How Good Are You at Evaluating People? Part One

We all have a tendency to think we are a good judge of people… in spite of the fact that most of us have a pretty shabby track record. For example, how many of us are on a second marriage, have mistakenly promoted a technician or sales person to a management position, or over estimated a friend’s job skills. It’s normal for people to make mistakes in judgment. The reasons can be found deep in the human psyche where emotions carry more weight than rational thought. Did you ever hear that impressions are made within the first 30 seconds of meeting? After 30 seconds, the ‘halo’ from our initial impression affects all further decisions about that person – from their choice of clothing to their skills on the job. You see, our brains come pre-programmed to evaluate others based on whether they are “threatening” or not. (Some say this is another Microsoft anti-competitive strategy, but I doubt this is true). Friendly people are “good” and unfriendly people are “bad.” Why else do you think people dress up and prepare smooth answers for an interview? This tendency to pre-judge others might have protected us as children, but it is a highly inaccurate way to measure job skills. Look over the following list of selection methods. Circle your favorite method(s) for measuring job skills. Then, estimate its accuracy using a zero to 100% rating. The “% Chance” column is the difference between your accuracy estimate and 100%

Method

%

Accuracy

%

Chance

Handwriting Analysis

? ?

Age

? ?

Amount of Education

? ?

Self Assessment

? ?

Projective Tests

? ?

Traditional Interviews

? ?

Grade Point Averages

? ?

Expert Recommendations

? ?

Personality Tests

? ?

Motivation

? ?

Reference Check

? ?

Biographical Data

? ?

Situational Interviews

? ?

Behavioral Event Interviews

? ?

Mental Ability Tests

? ?

Content Valid Simulations

? ?

You might know of a few more methods, but these represent the most common ways people assess job skills. They range from the subjective (e.g., handwriting, age, grades) to the objective (e.g., mental ability, simulations). Probably the most common method is a traditional interview accompanied with a few favorite questions. Each of these methods, however, has been tried and tested by hundreds of investigators who have studied thousands of people in hundreds of jobs. They were, of course, trying to determine the accuracy of each method for predicting job performance. The measures commonly used in these types of studies are called “correlation coefficients.” Correlation is measure of agreement between two factors. For example, a 1.0 correlation means perfect agreement, -1.0 is perfect disagreement, and 0 means there is no agreement between the numbers. That would be just fine if statistics were decipherable by normal people, but correlation coefficients must be squared before they can be used to explain performance. In other words, a traditional interview with a .10 correlation coefficient does not mean it explains 10% of the variance, it means it explains 1% of the variance! (.10 x .10 = .01 x 100 = 1%). For those of you who are not math majors, this leaves 99% of the variance in performance the chance! In other words, data gathered using a selection method that has a .10 correlation with performance would have about 1% accuracy. Would you make a $50,000 investment based on having 1% of the information you needed? I’ve known people to take more time when selecting a $200 printer (but it was their own money). The following diagrams should help explain what I mean graphically. The “A” represents job-related data you need to gather from the applicant. The “C” represents information left to chance. There are 100 data points. The first diagram illustrates zero variance – basically, there is no data that will predict future performance – everything is chance. The second diagram illustrates 1% evidence and 99% chance, and so on. Chance is what you discover after the applicant comes to work. Zero % Performance Information

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ccccc In part two we’ll continue our exploration into selection methods. Meanwhile, go back to the chart and give it your best shot. We’ll share the research findings in the next addition.

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