Table 1: Dyslexia Index Factors for research subgroup DNI

Table 2: Dyslexia Index Factors for research subgroup DI-600

Table 3: Dyslexia Index Factors for research subgroup ND-400

 

2. Academic Behavioural Confidence (ABC) Factors

 

A similar process of Principal Component Analysis has been executed on the data collected on Academic Behavioural Confidence, also through SPSS.

This has been prompted because a revised, 17-item ABC Scale has been established by Sander & Sanders (2009) following a re-inspection 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 24-item scale. Their (relatively) large composite dataset (n = 865) was established by bringing together data collected from five earlier studies between 2001 and 2006 with psychology undergraduates (n=507) from one university in South Wales, together with a further dataset of ABC values obtained from first-year 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). There are both differences and similarities in the cohorts of students in Sander & Sanders' combined dataset when compared with features of my dataset. For example, a differences was that in the Sander & Sanders studies, students were all undergraduates whereas in my study all students in the university community were invited to participate in the research which produced an overall ratio between undergraduates and other students of 75% : 25%. ('undergraduates includes students attending foundation or access courses and 'other students' comprises post-graduates, research students and a very small number of others who did not disclose their study level (3)). However, a similarity was that in Sander & Sanders' datasets, students' previous academic achievement at A-level was recorded as across the complete range of grades and since this data was not requested in my study it is reasonable to assume that students who responded presented an equally wide range of prior academic achievement. In the Sander & Sanders' datasets, students were drawn from a narrow range of subject specialisms whereas in my study, subject studied at university was not recorded so it is also reasonable to assume that students from across the complete curriculum are as likely to have participated as not.

It was felt, therefore, that there are reasonable grounds for conducing PCA on my data because there may be bias in Sander & Sanders' analysis due to students being all undergraduates and all from similar subject specialisms whereas students in my datapool can be assumed to be representative of the complete student community at university as no data was collected in relation to their academic discipline. Hence the specific factor structure that my be revealed from my PCA may be more appropriate to use in my comparative analysis with Dyslexia Index as opposed to relying on the factor structure and revised 17-item ABC scale determined from the Sander & Sanders' collection of studies. Research data collected through the Academic Confidence Scale in an existing study (Corkery, 2011) was conducted to identify factors relevant to that data and these emerged as different from the factor analysis conducted by Sander & Sanders so a precedent is set for this approach to PCA within a research project.

 

Nevertheless, for this research project, the original 24-item ABC scale has been used in the main research QNR which has proved expedient since both the aggregated ABC-24 and ABC-17 values can be established for all the respondents in the datapool. In addition, data sifted through the Sander & Sanders factor structure can be compared with a similar process with factors established from my own PCA - indeed, a valuable exercise has been to compare which individual scale items map to which factors in each of the PCAs, the result of which is reported on the StudyBlog.

Briefly, the PCA on Academic Behavioural Confidence for this project's data resulted in 5 factors being identified and although the factor structure was not as simple as is desirable because a few dimensions (that is, ABC scale items) mapped to more than one factor, it is felt that in the context of this enquiry, there is justification for accepting the PCA outcomes. A report about this is presented also on the StudyBlog and this will be explored more fully in the final thesis. The 5 factors identified have been categorized as:

• Study Efficacy
• Engagement
• Academic Output
• Attendance
• Debating

This process, in combination with the PCA on Dyslexia Index has enabled a very interesting matrix of effect sizes and t-test results to be compiled which identifies significant interrelationships between much more than the overall ABC and Dx values of all respondents in the project.

The table below sets out Hedges 'g' effect sizes and Student's t-test p-values for differences in ABC on a Dx factor-by-factor basis. This means that when respondents are sifted into the three respective research subgroups DNI, DI-600 and ND-400 according to each of the Dyslexia Index Factor values, that row of data presents the effect size differences (and t-test 'p' values) between the research subgroups for each ABC factor. For example, when sifting respondents into subgroups using Dx Factor 2 Processing as the criteria, the ABC Factor 2 Engagement showed an effect size of 0.43, considered as 'medium', between research subgroup DNI and DI-600, together with a very nearly significant difference between the sample means. In other words, students with unreported dyslexia-like profiles presented a measurably higher Engagement factor of Academic Behavioural Confidence than their identified-dyslexic peers.

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 t-test for independent sample means is used in the one-tail format because in almost all cases, the mean ABC24 values for research subgroup DNI exceeded those for the (control) research subgroup DI-600.

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