When evaluating the validity of a new assessment or measurement tool, one important consideration is its predictive validity. This concept provides insights into how well your measure can forecast or anticipate future outcomes of the assessed construct.

Example: Predictive validity

A college admissions test has predictive validity if it accurately forecasts which applicants will have strong academic performance during their first year. If students’ test scores show a high positive correlation with their first-year GPA (e.g., r = 0.75), the admissions test has good predictive validity for academic success in college.

What is Predictive Validity?

Predictive validity refers to the degree to which scores on a new test or assessment can predict some future criterion or outcome. In other words, it examines how well performance on your measure aligns with and predicts people’s real-world behaviors or achievements.

The basic idea is that if your new measure truly captures something meaningful and important, it should be able to successfully forecast relevant future events or achievements. Strong predictive validity suggests that your new assessment tool has practical utility and can be relied upon to make accurate predictions.

Predictive Validity Example

A technology company faces high turnover among its newly hired software engineers. To address this issue, the HR team evaluates the predictive validity of their current job interview process.

The company has traditionally relied on a multi-stage interview process involving technical assessments, behavioral interviews, and a final round with senior leaders. To establish the predictive validity of this process, the HR team takes the following steps:

  • Collect data: During the hiring process, the HR team records detailed scores and feedback for each candidate across the various interview stages.
  • Track employee performance: Over the next 12 months, the HR team closely monitors the newly hired software engineers’ job performance and retention rates.
  • Analyze the correlation: The HR team analyzes the relationship between the candidates’ interview scores and their subsequent job performance and retention and calculates the correlation coefficient between the two variables.
  • Evaluate predictive validity: If the correlation coefficient is high, it indicates that the interview process has strong predictive validity. This means the interview scores can effectively predict how well the new hires will perform and how long they will likely stay with the company.

Predictive vs. Concurrent Validity

It’s important to distinguish predictive validity from concurrent validity.

Concurrent validity examines how well a new measure agrees with or correlates with an existing “gold standard” assessment in the present moment. The focus is on demonstrating alignment between the new measure and a well-established benchmark.

Predictive validity looks at how the new measure’s scores can predict some future criterion or outcome. The emphasis is on the tool’s ability to successfully forecast real-world events or achievements rather than just aligning with other current assessments.

Predictive and concurrent validity provide important information but speak to slightly different aspects of a measure’s validity and utility. Establishing both types of validity can help build a comprehensive case for a new assessment’s overall usefulness and accuracy.

How to Measure Predictive Validity

Predictive validity is typically evaluated using longitudinal study designs. Researchers first administer the new measure and then follow up later to assess the criterion or outcome of interest.

For example, the university admissions test example would involve:

  1. Administering the new admissions test to applicants
  2. Tracking the academic performance of those admitted students over the next few years
  3. Examining the correlation between initial test scores and students’ eventual GPAs or graduation rates

A strong, positive correlation between the initial test scores and the future criterion would provide evidence of the test’s predictive validity. It would suggest that the admissions exam successfully forecasts who will thrive academically.

Researchers may also use regression analyses to more precisely quantify the predictive power of the new measure. The goal is to demonstrate that the new test can account for a significant portion of the variance in future outcomes, even after controlling for other relevant factors.

Establishing robust predictive validity is crucial, as it indicates a measure has practical, real-world applications and utility. A tool that can accurately forecast relevant future events or achievements is more likely to be valuable and trustworthy by experts and end-users.