Academic confidence and dyslexia at university

Research

 

Section 3 revised March-May 2020

Research Design - Methodology and Methods

 

3.1 Research Design Overview

The research aim was twofold: firstly, to establish the extent of all participants' 'dyslexia-ness', this to be the independent variable; secondly, to gauge their academic confidence in relation to their studies at university, the dependent variable, so that associations between the variables could be explored. This section describes the strategic and practical processes of the project that were planned and actioned to meet tha research aim. Details are provided about how practical processes have been designed and developed to enable appropriate data sources to be identified; how the research participants were identified and contacted; how data has been collected, collated and analysed so that the research questions can be properly addressed. This section of the thesis also sets out the development of the Dyslexia Index Profiler, the metric designed and developed exclusively for this project to gauge dyslexia-ness (Section 3.6, below). The rationales for research design decisions are set out and justified, and where the direction of the project has diverted from the initial aims and objectives, the reasons for these changes are justified.

Design Focus

The project has taken an explorative, mixed methods design focus (see Section 1.2/II-1). This is because little is known about the interrelationships between academic confidence and dyslexia. Hence, no earlier model has been available to provide guidance. Data were  collected through a self-report questionnaire, were mainly quantitative, and were collated to enable between-groups analyses to be conducted. This rationale falls within the scope of survey research methodology in which the process of asking participants questions about the issues being explored are a practical and expedient process of data collection, especially where more controlled experimental processes such as might be conducted in a laboratory, or other methods of observing behaviour are not feasible (Loftus et al., 1985). Likert scale item responses were transformed into numerical data for analysis. Some qualitative data were also collected through a free-writing area in the questionnaire for a 'softer' exploration about participants' more general feelings and attitudes to studying at university. In this way, hypotheses formalized from the research questions were addressed objectively using the outputs from the statistical analysis of the quantitative data, with qualitative data used to elaborate discussion points later. This is reported fully In Section 4, Results and Analysis.

 
 

3.2      Research participants:

 

Groups and Subgroups

The participants were all students at university and no selective nor stratified sampling protocols were used in relation to gender, academic study level or study status - that is, whether an individual was a home or overseas student. However, all three of these parameters were recorded for each participant, and these data have been used throughout the analysis and discussion when considered apposite. It is possible that a later study may re-visit the data to explore differences that might emerge through stratified analysis.

The objective was to establish a sizeable research datapool through convenience sampling that comprised two groups: the first was to be as good a cross-section of HE students as may be returned through voluntary participation in the project. Participants in this group were recruited through advertisements posted on Middlesex University’s student-facing webpages during the academic year 2015-16. The second group was to be students known to have dyslexic learning differences. These were recruited through the University’s Dyslexia and Disability Service student e-mail distribution list. Recruitment was incentivized by offering participants an opportunity to enter a prize draw subsequent to completing the questionnaire. Amazon vouchers were offered as prizes. From the group of non-dyslexic students, it was hoped that a subgroup of students presenting quasi-dyslexia could be identified. It was of no consequence that students with dyslexia may have found their way to the questionnaire through the links from the intranet rather than as a response to the Disability and Dyslexia Service's e-mail, because the questionnaire requested participants to declare any dyslexic learning challenges. Hence, participants would be assigned into the appropriate research group from either recruitment process.

 

Thus, two distinct datasets were established:

  • Students with known dyslexia - designated Research Group DI, and/or referred to as 'the dyslexic group';

  • Students with no known dyslexia - designated Research Group ND, and/or referred to as 'the non-dyslexic group';

 

Through the data collation process, a sub-group of students was established from non-dyslexic group, being those who presented quasi-dyslexia, as identified by the Dyslexia Index Profiler. This dataset was designated Research Group DNI, and was also referred to as 'the quasi-dyslexic subgroup'.

Hence, it was possible to compare levels of academic confidence between the three groups.

3.3  Data Collection

I - Objectives

As this project was focused on finding out more about the academic confidence of university students and relating this to levels of dyslexia-ness, the data collection objectives were:

  • to design and build a data collection instrument that could gather information about academic confidence and aspects of dyslexia-ness, expediently and unobtrusively from a range of university students, in information formats that could easily be collated and statistically analysed once acquired;

  • to ensure that the data collection instrument was as clear, accessible and easy-to-use as possible, noting that many respondents would be dyslexic;

  • to ensure that the data collection instrument could acquire information quickly (15 minutes was considered as the target) to maintain research participant interest and attention;

  • to design an instrument that could be administered online for participants to engage with at their convenience;

  • to enable participants to feel part of a research project rather than its subjects, and hence engage with it and provide honest responses;

  • to maximize response rates and minimize selection bias for the target audience;

  • to ensure compliance with all ethical and other research protocols and conventions for data collection according to guidelines and regulations specified by the researcher's home university.

 

These objectives were met by designing and building a self-report questionnaire. Carefully constructed survey questionnaires are widely used to collect data on individuals' feelings and attitudes that can be easily quantified to enable statistical analysis (Rattray & Jones, 2007). Questionnaires are one of the most commonly used processes for collecting information in educational contexts (Colosi, 2006). Evidence shows that self-report questionnaires have been found to provide reliable data in dyslexia research (e.g.: Tamboer et al., 2014; Snowling et al., 2012). Developments in web-browser technologies and electronic survey creation techniques have led to the widespread adoption of questionnaires that can be delivered electronically across the internet (Ritter & Sue, 2007) and so this process was used. The ability to reach a complete community of potential participants through the precise placement and marketing of a web-based questionnaire was felt to have significant benefits. These included:

  • the ability for the researcher to remain inert in the data collection process to reduce any researcher-induced bias;

  • the ability for participants to complete the questionnaire privately, at their own convenience and without interruption, which it was hoped would lead to responses that were honest and accurate;

  • ease of placement and reach, achieved through the deployment of a weblink to the questionnaire on the home university's website;

  • ease of data submission, and data conversion on receipt;

  • the facility for strict confidentiality protocols to be applied whereby a participant's data, once submitted, were to be anonymous and not attributable to the participant by any means.

 

Every questionnaire response received was anonymised at the submission point with a randomly generated 8-figure Questionnaire Response Identifier (QRI). The QRI was automatically added to the response dataset by the post-action process for submitting the form as an e-mail. Should any participant subsequently request revocation of data submitted, this was achieved by including the QRI in the revocation request form, also submitted electronically and received anonymously. No participants requested this.

 

II - Questionnaire design rationales

The questionnaire was designed to be as clear and as brief as possible. Notably, guidance provided by the British Dyslexia Association was helpful in meeting many of the design objectives. Additional literature was consulted about designing accessible online and web-based information systems, with particular attention to text formats and web design for visually impaired and dyslexic readers to ensure dyslexia-compliant readability (Gregor & Dickinson, 2007; Kurniawan, 2007; Al-Wabil et al., 2007; Beacham & Alty, 2006; Evett & Brown, 2005); to explore how dyslexia-friendly online webpage design may have been reviewed and updated in the light of the substantial, relatively recent expansion of online learning initiatives (e.g.: Rello et al., 2012; Chen et al., 2016; Berget et al., 2016); and how strong accessibility protocols not only enabled better access for those with dyslexia, or who experienced visual stress or other vision differences, but provided better accessibility and more straightforward functionality for everyone (McCarthy & Swierenga, 2010; Rello et. al, 2012; de Santana et.al., 2013). Other literature was consulted about the impact of design and response formats on data quality (Maloshonok & Terentev, 2016), on response and completion rates (Fan & Yan, 2010), on the effectiveness of prize draw incentivizations (Sanchez-Fernandez et al., 2012), and invitation design (Kaplowitz et al., 2011), and about web form design characteristics recommended for effectiveness and accessibility (Baatard, 2012). The questionnaire design stage reviewed existing web survey applications for customizability and flexibility, noting that Google Forms (Google, 2016), SurveyMonkey (Survey Monkey, 2016), SurveyLegend (Survey Legend, 2016), Polldaddy (Automattic, 2016), Survey Planet (Survey Plant, 2016), Survey Nuts (Zapier Inc., 2016), Zoho Survey (Zoho Corp., 2016) and Survey Gizmo (Widgix, 2016), were all limited by strictly constrained design and functionality options; advertising, or custom branding. None of the apps reviewed included the functionality of range input sliders.

 

Hence the project questionnaire was designed according to these design rationales:

  • it was an online questionnaire that rendered properly in at least the four most popular web-browsers: Google Chrome, Mozilla Firefox, Internet Explorer, Safari (usage popularity respectively 69.9%, 17.8%, 6.1%, 3.6%, data for March 2016 (w3schools.com, 2016))

  • text, fonts and colours were carefully chosen to ensure that the questionnaire was attractive to view and easy to engage with, meeting W3C Web Accessibility Initiative Guidelines (W3C WAI, 2016);

  • an estimate was provided about completion time (15 minutes);

  • questions were grouped into five, short sections, each focusing on a specific aspect of the research, with each question-group viewable one section at a time. This was to attempt to reduce survey fatigue and poor completion rates (McPeake et al., 2014; Ganassali, 2008; Flowerdew & Martin, 2008; Marcus et al., 2007; Cohen & Manion, 1994); In the event, only 17 of the 183 questionnaires returned were incomplete (9.2%).

  • the substantial part of the questionnaire used Likert-style items in groups, presenting response options using range sliders to gauge agreement with statements;

  • the questionnaire scale item statements were written as neutrally as possible, or in instances where this was difficult to phrase, a blend of negative and positive phrasing was used (e.g.: Sudman & Bradburn 1982). This was an attempt to avoid tacitly suggesting that the questionnaire was evaluating the impacts of learning difficulty, disability or other learning challenge on studying at university, but rather that a balanced approach was being used to explore a range of study strengths as well as challenges. Account was taken of evidence that wording polarity may influence respondents' answers to individual questions with 'no' being a more likely response to negative questions than 'yes' is, to positively worded ones (Kamoen et al., 2013); but that the widely claimed supposition that survey items worded negatively as an attempt to encourage respondents to be more attendant to them, or that mixing item polarity may be confusing to respondents, claimed through internal reliability analysis, was dubious at best (Barnette, 2000). Hence applying scale item statement neutrality where possible, was considered as the safest approach for minimizing bias;

  • a free-writing field was included to encourage participants to feel engaged with the research by providing an opportunity to make further comments about their studies at university in whatever form they wished. This had proved to be a popular feature in the preceding dissertation questionnaire (Dykes, 2008), providing rich, qualitative data;

The questionnaire was built, tested and published on the project webpages which had been established and hosted on the researcher's private web server, not least as this presented the most expedient means to retain complete control over both the content and security of the webpages. The questionnaire remains available here.

III - Questionnaire components

The questionnaire comprised three main sections: The first presented demographic data fields that all participants were to complete. The second section comprised quantitative data collection fields to explore academic confidence and dyslexia-ness. The final section collected qualitative data.

1. Demographic data

 

Data were collected on gender, student domicile ('home' or 'overseas') and student study level, with options provided from Foundation Level 3/4 to post-doctoral researcher Level 8 (QAA, 2014). This preliminary section also asked students with dyslexia how they learned of their dyslexia by selecting options from two drop-down menus to complete a sentence (Figure 8), thus collecting data to address Hypothesis 3 (see sub-section 1.4).

 

 

 

 

 

 

 

 

 
2. Quantitative data

Likert Scales

Likert-style scales were used to collect quantitative data throughout the questionnaire.Participants reported their degree of agreement with each scale-item statement using a continuous response scale approach. This was developed for this project in preference to traditional, fixed anchor point scale items because the data produced are arbitrarily coded so that they can be statistically analysed but this makes the data neither authentic nor actual (Carifio & Perla, 2007; Carifio & Perla 2008; Ladd, 2009). Hence by using range sliders, data quality may be increased (Funke & Reips, 2012), and would be as close to continuous as possible, thus enabling parametric analysis to be reasonably conducted (Jamieson, 2004; Pell, 2005; Carifio & Perla, 2007; 2008; Grace-Martin, 2008; Ladd, 2009; Norman, 2010; Murray, 2013, Mirciouiu & Atkinson, 2017). In this questionnaire, the continuous scales were set as percentage agreement, ranging from 0% to 100%, hence corresponding to participants strongly disagreeing to strongly agreeing respectively, with each statement.

 

1.  The Academic Behavioural Confidence Scale:

 

Academic confidence was assessed using the existing, ABC Scale (Sanders, 2006b), which is known to be a reliable evaluator of the academic confidence of university-level students by examining their study behaviours and actions (see Section 2.2). Using the 24-item scale, Sander and colleagues reported it to possess an internal reliability of ɑ = 0.88 (2007), based on data acquired from a sample of 284 participants drawn from two UK universities. All other studies using the ABC Scale found to date, appear to have either relied on this ɑ-value, or only report the internal reliability of the ABC Scale's sub-scales, as derived by prior dimension reduction (op cit). With one exception, no other studies were found that indicated item redundancy analysis nor dimension reduction of the ABC 24-item scale as a mechanism for a more nuanced analysis of local data. The exception was a short conference paper detailing a statistical evaluation of the factor structure of the preceding, Academic Confidence Scale, that used data collected from a local university (Corkery, et.al., 2011), and although no overall measure for scale reliability was indicated, coefficients for the three subscales were presented, with values ranging from 0.711 < ɑ < 0.880.

Currently, no other metrics exist which explicitly focus on gauging confidence in academic settings (Boyle et al., 2015). Evaluators exist to measure self-efficacy or academic self-efficacy, which, as also described in Section 2, is considered to be the umbrella construct that includes academic confidence (Sander & Sanders, 2003). However, of all such measures, the ABC Scale most closely matched the research objectives of this study. The full scale of 24 items includes dimensions such as: 'I am confident that ...

  • ... I can study effectively in independent study';

  • ... I can present to a small group of peers';

  • ... I can prepare thoroughly for tutorials'.

 

The complete scale is listed in Appendix 8.1(II)

 

2.1  Six Psychometric Scales:

 

The data collection process of the earlier, MSc dissertation (Dykes, 2008) had developed psychometric scales where the purpose was to explore feelings and attitudes of dyslexic students to their dyslexia in the context of their university studies. The rationale was based on evidence from literature which suggested that discernible differences between dyslexic and non-dyslexic individuals for each of these six constructs. For example, levels of self-esteem are depressed in dyslexic individuals in comparison to their non-dyslexic peers (e.g.: Riddick et al., 1999; Humphrey, 2002; Burton, 2004; Alexander-Passe, 2006; Terras et al., 2009; Glazzard, 2010; Nalavany et al., 2013); and Klassen et al. (2008) found that dyslexic students exhibit significantly higher levels of procrastination when tackling their academic studies at university in comparison to students with no indication of dyslexia. In the early stage of the research design process for this current study, it was planned that these six subscales would be combined into a profile chart to enable quasi-dyslexic students to be discriminated from the group of non-dyslexic students by comparing their profiles with mean-data profiles of the dyslexic and non-dyslexic groups overall.

 

The resulting, overlapping visualizations were distinct (Figure 9, generated from observed data collected later from the quasi-dyslexic subgroup), but it was considered doubtful that the complete set of profiles would show sufficient discriminative power to be reliable for identifying quasi-dyslexic students. Hence this approach was abandoned in lieu of developing an alternative, quantitative process as the discriminator between dyslexic, non-dyslexic, and quasi-dyslexic students, which emerged as the Dyslexia Index Profiler (2.2 below, and Section 3.6, bottom). Nevertheless, the profile chart visualizations were intriguing, suggesting that this data may have value, and so this section of the questionnaire was not deleted, and has been reserved so that the idea may be explored and reported later, perhaps as part of a subsequent study.

 

Figure 8:  Selection how dyslexic students learned of their dyslexia

Figure 9: The profile chart for a respondent in the quasi-dyslexic sub-group 

2.2  The Dyslexia Index Profiler

 

The Dyslexia Index (Dx) Profiler was a 20-item scale developed especially for this project (see Section 3.6 below for an account of the rationale, theoretical underpinnings, and development processes, including the small-scale enquiry that was conducted to validate the Profiler). It became necessary as a consequence of significant reservations about the likelihood of the visual profile approach (based on outputs from the six psychometric scales) to discriminate the sub-group of quasi-dyslexic students from the non-dyslexic group reliably and with sufficient precision.

The final iteration of the Profiler comprised 20 scale items, gauged with the continuous range input sliders consistent with quantitative data collection processes devised for the other sections of the questionnaire. Scale items explored a range of dimensions of dyslexia, expressed as statements to which participants registered levels of agreement on a range of 0-100%. The combined output enabled an aggregated Dyslexia Index 'score' to be generated, considered as the level of dyslexia-ness (for the purposes of this study). Scale items included for example:

  • 'When I was learning to read at school, I often felt I was slower than others in my class';

  • 'I have difficulty putting my writing ideas into a sensible order';

  • 'I get in a muddle when I'm searching for learning resources or information';

 

The complete scale is listed below in sub-section 3.6 and within the completed questionnaire, in Appendix 8.1(II).

 

As the Dx Profiler was developed for this project and no previous studies have devised or included any similar scales or gauging processes for evaluating dyslexia-ness, no prior reliability data were available. However, an internal scale reliability assessment was conducted post hoc using the conventional Cronbach's ɑ procedure, which delivered a scale reliability coefficient of ɑ = 0.849 (see sub-section 4.3/III.1 below).

 
 
 

 

3. Qualitative Data

The final part of the questionnaire collected qualitative data in an optional, unlimited free-writing area. Participants were invited to comment on any aspects of the research, the questionnaire, or their learning experiences at university more generally. Including this final section was based on the usefulness of the rich and varied data that had been acquired in a similar way in the questionnaire used in the earlier, MSc. dissertation. In that study, it became evident that providing a conduit for students with dyslexia to provide comments and feedback about how they felt about their study at university was heartily welcomed. The data captured was used to elaborate the discussion element of the dissertation. Hence it was considered that adopting a similar approach in this current study would be of value.

4. Questionnaire pilot

The questionnaire was trialled amongst a small group of students (n=10) local to the researcher to gain feedback about its style of presentation, ease of use, the clarity of the questions and statements, the quality of the introduction, the length of time it took to complete, any issues that had arisen in the way it had displayed in the web-browser used, and to elicit any other comments that might indicate that a review or partial review would be necessary before deployment to the target audience. The outcome of this pilot indicated that other than some minor wording changes, no amendments were required.

 
 
 

3.4  Procedure

On completion of the design, development, testing and piloting processes, the questionnaire was uploaded to the project's webpages for electronic deployment. To recruit students into the dyslexic group, co-operation from the University's Dyslexia and Disability Service was obtained so that an Invitation to Participate in the project could be sent to all students registered with the Service through an e-mail distribution list. The invitation included a link to the questionnaire. To recruit non-dyslexic students, similar co-operation was obtained from the University's website development team to enable publicity about the project to be posted on the student-facing intranet home page, which included the Invitation to Participate and a link to the questionnaire.

Completed questionnaires were submitted automatically by e-mail to the researcher in a format that permitted direct transfer to an Excel spreadsheet for collation and subsequent inspection and analysis.

 
 

3.6 Developing the Dyslexia Index Profiler

3.5 Data reduction

A Dyslexia Index was calculated for each participant using the weighted mean average process applied to the 20 scale-items, developed at the design stage of the Dx Profiler in the light of the analysis of the pilot study (see Section 3.6 below). This value was scaled up by a factor of ten so that it was easy to discriminate from the similarly gauged level of Academic Behavoural Confidence, and this Dx value was taken to indicate each participant's level of dyslexia-ness.

 

Students from the non-dyslexic group whose Dyslexia Index exceeded critical boundary values were categorized as quasi-dyslexic (see sub-section 4.3/III.1) for the discriminating rationale).

 

Later reliability analysis of the Dx Profiler indicated a possible, reduced-item scale where 4 scale items were identified as likely to be redundant (see sub-section 4.3/III(I) below) which enabled alternative measures of dyslexia-ness to be calculated for each participant. In the event, both outputs were considered of merit and implications are reported below (Section 4.3).

 

To gauge academic confidence, each participant’s ABC value was initially calculated using a non-weighted mean average of the 24 scale-item responses (which each offered a range from 0 to 100), leading to an aggregated output of 0 < ABC < 100. Subsequent to dimension reduction analysis later, (see sub-section 4.5 below), three further ABC Scales were used to re-calculate values. Hence this complete process led to permutations of the two Dx Profilers with the four ABC Scales being available to consider later. The complete datapool was transferred into SPSS v24 (IBM Corp, 2016) for further analysis.

Given that both scales were gauging multi-dimensional, continuous variables, further analysis was subsequently conducted to determine whether dimension reduction could reveal meaningful factor structures. Early iterations suggested a local factor structure for the ABC Scale was likely to emerge, although for the Dx Profiler, outcomes were less clear. Hence a parallel (simulation) process was applied through the Eigenvalue Monte Carlo Simulation protocols to determine the number of factors which are likely to occur using multiple simulated reductions of randomized versions of the experimentally acquired data. Outcomes confirmed a factor structure for the ABC Scale, and also indicated that the Dx Profiler, especially when already reduced to a 16-item scale was most likely to be uni-dimensional.

3.6  Developing the Dyslexia Index Profiler

Developing the Dyslexia Index (Dx) Profiler became a major component of the research design process. The entire project relied on this, as the main focus was to discover whether levels of academic confidence are influenced differently by dyslexia, quasi-dyslexia, and non-dyslexia, and that differences that emerge may be attributable to the dyslexic label. Many students with dyslexia at university may have developed strategies to compensate for literacy-based difficulties experienced in earlier learning histories, partly by virtue of their academic capabilities (see Section 2). Hence in HE contexts, other aspects of the dyslexic self may impact significantly on academic study. For example, it has been argued that to consider dyslexia to be only a literacy issue, or to focus on cognitive aspects such as working memory and processing speeds, may be erroneous (Cameron, 2015), and developing procedures to operationalize effective self-managed learning strategies need to be considered (Mortimore & Crozier, 2006). This is especially so, as self-regulated learning processes are recognized as a significant feature of university learning experiences (Zimmerman & Schunk, 2011; Broadbent & Poon, 2015). Hence, a metric was required which viewed university study attributes and behaviours through the lens of dyslexia, but which was not designed to be a dyslexia screener.

I  Background and rationale

Development of the Dx Profiler has been a complex process that built on pertinent theory about the broad and multifactorial nature of dyslexia (discussed in Section 2.1/II.6). To have used a proprietary dyslexia screener would have raised ethical challenges related to disclosure for participants in the non-dyslexic group, hence compromising the requirement for data collection anonymity. Stated use of a screener may also have introduced bias where participants who were not (identified as) dyslexic may have answered some parts of the questionnaire untruthfully through fear of being identified as dyslexic. Such fear is widely reported, in particular, amongst health professionals (e.g.: Shaw & Anderson, 2018; Evans, 2014; Ridley, 2011; Morris & Turnbill, 2007; Illingworth, 2005).

 

II  Establishing the Dyslexia-ness Continuum

The broad definition of dyslexia outlined by the BDA acknowledges much of this wider discourse about the nature and aetiology of the syndrome, discussed throughout Section 2.1. Critically, this definition frames dyslexia as a continuum, which firstly acknowledges that categorical distinctions within the syndrome are problematic; but also suggests that no clear-cut point along this continuum can be universally fixed to indicate the boundary between dyslexic and non-dyslexic individuals. This is despite the desire to do so, not least to enable decisions to be made concerning the award of financial learning support allowances for students at UK universities.

Adopting the continuum approach, therefore, adds substance to the concept of 'dyslexia-ness', introduced for this current study. Thus, it is reasonable to infer that the characteristics and attributes of dyslexia that are embraced within the definition, and which are the components of dyslexia-ness, might be measured in some way once distilled back into dimensions. This leads to the possibility for exploring either dimensions unilaterally, or groups of dimensions (perhaps combined into factors), or the complete portfolio of dimensions - that is, dyslexia-ness. According to their dyslexia-ness 'score', it will be possible to locate quasi-dyslexic and non-dyslexic individuals at some point along the continuum relative to their more dyslexic peers, or sift individuals who share similar levels of dyslexia-ness into sub-groups.

 

Hence, The Dyslexia-ness Continuum is established (Figure 10), and can be regarded as a continuous, independent variable against which other study attributes, such as academic confidence, can be examined as the corresponding dependent variable. In this way, tentative comparisons might then be made between groups and sub-groups of, in this case, students at university, naturally leading to a mechanism for deducing more generalized results. Indeed the idea of a dyslexia-ness continuum, might warrant further development, the first part of which should be to devise an alternative descriptor for it that removes, or at least dilutes, the allusion to the continuum being an evaluation of dyslexia, instead, that it is a continuum of learning development characteristics, skills and behaviours that has meaning and relevance in higher education contexts. Whilst this is not to ignore or dismiss the idea of dyslexia per se, such a process might help to relocate it more positively within a multifactorial portfolio of learning and study attributes that could also reduce much of the stigmatization associated with 'difference' in learning contexts (Osterholm, et.al., 2007; Ho, 2004; Riddick, 2000).

 

 

To operationalize The Dyslexia-ness Continuum through the Dx Profiler so that each partipant's Dyslexia Index would generate the continuum locator, these design criteria were established:

  • the profiler was to be a self-report tool requiring no administrative supervision;

  • the profiler was to be ethically non-controversial, not labelled as a dyslexia screener, and with data collected anonymously;

  • the profiler item statements were to be as applicable to non-dyslexic as to dyslexic students;

  • it would include a balance of literacy-related and wider, academic learning-management and study-behaviour evaluators;

  • it would include elements of learning biography;

  • although Likert-style based, scale item statements were to avoid fixed anchor points by presenting respondent selectors as a continuous range option;

  • scale item statements would aim to minimize response distortions potentially induced by negative affectivity bias (Brief, et al., 1988);

  • scale item statements would aim to minimize respondent auto-acquiescence, that Is, 'yea-saying', being the often-problematic tendency to respond positively to attitude statements (Paulhaus, 1991). Thus, the response indicator design would require a fine gradation of level-judgment to be applied;

  • although not specifically designed into the suite of scale-item statements at the outset - which were presented in a random order - natural groupings of statements as sub-scales were expected to emerge, leading to the possibility for factor analysis to be applied later, if appropriate;

  • scale item statements were to avoid social desirability bias, that is, the tendency of respondents to self-report positively, either deliberately or unconsciously. In particular, an overall neutrality should be established for the complete Dx Profiler so that it would be difficult for participants to guess how to respond to present themselves in a favourable light (Furnham & Henderson, 1982).

 

III  Designing the Dx Profiler

In addition to being grounded in the most recent BDA definition of dyslexia, several other evaluators were consulted for guidance. In particular: the BDA's Adult Checklist developed by Smythe and Everatt (2001); the original Adult Dyslexia Checklist proposed by Vinegrad (1994), upon which many subsequent checklists appear to be based; and the later, York Adult Assessment (YAA) (Warmington et al., 2012) which has a specific focus as a screening tool for dyslexia in adults, were all explored. Despite the limitations outlined earlier (sub-section 2.1(VII)), the YAA was found to be usefully informative. But also consulted and adapted has been the 'Myself as a Learner Scale' (Burden, 2000); the useful comparison of referral items used in screening tests which formed part of a wider research review of dyslexia by Rice and Brooks (2004); and especially more recent work by Tamboer and Vorst (2015) where both their own self-report inventory of dyslexia for students at university, and their useful overview of previous studies were consulted.

 

Drawing from all of these sources, and from supporting literature, a portfolio of 20 statements was devised for gauging attributes of study behaviours and learning biography that are known to present characteristic differences between dyslexic and non-dyslexic students, thus setting out the framework for the Dx Profiler (Table 2). Dimensions are listed in the order in which they appeared in the final iteration of the main research questionnaire. Participants were requested to gauge the magnitude of their agreement with each of the statements by adjusting the position of the range input slider from its default, 50%, position towards 0% or 100% agreement accordingly.

 
 

Figure 10: The Dyslexia-ness Continuum - displaying data from this current study

 
 

Table 2: Dx Profiler statements, dyslexia attributes, and supporting references

* [...] refers to sub-sections in this thesis where this reference is used to support the discussion point

The Profiler was to be aligned with the BDA (2018) definition of dyslexia, as adopted for this current study, (see Section 2.1/I), and this definition was distilled into three components: language and literacy skills; thinking and processing skills (encompassing issues related to working/short-term memory, but also to include creative strengths); and organization and time-management competencies. The statements in the Profiler were located across the three components accordingly (below), setting out a framework that might be validated from post-hoc factor analysis of results acquired from participants in this study later given that this was a newly devised metric (see sub-section 4.5/III).

COMPONENT: Literacy and language

  • accurate and fluent word reading and spelling;

  • phonological awareness;

  • [other] aspects of language (eg: writing coherence);

  • visual processing challenges;

Dimension:

'When I was learning to read at school, I often felt I was slower than others in my class'

'My spelling is generally good'

'In my writing, I frequently use the wrong word for my intended meaning'

'When I'm reading, I sometimes read the same line again or miss out a line altogether'

'I have difficulty putting my writing ideas into a sensible order'

'In my writing at school I often mixed up similar letters, like 'b' and 'd' or 'p' and 'q''

'My tutors often tell me that my essays or assignments are confusing to read'

'I get really anxious if I'm asked to read 'out loud''

COMPONENT: Thinking, processing, memory

  • verbal memory;

  • verbal processing speed;

  • mental calculation;

  • concentration;

  • information synthesis;

  • design, problem-solving ingenuity, creativity;

Dimension:

'I can explain things to people much more easily verbally than in my writing'

'I get in a muddle when I'm searching for learning resources or information'

'I'm hopeless at remembering things like telephone numbers'

'I find following directions to get to places quite straightforward'

'I prefer looking at the 'big picture' rather than focusing on the details'

'My friends say I often think in unusual or creative ways to solve problems'

'I find it really challenging to follow a list of instructions'

'I get my 'lefts and 'rights' easily mixed up'

COMPONENT: Organization and time management

  • personal organization;

Dimension:

'I find it very challenging to manage my time efficiently'

'I think I am a highly organized learner'

'I generally remember appointments and arrive on time'

'When I'm planning my work, I use diagrams or mindmaps rather than lists or bullet points'

The multifactorial nature of the syndrome implies that attributes are presented in varying degrees in each individual, and that some of the attributes devised are not likely to be uniquely located into any single component. For example, it is reasonable to suppose that the statement 'I get in a muddle when I'm searching for learning resources or information' may be variably influenced by criteria from the skillsets of all three components. How this variability may appear was unknown at the design stage of the Dx Profiler due to the unique, individual distribution of attributes across factors. Nevertheless, a draft of a possible mapping was constructed (Figure 11) which would be compared later with the output derived from the dimension reduction analysis of observed data, where attribute-factor overlap would be determined by relative factor loadings should these emerge from this process.

Figure 11: Dyslexia dimensions distributed across BDA components

IV  Validating the Dx Profiler

Before deploying the Dx Profiler as part of the research questionnaire, two further factors were considered pertinent: firstly, it was important to gain a tentative confirmation that the statements devised resonated with the learning and study experiences of students at university, and hence were likely to be a realistic attempt to gauge the levels of dyslexia-ness of participants in this current project; and secondly, that a reasonable estimate of the prevalence of each dimension so that the overall output of the Dx Profiler would be generated from a weighted rather than a simple mean-average of scores obtained from the complete set of 20 dimensions. It was reasonable to suppose that were prevalence data ignored, outputs from the Profiler would be less realistic, if not significantly skewed.

To meet these objectives, feedback was sought about the proposed portfolio of statements ahead of finalizing the Dx Profiler and incorporating it into the main research questionnaire. As the Profiler was to be a metric for use in university settings, the rationale for obtaining such feedback focused on obtaining data from that environment, specifically, from dyslexia support professionals. It seemed reasonable to assume that these members of university support services staff are likely to have day-to-day interactions with dyslexic students at university, and hence should have a good sense of how regularly they encounter the dimensions of dyslexia enshrined in the statements. Hence, a small-scale enquiry was devised, being a short, online poll designed, built and hosted on the project's webpages which sought to gauge the prevalence and frequency of dyslexia characteristics and attributes that were to be incorporated into the Dx Profiler.

1. Rationale, Methods and Processes

The rationale for the enquiry was threefold:

  • By exploring the prevalence of attributes (dimensions) of dyslexia observed in the field in addition to those distilled through the theory and literature reviewed to that point, it was hoped that the data acquired would confirm that the dimensions being gauged were appropriate and recognizable features of the learning and study profiles of dyslexic students at university;

  • Through analysis of the data collected, value weightings could be ascribed to each dimension based on their reported prevalence. Hence the output of the Dx Profiler in the main research questionnaire could account for the likely relative influence of each dimensions by generating a weighted-mean average level of dyslexia-ness for each participant;

  • Feedback could be sought about the design and operation of the continuous range input sliders (Figure 12) being trialled in this poll, as these were planned to be extensively used in the main questionnaire later. 

The poll (available here) contained 18 statements, mirroring those to be used in the Dx Profiler later. The list of statements was prefixed with the question: 'In your interactions with students, to what extent do you encounter each of these dimensions?' Respondents recorded their answer as a percentage where 0% indicated 'never encountered', 50% indicated 'encountered in about half of interactions', and 100% indicated 'all the time' (Figure 12). The default position was set at the midpoint of the slider scale, noting that the default position of input range sliders has been reported to have no significant impact on output (Couper et al., 2006).

 

 

 

 

 

 

2. Recruitment of Participants

 

Of the 132 UK Higher Education Institutions identified through the Universities UK database, 116 were identified with Student Support Services that included an indicated provision for students with dyslexia, generally as part of more general services for students with disabilities. These were established through inspection of institutions' outward-facing webpages. Most provided a specific e-mail address for contacting the team of dyslexia specialists directly, or otherwise a more general enquiry address for student services was available. All 116 institutions were contacted by to invite participation in the enquiry by including a link to the poll in the e-mail. The response rate of 30/116 institutions was disappointing, although was considered sufficient for meeting the objectives of the poll given the absence of any alternative data.

3. Process

 

An introduction to the poll described its purpose, provided instructions about how to complete it, and how to request withdrawal of data (revocation) after submission in the event that a participant had a change of heart about taking part. The relationship of the poll to the current study's main research was also stated, as was an offer to share the findings of the poll given that a contact e-mail address was supplied.

 

It was expected that respondents would count a multiple-visit student only once in their estimates of dimension prevalence although to do so was not made explicit so that the poll preamble remained as brief and uncomplicated as possible. Space was provided near the end for participants to submit any comments about either the enquiry itself or about features of the poll. An invitation was also made to submit information about any additional attributes or characteristics of dyslexia-ness that were regularly encountered.

Submitting the completed poll sent the dataset to the researcher's university e-mail account, where it was downloaded into an Excel spreadsheet for collation and analysis.

4. Results and Outcomes

 

Data received from the poll submissions were collated, and in the first instance the mean average prevalence for each dimension was calculated, derived from the average frequency (that is, extent) that each dimension was encountered (Table 3).

 

Fig.12 Continuous range input slider for Dx Dimension 04

 

Table 3: Prevalence of dyslexia dimensions

24 participants reported additional attributes encountered in their work with dyslexic students, and where these were provided, most also included % prevalence:

  • poor confidence in performing routine tasks [reported by 4 respondents with prevalence respectively: 90%; 85%; 80%; % not reported (n/r)]

  • slow reading [100%; 80%; n/r]

  • low self-esteem [85%; 45%]

  • anxiety related to academic achievement [80%; 60%]

  • pronunciation difficulties / pronunciation of unfamiliar vocabulary [75%; 70%]

  • finding the correct word when speaking [75%; 50%]

  • difficulties taking notes and absorbing information simultaneously [75%; n/r]

  • getting ideas from 'in my hear' to 'on the paper' [60%; n/r]

  • trouble concentrating when listening [80%]

  • difficulties proof-reading [80%]

  • difficulties ordering thoughts [75%]

  • difficulties remembering what they wanted to say [75%]

  • poor grasp of a range of academic skills [75%]

  • not being able to keep up with note-taking [75%]

  • getting lost in lectures [75%]

  • remembering what's been read [70%]

  • difficulties choosing the correct word from a spellchecker [60%]

  • meeting deadlines [60%]

  • focusing on detail before looking at the 'big picture' [60%]

  • difficulties writing a sentence that makes sense [50%]

  • handwriting legibility [50%]

  • being highly organized in deference to 'getting things done' [25%]

  • having to re-read several times to understand meaning [n/r]

  • profound lack of awareness of their own academic difficulties [n/r]

 

The additional attribute reported by the most respondents (four) related to confidence, with slow reading being reported by three respondents. Most other additional attributes were reported by only one respondent.

5. Discussion

Although the response rate for this small-scale poll was disappointing, (30 respondents out of 116 invitations to participate), it was considered that the data collected was sufficient to affirm that appropriate attributes of dyslexia had been selected to resonate with the typical field experiences of dyslexia support professionals, and hence were reasonably representative of the profiles of dyslexic students at UK universities. Although an additional 24 attributes to the 18 provided in the poll were reported, most with a corresponding level of prevalence, the majority of these were reported by only one respondent each, and hence were not considered indicative of a significant omission in the poll design. The additional attribute related to confidence was considered to be accounted for in the Academic Behavioural Confidence Scale, itself forming a major section of the main research questionnaire.

Hence, the 18 dyslexia dimensions were considered to have been validated to a sufficient degree by the outcomes of the poll to form the basis of the Dyslexia Index Profiler. In the first instance, these dimensions were formatted to be more concise; converted into the first person so that participants would feel engaged with the research; and re-phrased where necessary so that the Profiler would be relevant to all students. Secondly, the two additional dimensions relating to learning biography were now included (concerning letter reversal and slow uptake in learning to read). These did not form part of the validation poll as it was assumed that their context would be outside the frame of experience of the dyslexia tutors consulted.

 

The final iteration of the complete set of 20 dimensions that formed the Dx Profiler (Table 4), with weightings assigned as derived directly from the prevalence of dimensions established from the poll, were supplemented two additional dimensions were both assigned weightings of 0.61, this being the mean average weighting of the other 18 dimensions. This was considered reasonable given that no studies were found that were able to offer evidence of the prevalence of these dimensions in adults with dyslexia. The statements were ordered randomly to reduce the likelihood of order-effect bias. This is an error attributable to the sequence of questions or statements in a survey inducing a question-priming effect, such that a response provided for one statement or question subsequently influences the response for the following question, when these appear to be gauging the same or a similar aspect of the construct under scrutiny (McFarland, 1981).

 
 

Table 4: Weightings assigned to dyslexia-ness dimension statements

6. Generating the Dyslexia Index

Reverse coding

The objective of the Profiler was to generate a numerical output for every student participant - their Dyslexia Index (Dx) - and it was considered appropriate to aggregate the input-values of the Profiler in such a way that a high final Dx value indicated a high level of dyslexia-ness. However, as the Dx Profiler was designed to include a balance of positively and negatively phrased statements (see sub-section 3.3/II), if dimension-statement values were aggregated without taking account of whether a high or a low value for any particular statement was a marker of a high level of dyslexia-ness, the Dyslexia Index value would be compromised. For example, for Dimension #2: 'My spelling is generally very good', it is reasonable to expect that a strongly dyslexic participant would be likely to disagree with this statement, and hence record a low value for this dimension. Whereas for Dimension #1, relating to slow uptake of early years basic reading skills, the same respondent may be likely to record a high value, indicating strong agreement with the statement. Hence the value outputs for some statements needed to be reverse-coded to ensure that high values on all statements indicated high levels of dyslexia-ness.

As for identifying other dimensions that should be reverse-coded, this was a process that could only be achieved after data had been collected from participants in the research later. Several methods were trialled although after several iterations, the most likely outcomes were established by running a reliability analysis of the complete scale to generate Cronbach's ɑ reliability coefficients. When these outputs were integrated into the dimension reduction techniques later in the data analysis, it was possible to verify that Dimensions #5, and #7 also required data reverse coding. The reliability analysis also identified some dimensions that may be redundant, leading to a reduced scale of 16 dimensions, which is reported below (sub-section 4.3/III(I)). This aspect of the Dx Profiler requires developmental work and this may form the topic for a later project. However, in this current study, given the caveats mentioned, the process was considered robust enough to enable the outputs from the Profiler to be used.

Calculating Dyslexia Index (Dx)

The weighted mean calculation of the Dyslexia Index (Dx) using the raw scores (observed values) from a randomly chosen participant - a female, home, undergraduate who had declared dyslexia - has been used as an example of the process (Table 5). The Dx output was scaled to a value between 0 and 1000 to more easily distinguish it from the participant's ABC value, derived directly from the unscaled, unweighted mean average of their responses to the 24 statements of the ABC Scale, each gauged in the range 0 to 100.

Table 5:  Example calculation of Dyslexia Index

 

7. Concluding summary

 

In summary, the Dx Profiler calculated a Dyslexia Index for each respondent in the research datapool, being a weighted mean average of responses to 20 Likert-style item statements, where each aimed to capture data relating to a specific study attribute or behaviour, or an aspect of learning biography. Respondents recorded their strength of agreement with each statement along a continuous range from 0% to 100%. Weightings were derived from the prevalence of characteristics determined through a poll of dyslexia support practitioners. The weighted mean was scaled to provide an output, Dyslexia Index (Dx), in the range 0 < Dx < 1000. With data available following deployment of the main research questionnaire, dimensionality reduction was applied (PCA) to explore the factor structure of the Dx Profiler. This was firstly to compare the output with the speculated structure based on the BDA definition of dyslexia, and secondly to determine whether a useful cross-factorial analysis might be conducted with outputs from the ABC Scale. The aim was to explore more thoroughly the associations revealed between academic confidence and dyslexia-ness (reported in sub-section 4.6). This analysis remains tentative and to an extent, speculative, because the size of the sample (n=98) from which it was generated is quite small. A later study could aim to develop the Dx Profiler by collecting data from larger and more varied samples, hence enabling PCA to be more confidently applied.

The outcome of the development process was that the Dx Profiler was considered to have met its design specifications and was used confidently to gauge the dyslexia-ness of the participants in the study.

 
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Academic confidence and dyslexia at university

A PhD Research Project October 2014 - May 2019

Middlesex University, London

Andrew Dykes B.Ed, M.A, M.Sc, FHEA

ad1281@live.mdx.ac.uk; academic@ad1281.uk

+44 (0)79 26 17 20 26