## [ Archives ]

#### Multiple regression – can Dx predict ABC?

Applying multiple regression analysis to my data: Is this valid? Does the output tell me anything useful about the interrelationship between dyslexia and academic confidence? Tops, W., Callens, M., Lammertyn, J., van Huus, V., Brysbaert, M., 2012, Identifying students with dyslexia in higher education, Annals of Dyslexia, 62(3), 186-203. The paper recently digested […]

#### ABC 24-item scale – my 5-factor analysis

24-item Academic Behavioural Confidence Scale and my Factor Analysis outputs As reported elsewhere on the project webpages, Principal Component Analysis has been executed on the data collected in this project for both of the principle variables: Dyslexia Index and Academic Behavioural Confidence. In using Sander & Sander’s (2003) original 24-item ABC Scale, a basic analysis […]

#### Dyslexia Index factor profiles

Dyslexia Index Factor Profiles In the previous blog-post I identified that the factor analysis of my Dyslexia Index metric generates different cohorts of students in each factor when regarding Dyslexia Index (Dx) as the independent variable – that is, the one I’ve fixed or chosen. This is because the process of considering the aggregate […]

#### principal component analysis

Principal Component Analysis (PCA) Introduction The process of Principal Component Analysis (PCA) performs dimensionality reduction on a set of data, and especially a scale that is attempting to evaluate a construct. The point of this process is to see if a multi-item scale can be reduced into a simple structure with fewer components (Kline, 1994). For example, […]

#### research subgroup Dx boundary value, and confidence intervals for μ

Research groups and subgroups’ summary data for Dx ranges and CIs for μ; Rationale for boundary value adjustment to Dx = 592.5 As far as possible it is necessary for the principal research subgroups DNI and DI-600 to share the similar Dyslexia Index characteristics in order that the Academic Behavioural Confidence can be compared […]

#### coding categorical data, and using Student’s t-test in SPSS

Coding categorical data into SPSS SPSS is a powerful application and is a well-respected analytical tool. Although the datasets generated by the responses to my eQNR have been captured into Excel – not the least because the output from the PHP form-based QNR has arrived in e-mail format with the data attached as a .csv […]

#### cronbach’s alpha

Applying a test for internal reliability to my e-Questionnaire Everyone uses Cronbach’s Alpha (α) to establish the supposed internal reliability of their data collection scales. It is important to take into account, however, that the coefficient is a measure for determining the extent to which scale items reflect the consistency of scores obtained in specific […]

#### Reverse coding

The eQNR that has collected my data contains 8 Likert Scales which each comprise collections of stem statements. I’ve tried hard to word the stem statements in such a way to minimize an overall sense that the questionnaire takes either a generally negative or generally positive sense. My questionnaire asked respondents to provide a level […]

#### collating the data -> first analysis summary

The position to date (July 2016); 166 good datasets have now been received in total, thus forming the datapool. The dataset generated from each eQNR reply has been received as a .csv file so in the first instance these have been collected together into a ‘master’ Excel spreadsheet. SPSS will be used later for a […]

#### Dimensions of dyslexia 2: revisiting the data; looking for meaning

Part 1 of this commentary on my preliminary enquiry about dimensions of dyslexia outlined my increasing unease about pitching my project into the plethora of research about dyslexia as I learned more about the wide range of viewpoints on not only the nature of dyslexia as a syndrome (or not), the diversity of perspectives on […]