SELECT PERSPECTIVES BLOG

What is Data-Driven Decision Making? A Guide to Getting More from Your Data

Posted by  Mavis Kung, Ph.D.

Data is everywhere! Twenty years ago, people would say "you’re living in the information age.” With technology advancements, pieces of data are thrown at us more than ever. Your “likes” on social media are tracked and stored in a “data lake” somewhere so some sophisticated algorithm can determine what ads appear on the side bar in your internet browser. You can install a device in your car to monitor your driving habits, such as your speed and how hard you hit your brakes. Then, the insurance company can use this data to adjust your insurance rate. And, if you're like me, you probably have considered buying a fitness tracker to monitor your daily steps, heart rate, and sleep patterns to help you reach your health goals. With or without your consensus (and awareness), we are in an era where data is being created and used constantly.

If data can help companies market products based on your interests, give you reasonable car insurance rates, and motivate you to live healthier, wouldn’t it be cool if data could help you find the right people to work with you? What if the data could help you optimize your talent pool? It can!


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Outliers: An Analysis Challenge in Employee Assessment Validation

Posted by  Mavis Kung, Ph.D.

Growing up, I was an outlier. I was short, chubby, wore red glasses, listened to music by artists unknown to my friends, did not practice Buddhism, and was nerdy. I felt that I was different from everyone else – and not in a particularly good way. I just did not fit into the mainstream. I was a complete outlier from stereotypical image of Asian teen girls. 


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Our 5 Most Popular Blog Posts of 2016

Posted by  Mark Rogers

Countdown.jpgIt’s been another great year for Select International’s blog. We set new records for number of views, shares, and subscribers. That’s a direct result of our goal for this blog. We always try to create quality content that informs, educates, and sometimes entertains our readers. In 2016, we published 124 posts on this blog. That’s more than we’ve ever published, and it has really paid off.

With the year ending in just a few days, we thought it would be a good time to countdown our top 5 blog posts of the past year, just in case you missed any.

Let’s start with number 5…


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The Top 5 Takeaways From SIOP 2016

Posted by  Amie Lawrence, Ph.D.

SIOPSocial16.gifLast week was the 31st annual conference for the Society for Industrial/Organizational Psychology (SIOP). Every year at this conference aspiring I/O psychologists, academics, and practitioners gather to share their research and discuss best practices. Select International had a large contingent of psychologists sharing their knowledge (with over 20 presentations) and attending sessions. We have pooled our knowledge and identified five big takeaways from the conference related to personnel selection and assessment.

1) Size Matters

When it comes to pre-employment assessments and mobile devices, screen size does matter for some types of assessments. Mobile testing has been a big topic of discussion for the past few years. This year, researchers continued to investigate different kinds of assessment methodologies to better understand if mobile candidates are being affected by the device. For simulations and items measuring cognitive ability, the research is consistently pointing in the direction of “YES.” In general, the findings suggest for sections that measure problem solving, analysis, and processing speed, candidates are performing worse as a result of the device (smaller screens are one of the factors contributing to the decline). Given the link between diversity and mobile devices, it’s important for organizations to be aware of their assessment content and the devices candidates are using.


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5 More Do's and Don'ts of HR Analytics

Posted by  Greg Kedenburg

HR-Analytics-2.jpgIn our last blog post, we reviewed several do’s and don’ts for conducting internal HR analytics. To refresh your memory, HR analytics refers to the process of analyzing data related to your company and employees, and can be a goldmine of information if it’s tapped correctly. We reviewed several strategies, tips, and tricks in the previous blog but are back again with 5 more guidelines to adhere to that will help the endeavor of analyzing your employee data go much more smoothly.


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The 6 Do's and Don'ts of HR Analytics

Posted by  Greg Kedenburg

HR-analytics.jpgHuman resources analytics is a very popular topic right now. The opportunity to learn more about your employees/applicants based on data you likely already have can be an HR professional's dream come true. When done properly, analysis of your company’s HR data can yield highly valuable information related to topics ranging from employee engagement and satisfaction to turnover and counterproductive behaviors.

If you know both what you’re looking for, and how to look for it, you can uncover extremely useful data about your employees, which you can then use to improve their experience at work and organizational performance overall. However, the process of investigating HR data can be tricky, there are various pitfalls when it comes to the actual analysis piece of the puzzle. To this end, we’ve put together some general recommendations and guidelines to help keep anyone interested in utilizing their own HR data on the right track.


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Big Data is Getting Bigger

Posted by  Doug Wolf

Big_DataDue to an unfortunate and unsuccessful attempt at multitasking, I recently had to get a new smartphone. It’s the same brand and operating system as my previous device. But, I began to notice some differences – surprising ones at that.

One morning my phone alerted me that my drive time to work was normal. Curious, I opened this alert. Sure enough, there was a blue line mapping the exact route I took to work each morning. What in the heck? Not long after, I got another alert asking me if I wanted to mark a bar/restaurant as a favorite spot. Okay, so maybe I went there more than once in a 30-day period, but this is too much. Are they tracking me everywhere I go? Further, I didn’t do anything to initiate this destination marking. I didn’t visit this restaurant’s webpage, like them on a social media site, or anything else. This is just purely driven by the GPS in my phone. I feel like a delivery truck.


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Big Data or Big Dustbowl?

Posted by  Matthew O'Connell, Ph.D.

The HR world is now abuzz with Big Data.  But do we have a good feel for what Big Data is and how it will be used?  According to bestselling author Bernard Marr, Big Data is “our ability to collect and analyze the vast amounts of data we are now generating in the world.”  But just because we now have the processing horsepower coupled with vast amounts of information doesn’t mean that we’re going to come up with magical solutions.  If you really think about it, in the field of selection and hiring we’ve been working with Big Data for decades, albeit maybe it wasn’t so big. describe the image

Biodata is essentially Big Data.  Biodata is any information on an individual, usually a job candidate, that relates to their personal history, background, experience, etc.  For instance, where they went to school, the degree they obtained, how many jobs they’ve had, the types of jobs, and even things such as their favorite courses.  The goal of biodata is to find a stable set of variables, drawn from information provided by applicants or incumbents, to predict meaningful outcomes such as turnover, sales quota attainment, injury risk, and a slew of other criteria.  I/O psychologists have been doing that since the turn of the century, the 20th not the 21st.  The biggest criticism of purely empirical biodata has long been that is just “dustbowl empiricism,” meaning that if it correlates it should be used, whether you know why or not.  That’s all fine and good but it is decidedly atheoretical and likely results in a lot of spurious findings that also “shrink” or disappear when they are appropriately cross-validated.  In addition, it can lead to some really crazy findings, many of which would not only be unethical or unstable but also potentially illegal.  Most I/O psychologists who use biodata now endorse more of a “rainforest empiricism” perspective, wherein there should be at least some theoretical basis for understanding correlations between two or more variables. 


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