To study complexity is to investigate how “specific changes and interactions at the individual level create, maintain, change or destroy local systems” (Williams & Dyer, 2017, p.3). Therefore, in order to understand what is happening in a complex system it is important to understand how specific individual differences may influence the wider system. Yet, as discussed in Section 3.2.2, the networks within this system are highly complex, and therefore, it may not be possible to fully understand the system of
interactions which may lead to a person getting a diagnosis of dyslexia, and may influence their outlook on academia. However, Robertson and
Patterson (2016) suggest that “although we cannot have a complete
knowledge of complex systems, use of this framework […] has the potential to provide researchers with more detailed knowledge of complex systems” (Robertson & Patterson, 2016, p.9). Therefore, studying a complex system is a case of ‘inference as the best explanation’: while we cannot know the exact underpinnings of this complex causal system, we can look for
92 This will give us more information about the dyslexic system than simply looking for linear relationships within the system.
Byrne (1998) states that “complexity involves both quantitative measurement and the development of mathematically formalized accounts of a reality based on those measurements- twin essentials of any quantitative programme of scientific understanding” (p. 55). Therefore, the study of complexity is quantitative in its nature. However, although quantitative methods may be the most appropriate to study complexity, they do not come without limitations. Looking at a system involves attempting to say
something about the whole, by looking at information about the parts. However, as is the nature of a complex system, the whole may contain many aspects which are inducible by looking at the parts. In studying complex systems is not a case of hypothesis testing in which key relationships between predetermined variables are examined. Rather, studying a complex system is “a reflexive process in which the theory serves as a basis for the organisation of the model but the data itself is also used to generate ideas in an exploratory way which are then taken back for further review” (Byrne, 1998, p.66). Therefore, while the previously discussed theory sets up possible relationships to explore in the data, the data may also offer new, undetermined relationships and interactions that may reveal more about the system which is currently undiscussed in theory alone.
Thus, it is too simplistic to look for relationships between variables and infer causality to them. Given the complexity of the social world, to show that elements interact and lead to an event occurring is unrealistic. On the concept of homelessness, Williams (2018) states that “what is probably going on is that there a number of overlapping and interacting mechanisms that evolve over time, but whether they can be captured by the proposition of an elegant mechanism framework is debatable” (p. 5). Therefore, while we may claim that there is a ‘dyslexic system’, operationalising and
studying this system is far from straight forward. Consequently, in order to research these aspects of the social world some inference is needed. In order to account for this, Williams (2018) proposed that we can think of
93 1. Ontological: the actually existing mechanism of ‘nature’ (in this
case of society);
2. Epistemology: the mechanisms that we propose to account for these outcomes (p.6).
Thus, in social science research, our aim is to align these mechanisms as closely as possible. While the systems are complex, they are not totally random, therefore by looking for patterns we can attempt to understand mechanism 1 by looking for relative invariances in mechanism 2. In the case of this research, we can look at variables that increase the probability of having dyslexia and use this knowledge to inform our understanding of what dyslexia is. Blalock (1961) states that:
Reality, or at least our perception of reality, admittedly consists of ongoing processes. No two events are ever exactly repeated, nor does any object or organism remain precisely the same from one moment to the next. And yet, if we are ever to understand the nature of the real world, we must act and think as though events are
repeated as if objects do have properties that remain constant for some period of time, however short (p.7).
Therefore, while what is really happening in the dyslexic system
(mechanism 1) may be increasingly complex, in order to understand it to the best of our ability we must use mechanism 2 to, at least in part, uncover the workings of the system.
4.1.1 Studying complex variables
While some aspects of the social world are complex, others are easier to measure, for example, sex at birth, age, and income, are variables in which a reality exists and therefore have a generally agreed means of measurement. On the other hand, some variables have been socially labelled and defined meaning that measurement may widely vary. A key example is social class; as discussed in Section 3.5.3 the relationship between dyslexia and social class is an interesting one to explore. However, social class is a construct or ‘interactive kind’ (Hacking, 2009); therefore, in order to measure it, it first needs to be socially defined. Furthermore, in social research it is unlikely that participants self-identify their social class, rather, they are sorted into their social class based on some characteristic or feature. The intricacies of
94 social class are reduced into researcher categorization, often ignoring the complexities of the social world.
Yet, as discussed above, in acknowledging that dyslexia is a complex system, it is plausible to suggest that any relationship between dyslexia and social class is also complex. Thus, like any complex system, it is not possible to draw causal conclusions between social class and dyslexia, rather, we can look for patterns in this relationship and question what this tells us about both dyslexia and social class. Therefore, when looking at social class we have to work with the data that we have and, again, use the concept of ‘inference as the best explanation’. While we may not be able to find a ‘true measure’ of social class, a significant corpus of work has been conducted into the most likely correlate of social class that can be measured. There are many different methods that have been used to measure social class. Platt (2016) states that these scales, most of which use occupational status as a starting point, “tend to provide largely consistent accounts of class structures” (p. 68). The present research will investigate social class using the NS-SeC scale. This is based on the same principles as the CASMIN scale, which was developed by assigning occupations to categories based on their “expected occupational rewards […] and the nature of the employment relations and the levels of control or oversight” (Platt, 2016, p. 70). The NS-SeC scale, used in the current survey, is also used by the UK Office of National Statistics to measure socioeconomic class. Therefore, while we are not measuring mechanism 1 (social class) in its true form, we are using a proxy in an attempt to align mechanism 2 (occupational status) with mechanism 1 (social class).
To study a system, a sample is drawn from the population and the sample is used to statistically describe the population with significance testing applied to understand the sample’s version of the phenomenon. Error terms are used as “calculable tolerances” (Williams & Dyer, 2017, p.3), in which the larger the sample, the smaller the error terms become. Therefore, in applying the rule of large numbers, it is assumed that the larger the sample, the more likely that they will represent the target population.
However, from the sample, we cannot necessarily infer the ‘average’ results of the sample to an individual unit within the population. Due to many individual differences within the social world, it would not make sense to
95 look at the sample, or even subcategories within a sample, as a homogenous group. However, complexity methods allow researchers to look for groups that may share common patterns and characteristics which give rise to similar outcomes. Thus, allowing investigation of how membership of a particular category can lead to an increase or decrease in probability of an expected outcome.
4.1.2 Experimental methods vs. the social survey
Two key quantitative methods could feasibly be applied to study the phenomenon of interest in this thesis. Firstly, experimental methods could be applied. To look at the impact of the dyslexia label this would involve diagnosing one sample with dyslexia, while not diagnosing another sample, to view the impact of this diagnosis. Ignoring the obvious ethical
implications of this research design, is has been argued that
experimentalism would only work if the world was linear and cause is simple and single (Byrne, 1998). However, this is not the case. As previously explored, the underlying cause of dyslexia is complex and
unclear, meaning that the impact on the individual is varied. Furthermore, as suggested by Bronfenbrenner’s model, in order to understand the individual, it is necessary to examine the environment that they are situated within. Therefore, manipulating the individual, and studying the impact on the individual, would be to ignore the complex system that they are in. Therefore, it would not be possible to conclude that giving a person the dyslexia label, or not, would linearly cause differences between the two samples. There are many other actors that may be involved in this relationship.
An alternative to this experimental method is social surveys. Marsh (1982) argues that social surveys are a preferable method as they do not deal with abstractions from reality, as implied in experimental research, but rather they look at reality for what it actually is. In social surveys “data is constructed as numbers from real knowledge of the world held by
respondents as information in the natural language of everyday life, then the non-positivist character can be seen for what it is” (Byrne, 1998, p.66). Therefore, rather than attempting to looking for unrealistic cause and effect
96 relationships, social surveys can allow some understanding of the complex social world.
It is also necessary to take into account time while investigating social phenomenon:
Where individuals are surveyed at successive time points, then it is possible to investigate how individual outcomes or responses are related to the earlier circumstances of the same individuals. This provides the framework for very powerful analyses of the processes experienced by individuals; it enables a model to be constructed which explicitly takes into account the earlier circumstances
suspected to have an effect which carries through into later life (Dale & Davies, 1994, p.2).
Therefore, the optimum way to examine a phenomenon is through longitudinal social surveys. Byrne (1998) discusses the importance of hierarchical structure in datasets for looking at complexity: “the important thing is that our data structures are hierarchical because they reflect the way in which the world is composed of a set of nested far from equilibric
systems” (Byrne, 1998, p.125). Furthermore, Skinner (1997) states that “complex features of datasets, such as longitudinal or multi-level structures may be of intrinsic interest”. Therefore, this research will use the
Millennium Cohort Study (MCS), a large-scale, longitudinal research project, to investigate the aspects involved within the dyslexic system. However, no such datasets exist that investigates teachers’ understandings of dyslexia. Therefore, to further investigate teachers’ understandings of dyslexia a large-scale, cross-sectional survey of teachers will be collected. Therefore, this research will take a two study approach: The first looking at a large-scale longitudinal dataset for patterns within dyslexia; the second, a primary survey conducted to look at teachers’ understandings of dyslexia.