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.
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. situation-specific), 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 non-standard 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 non-standard (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.
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., non-standard) 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 Causal
Pattern Categories
Consensus low Persons contrast Internal (Person)
high invariant External
Consistency low Time contrast Variable (Occasion)
high invariant Stable
Distinctiveness high Stimuli contrast Specific (Stimulus)
low invariant Global
Contingency high Actions contrast Controllable
low invariant 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 information-dimension 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.
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 LuckConsensus information
intern[a] intern extern extern
Frieze & Low Low High Low
Weiner (1971,
study 1)
Frieze & ns Low High ns
Weiner (1971,
study 2)
Weiner & Kukla Low Low ---- ----
(1970, study 6)
Fontaine Low Low High Low
(1975, study 1)
Fontaine ns ns ns ns
(1975, study 2)
Read & Stephan Low Low High Low
(1979)
Frieze & ns ns High Low
Bar-Tal (1980)
Consistency
information
stable[a] unstable stable unstable
Frieze & High ns High Low
Weiner (1971,
study 1)
Frieze & High Low High Low
Weiner (1971,
study 2)
Feather & High ---- ---- Low
Simon (1971a)
Feather & High ns ns Low
Simon (1971b)
Nicholls (1975) High ns ns Low
Read & Stephan High ns ns Low
(1979)
Frieze & High Low High Low
Bar-Tal (1980)
Distinctiveness information
global[a] global global specific
Frieze & Low ns ns High
Weiner (1971,
study 1)
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.
- 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.
Table 3
Mean Causal Judgments and F-values following the Information Variables : Studies 1 - 3.
Locus Stability Globality Control
Study 1
Consensus High 4.47 3.87 3.70 3.57
Low 2.64 3.94 3.84 4.17
F 25.06**** ---- ---- ----
Consistency High 3.63 4.52 3.74 3.68
Low 3.48 3.28 3.80 4.07
F ---- 26.81**** ---- ----
Distinctiveness Low 3.46 4.11 5.23 3.90
High 3.64 3.69 2.31 3.85
F ---- ---- 63.73**** ----
Contingency Low 4.02 4.41 3.75 3.09
High 3.09 3.39 3.80 4.65
F ---- ---- ---- 5.13*
Study 2
Consensus High 4.36 3.74 3.92 3.57
Low 3.28 3.76 4.05 4.20
F 23.03**** ---- ---- 6.04*
Consistency High 3.89 4.32 3.84 3.95
Low 3.92 3.17 4.12 3.82
F ---- 26.58**** ---- ----
Distinctiveness Low 3.81 4.09 5.39 4.03
High 4.01 3.40 2.58 3.74
F ---- ---- 53.81**** ----
Contingency Low 4.11 4.05 4.08 3.12
High 3.70 3.44 3.88 4.66
F ---- ---- ---- 25.40****
Study 3
Consensus High 3.40 3.48 3.56 4.23
Low 2.48 3.97 4.00 4.54
F 14.18*** 6.74* ---- ----
Consistency High 2.94 4.42 3.76 4.31
Low 2.93 3.03 3.80 4.45
F ---- 67.92**** ---- ----
Distinctiveness Low 2.81 4.15 4.76 4.35
High 3.06 3.30 2.80 4.41
F ---- ---- 27.62**** ----
Contingency Low 2.87 3.85 3.98 4.21
High 3.01 3.60 3.57 4.55
F ---- ---- 4.90* 4.38*
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.
- 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).
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.
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.
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.
]
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.
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.
Table 4:
Causal Attributions Weighted along their Dimensional Properties in function of the Information Variables : Study 4
Consensus 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
Consistency 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
Distinctiveness 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
Contingency 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 PropertyApplication Scenario
Annie has no interest in a secretarial i - - - job The personnel manager set high standards e - - - Annie was ill during the interview - v - - Annie lacks necessary aptitude for this - f - - job The personnel manager is unfriendly - - s - Annie has low general intelligence - - g - Annie was not prepared for the interview - - - c Annie is physically handicapped - - - uExam scenario
John has no interest in math i - - - The math examinator set high standards e - - - John was tired - v - - John has low general intelligence - f - - The math exam was held at an - - s - inconvenient moment John has a bad memory - - g - John did not study regularly - - - c John was ill for a long time - - - 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.
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 rule-based 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.
Anderson, C. A. (1983). The causal structure of situations : The generation of plausible causal attributions as a function of type of event situation. Journal of Experimental Social Psychology, 19, 185-203.
Bradbury, T. N. & Fincham, F. D. (1990). Attributions in marriage : Review and critique. Psychological Bulletin, 107, 3-33
Cheng, P. W., & Novick, L. R. (1990). A probabilistic contrast model of causal induction. Journal of Personality and Social Psychology, 58, 545-567.
Cheng, P. W., & Novick, L. R. (1991). Causes versus enabling conditions. Cognition, 40, 83-120.
de Jong, P. F., Koomen, W. & Mellenbergh, G. J. (1988). Structure of causes for success and failure : A multidimensional scaling analysis of preference judgments. Journal of Personality and Social Psychology, 55, 718-725.
Feather, N. T., & Simon, J. G. (1971a). Attribution of responsibility and valence of outcome in relation to initial confidence and success and failure of self and other. Journal of Personality and Social Psychology, 18, 173-188.
Feather, N. T., & Simon, J. G. (1971b). Causal attributions for success and failure in relation to expectations of success based upon selective or manipulative control. Journal of Personality, 39, 527-541.
Folkes, V. S. (1984). Consumer reactions to product failure : An attributional approach. Journal of Consumer Research, 11, 398-409.
Fontaine, G. (1975). Causal attribution in simulated versus real situations : When are people logical, when are they not ? Journal of Personality and Social Psychology, 32, 1021-1029.
Försterling, F. (1989). Models of covariation and attribution : How do they relate to the analogy of analysis of variance ? Journal of Personality and Social Psychology, 57, 615-625.
Frieze, I. H., & Bar-tal, D. (1980). Developmental trends in cue utilization for attributional judgments. Journal of Applied Developmental Psychology, 1, 83-93.
Frieze, I. H., & Weiner, B. (1971). Cue utilization and attribution judgments for success and failure. Journal of Personality, 39, 4, 591-606.
Heider, F. (1958). The psychology of interpersonal relations. New York : Wiley.
Hewstone, M. & Jaspars, J. (1987). Covariation and causal attribution : A logical model of the intuitive analysis of variance. Journal of Personality and Social Psychology, 53, 663-673.
Hilton, D. J., & Slugoski, B. R. (1986). Knowledge-based causal attribution : The abnormal conditions focus model. Psychological review, 93, 75-88.
Hilton, D. J., Smith, R. H., & Kim S. H. (1994). The process of causal explanation and dispositional attribution. Journal of Personality and Social Psychology, in press.
Hilton, D. J. & Jaspars, J. M. F. (1987). The explanation of occurrences and non-occurrences : A test of the inductive logic model of causal attribution. British Journal of Social Psychology, 26, 189-201.
Jaspars, J. (1983). The process of causal attribution in common sense. In M. Hewstone (Ed.) Attribution theory : Social and functional extensions (pp. 28-44). Oxford, England : Basil Blackwell.
Kammer, D. (1984). Die Kausaldimension Generalität : Vorauslaufende Bedingungen für das Zustandekommen globaler und spezifisher Attributionen. Zeitschrift für experimentelle und angewandte Psychologie, 1, 48-62.
Kelley, H. H. (1967). Attribution in social psychology. Nebraska Symposium on Motivation, 15, 192-238.
Kornblum, S., Hasbrouc, T., & Osman, A. (1990). Dimensional overlap : Cognitive basis for stimulus-response compatibility -- A model and taxonomy. Psychological Review, 97, 253-270.
Lunt, P. (1988). The perceived causal structure of examination failure. British Journal of Social Psychology, 27 171-179.
McArthur, L. (1972). The how and what of why : Some determinants and consequences of causal attribution. Journal of Personality and Social Psychology, 22, 171-193.
McArthur, L. (1976). The lesser influence of consensus than distinctiveness information : The person-thing hypothesis. Journal of Personality and Social Psychology, 33, 733-742.
Meyer, J. P. (1980). Causal attributions for success and failure : a multivariate investigation of dimensionality, formation and consequences. Journal of Personality and Social Psychology, 38, 704-718.
Mill, J. S. (1872/1973). System of logic (8th ed). In J. M. Robson (Ed.) Collected works of John Stuart Mill (Vols. 7, & 8). Toronto, Canada : University of Toronto Press.
Nicholls, J. G. (1975). Causal attributions and other achievement-related cognitions : Effects of task outcome, attainment value and sex. Journal of Personality and Social Psychology, 31, 379-389.
Orvis, B. R., Cunningham, J. D., & Kelley, H. H. (1975). A closer examination of causal inference : The role of consensus, distinction and consistency information. Journal of Personality and Social Psychology, 32, 605-616.
Peterson, C., & Seligman, M. E. P. (1984). Causal explanations as a risk factor for depression : Theory and evidence. Psychological Review, 91, 347-374.
Read, S. J. & Stephan, W. G. (1979). An integration of Kelley's attribution cube and Weiner's achievement attribution model. Personality and Social Psychology Bulletin, 5, 196-200.
Read, S. J. (1987). Constructing causal scenarios : A knowledge structure approach to causal reasoning. Journal of Personality and Social Psychology, 52, 288-302.
Ruble, D. N., & Feldman, N. S. (1976). Order of consensus, distinctiveness, and consistency information and causal attribution. Journal of Personality and Social Psychology, 34, 930-937.
Shanks, D. R. (1991). Categorization by a connectionistic network. Journal of Experimental Psychology : Learning, Memory and Cognition, 17, 433-443.
Shanks, D. R. (1993). Human instrumental learning : A critical review of data and theory. British Journal of Psychology, 84, 319-354.
Shiffrin, R. M. & Schneider, W. (1977). Controlled and automatic human information processing : II Perceptual learning, automatic attending and a general theory. Psychological Review, 84, 127-190.
Steven, L., & Jones, E. E. (1976). Defensive attribution and the Kelley cube. Journal of Personality and Social Psychology, 34, 809-820.
Van Overwalle, F., Heylighen, F., Casaer, S., & Daniëls, M. (1992). Preattributional and attributional determinants of emotions and expectations. European Journal of Social Psychology, 22, 313-329.
Van Overwalle, F. (1989). Structure of freshmen's causal attributions for exam performance. Journal of Educational Psychology, 81, 400-407.
Weiner, B., & Kukla, A. (1970). An attributional analysis of achievement motivation. Journal of Personality and Social Psychology, 15, 1-7.
Weiner, B. (1983). Some methodological pitfalls in attributional research. Journal of Educational Psychology, 4, 530-543.
Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92, 548-573.
Weiner, B. (1986). An attributional theory of motivation and emotion. New York : Springer-Verlag.