Emerging advancements including the exponentially growing availability of computer-collected data and increasingly sophisticated statistical software have led to a "Big Data Movement" wherein organizations have begun attempting to use large-scale data analysis to improve their effectiveness. Yet, little is known regarding how organizations can leverage these advancements to develop more effective personnel selection procedures, especially when the data are unstructured (text-based). Drawing on literature on natural language processing, we critically examine the possibility of leveraging advances in text mining and predictive modeling computer software programs as a surrogate for human raters in a selection context. We explain how to "train" a computer program to emulate a human rater when scoring accomplishment records. We then examine the reliability of the computer's scores, provide preliminary evidence of their construct validity, demonstrate that this practice does not produce scores that disadvantage minority groups, illustrate the positive financial impact of adopting this practice in an organization (N ∼ 46,000 candidates), and discuss implementation issues. Finally, we discuss the potential implications of using computer scoring to address the adverse impact-validity dilemma. We suggest that it may provide a cost-effective means of using predictors that have comparable validity but have previously been too expensive for large-scale screening. (PsycINFO Database Record
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 March 2021.
All research outputs
#8,782,559
of 32,836,612 outputs
Outputs from Journal of Applied Psychology
#1,616
of 3,815 outputs
Outputs of similar age
#93,759
of 332,986 outputs
Outputs of similar age from Journal of Applied Psychology
#12
of 20 outputs
Altmetric has tracked 32,836,612 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 3,815 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.3. This one has gotten more attention than average, scoring higher than 57% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,986 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
