Discussion

 

This section will open with a review summary of the results of the quantitative analysis which demonstrates that there is a measurable difference in Academic Behavioural Confidence between not only students with dyslexia and those with no indications of dyslexia, but also between those with identified dyslexia and the small group who presented dyslexia-like profiles, aligned with those of the dyslexic group.

A key analysis concept for inclusion in the discussion section is Klassen's idea about calibration and his key paper (2002) will form an important basis for this.

 

 

Discussion overview

 

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Factor profile visualizations

At an earlier point in the data analysis process, data visualization techniques were developed that produced profile charts which summarized the 6 psychoeducational constructs for which data was collected in the main questionnaire. As outlined in the previous section, 'Research Design', the original intention was to build on earlier work undertaken in the preceding study (Dykes, 2008) where it emerged that similar profile charts were an interesting way to present complex data relationships so that commonalities and contrasts could be more easily spotted. Hence by reverse-engineering this process, it was thought that it may be possible to align each student participants' profile visualizations to either of the two mean-value ones constructed from datapool controls and as a result, use this as a sifting process for identifying research participants that exhibited alignment with the determined, baseline profile characteristics and hence enable further comparison of attributes. In other words, use this as a the process for identifying non-dyslexic students who were nevertheless presenting a blend of psychoeducational characteristics which were more in line with their dyslexic peers than their non-dyslexic ones. However, as has been previously explained, when tested with the data in this current datapool it seemed unlikely that this process of visually sifting out students into the TEST subgroup would have been sufficiently robust to act as the dyslexia-ness discriminator that needed to be a fundamental process in the analysis of this datapool. Hence the Dyslexia Index Profiler was developed and used instead. Whilst the data collected on these 6 constructs has not been included in this thesis, it has not been discarded and shows promise for being explored in more detail at a later date where it may merit writing up as an independent paper.

Nevertheless, the technical processes learned for creating the visualizations clearly had merit as a means for presenting complex datasets. To this end, test charts have been constructed which display the profile of dimensions which when taken together, generate the output which is each participant's Dyslexia Index. This dimension profile is overlaid onto the mean value profiles for the two primary research subgroups, ND and DI - students with no dyslexia and students with declared dyslexia and although this visualization does not have a direct input into the data analysis outcomes, the profiles charts created are able to demonstrate in graphic detail the blend of strengths and weaknesses in the characteristics and attributes of dyslexia (in the context of this enquiry) presented by individuals and also collectively. In the first iteration of this process, dyslexia dimensions were grouped by apparent similarity rather than displayed in the order in which they were presented in the questionnaire. This is effectively a 'by eye' factor analysis and to facilitate this the dimensions were sifted into newly-designated categories thus:

• READING
• SCOPING, THINKING and RESEARCH
• ORGANIZATION and TIME MANAGEMENT
• COMMUNICATING KNOWLEDGE and EXPRESSING IDEAS
• MEMORY and PROCESSING

Dimensions that are tentatively sifted into each factor are shown in the graphic below, which is an example of one dyslexic respondent's profile: respondent #ND-18801333 who presented a Dyslexia Index Dx = 714 and hence is in the research subgroup DNI, students with no declaration of dyslexia but who present a dyslexia-like profile, which is the research subgroup I am particularly interested in.  For this initial inspection, my reduced 16-item Dx scale has been used. As in previous displays of a similar nature, each scale-item location point on the chart indicates the extent of acquiescence with the dimension scale-item statement. In the representation below this indicates, for example, that the respondent strongly agreed with the scale-item statement: 'At school I considered myself slower at learning to read than my peers' but disagreed strongly with the statement: 'At school I often mixed up similar letters in my writing'.

Several SPSS outputs have been generated, each one to explore the results of the dimension reduction that occurs by adjusting the calculation criteria. The summary of these is that using all 20 dimensions from the main research questionnaire and forcing SPSS to extract 5 dimensions has enabled an alternative to the 'factors by eye' graphic to be generated (presented below) which has alternative factor labels to be assigned. This visualization represents the same respondent (as above) who, on the 20-point scale, presented a Dyslexia Index of Dx = 683.

(compared with Dx = 714 on the 16-point scale. This is also interesting and prompted a quick analysis in the main, Excel data spreadsheet to explore the differences between respondents' Dx values on the 16-point and 20-point scales. What has emerged is that overall, the 16-point Dyslexia Index scale has the effect of lowering the Dyslexia Index of respondents at the lower end of the Dyslexia Index range and elevating Dx at the higher end. This appears to be consistent with other interpretations so far (eg using Cronbach's alpha) that by removing the four scale items 3.03, 3.05. 3.07 and 3.13, a more effective discriminator may be established because when left in the datasets, these four dimensions appear to be diluting the Dyslexia Index - that is, they effectively are reducing the variance between values. This is evidenced by the standard deviations of the respective sets of values: for the 20-point Dx scale in research group DI, the SD = 149.3 and the corresponding value for the 16-point scale is SD = 35.58. However for research group ND the situation is reversed with the corresponding standard deviations being: 20-point Dx scale, SD = 159.6  , 16-point Dx scale, SD =  185.7; this is puzzling. Does it mean that for students with previously identified dyslexia, the 16-point Dyslexia Index provides a more accurate determination of 'level of dyslexia' whereas for everyone else, the 20-point scale is keener? In due course I will refer back to the analysis of Cronbach's Alpha for the research subgroups to try to interpret this apparent contradiction more clearly). In the graphic below, which represents the same respondent's Dx scale-item values now regrouped accordingly, the dimensions that constitute each factor are displayed in order of factor loading in a clockwise direction around the diagram. For example, in 'Reading, Writing, Spelling', the dimension that presented the highest factor loading is 'I get anxious when asked to read aloud' and the dimension presenting the lowest factor loading is 'weak spelling'. This is interesting and might be suggesting that to some degree at least, this dyslexic student at university may have developed spelling competencies to remediate previous weaknesses to become less troublesome - perhaps through use of spelling aids or assistive software applications - and that other reading and writing issues are now more significant.

Given the dimensional re-assignment that emerged out of this factor analysis I have also re-labelled the factors:

• Factor 1:  READING, WRITING, SPELLING
• Factor 2:  THINKING and PROCESSING
• Factor 3:  ORGANIZATION and TIME MANAGEMENT
• Factor 4:  VERBALIZING and SCOPING
• Factor 5:  WORKING MEMORY

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