The Complex and Dynamic Nature of Quality in Early Care and Educational Programs: A Case for Chaos.

Cassidy, Deborah J.

Abstract. This article describes how complex dynamical systems theory (chaos theory) can be used to understand the nature of quality in early care and education settings. The authors review past research on quality and quality initiatives, suggesting that the complex dynamical nature of early care and educational settings present challenges to quality enhancement initiatives. An application of the tenets of chaos theory to early care and educational settings is provided. Recommendations for research and policy are included.

In response to concerns raised by researchers and the public, increased attention has been paid to improving the quality of early care and education. This attention is the result of research that finds an association between the quality of early care and educational experiences and long-term outcomes for children (Campbell & Ramey, 1995; Frede, 1995; Lally, Mangione, Honig, & Wittner, 1988; Ramey & Ramey, 1993; Schweinhart & Weikart, 1998), as well as the belief that child care is a social support service that families need in order to be economically viable in the new millennium (Gallagher & Rooney, 1999). The programs that have demonstrated long-term sustainable gains in child outcomes, however, are usually model demonstration projects in which attention has been given to a multitude of details (Devaney, Ellwood, & Love, 1997). Replication of these programs without the ancillary resources and supports provided by model demonstration projects yields equivocal results (Frede, 1995; Yoshikawa, 1995).

Nevertheless, there is a continued interest in attempting to identify those most salient variables that will result in clearly predictable improvements in quality, thereby resulting in clearly predictable child outcomes. This approach is very appealing to those policymakers who ask, "If I have one more dollar of public money to spend on improving quality for young children and their families, where should this next dollar go?" This is a very difficult question to answer, one without a universally correct response. Furthermore, our current approach to investigating the variables associated with quality in early care and educational settings fails to capture the complex process involved in creating quality programming. Our current practices take a reductionistic approach to determining variables related to quality. When the results of this research are translated into policy, the variables are further decontextualized, resulting in policies that have little chance of creating long-term gains in quality. In orde r to understand and thereby develop strategies to truly improve quality in early care and education, a more sophisticated and holistic approach to understanding quality must be employed.

Although there are numerous statistical techniques designed to address the complex nature of explaining and predicting the human condition, the social sciences, including the field of early care and education, continue to approach research with an assumption that variables will behave in a linear and predictable way. Furthermore, there is an implicit assumption that if we were to measure our independent and dependent variables with enough accuracy, we would be able to create formulas that would accurately predict outcomes. Recent advances in the study of complex dynamic systems, also known as the chaos theory, argue for a revision of our approach to understanding the manner in which complex human systems, a category that subsumes early care and educational programs, function.

This article attempts to demonstrate how chaos theory can be useful in understanding the factors that contribute to quality in early care and educational settings, and how the implications of chaos theory can be used as a guide in developing policy aimed at improving the quality of early care and education. In order to do this, the authors will first review past research on quality in early care and educational settings, examining the factors that have been used to develop policy aimed at improving quality. They will then demonstrate how early care and education programs reflect many characteristics indicative of chaotic systems, and conclude by applying the logic of chaos to recommendations for policy and research.

Past Research on Quality in Early Care and Educational Settings

Beginning with the National Child Care Study (Roupp, Travers, Glantz, & Coelen, 1979), there has been a growing awareness of the characteristics of quality early care and educational programs. Recent findings indicate a relationship between certain regulable characteristics of quality and the care that children receive. A report on child care centers (Helburn, 1995) finds that quality programming is associated with better staff-to-child ratios, better staff education, low teacher turnover, high levels of administrative experience, and effectiveness in curriculum planning. Teachers' wages, education, and specialized training were found to be the most important factors in discriminating among poor, mediocre, and good quality child care centers. Teachers with college degrees demonstrated more positive behaviors, such as sensitivity to children, and fewer negative behaviors, such as harshness and detachment. In addition, teachers with at least a bachelor's degree in early childhood or child development, or both, provided more appropriate caregiving, including appropriate curricular activities and room arrangements; were more sensitive; and were less detached than teachers with vocational training or less. Currently, the National Institute of Child Health and Development (NICHD) is following a cohort of young children, measuring both development and caregiving environments and caregiving patterns. The NICHD research (1998, 1999) also supports the relationship between both higher levels of formal training and experience, and the quality of care that providers demonstrate, both affectively and in the types of activities they provide.

In response to these findings, there have been initiatives--both at the state and national levels--to increase quality by using such strategies as increasing compensation (Miller, 1996; Whitebook, 1996), increasing education and training (Miller, 1996), or lowering ratios (Harrington, Walsh, & Bryant, 1997; Kontos & WilcoxHerzog, 1997). Several studies call into question those initiatives that are focused on a single aspect of quality. For example, the California mentor teacher project targeted teacher training as an intervention (Whitebook & Sakai, 1995). The project provided training for mentor teachers, as well as an opportunity for students to be mentored by these teachers. The students who were supervised by mentors were initially quite proficient in their interactions with children and were sensitive to children's needs, as measured by the Caregiver Interaction Scale (CIS). When the students returned to child care centers that were of lower quality than those sites in which their mentors supervised them , however, their scores on the CIS immediately declined. Cassidy, Hicks, Hall, Farran, and Gray (1998) report similar findings for an Americorps project in North Carolina (North Carolina Child Care Corps). Corps members who had been trained for 5 weeks, given a practicum experience, and received 4 hours of college-level credit were placed in community child care classrooms in five regions of the state. Preservice scores on the CIS were good after training; upon completion of 7 months of service in child care centers, however, the scores of the corps members on the two subscales of the CIS significantly declined.

Even projects that address several components of the quality equation may not be able to sustain improvements in quality over time. For example, the T.E.A.C.H. Early Childhood Project is designed to increase quality by increasing provider compensation and education. A study conducted on one cohort of T.E.A.C.H. scholarship recipients (Cassidy, Buell, Pugh-Hoese, & Russell, 1995) revealed that over the first year of the project, during which they earned 12-20 credit hours from a community college, they made significant gains on their scores on the Early Childhood Environmental Ratings Scale (ECERS) and the Infant Toddler Environmental Rating Scale (ITERS). The same was not true for a comparison group of teachers in child care centers.

However, a longitudinal follow-up investigation of a sample of scholarship teachers indicated that many of the teachers were unable, over the ensuing years, to maintain the gains made on the ECERS/ITERS during the first year of the project. Indeed, the scores on this measure returned to pretraining levels, although the scores of the teachers on the Teachers' Beliefs Scale (TBS) (Hart, Burts, Charlesworth, Fleege, Ickes, & Durland, 1990) were maintained at the levels achieved after completion of the first year of training. Although the sample size was small in this follow-up study, these results seem to indicate that teachers were aware of developmentally appropriate practices throughout the three-year investigation, and that they were able to implement the practices during the first year of the program. They were unable, however, to continue to implement appropriate practices during subsequent years.

Despite the important contribution to quality that improving education or lowering ratios can make, the linear nature of our understanding of quality in the early childhood settings, particularly those factors that are associated with the teacher, need additional scrutiny. For instance, 18 additional community college hours will not uniformly result in an 18 "unit" increase in the quality of care a provider offers; yet, the manner in which much research is conducted implicitly assumes this is so. This belief also leads to initiatives that focus on single factors designed to improve quality, rather than a more systemic approach. Indeed, as the aforementioned studies demonstrate, initiatives that are too narrow often do not result in sustainable gains. For example, when training and education are used as an intervention without systematically addressing other aspects of quality, the gains may not be maintained.

Clearly, there is a need for more research designed to investigate the wide range of factors associated with quality care for children and families. Furthermore, it is necessary to examine how these factors interact overtime. Given the diversity of background and experiences of people who practice in the early childhood field, a more systemic approach to understanding the issues associated with quality is needed.

Complex Dynamic Systems Theory: Chaos

A useful lens through which to examine these factors is the chaos theory. Chaos theory is less a unified theory and more a collection of tenets concerning the behavior of complex nonlinear systems. There is much disagreement about the definitions used in this emerging field (Goldstein, 1995). Often, the terms "chaos theory" and "complexity theory" are used interchangeably among researchers, who disagree about which theory subsumes which. Chaos is the more common, albeit evocative, term used to describe the field that focuses on nonlinear dynamic systems. The title of chaos comes from scientists' detection of underlying patterns in seemingly random (chaotic) phenomena by using nonlinear math and statistical techniques. Chaos theory has grown out of physics and mathematics, and it represents a paradigm shift in the hard sciences away from a Newtonian reliance of linear relationships to an understanding of how dynamic nonlinear systems operate (Gleick, 1988; Hayles, 1990).

Past social researchers have applied chaos theory to understanding organizational structure (Bak, 1997; Dooley & Van de Ven, 1999; Seel, in press), curricular issues (Doll, 1986, 1988; Goff 1998), and issues about education (Grover, Achleitmer, Thomas, Wyatt, & Vowell, 1997) and elementary school quality (Livingston, Bridges, & Wylie, 1998). Chaos and complexity theory has been used to explore early childhood teacher professional development (VanderVen, 1998) and to understand the nature of administering early care and education programs (VanderVen, 2000). Building on the ideas of these theorists, the following is an overview of how chaos theory can be used as a heuristic for understanding quality in early care and educational settings.

Several tenets of chaos theory are useful in attempting to understand the complex nature of the relationship among factors usually associated with quality in the early care and educational setting. Among these tenets are: 1) decomposability, 2) nonlinearity/nonpredictability, 3) sensitivity to initial conditions, 4) recursive symmetries between scales (also called self-similarity), 5) feedback mechanisms, and 6) the existence of attractors (Hayles, 1990; VanderVen, 1998).

Decomposability. Complex systems cannot be deconstructed. In chaos theory, many levels--from the provider and child dyad, to the system of policies and procedures that govern licensing and accreditation--must be considered, as they all contribute to quality. Changes at any level will affect the entire system.

Nonlinearity. Complex systems are characterized by a nonlinear relationship between cause and effect; small changes can have large effects, large changes can have minor effects, and all things in the system are not weighted equally. Furthermore, it is not possible to know, a priori, what things will and will not be relevant in causing change. Therefore, changes in the systems are not predictable. This is clearly demonstrated in the child care setting. For example, until the child attends a program, we cannot know the impact on the classroom dynamics that the enrollment of this one new child will have. However, depending on the personality characteristics and dispositions of a child, the addition of a single child can noticeably affect the quality of care the teacher is able to provide to all the other children in the classroom. Furthermore, the care the teacher offers that particular child may be markedly different from the care she can give to the other children. Likewise, the enrollment of that same child i n a different classroom may have no measurable effect on the care he or she receives in comparison to that any of the other children receive. The critical factor in determining how the enrollment of a new child affects a program is how that child relates to the teacher and the other children in the classroom. Now consider the addition of a child with a disability to the classroom. One can readily see the dramatic increase in the complexity of the situation. Because the classroom is a complex adaptive system, the addition of one single child has the potential to significantly alter the entire classroom.

Sensitivity to initial conditions. In complex systems, initial characteristics can have profound effects on later behavior. This means that small variations at the beginning of a process can have large effects in the end. This is what is often referred to as the butterfly effect--the theory that a butterfly flapping its wings in Nepal can make it rain on the other side of the globe. An example of this in an early childhood setting might be seen in the difference in teacher-child interactions created by room arrangement and space. For instance, two classrooms can start out with identical materials, group sizes, and teacher-child ratios. Let us speculate, however, that one classroom is located in a large basement room in a church with no adjacent outside area, and the other classroom is in a small house that has been fully converted, including the appropriate outdoor space, to meet the needs of young children and their caregivers. Over time, the quality of care and education received by the children in these cl assrooms may be dramatically different. Initially, we might see similar patterns of teacher-child interaction and activities in both programs. Gradually, however, the teachers may grow frustrated with the lack of natural light in the basement classroom and the need to walk a block to the nearest playground. This frustration may result in quite noticeable fluctuations in the quality of care and education in the classroom. However, the classroom with the same equipment, materials and group size, but in a more appropriate setting, may continue to provide a more consistent level of quality.

In truth, we cannot say what effect initial conditions will have on the long-term outcomes of programs, because most of the research we have on early care and educational programs and quality has not been longitudinal in nature. However, examples from large-scale initiatives such as Head Start seem to confirm the proposition that early care and education programs are sensitive to initial conditions. As the only national example of homogeneity in initial levels of funding and resources and, since 1975, explicit standards governing performance and operation, one would expect Head Start programs to offer uniform levels of quality. In reality, the quality of services provided by programs can be seen to diverge over time. Some programs have been in existence for over 35 years and provide exceptionally high-quality services, while others, funded and monitored at the same level, are in dire need of technical assistance and support in order to improve their quality.

Recursive symmetries among scale levels. At any level of measurement, there will be distinctive patterns that repeat themselves throughout the system. The classic example of this in the chaos literature is referred to as a fractal. The chief example of fractals is the Mandlebroit set, where at every level of magnification or iteration one can see consistent patterns of complexity (Gleick, 1988). A more mundane example of recursive symmetry (also referred to as self-similarity) is tree bark. Tree bark looks much the same when observed at a distance of one foot and when magnified 100 times.

Recursive symmetries of scale also can be detected in early care and education programs. Programs often have a characteristic "style" or organizational climate. According to Seel (in press), this style emerges from the various agents in the organization interacting with each other. Rather than taking a top-down or bottom-up approach to understanding this culture, Seel believes that one needs to start in the middle and permeate out. All agents contribute to the culture. This "culture" permeates throughout the program and can be detected from the board of directors to the individual teacher-child interaction. That is not to say that every classroom in a particular program will look the same; rather, it means that all the classrooms and aspects of a particular program will eventually reach a similar quality, either high or low. It is rarely, if ever, the case that a truly poor-quality classroom operates directly next to a high-quality classroom. Of course, this may be the case for a limited period; over time, ho wever, the classrooms would begin to resemble one another in organization and quality, because the same system would support both classrooms. Those programs that are high quality are so in nearly every way-from the teacher-child interactions to the approach the director takes with employee development.

Feedback mechanisms. Chaotic systems have feedback mechanisms whereby the results of one interaction are fed back into the system as input for subsequent interactions. An example of this in early childhood education can be found in employees' feelings of burnout (Manlove, 1993, 1994). Initial feelings of dissatisfaction can color subsequent interactions, which can, in turn, contribute to heightened feelings of burnout, and lead to someone quitting. Furthermore, one caregiver's feelings of burnout and discontent can then affect the feelings of burnout and stress experienced by other providers in the same program who interact with the distressed individual. Whether or not these feed back mechanisms are allowed to build and reinforce a negative situation, or if the situation will dissipate or resolve itself, will be dependent on other factors within the system. For instance, whether or not a teacher's feelings of frustration and disenfranchisement grow will depend on his or her interactions with fellow teachers, children, and supervisors (Manlove, 1993).

Attractors. The last, and perhaps most important, tenet of chaos for the application to early care and education is the construct of attractors. Attractors come in many varieties: fixed point, limit cycles, and strange. Fixed point attractors are those points of measurement that a system always returns to. The classic example is that of a pendulum coming to rest. A limit cycle denotes a system in which there are clearly defined limits or parameters that the system fluctuates between. In the literature on dynamic systems theory, the maximum and minimum numbers a group of predators and prey fluctuates between often depicts limit cycles. The numbers of predators and the number of prey keep each other within a set population boundary, but there is wide fluctuation within the groups. Finally, strange attractors are those points around which measurements hover but never achieve. In chaos literature, Lorenz's butterfly is often used as an example (Gleick, 1988). Lorenz's butterfly is created by the mapping of measur ement coordinates in phase space. In a three-dimensional space, if you were to put temperature on the y axis, pressure on the x axis, wind speed velocity on the z axis, and then plot the points over time, you would have a pattern of coordinates that would form something akin to a butterfly pattern. The lines orbit around the strange attractors, but do not achieve them. This is different from the other attractors, in which the coordinates attract the system. The strange attractors almost repel the system from achieving those exact measurements, instead letting the system orbit around them as if they carried a certain type of gravity (Casti, 1994; Gleick, 1988; Hayles, 1990). These attractors are considered strange, because it is not clear why the measurement points define the system, only that the system is being attracted to orbit, in a non-repetitive way, around them.

In the field of early care and education, licensing standards best exemplify attractors. Some programs do not consistently meet licensing standards, due to the use of substitutes, teacher turnover, and enrollment patterns. Some programs are constantly above standards. If a researcher conducted a series of site visits over a period of several days, measuring every aspect of health and safety procedures, teacher-child interactions and program implementation, at one-hour intervals, a program would not likely achieve the exact same score each time it is measured. Subtle differences from hour to hour and day to day would emerge.

The differences in quality scores from day to day in one program are amplified when one considers the variety of programs operating in a single community. Certainly, one can expect quality ratings to cluster around particular values. They rarely, if ever, fall exactly at the standards specified by licensing. Consumers and policymakers alike can be assured that some programs will fall below and some programs will exceed levels required by law; in general, few will ever precisely be in compliance with each and every guideline.

It should be clear that early care and educational programs can be considered other examples of nonlinear systems, given their resemblance to the aforementioned six characteristics. Therefore, models describing the behavior of these systems must take into account the nonlinear nature of the phenomena under scrutiny. In the past, research on quality has not adequately addressed this concern. We have maintained a positivist belief in a Newtonian paradigm of reality. As the above examples clearly demonstrate, the manner in which early care and educational programs function demonstrates many of the qualities associated with chaotic systems. Therefore, research methods that do not address the dynamic, nonlinear nature of the system are destined to be ineffectual at predicting outcomes for these systems. Indeed, it may be that the level of quality is nondeterministic, although certainly not random.

Research Implications

Accepting the proposition that early care and education programs are complex dynamic systems carries with it implications for how one conducts research on these systems. For instance, one-time "snapshot" approaches to data collection will not adequately assess the manner in which a nonlinear, complex system functions. Rather, research conducted over time that is able to measure many interactions at multiple levels of a system are needed in order to get a more complete understanding of an early care and educational program's operation. Longitudinal research would better allow researchers to understand the salient variables in a system's functioning, thereby allowing exploration on factors that can support optimal functioning. It is through research conducted over time that we begin to understand how recursive patterns affect functioning.

There are also issues with measurement, in particular with respect to defining quality (Dahlberg, Moss, & Pence, 1999). As Scarr (1999) points out, parents may have a different definition of quality than that used by early childhood professionals. Likewise, assessing the quality of teacher-child interactions may be very different, depending on which child or children a researcher chooses to observe. For instance, a teacher may be quite good at interacting with most children but may fall short when assessed on interactions with a child with special needs, especially if she does not have the proper training to work with such children. Therefore, a child care program may be judged as adequate by the parents of the children enrolled, yet may fail in a measure of quality that assesses the same center as a site for early intervention (Aytch, Cryer, Bailey, & Selz, 1999). A clearer understanding of how the many different levels of the system are assessed, and to whom it is of great importance, is needed if we are to truly address ways to support quality in early care and educational settings. In addition to research, the complex dynamical nature of early care and educational programs also has policy implications.

Policy Implications

Accepting the assertion that child care programs are indeed chaotic systems also has implications for policy. One of the characteristics of complex dynamic systems is that they may behave in orderly patterns until the energy in the system reaches a certain point. Once this point is reached, order evaporates and the system begins to act erratically until order is again established, either through self-organization at a higher level of functioning or by dissipating the amount of energy (Casti, 1994). Work on catastrophe theory clearly describes these jumps in organization, whereby a system abruptly goes from one form of functioning to another. The rise of democracy in the former Soviet republics is one example of this change, where public opinion supporting democracy reached a critical mass, leading to a dramatic shift in the way the system operates (Casti, 1994). In the field of early care and education, several variables can be seen as controlling the system allowing it to operate within "normal" parameters. Licensing standards that control ratios; group sizes, and teachers' educational level all can be seen as ways to keep the system within normal parameters. Although Scarr, Eisenberg, & Deater-Deckard (1994) found that licensing standards were not a good measure of the quality of centers, it has been consistently demonstrated that the quality of programs is higher in states with more rigid standards (Helburn, 1995).

This is true because chaotic systems are extremely sensitive to turbulence, which is created when changes of energy occur within the system. In the case of early care and education, energy can be understood as resources, both monetary and human. For instance, increased education and wages will change the dynamics of the system by affecting the way teachers perform on the job. Because there is a tremendous range in the educational levels of early care and education personnel, the potential exists for turbulence and irregularity. However, even though regulations assist in controlling the system, an exodus of teachers due to low wages may create enough "energy" in the system to cause erratic and dramatic changes in the level of quality. Therefore, to ensure a consistently high range of quality (although this can never be exact), resources must remain consistent. That means that from a policy standpoint, if one wants to ensure a consistent range of quality in a complex system such as an early care and education p rogram, it is best not to isolate only one factor of the system for improvement. Addressing only one factor would not likely result in sustainable change, and any change that does occur may not be in the predicted direction. From the position of trying to control quality, the best we can do is identify the parameters of the system that should be controlled in order to lessen turbulence and increase the likelihood that the system will function in a way that increases the consistency of high-quality services to children and families.

The best way to ensure quality is by a systematic approach to quality, complete with stringent and enforced licensing standards and resources in the system that allow programs to meet these standards without struggle or turbulence. This would then ensure that there is less variance in the system overall, thereby cutting down on the ability for the system to begin to act erratically, which in this case means offering fluctuating levels of quality. Policymakers could ensure that the "attractor" of the system would be set at a higher point, ensuring that when programs were unable to achieve that level of quality, the points that fell below that threshold would, on balance, be higher than they would be if the standards were less stringent.

If this strategy were adopted and licensing standards were made more stringent, however, the very act of doing this would create turbulence, thereby sending programs into a state of highly nondeterministic behavior as the systems adjust to this new level of "energy." In other words, according to the way complex systems function, any strategy designed to improve quality is likely to produce results that reflect both increases and decreases in quality, and these results would eventually change. Over time, the system would be able to reorganize itself and gain the stability to maintain a higher range of functioning

Past initiatives that have tried to augment resources--such as training or compensation--have not always resulted in a sustained increase in quality, because they have not been done systemically. Additional resources in the system can be seen as turbulence. When complex systems experience turbulence, the resulting behavior is unpredictable. Furthermore, it has been shown that erratic behavior brought on by turbulence can, by its very nature, create new patterns and dynamics. For instance, legislation governing Head Start mandates that at least 50% all classroom teachers have an associate's degree by 2003. This mandate may cause resistance and resentment in some teachers, and be embraced by others. Differences in the amount of resources or supports available to individual teachers may mean that some teachers may be unable to apply in the classroom all that they learned in college, in effect making the "intervention" not uniformly effective. Furthermore, additional resources, such as mentoring and improved comp ensation, may be needed to ensure effectiveness, further adding to the turbulence and the inability to predict unconditionally the success of the mandate.

The authors hereby suggest a framework for evaluating policies based on the complexity theory: 1) Evaluate the research on which any policy is based for evidence of a complex systems approach to evaluation. If the research on which the policy recommendation is based is reductionistic and based on a purely linear relationship between cause and effect, then generalizing the findings to policy is not likely to be effective. 2) Evaluate the levels of the system the policy will affect, ranging from the microsystem of a classroom to the macrosystem of public opinion. The more levels of the system that the policy affects, the more likely it is that the policy will have a sustained effect. 3) Evaluate the policy for its likelihood to increase the turbulence in the system. As mentioned previously, any change in energy can create turbulence in a complex system; however, effective polices will have mechanisms to ensure that the turbulence is dampened and that reorganization occurs efficiently. If policy alters the syste m in such a way that turbulence will be created, other means to lessen the turbulence should be introduced. For instance, in the aforementioned Head Start legislation, there is no mandate for determining how to attain a 50% compliance rate nationwide, how to ensure that programs will be able to increase salaries commensurate with increased professional development, and how to make it more likely that Head Start staff will be able to accommodate programs into their schedules. Therefore, this new policy, although designed to increase quality, is likely to create an unhealthy level of turbulence until the system reorganizes at a qualitatively different level of functioning.

Conclusion

Based on chaos theory, quality initiatives must address multiple factors if they are to create real and lasting improvement. Policy recommendations, such as those formulated by the National Research Council ("Eager to Learn," Bowman, Donovan, & Burns, 2000) and the Bush Center in Child Development and Social Policy ("Not By Chance," Kagan & Cohen, 1997) are the types of approaches that will result in sustainable gains in quality. The authors of "Eager to Learn" provide a coherent set of recommendations that would affect all levels of the system of early care and education. Their recommendations include a call for all professionals working with children ages 2 through 5 to have a bachelor's degree in early childhood development and education, and to generate public will to support the system of early care and education. Likewise, "Not By Chance" (Kagan & Cohen, 1997) offers a package of policy proposals that, if implemented as a complete package, promises to increase the quality of early care and education. Ag ain, the policy recommendations of "Not By Chance" range from establishing a system of provider licensure, to adjusting funding streams, to blending funds to create an infrastructure to support quality services and supports for children and families. When discussing the "Not By Chance" initiative, the policymakers have coined the phrase "8-1=0," meaning that if you take away any one of the eight component policy initiatives from the formula, the entire initiative is destroyed. Furthermore, "Not By Chance" does not propose a strictly formulaic approach to quality. For instance, its authors have boldly suggested that ratios and groups sizes may need to be flexible to accommodate the various needs of the children. This is a good example of complex systems thinking; that is, acknowledging the fact that not all children require the same degree of energy to ensure their care and education. However, the policy implications of who would decide the group size or ratio number--given the current state of for-profit cent ers and differences in funding--make this sort of policy threatening to many providers. Despite very real concerns about the balance of power and control in early care and education settings, if quality is to be achieved, this sort of holistic approach, which acknowledges the variability among the factors determining quality, is much needed.

An awareness of the tenets of chaos is imperative as policies and programs are initiated in an effort to increase the quality of early care and educational settings. By adopting strategies and policies aimed at increasing quality, the complex dynamic nature of early care and education programs means that we cannot actually predict the outcome of any quality initiative. In the long term, these initiatives are likely to produce quality increases, but in the short term, turbulence and chaos are likely.

Chaos theory is not new. Mathematicians, physicists, and social scientists have been basing work on these tenets for quite some time. The theory has not been widely discussed in the field of early childhood education research, however. Amid the growing concern about the quality of early care and education, chaos theory may help us chart a course of action that will improve quality.

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