We’ve all seen those math questions that include the words “Explain how…” and “Show your work”. And you know a lot of students are sitting at their desks thinking, “I’m in a math class, why am I writing an essay?!”
Of course, they’re not writing an essay. Just a sentence or two to demonstrate they understand the concept behind the problem. And laying out their work provides their teacher with a wealth of knowledge that cannot be discerned from placing a simple “42” in an answer space. Are they having trouble adding or are they lining up the digits incorrectly? Do they consistently skip a crucial step? Do they always seem to get stuck at the same point in the process? Are they showing signs of dyscalculia (similar to dyslexia, but related to mathematical skills)?
As adults, we are constantly bombarded with statistics meant to influence, shock, motivate, or congratulate us. A recent survey by the Society for Human Resource Management (SHRM) states that 59% of HR execs are challenged by the need to reward top employees, and 36% are concerned about creating an environment that attracts the top candidates to their organizations in the first place.
A savvy reader will go behind the numbers to look at the structure of the survey. How many execs responded? What fields were they in? Do they already have company recognition programs or are they starting from scratch? Are they geographically isolated or located near a plentiful pool of candidates? Without understanding these factors, the reader can’t truly decide how relevant the final statistics are to his unique situation.
The same principle lies behind online pre-employment assessments. After a candidate takes a test, the prospective employer receives a raw score. But what does that score actually say about the candidate? How can the prospective employer understand everything else the candidate’s performance has to tell her?
This is where good analytics come in. Valuable analytics will show a prospective employer how long it took the candidate to answer questions and how many they skipped. They can show if the candidate left the testing screen to open browsers or utilize other computer programs. They can even show if a candidate tried to copy the question. Top analytics will also measure the candidate against all others who have taken the test so the employer can compare apples with apples.
Of course, how a prospective employer interprets these data is dependent upon the situation and what she is looking for in an employee. To some, trying to copy a question indicates an effort to cheat and is not desirable. To others, copying a question shows initiative and a desire to solve problems. Skipping questions may indicate a candidate has tentative knowledge of the subject. It could also indicate a skilled test taker who knows it’s best to answer all questions he’s sure of, then return to questions that will take a little longer to attack.
But that is the purpose of analytics and raw data. They should be available to prospective employers for every candidate, every test. The interpretation is dependent upon the employer’s needs and values, not a predetermined “cookie-cutter” set of assumptions. When choosing an online employment testing platform, employers are wise to evaluate the extent of data available to them in the test results, and if that data will meet their current needs when making informed hiring decisions.