheless have appeared as a somewhat external
cause to other subjects. This noise can only be avoided if causes are individually tailored
for each subject, which is very difficult to accomplish.
Second, as some causes have extreme loadings on more than one dimension, the derived
dimension indices were not completely independent, resulting in noise in the dependent
measures. For example, by providing a high rating on an internal cause which also has
extreme loadings on some other dimension (say, stability), it may appear as if stability
influenced that causal rating as well. As noted before, one scenario was excluded from
the main study for this reason, although fully independent measures could not be obtained
in the two remaining scenarios either.
Third, the scenarios may involve scripted events so that subjects may have relied on
routine knowledge from which they extracted some stereotyped explanations which then
received stronger ratings for that reason only (cf. Read, 1987). In sum, the information
variables most strongly determined attributions to causes with the predicted dimensional
properties. However, these effects were not unique because other information variables
also had some influence, although it may be that these additional influences were due to
methodological artifacts.
A second prediction of this study was that attributional ratings would be equally strong
given invariant as well as contrastive information conditions. To test this prediction, we
calculated the mean of all original causal judgments in both conditions. The mean
judgment given invariant conditions was 4.55, which was slightly higher than the mean
judgment under contrastive conditions, 4.43. This difference tended towards statistical
significance, t(39) = 1.98, p=.055. This result contradicts many earlier attribution models
which predict that causal inferences should be weaker or absent following invariant rather
than contrastive information.
General Discussion
The basic goal of this article was to bring theory and research on the attribution process
developed from Kelley's (1967) covariation model more in line with other theories on the
dimensionality and social consequences of attributions (e.g., Abramson, Seligman &
Teasdale, 1978; Weiner, 1986). In particular, we argued that the standard attribution
responses should be extended to the opposite extremes of the fundamental dimensions of
causality, and that the variables of contingency and the related dimension of control
should be included. We further reasoned that Mill's (1872/1973) method of difference, as
realized in the principle of contrast (i.e., Cheng & Novick, 1990), is applied to infer
standard attributions and the attribution of control, whereas Mill's method of agreement,
as realized in the principle of invariance, is used to infer the opposite non-standard
attributions.
The data presented in this article were largely in agreement with the proposed joint
model. A review of earlier relevant work demonstrated that subjects choose causal
attributions in line with the proposed information-dimension linkages. Similarly, the
evidence from four studies suggested that subjects not only judge the properties of causes
on the basis of the predicted information variables (Studies 1 - 3), but that they also select
single attributions on the same basis (Study 4). Moreover, Study 4 confirmed that these
attributional judgments are made through the joint application of both principles of
contrast and invariance. The finding that attributions were somewhat stronger following
invariant rather than contrastive information is inconsistent with most earlier models
which predict that when data are invariant, attributions should be weaker (Cheng &
Novick, 1990) or even absent (Kelley, 1973; Försterling, 1989; Hewstone & Jaspars,
1987; Hilton & Slugoski, 1986). Admittedly, some authors (Hilton & Slugoski, 1986;
Cheng & Novick, 1991) have suggested that invariant information patterns may be
attributed to enabling conditions rather than causes. For instance, Cheng and Novick
(1990) proposed that enabling conditions do not covary with the effect in a given set of
observations (i.e., the focal set), but rather covary with the effect outside the focal set.
Although this distinction was not made in our research, explanations in terms of enabling
conditions require at least weaker causality judgments within a particular focal set, but
stronger judgments were found in Study 4.
We proposed four covariation variables that may carry contrastive/invariant information
about potential causes. Three of these have been suggested earlier by Kelley (1967) and
were related to the causal dimensions of locus, stability and globality. A fourth
information variable, contingency, was proposed to determine causal judgments of
control. The results, however, suggest that this latter linkage is relatively weak. Perhaps
the effect of contingency was undermined by confounds with the other information
variables, because an additional study revealed that the impact of contingency was
stronger if other covariation variables were omitted. Although the effect of contingency is
rather weak, it is important to note that contingency was the sole determinant of
perceived causal controllability, because the evidence from all four studies revealed that
none of the other covariation variables influenced perceived control on its own or in
interaction with other variables (except for a small main effect of consensus in Study 2).
Another possible explanation for the weak effect of contingency may lie in a fundamental
flaw in the logical relation between contingency and control. Although low contingency
necessarily indicates that control through one's actions was impossible because the
outcome did not change, high contingency does not necessarily imply that the reverse
implication of high control is true. It may be that the outcome changed through some trial
and error, lucky guesses or other random behaviors which involve no knowledge on the
part of the actor on how the outcome was changed. However, such knowledge is needed
actually to control the outcome in the present as well as in the future. The idea that
controllability involves a minimal level of knowledge has also been captured by Heider
(1958, p. 113) as he stated that a person "is considered responsible, directly or indirectly,
for any aftereffect he may have foreseen". Thus, it may be that to attribute full control,
information is needed not only on the contingency of the actor's behavior, but also on the
actor's state of knowledge concerning the consequences of his or her behavior.
The implications drawn from this research need some qualification because of the limited
scope of the present studies : They involved only four dimensions of covariation and
causality, were limited to achievement and social domains, were obtained from verbal
summary information and did not address process mechanisms. However, not all of these
limitations are equally problematic for the basic predictions of the joint model. For
instance, it seems plausible that the principles of contrast and invariance apply to other
covariation variables and causal dimensions than those we have investigated. Thus, for
example, covariation information may be given in terms of countries rather than persons,
so that locus is evaluated in terms of whether the cause is internal or external to the target
country. It is also conceivable that perceivers have an even more specific causal
hypothesis in mind that they want to test. Although the joint principles of contrast and
invariance can be extended to accommodate such individual cases, our data do not reveal
which rules or procedures are used to test specific causal questions. In particular, it is
unclear how the perceiver goes from general covariation information to particular causes
(see Study 4). Is this done with specific dimensions in mind, or is a causal hypothesis
formed first and are its dimensional properties then tested against the information given ?
A recent study from our laboratory may be relevant here (Van Overwalle, Heylighen,
Casaer & Daniëls, 1992). The data suggested that causal dimensions are automatically
available upon reading the relevant information variables. Indeed, a unique prediction of
the joint model is that there are unique linkages between eight information conditions and
eight attribution categories. Given these unique one-to-one mappings, it seems likely that
the repeated use of these linkages has become automated so that the mere presence of
covariation information may activate the related attributional dimensions (cf., Shiffrin &
Schneider, 1977; Kornblum, Hasbrouck & Osman, 1990).
Another concern is that our data were limited to success and failure in achievement and
social domains, whereas other studies investigated various other events such as emotions,
opinions and actions (e.g., McArthur, 1972). Insofar as these events are also desirable or
undesirable from the actor's perspective, it seems logical that the same covariation and
causal dimensions apply to them, although perhaps to a lesser extent. For example, some
reservations should be made for contingency and controllability which may be of less
relevance in the context of an action (e.g., Jack contributed a large sum of money)
because contingency takes as implicit the assumption that performing such a social action
is controllable in and of itself (although the successful completion or outcome of the
action might be uncontrollable).
Another limitation of the present studies is that covariation information was presented in
a verbal summary format. It is unclear to what extent such summary information made
the relevant dimensions more salient than if raw data were presented about a number of
events. Moreover, the underlying causality principles given pre-packed summary as
opposed to raw information may not be the same. This may seriously restrict not only the
present findings, but also those of other researchers using the same paradigm (e.g., Cheng
& Novick, 1990; Hilton & Jaspars, 1987; Hilton & Slugoski, 1986; Hilton, Smith & Kim,
1994; Hewstone & Jaspars, 1987; Jaspars, 1983; McArthur, 1972, 1976; Orvis,
Cunningham & Kelley, 1975; Ruble & Feldman, 1976). Although summary information
may tell us a lot about the processes governing attributions based on social (pre-digested
verbal) communication, it does reveal very little about spontaneous attribution search
from actual (raw) social behavior. Perhaps, a more fundamental question is whether
attributional inferences -- derived from summary or raw data --proceed on the basis of a
set of rules as implied by the joint model and all previous covariation models, or whether
another, evolutionary more primitive form of associative learning mechanism governs our
causal thinking (see Shanks, 1991, 1993). Associative models assume that causal
knowledge is represented in the form of mental associations between causes and effects,
and that these associations are gradually learned. Schanks (1993) argues that these
models can deal with the acquisition of causal knowledge over time and a number of
other phenomena such as blocking and inhibition, which are not well captured by rulebased covariation models.
Although the present findings confirm that causality can be inferred by applying the
principle of invariance, it should be realized that this principle may sometimes lead to
spurious inferences, because one cannot guarantee that the cause inferred by applying the
invariance principle is the only factor that co-occurs with the effect (e.g., attributing the
sunset to the cock's crow). Mill (1872/1973) also warned that the method of agreement is
"inferior" to the method of difference. It would be interesting, therefore, to delineate in
future research the conditions under which people prefer one principle over another in
causal reasoning.
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