UI/UX Articles and Interesting Tidbits of the Week
May//5//2023
Here are some interesting finds on UI/UX of the week!
1.
Survey of Recently Laid-Off Workers. Very interesting article from Zip Recruiter who has canvased laid-off workers from 2022 and 2023 to find some relevant trends (2000 + people were canvased). The article looks at aspects such as severance pay, how laid off workers are likely to be entertaining multiple job offers following that occurrence, people changing careers and industries, amongst many other topics. Well worth reading through. Highlight of the article includes:
“Research shows that laid-off workers do far worse if the unemployment rate is high than if it is low. One study found that laid-off men lose 2.8 years of pre-layoff earnings when the unemployment rate exceeds 8%, but only 1.4 years of earnings when the unemployment rate is below 6%. As of publication, the unemployment rate is just 3.4% — the lowest in more than 50 years. There is little prior research on how layoffs affect the economy when the unemployment rate is that low. Evidence from our recent survey suggests that they may be substantially less damaging, both to the individuals directly affected, and the broader economy.”
2.
Effective Password Management. Insightful and pertinent article from Iliya Garakh, published on Smashing Magazine and sponsored by Passwork. While there’s indeed an angle of product promotion associated with this article, it’s nonetheless an interesting look at the challenges that surround the topic of passwords and security in general. The author highlights topics such as Collaborative Password Management, Access Rights, Password Policy Best Practices, to name but a few. It’s a thorough showcase for a topic that has impacts on operations, but also on painless collaboration that is sorely needed amongst teams. Highlight of the article includes:
“Collaborative password management systems, like Passwork, provide secure sharing options, allowing employees to share access credentials with colleagues without exposing sensitive data to unauthorized users. This is the kind of feature that a company needs for frictionless sharing in a collaborative environment, but without exposing sensitive information as you might through another platform, like email. This way, sharing happens securely through the password app’s service.”
3.
Statistical Significance and Research. Very pertinent article from Nikki Anderson-Stanier, for the dScout Design blog focused on Statistical significance in Research endeavors. The author illustrates the difference between Qualitative and Quantitative research methods, and where Statistics play a role in providing context on the exact number of participants that are canvased and the role they play for the type of research that is being conducted. Understanding the type of information the study aims to fulfill also enables the most suitable type of Research and method associated with it. Again, worth reading through. Highlight of the article includes:
“Quantitative research is statistical because it has numbers attached to it. With quantitative research, we see averages or percentages. Qualitative research uses non-statistical methods. Quantitative methods allow us to measure variables and test specific hypotheses, while qualitative methods enable us to explore concepts and experiences in depth. So, when it comes to determining and using statistical significance, it simply does not make sense in qualitative research. Qualitative research, as it exists, is a non-statistical approach. With qualitative research, we do not try to measure certain variables, nor do we try to test specific hypotheses. Never in my life have I used qualitative research to determine whether or not a null hypothesis is supported. One of the other reasons that qualitative research is not statistical is that we are not trying to generalize or widen our scope of understanding. Instead, we are trying to understand what is taking place on a deep level. With qualitative research, we are in the first phases of our study. So we aren’t looking to understand if our findings represent a broader population because we don’t have findings yet. So we first have to go deep before we go wide, or we will miss crucial insights that we can later test at large.”
