An online application for advancing quantitative data analysis

For quantitative research into foreign language learning or teaching, the dominant approach is arguably inferential statistics for statistical significance testing. Here, a test statistic (p-value) is calculated from sampled data, and decisions on the variables being tested-whether to accept or reject them as in some way contributing to the processes under study-are made based on the calculated p-value. However, this approach has long been recognised by numerous methodologists and theorists as potentially flawed, possibly holding back much research from contributing to substantive theory creation. Alternative measures are recommended; if not rejecting the approach outright, it is suggested that the results of statistical significance tests are augmented with measures of effect size, confidence intervals, robust variations of inferential statistics, and data-rich graphical plots. This presentation introduces an online application designed to help researchers carry out quantitative analysis focused on these alternatives to significance testing. Aimed particularly at less-experienced researchers, it requires little more than the input of data for the output of a range of useful statistics and plots. The rationale behind and usage of the application will be covered.

Paul Collett works at Shimonoseki City University. His interests include research methodology and epistemology, and working towards better practices in these areas.

Please note: In keeping with chapter policy, all meeting attendees will be required to wear a mask during the meeting.