Chief: Research & Development
Professional Communications, Inc.
SUMMARY
This research outlines a study of over 1,500 test-retest surveys spaced up to 12.7 years apart. The study uses a natural design that identified the degree and direction of change in “I Opt” strategic styles and profiles over time.
The study found that a majority of “I Opt” dominant styles remained constant over the long time period covered by the study. Many of the dominant style changes that did occur did not represent major behavioral shifts. Rather the granular nature of rank order (i.e., ordinal) measurement tended to exaggerate behavioral change estimate
It was found that a majority (83%) of the dominant styles that did changed followed a predictable pattern. The changes appear to be governed by the principles of social economics and non-optimality. The effects of aging were also identified. Aging effects were statistically significant but of relatively modest consequence.
Finally, the study identified a single strategic style that was most resistant to change. This stability appears to be due to the flexible structure of the input and output strategies being employed.
A video summary is available on YouTube and can be accessed by clicking the icon to the right.
BACKGROUND
Most tools in the field find their roots in psychology (e.g., Myers-Briggs®, DiSC®, 16PF®, FIRO-B®, etc.). They believe that they are measuring a “hard wired” behavioral map. They may informally acknowledge that change occurs. But none offers an explicit, testable change mechanism.
“I Opt” ® is unique. It is based on information processing. It is focused on the behavior generated as the human being attempts to navigate a particular environment. If the environment is stable, their behavior is stable. If the environment changes they either adapt their behavior or exit to a more comfortable terrain.
The choice of particular behavior to adopt is guided by social economics. Some behavioral changes are harder than others. If the same goal can be reached by two different behavioral choices—one hard and one easy—they will choose the easy one. An obvious but important observation.
The adaptive behavioral choice does not involve optimization. Optimality implies that some best possible end condition is known. This is impossible in ever-changing social situations. “Good enough” is the typical standard. Once a “good enough” result is regularly obtained behavior again stabilizes. Adequacy is a governing principal of style choice.
“I Opt” is indifferent as to whether or not there is a “hard wired” component to human nature. It works whether it is there or not. Any change discovered must be due to factors other than psychology since that behavior is fixed by definition. The fact that environment affects behavioral choice is deemed so obvious as not to required explanation here.
STYLE CHANGE
As applied in organizational research “style” is a term applied to a typical pattern of behavior. It is usually assessed using ordinal (i.e., rank order) measures. This form of measurement has serious limitations.
For example, DiSC® allows you to say that a person is more “dominant” than “compliant.” But you cannot say that a person is twice as likely to use one style versus the other. It does not matter if you assign the numeral 2 to “sometimes” and 3 to “often.” You will still be dividing “sometimes” by “often.” The inability to assign a magnitude to a style means that only general, non-specific and somewhat vague assessments can be offered.
The underlying concept of “style” does have practical utility. Practitioners must convey knowledge in a manner that can be understood. Styles offer that vehicle. The problem lies in how style is measured. “I Opt” has overcome this problem by using exact ( i.e., ratio scale—like a ruler) rather than rank order calibration. This gives “I Opt” a far broader range than traditional tools.
For example, we can always reduce time, say 12.05PM, to “daytime.” The reverse is obviously not true. “Daytime” is not always 12:05PM. “I Opt” can emulate traditional rank order tools. Traditional tools cannot emulate “I Opt.” This means that “I Opt” can address issues using broad categories where appropriate but is not confined to that level.
Exact measurement also means that the theory underlying “I Opt” can be disproved. This is the essential quality of any scientific theory. Without precise measurement no experiment can be designed that could completely disprove the “hard wired” claims of traditional tools. The inability to disprove them relegates these traditional tools to the realm of speculation. That speculation may be true. No one will ever know for certain. However, even if unproven traditional tools can be useful. They need not be discarded.
Definitive theory, measurement capabilities and a scientific basis makes “I Opt” a unique assessment tool. It is in a class or category by itself. This means that it can be used in conjunction with any of the historically accepted tools. “I Opt” is addressing different things in a different way. Since they work on different dimensions, they cannot contradict each other. This means that they can be combined and used together if conditions warrant.
THE SAMPLE
This research tests the theoretical expectations and principals outlined above using evidence-based data. It draws on over 12 years of repeated measurements. Table 1 outlines the general characteristics of the sample used.
Table 1
GENERAL SAMPLE CHARACTERISTICS
The size of the sample is wide, large and diverse. It is a meaningful representation of the universe to which “I Opt” technology applies.
STUDY DESIGN
Organizations have used “I Opt” technology continuously since 1994. The technology was used purposefully in development efforts involving teams, departments, and work groups. It was also deployed in programs involving leadership development, conflict resolution and other similar areas. This purposeful use means it is not contaminated by “experimentation” bias. In other words, the participants did not consider it a “game”, toy or other form of diversion.
During the period of the study people participated multiple activities using “I Opt.” The time horizon was long enough for significant changes in life circumstances to occur. People got married, had children, were promoted, changed location and so on. Testing and retesting over this long period provides “I Opt” with a rock-solid base on which to test and extend the already strong foundation on which it rests.
RETEST TIMING
The time periods between test and retest was determined by business needs. Therefore the periods between test and retest vary widely. This is an advantage. There is no preordained period over which change is “suppose” to occur. Graphic 1 shows the distribution retests over time.
The distribution is obviously skewed. The reason is that inclusion in the study design requires that the person remain with a firm. The average tenure of males in the private sector dropped 15.5% in the 1973-83 era to 11.4 years in the 1996-2006 period. (Farber, 2008). Since the economic collapse in 2008 it has undoubtedly dropped still further. Fewer people remaining with an organization over time means that there are fewer people to retest. Hence the skewed distribution.
However, the sample size is large. Retests beyond the 2.7-year average retest period totaled 556 (37% of the sample). This means that long tenured people are well represented. This reduces the potential bias arising from inadvertently measuring one cohort (e.g., Gen-X’s, baby boomers, etc). Overall, the retest distribution appears to be a fair representation of what can be expected in typical organizational situations.
POPULATION LEVEL CHANGE
Styles cannot change without affecting the entire behavioral profile of a person. For example, if “dominance” increases there is less time left in which “compliance” can be expressed (i.e., DiSC). Increase any style and something else has to change to accommodate it.
GENERAL SAMPLE CHARACTERISTICS
The size of the sample is wide, large and diverse. It is a meaningful representation of the universe to which “I Opt” technology applies.
STUDY DESIGN
Organizations have used “I Opt” technology continuously since 1994. The technology was used purposefully in development efforts involving teams, departments, and work groups. It was also deployed in programs involving leadership development, conflict resolution and other similar areas. This purposeful use means it is not contaminated by “experimentation” bias. In other words, the participants did not consider it a “game”, toy or other form of diversion.
During the period of the study people participated multiple activities using “I Opt.” The time horizon was long enough for significant changes in life circumstances to occur. People got married, had children, were promoted, changed location and so on. Testing and retesting over this long period provides “I Opt” with a rock-solid base on which to test and extend the already strong foundation on which it rests.
RETEST TIMING
The time periods between test and retest was determined by business needs. Therefore the periods between test and retest vary widely. This is an advantage. There is no preordained period over which change is “suppose” to occur. Graphic 1 shows the distribution retests over time.
Graphic 1
TIME DISTRIBUTION OF RETESTS
TIME DISTRIBUTION OF RETESTS
The distribution is obviously skewed. The reason is that inclusion in the study design requires that the person remain with a firm. The average tenure of males in the private sector dropped 15.5% in the 1973-83 era to 11.4 years in the 1996-2006 period. (Farber, 2008). Since the economic collapse in 2008 it has undoubtedly dropped still further. Fewer people remaining with an organization over time means that there are fewer people to retest. Hence the skewed distribution.However, the sample size is large. Retests beyond the 2.7-year average retest period totaled 556 (37% of the sample). This means that long tenured people are well represented. This reduces the potential bias arising from inadvertently measuring one cohort (e.g., Gen-X’s, baby boomers, etc). Overall, the retest distribution appears to be a fair representation of what can be expected in typical organizational situations.
POPULATION LEVEL CHANGE
Styles cannot change without affecting the entire behavioral profile of a person. For example, if “dominance” increases there is less time left in which “compliance” can be expressed (i.e., DiSC). Increase any style and something else has to change to accommodate it.
Graphic 2
CHANGES IN THE POPULATION PROFILE
(n =1515)
Graphic 2 measures change the overall behavioral profile of the sample population. It says that the whole population (i.e., n = 1515) did not substantially change in the average 2.7 years between test and retest. Individual changes netted out. This is exactly what would be expected and predicted by “I Opt” theory.
A major change in the profile of an overall population would require the information flows or meanings used in a society to change. While there have been “tweaks” (e.g., faster Internet lines, another recession, etc.) the basic social substance has not changed. The government still functions, schools are still teaching and stocks are still being traded. “I Opt” reflects this consistency. This result lends support to theory underlying “I Opt.”
COMPOSITE LEVEL CHANGE
The sample of 1,515 retests can also be looked at individually in terms of “style” changes. While society many not have changed, the circumstances of many individuals within that society most certainly have. Using the “style” concept these changes would be most visible in a change in the dominant style—the style with the highest rank order. Looked at in this manner, rank is all that is important. The magnitude of change does not matter. Graphic 3 shows the results for the dominant style of the study participants.
CHANGES IN THE POPULATION PROFILE
(n =1515)
Graphic 2 measures change the overall behavioral profile of the sample population. It says that the whole population (i.e., n = 1515) did not substantially change in the average 2.7 years between test and retest. Individual changes netted out. This is exactly what would be expected and predicted by “I Opt” theory.
A major change in the profile of an overall population would require the information flows or meanings used in a society to change. While there have been “tweaks” (e.g., faster Internet lines, another recession, etc.) the basic social substance has not changed. The government still functions, schools are still teaching and stocks are still being traded. “I Opt” reflects this consistency. This result lends support to theory underlying “I Opt.”
COMPOSITE LEVEL CHANGE
The sample of 1,515 retests can also be looked at individually in terms of “style” changes. While society many not have changed, the circumstances of many individuals within that society most certainly have. Using the “style” concept these changes would be most visible in a change in the dominant style—the style with the highest rank order. Looked at in this manner, rank is all that is important. The magnitude of change does not matter. Graphic 3 shows the results for the dominant style of the study participants.
Graphic 3
CHANGES IN DOMINANT STYLE
(n =1515)
NUMBER OF RESPONSE CHANGES
IN ALL STYLE CATEGORIES
(n =1515)
It is worth noting that only 7% of the sample had no change at all (left-hand column on Graphic 4). This would suggest that over an average period of 2.7 years most people encounter some kind of change. This reconfirms the ubiquitous nature of change. Change is the one constant of life
CHANGES IN DOMINANT STYLE
(n =1515)
The dominant style of most people in the sample did not change. But a significant minority did. Rank order (i.e., ordinal) measurement does not tell us by how much. However it was enough to change the rank order of strategies employed. The first step in understanding change is to try to figure out the magnitude of the change. In other words, we want to know if the change in behavioral preference is a lot or a little.
Graphic 4 shows how many individual survey retest responses (e.g., "questions") changed style values among all 1,515 re-testers. This is a measure of actual change. It may not be reflected in a change in dominant style. It could be merely a change in emphasis. For example, a style may have increased or decreased without changing rank order. Graphic 4 shows that an average of 2.5 “I Opt” responses that affected style scores changed between test and retest.
Graphic 4Graphic 4 shows how many individual survey retest responses (e.g., "questions") changed style values among all 1,515 re-testers. This is a measure of actual change. It may not be reflected in a change in dominant style. It could be merely a change in emphasis. For example, a style may have increased or decreased without changing rank order. Graphic 4 shows that an average of 2.5 “I Opt” responses that affected style scores changed between test and retest.
NUMBER OF RESPONSE CHANGES
IN ALL STYLE CATEGORIES
(n =1515)
It is worth noting that only 7% of the sample had no change at all (left-hand column on Graphic 4). This would suggest that over an average period of 2.7 years most people encounter some kind of change. This reconfirms the ubiquitous nature of change. Change is the one constant of life
Graphic 5
RESPONSE CHANGES IN DOMINANT STYLE CATEGORY
DOMINANT STYLE CHANGED
(n =651)
Graphic 5 shows the number of retest response changes among those whose dominant style (i.e., rank order position) actually shifted. The column showing “0” change in dominant style changed responses merit explanation (left hand column on Graphic 5). These are the cases where the original score for the dominant style did not change. But peripheral styles did change. Some of these changes were enough to boost another style to the dominant category even when the dominant style stayed at the same level.
Combining Graphics 4 and 5 tells a story. On average, 2.5 responses on the 24-question survey changed for all members of the sample. Graphic 5 shows the same data only for surveys where the dominant style (rank order) changed. Here the average number of responses that changed was 3.5. In other words an average difference of 1-response is enough to flip a style from one category to another.
The response analysis tells us that people change over time. Most people do not change their dominant style (57%). Those that do change (43%) do not change by much. However, we can get a firmer fix on the actual magnitude of change in the styles by using the exact measurement capabilities of ‘I Opt.”
Graphic 6 compares the test and retest profiles of the surveys where the 43% of the sample where the dominant style changed. The change is discernible. But it is not a lot. It is likely that the cruder rank order measurements of the traditional tools would judge this overall profile to be unchanged. “I Opt”, on the other hand, is able to recognize and measure the small but actual changes in survey responses.
RESPONSE CHANGES IN DOMINANT STYLE CATEGORY
DOMINANT STYLE CHANGED
(n =651)
Graphic 5 shows the number of retest response changes among those whose dominant style (i.e., rank order position) actually shifted. The column showing “0” change in dominant style changed responses merit explanation (left hand column on Graphic 5). These are the cases where the original score for the dominant style did not change. But peripheral styles did change. Some of these changes were enough to boost another style to the dominant category even when the dominant style stayed at the same level.
Combining Graphics 4 and 5 tells a story. On average, 2.5 responses on the 24-question survey changed for all members of the sample. Graphic 5 shows the same data only for surveys where the dominant style (rank order) changed. Here the average number of responses that changed was 3.5. In other words an average difference of 1-response is enough to flip a style from one category to another.
The response analysis tells us that people change over time. Most people do not change their dominant style (57%). Those that do change (43%) do not change by much. However, we can get a firmer fix on the actual magnitude of change in the styles by using the exact measurement capabilities of ‘I Opt.”
Graphic 6 compares the test and retest profiles of the surveys where the 43% of the sample where the dominant style changed. The change is discernible. But it is not a lot. It is likely that the cruder rank order measurements of the traditional tools would judge this overall profile to be unchanged. “I Opt”, on the other hand, is able to recognize and measure the small but actual changes in survey responses.
Graphic 6
CHANGES IN THE AVERAGE PROFILE
WHERE DOMINANT STYLE CHANGED
(n =651)
This analysis has shown that group level behavior—as measured by “I Opt”—is relatively stable. This is true whether the group includes everyone or just those who changed enough to flip their dominant style. This finding invites explanation.
One possibility is obvious. The social network and technologies of a particular society create unique information flows. These flows create a need for certain levels of each of the “I Opt” styles. Social adjustment mechanisms keep things in balance. Compensation levels for particular jobs can be increased or decreased. Social status of particular activities can rise or fall. These adjustments can cause people whose profiles lie close the expressed need to shift. The result is an automatic stabilization centering on the needs of the society at any particular point. This is a testable hypothesis. If it is true we should expect different societies to have different global profiles. All we need do is to look.
INDIVIDUAL LEVEL CHANGE
Practitioners involved in global level of analysis (e.g., culture studies) can make use of the group level assessment immediately. Practitioners whose work is focused on individuals require a deeper level of assessment. For this we have to look at an individual level.
The first thing we need is a baseline. Graphic 7 compares all of the retest surveys (n = 1515) with those of random pairs of people (i.e., the columns in Graphic 7). The columns were constructed by drawing random people from our 60,000+ database and calculating the degree that the profiles of the selected people overlapped. This is the equivalent of the experience a person would have if they were to interact with random people on any particular street.
Graphic 7
DISTRIBUTION OF TEST-RETEST OVERLAP CHANGES
(n = 1,515 retests, n = 100 random pairs)
CHANGES IN THE AVERAGE PROFILE
WHERE DOMINANT STYLE CHANGED
(n =651)
This analysis has shown that group level behavior—as measured by “I Opt”—is relatively stable. This is true whether the group includes everyone or just those who changed enough to flip their dominant style. This finding invites explanation.
One possibility is obvious. The social network and technologies of a particular society create unique information flows. These flows create a need for certain levels of each of the “I Opt” styles. Social adjustment mechanisms keep things in balance. Compensation levels for particular jobs can be increased or decreased. Social status of particular activities can rise or fall. These adjustments can cause people whose profiles lie close the expressed need to shift. The result is an automatic stabilization centering on the needs of the society at any particular point. This is a testable hypothesis. If it is true we should expect different societies to have different global profiles. All we need do is to look.
INDIVIDUAL LEVEL CHANGE
Practitioners involved in global level of analysis (e.g., culture studies) can make use of the group level assessment immediately. Practitioners whose work is focused on individuals require a deeper level of assessment. For this we have to look at an individual level.
The first thing we need is a baseline. Graphic 7 compares all of the retest surveys (n = 1515) with those of random pairs of people (i.e., the columns in Graphic 7). The columns were constructed by drawing random people from our 60,000+ database and calculating the degree that the profiles of the selected people overlapped. This is the equivalent of the experience a person would have if they were to interact with random people on any particular street.
DISTRIBUTION OF TEST-RETEST OVERLAP CHANGES
(n = 1,515 retests, n = 100 random pairs)
On average, a random pairing of people produces an overlap of about 40%. The average overlap of retest surveys is 61.1%. The difference is statistically significant (p< .00001). This is a condition “I Opt” theory predicts.
People tend to live in stable environments. Most people go home to the same house each night, the same kids show up at the dinner table and they take the same route to work the next morning. But some changes do occur. Promotions, new babies and job losses are just some dislocations over the period measured here. “I Opt” theory would recognize both the consistency and change in life circumstances.
Graphic 7 bears out “I Opt” predictions. Adjustments did occur but they did not take people back to point zero (i.e. random pairings). People tended to tweak their existing profiles. Strategies that were useful in the stable portion of their lives tended to be preserved. That portion of their life that did change was met with changes in their style elections. The net effect is that the test-retest overlaps stay at higher levels than would be expected by pure chance. That is exactly what is shown on Graphic 7.
ANALYSIS OF INDIVIDUAL LEVEL CHANGE
The consistency displayed in Graphic 7 requires no explanation. The change element invites it. One potential explanation of change might be simple aging. Age affects biology and biology affects behavior. Graphic 8 shows that age has an effect. The longer the time period between test and retest (i.e., more time for aging to occur) the less the before and after profile resembled each other. However, the effect is small.
People tend to live in stable environments. Most people go home to the same house each night, the same kids show up at the dinner table and they take the same route to work the next morning. But some changes do occur. Promotions, new babies and job losses are just some dislocations over the period measured here. “I Opt” theory would recognize both the consistency and change in life circumstances.
Graphic 7 bears out “I Opt” predictions. Adjustments did occur but they did not take people back to point zero (i.e. random pairings). People tended to tweak their existing profiles. Strategies that were useful in the stable portion of their lives tended to be preserved. That portion of their life that did change was met with changes in their style elections. The net effect is that the test-retest overlaps stay at higher levels than would be expected by pure chance. That is exactly what is shown on Graphic 7.
ANALYSIS OF INDIVIDUAL LEVEL CHANGE
The consistency displayed in Graphic 7 requires no explanation. The change element invites it. One potential explanation of change might be simple aging. Age affects biology and biology affects behavior. Graphic 8 shows that age has an effect. The longer the time period between test and retest (i.e., more time for aging to occur) the less the before and after profile resembled each other. However, the effect is small.
Graphic 8
CHANGE IN RETEST OVERLAP WITH TIME
(n = 1515 retests)
The mathematical notation in Chart 8 shows that there is about -0.5% (i.e., half of one percent) a year change in overlap by year due to time. It is reasonable to see this as the natural affect of adjustment due to normal aging processes. It is not enough to explain the entire change distribution we saw in Graphic 7. However the R2 of 95% suggests that this source of change is real.
Since the change due to time alone is small it is reasonable to look to changes in the local environment to explain the major portion of the observed change. The sources of environmental change are probably infinite and are not of significance to “I Opt” theory. Any change for any reason that causes a significant deterioration in the success of the current behavioral strategy is a motive (i.e., reason) for change.
However, “I Opt” theory can predict the direction of change. Remember social economics mentioned in the first part of this article? It says that people will try to minimize the cost any change. This is best achieved by preserving as much of the present behavior pattern as possible. This can be read directly from the “I Opt” profile by looking at adjacent axes on the graph.
The “I Opt” graphic is constructed so that adjacent axis share one or another information-processing component (input or output). By moving to an adjacent axis the individual is preserving at least one element of information processing strategy that they are using to navigate life. This lowers the cost of the change.
The easiest shift that can be made is to change emphasis. This would happen when a person promotes their secondary style to primary status. They simply begin using a style with which they are already familiar—their current “fallback” option—more intensively. Table 2 tests this hypothesis.
CHANGE IN RETEST OVERLAP WITH TIME
(n = 1515 retests)
The mathematical notation in Chart 8 shows that there is about -0.5% (i.e., half of one percent) a year change in overlap by year due to time. It is reasonable to see this as the natural affect of adjustment due to normal aging processes. It is not enough to explain the entire change distribution we saw in Graphic 7. However the R2 of 95% suggests that this source of change is real.
Since the change due to time alone is small it is reasonable to look to changes in the local environment to explain the major portion of the observed change. The sources of environmental change are probably infinite and are not of significance to “I Opt” theory. Any change for any reason that causes a significant deterioration in the success of the current behavioral strategy is a motive (i.e., reason) for change.
However, “I Opt” theory can predict the direction of change. Remember social economics mentioned in the first part of this article? It says that people will try to minimize the cost any change. This is best achieved by preserving as much of the present behavior pattern as possible. This can be read directly from the “I Opt” profile by looking at adjacent axes on the graph.
The “I Opt” graphic is constructed so that adjacent axis share one or another information-processing component (input or output). By moving to an adjacent axis the individual is preserving at least one element of information processing strategy that they are using to navigate life. This lowers the cost of the change.
The easiest shift that can be made is to change emphasis. This would happen when a person promotes their secondary style to primary status. They simply begin using a style with which they are already familiar—their current “fallback” option—more intensively. Table 2 tests this hypothesis.
Table 2
SECONDARY STYLE PROMOTED TO PRIMARY
SCORES ARE AVERAGED PERCENT OF TOTAL FOR EACH CATEGORY
(n = 651 retests)
The original secondary style is highlighted in yellow in the original test section of Table2. The new primary style is highlighted in the retest section (i.e., right side) in bolder enlarged characters.
In a majority of cases the original secondary and final primary styles are in the same position. In 10 of the 12 categories (83%) the change in style behaved exactly as predicted. On average, people just adjusted emphasis. They used their secondary style more. This increased its rank order position. The dominant style (rank order position) changed but a consistency in the behavioral pattern is preserved.
The two cases where this did not happen are boxed in red. In these two cases the original secondary style was almost as strong as the style that ultimately evolved into the primary style on retest. In other words, people were already heavily using the style that ultimately became their primary style. They were just not using it heavily enough to raise the rank order to a secondary status. But there is also another possible reason.
Major environmental dislocations that invalidate both input and output strategies can occur. This would cause a global change in an individual’s strategy (both input and output). Major dislocations of this nature are rare. Those that do occur can often be anticipated. New babies give at least 9 months advanced warning. Layoffs are often preceded by losses and deteriorated working conditions. Serious illness is usually accompanied by increasing medical interventions. These “flags” may have had a role in moving the percentages in the red boxes closer together. In other words, people may have been preparing for an anticipated change in their environment.
The two HA and LP boxed areas in Table 2 are representative of a class. There were individuals in each of the four categories that made a total transition (input and output change). Table 2 worked on averages. In the two cases which escaped the boxed effect their strength was not enough to move the average—but individuals within those groups did make total transitions.
Table 3 shows the effect of those involved in a total transition of their information processing strategy more clearly. The “Focus Change” column on the right shows which element of the strategy changed. The designation “BOTH” indicates a total transition.
A change in the output strategy (action vs. thought) is always the top of the list. The reason is that this is the simplest and least expensive approach. Just change the output from thought to action or vice-versa. It is a one-step strategy.
A change to the input strategy (unpatterned vs. structured) is the next most frequent. This is a two-step process. New kinds of information must be acquired. Then effort must be expended to organize and understand the new information. It is more expensive and therefore less used.
A total transition strategy—change in both input and output—are the least used. This is a three-step process. New kinds of information must be acquired, it must be organized to be understandable and then it must be acted upon in an unfamiliar way. This is the most expensive strategy and the least used by a substantial margin.
The ordering of the strategy changes also evidences the operation of the adequacy principle of strategic style selection. People were selecting strategies that worked “good enough.” If some form of optimization were operating it is unlikely that the ordering of change would be the same in every set.
The analysis of individual changes dramatically confirms the theory (what causes what and why) underlying “I Opt” technology. The data demonstrates continuity in the transition process (Graphic 7). It is also able to capture the universally acknowledged maturation effect (Graphic 8). The concept of a style change economy was forcefully confirmed by the use of secondary styles as the principal transition vehicle (Table 2). And finally the order of transition (Table 3) evidences the operation of the social economics and style adequacy principles.
Table 5 shows that the difference between the RI (i.e. 35%) and other style change rate is statistically significant for the HA and LP (highlighted in yellow) and almost so for the RS (highlighted in green).
SECONDARY STYLE PROMOTED TO PRIMARY
SCORES ARE AVERAGED PERCENT OF TOTAL FOR EACH CATEGORY
(n = 651 retests)
The original secondary style is highlighted in yellow in the original test section of Table2. The new primary style is highlighted in the retest section (i.e., right side) in bolder enlarged characters.
In a majority of cases the original secondary and final primary styles are in the same position. In 10 of the 12 categories (83%) the change in style behaved exactly as predicted. On average, people just adjusted emphasis. They used their secondary style more. This increased its rank order position. The dominant style (rank order position) changed but a consistency in the behavioral pattern is preserved.
The two cases where this did not happen are boxed in red. In these two cases the original secondary style was almost as strong as the style that ultimately evolved into the primary style on retest. In other words, people were already heavily using the style that ultimately became their primary style. They were just not using it heavily enough to raise the rank order to a secondary status. But there is also another possible reason.
Major environmental dislocations that invalidate both input and output strategies can occur. This would cause a global change in an individual’s strategy (both input and output). Major dislocations of this nature are rare. Those that do occur can often be anticipated. New babies give at least 9 months advanced warning. Layoffs are often preceded by losses and deteriorated working conditions. Serious illness is usually accompanied by increasing medical interventions. These “flags” may have had a role in moving the percentages in the red boxes closer together. In other words, people may have been preparing for an anticipated change in their environment.
The two HA and LP boxed areas in Table 2 are representative of a class. There were individuals in each of the four categories that made a total transition (input and output change). Table 2 worked on averages. In the two cases which escaped the boxed effect their strength was not enough to move the average—but individuals within those groups did make total transitions.
Table 3 shows the effect of those involved in a total transition of their information processing strategy more clearly. The “Focus Change” column on the right shows which element of the strategy changed. The designation “BOTH” indicates a total transition.
Table 3
SOURCE AND AMOUNT OF CHANGE
(n = 651 retests)
Table 3 looks at that portion of the sample whose dominant style changed. It rank-orders change categories by the percent of surveys falling within that category. A pattern is clearly visible and is governed by the principles of social economics and style adequacy.SOURCE AND AMOUNT OF CHANGE
(n = 651 retests)
A change in the output strategy (action vs. thought) is always the top of the list. The reason is that this is the simplest and least expensive approach. Just change the output from thought to action or vice-versa. It is a one-step strategy.
A change to the input strategy (unpatterned vs. structured) is the next most frequent. This is a two-step process. New kinds of information must be acquired. Then effort must be expended to organize and understand the new information. It is more expensive and therefore less used.
A total transition strategy—change in both input and output—are the least used. This is a three-step process. New kinds of information must be acquired, it must be organized to be understandable and then it must be acted upon in an unfamiliar way. This is the most expensive strategy and the least used by a substantial margin.
The ordering of the strategy changes also evidences the operation of the adequacy principle of strategic style selection. People were selecting strategies that worked “good enough.” If some form of optimization were operating it is unlikely that the ordering of change would be the same in every set.
The analysis of individual changes dramatically confirms the theory (what causes what and why) underlying “I Opt” technology. The data demonstrates continuity in the transition process (Graphic 7). It is also able to capture the universally acknowledged maturation effect (Graphic 8). The concept of a style change economy was forcefully confirmed by the use of secondary styles as the principal transition vehicle (Table 2). And finally the order of transition (Table 3) evidences the operation of the social economics and style adequacy principles.
Table 4
DIFFERENCES IN TRANSITION FREQUENCY
DIFFERENCES IN TRANSITION FREQUENCY
Table 5 shows that the difference between the RI (i.e. 35%) and other style change rate is statistically significant for the HA and LP (highlighted in yellow) and almost so for the RS (highlighted in green).
Table 5
SIGNIFICANCE OF RATE OF CHANGE DIFFERENCES
The reason for the RI’s greater resistance to change is likely to be found in the structure of its information processing strategy. The RI uses unpatterned input. This means that the style can accept and use any form of input. The amount of information obtained may be less than for the structured HA and LP styles but it is still usable without the need to change style orientation. Remember that optimality is not at issue. Adequacy is all that is needed.
The other information-processing element is the RI’s use of thought output (e.g., ideas, options, etc.). This is infinitely flexible. Unpatterned input means that the RI is not bound by the rigor of the HA’s structured thought-based strategy. Inconvenient discrepancies can be ignored and returned to later if further definition or specification is needed. The reduced need for rigor means that a relevant response (i.e., output) can be offered to meet almost any situation. It does not have to be perfect, just “good enough.”
The combination of unpatterned input and thought output means that the RI is the most flexible of the styles. The RI style can more easily emulate any of the other styles—at least for a time. Since most transactions are relatively brief, this capacity allows the RI to “get by” in most situations. The ability to “get by” allows the RI to more easily maintain their approach in the face of environmental change. Hence they have the lowest rate of change. The data does not “prove” this theoretical reasoning but the author is at a loss for any other reasonable causal chain.
An informal confirmation of the logic offered above is found in the many leadership studies that have been done (Salton, various). They all find the dominance of the RI style to be characteristic of people in senior leadership positions. This is no accident.
Leaders typically must guide people who use a variety of styles. The higher the level, the more variety will likely be encountered (e.g., more functions, more people, etc). The flexibility of the RI is well suited to understanding and contributing to these various postures. This gives the RI an edge in rising to leadership—whether formal or informally gained.
Keep in mind that there is no such thing as optimality in social interactions. Adequacy is all that is needed. Thus it is not necessary that a leader “fully” appreciate the contribution of other styles. It is only necessary that the leader understand “well enough” to provide reasonably correct directional guidance.
RESEARCH IMPLICATIONS
The findings of this research offer insight of immediate value to the practitioner. For example, this paper has identified the kinds of change that will be easy or hard. This can be useful in establishing job progressions that are likely to yield success for both the individual and the organization.
The study has also alerted the practitioner to the fact that dominant styles are “sticky.” Most do not substantially change even over long periods of time. What this means is trying to change a persons “I Opt” strategy will always be a difficult undertaking. It can be done but it is not cheap. Clients looking for “quick fixes” to fundamental strategic postures are likely to be disappointed. This study provides the practitioner with hard data with which to make this case.
The study has also demonstrated that changing a “style” is easier than changing observable behavior. A style change only requires a change in rank order. The study has shown that on a global basis this happens with an average change in only 1 response (see Graphics 4 and 5). Even when this happens the change in the entire repertoire of behaviors is small (see Graphic 6). Practitioners should expect to continue to work with people and organizations even after “style” based measurements tell them that the job is done. It probably is not.
This paper further identified the degree of difficulty that can be expected in any change. Table 3 showed that the easiest change is redirecting output. Redirecting input is next. This knowledge can be very useful in areas such as leadership training.
The study also alerts the practitioner to some more subtle factors. Both the leadership edge and relative resistance of the RI to change probably rests on its flexibility. Observed changes may be simply temporary accommodations. Knowing this can cause the practitioner to make their own accommodations in their development initiatives.
“I Opt” represents a quantum leap beyond the capacities of traditional tools. It not only anticipates change but can identify how much actually occurs in the “real world.” It goes on to explain what changes, why it changes, how much it changes and the likelihood that it will change in a particular direction. In other words, “I Opt” theory accurately predicts what is going to happen in the real world as well as explaining what has happened.
The combination of a quantum leap in scope, accuracy of predictive capability and solid, testable reasoning creates a much more powerful tool than previously available. Both practitioners and theoreticians can deploy this tested and validated tool immediately to address issues being confronted in today’s world. The result is likely to benefit both the practitioner/theoretician and the organization to which it is applied.
TRADEMARKS
® IOPT is a registered trademark of Professional Communications Inc.
® MBTI, Myers-Briggs Type Indicator, and Myers-Briggs are registered trademarks of the MBTI Trust, Inc.
® FIRO-B is a registered trademark of CPP, Inc.
® DiSC is a registered trademark of Inscape Publishing, Inc.
® 16PF is a registered trademark of the Institute for Personality and Ability Testing, Inc.
SIGNIFICANCE OF RATE OF CHANGE DIFFERENCES
The reason for the RI’s greater resistance to change is likely to be found in the structure of its information processing strategy. The RI uses unpatterned input. This means that the style can accept and use any form of input. The amount of information obtained may be less than for the structured HA and LP styles but it is still usable without the need to change style orientation. Remember that optimality is not at issue. Adequacy is all that is needed.
The other information-processing element is the RI’s use of thought output (e.g., ideas, options, etc.). This is infinitely flexible. Unpatterned input means that the RI is not bound by the rigor of the HA’s structured thought-based strategy. Inconvenient discrepancies can be ignored and returned to later if further definition or specification is needed. The reduced need for rigor means that a relevant response (i.e., output) can be offered to meet almost any situation. It does not have to be perfect, just “good enough.”
The combination of unpatterned input and thought output means that the RI is the most flexible of the styles. The RI style can more easily emulate any of the other styles—at least for a time. Since most transactions are relatively brief, this capacity allows the RI to “get by” in most situations. The ability to “get by” allows the RI to more easily maintain their approach in the face of environmental change. Hence they have the lowest rate of change. The data does not “prove” this theoretical reasoning but the author is at a loss for any other reasonable causal chain.
An informal confirmation of the logic offered above is found in the many leadership studies that have been done (Salton, various). They all find the dominance of the RI style to be characteristic of people in senior leadership positions. This is no accident.
Leaders typically must guide people who use a variety of styles. The higher the level, the more variety will likely be encountered (e.g., more functions, more people, etc). The flexibility of the RI is well suited to understanding and contributing to these various postures. This gives the RI an edge in rising to leadership—whether formal or informally gained.
Keep in mind that there is no such thing as optimality in social interactions. Adequacy is all that is needed. Thus it is not necessary that a leader “fully” appreciate the contribution of other styles. It is only necessary that the leader understand “well enough” to provide reasonably correct directional guidance.
RESEARCH IMPLICATIONS
The findings of this research offer insight of immediate value to the practitioner. For example, this paper has identified the kinds of change that will be easy or hard. This can be useful in establishing job progressions that are likely to yield success for both the individual and the organization.
The study has also alerted the practitioner to the fact that dominant styles are “sticky.” Most do not substantially change even over long periods of time. What this means is trying to change a persons “I Opt” strategy will always be a difficult undertaking. It can be done but it is not cheap. Clients looking for “quick fixes” to fundamental strategic postures are likely to be disappointed. This study provides the practitioner with hard data with which to make this case.
The study has also demonstrated that changing a “style” is easier than changing observable behavior. A style change only requires a change in rank order. The study has shown that on a global basis this happens with an average change in only 1 response (see Graphics 4 and 5). Even when this happens the change in the entire repertoire of behaviors is small (see Graphic 6). Practitioners should expect to continue to work with people and organizations even after “style” based measurements tell them that the job is done. It probably is not.
This paper further identified the degree of difficulty that can be expected in any change. Table 3 showed that the easiest change is redirecting output. Redirecting input is next. This knowledge can be very useful in areas such as leadership training.
The study also alerts the practitioner to some more subtle factors. Both the leadership edge and relative resistance of the RI to change probably rests on its flexibility. Observed changes may be simply temporary accommodations. Knowing this can cause the practitioner to make their own accommodations in their development initiatives.
“I Opt” represents a quantum leap beyond the capacities of traditional tools. It not only anticipates change but can identify how much actually occurs in the “real world.” It goes on to explain what changes, why it changes, how much it changes and the likelihood that it will change in a particular direction. In other words, “I Opt” theory accurately predicts what is going to happen in the real world as well as explaining what has happened.
The combination of a quantum leap in scope, accuracy of predictive capability and solid, testable reasoning creates a much more powerful tool than previously available. Both practitioners and theoreticians can deploy this tested and validated tool immediately to address issues being confronted in today’s world. The result is likely to benefit both the practitioner/theoretician and the organization to which it is applied.
TRADEMARKS
® IOPT is a registered trademark of Professional Communications Inc.
® MBTI, Myers-Briggs Type Indicator, and Myers-Briggs are registered trademarks of the MBTI Trust, Inc.
® FIRO-B is a registered trademark of CPP, Inc.
® DiSC is a registered trademark of Inscape Publishing, Inc.
® 16PF is a registered trademark of the Institute for Personality and Ability Testing, Inc.
BIBLIOGRAPHY
Farber, Henry F., 2008. Employment Insecurity: The Decline in Worker-Firm Attachment in the United States. Princeton University: CEPS Working Paper No. 171, June 2008, page 6. Retrieved from http://www.princeton.edu/ceps/workingpapers/172farber.pdf on February 2, 2011.
Inscape Publishing (2005). DiSC Validation Research Report. Inscape Publishing, Minneapolis, MN. Retrieved from http://www.discprofile.com/downloads/DISC/ResearchDiSC_ValidationResearch
Report.pdf January 4, 2011.
Salton, Gary (2011), IOpt Style Reliability Stress Test, http:\\garysalton.blogspot.com.
Salton, Gary (Various):
Harvey, R J (1996). Reliability and Validity, in MBTI Applications A.L. Hammer, Editor. Consulting Psychologists Press: Palo Alto, CA. p. 5- 29.
Farber, Henry F., 2008. Employment Insecurity: The Decline in Worker-Firm Attachment in the United States. Princeton University: CEPS Working Paper No. 171, June 2008, page 6. Retrieved from http://www.princeton.edu/ceps/workingpapers/172farber.pdf on February 2, 2011.
Inscape Publishing (2005). DiSC Validation Research Report. Inscape Publishing, Minneapolis, MN. Retrieved from http://www.discprofile.com/downloads/DISC/ResearchDiSC_ValidationResearch
Report.pdf January 4, 2011.
Salton, Gary (2011), IOpt Style Reliability Stress Test, http:\\garysalton.blogspot.com.
Salton, Gary (Various):
- Salton, Gary (November 2010) Sales Management and Performance. http://garysalton.blogspot.com/2010/11/sales-management-and-performance.html
- Salton, Gary (October 2010) City Management http://garysalton.blogspot.com/2010/10/city-versus-corporate-executive.html
- Salton, Gary (September 2009). The Nursing Staircase and Managerial Gap http://garysalton.blogspot.com/2009/09/nursing-staircase-and-managerial-gap.html
- Salton, Gary (September 2008). Hierarchy Influence on Team Leadership
- http://garysalton.blogspot.com/2008/09/hierarchy-influence-on-team-leadership.html
- Salton, Gary (August 2008). Engineering Leadership. http://garysalton.blogspot.com/2008/08/engineering-leadership.html
- Salton, Gary (June 2008). The Pastor as a Leader. http://garysalton.blogspot.com/2008/06/pastor-as-leader.html
- Salton, Gary (May 2008). Fitting the Leader to the Matrix http://garysalton.blogspot.com/2008_05_01_archive.html
- Salton, Gary (October 2007). Leadership, Diversity and the Goldilocks Zone http://garysalton.blogspot.com/2008_01_01_archive.html
- Salton, Gary (October 2007). How Styles Affect Promotion Potential http://garysalton.blogspot.com/2007_10_01_archive.html
- Salton, Gary (November 2006). Gender in the Executive Suite http://garysalton.blogspot.com/2006_11_01_archive.html
- Salton, Gary (October 2006). CEO Insights http://garysalton.blogspot.com/2006_10_01_archive.html
Harvey, R J (1996). Reliability and Validity, in MBTI Applications A.L. Hammer, Editor. Consulting Psychologists Press: Palo Alto, CA. p. 5- 29.
