The Scenario A student recently approached me to review their dissertation proposal, which had been rejected by their school’s proposal committee. I had not been involved in the proposal. One of the key issues was that the student had confused the concepts of mediation and moderation, which led to flaws in the argument, hypotheses, statistical …
Lessons from My Students: Mediation versus Moderation Read More »
Less is More – Consolidation is Key The Scenario A recent dissertation I reviewed contained over 100 small tables and graphs, each presenting a single aspect of the student’s findings. Tables were presented per demographic and item, for example, the respondents’ level of education, years of experience, and age group (there were six such small …
In preparation for our upcoming webinar on Avoiding the Traps of Questionnaire Design and Analysis in Your Dissertation at 6 pm (GMT+2:00) on 19th September, I’m writing about an issue of reliability that is often problematic for students. An old bathroom scale explains the concept of reliability of the measurement instrument in your dissertation. Here …
Avoiding the Traps of Questionnaire Design and Analysis in Your Dissertation Read More »
In a previous post, I spoke about what a statistically significant result means and doesn’t mean. A significant result may mean very little, not necessarily anything to inspire you to throw a party or make a supportive parent proud. A significant result does not tell you how strong – or meaningful – your result is. …
How Strong are Your Statistical Results? The Concept of Effect Size Read More »
If you’re involved in research, you’ve likely come across the notation ‘p < .05’ in journal articles or elsewhere, or you’re using this notation in your writing to indicate a statistically significant result. It is used pretty much everywhere, almost as a mantra across studies. But do you know what p < .05 actually means? …
If you’re involved in research, you’ve likely come across the notation ‘p < .05’ in journal articles or elsewhere, or you’re using this notation in your own writing to indicate a statistically significant result. It is used pretty much everywhere, almost as a mantra across research studies. But do you know what p < …
Sometimes, despite all your efforts, the sample size of your quantitative study turns out to be smaller than you planned. Maybe you received a low response rate to your online survey, your database search for patient records returned many unusable or missing records, or you found yourself in another scenario with a similar outcome. And …
In our next webinar on Thursday 5th October @ 7 pm (GMT + 2:00), Professor Linda Bloomberg and I will be discussing analysing your qualitative and quantitative data. In this post, I discuss the step before the analysis of quantitative data. This is where you inspect and clean the errors in your data before starting …