I struggle with multiple choice quizzes. I can talk myself out of correct responses and into incorrect answers by overthinking the language. And in a topic where wording is so critical, this is quite the problem. 

I am so glad that we are given time for completing the quizzes. I am using a modified Cornell Notes format to help me process each question, but instead of writing notes in the right hand column, I am using part of a problem solving strategy my math students follow. My first step is to isolate the question by rewriting it in simpler terms. This helps me to ensure that I understand what I am being asked to find out. This will also help me to identify how the answer should appear. My next step is to list all of the information from the question that can help me to answer the question. If possible, I try to generate an answer (or a prediction/estimate/hypothesis) next. Then, I make some general notes of what I DO know. What is my relevant background knowledge? Finally, I work through each possible answer to explain why it is either a possibility or an unlikely response.

It’s not as time consuming as it sounds! But, what it has done for me is increase my confidence. It is very affirming to make a list of what I know in relation to a topic that has felt so intimidating – and to find that I *actually* have things to list! 

This system is not perfect as it falls apart when I narrow the possibilities to two responses. It always comes down to the wording – either in question or responses.

***I will be sharing the details from one question below, revealing my suggested answer on a quiz question – which may or may not be correct***

In some cases the extra time has been the enemy as it allows me to sit with my uncertainty and question my “gut response”. I have changed several correct answers on quizzes.

Progressing through the readings, lectures, and activities has taught me the importance of peer review in academic literature. I make errors while interpreting questions and responses – especially when the language doesn’t perfectly match the lecture or reading material. Consistency is helpful for interpretation. My critical learning at this point is that when I evaluate my data and publicly share it I must use clarity in my word choices and include appropriate visuals. This will allow an audience to cross-check my discussion. 

This course is making me a better consumer of statistical information. The end goal has now shifted to applying what I am learning from my experiences in completing the quizzes to ensure that I disseminate statistical information with clarity. My job in research is to interpret data into findings that are ready for use. I am also considering the additional complication of packaging data to share with people beyond academia. How will I report my findings in manageable ways?

Kona is hoping that I wrap up my studying soon. It has been a long summer of multiple stats texts covering multiple surfaces in preparation for this course! She is developing her own coping strategies for dealing with stats.

a black dog sleeps on an open textbook, covering both pages with her head

Image by @h_james18

My learning partner Nicole reported how useful she was finding the stats textbook by David Howell, so I purchased one. It arrived a couple of weeks ago and it has helped clarify SO MANY THINGS! I am in the process of resubmitting a quiz that I struggled with, and this text gave me a better understanding. Howell also maintains support sites (with some dead links) but there are question banks and lots of practice options. I couldn’t use all of the practice offered by the Field text because I was using my work computer. I know that I would have to explain why I downloaded a file entitled “porn” for analysis purposes (I chose not to download that data set!). I think his text is meant to be high-interest, but it didn’t offer the same detail I received from the Howell text. I know that I will have to continue with my learning beyond this course and Nicole and I have already made plans to meet. The areas I will continue to practice include:

  • working with raw data (analysis, display options, etc)
  • interpreting the raw data (what is important to observe, patterns, language usage)
  • reading the data shared by others (decoding, matching discussion with tables)

For now, I leave you with a final video by Howie Hua on factorials: