As outlined in the Research Methodology section, Principal Component Analysis (PCA) performs dimensionality contraction of data and hence is a valuable tool for determining whether scale items within a metric can be reduced into a set of factors, each effectively becoming a subscale in its own right. The main purpose of the analysis is to explain as much of the variance in the variables as possible using as few components (factors) as possible. However the process also enables an examination of the resulting subscales to be conducted which can be introduced into the analysis of the data to help in understanding how these components of the main metrics might interact with each other. This is important because by exploring these interactions, the ways in which aspects of dyslexianess might affect aspects of academic confidence may be more clearly revealed and hence, better understood.
Hence both the Dyslexia Index Profiler and the Academic Behavioural Confidence Scale have been reduced through PCA into a set of factors (components) and these have been assigned namelabels that reflect which dimensions of their parent metrics are their respective contributors. The software application SPSS has been used to work through the calculations and the report that follows distils the outputs generated from running these processes. As reported in the previous section, Sander & Sanders also applied a process of factorial analysis to their original, 24item Academic Behavioural Confidence Scale and later, to their revised, 17item Scale. With the exception of one study found to date (Corkery, 2011), all others that have used the ABC Scale in their research have utilized the Sander & Sanders factor structure for their analysis, had they chosen to explore their findings in the greater detail that the use of ABC factors permits. The original, together with Corkery's ABC factor structures have been investigated as part of my analysis development and reported elsewhere in this thesis (here), the outcome of which suggested that there would be merit in developing a factor structure that is unique to this project because the process may reveal an alternative set of projectspecific factors for the Academic Behavioural Confidence Scale which could be more relevant for exploring the interrelationships between components of academic confidence and components of dyslexianess that are the focus of this enquiry.
In order to run a principal component analysis there are a number basic assumptions which need to be fulfilled:
The first is met through the study design for this project which is that the variables that comprise the two comparator metrics are continuous. This has been incorporated into the questionnaire design through use of continuous range input sliders as the mechanism for acquiring research participant response scores for the two scales, Dyslexia Index and Academic Behavioural Confidence which have been developed specifically to enable parametric statistical processes to be used in the data analysis. As alternatives to the more conventional, fixed anchorpoint Likertstyle scales which would have produced discrete data values leading to data outputs being arbitrarily coded, this design feature of the data collection tool thus supports a more robust statistical analysis.
The second assumption is that there needs to be evidence of linear relationships between the variables that comprise the scales. This is because PCA is based on Pearson correlation coefficients and it is considered that a scaleitem variable that presents a correlation of r ≥ 0.3 with at least one other scaleitem variable is worthy of inclusion in the analysis (Hinton et al, 2004). In the case of the Dyslexia Index, there are 20 scale item variables and for Academic Behavioural Confidence there are 24. An analysis of the intervariable correlation matrix for both metrics showed that for Dyslexia Index, of the 190 possible correlation outcomes, 80 returned a correlation coefficient of r ≥ 0.3 with all variables bar one returning at least one correlation of r ≥ 0.3 with any other variable. For the Academic Behavioural Confidence Scale, of the 300 possible correlation outcomes, 138 returned a correlation coefficient of r ≥ 0.3 with all variables returning at least one correlation of r ≥ 0.3. Hence it is considered that the second assumption for applying a PCA to both metrics is met.
The fourth assumption tests the null hypothesis that there are no correlations between any of the variables. If this null hypothesis were accepted, this would be indicating that the set of variables may not be reducible to a smaller number of components (factors), which is after all, the rationale for applying a principal component analysis to the data. The measure for testing this null hypothesis is Bartlett's Test of Sphericity and the output being sought is a significant result (p < 0.05) which will enable the null hypothesis that there are no correlations between any of the variable to be rejected. When applied to the Dyslexia Index metric, the test returned an output of p < 0.005, and when applied to the ABC Scale, the output was also p < 0.005. Hence for both metrics, the null hypothesis that there is no correlations between the metrics' variables is rejected, which means that there are correlations between the variables and therefore justification for running the PCA is present
Hence the preliminary assumptions have been met for running a principal component analysis on both the Dyslexia Index and on the Academic Behavioural Confidence Scale for the data collected in this enquiry.
The key outcome of the factorial analysis of both metrics is that it has enabled a Dx Factor X ABC Factor matrix to be contsructed which, as expected, has revealed highly interesting and meaningful relationships between the components of the two metrics and this has contributed significantly to the discussion and final conclusions. The matrix and a more detailed report is presented below.
Recall that the Likert scale that is attempting to evaluate levels of dyslexianess, the Dyslexia Index (Dx) Profiler, is a 20item scale comprising scale items which attempt to evaluate the extent of each respondent's acquiescence with statements concerning specific aspects, that is, dimensions, of study, academic learning management and to an extent, learning history. The scale items were assembled in the questionnaire in a random order (left) and respondents were required to adjust the position of a slider control along a scale from 0 to 100% to register their degree of acquiescence with each of the statements. Each statement had its own slider. This process for recording participants' responses has been used throughout the earlier sections of the questionnaire. The design rationales are described in the previous section, 'Research Design' but the principal aim has been to mitigate the conventional Likertstyle scale limitations imposed through fixed anchorpoint selectors by generating a data output that can be considered as a measure of a continuous variable.
It is important to emphasize that the main function of factorial analysis in this project is to enable the factor subscales that emerge out of the Dyslexia Index metric to be related factorbyfactor to data acquired through the other main metric, Academic Behavioural Confidence (ABC) and its own factors (note that 'component' and 'factor' are used interchangeably throughout). The process of PCA determines that through interpretation of the output, scaleitem dimensions of the Dyslexia Index scale will be assigned to only one factor  hence none will have crossfactoral influence, and likewise for ABC. Note that there is an element of 'best option' choice involved in this process as usually dimensions load on to more than one factor and a decision has to be made about determining which balance of options is the most interpretable. More of this below.
A significant further element of PCA is that it enables statements to be constructed which explain the proportion of variance that can be explained by each component and this can be usefully interpreted as the strength of impact that a component has on the complete results. This has been discussed in more depth in the previous section 'Research Design'. [Note to me: expand this a little in the RD section]
PCA has also been used to help to identify scale items that might be considered as redundant  that is, are not contributing to the evaluation of the construct in a helpful way and hence might be discarded. A process of inspection for scaleitem redundancy has already been conducted through interpretation of the Cronbach's Alpha (α) internal reliability consistency analysis which has been thoroughly reported in the previous section, Research Design, however it is useful to provide a recap here as the outcomes contribute to this report on how the factors for Dyslexia Index (and ABC) have been established.
For the 20item Dx scale used to collect participant data in the main research questionnaire, Cronbach's α = 0.842 which appears to be indicating a strong level of internal reliability consistency. As stated earlier, according to Kline (1986) however, a value α > 0.7 of does not necessarily strengthen this further and may instead be suggesting that some scale items can be removed to have little impact on the αvalue and hence maintain the level of reliability of the scale without sacrifices possibly perceived as resulting from the subsequent reduced data acquisition.
When the potentially redundant Dx scale items were identified through this analysis and removed, the resulting 16item scale returned a value of α = 0.889 which is indeed indicating that the internal consistency reliability of the scale is maintained and hence suggests that discarding these 4 scale items from the Dyslexia Index Profiler, thus reducing it to a 16item scale, may have little impact on the overall Dx values.
The first iteration of this process which has regenerated 16point Dyslexia Index Scale warrants further testing and this may be the focus of a followup study in due course, one element of which will be to explore the impact on α when combinations of the possibly redundant items are withdrawn. Suffice to say that effect size results for differences in ABC between research subgroups established through the 20point Dx scale compared to those established through the reduced, 16point Dx scale were only marginally different and so for this primary analysis, the full, 20point scale has been used, pending the outcomes of the suggested followup study later.
Principal Component Analysis will produce as many components (factors) as variables and the process will 'explain' all the variance in each of the variables if all of the components are retained. Clearly the objective of the process is to decide how many factors are worth retaining in the final solution so that as much of the total variance as possible can be explained through all of these factors. Using the Eigenvalue1 extraction criteria (Kaiser, 1960) which is the default setting in SPSS on account of this criteria being claimed as the most popular, five components (factors) emerged from the analysis output in SPSS which between them accounted for 60.4% of the total variance. Respectively these factors accounted for 31.7%, 9.9%, 7.6%. 6.0% and 5.3% of the total variance for Dyslexia Index. Inspection of the scree plot that was generated as part of the output suggested that retaining these 5 components (factors) would be appropriate although it can be seen both from the scree plot and from the table (right) that it is possible that a sixfactor solution may be equally applicable because the initial eigenvalues for components 5 and 6 were both very close to 1, (1.06, 0.988), or even a fourfactor solution may be the most appropriate as the eigenvalue for the fourth component in the 5factor solution stood at a value of 1.20. To explore this, the principal component analysis was run twice more, firstly with a forced extraction of six components and secondly with four components. The sixcomponent extraction produced an output where the last two components comprised just one dimension each and so this analysis was dismissed. The fourcomponent extraction produced a highly similar output to the original, fivefactor solution with just two dimensions being alternatively placed into different factors. Hence it was concluded that the fivefactor solution can be accepted as a reasonable factor structure for the metric Dyslexia Index.
In interpreting factor analysis outputs, the Table of Communalities is the first result that is useful and from this, the groupings of dimensions into factors makes its first appearance. Five factors emerged and these have been labelled in keeping with the flavour of the dimensions which comprise them respectively:
The communalities are the proportions of each of the variables' variances that is accounted for by the principal component analysis. So for example, for the first dimension in the table below 3.20: I get really anxious if I'm asked to read 'out loud', the communalities extraction value of 0.573 indicates that 57.3% of this dimension's variance can be explained by the factors (components). The loading is the correlation between the variable and the factor and this is(are) the figure(s) presented in line with each dimension in the respective factor column. According to research convention, serious attention is paid to loading factors of > 0.32 (Dewbury, 2004, p309) with Dewbury citing a reference to an earlier work by Comry & Lee (1992) which proposes that a loading of > 0.71 is 'excellent'. Note that in the table below, although loadings are calculated for all dimensions in all factors, only factor loadings > 0.3 are presented to enable the table to be more easily assimilated. Hence the row of data for dimension 3.20 only shows the one value of 0.829 for a loading onto Factor 1, Reading, Writing, Spelling because the loadings onto the other four factors are less than 0.3, and so forth for all dimensions and factors. The communalities extraction figure of 0.573 is thus the proportion of this dimension's variance that can be accounted for by all of the factors.
These communalities are reported alongside the Rotated Component Matrix which is a table that groups the 20 dimensions into the 5 components/factors, where in each component, dimensions are listed in descending order according to the loading onto each variable ( = dimension). The table indicates 'rotated' components which is the mathematical process that places the factors in the best (geometrical) position to enable easier interpretation. SPSS uses a Varimax' rotation, this being an 'orthoganal' process meaning that factors are forced to be independent of each other (rather than taking into account correlations between them).
What emerges from this matrix (below) is that the factor structure is not quite as simple as would have been desirable because some dimensions load onto more than one factor, given the convention of a loading > 0.3 is indicating an influence that should be taken seriously. However, where this occurs, the troublesome dimension has been assigned to the factor onto which its loading is greatest  that is, where there is the greatest correlation between the dimension and the factor. Kline (1986), amongst other consulted, suggests that more often than not a single, simple factor structure is elusive and it should remains the task of the researcher to establish the most appropriate interpretation of the analysis that makes sense in the context of the project. This factor analysis for the Dyslexia Index Profiler seems reasonable and so for the purposes of the remaining analysis of the data collected in this project, this factor structure with the 20point Dyslexia Index comprising 5 factors will be retained.
Dyslexia Index (Dx) Factors by Research Subgroup
The factorial analysis for Dyslexia Index has enabled strongly visual radar charts to be constructed which present an overview of the distribution of Dx factor values for students in each of the three research subgroups in the study. The three charts (below) display the factor profile for every student in these subgroups with profiles overlaid to generate a composite profile map for each subgroup. In ways that are much easier to spot than through inspection of the full data tables, these graphical representations of the five factor values for both each, and all students in each subgroup are revealing. Stark differences are evident between the factor profiles of nondyslexic students in the Base subgroup (RG:ND400) and dyslexic students in the Control subgroup (RG:DI600). It is also apparent that the collective profile maps for students in the Test research subgroup, RG:DNI, are highly similar to the collective profile maps for students in the Control subgroup. In both of these subgroups students are presenting a Dyslexia Index value of Dx > 592.5 which is indicating that highly similar combinations of factor values are present in both subgroups, implying strong dyslexianess similarities between students with known dyslexia and the quasidyslexic students. Both of these are significantly different from the collective profile maps for students in the Base subgroup, RG:ND400, (Dx < 400). Notably, in the chart for students in the strongly, nondyslexic Base subgroup it is clear to see the noticeable skew away from the two Dyslexia Index factors, 'Reading, Writing, Spelling' and 'Thinking, Processing' but also more generally this profile map indicates reduced Dyslexia Index Factor values overall for students in this group in comparison with students with declared dyslexia and those nondyslexics who appear to be presenting dyslexic characteristics. Aside from being highly revealing of differences at the factorial level which will be discussed below (subsection 4.##), it is argued that this representation further demonstrate the validity of the Dyslexia Index Profiler as an effective discriminator for the purposes of this study. Each of these radar charts can be viewed in deeper detail in conjunction with each of the full data tables below.
The data tables collected here show a detailed summary of the factor values for every student in each of the three research subgroups of main interest. Two visualizations of each set of data values are available but it is clear to see how much easier it is to absorb the results collectively when presented as a radar chart in comparison to the scatter diagram of the same data which was the first attempt at visualizing the data.
[Respondents highlighted in green text in the tables are linked to their additional written commentaries submitted in the questionnaires and themes that have emerged in these will be included in the discussion of the results in the next section.]
The matrix tables (Tables 1315) explore these similarities and contrasts through a statistical process where the tables show a condensed view of the complete set of same Dx Factor mean value x same Dx Factor mean value combinations of research subgroups taken in pairs. The purpose of this is to present evidence of the significant or not significant differences between the Dx Factor means across the research subgroups as a first step towards a more detailed level of scrutiny of the subgroup differences in dyslexianess that have emerged.
Firstly, it is important to note that for all Dx factors, the tTest reports no significant differences between the samples means for each factor when comparing the Test subgroup DNI with the Control subgroup DI600. Hence respondents in the Test subgroup and the Control subgroup are presenting on average, similar Dx Factor mean values further implying that the Dyslexia Index metric appears to be successfully identifying students with dyslexialike profiles from the research group of students with no reported dyslexia: that is, quasidyslexic students. Thus, the foundation is laid for comparing the Academic Behavioural Confidence between the Test and the Control subgroups. As added verification, it can be seen that the converse outcome is established between the Control subgroup and the Base subgroup (ND400) where, with the exception of Dx Factor 3, Organization and Time Management, very highly significant differences between the Dx Factor means are recorded. This adds to the argument that overall, students from research group ND, which is students who declared no dyslexic learning difference, who comprised the Base subgroup of students who presented a Dyslexia Index of Dx < 400  can be considered as properly presenting very low levels of dyslexianess. Hence, it is argued that the Dyslexia Index Profiler is presenting good discriminatory properties in accordance with its design rationale.
However for Dx Factor 3, Organization and Time Management, the mean Dx Factor values for all three research subgroups are not significantly different from each other which suggests that students at university who present very low levels of dyslexianess may be experiencing similar issues with organization and time management in their studies as do their dyslexiaidentified or not, peers. In their of students with dyslexia at university and in addition to presenting their own results, Mortimore & Crozier (2006), draw on prior research (Gilroy & Miles, 1996, McLaughlin et al, 1994) to evidence the difficulties experienced by dyslexic students in organizing their study processes and timekeeping. Whilst the outcomes of their study were consistent with the earlier research, their enquiry was conducted amongst students with dyslexia only and did not explore how the organization and timekeeping aspect of academic learning management may be referenced against students with no reported dyslexia. Hence the data summary presented here fills this gap in addition to being consistent with the findings of Mortimore & Crozier's study amongst students with dyslexia, demonstrated by the mean Dx Factor 3 of Dx = 635.53 for the strongly dyslexic students in this project. Hence this suggests that according to this metric's results and analysis, students who are strongly nondyslexic in other areas may be just as 'dyslexic' in organizational and timemanagement skills at university as students with dyslexia. Viewing it another way, this is saying that most students at university tend to be disorganized and find timemanagement challenging, and that this aspect of academic learning management is not unique to students with learning differences. A reaction to this might be that if universities are motivated to ensure that all students across their learning communities become properly equipped to meet the learning challenges that they will be facing, then making early provision for upskilling students' organizational and time management competencies as part of the groundwork for enabling them to develop their academic learning management capabilities would be time wellspent.
Dx Factor Means Differences between the TEST subgroup (DNI) and the CONTROL subgroup (DI600) 


Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 


TEST Research Subgroup: DNI Factor Means 
Dx Factor 
CONTROL Research Subgroup: DI600
Factor Means 
763.01 
647.60 
635.53 
734.64 
668.82 
Dx 1 
794.50 
p = 0.3325;
no significant difference (5%) 




Dx 2 
700.42 

p = 0.1449;
no significant difference (5%) 



Dx 3 
615.72 


p = 0.6302;
no significant difference (5%) 


Dx 4 
772.72 



p = 0.4828;
no significant difference (5%) 

Dx 5 
589.20 




p = 0.2254;
no significant difference (5%) 

Dx Factor Means Differences between the BASE subgroup (ND400) and the CONTROL subgroup (DI600) 


Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 


BASE Research Subgroup: ND400 Factor Means 
Dx Factor 
CONTROL Research Subgroup: DI600
Factor Means 
276.43 
214.38 
586.78 
458.02 
377.68 
Dx 1 
794.50 
p < 0.0001;
very highly significant difference (0.1%) 




Dx 2 
700.42 

p < 0.0001;
very highly significant difference (0.1%) 



Dx 3 
615.72 


p = 0.3548;
no significant difference (5%) 


Dx 4 
772.72 



p < 0.0001;
very highly significant difference (0.1%) 

Dx 5 
589.20 




p < 0.0001;
very highly significant difference (0.1%) 

Dx Factor Means Differences between the BASE subgroup (ND400) and the TEST subgroup (DNI) 


Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 


BASE Research Subgroup: ND400 Factor Means 
Dx Factor 
TEST Research Subgroup: DNI
Factor Means 
276.43 
214.38 
586.78 
458.02 
377.68 
Dx 1 
763.01 
p < 0.0001;
very highly significant difference (0.1%) 




Dx 2 
647.60 

p < 0.0001;
very highly significant difference (0.1%) 



Dx 3 
635.53 


p = 0.2550;
no significant difference (5%) 


Dx 4 
734.64 



p < 0.0001;
very highly significant difference (0.1%) 

Dx 5 
668.82 




p < 0.0001;
very highly significant difference (0.1%) 

One outcome of the increasingly demonstrable robustness of its construct validity is that the Dyslexia Index Profiler, as it has been developed for this project more as a discriminator rather than an identifier, is showing not only merit as a screening tool for dyslexia  when dyslexia in higher education settings is framed in terms of parameters of academic learning management and studyskills  but possibly as a more widely available appraisal device in the toolkit for learning development and academic skills support services at university because it presents a readily comprehensible snapshot of any individual student's approach to study by generating a profile which identifies strengths that can be developed together with weaknesses that might be remediated. Warranted, further development would be requried.
Comparing differences in Dyslexia Index between research subgroups at a dimensional level
Further to examining differences in Dyslexia Index Factors, Dyslexia Index has been explored on a dimension by dimension basis as part of the process of trying to tease out which characteristics could be the more significantly responsible ones that might account for the differences in Academic Behavioural Confidence between the three research subgroups Test, Control and Base.
The summary table below lists all, 20 dimensions of Dyslexia Index (Dx) and shows the mean Dx levels firstly between the two, core research groups  students who declared their dyslexia, and students who declared no dyslexic learning difference; and secondly between the three research subgroups  the Control subgroup, the Base subgroup and the Test subgroup. Data in the top section of the table shows the mean Dx values for each dimension  recall that the Dx value indicates the level of respondents' acquiescence with the dimension statement. Note that the values are all 0 < Dx < 100 and that for each respondent in the research it has been the mean of the weighted aggregates of these dimensional values, scaled to 0 < Dx < 1000 which generates the respondent's overall Dyslexia Index (Dx). Underneath the actual mean values, both the ttest pvalues and the Hedges 'g' effect size differences between pairs of groups and subgroups are shown. Aside from indicating the stark differences in the majority of the mean values between students with dyslexia and those without (RG:DI and RG:ND) when these data are further reduced into differences between the research subgroups, fascinating information is revealed. For example: looking at the differences in means for the TEST subgroup and the CONTROL subgroup for the two dimensions that together constitute the Dx Factor: Verbalizing and scoping it can be seen that the ttest output together with a mediumtolarge effect size indicate significant differences.This may be evidence supporting the viewpoint that dyslexic students are likely to be academically more comfortable adopting planning strategies which permit a more holistic overview to be taken when approaching an assignment challenge rather than plan in lists or other linearthinking ways (Draffen et al, 2007)  hence the widely adopted feature of UK Disabled Students' Allowance provision of conceptmapping assistive technologies such as the applications 'Inspiration' and 'Mind Genius'. Both of these software tools are designed to foster creative thinking, to facilitate ideasbrainstorming and patternspotting, and to enable the grasshopper thinking of many dyslexic students to be developed into meaningful learning from which powerful knowledge structures can be built, ordered and converted into a linear writing process (Novak & Canus, 2010). Evidence has also shown that conceptmapping applications as learning technologies as opposed to assistive technologies are gaining traction in curriculum design, both as an additional and accessible learning tool (Nesbit & Adescope, 2006), as a mechanism for summative assessment (Anghel et al, 2010) and not least in higher education contexts as a means to promote flexible learning approaches (Goldrick et al, 2014), all of which are the embodiment of UDL. Additionally, and of high relevance to students presenting weak spelling competencies, whether attributed to a dyslexia or not, is evidence from studies concerning TEFL (Teaching English as a Foreign Language) learners where conceptmapping applications have been very successfully used to develop Englishlanguage spelling skills by enabling spoken phonemes to be connected with their written forms in a highly innovative and relationshipbuilding format (AlJarf, 2011) and for connecting vocabulary to concepts in different contexts (Betancur & King, 2014).
Secondly, even more striking differences between dyslexic and nondyslexic students for verbalizing ideas in preference to writing about them (Dimension 4) are evidenced where the Dyslexia Index dimension mean value of Dx=84.34 for the strongly dyslexic students in the Control subgroup contrasts sharply with the mean value of Dx=42.34 for the strongly nondyslexic students in the Base subgroup. Indeed, it can be seen that for most of the dimensions, the differences in mean Dx values between dyslexic and nondyslexic students (RG:DI and RG:ND) are equally salient with the largest absolute difference being for Dimension 20, I get really anxious if I'm asked to read out loud, with a Dx Index difference of 32.57 points (RG:DI Dx=77.40, RG:ND Dx=44.83) which corresponded to an effect size of 0.9654, conventionally categorized as 'large'. For the corresponding difference in mean Dx values between the strongly dyslexic and strongly nondyslexic research subgroups, we see an even more substantial absolute difference of 62.11 Dx Index points (RG:DI600 Dx=83.38, RG:ND400 Dx=21.27) which is as we would expect given that dyslexia is conventionally considered as principally associated with reading difficulties. This considerable absolute difference in Dx points is echoed for Dimension 1, When I was learning to read at school I often felt I was slower than others in my class, where an even greater difference of 65.29 Dx Index points is recorded (RG:DI600 Dx=78.34, RG:ND400, Dx=13.05).
Notable differences which emergefrom the data for Dimensions 2, My spelling is generally good (weak), 17, I get my 'lefts' and 'rights' easily mixed up and 10, In my writing at school, I often mixed up letter that looked similar: Students with declared dyslexia in the Control subgroup present significantly higher Dx mean values than for their peers in the Test subgroup (Dx=75.45 / 49.17; Dx=75.28 / 57.78; Dx=67.17 / 45.33 respectively). For Dimension 2, spelling, this may be indicating that weak spelling is not a characteristic of students in the Test subgroup where in most other respects, these students are presenting dimensional levels of dyslexianess that are at similar levels to their identified dyslexic peers. This might be a reason to explain why students in the Test subgroup who might otherwise be considered as unidentified dyslexics have not had their dyslexic learning differences previously spotted in their learning careers as weak spelling can be an early indication pf dyslexia.
Looking across the complete set of dyslexia dimensions, the outcomes that emerge when the Test and the Control subgroups are compared show that in all but 4 of the 20 dyslexia dimensions, the mean values for each of the dimensions respectively are very similar which is supported by generally small effect size differences and pvalues which indicate no significant differences between the means. This outcome is suggesting that the students in the Control subgroup who are presenting dyslexialike profiles are indeed dyslexic within the terms of reference of the Dyslexia Index Profiler. This adds to the construct validity of the Dyslexia Index metric as a mechanism for discriminating students who may be dyslexic amongst the research group of students who declared no dyslexia. Thus confidence is gained in using the measure as an index of a construct that is not directly observable (Weston & Rosenthall, 2003), in this project, termed 'dyslexianess'. Smith (2005) summarizes the seminal work of Cronbach & Meehl (1955) on construct validity which comprehensively argues 'that the only way to determine whether a measure reflects a construct validly is to test whether scores on the measure conform to a theory, of which the target construct is a part' (op cit, p405) and it is argued that by exploring the contrasts in Dx Index values at a dimensional level and commenting on the extent to which the differences that have been measured are in keeping with the more widely accepted theoretical underpinnings of at least some of the typically observed characteristics of dyslexia, the construct validity of the Dyslexia Index Profiler is strengthened and justified as the discriminator for which it was designed in this project.
4.2 Factorial analysis of the Academic Behavioural Confidence Scale
The process of Principal Component Analysis has also been applied to the data collected on Academic Behavioural Confidence and likewise this has been conducted through the software application SPSS.
This has been prompted because a revised, 17item ABC Scale had been established by Sander & Sanders (2009) following a reinspection of the combined data from several of their earlier studies where ABC had been measured, and this was developed through PCA on data collected through their original 24item scale. Their (relatively) large composite dataset (n = 865) was generated by aggregating data collected from five earlier studies conducted between 2001 and 2006 collecting psychology undergraduates from one university in South Wales (n=507) with a further dataset of ABC values obtained from firstyear medical students at one HE institution in The Midlands collected in 2001 (n = 182) and an additional dataset of health care students from a new university in South Wales attending 6 different courses ranging from podiatry to dental health care (n = 176). The two, smaller of these three datasets are of a similar size to the number of respondents in this current project (n=166).
It is of note that there are both differences and similarities in the cohorts of students in Sander & Sanders' (S&S) combined dataset when compared with features of my dataset. For example, one difference was that in the Sander & Sanders studies, students were all undergraduates from a broadly similar family of academic disciplines which may be considered as a limitation because some components of study skillsets might reasonably be expected to differ according to the academic discipline being studied. In the S&S studies it is likely that this may be due to convenience sampling rather than through research design and does not necessarily reflect on the quality of the data collected, although a consequence could be that results obtained may lack generalizability across the wider student community. Whereas in my study students across the university community were invited to participate in the research, with the participation response producing an overall ratio between undergraduates and other students of 75% : 25%. ('undergraduates' includes students attending foundation or access courses and 'other students' comprises postgraduates, research students and a very small number of others who did not disclose their study level (3)). In the Sander & Sanders' datasets, students were drawn from a narrow range of subject specialisms whereas in my study, subjects studied at university was not recorded so it is reasonable to assume that students from a range of curriculum specialisms are as likely to have participated as not. Thus, analysis outcomes from my study are likely to be a good crosssectional representation of the constructs being explored from across the complete student community, albeit through data acquired largely from just one institution. One similarity was that in Sander & Sanders' datasets, students' previous academic achievement at Alevel was recorded with the complete range of grades being presented and although this data was not requested in my study it is not unreasonable to suppose that students who responded to the invitation to participate presented an equally wide range of prior academic achievement.
The original Academic Confidence Scale (ACS) was formulated to operationalize an enquiry to explore stark differences in confidence observed between two very different student groups (Sander & Sanders 2003). The data collected was factoranalysed to reveal 6 subscales: Studying, Understanding, Attendance, Grades, Verbalizing and Clarifying although it was pointed out that this resulting factor structure was a bestcompromise solution as some statements in the ACS did not load on to only one factor, which would be the ideal outcome for a principal component analysis. The ACS was renamed shortly after its inception to the Academic Behavioural Confidence Scale to acknowledge that the scale was in fact more sharply focused on measuring students' 'confidence in actions and plans related to academic study' (Sanders & Sander, 2007, p635). The later, factor analysis of the aggregated data demonstrated that this revised scale consisted of 6 factors: Studying, Understanding, Attendance, Grades, Verbalising, and Requesting which was deemed a better representation of the subscale structure than the earlier 6factor analysis following a more detailed, Confirmatory Factor Analysis. Note that only the earlier factor 'Clarifying' was superseded by the later factor 'Requesting' so it might appear that differences between the factor structures are minimal. However as the factor loadings table of the earlier PCA is not published, it is not possible to comment on how scale item loadings may have shifted in generating the later factors despite 5 of the six factors retaining the same factor labels. An additional significant outcome of the later factor analysis was to identify some scaleitem redundancy which led to 7 scale items being removed from the original, 24item ABC Scale. A further factor analysis was then conducted which revealed a new factor structure with scale items loading onto only 4 factors, these being described as: Grades, Verbalizing, Studying and Attendance.
The data collected in this project has been acquired through the complete, original 24scaleitem scale and since Sander & Sanders' 17point scale was revised by merely omitted some scale items leaving the others exactly as they had been in the earlier, 24item scale, it has enabled both ABC24 and ABC17 outputs to be generated from my local data. The table below presents these outputs for comparison:
Research Group 
Research subgroup 
n 
ABC24 mean 
ABC24 sd 
ABC17 mean 
ABC17 sd 







DI 
CONTROL (RG:DI600) 
47 
57.89 
15.24 
57.49 
15.75 
Hedges' g effect size / Student's ttest pvalue: DI600:DNI: 

g = 0.483 / p = 0.041 

g = 0.521 / p = 0.032 

ND 
TEST (RG:DNI) 
18 
64.92 
12.43 
65.24 
12.26 








BASE (RG:ND400) 
44 
72.15 
12.35 
72.25 
12.66 
This reveals little difference between the mean ABC24 and mean ABC17 values for any of the three, principal research subgroups, showing that a slightly greater effect size is generated between the TEST subgroup and the CONTROL subgroup using the 17point ABC Scale. In both cases (ABC24 and ABC17) Student's ttest reveals that a significant difference (p < 0.05) is present between the sample means (onetail test, 5% level) of the TEST and the CONTROL subgroups which is an outcome that supports a rejection of the Research Null Hypothesis that there is no difference in Academic Behavioural Confidence between the TEST subgroup and the CONTROL subgroup.
Comparing ABC24 and ABC17 outputs
I, it is unusual to be able to use two, so closely related versions of a metric to evaluate the same construct and reflecting on this, first of all identified the merit of applying a statistical test to try to determine whether or not there is a significant difference in the effect size outputs that the two versions have generated for this local data. Should the result indicate no significant difference between the effect sizes, this would imply that whichever version of the metric were used, the broader outcomes would be the same, statistically at least. To date, no literature has been found where this idea has been converted into a workable statistical process that might be followed as an exemplar or to offer guidance about how a comparison of effect sizes in this context might be achievable, especially since the distribution of effect sizes is unknown. But it is possible to calculate a confidence interval for the population Cohen's 'd' effect size, 'δ', this being an effect size measure of which Hedges 'g' is a slightly more refined version. From this it may be possible to broadly establish whether these two effect sizes are in fact statistically the same. The Confidence Interval calculation process for Cohen's 'd' that is accessible (Cumming, 2012) generates the confidence interval for the estimated population effect size and when using the ABC24 Scale on my data Cohen's 'd' effect size difference between the ABC mean values between the Test and the Control subgroups emerged as 0.068 < δ < 1.032, and for the ABC17 mean values, 0.032 < δ < 1.070. Given the very close match between these two confidence intervals (wide as they are) this is suggesting that to all intents and purposes, the difference in effect sizes when using ABC24 compared with using ABC17 is marginal. Sander & Sanders claim that the criterion validity of the ABC Scale is enhanced through their factor analysis procedure and the subsequent reduction into a 17point scale where criterion validity is presumed to refer to predictive criterion validity although with my data at least, given the negligible variation between effect size differences when using the 17point or the 24point scale, it is not possible to argue the same point. Secondly, and as a further ‘bootstrap’ to explore whether significant differences in outputs from the ABC24 and ABC17 point scales existed, a somewhat contrived adaptation of the tTest was applied. Although not strictly meeting the criteria for the application of this test, by treating the ABC24 and ABC17 point scales as equivalent to a pre postintervention examination of observable differences in outputs for the same sample – in this case, the complete datagroup  a pairedsamples ttest could generate an output worthy of interpretation. On running this test, QQ plots showed distributions closely aligned with the leading diagonal indicating that normality could be assumed (Figure 29). The ttest itself indicated no significant differences between the outputs of the ABC24 and ABC17 point scales (t=0.099, p=0.921, 2tailed test).
Much has been drawn from the statistical rigour that Sander & Sanders have demonstrated to justify the robustness of their Academic Behavioural Confidence Scale and given the growing reputation that the metric is gaining as a wellproven and valid scale for exploring various aspect of academic confidence amongst university students (eg: Nicholson et al, 2013, Matoti & Junquiera, 2009, Hlalele, 2010, Taylor & House, 2010, Stevenson, 2010, Matoti, 2011, Chester et al, 2010, Willis, 2010, Chester et al, 2011, Wesson & DerrerRendall, 2011, Hlalele & Alexander, 2011, Keinhuis et al, 2011, Newstead, 2011, Aguila Ochoa & Sander, 2012, Hlalele, 2012, McLafferty et al, 2012, Kienhuis, 2013, Putwain et al, 2013, de la Fuente et al, 2014, Takahashi & Takahashi, 2015, Marek et al, 2015, Sanders et al, 2016, Braithwaite & Corr, 2016, Putwain & Sander, 2016), it is being used in this project without hesitation as the best metric available for exploring the issues being addressed by the research hypotheses.
However in the interests of trying hard to ensure that analysis of the ABC Scale's output is scrutinized very carefully and contextually in respect of the datapool that has generated the results, combined with Sander & Sanders' precedent for investigating the factor structure of the ABC Scale, there are reasonable grounds for conducting PCA on this project's local data. This will be to determine whether a similar or a different factor structure (to the S&S ABC17 or ABC24 point scales) emerges and which may then be more acutely integrated with the datapool's output for the factor structure of Dyslexia Index Profiler accordingly. Additionally, the possibility should be considered that there may be an unwitting bias in Sander & Sanders' analyses due to students being all undergraduates and all from similar subject specialisms which may mean that the factor structure of the ABC Scale as determined by their PCA has its best accuracy when applied to data collected from a student demographic that is reasonably comparable to their studies'. Whereas in this project, no data was collected about students' subject specialisms nor their levels of study and so drawing research conclusions based on outputs from analysis of data collected from my more broadbased source from the subscales of the existing ABC24 Scale's 6factor, or the ABC17 Scale's 4factor structure could be questionable. This cautious approach is partly to demonstrate an awareness of the need for data analysis processes to be as relevant and applicable as possible. But it is also a consequence of earlier attention drawn (subsection 2.##) to an example of the reportedly disappointing effectiveness of a constructevaluating metric developed from a closed cohort sample at a single university when used to explore the same construct as presented in a sample taken from a different university's student community (the YAA Adult Dyslexia Scale; (Hatcher & Snowling, 2002) which was adapted for use in an Australian university with disappointing results (Chanock et al, 2010)). Chanock's highlighted the limitations of the YAA due to its development being based entirely on data collected from a single source arguing that this reduced its adaptability for use in outwardly similar contexts but where, in this case, significant differences in testsubject demographics appeared sufficient to upset the results.
Hence the specific factor structure that is revealed through PCA locally is almost certainly likely be more appropriate for use in the project's comparative analysis with the Dyslexia Index metric, rather than relying on outputs from the factor structure and revised 17item ABC scale determined from the Sander & Sanders' collection of studies. It is reasonable to suppose that Corkery et al (2011) followed a similar line of reasoning to justify applying principal component analysis to the local data in their study and the factors which emerged showed differences in comparison to both the Sander & Sanders (S&S) factor structures for both the 24scaleitem and the 17scaleitem ABC Scales and indeed to the factor loadings and subscales which emerged from the PCA on my data. Hence we see an emerging precedent for applying PCA to local data within a research project in order to examine the outputs generated from the application of the Academic Behavioural Confidence Scale, at the very least in order to compare the resulting factor loadings and subscales with outputs generated from the existing, Sander & Sanders ABC subscales. Unfortunately this does raise an issue about the stability and hence the generalizability of ABC factors per se and suggests that researchers choosing to use the metric in their studies may be wise to explore the factor structre of the ABC Scale in relation to their local data unless it could be shown that the demographics of their research cohorts may closely resemble those of Sander & Sanders' original (combined) studies. On a more general point but also in line with Chanock's (2010) observations in respect of the York Adult Assessment (for dyslexia) and using evaluatative metrics developed entirely from one single institution's local datapool in a different institution, this suggests that research outputs may be more robust were researchers more inclined to extend their data analysis procedures to include principal component analysis on their local data when using metrics developed elsewhere.
The Principal Component Analysis applied to the 24scaleitem Academic Behavioural Confidence Scale used to acquire this project's data has resulted in 5 factors being identified. However, just as for the factorial analysis conducted on the Dyslexia Index scale, it can been seen from the Rotated Component Matrix (below) that the factor structure that emerged from this analysis of the ABC Scale was not as simple as is desirable because a few dimensions (that is, ABC scale items) loaded on to more than one factor. By applying an element of best reasonable judgement, it is considered that there is justification for accepting the outcomes and in accordance with the nature of scale items that emerged as sensibly loading onto each of the 5 factors, these have been categorized as:
 ABC24 Factor 1:  Study Efficacy
 ABC24 Factor 2:  Engagement
 ABC24 Factor 3:  Academic Output
 ABC24 Factor 4:  Attendance
 ABC24 Factor 5:  Debating
Rotated Component Matrix for Academic Behavioural Confidence 24point scale 
item # 
item statement 
Factor 
Communalities 
ABC 

1 
2 
3 
4 
5 
Extraction 


study efficacy 
engagement 
academic output 
attendance 
debating 

121 
 plan appropriate revision schedules 
0.809 




0.761 
101 
 study effectively in independent study 
0.703 




0.637 
104 
 manage workload to meet deadlines 
0.695 




0.593 
113 
 prepare thoroughly for tutorials 
0.665 




0.578 
122 
 remain adequately motivated throughout my time at university 
0.639 




0.555 
119 
 make the most of university study opportunities 
0.637 




0.570 
114 
 read recommended background material 
0.602 


0.318 

0.530 
103 
 respond to lecturers' questions in a full lecture theatre 

0.799 



0.662 
110 
 ask lecturers questions during a lecture 

0.774 



0.707 
112 
 follow themes and debates in lectures 

0.654 



0.610 
105 
 present to a small group of peers 

0.624 



0.483 
102 
 produce your best work in exams 

0.605 


0.444 
0.692 
111 
 understand material discussed with lecturers 

0.597 



0.516 
117 
 ask for help if you don't understand 

0.454 



0.406 
116 
 write in an appropriate style 


0.819 


0.736 
115 
 produce coursework at the required standard 


0.814 


0.805 
107 
 attain good grades 
0.383 

0.740 


0.740 
120 
 pass assessments at the first attempt 


0.696 


0.593 
123 
 produce best work in coursework assignments 
0.492 

0.511 

0.344 
0.649 
106 
 attend most taught sessions 



0.812 

0.739 
124 
 attend tutorials 



0.772 

0.675 
118 
 be on time for lectures 



0.676 

0.522 
108 
 debate academically with peers 

0.435 


0.640 
0.652 
109 
 ask lecturers questions in oneone settings 
0.321 
0.346 


0.632 
0.624 








The output from the analysis conducted in the application SPSS, also indicated a KaiserMeyerOlkin measure of sampling adequacy of 0.866, regarded as 'meritorious' (Kaiser, 1974), which confirms that conducting principal component analysis has been a useful technique to undertake; and the Bartlett test of sphericity for the null hypothesis that there are no correlations between any of the variables showed a level of significance of p < 0.0005 which is a highly significant result, indicating that this null hypothesis is to be rejected and that there are correlations between variables so that applying PCA to the data is likely to reveal a useful factor structure.
Proportions of variance explained
As outlined above for the principal component analysis conducted for the Dyslexia Index metric, the process attempts to account for all the variance in each of the variables if all of the components are retained. Using the same, Eigenvalue1 extraction factor, the five components (factors) which emerged from the analysis of the ABC24 Scale accounted between them for 62.6% of the total variance with the most significant influence being from Factor 1, study efficacy which explained 35.0% of the total variance.
Consistent with this Table of Variances Explained, it can also be seen from the factor loadings table above (Rotated Component Matrix) that in terms of the number of scale items loading on to factors, the strongest influence to Academic Behavioural Confidence overall appears to be attributable to Factors 1 and 2, study efficacy and engagement respectively, and to a lesser extent, Factor 3, academic output. Given the foundations of the ABC Scale being firmly rooted in Bandura's Social Cognitive Theory and all it says about selfefficacy where it has been demonstrated that mastery experience is one of the key contributors, it is pleasing to note that these three factors are strongly indicative of the relationship between academic confidence and academic learning management processes, success in which might be argued as strong evidence of a learner's developing academic mastery.
But this table of factor loadings also shows the significance that developing strong academic writing styles has on academic confidence with a factor loading of 0.819 being the highest of all 24 loadings. Where this impacts on students presenting high levels of dyslexianess is that clearly while education systems remain steadfastly rooted in literacy competencies with academic outputs based on writing skills being the principal form upon which assessments of academic capabilities are gauged, students with a dyslexia that has not been strategically ameliorated, whether unknowingly or through learning support and development, will continue to be disadvantaged. This is a significant point and returns again to my argument in support of Universal Design for Learning where access to learning becomes more adaptable to learner needs and less constrained by conventional and traditional processes for the transmission of knowledge and the expression of ideas, all discussed in an earlier section.
Comparing factor structures
It is useful to consider this factor analysis in the light of that of Sander & Sanders (2009) study, reproduced below. An attempts has been made to compare the grouping of dimensions into factors that emerged from their PCA to the PCA on this project's local data, indicated by what has been termed the 'closest map'. This is where dimensions from both the S&S PCA and my own PCA result in similar dimensional groupings. It has been necessary of course to revert back to Sander & Sanders original 24item scale to make this comparison. It can been seen that the first four factors of both analyses present similar loadings and groupings of dimensions although Sander & Sanders' factors 5 and 6 draw no obvious mapping to the factor that have emerged out of my PCA.
Rotated Component Matrix for Academic Behavioural Confidence 24point scale
(adapted from: Sander & Sanders, 2009, p25) 
item # 
item statement 
Factor 
ABC 

1 
2 
3 
4 
5 
6 

Sander & Sanders' factor designations: 
studying 
verbalising 
grades 
attendance 
understanding 
requesting 

closest map to ABC24(5) in my data: 
study efficacy 
engagement 
academic output 
attendance 
no mapping 
no mapping 
121 
 plan appropriate revision schedules 
0.80 





101 
 study effectively in independent study 
0.72 





122 
 remain adequately motivated throughout my time at university 
0.62 





104 
 manage workload to meet deadlines 
0.56 





103 
 respond to lecturers' questions in a full lecture theatre 

0.85 




105 
 present to a small group of peers 

0.81 




108 
 debate academically with peers 

0.67 




110 
 ask lecturers questions during a lecture 

0.58 




120 
 pass assessment at the first attempt 


0.83 



115 
 produce coursework at the required standard 


0.74 



116 
 write in an appropriate style 


0.67 



107 
 attain good grades 


0.66 



123 
 produce best work in coursework assignments 


0.55 



102 
 produce best work in exams 


0.51 



124 
 attend tutorials 



0.86 


106 
 attend most taught sessions 



0.82 


118 
 be on time for lectures 



0.40 


119 
 make the most of university study opportunities 
0.17 
0.24 

0.29 

0.21 
113 
 prepare thoroughly for tutorials 




0.73 

112 
 follow themes and debates in lectures 




0.72 

111 
 understand material discussed with lecturers 




0.68 

114 
 read recommended background material 




0.68 

109 
 ask lecturers questions in oneone settings 





0.85 
117 
 ask for help if you don't understand 





0.83 








It is of note that scale item 119 was not attributed to any of Sander & Sanders' factors with the highest loading of just 0.29 with the factor 'attendance'. The extraction commonalities was not published in Sander & Sanders 2009 paper from which this data has been drawn.
A cursory inspection of the two tables sidebyside shows that:
 My Factor 1, 'study efficacy' includes all four dimensions in S&S Factor 1: 'studying', with two of the remaining 3 dimensions attributed into S&S Factor 4, 'understanding' and the final dimension, 119, 'make the most of university study opportunities' being unattributed in the S&S analysis; however with a loading factor of 0.637 in my data, this dimension should clearly be included in my Factor 1. Where Sander & Sanders designate these dimensions into two factors 'studying' and 'understanding', these two factors together map to my Factor 1, so aspects of academic confidence at university that Sander & Sanders call 'studying' and 'understanding', I call 'study efficacy'. Sander & Sanders renamed their Scale as a measure of Academic Behavioural Confidence. so the dimension 111 'understand material discussed with lecturers' might be at odds with the revised rationale for the scale which is to assess actions and plans related to academic study because to understand is more of an executive, cognitive function rather than a behavioural one. Indeed the S&S Factor 'understanding' is perhaps inappropriately named for this same reason not least as the other dimensions in this factor are more actionoriented. Dimension 111 is one of the 7 dimensions of my Factor 2 engagement which is arguably a better place for this dimension because participants in this project at least have focused more on the interactional process with their lecturers implied in this dimension's stem statement rather than the cognitive process of understanding.
 3 of S&S's 4 dimensions in their Factor 2, 'verbalising' map to to the same 3 out of 7 dimensions in my Factor 2, 'engagement'. S&S include dimension 108 'debate academically with peers' into their Factor 2 whereas I attribute this dimension to my Factor 5, 'debating'. Two further dimensions in my Factor 2 are attributed in the S&S analysis to their Factor 4, 'understanding' so this is suggesting that the S&S Factors 'verbalising' and 'understanding' when taken together make a close map fo my Factor 2 'engagement'.
 In S&S Factor 3, 'grades', 5 out of the 6 dimensions in this factor also appear in my Factor 3, 'academic output' so this is a close mapping between the two factors. The additional dimension, 'produce best work in exams' in S&S Factor 3 however, presented a higher loading with Factor 2, 'engagement' in my analysis.
 My Factor 4, 'attendance' contains exactly the same 3 dimensions as S&S Factor 4, 'attendance' so there is an exact mapping here.
 S&S designated a Factor 6, 'requesting' which contained dimensions 109 and 117, 'ask lecturers questions in oneone settings' and 'ask for help if you don't understand' whereas the former of these (109) is grouped with dimension 108, 'debate academically with peers' in my PCA with these two dimensions alone forming the final Factor 5, 'debating' in my analysis, so this acknowledges more of a dialogical interactional relationship between not only students and their peers, but also with their academic staff.
In summary, it can be seen from this brief discussion relating to differences and similarities between the assignment of dimensions into factors according to the PCA conducted on my local data in comparision to the Sander & Sanders analysis, that there appears to be merit in the application of a local principal component analysis on data collected through the Academic Behavioral Confidence Scale but perhaps only where the metric is being used in conjunction with another evaluator  in the case of this study, Dyslexia Index. Tops et al (2012) in a discussion about how to identify dyslexia in higher education students argued in support of the generalizability of a new model for exploring meaning in data rather than accepting that the model may only be valid for the data from which it is derived (ibid, p7) and although the reference was more so part of a point relating to post hoc discriminant analysis where tests are applied to data first, followed by an interpretation about partipants may subseqently be classified, it is relevant here because in transferring the point to this context, this is perhaps suggesting that the ABC Scale, at least in terms of its factorial components, remains at an early stage of development. This is not least as the discussion above has shown that the establishment of a factor structure for the scale as an outcome of a principal component analysis on a local datapool does not necessarily produce the same factor structure as the originators of the scale have proposed. The outcome of the Corkery et al (2011) study implies a similar conclusion where the PCA on their local, Academic Confidence Scale data collected from university students showed that 20 of the 24 dimensions of the Scale loaded onto just three factors with the remaining 4 dimensions being excluded because they loaded substantially on to two or more factors. The three factors in this study were labelled 'Study', 'Interact' and 'Attend' reflecting the groupings of dimensions accordingly. The Rotated Component Matrix was not published so scrutiny of which dimensions loaded onto which factors has not been possible, however the labels attributed to the Corkery factors imply similarities to Factors 1,2,4 in my study (Study Efficacy, Engagement, Attendance). [This para isn't very good  I need to rethink and rewrite this]
4.3 Dx Factor x ABC Factor Matrix
The principal component analysis applied to the data acquired through the Dyslexia Index metric suggests that it loads onto 5 factors and these have been designated:
 Reading, Writing, Spelling
 Thinking and Processing
 Organization and Timemanagement
 Verbalizing and Scoping
 Working Memory
and that the PCA applied to data collected on Sander & Sanders full, 24item Academic Behavioural Confidence Scale has also loaded onto 5 factors, designated:
 Study efficacy
 Engagement
 Academic output
 Attendance
 Debating
The dimensions that constitute all of these factors are listed again in the table below as these will be referred to in the analysis which follows:
To explore the interrelationships between each of these two sets of 5 factors, a 5 x 5 cell matrix has been constructed, presented and discussed in more detail below, which sets out Hedges' 'g' effect size and Student's ttest pvalues between the three research subgroups, DNI (TEST), DI600 (CONTROL) and ND400 (BASE) when these are reestablished according to each of the Dyslexia Index factors in turn. This means that respondents' datasets have been resifted 5 successive times using each Dx Factor in turn as the sole sifing criteria. The outcome of the sifting process produces different research subgroups when the Dx boundary value of Dx = 592.5 is freshly applied. So for example, when research group ND is sorted with Dx Factor 1 as the sort specifier, the TEST subgroup and the BASE subgroup will each contain a different set of respondent datasets in comparison to those obtained when Dx Factor 2 is the sort specifier, and so forth. To arrive at the Dx factor values, the weighted mean average process applied to the dimensions has been retained with only a scaling factor being introduced to adjust the weighted mean averages to a value #/1000. The rationale for using the weighted mean average has been described earlier in the Research Design section.
Thus, ordering and reordering the datasets into the three subgroups according to Dx values for each Dx factor has generated a fresh opportunity to analyse the data from a deeper perspective. It is drawing on the more recent view that dyslexia, such that it can be defined, is most likely to be a multifactoral condition and that the relative balances of the factors can be significantly different from one dyslexic individual to another whilst both are still identified as dyslexic. This principle of theory has been described more fully in the Section: Theoretical Persectives, above but in summary, the arguments supporting the multifactoral approach have been developed most lately by Tamboer, Vorst and Jon (2017), building on the earlier ideas of Pennington (2006) and more lately of Le Jan et al (2011) and Callens et al (2014).
The objective in exploring the DxABC matrix is to see if this can lead to a better understanding about the impacts of specific groups of dyslexia dimensions on not only academic confidence overall but also on the components of academic behavioural confidence where these too, have been established by the process of principal component analysis.
To understand more clearly the process of dataset ordering/reordering according to Dx Factor consider as a case study example, student respondent #96408084 in the datapool who is in research group ND because no reported dyslexia was declared but who presented an overall Dyslexia Index of Dx = 604.94. At the outset, this Dx value placed this respondent into the TEST research subgroup, DNI, which is students with an unreported dyslexialike profile and as with the complete datapool, together with the initial declaration of dyslexia/no dyslexia, applying the Dx boundary fence has sorted all datasets into groups and subgroups accordingly which has enabled an overall idea to have been established about the impact of dyslexia on academic confidence, summarized in Key Outcomes 1 and 2 above.
However when this student's dataset is reviewed on a factor by factor basis, the Dx factor values are quite disparate ranging from Dx = 824.11 in Factor 1, Reading, Writing, Spelling, to Dx = 80 in Factor 4, Verbalizing and Scoping. So although this respondent is sifted into research subgroup DNI on the basis of overall Dx value, because at Dx = 604.94 this is above the boundary value of Dx > 592.5, when the Dx value for each factor is considered in turn this student will be sifted into research subgroup DNI only for Factors 1 and 2, as Dx values for the remaining factors are below the boundary value of Dx = 592.5. Indeed with a Dx Factor 4 value of Dx = 80, this particular student would actually feature in research subgroup ND400 for Dx Factor 4 Verbalizing and Scoping, which is the research subgroup of students presenting very low levels of dyslexianess, and for Dx Factors 3 and 5, this student's data would not be sifted into any of the three, key research subgroups on which values of Academic Behavioural Confidence are being compared.
Dx overall 
Student respondent 
Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 







604.94 
#96408048 
824.11 
746.99 
512.26 
80.00 
489.51 
= RG:DNI (TEST) 

= RG:DNI (TEST) 
=RG:DNI (TEST) 
=RG:NDx400 
=RG:ND400 (BASE) 
=RG:NDx400 







The significant point is that the Dx values for Dx Factors 1 and 2 are high, suggesting that this particular student is presenting a strongly dyslexic profile in the two (factor) areas of Reading, Writing, Spelling, and Thinking & Processing – conventionally regarded throughout decades of dyslexia research with children as being key indicators of the syndrome. Although this quasidyslexia is only indicated through the selfreport output of the Dyslexia Index Profiler, which, as has been established earlier is not, and is not claiming to be a dyslexia screener, it is nevertheless possible that this output may be indicating that this student does present a dyslexia that so far has been unidentified. The contention that this research project is aiming to justify however, is that such a student may be better left alone to pursue her studies in her own way rather than be formally screened and possibly identified as dyslexic because to do so may weaken her academic confidence. To support this, consider the outputs from her responses to the Academic Behavioural Confidence Scale and how these compare to the mean ABC Factor values for the research groups of nondyslexic students (RG:ND) and dyslexic students (RG:DI):

ABC24 
ABC241
Study Efficacy 
ABC242
Engagement 
ABC243
Academic Output 
ABC244
Attendance 
ABC245
Debating 
RG:ND400 mean values 
72.31 
67.75 
66.92 
79.85 
83.05 
72.19 
RG:ND400 standard deviations 
12.35 
16.57 
16.05 
13.57 
19.44 
21.58 







RG:ND mean values 
67.2 
65.5 
61.1 
73.9 
80.9 
68.1 
RG:ND standard deviations 
13.9 
17.8 
18.1 
16.4 
19.2 
21.3 







no. SD's above + / below  RG:ND means 
+0.19 
0.02 
+0.04 
+1.39 
+0.47 
1.93 
This respondent #96048048 
70.7 
65.1 
61.9 
96.8 
90 
27 
no. SD's above + / below  RG:DI means 
+0.79 
+0.43 
+0.76 
+1.68 
+0.47 
1.59 







RG:DI mean values 
58.4 
54.9 
47.7 
62.8 
80.4 
64.4 
RG:DI standard deviations 
15.5 
23.1 
18.6 
20.2 
20.1 
23.5 







RG:DI600 means 
57.89 
55.92 
45.76 
59.89 
81.82 
66.38 
RG:DI600 standard deviations 
15.24 
21.56 
19.31 
20.24 
17.87 
22.59 
Aside from being a very interesting overview of this student's ABC values both overall and at a factorial level, by viewing in relation to the mean values of both the nondyslexic (RG:ND) and the dyslexic (RG:DI) groups a picture emerges which shows this student's academic confidence is approximately at or above the mean values for nondyslexic students, with values ranging from 0.02SD to +1.39SD except for ABC Factor 5, Debating, where we see a value of nearly 2 standard deviations below the mean value for the nondyslexic group. When compared with mean values for the dyslexic group which as can be seen are all depressed relative to the nondyslexic means although only very marginally for the ABC Factor 4, Attendance, we see an even starker contrast where this student's mean ABC values range from +0.43SD to +1.68SD above the dyslexic group's mean values with again, only the ABC Factor 5 mean value showing a contrary result.
In a realworld, universitylearning context it can argued that gaining a perspective on this student's blend of academic learning management strengths and weakness and about their academic confidence across the spectrum learning and study behaviours and preferences related to academic study could be highly useful, not only for university learning development tutors for assessing where this individual might benefit from advice and development to mitigate the impact of apparent learning challenges whilst at the same time capitalize on areas of strong competency, but also to the student themselves as a means to enhance their metacognition and metalearning to enable them to become more at ease with their own study routines, to enable them to reflect on how some of these may be modified to mitigate a variety of affective responses to the challenges of study that they may come to realize are inhibiting factors in relation to their academic performance, In addition to the Dx Factorbased profile that has been generated and displayed earlier in this thesis report, a more detailed profile map which presents data for every Dyslexia Index dimension collectively in the form of a 'rose' chart may also be a powerful gauging instrument and as an exemplar, the rose chart has been generated for this casestudy example student respondent #96408048 (below). Although not constructed and displayed here, it is of course possible to construct a similar rose chart profile diagram for this student's blend of actions, plans and behaviours related to their academic study as revealed by their selfreport output on the Academic Behavioural Confidence Scale which would be equally valuable.
[Dimensions are grouped according to the Dx Factors which contain them rotating clockwise thus: Dx Factor 1, Reading, writing, spelling: Dimensions Dx20 > Dx02; Dx Factor 2, Thinking and processing: Dimensions Dx15 > Dx16; Dx Factor 3, Organization and Timemanagement: Dimensions Dx05 > Dx07; Dx Factor 4, Verbalizing and scoping: Dimensions Dx 14, 04; Dx Factor 5, Working memory: Dimensions Dx13, 12].
Consider the summary table and rose chart (below) for another respondent whose overall Dyslexia Index value of Dx=306.04 placed this student in the BASE research subgroup of those whose Dyslexia Index indicated low levels of dyslexianess (Dx<400). As would be expected, levels of dyslexianess are generally low although some significant anomalies are immediately apparent, notably where this respondent has selfreported as an holistic thinker (Dx14) and also reports significant challenges in organization and timemanagement which has emerged as not uncommon across the complete datapool and is discussed further below.
Dx overall 
Student respondent 
Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 







306.04 
#65118727 
288.88 
259.95 
504.69 
539.22 
293.90 
= RG:ND400 (BASE) 

= RG:ND400 (BASE) 
=RG:ND400 (BASE) 
=RG:NDx400 
=RG:NDx400 
=RG:ND400 (BASE) 







As a final comparison, the rose chart below presents the blend and balance of dyslexia dimensions presented by a respondent with identified dyslexia and with a significant level of dyslexianess of Dx=719.63 as determined through the Dyslexia Index Profiler which falls at the median position in the CONTROL research subgroup. It can be seen how generally different this profile is in comparison to the profile directly above for a respondent from the nondyslexic BASE research subgroup although some similarities are present. More interesting and significant is the likeness of the chart of the respondent from the TEST subgroup of quasidyslexic students (top) to the dimension profile below of the dyslexic student.
Dx overall 
Student respondent 
Dx Factor 1 
Dx Factor 2 
Dx Factor 3 
Dx Factor 4 
Dx Factor 5 


Reading, Writing, Spelling 
Thinking & Processing 
Organization & Time Management 
Verbalizing & Scoping 
Working Memory 







719.63 
#17465316 
752.00 
647.03 
635.03 
764.53 
516.34 
= RG:DI600 (CONTROL) 

= RG:DI600 (CONTROL) 
= RG:DI600 (CONTROL) 
= RG:DI600 (CONTROL) 
= RG:DI600 (CONTROL) 
=RG:DIx600 







It is immediately apparent that these three students present different blends of strengths and weakness in academic learning management and dyslexianess characteristics with not only contrasts, but also similarities being clearly visible and whilst some further developmental work needs to be conducted to validate and refine the Dyslexia Index Profiler (or however it may be more properly titled), the concept shows promise for becoming a useful mechanism to enhance the targetting of learning development or study skills initiatives in university contexts. It is hoped that there will be an opportunity to return to this at a later date.
In summary:
It must be reemphasized that the datasets which comprise research subgroups ND400, DNI and DI600 after sifting the datapool according to Dyslexia Factor 1 values for example, will or may contain a different collection of datasets when the sifting criteria is according to Dx Factors 2, 3, 4 and 5. The table below summarizes how the sizes of the research subgroups change as a result of this process. These redistributions of the datapool enable a alternative insight to be taken into differences in academic confidence (at a factoral level) between students with reported dyslexia and those with unreported dyslexialike profiles on a Dx factorbyfactor basis. At the outset this appears to be overcomplicating the analysis process but it is justified because the results that emerge appear to be showing a range of significant differences across the research subgroups when these are examined with Dyslexia Index Factor as the determining criteria. As reported in the literature review earlier, examining dyslexia at a factorial level has been gaining traction in recent research and particularly a late study conducted with students at the University of Amsterdam (n=154) demonstrated nine distinguishable factors of dyslexia. These were classified as: Spelling, Reading, Rapid Naming, Attention, ShortTerm Memory, Confusion, Phonology, Complexity, and Learning English (Tamboer et al, 2017). Note the researchers' comment that this final factor, Learning English, might only have become significant in their cohort of students because these individuals were highly academically competent in their own language (Dutch) and likely to have overcome many of their earlier linguistic difficulties in their native tongue, but not with foreign languages. It may be reasonable to discount this factor in student coherts with L1 English. This paper has been particularly singled out not only as an example of a study that has explored dyslexia at a factorial level, but also because the cohort of research participants used in the study closely resemble those in my project, particularly because they comprised known dyslexic students, those who were clearly presenting no indications of dyslexia as determined by any of the conventional criteria, but also, a significant subgroup of maybedyslexics emerged out of the analysis. The outcomes of the study have a bearing on the factorial analysis outcomes in my project due to the similarities of the factors and the process by which they were established. This is despite Tamboer et al's principle interest being determination of the predictive validity of their newlydeveloped screening test for dyslexia in the Dutch language. Their research suggested that this validity was strong, leading to the conclusion that the screening test that had been developed would be useful as a dyslexia identifier in higher education contexts. Of particular interest was that the selfreport questions that had been included in their data collection instrument also returned high construct validity and significantly, an even higher predictive validity than the other tests that had been included in the screener (ibid, p167). A more reflective review of this and other relevant studies recently conducted in The Netherlands has been included as part of literature review in the Theoretical Perspectives section earlier. To date, no studies have been found which use a factorial analysis of a dyslexia evaluator in higher education settings as an independent variable correlator for exploring another construct, in the case of this project, academic confidence.
Reconstructed Research Subgroups
Thus, the outcomes of the process described directly above is shown in the table below which summarizes the relative sample sizes of the reconstructed research subgroups when these are sifted according to Dyslexia Index Factor using the established boundary value of Dx=592.5. Recall that this boundary value provided the criteria for sorting students from research group ND (n=98) into the TEST subgroup: RG:DNI and the CONTROL subgroup: RG:ND400, and for sorting students from research group DI (n=68) into the CONTROL subgroup: RG:DI600.
The table is important and is interpreted thus:
 For each Dx Factor, the top three rows of data list how the core research groups ND and DI are sifted into research subgroups giving the each new sample size as well as its percentage of the respective parent research group. So for example when research groups are sifted according to Dx Factor 1, the new research subgroup ND400 is sample size n=40, which represents 41% of the parent research group ND (n=98).
 The lower section of the table shows sample mean Dx values for each of the subgroups. The main focus is on the mean Dx values for subgroups DNI and DI600, with outputs from the ttest for significant differences between sample means (a 2tail test at the 5% level of significance) being reported. The sample mean Dx values for research subgroup ND400 is also provided for comparison between the absolute values.
In the complete datapool it has been established through the ttest that by setting the boundary Dx value at Dx=592.5, the mean (overall) Dx values were not significantly different between the TEST and the CONTROL subgroups and therefore it is reasonable that the sample mean Academic Behavioural Confidence can be properly compared betweeen these two subgroups because they are presenting (statistically) the same mean Dyslexia Index. Thus it has been important to apply the ttest comparator again when datasets have been sifted into reconstructed research subgroups on a Dyslexia Index factorbyfactor basis because the datasets comprising each research subgroup are reconstituted 5 different ways according to each of the 5 Dx factors and consequently, the mean Dx values have to be recalculated. This process is to ensure that the Dyslexia Index means remain not significantly different between the reconstructed research subgroups of interest (TEST and CONTROL) and that therefore it will be appropriate to apply the earlier rationale, that it is justifiable to consider ABC effect sizes for differences in mean ABC values on an ABC factorbyfactor basis. It can be seen that for every Dx Factor there remains no significant differences between the respective Dx sample means of the TEST and the CONTROL subgroups and hence this criteria for subsequently examining respective values of Academic Behavioural Confidence is reasonable. [Note: the designation 'Dx20(5)' is reminding us that the 20scaleitem Dyslexia Index Profiler with 5 factors is the data source.]
However some notable features emerge out of this summary table that are worthy of comment and reflection first:
 When the datapool is sifted according to Dx Factor 3, Organization & Time management, the proportion of students in research group ND (those with no declared dyslexia), whose Dyslexia Index value places them into the TEST research subgroup DNI, rises from 18% of their parent research group to a new proportion of 50%. This is telling us that 50% of students with no declared dyslexia are nevertheless presenting levels of dyslexianess that are comparable to declared dyslexic students for dimensions that are gauging their levels of organization and timemanagement in their academic study behaviours. Given that 51% of students in research group DI (those with declared dyslexia) are sifted into the CONTROL research subgroup by the same criteria this outcome is suggesting that as many students with dyslexia as those without consider themselves to have poor levels of organizational and timemanagement competencies in their studies. We know that this aspect of academic learning management commonly presents issues for students with dyslexia at university (Mortimore & Crozer, 2006, Kirby et al, 2008, Olofsson et al, 2012, MacCullagh et al, 2017) but it is of note that the nondyslexic students in this project appear similarly challenged which may be suggesting that weaknesses in developing effective strategic competencies in organizational and timemanagement skills is widespread amongst student communities and not limited to those with specific learning difficulties.
 In sifting the datapool according to Dx Factor 1, Reading, writing, spelling, the Dyslexia Index Profiler appears to offer a concurrent identification of these conventionally accepted aspects of dyslexia to other dyslexia identifiers, this being indicated by 79% of participants in research group DI returning substantive levels of dyslexianess (Dx > 592.5) on dimensions that constitute this factor in the Dx Profiler. However by considering how this Dx Factor distributes students in research group ND, we notice that nearly twice as many would be categorized with levels of dyslexianess that would sift them into the TEST research subgroup were this the only criteria in comparison to the number sifted into this research subgroup according to the overall Dyslexia Index value. This appears to be suggesting two things: firstly that issues with reading, writing and spelling also occur quite commonly amongst nondyslexic students, and secondly, other Dx Factors aside from Factor 1 appear to be making a greater contribution to the overall Dyslexia Index value criteria that sifts apparently nondyslexic students into the TEST research subgroup of students presenting high levels of dyslexianess. This outcome may be indicating that the more conventionallyapplied dyslexia screening tools are weighted towards identifying dyslexia through apparent weaknesses in literacy skills because those who do not present such weaknesses but who are indicated as having significant other challenges in their academic learning management competencies are not identified as dyslexic. It is also possible that this bias towards identifying deficits in literacy skills is a legacy of childfocused dyslexia identifying processes where issues in acquiring reading skills in early years learning are well documented as possible indicators of dyslexia.
 The effect of sifting the datapool according to Dx Factor 2, Thinking and processing, is also worthy of comment where we see that more than any of the others, this factor sorts the highest proportion of students in research group ND into the BASE research subgroup, which is students showing very low levels of dyslexianess (Dx < 400) and only 16% of the research group ND being sifted into the TEST research subgroup (Dx > 592.5) on the basis of this Dx Factor alone.
Outcome of the data reorganization process
Thus this process of exploring how principal component analysis impacts on the datapool for both variables, Dyslexia Index and Academic Behavioural Confidence, has led to the construction of the Dx Factor x ABC Factor Matrix (below).
The matrix is complex and difficult to understand but is attempting to find a way to explore differences in academic confidence between both dyslexic and nondyslexic students and more so, between dyslexic and quasidyslexic students at a factorial level but by doing so it is possible that this is an overcomplicated analysis although an attempt will be made to interpret the output. The matrix presents effect size differences and ttest outputs between mean Academic Behavioral Confidence values for the Test research subgroup, DNI, the Control research subgroup, DI600, and the Base research subgroup, ND400 when these research subgroups are reconstructed on the Dyslexia Index factorbyfactor basis. Two sets of comparators are considered to be pertinent: firstly between the group of students considered to be very unlikely to be dyslexic by presenting low levels of dyslexianess and those others considered to be highly dyslexic (RG:ND400 and RG:DI600 respectively) by presenting levels of dyslexianess Dx > 592.5; and secondly between the Control research subgroup (RG:DI600) and the Test subgroup (DNI) and these data are presented in rowpairs for each Dyslexia Index Factor. In addition to the effect size differences being shown, the absolute ABC values are provided to contextualize the effect size values and the overall key findings of the analysis which relate back to the research hypotheses are indicated in the bottomright of the matrix (the red box). To aid clarity, pvalues for differences between factor mean ABC values for research subgroups ND400 and DI600 have been omitted with the exception of the overall result (bottomright). However, where these are apposite, these data are provided in the discussion which follows below.
For example: consider the row of data for Dyslexia Index Factor 3: Organization and Time Management. When research subgroup DNI (from research group ND) and research subgroup DI600 (from research group DI) are reconstructed using Dx Factor 3 as the sifting criteria (n=49, n=35 respectively, from the table of Sample Sizes above), it can be seen that the mean average for ABC Factor 1: Study Efficacy (for example) for the respondents in these reconstructed subgroups shows an effect size of 0.42 supported by a significant difference between the ABC Factor 1 sample means (p=0.0299). The represented the statistical analysis of the differences between the mean ABC values of ABC=65.3 and ABC=57.0 for research subgroups DNI and DI600 respectively. In other words, students with declared dyslexia present Academic Behavioural Confidence that is significantly lower, statistically, than students in the datapool with no declared dyslexia but who present dyslexialike profiles, when the data is arranged according to the Dx Factor, Organization and Time Management.
[table source: Excel sheet: CompleteDataset DxCompPCA BD425:BS451]
Hedges 'g' has been used because this calculation uses a weighted mean process for pooling the standard deviations of each dataset being considered which is important when the datasets are of different sizes; Student's ttest for independent sample means is used in the onetail format because this is consistent with the research null and alternate hypotheses stated above although aside from the overall analysis outcome (redboxed) ttest outcomes are only provided for the differences in mean values between the CONTROL and the TEST research subgroups, not least to make the data table easier to comprehend. Effect size values between the BASE and the TEST subgroups are considered a sufficient indicator of differences.
Effect size between Academic Behavioural Confidence:
The matrix of effect sizes above is the most substantial data analysis outcome of the research because as well as highlighting the overall effect size between the Academic Behavioral Confidence values that address the main research hypotheses, it has also identified significant effect sizes in ABC factors that may not have otherwise been revealed.
The central analysis outcome evidences a very highly significant difference between the mean values of Academic Behavioural Confidence between students with identified and declared dyslexia (RG: DI) and students who declared no known, dyslexic learning challenges (RG:ND) leading to Key Outcome 1:
Key Outcome 1:
KEY OUTCOME 1:
On the basis of the analysis of this datapool, students with no indications of dyslexia are shown to have, on average, a substantially higher level of Academic Behavioural Confidence than their dyslexiaidentified peers.
[RG:ND400, ABC=72.3; RG:DI600, ABC=57.9; Effect size: g=1.04; Ttest: t=5.037 , p<0.0001]
The second, important overall key finding, which is of the essence of this project, is that the analysis identifies a medium, Academic Behavioural Confidence effect size of 0.48 between the TEST research subgroup: DNI and the CONTROL research subgroup:DI600 (shown in the red box gridsector extreme bottomright of the matrix). This is the effect size difference between the mean ABC values of research subgroups that are constructed on the basis of the overall Dx values of respondents. Taken together with the independent sample means ttest this returns a pvalue of p=0.043 (t=1.743, onetail test), these outcomes are indicating a significant difference between the ABC sample means of the TEST and the CONTROL subgroups which address the second of the enquiry hypotheses and enables a further, more significant key outcome to be stated:
Key Outcome 2:
KEY OUTCOME 2:
On the basis of the analysis of this datapool, quasidyslexic students, that is, students with unreported, dyslexialike profiles, are shown to have, on average, a significantly higher level of Academic Behavioural Confidence than their dyslexiaidentified peers.
[RG:DNI (CONTROL), ABC=64.9; RG:DI600 (TEST), ABC=57.9; Effect size: g=0.48; Ttest: t=1.743 , p=0.043]
These two key outcomes directly address the project's core, research hypotheses: firstly that students with dyslexia present a significantly lower academic confidence, as measured through the Academic Behavioural Confidence Scale, and therefore probably a lower level of academic selfefficacy, than their nondyslexic peers. Key Outcome 1 evidences this conclusion. Secondly, Key Outcome 2 is indicating that students with quasidyslexia present a measurably and significantly higher level of academic confidence than their equivalently dyslexic peers, as determined through the ABC Scale, and also most probably a higher level of academic selfefficacy. These are highly significant results and they are suggesting that students at university with dyslexia but who may be unaware that they may be dyslexic are best left not knowing this because to otherwise suggest that they consider taking a dyslexia screening test, possibly leading to a full dyslexia assessment  such as this may be  and which may subsequently indicate that they have a dyslexic learning difference might adversely affect their academic confidence, their academic selfefficacy and perhaps their academic achievement.
Academic Behavioral Confidence differences on a factorbyfactor basis
The bottom part of the table (above) provides effect size differences and ttest outcomes in ABC Factors for the three research subgroups ND400 (BASE), DNI (TEST) and DI600 (CONTROL) with datasets sifted into each of the subgroups according to the overall Dyslexia Index value. From this row of data we can observe the contributions made to the overall ABC24 effect size differences and ttest outcomes by each of the five ABC Factors and from this an interesting picture emerges: firstly, it can be seen that very little, if any contribution is made by the two ABC Factors 4, Attendance, and 5, Debating, with small or negligible effect sizes between the Test and the Control subgroups and between the Control and the Base subgroups. This seems to be indicating that dyslexia, quasidyslexia or nondyslexia makes little difference to students' academic confidence in relation to their attendance regimes, and the ways in which they interact academically with their peers and with their teachers in oneone settings. This is not to say that there are no differences in attendance regimes and peer and lecturer interactions between dyslexic, quaisdyslexic or nondyslexic students, it is indicating that academic confidence is not a factor which influences this. Whereas the greatest contribution to the overall effect size comes from ABC Factor 2, Engagement (g=0.61) where the ttest also reveals a significant difference at the 5% level between the mean ABC Factor values of the Test and the Control subgroups (ABC242: 57.4, 45.8 respectively, t=2.197 , p=0.0159), and from ABC Factor 3, Academic Output (g=0.41) where although the ttest does not return a significant result at the 5% level for the difference between the Test and Control subgroup sample means (ABC243: 68.2, 59.9 respectively, t=1.475, p=0.0726) a 'medium' effect size is obtained.
ABC242, Engagement, is concerned with the processes and actionactivities of study and include for example, following themes and debates and asking questions in lectures, and 'presenting' to student peers and so this medium to large effect size between ABC242 values for the Test and the Control subgroups is indicating a substantial difference in academic confidence between dyslexic and quasidyslexic students. If we are to take 'academic confidence' in this context as a reflection of selfconfidence then we can locate this difference as a marker about how selfappraisal of efficacy can be strongly influenced by social comparisons (Bandura, 1997b). This may be particularly significant for students with dyslexia who feel socially stigmatized as a consequence of internalizing their learning difference as a disability that is perceived negatively in peer comparison situations (Murphy, 2009, Dykes, 2008), and hence are less likely to participate and engage in study actionactivities which may expose their dyslexia. One respondent typifies these feelings of disenfranchisement through the comments that he added in his questionnaire answers:
 "I don't like feeling different because people start treating you differently if they know you have dyslexia and normally they don't want to work with you because of this ... I don't speak in class because I am not very confident at answering questions in case I get them wrong and people laugh" (Respondent #85897154; RG:DI; ABC = 47.3; Dx = 797.89)
Cameron (2016) in a case study of dyslexic students in higher education reported similar findings of reticence in voicing opinions in the company of peers and clear feelings of social disenfranchisement were evidenced in study exploring feelings and attitudes of a similar group of students to university:
 "I find the world is not arranged in a way that uses my abilities. Rather it is arranged in a way that emphasises my problems" (Thompson et al, 2015, p1338).
Another respondent from the dyslexic group demonstrated the lasting impact of feelings of difference stemming from experiences in earlier schooling:
 "I do have to battle with elements of doubt ... particularly influenced by bullying at primary and secondary school to do with 'stupidity' and 'slowness' and my seemingly unrelated comments to topics at the time" (Respondent #87564798; RG:DI; ABC = 49.2; Dx = 751.23)
although some students with dyslexia are clearly endowed with inner strength that can enable them to mitigate earlier disparagement and build sufficient levels of confidence to tackle university study:
 "When I was at school I was told that I had dyslexia; when I told them I wanted to be a nurse they laughed at me and said I would not achieve this and I was best off getting a job in a supermarket. Here I am now, doing nursing!" (Respondent #48997796; RG:DI; ABC24 = 84.6; Dx = 835.65)
The large effect size of g=1.19 between the Base and the Control subgroups for this ABC Factor is the secondlargest of all the effect size differences between these subgroups and with the mean ABC Factor 3 values of 66.9 (Base) and 45.8 (Control) is highly indicative of the differences between the academic confidence of nondyslexic students and strongly dyslexic students amongst this family of actionactivities in study at university.
ABC243, Academic Output, encompasses academic performance including dimensions such as writing in an appropriate style, attaining good grades and producing good quality coursework and with a medium effect size between the Test and the Control subgroups of g=0.41 this also indicates a strongly measurable difference between the academic confidence of quasidyslexic and dyslexic student in this ABC Factor. Although at face value this also appears to be indicating that students with identified dyslexia present lower levels of academic confidence about performing at a good standard academically in comparison to their quasidyslexic peers, without controlling for other variables such as academic aptitude or legacies from prior academic history and attainment, this outcome should be viewed cautiously. However a highly significant difference between the ABC Factor 3 values of the Test subgroup of strongly dyslexic students and the Base group of nondyslexic students is indicated by a large effect size (g=1.15) with ABC243 values of 59.9 (Test) and 79.9 (Base) and with the sample sizes of these subgroups of n=47 (Test) and n=44 (Base) being respectable, this is a strong indication that the academic confidence of students with dyslexia towards their academic performance outcomes is strongly depressed in comparison to their nondyselxic peers. This may be related to attitude to tackling difficult work where high standards are expected. One respondent from the nondyslexic group wrote:
 "As soon as I get a piece of coursework I try to get it doen to a high standard ... Overall I don't think I pick things up quick[ly]. I'm more of a hard worker than a natural learner. Some of my friends can interpret data straight away whereas I have to take my time to understand it" (Respondent #60017207; RG:ND; ABC24 = 90.2; Dx = 466.90)
which also indicates that this student has not only developed a good workstudy ethic but also has an understanding of his own metalearning processes.
Exploring Academic Behavioural Confidence on a Dyslexia Index factorbyfactor basis
By looking in more detail at the matrix of effect size and pvalue results for the component analysis for both metrics (Dx and ABC) it is possible to explore where the contributing differences between Dx and ABC for each of the subgroups lies and speculate about what this may mean:
Sifting the datapool by Dyslexia Index Factor 1: Reading, writing, spelling
When the data is sifted according to Dx Factor 1 only, there is a negligible ABC (overall) effect size between the Test and the Control subgroups (0.07) suggesting that were dyslexia attributable to only the literacy family of dimensions, there is no difference in academic confidence between students who know about their dyslexia and students who may have an unidentified dyslexia, or may be quasidyslexic which here may be indicating students who are 'garden variety' poor readers and spellers (Stanovich, 1996, p157)  that is, are resembling dyslexia. However the effect size between dyslexic students in the Control subgroup and nondyslexic students in the Base subgroup is large (g=1.14) with the outcome from the ttest indicating a very highly significant difference between the mean ABC values (t=5.286, p < 0.00001, ABC24: 58.4(Control), 73.8(Base)) which is indicating that competency in literacy skills is likely to have a very significant impact on academic confidence. This appears to be adding to the argument that while competency in literacy remains a significant conduit for academic ability to be gauged at university, students who know that they struggle in this area will be further impacted by reduced academic confidence  altogether not an unsurprsing deduction but nevertheless highlights that were curriculum delivery and especially assessment processes broadened to reduce the reliance on literacy skills, courses would become more accessible and inclusive, and those who have better processes for expressing their ideas and communicating their knowledge would not be disadvantaged.
For ABC Factor 4, Attendance, we see a medium effect size of 0.40 between the Test and the Control subgroups and this is the only outcome where the difference between the mean ABC Factor 4 values is significant. ABC Factor 4 is an evaluation of the study behaviours relating to attending lectures and being on time for them, and attending tutorials, so this result seems to be indicating that the quasidyslexic students in this datapool, which in this sifting process comprise 39% (n=35) of the students in the nondyslexic research group (n=98) representing an increase in the Test subgroup sample size by nearly 100%, may be the least diligent in attending their classes. This could be an indication that the quasidyslexic students in this datapool are indeed unidentified dyslexics and these students tend to avoid lectures, classes and tutorials more than their peers because they find them particularly challenging, possibly for any number of reasons but significantly, they may be unaware that the literacy challenges that they experience may be related to an unidentified dyslexia. Significant evidence has been cited in the literature review section of this thesis relating to levels of unidentified dyslexia in university students or where dyslexia is identified at some point during these students' courses. For identified dyslexics, it is possible that these students support themselves with a variety of strategies and studyaid devices and techniques which enable them to engage more effectively with formal teaching situations and hence, they are not deterred from attending them. It is significant to note that for the three ABC Factors, Study Efficacy  which gauges expediency in working through academic tasks by measuring dimensions such as 'studying effectively independently' or 'remain adequately motivated throughout university' for example; Engagement  which provides a measure of studentteacher and studentstudent interaction, exam performance and willingness to ask for help; and Academic Output  broadly gauging confidence in assessment performance, the effect sizes between nondyslexic students and their dyslexic peers are substantial (g=0.85, 1.23, 1.19 respectively) which is indicating strongly depressed academic confidence in students with dyslexia in these factors of Academic Behavioural Confidence.
Dyslexia Index Factor 2: Thinking and processing
Sifting the datapool according to Dx Factor 2 reduces the sample size of the Test subgroup to n=16 (from n=18, representing an 11% reduction), hence establishing just 16.3% of the nondyslexic students to be quasidyslexic if this criteria alone is the determining attribute. However a similar reduction is also observed in the sample size of the Control subgroup which reduces to n=39 (from n=47) which may be suggesting that the family of dimensions that constitute Dx Factor 2 are less significant in determining dyslexianess overall because fewer students in the complete datapool are then presenting levels of dyslexianess that is above the fence of Dx > 592.5. In the complete datapool, these students would then represent 33.1% of the total number of research participants (n=55/166) whereas in applying the Dyslexia Index fence Dx > 592.5 to each research group  that is, to the group of declared dyslexic students (RG:DI) and to the group of declared nondyslexic students (RG:ND)  and then combining these, this would represent a total of n=65/166, being 39.2% of the total datapool. Thus if the attribute for determining whether a student is dyslexic or not according to the Dyslexia Index Profiler were solely based on the family of dyslexia dimensions related to thinking and processing, fewer students would be considered as dyslexic. This might be suggesting that were we to think of dyslexia primarily as a thinking and processing difference, it would be less prevalent, However it is noticeable that there is a significant difference at the 5% level between the mean ABC values of the Test subgroup and the Control subgroup in ABC Factor 3, Academic Output, (t=2.175, p=0.0172) with mediumtolarge effect size of g=0.66, and for ABC Factor 2, Engagement, a closetosignificant difference in mean ABC values with a medium effect size of g=0.43 which is suggesting that even were Thinking and Processing the determining factor of dyslexia, a student who is identified is likely to present substantially lower levels of academic confidence than were they not to be identified, in aspects of their studies at university that are part of the academic processes of engaging with academic materials both independently, with their teachers and with their peers, and in assessment processes. A theme which emerged out of respondents' comments suggests that some felt inadequately prepared for independent learning or finding out more about their own learning processes, characteristics that are recognized as desirable in university study, with some observing that tutorial sessions for study or academic skills missed the target. Two respondents from the dyslexic group said respectively:
 "... universities provide support with tutorials geared at helping the individual with learning but somehow they seem to expect that a person understands what they find difficult ... because they have been living with it their whole lives and can't see objectively what is 'wrong' "(Respondent #87564798; RG:DI; ABC = 49.2; Dx = 751.23)
 "I find independent learning quite difficult and would prefer more in depth help from tutors to give a clear[er] idea of what is accept[able]" (Repondent #17465316; RG:DI; ABC = 56.5; Dx = 719.63)
Another respondent, in this case from the nondyslexic group commented:
 "Ways that studying at university can be improved is by far, to teach students how to learn. We're always taught the content for a specific subject but has anyone ever taught a student on how to learn?"(Respondent #52289216; RG:ND; ABC = 56.9; Dx = 570.73)
These comments may reflect a lack of progress in how some institutions deal with student preconceptions about what it is to study at university and be an independent learner in response particularly to the surge in students now attending universities as an outcome of widening participation initiatives that aim to especially enrol learners from traditionally poorly represented backgrounds. For many of these students the transition to university initiates a conflict in values bringing a challenge to an earlierestablished identity and poses a threat to familiar ways of knowing and doing (Krause, 2006 in Brownlee et al, 2009). Information processing and thinking about it are rightly considered to be critical components of learning and if there are now indications that many students attending university feel unprepared for these cognitive demands this may also be a reflection on the style and structure of their prior learning experiences which, in the UK at least, may have become increasingly reversive towards old learning structures grounded in rote in order to meet demands for greater accountability and in response to institutional academic competitiveness with an equally increased dependency on supplementary subject tutoring and exam coaching.
Thus evidence from the data collected in this project indicates a substantial disparity in academic confidence between dyslexic and nondyslexic learners in the factors related to engagement and to academic output not only overall, as described above, but also when the datapool is sifted according to the Dyslexia Factor 2 criteria, Thinking and Processing. Very large effect sizes are recorded between the Control and the Base subgroups in these two ABC factors (g=1.09, g=1.12 respectively) with the differences in absolute mean values being considerable (ABC242: 46.6(Control), 65.3(Base); ABC243: 59.0(Control), 78.0(Base)). Also indicated is a medium effect size between dyslexic and quasidyslexic subgroups in overall Academic Behavioural Confidence (g=0.45; ABC24: 57.3(Control); 70.6(Test)) with a closetosignificant difference between the means (p=0.064) at the 5% level, which indicates that when the datapool is sifted according to Dyslexia Index Factor 2, Thinking and Processing, the academic confidence of dyslexic students in the Control subgroup is substantially depressed in comparison to quasidyslexic students in the Test subgroup. Further, the academic confidence of dyslexic students in the Control subgroup is very significantly depressed in comparison to nondyslexic students in the Base subgroup (t=3.424, p=0.00047: highly significant at the 1% level; g=0.95 'large'; ABC24: 57.3(Control); 70.6(Base)).
Dyslexia Index Factor 3: Organization and time management
When the datapool is organized according to Dyslexia Index Factor 3: Organization and Time Management, notable effect size differences arise between the Test and the Control research subgroups in all five factors of Academic Behavioural Confidence. There are several features of this data reorganization that warrent comment: firstly, it produces a Test subgroup that is the most sizeable (n=49/98 = 50%) in comparison to the four other Dx Factor sifing processes, together with the smallest Base research subgroup (n=8/98 = 8.2%). In other words, using Dx Factor 3 as the marker for dyslexianess, 50% of the nondyslexic research group would be classified as quasidyslexic. Secondly, effect size ‘g’ values between the Test subgroup and the Control subgroup range from g = 0.38 in ABC factor 5: Debating with the ttest indicating an albeit only just significant difference between the sample means (p= 0.046); to an effect size of g = 0.89 in ABC factor 2: Engagement. The ttest returned a very highly significant pvalue of p=0.0001 (rounded to 4 dp, the actual pvalue is p = 0.0000569). Given that effect size differences are onetail, that is, are set so that a positive effect size indicates that ABC is higher for the Test subgroup than the Control subgroup, these results are indicating that students with reported dyslexia exhibit significantly lower levels of academic confidence when sifted according to the Organization & Time Management factor of Dyslexia Index. Recall that Dx Factor 3 comprises the dyslexia dimensions: 'I think I am a highyly organized learner', 'I find it very challenging to manage my time effectively', and 'I generally remember appointments and arrive on time'. Given that in total, 50.6% (n=84/166) of the complete datapool are presenting significant levels of dyslexianess when gauged through this Dx Factor alone this implies firstly that issues with organizational skills and time management are by no means endemic amongst the dyslexic student community at university alone, more so this outcome is suggesting that developing into an organized and timeefficient learner may be challenging for significant proportion of all students. But of particular note is the outcome which is showing an effect size of g=0.78 for ABC24 overall between the Test and the Control subgroups when these are determined by Dx Factor 3. In addition, the ttest outcome of p=0.0003 (t=3.528) indicates a highly significant difference at the 1% level between the mean ABC24 values (Test: 70.9; Control: 59.7) showing that quasidyslexic students are presenting a significantly higher level of academic confidence than their dyslexiaidentified peers when viewed through the lens of organization and time management. Firstly this is suggesting that given we apply the useful definition of academic confidence from Sander & Sanders' earlier, (2003) study stated as '... the mediating variable that acts between individuals' inherent abilities, their learning styles and opportunities afforded by the academic environment of higher education', being identified as dyslexic significantly depresses academic confidence, and might also be indicative of the ineffectiveness of dyslexiasupporting learning development strategies designed to assist with organization and timemanagement accorded to students with dyslexia at university, assuming that these have been recommended and made available to identified dyslexic students by their higher education institution. Not knowing that you may be dyslexic appears to be better for you when it comes down to the studyskill attribute of organization and time management, however comments returned in the questionnaire do appear to confirm that issues with organization and time management are common across the student community: One respondent from the dyslexic student group located their dyslexia in the context of organizational challenges thus:
 "My dyslexia affects my organization abilities mostly. I'm strong academically ... despite quite strong learning difficulties because I have a good memory. [But] I am chronically late, disorganized and often have large dips in academic confidence" (Respondent #99141284; RG:DI; ABC24 = 33.8; Dx = 496.66)
Another respondent, this time from the nondyslexic group provided a similar reflection, who with an overall Dx=346.15 is located in the Base research subgroup but presented a Dx Factor 3 value of Dx=576.29:
 "I have issues with procrastinating, time management and making an effective plan of knowing where to start ... I leave starting my work to the last minute and ... I leave little time for editing and improvements" (Respondent #21294241; RG:ND; ABC24 = 80.5; Dx = 346.15)
Another respondent echoed poor levels of institutional support that has been suggested by this analysis:
 "I think there could be more support for students with learning difficulties. As of yet, the dyslexic team haven't been very helpful or supportive" (Respondent #61502858; RG:DI; ABC24 = 61.9; Dx = 633.07)
although without learning more about this students circumstances and knowing something of the study support regimes offered by the Dyslexia Support Team if would be inappropriate to read too much into this student's comments.
However a different picture appears to emerge when looking at the differences between the dyslexic students in the Control subgroup and the nondyslexic students in the Base subgroup when these are determined according to Dyslexic Index Factor 3. In all ABC24 factors except ABC244, Attendance, the effect size between these subgroups is small or negligible, contributing to an effect size difference in overall ABC24 of virtually zero (g=0.09, ABC24: 59.7(Control), 58.4(Base). For ABC24 Factor 4, Attendance, a large negative effect size between these two sets of students (g=0.72) although this was not supported by a ttest outcome which indicated only a closetosignificant result at the 5% level (t=1.532, p=0.0647). This outcome seems to be suggesting at face value that disorganized and poorly timemanaged nondyslexic students may also be less diligent in attending their teaching classes and tutorials in comparison to their dyslexic peers but with such a small sample size of nondyslexic students in the Base subgroup (n=8) these outcomes can not be considered as properly indicative of any significant differences.
Dyslexia Index Factor 4: Verbalizing and scoping
The picture which emerges when Dx Factor 4 is applied as the sifting criteria for establishing the three research subgroups is also interesting. Firstly, it can be seen that there is a medium effect size (g=0.61) between the Control and the Test subgroups and although the absolute difference in mean ABC24 values does not appear to be particularly large (ABC24: 57.1(Control), 66.0(Test)) the ttest outcome indicated that this is a highly significant difference (t=2.861, p=0.0026). The principle contributor to this effect size is again arising from ABC24 Factor 2, Engagement as has been reported above in the discussion about outputs when the datapool is sifted according to Dx Factors 2, Thinking and Processing, and 3, Organization and Timemanagement, but also significant contributors are from the ABC Factors 1, Study Efficacy (g=0.38) and 3, Academic Output (g=0.44). For ABC242, Engagement, the effect size between the Control and the Test subgroups is large (g=0.81) with a substantial difference in absolute ABC242 mean values (46.1(Control), 60.0(Test)) supported by a ttest outcome indicating a highly significant difference (t=3.765, p=0.0002); For ABC241, Study Efficacy, a lowmedium effect size (g=0.38) reflects the modest differences between absolute ABC241 mean values (52.8(Control), 61.1(Test) although the ttest outcome indicated a significant difference between these means at the 5% level (t=1.761, p=0.0409) and for ABC243, Academic Output, the respective absolute ABC243 mean values, effect size and ttest outcomes are similar (61.3(Control), 70.1(Test); g=0.44; t=2.065, p=0.0209). For the remaining ABC24 Factors 4 and 5, negligible effect sizes are observed.
These outcomes suggest that when students are categorized into the research subgroups according to Dx Factor 4, which incidentally generates Control and Test subgroup sample sizes that are similar (n=40 (Control), n=48 (Test)) significantly increasing the number of students identified as quasidyslexic using this criteria by more than double in comparison to the Test subgroup sample size using the overall Dyslexia Index values, there are marked differences in academic confidence between the Control and the Test subgroups in three of the five factors of Academic Behavioural Confidence. ABC dimensions in these impacting factors are relating to the ways in which students are efficacious, aware of and able to meet their assessment targets, but particularly in relation to their levels of engagement where there exists a highly significant difference in academic confidence in this factor. Although it must be stated that with only two dimensions in the sifting criteria, Dyslexia Index Factor 4, these being indicators of the means by which students appraise theories, ideas or tasks in their study courses and also the ways in which they express a preference to communicate what they know and how they might interpret this knowledge to others verbally rather than in writing, given that these two (dyslexia) dimensions might be considered as markers of atypical and more holistic thinking that is reflected in likely challenges in translating this into ordered, structured and linear writing processes, it is significant that the quasidyslexic students are presenting higher levels of academic confidence than their dyslexic peers. Again, this suggests that students who know about their dyslexia and perhaps are receiving study support in one form or another nevertheless remain challenged by academic processes that are core components of their study courses despite any learning support that they may be receiving. It is possible that it is the very help that they are receiving that may be a factor in reduced academic confidence, wellmeaning as it will no doubt be (It should be pointed out that no direct evidence about dyslexic students' access to or receipt of support was queried in the research questionnaire and so suggesting that students may have received this is supposition only). Evidence for this emerges from some of the comments students provided:
In the earlier, Masters' dissertation pilot study students complained:
 "Extra support is not given in the right way. How doe extra time in exams help? It doesn't reflect what would happen in the real world. Changing the assessment techniques would be better" (Respondent QNR #7; Dykes, 2008, p82)
 "I did not use dyslexia support at all last year. I would prefer to ask for help when needed and I find the extra time in having to organize dyslexia support well in advance is not helpful" (Respondent QNR #28, ibid, p86)
 "I am unable to use support study sessions as I am already finding it hard to keep up with coursework and don't have time" (Respondent QNR #34, ibid, p89)
 "Going for help with studies takes up more of my time when I'm already struggling with too much work and not enough time; and it rarely helps as I can't explain why I'm struggling  otherwise I would have just done it on my own in the first place" (Respondent QNR #20, ibid, p99)
where it can be seen that systemic failings related to how support services are delivered are an impacting factor on some students' uptake of them. In this current study, conducted nearly a decade later and with students at a different institution, not dissimilar comments were provided:
 "[Support] should not just be for one type or group of people such as those with particular learning difficulties. [I] think that puts many people off as soon as they see the term 'learning difficulties' " (Respondent #71712644; RG:DI; ABC24 = 86.6; Dx = 592.48)
 "Lecturers need to be more supportive instead of referring me to learning support" (Respondent #67632469; RG:DI; ABC = 41.7, Dx = 682.21)
But evidence was also provided which did identify atypical preferences for thinking about and accessing academic work and how to communicate knowledge:
 "I am a visual person and for me it's easier to remember something if I am shown an image of that thing" (Respondent #90023507; RG:DI; ABC24 = 38.3; Dx = 748.93)
 "I usually use very visual ways to learn, for example drawing funny pictures to remember medication names ... and more interactive lectures would benefit me" (Respondent #74355805; RG:DI; ABC = 30.6, Dx = 699.15)
 "I found audio recording lectures was quite helpful; also when lectures were interactive or when images or films were included I got a better understanding of the subject" (Respondent #16517091; RG;DI; ABC = 59.7; Dx = 339.92)
 "I thoroughly enjoy seminars and lab classes and feel that I benefit much more academically in this setting [in comparison to] some days I have three consecutive hours of lectures ... after a while my attention wavers and I stuggle to focus" (Respondent #39243302; RG:ND; ABC = 56.5; Dx = 345.22)
 "I can sometimes have allornothing thinking which makes it difficult to be critical and explain in detail  Sometimes it feels as if my mind spirals when I think about one topic for too long and I lose track of my original idea/thought" (Respondent #69417357; RG:ND; ABC = 56.6; Dx = 334.95)
which adds to this project's argument in support of a thorough revision of processes of curriculum delivery and assessment mechanisms so that these might be more in line with the ethos of Universal Design for Learning, cited earlier in the literature review. Evidence here suggests that the academic confidence of students with dyslexia is likely to be less negatively impacted were UDL more widespread in university learning but evidence is also provided that nondyslexic students also evidence atypical thinking and information processing preferences or difficulties.
Dyslexia Index Factor 5: Working memory
Individuals with dyslexia are cited in literature as often experiencing differences in immediate memory function, commonly evidenced by scores in Digit Span and LetterNumber Sequencing subtests of wider assessments, for example as part of the WAISIV (Weschsler Adult Intelligence Scale) (Egeland, 2015) or phonological loop processing tests which deal with acoustic information, that is, speech sounds, and also written words, how these are converted into speech sounds in the mind, and how effectively these are retained in the shortterm, or working memory. Information is temporarily stored and manipulated in the working memory and the phonological loop comprises the 'inner ear', which is linked to speech perception and information in speechbased form, that is, words, and the 'inner voice', related to speech production and is used to rehearse and store verbal information through the articulatory control process where written words are converted into speech sounds. These fundamental concepts were proposed in a seminal paper by Baddeley and Hitch (1974) who, through their Working Memory Model, argued that the initial stages of the memory system are complex, and in addition to the phonological loop components of the 'inner ear' and the 'inner voice', visual and spatial information is dealt with by the 'inner eye', termed as the visuospatial sketchpad and the complete process is overseen by a central executive. The earlier model was later updated to include an additional component, the episodic buffer, which integrates with the central executive and determines how much prior knowledge is drawn from the long term memory to aid working memory processes. (Baddeley, 2000). In individuals with dyslexia it has been widely demonstrated that reduced phonological awareness is strongly associated with reduced digit span performance (MelgyLervag et al, 2012, Gooch et al, 2011) which hence explains the interest and apparent relevance in identifying deficits in these capabilities in comparison with nondyslexic norms amongst individuals who present for dyslexia screening.
There is not the scope in this project for a wider discussion about the relationship between working memory and dyslexia as this would be more than enough for a project in its own right. However, gaining some small measure of working memory differences amongst this project's participant datapool was considered useful. Hence just two dimensions were included in the Dyslexia Index Profiler which attempted to acquire at least a superficial overview of working memory capabilities. However by sifting the datapool according to Dx Factor 5, Working Memory, some useful differences have emerged. Firstly, this sifting process has the effect of nearly doubling the number of respondents classified as quasidyslexic in the Test subgroup (DNI) from a sample size of n=18 to n=31 whilst simultaneously reducing the sample size of the Control subgroup (DI600) of identified and strongly dyslexic students from n=47 to n=36. Comparing the new sample mean ABC24 overall values between these two subgroups shows a lowmedium effect size (g=0.40) between the absolute ABC values (60.3 (Control), 66.5 (Test)) with a ttest outcome that is closetosignificant at the 5% level (t=1.646 , p=0.0523) but this does indicate that overall, academic confidence of quasidyslexic students substantially exceeds that for dyslexic students. The difference is more pronounced between the Control and the Base subgroups (60.3 (Control), 69.2 (Base) g=0.61, t=2.619, p=0.0054) represented by a largemedium effect size and a ttest outcome that is highly significant at the 1% level showing that a nondyslexic students are presenting a strongly elevated academic confidence in comparison to their dyslexic peers when the data is reanalysed according to the Dyslexic Index Factor, Working Memory. In both comparisons, it is ABC Factor 2, Engagement, which appears to be making the most significant contribution to the overall effect size differences (g=0.64 Control/Test, ABC242: 48.9/61.2; g=0.70 Control/Base, ABC242: 48.9/70) which in both cases is supported by a ttest outcome indicating highly significant differences at the 1% level (Control/Test: t=2.602, p=0.0057; Control/Base: t=3.036 , p=0.0017).
Some respondents reported issues of memory in their questionnaire submissions:
 "I find exams [particularly] stressful as I feel [they] are a memory test even though they may be posed as 'not a memory test' ... [and] my anxiety gets in the way of my concentration and memory for exams" (Respondent #44317730; RG:DI; ABC24 = 54.6; Dx = 563.23)
 "My learning difficulty is related to my working memory" (Respondent #99268333; RG:ND (DNI); ABC24 = 47.9; Dx = 654.82)
 "Having dyslexia ... sometimes affects the memory where in the moment you forget everything and don't know what you need to write" (Respondent #11098724; RG:DI; ABC = 62.4; Dx = 679.84)
although amongst all comments submitted, those that referred to memory constituted only a small proportion (5/78 = 6.4%). With hindsight and a better understanding of cognitive load theory (Sweller, 1988) at the time, designing these two dimensions of the Dyslexia Index Profiler more carefully may have generated outcomes that were more meaningful and relevant to the wider issues in the literature related to working memory in students with dyslexia. especially in the light of the dualchannel theory which suggests that information is processed through both an auditory and a visual channel in working memory (Baddeley, 1995 in Knoopvan Campen et al, 2018) working in parallel in ways that generate cognitive load in the working memory, that is, the amount of information that can be accommodated at a time.
Sifting the datapool by Dx Factor  summary
Although the matrix highlights many interesting feature and differences which will be discussed further below, it is significant to note that when the datapool is sifted according to Dx Factor 1: Reading, Writing, Spelling, the differences in Academic Behavioural Confidence between students with dyslexia in the Test subgroup and those with quasidyslexia, which may imply an unidentified dyslexia, in the Control subgroup, are small and statistically not significant neither for the overall ABC value nor for any of the ABC factor values with the exception of ABC Factor 4, Attendance where students in the Control subgroup present a higher ABC244 mean value than their peers in the Test subgroup. This may be suggesting that where students in each of these research subgroups respectively may be presenting similar levels of dyslexiarelated issues in the context of literacy skills, these do not impact significantly on differences in levels of academic behavioural confidence. Although at face value this might be considered as a surprising outcome, it could be consistent with arguments which support the view that for many dyslexic individuals operating at the higher levels of academic capability required at university, earlier literacy difficulties associated with their dyslexia which may have been apparent in preuniversity learning and indicated through value outputs in dimensions of the Dyslexia Index Profiler that have enquired about these earlylearning challenges, may have responded to the development of strategies and compensations that have made literacy challenges less problematic. Whereas by sifting the datapool according to all of the other four Dx Factors, differences in ABC are more pronounced and supported by effect sizes ranging from medium (g=0.45) when the datapool is sifted according to Dx Factor 2, Thinking and Processing, to large (g=0.78) when Dx Factor 3, Organization and Timemanagement is the sort parameter.
[another para or two to write]