Article Summary: Feld et al. (2022) Writing Matters


To examine how the quality of writing in academic papers affects the perceived quality of work and publication rates.

Writing Quality in Academia

The low quality of academic writing is so ubiquitous that it has become a meme. While many academics feel frustrated while reading poorly written papers, this experience does not necessarily motivate us to produce well-written papers. After all, we have many skillsets to develop and demands on our time, and learning to write well involves copious practice and individualized feedback. Research has found that this investment does not necessarily result in higher scientific impact. Further, the ubiquity of low-quality papers shows that such papers are publishable, so it’s not obvious that improving our writing will provide us with tangible benefits. To determine the tangible benefits of investing in writing quality, this paper uses a highly controlled experiment to examine the effect that writing quality has on the perceived quality of work and recommendations for accepting a paper.

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Wellness: Good Stress: Discomfort and Doing Hard Things

Les Brown said it best when he said, “If you do what is easy, your life will be hard. However, if you do what is hard, your life will be easy.” He and many others have espoused the value of doing uncomfortable and hard things. Part of the value is physical, pushing our boundaries and expanding what feels comfortable to us. Another part of the value is psychological, showing ourselves that we are tough and that the anticipation of discomfort is often worse than the discomfort itself. The latter is where I have found the most value from practicing discomfort, reminding me of another great quote.

“I’ve experienced a great deal of pain and suffering in my life. Most of which never happened.”

Mark Twain
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Research Design: Improve Your Design Before Collecting Data

I’m about to share the secret sauce of designing rigorous and compelling research projects. I call it a secret sauce, but it’s actually a tool that I was taught as an undergrad research assistant in psychology. Thus, it’s just one of those skills I learned through apprenticeship rather than formal coursework. The tool is using your hypotheses to iteratively improve your research design.

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Research Design: Additional Analyses – Interrater Reliability and Demographics

This post discusses additional analyses that can help you to establish conclusion validity. Conclusion validity means that your conclusions are reasonable given the evidence that you have, especially that you do not overstate the generalizability of your data (i.e., overstate what your sample says about the population). Two common tools to establish conclusion validity are interrater reliability, which ensures your data are as objective as possible, and demographic analyses, which examine differences within your data based on learner characteristics.

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Research Design: Interpreting and Calculating Effect Sizes

When using inferential statistics, we commonly use two paradigms to understand our results: null hypothesis significance testing (NHST, aka statistical significance) and effect sizes (aka the quantitative difference between groups). NHST determines whether there is a difference between groups, and effect sizes describe the difference between groups. While there is a movement away from NHST towards effect sizes, the current best practice typically includes both. This post explains how to interpret and calculate effect sizes.

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Research Design: Inferential Statistics for Causal Questions

The last post discussed inferential statistics for relational questions. This post will discuss inferential statistics for causal questions. Most commonly when asking a causal research question, an experimental design is appropriate. In this case, the purpose of inferential statistics is to determine whether the difference between groups is greater than what would be expected due to chance. For example, if you tested a new teaching method and found that students who received this new teaching method scored 5 points higher on a test than students who didn’t, it would matter if the standard deviation were 2 points or 20 points. If the standard deviation were 2 points, then the mean of the intervention group is 2.5 standard deviations higher than the other group, and the average student in the intervention group scored better than 98% of the other group. However, if the standard deviation were 20 points, then the difference in means likely isn’t greater than what would be expected due to chance.

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Research Design: Inferential Statistics for Relational Questions

The purpose of inferential statistics is to determine if the features of a sample (e.g., participants in a study) are representative of a population (i.e., all people belonging to a group). For example, the question “Do first-generation college students study more than other students?” must be answered with inferential statistics unless you can collect data from all college students. Thus, inferential statistics allow you to make some generalizations about your findings beyond your sample, and they are used to answer relational and causal research questions. The posts on inferential stats will cover many topics (at least two semesters of stats classes), so to break it up, this post will focus on only relational questions.

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Wellness: Good Stress: Multi-day Fasting Reasons and Tips

I thought today would be a particularly good day to write this post because I’m currently 68 hours into a 3-day fast. It’s a good time to show that I’m still functioning normally after nearly 3 days without eating and to ensure that I remember all my tips and tricks, in case you’re interested in trying it. If you’re thinking that multi-day fasting seems impossible or ill-advised, I hope that I can dispel some of the myths.

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Research Design: What Statistical Significance Means

In the scientific method, we collect data to support or refute hypotheses, not to prove or disprove them. We frame scientific research in this way because there might be factors that we are unaware of that affect the results. In human-subjects research, one reason we cannot prove or disprove hypotheses is that we use samples to represent populations rather than whole populations.

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