ENTITIES
4.4 Integrating the results of the analyses
Although this study draws on multiple methods, the intention is not to triangulate the results of each mode of analysis as one would in a more positivist study.Inthis case, the two methods are exploring different aspects of the problem and the intention is to build a layered and composite understanding of attribution in talk. The endpoint is not a simple validation or refutation of the cognitive theories of attribution, but rather an exposition of how the modes of talk identified by cognitive attribution theorists may be used (or may not be used) to achieve multiple social ends in a particular context.
5 - Counting categories: correspondence between cognitive theories and observed interactions
The first stage in analysis was to detennine whether the traditional theories do, indeed, describe real-life attributional processes. This was done by attempting to match the type of infonnation expected by the theories to the infonnation actually exchanged in real-life troubleshooting situations. The presence of these types of infonnation in observed conversation does not prove that these processes operate in the brain, and nor does their absence prove that they do not. However, if the infonnation required by cognitive theories of attribution appears (or does not appear) with regularity in dialogue then it will at least show that these theories offer some insight into attributional processes as they occur in this everyday context.
Once talk was categorised and categories were counted, as described in 4.3.3.1, above, it became clear that the vast majority of infonnation exchanged or requested by participants was about the problem and corresponded well with Kelley's (1967) 'man-the-scientist' model. In comparison, talk concerning infonnation about participants' intentions and abilities corresponding with Jones & Davis' (1965) model of correspondent inferences was scarce.
Initial coding only identified 17 instances of this type ofinfonnation exchange compared to 254 instances where infonnation was exchanged about the problem (i.e. concerning
consensus, consistency and/or distinctiveness infonnation). Talk about intentions and ability was generally treated cautiously and often approached circuitously compared to the direct approach taken to exchanging infonnation about the problem. Although this makes the analysis a little less straightforward, it is far from problematic and integrates well with social and discursive models of attribution, as we shall see a little later.
To explore the ways in which problem-related information was spoken about, Table 2 lists each problem along with the eventual outcome of the troubleshooting process and the presence or absence of information about dispositions or intentions (corresponding to the models of Heider, 1958 and Jones& Davis, 1965), and consensus, consistency and
distinctiveness information (corresponding to the model of Kelley, 1967) in the course of the interaction. The most striking feature is that consistency and distinctiveness information was exchanged or requested in 93% and 97% of problems respectively, compared to consensus information which was observed in 66% of the problems, and information about dispositions or intentions that was exchanged in only 28% of problems. This is roughly consistent to the empirical findings from experimental tests of Kelley's (1967) model of attribution which have found that consensus information is generally under-represented compared to consistency and distinctiveness information (see page 36).
Table 2:Consensus, consistency, and distinctiveness information by problem4
' " Cl) Cl)
»
Cl)Problem characters,Length (in o
=.... =
o~.s = = =
~~~....
CJ=
~~ ~~>=
~including :e~~
.s =
0.... =
~.... ....
CJtranscription c::l.. U U0
.s ....
conventions)
....
~.
~...
~ ~
1 18926 Yes Yes Yes Yes
2 464 Yes No Yes No
3 5055 No No Yes Yes
4 16392 Yes Yes Yes Yes
5 10729 No No Yes Yes
6. 4072 No Yes Yes Yes
7. 9253 No Yes Yes Yes
4See Table 1 on p. 81 for more detailed descriptions of problems and information about missing problems.
"" lIS lIS
»
lISProblem Length (in o"'i:
=
0=
" , (J=
" ,~=
~=
characters,
8 =
~" ,...
" , ~>including
....
~= .... ....
;:::
...
" ,...
~ .5 0
=
(Jtranscription Cl. U
u
0 .5...
conventions)
....
" ,....
" ,~ ~
8. 3189 No No No Yes
9. 11504 No Yes Yes Yes
10. 7524 No Yes Yes Yes
11. 30322 No Yes Yes Yes
12. 4392 No Yes No Yes
13. 13316 Yes Yes Yes Yes
14. 2115 No No Yes Yes
15. 3925 No No Yes Yes
19. 15839 No Yes Yes Yes
22. 884 No Yes Yes Yes
23. 9788 No Yes Yes Yes
24 1192 No No Yes Yes
26. 17129 Yes Yes Yes Yes
27. 5308 No Yes Yes Yes
28. 2932 No No Yes Yes
29. 25920 No Yes Yes Yes
30. 2369 No Yes Yes No
31. 14043 Yes Yes Yes Yes
32. 1731 No No Yes Yes
33. 1750 No No No Yes
34. 9915 Yes Yes Yes Yes
35. 3569 No Yes Yes Yes
Frequency 8 19 27 28
Total valid attributional 29
problems
The types of information required by cognitive models of attribution are clearly being offered and sought by participants. However, certain types of information are scarce, particularly
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infonnation about intentions and dispositions and consensus infonnation. The analysis will initially focus on exploring the patterns of infonnation relating to Kelley's (1967) model and will then return to the general pattern of under-representation with a possible explanation.
Itis puzzling that consensus infonnation is under-represented (observed in 19/29 problems) compared to consistency (26/29) and distinctiveness (27/29) infonnation. In Table 3, below, this infonnation is condensed, sorted by the length of the problem and highlighted if
consensus infonnation was observed. Itis quite striking that consensus infonnation was exchanged least often during short problems and most often during more extended problems.
Table 3: Consensus, consistency, and distinctiveness information sorted by the length of the problem and highlighted (in grey) if consensus information is present
Problem Length (in
characters, including transcription
conventions)
t>
~
=
v.>....
v.>=
Uo
Problem Length (in characters, including
transcription conventions)
.."
=
o1:1
~o 1:1Q
U
Itis worth exploring these intuitive observations further to detennine whether they can be considered statistically significant or due to sampling error or chance. I will do so in two stages: firstly I will use the Binomial test to see whether consensus infonnation can be considered significantly under-represented compared to consistency and distinctiveness infonnation, and secondly, I will use the Gamma statistic for ordered categorical variables to detennine whether consensus infonnation is more likely to be absent from short interactions than long ones. For the purposes ofthis discussion, consensus infonnation will be referred to as Cs, consistency infonnation as Cy and Distinctiveness infonnation as Dt. The presence or absence of each type of infonnation will be referred to by a plus or minus sign. For example, Cs+indicates that consensus infonnation is present for a particular problem.
The binomial test is used to detennine whether an observed distribution of a dichotomous variable can be thought to significantly deviate from a predetennined pattern - detennined by the probability of the variable falling into either of the two conditions under the null
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hypothesis (Siegel & Castellan, 1988). In this case, Kelley's (1967) model predicts that attribution typically requires all three types of infonnation to generate a "subjectively veridical" attribution and this will fonn the null hypothesis. Table 4 displays the observed frequency of problems displaying each combination of Cs, Cy and Dt and, clearly, the majority of cases (n=17) are consistent with Kelley's model and display all three types of infonnation. However, the remaining 12 cases are inconsistent.
Table 4: Frequency of problems displaying different combinations of consensus, consistency and distinctiveness information
Combination of Cs, Cy and Dt Deviation Consensus Frequency from Present!Absent
Kelley's model
[Cs-] [Ci] [Dt+] Yes - 2
[Cs-] [Cl] [Df] Yes
-
1[Cs-] [Cl] [Dt+] Yes
-
7[Cs+] [Ci] [Df] Yes + 0
[Cs+] [Ci] [Dt+] Yes + 1
[Cs+] [cl] [Df] Yes + 1
[Cs+] [Cy+] [Dt] No n/a 17
While Kelley (1967) admits that attributions may sometimes be made with incomplete infonnation, he provides no finn grounds for suggesting that one type of infonnation is less likely to be used than the others. Therefore, in Kelley's model, it would be equally likely for any of the three types of infonnation to be lacking in any particular problem-solving
interaction. However, eyeballing the data (see Table 3) suggests that consensus infonnation is more likely to be lacking (10/29) than either consistency (3/29) or distinctiveness (2/29).
Therefore it seems reasonable to divide the various possible combinations of Cs, Cy and Dt that deviate from Kelley's model into those in which Cs infonnation is present and those in
which it is not, as I have done in Table 4, above. We now have two groups that each contains three ofthe six possible ways of deviating from Kelley's model. Under the null hypothesis (where each possible pattern of deviation is equally unlikely) there is a 1/6 chance that any individual deviation will match any of the patterns displayed in table 3. Since three patterns fall into each group, there is a 50/50 chance that any individual deviation would fall into either of the groups - and we can therefore use a binomial test to see whether the patterns of deviation observed deviate significantly from the uniform pattern predicted by Kelley's model.
Given that 10 of the deviant cases fall into the Cs-absent group, compared to two in the Cs- present group, it seems unlikely that these results are random. A two-tailed binomial test confirms this (n= 12;p = 0.039) and allows us to reject the null hypothesis that the observed pattern is not significantly different to that predicted by Kelley's (1967) model. Inother words, when deviations to Kelley's pattern occur, they are significantly likely to be related to the absence of consensus info.
Having shown that there is something significantly different about consensus information, the second task is to investigate the relationship or association between consensus information (or the lack thereof) and the length of interaction. Here we are looking to determine the strength of association between a variable measured on a ratio scale (length) and an ordinal dichotomous variable (absence or presence of consensus). The most obvious test of
association would be to calculate Spearman's correlation coefficient(rs ),but this is not appropriate due to the large number of tied values present in the dichotomous variable.
Obviously, if the variable length is converted into a categorical variable ("short" and "long") then a chi-square test of association can be performed. However, a drawback of this approach is that chi-squared will not directly measure the strength or direction of the association.
Instead, a less commonly used categorical statistic, Gamma, will be used. Gamma measures both the strength and direction of the relation between two ordered categorical variables.
While it may perform a similar function to Spearman'srs,it does not have the same problems when ranks are tied (Siegel& Castellan, 1988). Much like a correlation, Gamma may range between -1 and 1, where -1 represents a strong negative association and 1 represents a strong positive one.
Inorder to calculate Gamma, the length of interaction was first converted into a categorical variable by splitting the cases into two equally sized groups and coding them "short" and
"long". (The small sample size prevented the use of more categories.) This was cross- tabulated with existing information regarding the presence or absence of consensus information to calculate Gamma. This resulted in a 2x2 contingency table where both dichotomous variables could be considered ordinal (directional) rather than nominal.
The calculated Gamma statistic is significant(p <0.0005), and high(y= 0.924), allowing us to conclude, firstly, that the presence or absence of consensus information cannot be
considered independent of length. In other words, there is an association between the length of the interaction and the presence or absence of consensus information. Secondly, Gamma is positive and close to 1, indicating that the association between consensus and length is
positive and very strong. In other words, short interactions are significantly less likely to contain consensus information and vice versa.
One problem with this analysis is that it stands to reason that the longer the interaction, the more likely it is that any particular type of information is represented - and it is therefore likely that length has a similar relationship to consistency and distinctiveness information.
However, both consistency and distinctiveness are represented in such a large proportion of the interactions observed (26/29 and 27/29 respectively) that calculating the relationship
between their absence and the length of interaction is not a particularly meaningful question.
What the analysis does show is that consensus information is significantly more likely to be absent than consistency or distinctiveness information and that consensus is far more likely to be absent in shorter interactions than longer ones. There is clearly something different about the practice of exchanging consensus information - compared to consistency and
distinctiveness information - such that participants exchange it seldom and do so only after other avenues have been tried. Itis possible that consensus information is less useful for returning computers to functionality, but this is not predicted by Kelley's model. In fact Kelley (1967) postulates that, if anything, consistency information should pose the greatest problem in everyday attribution because ofthe difficulty of running multiple 'trials' of everyday problems. So now there are two major puzzles concerning the types of information actually exchanged in relation to the cognitive models oflones & Davis (1965) and Kelley (1967). Firstly the information actually exchanged is largely about the problem rather than about the agents and, secondly, information about the problem is far more likely to be oriented to Kelley's axes of consistency and distinctiveness than to that of consensus.
In order to make sense of these puzzles it will be useful to examine some examples of these types of talk in detail. The first example is one in which most of the attributional traces predicted by cognitive attribution theory are present and will be a useful illustration of the kinds of ways that participants approach these different types of information exchange. The second example is one in which the information exchanged is a site of struggle with clear implications for dispositional attributions. The third is an exchange in which information is exchanged in such a way that dispositional roles are amicably reinforced.