The exploratory study validated the approach of deconstructing the thinking of post-graduate students using what were previously called Meta-Programmes and are now referred to as Cognitive Intentions, via the Identity Compass profile system. Study 2 (chapter 4) also supported the concepts of Thinking Styles, the Thinking Quotient (TQ) benchmark tool and the potential for the TQ to be a measure of intention and awareness of CI-use. However, even though a number of dimensions within the data were demonstrated, the two previous studies’ findings were non-significant regarding the TQ scale. It was therefore necessary to test the hypotheses with a larger number of profiles to ascertain if the results were replicable.
The current study had the potential to be either a validation or refutation of the aims and objectives of the previous studies by virtue of acting as a large control group. Thus, the data in the current study set out to test the third incarnation of the quantitative research question:
Does Dynamic Intelligence exist as a conceptual measure of self-awareness in the moment?
Having determined that objective 5 from study 2: can a benchmark tool be created to normalise the data output of the Identity Compass profile tool? was possible, and having used its principles to discover the findings were non-significant on n=177 profiles from a Thinking Styles perspective, the current study aimed to apply the findings to a larger dataset (n=8,200) to determine if the results were replicable and significant. Therefore, it was necessary to expand the objectives to accommodate the third study:
- To determine if there are Cognitive Intentions common to 8,200 profiles
- To determine if the larger dataset confirms or contradicts the existence of Thinking Styles by virtue of the various combinations of Cognitive Intentions
- To determine if the Thinking Quotient scale is valid for 8,200 profiles as it was for 177
The hypotheses that emerged from study 2’s findings were:
The Factor analysis in study 2 uncovered five dimensions that differentiated a potential ‘academic thinking style’ as per objective 2 above. The aim was now to differentiate between the academic data and the larger data set to discover if the principles transfer with the larger dataset acting as the control group.
The research in the current study was the same as in the pilot study and study 2 in that the Identity Compass profile tool was used to determine the construction of circa 8,200 individuals’ thinking using fifty Cognitive Intentions. With the data pool being so large compared to the previous two studies, Fabrigar, Wegener, MacCallum and Strahan (1999) articulated that if data are relatively normally distributed, maximum likelihood is the appropriate method because:
“it allows for the computation of a wide range of indexes of the goodness of fit of the model [and] permits statistical significance testing of Dimension loadings and correlations among factors and the computation of confidence intervals.” (p. 277).
There are very few rules for sample sizes within a factor analysis. Studies have shown that adequate sample size is determined in part by the nature of the data (Fabrigar et al., 1999; MacCallum, Widaman, Zhang, & Hong, 1999). Usually, the stronger the data, the smaller the sample can be for a precise analysis. ‘Strong data’ in a factor analysis means homogeneously high communalities without cross loadings, plus numerous variables loading strongly on each factor.
The current study made use of secondary data in the form of a database of circa 8,200 Identity Compass profiles donated to this study by the profile owner (see Appendix 7). Each profile was anonymous, with only the country of origin being a differentiator. For example: if the profile consultant were from the UK, their profiles began with the letters “UK”. If they were from Germany, their profiles began with the letters “DE”. It was not possible to determine sex or age from the profile data as this was not part of the data spreadsheet received from the profile owner. Their countries of origin were, but not limited to: England, Germany, Italy, Sweden, India, Pakistan, Korea, China, Japan, South Africa and Nigeria. The common denominator amongst the profile participants was their status within their respective organisations as the profile tool was predominantly aimed at medium-to-large sized businesses as a method of profiling their middle managers and higher-level managers.
The secondary data in the current study was leveraged in order to examine a new perspective on the original question from study 2. A justification for utilising secondary data to answer the existing research question began with it being highly preferable (Doolan, Winters & Nouredini, 2017), and that the data were already clean and stored in a format familiar to the researcher. They were also already pursuant to the output of the Thinking Quotient tool. A further justification for using the large participant dataset was that the larger the (participant) sample size, the more precise the mean, which in turn allowed for the pinpointing of outliers. From a participant perspective, the outliers were important for the substantiation of the theory emerging from the data.
Next, the volume of data within the dataset would have been impossible to collate by the researcher individually. Finally, time was a factor for both the researcher and potential participants. Having secondary data in the form of profiles already completed saved potentially thousands of (wo)man/hours.
As this study was an extension of the previous two studies, all measures and methods were replicated from study 2. The only differentiator between studies 2 and 3 was the number of participants: n=177 and n=8,200 respectively. This meant that the Identity Compass output data were used for all 8,200 participants, and the Thinking Quotient score was used to benchmark their thinking in the same manner as in study 2.
Ethical approval was granted from Coventry University ethics committee to use the historical data within the Identity Compass profile database in Germany. The profile owner granted permission to use the dataset, provided it remained anonymous, and a gatekeeper letter was signed allowing the use of the anonymised data (see Gatekeeper letter in Appendix 7).
In order to ensure continuity, the same statistical tests were conducted on the large dataset as were on the student dataset in study 2. Fifty Cognitive Intentions were included in the initial exploratory factor analysis in order to determine the latent dimension structure, using IBM SPSS Statistics 25 software. As the data used in this study were not normally distributed, according to Kolmogorov-Smirnov test (p < .05 for all the variables included) as well as histograms, the principal axis factoring method of factor analysis was used (Fabrigar, Wegener, MacCallum, & Strahan, 1999).
Before performing the analysis, it was checked if the basic assumptions of exploratory Factor analysis were met. The sample size of N = 8243 was adequate (Tabachnick, & Fidell, 2007). Tabachnick and Fidell (2001) cite .32 as a good rule of thumb for the minimum loading of an item, which equates to approximately 10% overlapping variance with the other items in that factor. According to the correlation matrix there were many correlations between the items higher than r = .03 (although most of them were lower). Kaiser-Meyer-Olkin Measure of Sampling Adequacy was equal to .94, which was higher than the recommended value of .60 (Kaiser, 1974). Bartlett’s Test of Sphericity (Bartlett, 1954) was statistically significant (p < .001). This all suggested that the data were overall appropriate for factor analysis.
The principal axis factoring method revealed ten factors with eigenvalues greater than λ = 1.00, explaining 59.37% of the variance. According to the scree plot, keeping the first five factors seemed to be the best solution, according to the Cattell’s criteria (1966).
The five factors explained 47.29% of the variance. Direct oblimin rotation was used, in order to get more accurate results, since some of the factors were correlated themselves, as presented in Table 5.19. Many items had communalities higher than .40, except for Seeing, Hearing, Feeling, Things, Observer, External, Caring for Self, Caring for Others, Pre-Active, Post Active, Sameness, Team Player, Past, Long Term, Short Term, Doing, and Trustful, which, due to low communality, did not correlate with the other items. However, only variables Hearing, Looking, Pre-Active, Doing, Achievement, and Caring for Others had Dimension loadings lower than .32, so they were excluded from the final five-Dimension structure (Tabachnick, & Fidell, 2007).
Table 5.19: Correlations of the extracted factors
Table 5.19 presents the Dimension structure, i.e. the new five dimensions created based on Pattern Matrix (see Appendix 8), keeping those with dimension loadings higher than .32, and attaching each item to the dimension on which it had the highest dimension loading. Item ‘Reactive’ had the same dimension loading size on the first and the third dimension (although positive on the first, and negative on the third), so, as it correlated just slightly more with the third Dimension (r = .50 with the first, and r = -.51 with the third), it was finally attributed to the third factor.
Table 5.20: Five factors of cognitive intentions
|Dimension 1:||Dimension 2:||Dimension 3:||Dimension 4:||Dimension 5:|
|Long-Term||Away From||Quality Control||Influence||External|
|Difference||Task||Caring for Self||Listening|
Based on the results of the factor analysis it can be concluded that there exists a subset of latent dimensions (objective 3) underlying the fifty investigated Cognitive Intentions amongst the general population.
In order to test objective 4, the effects of the five dimensions on the TQ score were tested using multiple linear regression, with five cognitive dimensions as independent variables, and the TQ as dependent variable. As with the first regression model, 84 cases were identified as outliers, since they had Mahalanobis distances higher than the recommended threshold of 20.52 (Tabachnick, & Fidell, 1996). The final multiple regression was conducted without them, on the sample of 8159 participants. Before that, the assumptions of conducting multiple regression analysis were evaluated.
The sample size of 8159 was adequate for the linear regression model with five predictors (Tabachnick, & Fidell, 2007, p. 123). The assumption of multicollinearity and singularity was not violated. There were many moderate and high intercorrelations of cognitive dimensions, and four low significant correlations of cognitive dimensions with the TQ score (Cohen, 1988; Table 5.21). Tolerance and Variance inflation Dimension (VIF) measures suggested that there were no problems with multicollinearity between any pair of the independent variables.
Table 5.21: Correlations between the variables in the model
|2. Dimension 1||.27***||1.00|
|3. Dimension 2||.26***||.60***||1.00|
|4. Dimension 3||-.02*||.64***||.62***||1.00|
|5. Dimension 4||.01||.49***||.42***||.48***||1.00|
|6. Dimension 5||.20***||.59***||.61***||.51***||.32***||1.00|
*. Significance of p < .05, 2-tailed.
***. Significance of p < .001, 2-tailed.
Normal P-P Plot of Regression Standardized Residual suggested that the data were very close to normally distributed. Scatterplot visualization suggested that there were not many atypical points, and no violation of some of the assumptions of linearity and homogeneity of variances. Maximum Cook’s distances lower than 1 also indicated that there were no atypical points, and hence no cases to exclude from the analyses.
The results of the multiple regression analysis were that the overall regression model was highly significant, explaining 20.2% of the variance of the TQ, F (5, 8153) = 412.26, p < .001. All five cognitive dimensions predicted the TQ at 1% (Dimension 1, Dimension 2, Dimension 3 and Dimension 4) or 3% level of statistical significance (Dimension 5), as presented in Table 5.22.
Table 5.22: Regression coefficients of cognitive dimensions with TQ as the outcome
|Unstandardized Coefficients||Standardized Coefficients||t-value||Sig.|
1Three decimals maintained due to small numbers.
|TQ = (3+).40 * Dimension 1 + .31 * Dimension 2 – .43 * Dimension 3 – .12 * Dimension 4 + .04 * Dimension 5|
The final multiple regression equation looked like this:
It was apparent that Dimension 1 had the strongest positive effect on the TQ, with Dimension 2 having a slightly smaller effect, and Dimension 5 having a very small effect. Dimension 3 had a negative effect equivalent to dimension 1, whilst Dimension 4 had a lower negative effect in predicting the TQ. In terms of un-standardised coefficients, when Dimension 1 increased for one percentage point, the TQ score increased for .010 at 2.40-4.90 of the TQ scale. When Dimension 2 increased for one point, the TQ increased for .007. When Dimension 3 increased for one point, the TQ decreased for .012, and when Dimension 4 increased for one level, the TQ became, on average, .003 points lower at 2.40 – 4.90 scale. Finally, if Dimension 5 increased by 1, the TQ increased for only .001, but still significantly.
Objective 2 asked: Does the larger dataset confirm the existence of Thinking Styles by virtue of the various combinations of Cognitive Intentions? In order to answer this, the TQ Score was divided into levels to give a spread of the scale: 2.5, 2.7, 3.0, 3.2, 3.3, 3.5+. From the data output, this was the most appropriate way to differentiate the levels in order to determine any clustering. The data demonstrated that at each of these levels, there were five different dimensions, which supported the hypothesis of automorphic Thinking Styles based on the combination of Cognitive Intentions (see Figure 5.5). Informally, an automorphism is a map that preserves sets and relations among elements.
Figure 5.7: Visual isomorphism of thinking styles as per DI
Contrary to the previous two studies’ findings, the results of the current study were significant, which supported the hypotheses that:
In Table 5.20, it can be seen that Dimension 3 had a negative effect equally as large as Dimension 1’s positive effect in predicting the TQ. In terms of the literature, and as was mentioned in study 2, this was suggestive of what Piaget called Disequilibrium in that the positive effects of those Cognitive Intentions within Dimension 1 were held in place by their polar CI in Dimension 3. As also mentioned in study 2, in order to grow the individual’s thinking, it would be necessary to stretch them to do ‘Procedures’ where they normally and comfortably do ‘Options’ (Dunn, Griggs, Olson, Beasley & Gorman, 2010). This supported hypothesis 4 that Thinking Styles (and thus behaviour) are influenced by polar CI intervention. This helps to form the basis of what could be called Dynamic Intelligence. This will be discussed in chapter 8.
Objective 1 asked if there were Cognitive Intentions common to 8,200 profiles. With hindsight, a more appropriate question would have been: which were the most influential CI’s on the participant’s thinking? Dimension 1 in Table 5.20 demonstrated that the 13 Cognitive Intentions listed had the most effect on the Thinking Quotient score, whereas the five CI’s listed in Dimension 5 had the least effect. As mentioned in the previous study, a factor analysis says: if you do this, then you probably do the other to a similar degree. In context, this suggests that Dimension 1 contained those CI’s most accessible to the participants, and those in Dimension 5 the least accessible in context. What was interesting about the factors was that Dimension 3 contained those Cognitive Intentions that oppose those in Dimension 1 (see Figure 5.6), and the regression model demonstrated that increases in Dimension 1 scores increased the TQ score, whereas increases in Dimension 3 CI’s decreased the TQ score (see Table 5.20). This again supported hypothesis 4, and further reinforced the findings from study 2 that in order to influence a participant’s thinking long term, they must incorporate elements of the opposite heuristic in order to create future choice (Dunn, et al., 2010). As was mentioned in the literature review, it was this choice (of constructing one’s thinking) that was missing in current profile tools. In other words, in order to grow one’s thinking, where an individual habitually and unconsciously used ‘Options’ as a heuristic, they would need to consciously consider a ‘Procedural’ approach going forward.
Figure 5.8: Creating choice in our thinking construction
As determined in study 2, this influence and detraction from the TQ score aligned with Piaget’s (1971) Cognitive Developmental Theory, specifically his accommodation and assimilation. The output also aligned with Piaget’s (1985) disequilibrium concept, and Ashforth, et al.’s (2008) theory of ‘Identicide’, in that one must place one’s thinking into a state of disequilibrium before one can grow. Piaget maintained that the brain tries to achieve a sense of equilibrium, to exist in harmony with its environment and reduce disequilibrium. The current study supports future research on the potential for interventions using this specific polar-CI (disequilibrium) strategy. Palus & Drath (1995) questioned the validity of short-term interventions that cause disequilibrium in an individual’s meaning-making system, in favour of more long-term development methods. Therefore, a fundamental change in one’s conscious use of ‘Towards’ and ‘Away From’, for example, could be considered a long term-strategy for growth as it disrupts an individual’s meaning-making and thus world view, which can only be resolved when a more adaptive, more sophisticated mode of thought is adopted (Piaget, 1985).
Objective 2 asked: does the larger dataset confirm or contradict the existence of Thinking Styles by virtue of the differing combinations of Cognitive Intentions? Study 2’s data were non-significant in this regard, however there was a specific combination of Cognitive Intentions that appeared influential in a post-graduate student’s thinking (see chapter 4, Table 4.12). The current study demonstrated that a larger population had a different combination of Cognitive Intentions in Dimension 1 due to the context of the questions and the general managerial roles of the participants, which supported the hypothesis from study 2 that an academic Thinking Style does exist, as does an organisational Thinking Style.
It was also argued that the TQ scale was not indicative of a hierarchical stage of development. There were myriad stage theories to link development to levels, including Maslow, (1968); Loevinger, (1976); Cook-Greuter, (1999); Rooke & Torbet, (2005); Joiner & Josephs, (2007); Laske, (2008) Kegan and Lahey, (2009); Commons, (2011); Eigel & Kuhnert, (2016) and Anderson & Adams, (2015). Instead, these represented differing Thinking Styles that were labelled numerically for ease of differentiation within this thesis.
In order to address this point, the TQ scale was divided into output measures to give a spread of the scale: 2.5, 2.7, 3.0, 3.2, 3.3, 3.5+ (see Figure 5.8). From the data output, this was the most appropriate way to differentiate the measure in order to determine any clustering. The data (and Figure 5.8) demonstrated that at each of these measures, there were five different dimensions, which supported the hypothesis of Thinking Styles based on the combination of CI’s.
With the above in mind, by virtue of the measures described in the TQ scale, each was intended to be a way of constructing one facet of one’s thinking in the moment (see Figure 5.9). The Constructed Development Onion is an illustration of growth within the framework of CDT via Cognitive Intention awareness. This will be discussed in greater detail in chapter 8.
Figure 5.9: The Constructed Developmental Onion
The principle of onion avoided the inherent assumption of betterment, as seen in Laske’s or Kegan’s scales where a reader might assume Stage 4 was ‘better’ than Stage 2 by virtue of it being numerically higher, when in reality, from a Constructed Development perspective, it was simply a different combination of Cognitive Intentions and thus a different way of constructing oneself in the moment. This was akin to Siegler’s (1996) overlapping waves theory mentioned in the literature review, where he stated that children have several strategies available to them at any age in order to figure things out. A more capable child would have greater facets of thinking available to inform their construction of a strategy in the moment. Siegler wanted to address cognitive variability better than existing models so developed his own model, as he contended that the metaphorical staircase of Piaget did not sufficiently differentiate between stages of development (Slavin, 2012). It was suggested here that the theory of Constructed Development offered a facet of cognitive growth that can be manipulated and measured, which the adult stage development theories lacked. In other words, each growth ring of the onion was an (un)conscious Intention, and where it was repeated, had become a conscious Intention. This contributes to theory by demonstrating that stage development was not necessarily a staircase, as described by Piaget and others, nor a wave, as described by Siegler, but instead was constructed of Cognitive Intention awareness growth rings.
From an adult perspective, a more self-aware thinker would have a number of response strategies available to them based on their level of Dynamic Intelligence. This capacity to respond in the moment was called their Dynamic Responsiveness. What was intrinsic in their Thinking Style was their level of ‘Intention, Awareness, Choice and Response’ (as discussed in study 2) was different at TQ2 than TQ4, which manifested in their outward thinking and behaving. The following list explains the construction difference:
The research question from studies 1 and 2 asked: does Dynamic Intelligence exist as a conceptual measure of self-awareness in the moment? It can be concluded, based on the results of the current study that the evidence supported the hypothesis.
Figure 5.10: Visual isomorphism of Thinking Styles as per DI
As mentioned, a factor analysis states that “if you do this Cognitive Intention (dimension 1) then you’re also likely to do this CI (dimension 3) to a similar degree”. At TQ2.5 the average for dimension 1 was 64.6% and the average for dimension 3 was 75.4%. This was an 11% difference, which pointed towards the CI’s in dimension 3 being unconsciously higher than their paired CI in dimension 1 at TQ2.5. This supported the hypothesis that the low TQ score was indicative of an imbalance in Cognitive Intention scores (by an average of 11%), and thus a lack of Intention, Awareness, Choice and Response. To reinforce this premise, the difference at TQ4 was only -1.54, thus demonstrating greater balance between the same CI pairs than TQ2.5, and hence a greater choice of response in the moment.
The same principle applied to dimension 4. Dimension 4 (in Table 5.20) consisted of five Cognitive Intentions: Own, Individualist, Internal, Influence and Caring for Self. Each of these could be considered the building blocks of a TQ2 [and TQ4] profile. When someone only utilised ‘Internal’, they would be incapable of doing ‘External’, thus they were TQ2. However, the graph demonstrated that with awareness and greater balance, ‘Own’ and ‘Internal’ take on a completely different meaning when there was balance with ‘Partner’ and ‘External’ (Dimension 5). With this balance, they were more akin to TQ4 thinking. By virtue of the fact that TQ4 was a higher score compared with TQ2, it was indicative of the greater balance in the participant’s CI scores, and thus their greater choice (and capacity) can be inferred from the results, as mentioned above. In other words, the foundational aspect of Constructed Development as a theory would be: the greater the balance between the Cognitive Intention pairs, the greater the awareness of the participant to choose a response in the moment.
The heuristic ‘Options’ at TQ2 was very different in its intention and awareness (and thus Dynamic Responsiveness) to ‘Options’ at TQ4. At TQ2, one can only respond in one direction, whichever the CI bias was. At TQ4, one could choose between ‘Options’ or ‘Procedures’ and thus choose a more dynamic response in the moment.
Objective 3 asked: if a subset of dominant Cognitive Intentions arises in the current study different to the previous two studies. Based on the results of the factor analysis, and as was described in the factor analysis, it was reasonable to state that there exists a subset of latent dimensions underlying the fifty investigated Cognitive Intentions amongst the general population of the current data. Further to this, if one were to investigate each Identity Compass profile for each participant, it would be evident that a different combination of CI’s exists, unique to each individual. The idea that these unique combinations were representative of a level of awareness on a scale that united all profiles was the essence of the thesis and was supported by the fact that five dimensions existed at varying levels on the TQ scale (see Figure 5.8).
Objective 4 asked: if the Thinking Quotient benchmark scale is valid for 8,200 profiles as it was for 177? Study 2 demonstrated the same principles of normalisation using the TQ score, however the data were again non-significant. In the current study, the multiple linear regression demonstrated that the 8,159 participants were adequate and that the results of the overall regression model were highly significant, explaining 20.2% of the variance of the TQ.
Below are the factor analysis tables (5.23 & 5.24) from study 2 and study 3 for comparison.
Table 5.23: Five dimensions of Cognitive Intentions – Study 2
|Dimension 1||Dimension 2||Dimension 3||Dimension 4||Dimension 5|
|Achievement||Team Player||Quality Control||Own||Abstract|
|Activity||Caring for Others||Procedures||Internal||Vision|
|Caring for Self||Affiliation||Things||Polar|
Table 5.24: Five factors of Cognitive Intentions
|Dimension 1:||Dimension 2:||Dimension 3:||Dimension 4:||Dimension 5:|
|Long-Term||Away From||Quality Control||Influence||External|
|Difference||Task||Caring for Self||Listening|
All five cognitive factors predicted the TQ at 1% (Factors 1 to 4) or 3% level of statistical significance (Dimension 5), as presented in Table 5.20. It was shown that Dimension 1 had the strongest positive effect on the TQ, with Dimension 2 having slightly smaller effect, and Dimension 5 having very small effect. Dimension 3 had a negative effect equating to Dimension 1’s positive effect, while Dimension 4 had a lower negative effect in predicting the TQ. The importance here was that when Dimension 1 increased by a certain percentage score, then the TQ score increased by a predictable amount. This fundamentally supported the premise and utility of a scale for self-awareness within CDT.
By comparison to the post-graduate student data in study 2, it can be seen in Figure 5.9 that overall, the level of self-construction of the students (mean = 3.2) was close to the greater population (mean = 3.19). This suggested that in order to become a post-graduate student, one might not need to be ‘smarter’ in IQ terms, but instead, one needs to have an adaptive Thinking Style that best-suits academia.
Our thoughts, actions and perceptions become our habits of mind, eventually becoming the subjects and objects of our experience, and also our experience of self (Vago, 2014). Previous research suggested that how people perceived others, and even themselves was often inaccurate, which might have contributed to negative outcomes for their well-being (Vago, 2014).
Figure 5.11: Mean TQ Scores
A consequence of these studies was an emerging field of contemplative science that looked at the underlying processes of such constructs as mindfulness from a neurophenomenological perspective in order to investigate a self-awareness that was meta-to self-awareness. It thus took awareness as an actual object of attention, allowing an individual to disassociate from self and account for changes in self-awareness based on lived experience. This contributed to our understanding of self but also the relationship between awareness and our habituated thinking. Habituated thinking manifests as habituated behaving, often called automaticity. Automaticity reflects the reinforcement between past and future behaviour and the circular structure of repeated patterns (Vago, 2014).
In order to transform this and change one’s habits and biases in both cognitive and emotional systems, a form of interoception was required that focuses on what were referred to in the literature review as VAK: visual, auditory and kinaesthetic (Young, 2013). This aligned with the principles in the current study that hypothesised that awareness of one’s VAK was the primary filter before Intention and Choice began, which also conformed to evidence of early attentional processing of sensory information existing before there was conscious awareness (Pessoa, 2005; Sperling, 1960). The current study goes some way to supporting Vago’s (2014) perspective in that habituated Cognitive Intention use reifies over time until such a time that an adult unconsciously processes incoming data with an Intention outside their awareness, which then influences their thinking and behaving. This is done in context, which also suggests that in different contextual scenarios, an individual might construct their thinking differently. In essence: they use a different Thinking Style in each context.
A final consideration for the effects of awareness on an individual’s score for self-awareness was their propensity for alexithymia, which was their ability to process emotions explicitly. A study by Laws and Rivera (2012) demonstrated that individuals might have discrepancies with their self-image which might cause an internal inconsistency, only alleviated by externalised positive affirmations of their character (Petty and Briñol, 2009). From Kegan’s perspective, this would be a stage 3 response (emotional) to a stage 2 reaction (alexithymia). From a Cognitive Intentions perspective, the disequilibrium caused by a lack of awareness between emotional CI pairs could be remedied by exposure to their opposite in order to create choice where none existed originally. Alexithymia might be an extreme example, however, the potential for investigation of efficacy for Constructed Development would be interesting. The net effect of constructing disequilibrium by CI pairs might disengage an actor from their alexithymia mindset. Table 4.10 illustrates the emotional Cognitive Intentions.
The 8,200 profiles were from differing countries where cultural differences influence ways of thinking (REF XXX). From the information gained from the profile owner, the profiles were predominantly of middle-to-upper management in a variety of large organisations across the world. This suggested that the starting point for their [Constructed] development would be around the median and higher stages.
The findings of the current study are important to the theory of Constructed Development and Dynamic Intelligence in that they supported the hypothesis that based on the difference between a participant’s score for paired (or otherwise) Cognitive Intentions, there was a measurable difference in their Intention, Awareness, Choice and Response. There was also a demonstrable difference in how they process incoming information and sort it for social or cognitive intentions. This has a significant impact on stage development psychology specifically as it implies that an individual’s level of self-awareness is an indication of their residence at a specific stage of adult development. This can be seen in the alignment between the TQ score and Kegan’s scale. In other words, their level of Dynamic Intelligence is akin to their epistemic stance (Laske, 2008).
What was also apparent in the data was the participant’s capacity to manage their choice of response in the moment based on their use of certain Cognitive Intentions. In other words, where they did not know they were using a Cognitive Intention such as ‘Internal’ as a heuristic, they had no choice to not use it, and the data supported this. In essence, this meant that TQ2 aligned with Kegan’s LoAD Stage 2 behavioural output. And an individual with the relevant combination of External and Partner, and so on, would align to Kegan’s Stage 3, Socialised Mind. The implications of this from an intervention perspective will be discussed further in the final Discussion chapter. However, this lack of awareness was interesting because it was predicted in the data, thus further reinforcing the CDT perspective of Cognitive Intention relationships as a determinant of adult development. Again, this has a significant impact on stage development psychology as it offered further support for CDT as a theory and how it might thus change the perception of adult development going forward. In order to reinforce the findings in this study, a qualitative study will be devised that investigates a participant’s lack of awareness in order to gauge how true the quantitative support was, and the degree to which CDT might impact stage development theory. Now that it had been established that stages are potentially only one avenue for determining adult cognitive growth, the next study to logically follow was to investigate an individual’s awareness of their Thinking Style, and to test the hypothesis of opposing CI’s being the necessary disequilibrium catalyst for growth. Study 4 aimed to discover if there was a correlation with a person’s awareness of self and their individual Cognitive Intention scores, and if this correlation could help to define a person’s thinking capacity, as per the levels of adult development by Kegan (1994). It also aimed to determine if a person’s level of awareness of the relationship between Internal and External (for example) defined their behavioural output. This would further interrupt the stage development perspective on cognitive and social-emotional growth.