K - O
Local Validity Study
Performing a local validity study is a powerful way to validate a pre-employment test. Most organisations using pre-employment tests often rely on general validity evidence provided by test publishers to demonstrate the validity of the test. However, whenever possible, companies should seek to verify that the test in question is valid for the precise position for which the company will use it. To ensure this is the case, companies can perform local validity studies within their organisations by administering the test to their current employees.
For instance, if a company wants to see if an aptitude test is a valid test for its financial analysts, the company can administer the test to its current employees in that role and then compare their test scores with some measure of their job performance. If the current financial analysts’ test scores are significantly correlated with their job performance, then the aptitude test would be valid for that position within that organisation.
Local validity tests demonstrate the correlation between two variables (in this case, test scores and performance) across a large group of individuals. This means that local validation studies require large sample sizes. Smaller companies or companies that do not already have a lot of incumbent employees in a particular position will not be able to perform local validity studies within their organisations. As an alternative, they can turn to validity generalisation, relying on the "transportable validity" from test publishers who have demonstrated the validity of a specific test within widely applicable employment settings.
As it relates to pre-employment testing, offsite testing refers to any time a job candidate takes a test away from a designated testing location or employer’s place of business. What this generally means is that rather than taking tests at an office or jobsite, candidates take tests at home or at any other location of their choosing. Offsite tests are usually unproctored and most taken online using an internet-connected device.
Offsite testing, also called remote testing, is especially useful for screening candidates at the beginning of the hiring process. By administering tests offsite, employers can use test scores to filter out unqualified applicants before reaching out to a select number of candidates for interviews. As for the concern about cheating in offsite testing, many tests can detect suspicious test-taking behaviours linked with cheating. However, evidence suggests that when testing is properly positioned, cheating actually happens far less than expected with offsite testing.
In statistics, an outlier is a data point that differs greatly from other values in a data set. Outliers are important to keep in mind when looking at pools of data because they can sometimes affect how the data is perceived overall.
In pre-employment testing, the most common data that is observed are test scores, usually plotted against a measure of employee performance. Examining the data will help determine how correlated test scores are with test performance. In any given sample, however, there are likely to be outliers, and it is important to avoid focusing on outliers as opposed to the trends presented by the data on the whole. Take the example below:
A company administers an aptitude test to their current sales staff to see how correlated their test scores are with job performance, quantified in this case by monthly sales. These were the results:
At a first glance, employee #2, the employee with the second highest monthly sales, scored rather poorly on the test. On the other end of the spectrum, employee #19, with the second lowest monthly sales, scored quite well on the test. These two employees, when examined individually, make it seem as if the test is not very predictive of job performance. However, when viewing the data sample on the scatter plot above, a clear and positive correlation is evident between test scores and job performance. In other words, as test scores rise, monthly sales rise as well. Employees #2 and #19 are both outliers because their data values exist outside of the general trend in the overall data sample. When determining whether a correlation exists, it is important to look at the overall trends in the entire data sample instead of focusing on a few outliers that seemingly contradict those trends.