Elena Fitkov-Norris

Validation of VARK learning modalities questionnaire using Rasch analysis

This article discusses the application of Rasch analysis to assess the internal validity of a four sub-scale VARK (Visual, Auditory, Read/Write and Kinaesthetic) learning styles instrument. The results from the analysis show that the Rasch model fits the majority of the VARK questionnaire data and the sample data support the internal validity of the four sub-constructs at 1% level of significance for all but one item. While this suggests that the instrument could potentially be used as a predictor for a person’s learning preference orientation, further analysis is necessary to confirm the invariability of the instrument across different user groups across factors such as gender, age, educational and cultural background.

Measuring Study Habits in Higher Education: The Way Forward?

This article reviews existing study habit measurement instruments and discusses their drawbacks, in the light of new evidence from neuroscience on the workings of the brain. It is suggested that in addition to traditional frequency based past behavioural measures, the predictive accuracy of study habit measurement instruments could be improved by including measures of habit strength that take into account behaviour automaticity and efficacy, such as the Self-Report Habit Index (SRHI) developed by [1]. The SRHI has shown high reliability and internal validity in a wide range of contexts and its applicability and validity in the context of learning and higher education as an enhancement to study habit measurement instruments is as yet to be tested.

Evaluating the Impact of Categorical Data Encoding and Scaling on Neural Network Classification Performance: The Case of Repeat Consumption of Identical Cultural Goods

This article investigated the impact of categorical input encoding and scaling approaches on neural network sensitivity and overall classification performance in the context of predicting the repeat viewing propensity of movie goers. The results show that neural network out of sample minimum sensitivity and overall classification performance are indifferent to the scaling of the categorical inputs. However, the encoding of inputs had a significant impact on classification accuracy and utilising ordinal or thermometer encoding approaches for categorical inputs significantly increases the out of sample accuracy of the neural network classifier. These findings confirm that the impact of categorical encoding is problem specific for an ordinal approach, and support thermometer encoding as most suitable for categorical inputs. The classification performance of neural networks was compared against a logistic regression model and the results show that in this instance, the non-parametric approach does not offer any advantage over standard statistical models.

Use of measurement theory for operationalization and quantification of psychological constructs in systems dynamics modelling

The analytical tools available to social scientists have traditionally been adapted from tools originally designed for analysis of natural science phenomena. This article discusses the applicability of systems dynamics - a qualitative based modelling approach, as a possible analysis and simulation tool that bridges the gap between social and natural sciences. After a brief overview of the systems dynamics modelling methodology, the advantages as well as limiting factors of systems dynamics to the potential applications in the field of social sciences and human interactions are discussed. The issues arise with regards to operationalization and quantification of latent constructs at the simulation building stage of the systems dynamics methodology and measurement theory is proposed as a ready and waiting solution to the problem of dynamic model calibration, with a view of improving simulation model reliability and validity and encouraging the development of standardised, modular system dynamics models that can be used in social science research.

Impact of sampling on neural network classification performance in the context of repeat movie viewing

This paper assesses the impact of different sampling approaches on neural network classification performance in the context of repeat movie going. The results showed that synthetic oversampling of the minority class, either on its own or combined with under-sampling and removal of noisy examples from the majority class offered the best overall performance. The identification of the best sampling approach for this data set is not trivial since the alternatives would be highly dependent on the metrics used, as the accuracy ranks of the approaches did not agree across the different accuracy measures used. In addition, the findings suggest that including examples generated as part of the oversampling procedure in the holdout sample, leads to a significant overestimation of the accuracy of the neural network. Further research is necessary to understand the relationship between degree of synthetic over-sampling and the efficacy of the holdout sample as a neural network accuracy estimator.

Evaluating the impact of a Quasi-ipsative scoring approach on the scoring of a VARK style questionnaire

This paper demonstrated the application of a quasi-ipsative scoring approach to assess the relative strengths of individual preferences in a VARK style questionnaire. The approach identified a chi-squared test as more suitable method for analysing the type of data gathered by the VARK questionnaire. The results suggest that the quasi-ipsative chi-squared based approach does not appear to be as sensitive as the original t-test approach in identifying significant modalities. In order to increase the sensitivity of the test, the requirement for overall test significance has to be relaxed and individual cells considered in the analysis. The findings also put some doubt on the statistical validity of the original t-test approach as it also uses the deviation from the means (in the form of standard deviation) rather than statistical significance, as a tool for assessing the strength of individual preferences. The causes of the discrepancies between the two scorings techniques need to be examined further, before recommendations can be made on which approach is better suited to identifying the strength of individual preference for information input modality.