Tuesday, December 20, 2011

K-12 School System Management

By: Gary J. Salton, Ph.D.
Chief: Research & Development
Professional Communications, Inc.

This research reports on an evidenced-based analysis of the K-12 school system in the United States. A companion video both abbreviates and expands on the research. It can be viewed on www.iopt.com or by clicking the icon on the right.

Public schools in the United States use a four-tier management system. An elected school
board sits at the top. It is charged with providing the educational resources—both people and funding—and sits in judgement of performance. A direct analysis of this tier is out of the reach of this study. However, some tendencies can be inferred from the character of the system that these boards have created.

The school system is a hierarchy. It operationally consists of superintendents, principals and teachers. This research focuses on the implications of the information processing strategies employed at these various levels (except for the school board). “I Opt” technology captures this in the form of strategic styles. These styles mediate the content of whatever particulars flow through them. “I Opt” is ideologically agnostic and indifferent.

The superintendent of schools has locally defined responsibilities. They are typically described as the Chief Executive Officer of a school system. In that capacity they are expected guide educational policy, procedure and practice for a school district. They typically (but not always) are hired by and report directly to an elected school board.

The sample used in this study is of 37 School District Superintendents from 11 states as shown in Table 1. The sample is not large but meets the criteria for the use of standard (i.e., parametric) statistics. It is also sufficiently diverse to represent the range of circumstances that the people occupying this position face.

Table 1

Graphic 1 shows that the superintendents in the sample favor the idea-oriented Relational Innovator (RI) strategy as a principal method of navigating life. Secondarily, the group favors the disciplined, methodical Logical Processor (LP) strategy. This is not a common finding among people sharing the same role.

Graphic 1

The unusual character arises from the double peak on the RI and LP style dimensions. These are diametrically different strategies. The targeted outcome of the LP strategy is reliability and quality. This is typically gained by the use of proven methods. The RI strategy usually targets major gains realized using new and creative methods, options and discoveries. By definition new methods are unproven. These two dominant styles are not opposites but are mutually exclusive.

There are other unusual aspects to Superintendents. Table 2 displays the unique nature of the role. It shows the R2 (“R Square”) or "goodness of fit" of Superintendent profiles versus various levels in other organizations. R2 is the proportion of fluctuation (i.e., variance) of one variable that is predictable from the other. Roughly put, R2 is a measure of how well one distribution matches another.

Table 2
(As measured by the Coefficient of Determination )

The Superintendent’s approach to decision issues is distinct. A visual examination of Table 2 (above) suggests that the core of the difference lies in the action-oriented strategies. Graphic 2 (below) shows that Superintendents (red column) have among the lowest decisive action RS score of any management level. Even college professors, a group not known for quick action, have more of an RS inclination.

Graphic 2

The methodically deliberate LP inclinations of Superintendents show exactly the reverse condition. Table 3 shows that Superintendents are 43% more inclined than CEOs to rely on proven, traditional methods (i.e., <26.9%-18.8%>/18.8%=43.1% more). That is a lot.

Graphic 3

The picture painted by Graphics 2 and 3 are pretty clear. As a group, Superintendents are powerfully disinclined toward responsive action. They will see very little need to act quickly on most decision matters. A sense of urgency is unlikely to be visible in behavior.

Styles of RS and LP are real and visible aspects of behavior that we can all see in each other. But they are not everything. Everyone can be responsive (RS), plan (HA), get ideas (RI) and act methodically (LP). What matters is just how these are balanced one against the other. "I OPT" technology has a way of addressing this issue.

The overall behavioral tendency of a person can be measured by calculating a Cartesian average. This well-recognized tool distills a person's entire behavioral profile into a single point. This is illustrated by the example in Graphic 4. The centroid (yellow dot) characterizes the general tendency of a person across all of the decisions that they are likely to make.

Graphic 4

Using centroids individual superintendents can be compared with actual individual CEOs (profit, non-profit, proprietorships). Graphic 5 compares these two groups. It identifies the kind of issue resolution method likely to be considered. Every dot is a specific individual. The results are striking.

Graphic 5

Superintendents are far less inclined to both generate and accept new approaches to issue resolution than are Corporate CEOs (74%). However a majority of Superintendents (58%) are still somewhat inclined toward the RI strategy. Graphic 6 fills in the picture. It shows what is likely to be done with whatever approach is selected to be applied.

Graphic 6

Once again the difference is striking. Corporate CEOs (as a group-there are individual exceptions) are much more inclined to use the responsive RS strategy (55%). This behaviorally evidences itself in an experimental approach. CEOs tend to give a new approach a try. If it fails they cut it off quickly. If it works they let it run. They get the benefits from success sooner and thus enjoy them longer. They control cost by a willingness to cut their losses fast.

Superintendents (as a group) take a different course. They strongly favor an analytical approach (68%). They invest heavily in trying to insure that whatever they try will work. This is a risk adverse strategy. There will always be a “good reason” that a particular choice was made. But it also carries the risk “throwing good money after bad.” The heavy analytical investment makes it hard to walk away from failing strategies.

This actual data suggests that Superintendents (as a group) are not likely to be successful if they were transposed into a corporate environment. However, the reason that most (not all) Superintendents share a cautious posture is that a hiring authority (i.e., a school board) believes it to be appropriate to the mission. “I Opt” data on school board composition is unavailable and any judgements are entirely speculative. However, it is worth noting that the “I Opt” profile of the general population from which school board members is drawn would favor a cautious, analytical approach. It is probable that school board members would see themselves as doing things the “right” way and would probably demand the same of the superintendents that they hire.

The size of the Superintendent sample is not large. Questions as to its representativeness are legitimate. But its size is not inconsequential. In addition, the data is striking in its strength and consistency. Further, the analysis appears to reflect actual “real world” experience. It is reasonable to accept the sample as strongly indicative if not exactly representative.

The sample used in this study includes 208 Principals from 12 states in the United States as shown in Table 3. The sample includes K-12 public, private and chartered schools and incorporates a very small representation of schools dedicated to special education.

Table 3

Principals are responsible for the effective functioning of (usually) an individual school. They operate within the framework (i.e., policies, procedures, practices, etc.) established by the Superintendent. This parallels typical corporate supervisory relationships. Table 4 shows how well the Principal’s profile matches those of various levels in other areas of society.

Table 4
(As measured by the Coefficient of Determination (R2) )

The R2 column in Table 4 measures the “goodness of fit” and has the same meaning as explained in Table 2. While Superintendents are markedly distinct from their non-school counterparts, Principals appear to fit right in. This relation is visually shown in Graphic 7 (below).

Graphic 7
(Manager/Director n = 5,313, Principal n = 208)

While similar, Principals are significantly less inclined to use the fast-acting RS strategy (p = 0.00085) than are their corporate counterparts. The tendency to use the cautiously methodical LP strategy approaches but does not quite meet academic significance (p = .068). In total, Principals tend to share the Superintendents cautious posture. This is visually shown in Graphic 8 (below).

Graphic 8
(Superintendent n = 37, Principal n = 208)

The comparison shows a matter of degree and not kind. Superintendents are more distant from their corporate counterparts than are Principals. If school executives were transported to corporate America, Principals would “fit in” better than would Superintendents. But neither would fit perfectly.

Different organizational levels consistently favor particular “I Opt” profiles (Various Studies, #1 in bibliography). The reason is simple. Different levels have different roles. Time horizons, decision complexity, level of detail, degree of specification and the availability of resolution formats systematically vary by level. For example, corporate CEOs are typically unconcerned with a particular plant’s daily production. A supervisor usually does not spend much time considering the consequences of a long-term strategic sourcing decision. The gaps in these “I Opt” profiles between adjacent levels can give insight into the operation of a hierarchical system. This is done in Graphic 9 (below).

Graphic 9

The profile gap in Graphic 9 measures the direction and degree of difference between supervisor and subordinate in each “I Opt” information processing style. The zero (red) line indicates no difference. Above that line indicates the leader is stronger in that dimension. Below the line the subordinate is stronger.

The direction of both school and corporate executive relationships is the same. Both lines tend up and down in unison. They are directionally consistent. This suggests that the school-corporate distinction is a difference in degree rather than kind. This in turn tends to confirm that the systematic and predictable nature of the separation between hierarchical levels found in other studies.

The green corporate line in Graphic 9 shows that non-school executives interact to make distinct contributions in every “I Opt” dimension. The average absolute difference between corporate executive levels is 7.8% (see Table 5 below). This is enough to inform both the decisions and their execution. It is not enough to impede communication. In other words, the non-school superiors and subordinates combine to both broaden and deepen the view beyond that which either level would achieve operating alone.

Table 5

The blue school executive line in Graphic 9 tells a different story. Superintendents and Principals hold virtually identical positions on the action-based strategies (RS and LP). Neither broadens the perspective of the other on this dimension. Both levels are likely to quickly agree on the direction any actions—whether decisions or their execution—without challenge.

It is a different picture for the thought-based strategies (HA and RI) of school executives. Table 5 shows a dramatic difference. The average gap between school executive levels is more than twice as much as their corporate counterparts (19.7/8.2=2.4 times). Graphic 10 (a duplicate of Graphic 8) shows that this gap is statistically significant for both the analytical HA and idea-oriented RI. In deciding on a resolution strategy Superintendents and Principals are likely to see entirely different worlds.

Graphic 10
(Superintendent n = 37, Principal n = 208)

The high idea-oriented RI posture of Superintendents generates a wealth of new ideas. But the focus is on expressions of creativity rather than as proposals for immediate implementation. The high analytical HA posture of Principals welcomes new ideas. But as “fodder” for analysis rather than as opportunities for near-term improvement. Both parties celebrate new ideas. Neither is prepared to act on them. The net result is a cauldron of intellectual vitality that appears to outsiders to be aggressively laying groundwork for improvement. But few things actually get done.

Contrast schools with corporations. In corporations fewer ideas are analyzed in lesser depth. Ideas move more quickly to proposals that are then tested experimentally. Ideas that fail are discarded more quickly thus limiting risk. The cauldron of intellectual vitality bubbles less vigorously in corporations but more actually gets done.

The overall picture offered by this assessment is a distortion of the executive interplay. No school system level is focused on using experimental risk as a tool of advancement. The actions school systems will see as “experiments” are probably so well-planned as to be rehearsed procedures. They carry a heavy analytical investment made by many people with vested interests. In these circumstances “failure” is not an information-gathering tool. Failure is a “bad” thing that should be admitted only after evidence has piled up to a point where only the comatose have not recognized it.

It is worth noting that corporations are not a particularly high comparative standard. The ineptitude evidenced in the ongoing recession occurring at the time of this writing is sufficient evidence of its shortcomings. However, in the long run it has shown the ability to adjust and recover. The school system as a whole has shown no such evidence.

It is also fair to note that there are individual Superintendents and Principals who if paired could (and in some cases have) create a dynamic system. Such a system would be capable of producing rapid and substantial advances. However, on an overall system-wide basis the probability of a dynamic system actually occurring on a broad scale is relatively low. There are simply not enough people who hold appropriate profiles to effect this kind of wide-scale
structural change. And that’s not the whole story. There is still another level.

The sample used in this study includes 218 teachers from 11 states in the United States as shown in Table 6. High, middle and elementary schools are included with a small representation of preschool teachers. The schools include public, private, charter and religious institutions.

Table 6

Teachers are the delivery vehicle of the school system. Their position parallels the various positions in the corporate world where things are actually done. Table 7 shows how teacher’s profile matches that of other operating levels in the economy.

Table 7
(As measured by the Coefficient of Determination (R2) )

It is important not to view the various levels in status terms. All of the categories are necessary for a society to operate. They are linked or distinguished in Table 9 (above) by the nature of the information flows necessary to fulfill their mission under the system within which they operate. For example, nurses, teachers and hourly manufacturing all deal with “products” whose quality can be directly and immediately judged. Trainers, Superintendents and Scientists (those on the bottom of the list) produce nothing whose quality or value is immediately visible. This kind of immediacy would favor strategies that put a premium on detailed, methodical and certain strategies. There are other factors but this illustrates the relevance of information flows to job demands.

Positioning teachers in the School System management matrix is perhaps best accomplished by first viewing the typical relationships of all levels in a corporate environment. Graphic 11 (below) shows the “I Opt” information processing styles for all levels of corporate management from CEO to non-management. The stair step pattern is typical. The same relationship has been found in multiple studies of different functions and activities (#1, Various Studies in bibliography). It is there because it is functional within a hierarchical system.

Graphic 11

Graphic 12 (below) shows the hierarchical relationships in the school system. The stair step has disappeared from three of the four styles. The only one that remains is the Relational Innovator strategy. And that one is more severe than its corporate counterpart.

Graphic 12

However, for the purposes of assessing the teacher position it is sufficient to note that the LP style stands out as an anomaly. This can be more clearly seen by positioning the corporate and school system hierarchies on the same graphic. This is done in Graphic 13.

Graphic 13

The sections have been numbered to facilitate reference. The LP style in Section 2 (above) stands out. School management is far more committed to the process-oriented LP style at every level. The teachers are the most committed. As a group they put high value on consistency, predictability and reliability. This is typically achieved by relying on proven methods that have worked in the past. Teachers can be expected to only reluctantly change their teaching practices. The LP is also most comfortable in highly stable situations governed by predictable routines. Change is effectively defined as anything that disrupts established patterns anywhere. This makes any kind of change VERY difficult.

The average school executive shares the Teachers high levels of LP relative to their corporate counterparts. The Superintendent and Principal’s commitment to the LP strategy is only exceeded by the non-management corporate ranks. This builds in a very strong system wide bias against change. The measurements in Section 1 (above) confirm the reluctance toward anything but absolutely certain action. Everyone in the school management structure—from Superintendent to Teacher—has a lower tolerance for change than does their corporate counterpart.

While there is a reluctance to “do” new things, there is no reluctance to talking about them. Principals and Teachers are heavily inclined to study, evaluate, assess and analyze things (see Section 3 in Graphic 13). They are likely to want “justification” on even minor matters. And that justification will probably be required to meet stringent standards. Quibbles are likely to abound and will be taken very seriously at these levels. This drives the cost of a decision up to higher levels than experienced by corporations.

Superintendents are unlikely to concur with either the depth or scope of the analysis sponsored by lower levels. However, they will probably be confronted with group pressures born of the conformity of Principal and Teacher viewpoints. In addition, the pressures of teacher unions are likely to reinforce careful, thorough analysis and assessment.

Finally, Section 4 (i.e., idea-oriented RI) shows that teachers are among the least inclined to offer new ideas that break from current practice. Teachers are likely to take issue with this. They will probably see themselves as idea generators. This perspective will be reinforced by their strong HA inclination. They are likely to welcome the ideas offered by Superintendents and Principals. But this reception will be focused on analysis (HA, Section 3) rather than implementation. In final analysis Teacher ideas will likely be confined to offer only incremental improvements to existing practices. Ideas involving quantum leaps or complete "tear ups" are probably out of range for most teachers.

Teachers are the rock solid base of the school system. Things are done consistently, reliably and with attention to a level of excellence. Interest, compassion and concern typify their stance on non-academic elements of teaching. There is much merit to the LP posture. The challenge is how to integrate it with the demands of changing times.

The school system’s organization structure is poorly suited to eras where knowledge advances quickly, where resources fluctuate and students with varying needs must be serviced. The likely response to changing conditions will be to apply the existing tools more intensively. Everyone works harder. The effort expended will be taken as evidence that something other than them (i.e., superintendents, principals, teachers) is the cause of negative outcomes. The likely result is a circular firing squad. Everyone ends up blaming everyone else. That is exactly what appears to be happening as this research report is being written.

This report has demonstrated that the organizational impediment to change is structural. Whatever new teaching technique, method or process that is attempted will have to pass through this structure on its way to implementation. In other words, things that appear to work in isolated test or experimental situations are likely to be problematic when applied to the general school population.

Earlier research has shown that the probability of major changes in the information processing structure of anyone is small (#4, Style Stress test; #5, Pattern Stress Test in the bibliography). There are 7.2 million teachers in about 99,000 public schools each with a Principal (
#2, US Census Bureau, 2011) embedded in over 14,000 different school districts with their attendant Superintendents (#3, US Census Bureau, 2011).

Investments attempting to change the information processing structure of existing participants on this scale is possible but difficult. Transitioning the balance of styles over time is a viable long-term strategy. Execution of this strategy does not require everyone to change. The need is for a shift in the structural profile of the group as a whole. This can be accomplished with scattered individual changes. Natural group dynamics will do the rest.

Given the conditions outlined in this article, initiatives undertaken before the structural change occurs should consider the their effect within the current structure. People using highly structured strategies will require a substantial time investment. Highly detailed specification and strong evidence that the proposed initiative will be successful will be needed. The time and detail will probably extend beyond that which people using a more
spontaneous strategy will deem reasonable. But without this investment the probability of success of any initiative is seriously compromised.

There are other strategies that can ameliorate but not resolve the situation. Specifying them is beyond the scope of this research. However, it is worth noting that the current environmental condition will not exist forever. Building organizational structures that can adjust to changing conditions would do much to limit the probability of repeatedly encountering structural issues of this and other types. Altering the structure in favor of the "stair step" configuration typical in other organizations will accomplish this objective.


(1) 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
(2) US Census Bureau, 2011. Newsroom Facts and Figures. http://www.census.gov/newsroom/releases/archives/facts_for_features_special_editions/cb11-ff15.html

(3) US Census Bureau, 2011. School Districts. http://www.census.gov/did/www/schooldistricts/

(4) “IOPT” Style Reliability Stress Test, Research Blog http://garysalton.blogspot.com/2011/03/i-opt-style-reliability-stress-test.html Video summary, 10 Minutes. http://www.youtube.com/watch?v=Vs6eoIsqVkc.

(5) “IOPT” Pattern Reliability Stress Test, Research Blog http://garysalton.blogspot.com/2011/03/i-opt-pattern-reliability-stress-test.html. Video summary, 15 Minutes. http://www.youtube.com/watch?v=0SLg28BhNHU

Friday, August 05, 2011

University Management

By: Gary J. Salton, Ph.D.
Chief: Research & Development
Professional Communications, Inc.

The information processing structure of universities is studied though a sample of 732 positions at over 100 different universities. Statistics reveal that there are structural disjoints that will result in persistent systematic issues. These structural “disjoints” need not be corrected. They are necessary for the effective functioning of the university,
A self-imposed standard of confining research blogs to about 2000 words requires omitting some useful elaborations. A complementary video which both expands on and abbreviates this research blog is available by clicking the icon to the right.

The university has two goals. The inter-generational transfer of knowledge preserves the knowledge gains of past generations. The creation of new knowledge objective is focused on the legacy to be left to the future.

Professors control knowledge content. Surrounding them is a management structure that controls the circumstances through which the content is delivered. Effective functioning requires that the information processing strategies of these elements —their “I Opt” strategic styles and patterns—be aligned.

Each management component serves a different function its strategy must be aligned to fulfill this mission. That strategy must likewise “fit” with the mission of other organizational elements. How well these strategies mesh will determine university success.

University management is arranged in organizational layers. For purposes of this study these units are grouped. Directors are non-academic executives that head functions or programs. Administrators include managers and other supervisory staffs that guide the operations of the functions or programs.The staff consists of professionals and other support personnel who actually execute the needed functions. Table 1 outlines the sample based on these groupings. The size and diversity appears sufficient for it to be considered representative.

Table 1

The Professor staff has been analyzed in a separate research blog ("The Professors"). There is no statistically significant difference based on academic rank. Therefore the “I Opt” profiles of all 254 Professors can be consolidated and their averages are shown in Graphic 1

Graphic 1
(n = 254)

The combination of a very high idea-generating RI style (31.5% on Graphic 1) and a secondary analytical HA approach (25.6% on Graphic 1) describes a “Perfector” pattern. In colloquial terms the "Perfector" pattern of behavior can be described as “Great idea!! Let’s think it through completely.” This is not a formula for decisive management but does serve the Professor’s instrumental role of knowledge creation and transfer within a university structure.

A total of 140 Directors drawn from 69 universities were used in the sample. This category includes Deans whose position do not have academic content. Table 2 outlines the range of functions of Directors included in this study. Overall they appear to fairly represent the range of activities needed to support the university mission.
Table 2

Directors set policy, practice and procedure. In doing this they extract information from the environment, process it using their own standards and issue output aligned with their responsibilities. Graphic 2 shows how well their profile—and thus their interpretation of decision issues—matches that of the Professors.
Graphic 2

Overall, the Directors and Professors are “cut from the same cloth.” The single significant difference is in the decisive action Reactive Stimulator style. Directors are inclined toward acting more responsively than are their Professor counterparts. Graph 3 shows the clear shift in this style preference.

Graphic 3

This category of management consists of managers, assistant Directors, associate deans (non-academic) and similar titles sourced from 55 universities. The role of this level of management is in guiding relatively near-term operations in various functional areas. Table 3 identifies some of the areas of responsibility. It appears reasonably representative of the range functions typically involved in university operations.
Table 3

Administrators are likely to periodically interact with both Professors and Directors on decision matters. The degree to which their information processing profiles match will influence the level of managerial harmony. Matching profiles indicate people are “talking the same language.” Divergent profiles mean that the parties are paying attention to different aspects of an issue, weighting them differently and seeking a different resolution. Table 4 shows the statistical significance of the difference for each strategic style.

Table 4
Probability that observed differences are due to chance alone

Professors vs. Administrators:
The academic standard for statistical significance is 5% or less. Table 4 (above) shows that Administrators differ form both Professors and Directors but in different ways. Administrators fall short of the Professors idea-generating RI tendencies. In general Professors are likely to view Administrators as a bit shortsighted and unimaginative. Administrators are likely to see Professors as a tad “blue sky” in orientation and somewhat out of touch with the real world. The degree of difference probably nets out to more of an annoyance than a basis of hostility. Graphic 4 (below) gives a picture of the Professor-Administrator differences across the range of strengths which the RI style is held..

Graphic 4

Directors vs. Administrators:
Administrator and Director differences center on the analytical HA style. Graphic 5 shows that it is localized to a particular group of Administrators (see blue arrow).

Graphic 5

Directors are likely to view most Administrators as roughly in line with their view of issues. But one small Administrator group (blue arrow in Graphic 5) will probably be seen as spending too much time, energy and probably money on analysis, assessment and evaluation. For their part, this Administrator subset is likely to view the Directors as a bit superficial.

Overall, Directors and Administrators are reasonably well matched. The information processing disjoint is local and focused on a small percentage of Administrators (i.e., 17%). Overall, Directors and Administrators may not come to exactly the same conclusion on particular issues. However, they are likely to see the same variables as important, process them in roughly the same way and more or less agree on the degree of analysis appropriate to an issue.

The staff is technically not part of management. However, their roles affect the information available to and decisions made by management. In addition the ability of the staff to execute the directives given by management fundamentally influences the functioning of the university system. Non-management staffs matter.

Table 5 specifies some of the roles performed by the 203 staff members from 59 universities included in the sample. It appears to be sufficiently broad as to be representative of the universities non-managerial staffs.

Table 5

Table 6 (below) shows 12 “I Opt” relationships between the staff versus Professors, Directors and Administrators. Eight of the 12 meet the 5% or less standard of academic significance. In other words most staff members will tend to “see” issues in fundamentally different terms than do the various levels of management.
Table 6
Probability that observed differences are due to chance alone

Table 7 (below) shows 12 relationships between the various management levels without staffs considered. Only 3 or 25% (3/12=.25) of the relationships register a statistically significant difference. Management is a relatively homogeneous group. What this means is that the university’s challenge is integrating the staffs into the management structure in a way that people can “understand” each other. Left unattended this can lead to misinterpretation, misdirected outcomes and emotional stress for all involved.

Table 7
Probability that observed differences are due to chance alone

The university structure directly addresses this potential issue in a traditional manner. The interaction of management and non-management staffs is mediated. Administrators and non-management have a marginally significant disjoint on only two levels strategic style levels (i.e., the LP and RI on Table 6). Effectively, Administrators act as a “bridge” between the Staffs and the various levels of management.

The idea-generating RI is one of the points of difference between Administrators and Staff. Graphic 6 shows that the difference divides neatly.

Graphic 6

The red arrow shows that the staff tends to prevail the lower levels of RI. The blue arrow indicates that Administrators tend to hold sway at the higher levels. Overall, Administrators are 13% more committed to the RI strategy. Staffs will contribute ideas. But they are likely to be fewer in volume and less radical in content.

The difference in idea-generation is unlikely to be a major issue. Administrators will not feel a shortage of ideas. They are likely to have an overabundance from the Professor and Director levels. Administrators will probably have been learned by experience that staffs are not a productive source of major change options. Hence staffs are not likely to be pestered to generate ideas and options of a major nature.

It is unlikely that the staff level will feel “left out” of the major decision process. It is not a component of their role so their expectations will not be violated. In addition, the university culture typically dictates that major changes are “hashed out” in relatively public discussions. This gives everyone involved a sense of having an “input.”

Staffs are about 17% more committed to the disciplined Logical Processor (LP) style than are Administrators . This is the cumulative result of small differences over a broad range. The general configuration of the profiles of the two groups are aligned. This close “tracking” is shown in Graphic 7.

Graphic 7

Overall both Administrators and staff are likely to see each other as having about the “right” level of commitment to methodical, deliberate and careful action. The difference in information processing profiles is likely to be of only minor consequence.

Graphic 8 shows the integration of all of the various levels more explicitly. Note that the bold green Administrator line breaches all of the various management levels.

Graphic 8

In general, the administrative management of universities appears to represent an effective bridge. The bridge goes both ways. Administrators are likely to effectively represent staff interests to the other management levels. They are also likely to effectively interpret Professor and Director concerns to staffs.

The information processing compatibility among the management levels is not perfect. But it is structurally efficient and effective. Overall the differences between levels are functional. For example, the decisive action tendencies of Directors make sense in terms of their role in getting things done. The Professors idea-oriented RI tendencies directly address their role in creating new knowledge. The staff’s LP capacities are essential for smooth day-to-day operations.

The structural differences will give rise to “bumps in the road.” The decisive Directors are likely to cause Professors to see themselves as “left out” of decision making. The segment of Administrators holding a high analytical HA commitment may frustrate the Directors need to get things done. Administrators are likely to feel themselves “caught in the middle” in trying to balance off the needs of the various levels. These “bumps” are structurally built-in. A modest level of tension is probably needed for the university to function.

Locally there may be information processing disjoints that give rise to difficulties of consequence. But when viewed across all universities the “bumps” are likely to be more of an annoyance than a source of hostility. The picture painted by the 732 person sample of this study is one of general satisfaction but with a tinge of annoyance over one or another issue generated by one or another management level. The fact that the source of the discomfort changes with time is evidence that there is no fundamental structural problem.

In summary, universities are to be celebrated. They appear to have devised about as good of a management structure as is possible given their mission and constraints.

Monday, August 01, 2011

The Professors

By: Gary J. Salton, Ph.D.
Chief: Research & Development
Professional Communications, Inc.

The information processing profiles of 254 professors from 109 different universities showed an unexpected finding. A double peak in the idea-generating Relational Innovator (RI) dimension appears to create two distinct classes of professors. This condition exists across academic disciplines. It applies equally to both the hard and soft science disciplines.

The dual peak appears to be the result of the tenure system that favors two distinct activity patterns that give rise to the dual peak. The result appears ideal for the creation and maintenance of reliable knowledge. A corollary to this finding is that threats to the tenure system may compromise the knowledge generation role of the university.

An unintended consequence of the system is that negative “Rate my Professor” ratings are “built in”. They are likely be the result of the strategic style alignment of the students and one category of professors.

A video that both expands on and abbreviates this research blog can be viewed by clicking the icon on the right.

Professors teach many subjects. Table 1 shows that the sample used here can be considered to reasonably representative of professors in the average university.

Table 1

Professors also are differentiated by rank. Some are tenured (usually Full and Associate), some aspire to tenure (usually Assistant) and some are contracted teachers (usually Adjunct, Lecturers, Readers, etc.). Table 2 shows that the representation of each group within the sample is sufficient to be representative.

Table 2

Universities also differ in size, specialization, endowments, geography and other areas. Table 3 suggests that the range of universities represented in the sample is wide enough to characterize universities as an institutional class.

Table 3

In total, the sample population can reasonably be accepted as a fair representation of university professors in general. Its conclusions are likely to be generally applicable.

Information processing elections affect how professors teach, the nature of their research and the likely character of their interactions. “I Opt” technology measures these strategies in as “strategic styles.” These are specific combinations of input and output elections. “I Opt” is fully validated across all eight dimensions of validity and its reliability has been repeatedly checked under both normal and stressed conditions. Its 20-year use in virtually every segment of society further testifies to its relevance. The “I Opt” tool can be trusted as a tool of research. Its appliction to the various professor ranks is shown in Graphic 1.

Graphic 1

The red quotes on the bottom of the graph characterize the style in colloquial terms to facilitate understanding at the sacrifice of some level of accuracy.

The structural commonality between the ranks is visually apparent. All professors put little emphasis on action (the RS and LP strategies). The idea-oriented RI dominates followed by a strong preference for analysis (HA style).

Table 4
Expressed as a percent for ease of understanding (vs. p< 05, etc.)

Table 4 shows that the differences shown in the line graph are not material. One statistic (Full vs. Associate professor) approaches the 5% academic standard (p < .05) of significance. However, it is isolated and marginal in value. This argues that it can be considered an anomaly. For the purposes of this study all professors can be seen as being cut from the same cloth.

Profile commonality means that professors are looking at the world through the same set of glasses. They will tend to view issues in roughly the same way. They will tend to focus on roughly the same kind of variables, weight the factors in a similar fashion and arrive at a similar character of conclusion.

The above commonality invites stereotyping. People outside the academy live in more mottled environments. They interact with people of a more varied character. To them, the judgements and proclamations of professors are likely to seem strangely coordinated.

To the non-academic this “coordinated” view is likely to be seen as missing obvious factors that are fully visible to those living in more varied non-academic environments. The net result is that professors will tend to be judged as having an oversimplified view of reality. As a group, professors are likely to be viewed as being a bit “out of kilt” with the “real” world.

Unlike non-academic outsiders, insiders are able to contrast professors individually and over time. Graphic 2 plots the strength of the idea-oriented RI style for each academic rank. It suggests that they are likely see two distinct groups of professors based on the volume, scope and quality of idea generation.

Graphic 2

Except for the non-tenure track adjuncts (green line), a distinct dual peak pattern is visible. These peaks are distinct clusters of professors. One group is about average in the idea-generating RI style. The other is highly committed. This unusual gap separates the two groups. It invites examination.

The subject being taught is a probable cause. Grouping the 254 sampled professors tests this hypothesis by discipline. The groups must be large enough to be representative. They must also be small enough to reveal differences. The balance that was used in this research is shown in Table 5.

Table 5

The basis of the groupings is structural rigor. The “constrained” group is governed by relatively well defined, stable theories. These theories are a framework against which new knowledge can be tested.

The “expansive” group has less well-defined theory. The “looser” theory provides fewer comparison points against which to test new knowledge. This difference in structure of the subject matter “should” result in different information processing approaches.

Table 6 tests whether the subjects in each “bundled” group are really using the same basic strategy. It shows that there is no significant difference between the disciplines “bundled” within each of the two groups (i.e., none of the values are less than 5% or p < .05). In other words the bundling appears to be a reasonable approximation of the information processing styles used by the professors sampled.

Table 6
Expressed as a percent for ease of understanding (vs. p< 05, etc.)

The row at the bottom of Table 6 is a between group comparison. It shows that the two professor “bundles” really do use different information processing strategies. The profiles (i.e., the combination of strategic styles) used by the two groups differ in a statistically significant ways. Graphic 3 defines those differences.

Graphic 3

The constrained group (e.g., exact sciences, medicine, etc.) puts more emphasis on analysis (HA) and less on new ideas and options (RI). This makes some sense. Well-defined theory makes new ideas costly. A professor’s time and resources in have to be invested in evaluating, assessing and validating the new knowledge against the existing theory. This is a “cost” of new ideas. More ideas, more cost.

The “looser” theory of the expansive group (e.g., management, learning, etc.) means that there is less to analyze. Assessment and validation is done with experimentation. For the professor this is much less expensive. There are fewer papers to write, fewer equations to run and fewer differences to reconcile. All you need do is to “try it.” Ideas are cheaper so you can afford more of them.

It is clear that the two “bundles” or groups of professors are really different in terms of the way they process information. The hard and soft sciences really do tap different information processing capabilities. But does the difference in subject matter (e.g. constrained vs. adaptive) account for the double RI peak? If it did, we would expect to find one peak in each subject matter group. The peaks would be in different places. The merged result would thus give us the double peak. Graphic 4 shows that the cause is not the result of this merging of two single peaks.

Graphic 4

A double peak appears in both groups. This means the source is not in the field of study. Something common to all areas of study and all academic ranks is causing it. The cause must lie somewhere in the structure of the university itself.

The tenure system is a prime candidate. Tenure can be achieved via two routes. One focuses on the creation of new knowledge. This speaks to the university’s knowledge creation mission. If the quality, quantity and scope of new knowledge is high enough, this is all that is needed. However, this is not the only route to tenure.

After realizing some minimum level of published new knowledge other qualities can weight into the tenure decision. Speaking (i.e., representing the university), teaching skill, graduate studies supervision and administrative contributions can also argue for granting tenure. These activities support the university’s knowledge transfer mission. Both routes are valuable to the university. Both types of professors are needed for the university is to fulfill its mission.

A consequence of the double peak can be seen in Graphic 5. Professors highly committed to the idea-oriented RI tend to have a lower commitment the disciplined LP and analytical HA strategies. Professors with a lower commitment to the idea oriented RI strategy tend to favor the more rigorous analytical HA and LP strategies.

Graphic 5

The net effect of the interplay between the types of professors is that new knowledge is systematically tested. The knowledge created by professors on the upper peak will be tested in detail by the rigorous capacities of professors on the lower peak. The result is the culling substandard work.

All professors have an absolutely high level of RI. Professors on the lower peak will also create new knowledge. However, it is more likely to be of an incremental character. Professors on the higher peak will tend to generate knowledge of a “quantum leap” character. These abrupt transitions will always merit more focused challenge. They do not have the validating history of established knowledge.

The rival information processing postures of the two groups of professors thus create a critically important system. Acting together they insure that the knowledge being created is worth preserving. In other words, they act to create and maintain society’s store of reliable knowledge.

The existence of the double peaked idea oriented RI is a fact. The difference in the disciplined postures of HA and more spontaneous RI is a fact. Assigning its cause to the tenure system is a testable hypothesis. However, regardless of its cause the existence of the double peak has university level implications for its students.

An unintended consequence of the double peak effect is its effect on the student’s “Rate my Professor” judgment. The ability of a processor to transfer knowledge is governed by the match of the professor and student’s information processing profiles.

Graphic 6
(Professor n=254, Student n = 1800)

The student’s profile in Graphic 6 (blue line) is based on a sample of 1,800 graduate and undergraduate students from 31 different colleges and universities. The coefficient of determination (R2 or R squared) is cited on the graph. R2 puts a number on the visual differences. R2 is the proportion of fluctuation (i.e., variance) of one variable that is predictable from the other. Roughly put, R2 is a measure of how well one distribution matches another.

Graphic 5 locates the disjoint between students and professors. The high R2 in the Reactive Stimulator and Hypothetical Analyzers dimensions confirm these will not be a problem. On these dimensions the professor and student are “talking the same language.”

The Logical Processor dimension will create a bit of an annoyance. Students have a stronger desire for a disciplined, step-by-step procedure than does the professor. However, the R2 of 52.3% suggests that negative “Rate my Professor” comments based on this factor will be restrained—more like “grousing” than true complaints.

The RI graphic in the lower right box of Graphic 5 is the major disjoint. The R2 of 33.7% is concentrated on professors holding a position on the second high RI peak. Negative “Rate my Professor” complaints are likely to center on this specific group of professors.

The nature of the “disjoint” is likely to focus on professor’s ability to offer the student an uninterrupted stream of “what causes what and why” logic. The RI style uses fragmented knowledge in its processing. The style is also susceptible to diversion by an interesting comment or speculation. The net result is that the student must try to piece together a consistent “chunk” of knowledge that is understandable in terms of their way of viewing the world—i.e., their strategic profile.

A means of at least partially remedying this condition is readily available. Professors holding a position on the second peak can be taught to align their instruction with the student needs. The professors do not have to change their own strategies. They just have to adopt in their student’s perspective for the period in which they interact.

This is an economical and “doable” option. There is no need to involve all professors. Just those on the second peak. The only intervention needed is some relatively simple instruction. The affected professors only need to know how to suppress a “natural” approach for the duration of a class or meeting. They then need only to know what to substitute for their “natural” approach. This process can realign the strategic style profiles on a temporary basis. That is all that is needed for learning.

The result will not be perfect. But it will be a step forward. The transfer of knowledge will be better served without sacrificing knowledge creation. The only difficulty will be convincing the professors that their way is not the only “right way.” That could be the real challenge.

This study finds that all professors share a basic, overall strategic profile that favors new ideas and careful analysis. This commonality lends some justification to the popular characterizations of professors as a bit “out of kilt” with general society.

A double peak was found on the idea generating RI dimension among tenure track professors. Two distinct groups of professors appear to populate the academy. One is well suited to the creation of “paradigm shifting” knowledge. The other is ideal for testing new knowledge. The result of their interaction is reliable knowledge. .

The university’s knowledge creation mission appears to hinge on the tenure system. Recognizing excellence in both knowledge transfer (the lower RI peak) and knowledge creation (the upper RI peak) creates the dual peak. This suggests that tenure is not just useful in motivating individual professors. It is a system selection mechanism. It creates a system of two complementary, contesting components that act together to insure the consistent creation of reliable knowledge.

A corollary of the twin peak tenure hypothesis is that were the tenure system to be replaced, there is a strong probability that the replacement system would not create the twin peak profile. The result could be the compromise of a critical component of the university’s knowledge creation mechanism. Caution is in order.

Finally, nothing comes without a price. The price here is that the upper peak professors will tend to accumulate more negative “Rate My Professor” comments. This testifies to a structurally based compromise of the university’s inter generational knowledge transfer function. However, this can be ameliorated if not resolved through a simple focused educational process.