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The Kirton adaption-innovation inventory: Should the sub-scales be orthogonal?
Pwson. Printed
inllirrd.
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Vol.
10, No.
inGreat Bntain.All
rights
9, pp. 921-929.
1989
0191-8869’89
reserved
Copyright
C 1989 Maxwell
$3.00 + 0.00
PergamonMacmillan plc
THE KIRTON ADAPTION-INNOVATION INVENTORY: SHOULD THE SUB-SCALES BE ORTHOGONAL? WILLIAM G. K. TAYLOR Sheffield City Polytechnic, Totley Hall Lane. Sheffield Sl7 4AB, England (Received
I4 October
1988)
Summary-The paper examines one of the fundamental tenets of Adaption-Innovation theory, that the KAI measure is uni-dimensional. Attention is focused on the orthodox three-factor structure and by a simple process of refinement it is demonstrated that the sub-scales are essentially orthogonal. It is further shown that evidence for the inherent independence of the three sub-scales of originality, efficiency and conformity lies in data published in the first KAI Manual. Some implicationsfor Adaption-Innovation theory are outlined.
INTRODUCTION
The Kirton Adaption-Innovation inventory (KAI) has attracted considerable attention as a measure of the characteristic approach a person has towards problem solving and decision making. Adaption-Innovation theory underlying the KAI posits that a person can be located on a single dimension of cognitive style, with the extremes labelled as Adaptors and Innovators. Since its inception in 1976, the KAI has almost always been used as a single scale. Considerable evidence for its validity has accumulated and it has been shown by a number of researchers in several countries to have good psychometric properties (Kirton, 1987). Nevertheless, factor analysis of the KAI, initially by Kirton (1976) and by others subsequently (Mulligan and Martin, 1980; Prato Previde, 1984; Goldsmith, 1985; Hammond, 1986; Taylor, 1989), has afforded ample evidence for the extraction of three or more factors. However, the scores based on a three-factor model have been shown to have only modest correlations with each other. The factor correlation matrix published by Kirton (1977) based on his general population sample of n = 532 is given in Table 1. Subsequent work by Lowe and Taylor (1986) using a sample, n = 93, drawn from research scientists in three industrial organisations, corroborated these correlations. Their data was: 0 vs E, r = 0.37
0 vs R, r = 0.56
E vs R, r = 0.39
None of the differences between the two sets of correlations were significant statistically. The use of an aggregate KAI score in the face of these modest correlations is questionable. Payne (1987) argued that it is inevitable that there will be people with all possible combinations of scores on the three dimensions of KAI since the inter-correlations are not strong. For people with KAI scores lying roughly in the middle of the range, to use the total KAI score is to conflate the three dimensions. For example, a person of about average total KAI score may be well above average on the 0 sub-scale, well below average on the E sub-scale, and about average on the R sub-scale. Such a person can be expected to be very different from one who is well below average on 0, well above average on E and about average on R, yet these two people would have similar total KAI scores. An example of this nature was illustrated by Lowe and Taylor (1986) in their study of R and D scientists. Their findings led them to reject the total KAI score in favour of the separate sub-scales. However, a characterisation using the sub-scales would, ideally, require the sub-scales to be orthogonal. As the KAI presently stands, the sub-scale are neither strongly enough correlated to justify the use of the total score nor weakly enough correlated to encourage the use of the separate sub-scales. Recent work by Taylor (1989) has re-examined the factor structure of the KAI. Although the structure of the orthodox three-factor model was confirmed, as were the high internal reliabilities, concern was expressed about the use of only three factors. Corroborating the work of Hammond 921
W. G. K. TAYLOR
TableI.Inter-correlations of KAI fdcror traits (Kmon. 1977) E
R
I 0.42
I
0 0 E R
I
0.36 0.47
(1986), a highly significant amount of KAI item variance remained unaccounted for by the extraction of only three factors. Evidence was presented to suggest that the 0 sub-scale in the orthodox three-factor model was not homogeneous, and consisted of a major component concerned with idea generation and a subsidiary element concerned with preference for stability/change. One possibility for exploring developments in the KAI to achieve more homogeneous and orthogonal sub-scales was, clearly, the culling of ‘poor’ items from the inventory. The present paper describes this approach. METHOD
The sample consisted of 305 professionally employed people drawn from two sources. Group A was taken from the R and D departments of four large manufacturing companies and consisted of 119 graduate staff spanning a range of scientific disciplines. Group B was taken from mature students studying part-time on a DMS/MBA programme and consisted of 186 people, the majority of whom were graduates. In both groups the ratio of male:female Ss was approx. 4:l. Descriptive statistics were calculated for each group separately and for the combined sample. Factor analyses were initially carried out for each group separately and for the combined sample, extracting three factors in accordance with Kirton (1976). The maximum likelihood method was utilised, followed by varimax rotation, as implemented in the SPSSX programme package. Finding similar factor structures and internal reliabilities in the two sub-groups and in the combined sample, further studies were restricted to the combined sample, n = 305. Details regarding the preliminary analyses can be found in an earlier paper (Taylor, 1989). The starting point for examining reduced item inventories was the orthodox three-factor model (Kirton, 1976). The essential criterion used in selecting items for removal from the inventory was that the sub-scales should be made as orthogonal as possible by deriving three scales which were each homogeneous conceptually. The process involved a scrutiny of the inter-item correlations and the magnitude of the loadings of each item on each factor. At the same time, test reliability was taken into account, using the criterion that Cronbach’s r must not fall below 0.7 for either the total KAI or any of the sub-scales. After each step in the progressive removal of items from the inventory, factor analysis was carried out and the inter-factor correlations and reliability coefficients were determined. RESULTS
AND
DISCUSSION
The initial three-factor model employing all 32 items of the KAI inventory is given in Table 2 alongside the factor structure reported by Kirton (1977). Following Kirton, only loadings of 0.30 or more are shown (except where an item had no loading equal to 0.30 in which case the highest loading is shown in parentheses). To facilitate comparison with Kirton, the items are listed in loading order as given by Kirton (1977) and where an item is misplaced relative to Kirton, the loading is marked with an asterisk. Decimal points are omitted. It is apparent that the present KAI data leads to a very similar three-factor model to that of Kirton (1977). The percentage of items allocated to the same factors as Kirton is 91% and moreover all of the 17 items loading >0.50 in the Kirton general population sample are classified ‘correctly’ in the present study. The unsatisfactory feature in the Kirton model of a substantial number of items failing to reach a loading of 0.40 on any factor is also confirmed. Statistical data on the KAI and its sub-scales is given in Table 3. As noted in an earlier paper (Taylor, 1989), the scale statistics and reliability coefficients are in accord with data reported in the literature. The mean inter-item correlations provide a measure of the degree to which the scales
The Kirton Table
Adaption-Innovation 2. Three-factor
inventory
models cornoared
Kirton Item
0
21 23 19 16 3 5 II 26 I2 24 I8 31 13 14 22 25 4 15 17 28 30 2 20 8 7 6 29 33 32 9 27 IO
77 74 64 60 52 52 51 47 37 36 34
923
Present study
E
R
n
F
R
67 71 56 65 45 34 67 54
39 (26)
45 41
30
35 31
35
77 75 74 63 48
76 56 81 56 42 49 43
(35,
75 68 60 57 54 51 48 44 36 34 30 0
40 57 63 41 50 57 52 41 36 40 (29) 38
34 33 34 34
36
are homogeneous and reveal the modest level in the 0 and R sub-scales, and the low level in the total KAI. The coefficient of variation, rarely quoted in the KAI literature, indicates how each scale discriminates between Ss in the sample. Not surprisingly for a scale formed by the aggregation of three modestly correlated factors, the KAI has the lowest coefficient of variation. Initial identification of poor inventory items
A scrutiny of the 32 KAI inventory items using the criteria already mentioned identified seven poor items for removal. These are detailed below with a critique on each item. Item 10: ‘Holds back ideas until obviously needed’. This can be regarded as an unsatisfactory item on several criteria. It failed to achieve a loading of 0.30 on any of the three factors in Kirton’s (1977) analysis and also in Hammond’s (1986) work and in the present study. Conceptually it has a connection with ideas, which could give it a place on the 0 sub-scale if one argues that sufficiency/proliferation of ideas would have a bearing on the readiness with which ideas were put forward or held back. On the other hand, it clearly relates to group interaction and might be determined by the level of group conformity of an individual. It could therefore have a place on the R sub-scale. The present study placed it on the 0 sub-scale, the Kirton (1977) study placed it on the R sub-scale, and the Hammond study gave it equal loadings on the 0 and R sub-scales. In the present study, it had a multiplicity of weak correlations (between r = 0.20 and r = 0.29) with items on the 0 and R sub-scales of Kirton and no correlations of r = 0.30 or greater. Whether it appears on the 0 or R sub-scales, it will be an item which impairs the orthogonality of the sub-scales and makes a poor contribution to the reliability of the scale on which it is placed. Table
Mean Standard deviation Coeff. of variation Cronbach’s a Mean inter-item con.
3. Statistical
data: 32-item
KAI
0
100.9 IS.0 14.9% 0.88 0.19
44. I 7.2 16.3% 0.82 0.26
inventory E 19.4 4.8 24.7% 0.77 0.32
R 37.4 6.8 18.2% 0.80 0.25
W.G.K.
921
TAYLOR
item 12: ‘Likes to vary set routines at a moment’s notice’. This item was placed on the 0 sub-scale by Kirton and by Hammond, but in both cases the loading was ~0.40. In the present study, its greatest loading (0.26) was on the R sub-scale. Conceptually, it could be argued that a propensity to vary set routines could be determined in part by sufficiency-proliferation of ideas, i.e. by a wish to follow up ideas regardless of agreed plans. However, it could also be argued that varying set routines at a moment’s notice is also determined by the degree of non-conformity of a person. In the present study, Item 12 had correlations r >0.20 with items on all three scales in roughly equal proportions. Like Item 10, it is likely to impair the orthogonality of the sub-scales regardless of the scale on which it is located. Item IS: ‘Is a steady plodder’. This item was placed on the E sub-scale by Kirton (1977) on the R sub-scale by Hammond (1986), and on the 0 sub-scale in the present study. Conceptually, Item 15 might be regarded as ambiguous by some respondents who may view the two descriptions ‘steady’ and ‘plodding’ as indicators of different characteristics. Some people may feel able to affirm stability (steady) yet not wish to affirm a plodding disposition. Scrutiny of the correlation matrices of Kirton and of the present study revealed a large number of significant inter-item correlations involving Item 15. In seeking factor orthogonality, this item must be deleted. Item 18: ‘Can stand out in disagreement against the group’. Quite remarkably, this item achieved its greatest loading on the 0 sub-scale in the work of Kirton and Hammond and in the present study, achieving only a subsidiary loading on what would seem conceptually to be the obvious location for it, the R sub-scale. Nevertheless, with both Kirton and the present work Item 18 had significant correlations with about as many R items as with 0 items. It would be a satisfactory item for use on a single scale, but with the objective of orthogonality, Item 18 must be deleted. Item 27: ‘Works without deviation in a prescribed way’. This item achieved very low loadings in the Kirton and Hammond studies (0.30 and 0.26 respectively, both on the R sub-scale). In the present work, it had loadings between 0.34 and 0.38 on all three factors. Conceptually this item has connotations of conformity (R) and of disciplined methodology (E). Examination of the Kirton correlation matrix reveals significant correlations with 19 other items, including items from all three sub-scales in not very dissimilar proportions. The correlation matrix of the present study is similar. Loadings on all three factors, as in the present work are not surprising, therefore. Like Items 15 and 18, this item would be satisfactory for use in a single scale, but must be excluded if orthogonal factors are sought. With the above five items removed from the inventory, factor analysis gave a three-factor model in which all of the items were classified according to Kirton (1977), there being 11, 6 and 10 items in the 0, E and R sub-scales respectively. Statistical data on the whole scale and the subscales are given in Table 4. The most remarkable feature of this data is contained in the reliability coefficients. Figures for Cronbach’s a show only very slight impairment, given that 16% of the inventory items have been deleted. Comparison with Table 3 shows that the subscales have improved homogeneity, as indicated by the mean inter-item correlations. The coefficients of variation of the sub-scales were also marginally improved. Inter-factor correlations, shown in Table 5, also reveal considerable progress towards achieving scales which are orthogonal. The mean inter-factor correlation is now 0.34, compared with 0.44 for the orthodox 32 item inventory (Kirton’s (1977) data gave 0.42). Further
deletions from
the inventory
In an earlier paper (Taylor, 1988), it was concluded that there was strong evidence for regarding the 0 sub-scale in the orthodox three-factor model as consisting of two elements. A major component concerned with idea generation comprised all of the items loading strongly on the 0 Table
Mean Standard deviation Coeff. of variation Cronbach’s r Mean inter-item cow.
4. Statistical
data: 27-item
inventory
KAI
0
E
R
83.9 12.4 14.8% 0.86 0.18
37.5 6.3 16.8% 0.81 0.29
16.1 4.3 26.7% 0.79 0.38
30.3 5.8 19.1% 0.78 0.26
The Kirton
Adaption-Innovation
Table 5. Inter-correlations
inventory
92s
of sub-scales:
27-item inventory
0 E R
0
E
R
0.22
I 0.37
I
I 0.43
sub-scale in the Kirton (1977) model, and a subsidiary element concerned with preference for stability/change comprised Items 12, 13, 31 and 24. In the next step towards increasing the homogeneity and orthogonality of the sub-scales, these items were obvious candidates for deletion. (Item 12 had already been removed for other reasons.) In addition, several other items were identified for deletion using the criteria applied earlier. These items were as follows: Item 28 ‘Imposes strict order on matters within own control’, Item 9 ‘Likes bosses and work patterns which are consistent’, Item 33 ‘Is predictable’, Item 29 ‘Likes the protection of precise instructions’. With the removal of these further items, leaving an inventory of 20 items, factor analysis gave a very satisfactory three-factor model with no item failing to load co.35 on its principal factor. The clear-cut factor structure is given in Table 6. Statistical data relating to this 20-item inventory, given in Table 7, shows the remarkable effect on the 0 sub-scale achieved by the removal of these further items. The increase in the mean inter-item correlation within the 0 sub-scale has permitted the loss of three items without any reduction in the value of Cronbach’s a. The E sub-scale has also lost nothing in internal reliability, but the reliability of the R sub-scale has been impaired, as also, inevitably, has the reliability of the total scale. Coefficients of variation were marginally improved with all three sub-scales. Perhaps the most remarkable effect of these deletions, however, is shown in Table 8. The mean inter-factor correlation has been reduced to 0.25, and the 0 and E sub-scales have become virtually orthogonal (r’= 0.012). It is suggested that this 20-item inventory provides an attractive way to operationalise Adaption-Innovation theory. Since the reduced inventory contains all of the items which loaded 0.50 or more on each of the three factors in the Kirton reference sample, it clearly embodies the Table 6. Factor structure: 20-item inventory Item
0
23 21 II I6 19 26 3 5 25 14 4 22 17 2 7 30 6 8 20 32
74 71 70 58 56 54 46 35
Proliferation ideas Has original ideas Has fresh perspectives on old problems Copes with several new ideas at same time Is stimulating Often risks doing things differently Will always think of something when stuck Would sooner create than improve Is methodical and systematic Is thorough Enjoys detailed work Masters all details painstakingly Is consistent Conforms Never acts without proper authority Fits readily into the ‘system’ Is prudent when dealing with authority Never seeks to bend or break the rules Readily agrees with the team at work Prefers colleagues who never rock the boat
E
R
38
79 79 58 55 47
31
39 63 59 57 55 50 37 35
All loadings of 0.20 or more are shown. Table 7. Statistical data: 20&m
Mean
Standard deviation Coeff. of variation Cronbach’s a Mean inter-item corr.
inventory
KAI
0
E
R
62. I 9.3 15.0% 0.82
27.7 4.9 17.7%
13.3 3.8 28.6% 0.79 0.43
21.2 4.4 20.8% 0.73 0.28
0.18
0.81 0.35
926
W.
Table
8.
G.
K.
TAYLOR
Inter-correlations 20-item
0 E R
of
sub-scales:
inventory
0
E
R
I 0.1 I 0.33
1 0.31
I
same primary concepts as the orthodox KAI inventory. It does so with much more homogeneous and orthogonal sub-scales, which makes the use of these sub-scales a more satisfactory proposition. [Nevertheless, the doubts expressed in an earlier paper (Taylor, 1989) regarding the conceptual status of the 0 sub-scale remain.] The question as to whether or not the concepts underlying the three sub-scales should be orthogonal has received scant attention in the literature. Kirton (1976) recommends a Varimax orthogonal solution for exploring the factor structure of his measure. However, the sub-scales he derived proved to be far from orthogonal. Hammond (1986) briefly comments that the sub-scales, being aspects of the Adaptor-Innovator domain, should be expected to be inter-related. This seems to be an expectation which has survived the decade of use of KAI without being subjected to analysis. Yet throughout this period Varimax orthogonal solutions have been the standard approach. Considering the 0 and E sub-scales, it is difficult to provide an argument, outside Adaption-Innovation theory, that methodical Weberianism should be related to a person’s level of idea generation. The evidence of the present research suggests that as weakly loading items are removed, the 0 and E sub-scales become progressively more orthogonal. There is the prospect that better measures could lead to orthogonal scales of high reliability. Further indication of this prospect can be obtained from a scrutiny of the correlation matrix. Using the Kirton (1977) study and taking the four highest loading items from his 0 and E factors, all 16 of the inter-item correlations were specified as co.20 (actual figures were not given). A similar scrutiny applied to the present work gave the data in Table 9. The mean inter-item correlation was 0.04, and this gives little or no support to the suggestion that these concepts are other than orthogonal. If the 0 sub-scale is interpreted in the orthodox manner as a person’s preference for sufficiency/proliferation of ideas, then one could argue that some correlation between the 0 and E sub-scales is to be anticipated. That is to say, a person with a preference for the thoroughness which is characteristic of Weberianism might be expected to prefer to work with only a sufficiency of ideas, since a proliferation of ideas might jeopardise thoroughness. The fact that Table 9 exhibits virtually zero correlation, offers some support for the alternative interpretation of the 0 concept propounded in an earlier paper (Taylor, 1989). The situation is somewhat different regarding the 0 and R sub-scales. Whether the 0 subscale is interpreted in the orthodox way as indicating a preference in sufficiency/proliferation of ideas or as a measure of fecundity in idea generation, it can be argued that, other things being equal, a person high on 0 is more likely to be perceived as a non-conformer than someone low on 0. This could arise by virtue of his/her freedom in handling ideas. To what extent this effect would be reflected in a correlation between the two sub-scales is not clear. Taking the four items loading most heavily on the 0 and R factors in the Kirton (1977) study, only two of the 16 correlation coefficients were reported by Kirton to equal 0.20 or more. Using data from the present study, the matrix of coefficients given in Table 10 resulted. Though very weak correlations, 75% of them were statistically significant, and the mean of 0.14 suggests that truly orthogonal sub-scales are unlikely. Table
9.
Inter-item
correlations
for
principal*
0
and
E
Table
IO.
Inter-item
correlations
0
I4
E items
*As
given
by
items
0
21
23
I9
16
-0.01
0.00
-0.05
-0.04
22
0.12
0.03
0.03
0.06
25
0.08
0.13
0.02
0.10
4
0.05
0.06
0.00
0.04
Kirton
(1977).
for
principal*
0
and
items
items
R items
‘As
given
by
items
21
23
19
16
30
0.18
0.21
0.08
0.14
I
2
0.20
0.1
0.10
0.13
20
0.09
0.18
0.02
0.19
8
0.22
0.14
0.08
0.18
Kirton
(1977).
R
The Kirton Table II.
Adaption-Innovation
inventory
Inter-item correlations for prmcipal* Items
927
E and R
E items
30 R items
20 8
I4
22
25
0.17 0.06 -0.01 0.16
0.16 0.06 0.04 0.16
0.27 0.12 0.02 0.2 I
4 0.12 0.01 0.07 0.27
*As given by Klrton (1977).
A similar situation exists in the case of the E and R sub-scales. People of low Weberian efficiency (high E), not disposed to thorough, painstaking work, could be perceived as not conforming to organisation norms. To what extent this might be reflected in a correlation between these two sub-scales is again not clear. Analysis of the four principal items on the E and R factors in the Kirton (1977) factor model gave similar results to the previous case. The matrix of coefficients using data from the present is given in Table 11, the mean correlation being 0.12. A minimum size incentory Given the very low level of correlations between principal loading items on different factors, as shown in Tables 9, 10 and 11, the concluding step in this study was an examination of the KAI inventory reduced to the minimum size consistent with the criteria stated earlier. The factor structure of the 13-item inventory which resulted is given in Table 12. All items loaded 0.50 or higher on their principal factor, and no subsidiary loading reached 0.20. These three factors have continued to increase in homogeneity as judged by the mean inter-item correlations. In consequence, the internal reliabilities have held up remarkably well, particularly in the case of the 0 and E sub-scales. Cronbach’s z for the aggregate scale fell most of all because the homogeneity of the whole scale remained low. Coefficients of variation for these grossly reduced scales were also satisfactory, having shown progressive improvement as items have been deleted. Regarding inter-factor correlations, the 0 and E scales finally reached a level of correlation which was not statistically significant. The correlation between the E and R scales fell less than was anticipated, but these three sub-scales can now be regarded as effectively orthogonal. Admittedly, the internal reliability of the R sub-scale and the aggregate scale have suffered in this culling process, but this study has demonstrated the prospect of essentially orthogonal scales of high reliability, given further development. A summary of the changes in internal reliability, mean inter-item correlation, and inter-factor,correlation as ‘poor’ items are culled from the inventory is illustrated in Fig. 1. Table I?. Factor structure: 13-item inventory Item
0
23 21 II I9 I6 25 14 4 22 2 30 6 7
79 69 69 56 55
Proliferates ideas Has original ideas Has fresh perspectiveson old problems Is stimulating Copes with several new ideas at same time Is methodical and systematic Is thorough Enjoys detailed work Masters all details painstakingly Conforms Fits readily into the ‘system’ Is prudent when dealing with authority Never acts without proper authority
E
R
79 78 60 56 66 65 60 50
All loadings of 0.20 or more are shown.
Table 13. Statistical data: 13-item inventorv
Mean Standard deviation Coeff. of variation Cronbach’s I Mean inter-item corr.
KAI
0
E
R
39.6 6.4 16.2% 0.74 0.18
17.2 3.4 19.8% 0.79 0.43
10.9 3.4 31.2% 0.79 0.48
Il.4 2.9 25.4% 0.70 0.37
W.
928 Table
14.
G.
K.
TAYLOR
Inter-correlations 13-item 0
0
of
sub-scales:
inventory E
R
I
I
E
0.07
I
R
0.17
0.23
CONCLUSIONS These findings strike at one of the basic tenets of orthodox Adaption-Innovation theory. From the outset, the Adaptor-Innovator concept has been held to be uni-dimensional (Kirton, 1976) and this view is still asserted in the latest KAI Manual (Kirton, 1987). The present work has demonstrated that the apparent uni-dimensionality of the KAI stems from the use of impure sub-scales. It has been shown that evidence for essentially orthogonal factors lies in the published inter-item correlations (Kirton, 1987). A process of refinement which does no more than cull items 0.50
r
(a
)
Mean
inter-
item
0.20
correlotlon
.
.
o-0 KAI t 0.10
1
0.90
r
0.70
L
0.6
0.0
r
I
I
I
I
32
27
20
13
( b)
Cronboch’s
alpha
I
I
I
32
27
20
(C
1
Inter
-factor
13
correlation
* 32
27 Number
Fig. 1. Reducing
the KAI
20 of
13
items
inventory-a
summary.
The Kirton Adaption-Innovation
inventory
929
which are ‘tail-enders’ in the orthodox three-factor model leads to sub-scales which are effectively homogeneous and orthogonal, and have tolerable reliability coefficients. It is difficult to see how KAI theory can develop satisfactorily without recognising this multi-dimensionality. A decade of using KAI which was claimed to be uni-dimensional has seen much empirical data published which relates only to the total score. (The KAI Manual in use from 1977 to 1987 stated “The sub-scales have not been used independently . . .“. ) In the light of the present work, much of the reported data is seen to be of limited value because the middle-ranking KAI scores, say those within the inter-quartile range, conflate three essentially independent measures. What is now needed is the development of KAI methodology using orthogonal scales which makes characterisations based on the multi-dimensional nature of Adaption-Innovation. For example, by dividing each of three dimensions into high and low scores, a KAI typology of nine classes would result. Such a development would hold the prospect of a much richer understanding of behaviour patterns, making the KAI a more powerful psychometric instrument. REFERENCES Goldsmith R. E. (1985) A factorial composition of the KAI Inventory. Educ. Psychol. Measmr 45, 245. Hammond S. M. (1986) Some pitfalls in the use of factor scores: the case of the Kirton Adaption-Innovation
Inventory.
Person. indiGd. Dtfl. 7, 401.
Kirton M. J. (1976) Adaptors and innovators: a description and measure. J. appl. Psychol. 61, 622. Kirton M. J. (1977) Manual of the Kirlon Adaption-Innocarion Inuenrory. National Foundation for Educational Research, London. Kirton M. J. (1987) Manual of rhe Kirlon Adaption-lnnooafion Inventory, 2nd edn. Occupational Research Centre, Hatfield. Lowe E. A. and Taylor W. G. K. (1986) The management of research in the life sciences: the characterisation of researchers. R&D Mgmt 16, 45. Mulligan G-and Martin W. (1980) Adaptors, Innovators and the KAI. Psychol. Rep. 46, 883. Payne R. L. (1987) Individual differences and performance amongst R & D personnel: some implications for management development, R&D Mgmr 17, 153. Prato Previde G. (1984) Adattatori ed Innovatori: i resultati della standardizzazione italiana de1 KAI. Ric. Psicol. 4, 81. Taylor W. G. K. (1989) The Kirton Adaption-Innovation Inventory: a re-examination of the factor structure. J. Orgn Behar. IO. In press.
inllirrd.
01%
Vol.
10, No.
inGreat Bntain.All
rights
9, pp. 921-929.
1989
0191-8869’89
reserved
Copyright
C 1989 Maxwell
$3.00 + 0.00
PergamonMacmillan plc
THE KIRTON ADAPTION-INNOVATION INVENTORY: SHOULD THE SUB-SCALES BE ORTHOGONAL? WILLIAM G. K. TAYLOR Sheffield City Polytechnic, Totley Hall Lane. Sheffield Sl7 4AB, England (Received
I4 October
1988)
Summary-The paper examines one of the fundamental tenets of Adaption-Innovation theory, that the KAI measure is uni-dimensional. Attention is focused on the orthodox three-factor structure and by a simple process of refinement it is demonstrated that the sub-scales are essentially orthogonal. It is further shown that evidence for the inherent independence of the three sub-scales of originality, efficiency and conformity lies in data published in the first KAI Manual. Some implicationsfor Adaption-Innovation theory are outlined.
INTRODUCTION
The Kirton Adaption-Innovation inventory (KAI) has attracted considerable attention as a measure of the characteristic approach a person has towards problem solving and decision making. Adaption-Innovation theory underlying the KAI posits that a person can be located on a single dimension of cognitive style, with the extremes labelled as Adaptors and Innovators. Since its inception in 1976, the KAI has almost always been used as a single scale. Considerable evidence for its validity has accumulated and it has been shown by a number of researchers in several countries to have good psychometric properties (Kirton, 1987). Nevertheless, factor analysis of the KAI, initially by Kirton (1976) and by others subsequently (Mulligan and Martin, 1980; Prato Previde, 1984; Goldsmith, 1985; Hammond, 1986; Taylor, 1989), has afforded ample evidence for the extraction of three or more factors. However, the scores based on a three-factor model have been shown to have only modest correlations with each other. The factor correlation matrix published by Kirton (1977) based on his general population sample of n = 532 is given in Table 1. Subsequent work by Lowe and Taylor (1986) using a sample, n = 93, drawn from research scientists in three industrial organisations, corroborated these correlations. Their data was: 0 vs E, r = 0.37
0 vs R, r = 0.56
E vs R, r = 0.39
None of the differences between the two sets of correlations were significant statistically. The use of an aggregate KAI score in the face of these modest correlations is questionable. Payne (1987) argued that it is inevitable that there will be people with all possible combinations of scores on the three dimensions of KAI since the inter-correlations are not strong. For people with KAI scores lying roughly in the middle of the range, to use the total KAI score is to conflate the three dimensions. For example, a person of about average total KAI score may be well above average on the 0 sub-scale, well below average on the E sub-scale, and about average on the R sub-scale. Such a person can be expected to be very different from one who is well below average on 0, well above average on E and about average on R, yet these two people would have similar total KAI scores. An example of this nature was illustrated by Lowe and Taylor (1986) in their study of R and D scientists. Their findings led them to reject the total KAI score in favour of the separate sub-scales. However, a characterisation using the sub-scales would, ideally, require the sub-scales to be orthogonal. As the KAI presently stands, the sub-scale are neither strongly enough correlated to justify the use of the total score nor weakly enough correlated to encourage the use of the separate sub-scales. Recent work by Taylor (1989) has re-examined the factor structure of the KAI. Although the structure of the orthodox three-factor model was confirmed, as were the high internal reliabilities, concern was expressed about the use of only three factors. Corroborating the work of Hammond 921
W. G. K. TAYLOR
TableI.Inter-correlations of KAI fdcror traits (Kmon. 1977) E
R
I 0.42
I
0 0 E R
I
0.36 0.47
(1986), a highly significant amount of KAI item variance remained unaccounted for by the extraction of only three factors. Evidence was presented to suggest that the 0 sub-scale in the orthodox three-factor model was not homogeneous, and consisted of a major component concerned with idea generation and a subsidiary element concerned with preference for stability/change. One possibility for exploring developments in the KAI to achieve more homogeneous and orthogonal sub-scales was, clearly, the culling of ‘poor’ items from the inventory. The present paper describes this approach. METHOD
The sample consisted of 305 professionally employed people drawn from two sources. Group A was taken from the R and D departments of four large manufacturing companies and consisted of 119 graduate staff spanning a range of scientific disciplines. Group B was taken from mature students studying part-time on a DMS/MBA programme and consisted of 186 people, the majority of whom were graduates. In both groups the ratio of male:female Ss was approx. 4:l. Descriptive statistics were calculated for each group separately and for the combined sample. Factor analyses were initially carried out for each group separately and for the combined sample, extracting three factors in accordance with Kirton (1976). The maximum likelihood method was utilised, followed by varimax rotation, as implemented in the SPSSX programme package. Finding similar factor structures and internal reliabilities in the two sub-groups and in the combined sample, further studies were restricted to the combined sample, n = 305. Details regarding the preliminary analyses can be found in an earlier paper (Taylor, 1989). The starting point for examining reduced item inventories was the orthodox three-factor model (Kirton, 1976). The essential criterion used in selecting items for removal from the inventory was that the sub-scales should be made as orthogonal as possible by deriving three scales which were each homogeneous conceptually. The process involved a scrutiny of the inter-item correlations and the magnitude of the loadings of each item on each factor. At the same time, test reliability was taken into account, using the criterion that Cronbach’s r must not fall below 0.7 for either the total KAI or any of the sub-scales. After each step in the progressive removal of items from the inventory, factor analysis was carried out and the inter-factor correlations and reliability coefficients were determined. RESULTS
AND
DISCUSSION
The initial three-factor model employing all 32 items of the KAI inventory is given in Table 2 alongside the factor structure reported by Kirton (1977). Following Kirton, only loadings of 0.30 or more are shown (except where an item had no loading equal to 0.30 in which case the highest loading is shown in parentheses). To facilitate comparison with Kirton, the items are listed in loading order as given by Kirton (1977) and where an item is misplaced relative to Kirton, the loading is marked with an asterisk. Decimal points are omitted. It is apparent that the present KAI data leads to a very similar three-factor model to that of Kirton (1977). The percentage of items allocated to the same factors as Kirton is 91% and moreover all of the 17 items loading >0.50 in the Kirton general population sample are classified ‘correctly’ in the present study. The unsatisfactory feature in the Kirton model of a substantial number of items failing to reach a loading of 0.40 on any factor is also confirmed. Statistical data on the KAI and its sub-scales is given in Table 3. As noted in an earlier paper (Taylor, 1989), the scale statistics and reliability coefficients are in accord with data reported in the literature. The mean inter-item correlations provide a measure of the degree to which the scales
The Kirton Table
Adaption-Innovation 2. Three-factor
inventory
models cornoared
Kirton Item
0
21 23 19 16 3 5 II 26 I2 24 I8 31 13 14 22 25 4 15 17 28 30 2 20 8 7 6 29 33 32 9 27 IO
77 74 64 60 52 52 51 47 37 36 34
923
Present study
E
R
n
F
R
67 71 56 65 45 34 67 54
39 (26)
45 41
30
35 31
35
77 75 74 63 48
76 56 81 56 42 49 43
(35,
75 68 60 57 54 51 48 44 36 34 30 0
40 57 63 41 50 57 52 41 36 40 (29) 38
34 33 34 34
36
are homogeneous and reveal the modest level in the 0 and R sub-scales, and the low level in the total KAI. The coefficient of variation, rarely quoted in the KAI literature, indicates how each scale discriminates between Ss in the sample. Not surprisingly for a scale formed by the aggregation of three modestly correlated factors, the KAI has the lowest coefficient of variation. Initial identification of poor inventory items
A scrutiny of the 32 KAI inventory items using the criteria already mentioned identified seven poor items for removal. These are detailed below with a critique on each item. Item 10: ‘Holds back ideas until obviously needed’. This can be regarded as an unsatisfactory item on several criteria. It failed to achieve a loading of 0.30 on any of the three factors in Kirton’s (1977) analysis and also in Hammond’s (1986) work and in the present study. Conceptually it has a connection with ideas, which could give it a place on the 0 sub-scale if one argues that sufficiency/proliferation of ideas would have a bearing on the readiness with which ideas were put forward or held back. On the other hand, it clearly relates to group interaction and might be determined by the level of group conformity of an individual. It could therefore have a place on the R sub-scale. The present study placed it on the 0 sub-scale, the Kirton (1977) study placed it on the R sub-scale, and the Hammond study gave it equal loadings on the 0 and R sub-scales. In the present study, it had a multiplicity of weak correlations (between r = 0.20 and r = 0.29) with items on the 0 and R sub-scales of Kirton and no correlations of r = 0.30 or greater. Whether it appears on the 0 or R sub-scales, it will be an item which impairs the orthogonality of the sub-scales and makes a poor contribution to the reliability of the scale on which it is placed. Table
Mean Standard deviation Coeff. of variation Cronbach’s a Mean inter-item con.
3. Statistical
data: 32-item
KAI
0
100.9 IS.0 14.9% 0.88 0.19
44. I 7.2 16.3% 0.82 0.26
inventory E 19.4 4.8 24.7% 0.77 0.32
R 37.4 6.8 18.2% 0.80 0.25
W.G.K.
921
TAYLOR
item 12: ‘Likes to vary set routines at a moment’s notice’. This item was placed on the 0 sub-scale by Kirton and by Hammond, but in both cases the loading was ~0.40. In the present study, its greatest loading (0.26) was on the R sub-scale. Conceptually, it could be argued that a propensity to vary set routines could be determined in part by sufficiency-proliferation of ideas, i.e. by a wish to follow up ideas regardless of agreed plans. However, it could also be argued that varying set routines at a moment’s notice is also determined by the degree of non-conformity of a person. In the present study, Item 12 had correlations r >0.20 with items on all three scales in roughly equal proportions. Like Item 10, it is likely to impair the orthogonality of the sub-scales regardless of the scale on which it is located. Item IS: ‘Is a steady plodder’. This item was placed on the E sub-scale by Kirton (1977) on the R sub-scale by Hammond (1986), and on the 0 sub-scale in the present study. Conceptually, Item 15 might be regarded as ambiguous by some respondents who may view the two descriptions ‘steady’ and ‘plodding’ as indicators of different characteristics. Some people may feel able to affirm stability (steady) yet not wish to affirm a plodding disposition. Scrutiny of the correlation matrices of Kirton and of the present study revealed a large number of significant inter-item correlations involving Item 15. In seeking factor orthogonality, this item must be deleted. Item 18: ‘Can stand out in disagreement against the group’. Quite remarkably, this item achieved its greatest loading on the 0 sub-scale in the work of Kirton and Hammond and in the present study, achieving only a subsidiary loading on what would seem conceptually to be the obvious location for it, the R sub-scale. Nevertheless, with both Kirton and the present work Item 18 had significant correlations with about as many R items as with 0 items. It would be a satisfactory item for use on a single scale, but with the objective of orthogonality, Item 18 must be deleted. Item 27: ‘Works without deviation in a prescribed way’. This item achieved very low loadings in the Kirton and Hammond studies (0.30 and 0.26 respectively, both on the R sub-scale). In the present work, it had loadings between 0.34 and 0.38 on all three factors. Conceptually this item has connotations of conformity (R) and of disciplined methodology (E). Examination of the Kirton correlation matrix reveals significant correlations with 19 other items, including items from all three sub-scales in not very dissimilar proportions. The correlation matrix of the present study is similar. Loadings on all three factors, as in the present work are not surprising, therefore. Like Items 15 and 18, this item would be satisfactory for use in a single scale, but must be excluded if orthogonal factors are sought. With the above five items removed from the inventory, factor analysis gave a three-factor model in which all of the items were classified according to Kirton (1977), there being 11, 6 and 10 items in the 0, E and R sub-scales respectively. Statistical data on the whole scale and the subscales are given in Table 4. The most remarkable feature of this data is contained in the reliability coefficients. Figures for Cronbach’s a show only very slight impairment, given that 16% of the inventory items have been deleted. Comparison with Table 3 shows that the subscales have improved homogeneity, as indicated by the mean inter-item correlations. The coefficients of variation of the sub-scales were also marginally improved. Inter-factor correlations, shown in Table 5, also reveal considerable progress towards achieving scales which are orthogonal. The mean inter-factor correlation is now 0.34, compared with 0.44 for the orthodox 32 item inventory (Kirton’s (1977) data gave 0.42). Further
deletions from
the inventory
In an earlier paper (Taylor, 1988), it was concluded that there was strong evidence for regarding the 0 sub-scale in the orthodox three-factor model as consisting of two elements. A major component concerned with idea generation comprised all of the items loading strongly on the 0 Table
Mean Standard deviation Coeff. of variation Cronbach’s r Mean inter-item cow.
4. Statistical
data: 27-item
inventory
KAI
0
E
R
83.9 12.4 14.8% 0.86 0.18
37.5 6.3 16.8% 0.81 0.29
16.1 4.3 26.7% 0.79 0.38
30.3 5.8 19.1% 0.78 0.26
The Kirton
Adaption-Innovation
Table 5. Inter-correlations
inventory
92s
of sub-scales:
27-item inventory
0 E R
0
E
R
0.22
I 0.37
I
I 0.43
sub-scale in the Kirton (1977) model, and a subsidiary element concerned with preference for stability/change comprised Items 12, 13, 31 and 24. In the next step towards increasing the homogeneity and orthogonality of the sub-scales, these items were obvious candidates for deletion. (Item 12 had already been removed for other reasons.) In addition, several other items were identified for deletion using the criteria applied earlier. These items were as follows: Item 28 ‘Imposes strict order on matters within own control’, Item 9 ‘Likes bosses and work patterns which are consistent’, Item 33 ‘Is predictable’, Item 29 ‘Likes the protection of precise instructions’. With the removal of these further items, leaving an inventory of 20 items, factor analysis gave a very satisfactory three-factor model with no item failing to load co.35 on its principal factor. The clear-cut factor structure is given in Table 6. Statistical data relating to this 20-item inventory, given in Table 7, shows the remarkable effect on the 0 sub-scale achieved by the removal of these further items. The increase in the mean inter-item correlation within the 0 sub-scale has permitted the loss of three items without any reduction in the value of Cronbach’s a. The E sub-scale has also lost nothing in internal reliability, but the reliability of the R sub-scale has been impaired, as also, inevitably, has the reliability of the total scale. Coefficients of variation were marginally improved with all three sub-scales. Perhaps the most remarkable effect of these deletions, however, is shown in Table 8. The mean inter-factor correlation has been reduced to 0.25, and the 0 and E sub-scales have become virtually orthogonal (r’= 0.012). It is suggested that this 20-item inventory provides an attractive way to operationalise Adaption-Innovation theory. Since the reduced inventory contains all of the items which loaded 0.50 or more on each of the three factors in the Kirton reference sample, it clearly embodies the Table 6. Factor structure: 20-item inventory Item
0
23 21 II I6 19 26 3 5 25 14 4 22 17 2 7 30 6 8 20 32
74 71 70 58 56 54 46 35
Proliferation ideas Has original ideas Has fresh perspectives on old problems Copes with several new ideas at same time Is stimulating Often risks doing things differently Will always think of something when stuck Would sooner create than improve Is methodical and systematic Is thorough Enjoys detailed work Masters all details painstakingly Is consistent Conforms Never acts without proper authority Fits readily into the ‘system’ Is prudent when dealing with authority Never seeks to bend or break the rules Readily agrees with the team at work Prefers colleagues who never rock the boat
E
R
38
79 79 58 55 47
31
39 63 59 57 55 50 37 35
All loadings of 0.20 or more are shown. Table 7. Statistical data: 20&m
Mean
Standard deviation Coeff. of variation Cronbach’s a Mean inter-item corr.
inventory
KAI
0
E
R
62. I 9.3 15.0% 0.82
27.7 4.9 17.7%
13.3 3.8 28.6% 0.79 0.43
21.2 4.4 20.8% 0.73 0.28
0.18
0.81 0.35
926
W.
Table
8.
G.
K.
TAYLOR
Inter-correlations 20-item
0 E R
of
sub-scales:
inventory
0
E
R
I 0.1 I 0.33
1 0.31
I
same primary concepts as the orthodox KAI inventory. It does so with much more homogeneous and orthogonal sub-scales, which makes the use of these sub-scales a more satisfactory proposition. [Nevertheless, the doubts expressed in an earlier paper (Taylor, 1989) regarding the conceptual status of the 0 sub-scale remain.] The question as to whether or not the concepts underlying the three sub-scales should be orthogonal has received scant attention in the literature. Kirton (1976) recommends a Varimax orthogonal solution for exploring the factor structure of his measure. However, the sub-scales he derived proved to be far from orthogonal. Hammond (1986) briefly comments that the sub-scales, being aspects of the Adaptor-Innovator domain, should be expected to be inter-related. This seems to be an expectation which has survived the decade of use of KAI without being subjected to analysis. Yet throughout this period Varimax orthogonal solutions have been the standard approach. Considering the 0 and E sub-scales, it is difficult to provide an argument, outside Adaption-Innovation theory, that methodical Weberianism should be related to a person’s level of idea generation. The evidence of the present research suggests that as weakly loading items are removed, the 0 and E sub-scales become progressively more orthogonal. There is the prospect that better measures could lead to orthogonal scales of high reliability. Further indication of this prospect can be obtained from a scrutiny of the correlation matrix. Using the Kirton (1977) study and taking the four highest loading items from his 0 and E factors, all 16 of the inter-item correlations were specified as co.20 (actual figures were not given). A similar scrutiny applied to the present work gave the data in Table 9. The mean inter-item correlation was 0.04, and this gives little or no support to the suggestion that these concepts are other than orthogonal. If the 0 sub-scale is interpreted in the orthodox manner as a person’s preference for sufficiency/proliferation of ideas, then one could argue that some correlation between the 0 and E sub-scales is to be anticipated. That is to say, a person with a preference for the thoroughness which is characteristic of Weberianism might be expected to prefer to work with only a sufficiency of ideas, since a proliferation of ideas might jeopardise thoroughness. The fact that Table 9 exhibits virtually zero correlation, offers some support for the alternative interpretation of the 0 concept propounded in an earlier paper (Taylor, 1989). The situation is somewhat different regarding the 0 and R sub-scales. Whether the 0 subscale is interpreted in the orthodox way as indicating a preference in sufficiency/proliferation of ideas or as a measure of fecundity in idea generation, it can be argued that, other things being equal, a person high on 0 is more likely to be perceived as a non-conformer than someone low on 0. This could arise by virtue of his/her freedom in handling ideas. To what extent this effect would be reflected in a correlation between the two sub-scales is not clear. Taking the four items loading most heavily on the 0 and R factors in the Kirton (1977) study, only two of the 16 correlation coefficients were reported by Kirton to equal 0.20 or more. Using data from the present study, the matrix of coefficients given in Table 10 resulted. Though very weak correlations, 75% of them were statistically significant, and the mean of 0.14 suggests that truly orthogonal sub-scales are unlikely. Table
9.
Inter-item
correlations
for
principal*
0
and
E
Table
IO.
Inter-item
correlations
0
I4
E items
*As
given
by
items
0
21
23
I9
16
-0.01
0.00
-0.05
-0.04
22
0.12
0.03
0.03
0.06
25
0.08
0.13
0.02
0.10
4
0.05
0.06
0.00
0.04
Kirton
(1977).
for
principal*
0
and
items
items
R items
‘As
given
by
items
21
23
19
16
30
0.18
0.21
0.08
0.14
I
2
0.20
0.1
0.10
0.13
20
0.09
0.18
0.02
0.19
8
0.22
0.14
0.08
0.18
Kirton
(1977).
R
The Kirton Table II.
Adaption-Innovation
inventory
Inter-item correlations for prmcipal* Items
927
E and R
E items
30 R items
20 8
I4
22
25
0.17 0.06 -0.01 0.16
0.16 0.06 0.04 0.16
0.27 0.12 0.02 0.2 I
4 0.12 0.01 0.07 0.27
*As given by Klrton (1977).
A similar situation exists in the case of the E and R sub-scales. People of low Weberian efficiency (high E), not disposed to thorough, painstaking work, could be perceived as not conforming to organisation norms. To what extent this might be reflected in a correlation between these two sub-scales is again not clear. Analysis of the four principal items on the E and R factors in the Kirton (1977) factor model gave similar results to the previous case. The matrix of coefficients using data from the present is given in Table 11, the mean correlation being 0.12. A minimum size incentory Given the very low level of correlations between principal loading items on different factors, as shown in Tables 9, 10 and 11, the concluding step in this study was an examination of the KAI inventory reduced to the minimum size consistent with the criteria stated earlier. The factor structure of the 13-item inventory which resulted is given in Table 12. All items loaded 0.50 or higher on their principal factor, and no subsidiary loading reached 0.20. These three factors have continued to increase in homogeneity as judged by the mean inter-item correlations. In consequence, the internal reliabilities have held up remarkably well, particularly in the case of the 0 and E sub-scales. Cronbach’s z for the aggregate scale fell most of all because the homogeneity of the whole scale remained low. Coefficients of variation for these grossly reduced scales were also satisfactory, having shown progressive improvement as items have been deleted. Regarding inter-factor correlations, the 0 and E scales finally reached a level of correlation which was not statistically significant. The correlation between the E and R scales fell less than was anticipated, but these three sub-scales can now be regarded as effectively orthogonal. Admittedly, the internal reliability of the R sub-scale and the aggregate scale have suffered in this culling process, but this study has demonstrated the prospect of essentially orthogonal scales of high reliability, given further development. A summary of the changes in internal reliability, mean inter-item correlation, and inter-factor,correlation as ‘poor’ items are culled from the inventory is illustrated in Fig. 1. Table I?. Factor structure: 13-item inventory Item
0
23 21 II I9 I6 25 14 4 22 2 30 6 7
79 69 69 56 55
Proliferates ideas Has original ideas Has fresh perspectiveson old problems Is stimulating Copes with several new ideas at same time Is methodical and systematic Is thorough Enjoys detailed work Masters all details painstakingly Conforms Fits readily into the ‘system’ Is prudent when dealing with authority Never acts without proper authority
E
R
79 78 60 56 66 65 60 50
All loadings of 0.20 or more are shown.
Table 13. Statistical data: 13-item inventorv
Mean Standard deviation Coeff. of variation Cronbach’s I Mean inter-item corr.
KAI
0
E
R
39.6 6.4 16.2% 0.74 0.18
17.2 3.4 19.8% 0.79 0.43
10.9 3.4 31.2% 0.79 0.48
Il.4 2.9 25.4% 0.70 0.37
W.
928 Table
14.
G.
K.
TAYLOR
Inter-correlations 13-item 0
0
of
sub-scales:
inventory E
R
I
I
E
0.07
I
R
0.17
0.23
CONCLUSIONS These findings strike at one of the basic tenets of orthodox Adaption-Innovation theory. From the outset, the Adaptor-Innovator concept has been held to be uni-dimensional (Kirton, 1976) and this view is still asserted in the latest KAI Manual (Kirton, 1987). The present work has demonstrated that the apparent uni-dimensionality of the KAI stems from the use of impure sub-scales. It has been shown that evidence for essentially orthogonal factors lies in the published inter-item correlations (Kirton, 1987). A process of refinement which does no more than cull items 0.50
r
(a
)
Mean
inter-
item
0.20
correlotlon
.
.
o-0 KAI t 0.10
1
0.90
r
0.70
L
0.6
0.0
r
I
I
I
I
32
27
20
13
( b)
Cronboch’s
alpha
I
I
I
32
27
20
(C
1
Inter
-factor
13
correlation
* 32
27 Number
Fig. 1. Reducing
the KAI
20 of
13
items
inventory-a
summary.
The Kirton Adaption-Innovation
inventory
929
which are ‘tail-enders’ in the orthodox three-factor model leads to sub-scales which are effectively homogeneous and orthogonal, and have tolerable reliability coefficients. It is difficult to see how KAI theory can develop satisfactorily without recognising this multi-dimensionality. A decade of using KAI which was claimed to be uni-dimensional has seen much empirical data published which relates only to the total score. (The KAI Manual in use from 1977 to 1987 stated “The sub-scales have not been used independently . . .“. ) In the light of the present work, much of the reported data is seen to be of limited value because the middle-ranking KAI scores, say those within the inter-quartile range, conflate three essentially independent measures. What is now needed is the development of KAI methodology using orthogonal scales which makes characterisations based on the multi-dimensional nature of Adaption-Innovation. For example, by dividing each of three dimensions into high and low scores, a KAI typology of nine classes would result. Such a development would hold the prospect of a much richer understanding of behaviour patterns, making the KAI a more powerful psychometric instrument. REFERENCES Goldsmith R. E. (1985) A factorial composition of the KAI Inventory. Educ. Psychol. Measmr 45, 245. Hammond S. M. (1986) Some pitfalls in the use of factor scores: the case of the Kirton Adaption-Innovation
Inventory.
Person. indiGd. Dtfl. 7, 401.
Kirton M. J. (1976) Adaptors and innovators: a description and measure. J. appl. Psychol. 61, 622. Kirton M. J. (1977) Manual of the Kirlon Adaption-Innocarion Inuenrory. National Foundation for Educational Research, London. Kirton M. J. (1987) Manual of rhe Kirlon Adaption-lnnooafion Inventory, 2nd edn. Occupational Research Centre, Hatfield. Lowe E. A. and Taylor W. G. K. (1986) The management of research in the life sciences: the characterisation of researchers. R&D Mgmt 16, 45. Mulligan G-and Martin W. (1980) Adaptors, Innovators and the KAI. Psychol. Rep. 46, 883. Payne R. L. (1987) Individual differences and performance amongst R & D personnel: some implications for management development, R&D Mgmr 17, 153. Prato Previde G. (1984) Adattatori ed Innovatori: i resultati della standardizzazione italiana de1 KAI. Ric. Psicol. 4, 81. Taylor W. G. K. (1989) The Kirton Adaption-Innovation Inventory: a re-examination of the factor structure. J. Orgn Behar. IO. In press.