Argh! I thought I liked numbers, but I’m totally baffled by the statistics class I’m taking. The teachers are wonderful and the textbook is great, but I just can’t get my head around the null hypothesis that’s used to calculate statistical significance. It’s like my brain is willing to go just so far, and no further.
Our teachers ask often if there are any questions, and no one asks. It’s either because everyone else understands it, or perhaps, like me, they don’t understand enough to even formulate a question. This is incredibly frustrating, as my research is quantitative, and I totally need statistics!
The only thing I understood today was when the teacher asked “Do we have enough evidence to recommend that our patients switch to another brand of cigarettes?” And the answer was no – we should recommend that they quit smoking instead. But that’s not exactly statistics…
Hopefully, before the exam on April 11th, I’ll somehow understand more – otherwise I’m in trouble.
Thanks, Matthias, for trying to make statistics fun:
One of the sources of data for my research is a survey which attempts to measure students’ levels of information literacy. A pilot study should always be done before finalizing a survey in order to find and eliminate questions that are unclear or don’t serve their purpose. This is one way of validating a survey – checking that the survey questions actually address the research question, and that the respondents understand the answer alternatives.
After working on the survey for several weeks I started collecting data yesterday. The survey is made in Qualtrics, an online survey tool that makes distributing and collecting data relatively easy. I presented my research and the survey for a class here at UiT and encouraged them to participate. I’ll be doing the same for several different classes over the next weeks. My goal is to get 250 responses, which is 5 times the number of questions in the survey – a good rule-of-thumb. So far I’ve gotten 29.
When I get enough responses I’ll analyze the results to see which questions work well, and then eliminate the least useful questions, perhaps halving the total amount. It’s not a good idea to have a survey that’s too long because that discourages participation.
The survey is only one of three tools that I’ll be using to measure information literacy. The other two tools will measure what students actually do – in this case how they critically evaluate information sources and how they cite their sources in their writing.
And otherwise – I will start my first PhD course today! It’s called “Take control of your PhD journey – from (p)reflection to publishing.” It will be 3 days of seminars/instruction, some reading, and a final paper. The course is given by senior academic librarians, so I’m sure it’ll be great.
And today is my son Daniel’s 23rd birthday! (I know that this blog is about becoming a researcher, but some exceptions must be made. 😉 )
Together with Torstein, I’ve now made a “Data Management Plan” (DMP) for my study. It’s highly recommended, but not mandatory, to make a DMP within the first 6 months of a study. (The trick is then to remember to do what the plan prescribes!)
For the DMP, I used the Norwegian Centre for Research Data’s (NSD) template – the same organization which I notified about how I’m going to process personal data in my study. These are some of the questions which are addressed in the DMP:
brief description of my project , with an explanation of how data can help to answer my research questions
whether I’ll collect the data myself or use already existing data in a research archive
how my data could be useful for other researchers, and keywords to make it searchable by others
technical questions about the data itself, and methods and programs used to collect, store and analyze the data (here I got to use cool words like R-script and SPSS-syntax)
ethical and legal issues about personal data (where individuals can be identified)
security in the handling and storage of the data
systematic naming of data files so they can be interpreted by others
anonymization of personal data (when I’m done with all analyses)
Research is much different now than 10-20 years ago. Many institutions now require scientists to publish both their articles AND THEIR DATA open access, in archives like UiT’s, making them accessible to others.
This is actually a REVOLUTION in the world of science! Scientists now have access to each others’ data, making it possible for them to check results for scientific misconduct such as falsification or fabrication of data, calculation errors, plagiarism, etc. This leads to the retraction of several hundred articles every year, also in prestigious journals (see Retraction Watch). But unfortunately, before they get retracted, many of these articles are cited by others. This is bad science.
This mug was given to me by my supervisors, to celebrate the acceptance of my research proposal. They made a word cloud from the text of the proposal! (IL = information literacy) Isn’t it great? 🙂
After sending in the last of the forms, I’m now properly enrolled in the Faculty of Health Sciences. This means that I can (finally) enroll in PhD courses, so I’ll be taking these two courses this semester:
Take control of your PhD journey: From (p)reflection to publishing
Quantitative research methods
I also got feedback from NSD (Norway’s Data Protection Services), approving of my plan for the processing of personal data in my project. Another small milestone.
Next week’s project is to write a Data Management Plan (DMP).