What is Correlational Research and How Can It Improve Your Recruitment Strategy?

Correlational research is a powerful, non-experimental method that allows businesses to identify meaningful relationships between key variables, leading to more strategic and data-driven recruitment decisions. By analyzing how factors like interview scores, skills assessments, and employee engagement surveys interrelate, HR professionals can significantly enhance hiring accuracy, predict employee success, and improve retention rates. This approach moves beyond gut feelings to create a more efficient and effective talent acquisition process.

What is Correlational Research in HR?

In the context of human resources, correlational research is a type of data analysis used to assess the relationship between two or more workforce-related variables without manipulating them. Unlike experimental research, it doesn't prove causation but reveals important trends and connections. For example, a company might use it to understand if there's a link between an employee's performance on a pre-employment assessment and their first-year performance review score. The primary types of relationships identified are:

• Positive Correlation: As one variable increases, the other also increases (e.g., higher investment in training programs correlates with higher employee retention rates).
• Negative Correlation: As one variable increases, the other decreases (e.g., an increase in overtime hours correlates with a decrease in employee satisfaction scores).
• No Correlation: There is no discernible relationship between the variables (e.g., the color of a candidate's resume has no link to their interview performance).
• Correlation Strength: This indicates how consistently the relationship holds, from strong (data points are close to the trend line) to weak (data points are widely scattered).

A key characteristic of this research is that it is observational, meaning HR professionals collect data without intervening in the natural work environment. However, based on our assessment experience, it's critical to remember that external factors not measured in the study can influence outcomes, so findings should be a starting point for deeper investigation.

How Can You Apply Correlational Research to Recruitment?

Applying this method can transform your hiring process from reactive to predictive. The goal is to pinpoint which candidate attributes and hiring process steps are most indicative of long-term success within your organization.

A practical application is analyzing the correlation between different stages of your candidate screening process. For instance, you might collect data on:

• Resume keyword match rates versus interview scores.
• Scores from structured interview questions versus 90-day performance reviews.
• Pre-employment skills test results versus time-to-productivity.

By plotting this data, you can identify which screening tools have the strongest positive correlation with successful hires. This allows you to optimize your process, potentially shortening time-to-hire by focusing on the most predictive assessments. The table below illustrates a simplified example of what this analysis might reveal:

This data-driven approach helps build a more objective and fair hiring process by reducing unconscious bias and focusing on empirically supported indicators.

What Are the Practical Methods for Collecting HR Data?

To conduct correlational research, you need reliable data. Here are three effective methods tailored for HR:

1. Analyzing Existing HR Metrics (Archival Research): This involves leveraging your current data archives, such as your Applicant Tracking System (ATS), performance management software, and employee surveys. It's a cost-effective starting point. You can analyze historical data to find correlations between employee referral sources and retention rates, or between starting salary and promotion velocity. This method is ideal for established companies with rich historical data.

2. Deploying Targeted Surveys: Surveys are an active method to gather specific data from employees or candidates. For example, you could survey new hires after 6 months to quantify their onboarding experience and correlate those scores with their engagement levels a year later. To ensure quality data, use a consistent structured interview format for your survey questions and aim for a high response rate across different departments.

3. Naturalistic Observation in the Workplace: This involves observing behaviors in their natural context without interference. While more common in operational settings, an HR example could be anonymously observing team interactions during a group interview exercise to see if certain collaborative behaviors correlate with later performance reviews. This method provides authentic behavioral data but requires careful handling to maintain ethical standards and participant confidentiality.

What Are the Limitations and Best Practices?

While powerful, correlational research has limitations. The most important is that correlation does not equal causation. Just because two variables move together doesn't mean one causes the other. A classic example: a company might find a positive correlation between employees who use the company gym and higher productivity. This doesn't mean the gym causes productivity; a third variable, like overall health and energy levels, might influence both.

To use this research effectively:

• Use it as a starting point: A discovered correlation should lead to further investigation or a controlled experiment.
• Focus on strong, logical relationships: Prioritize actions based on correlations that make intuitive sense and are supported by strong data.
• Combine with other data: Triangulate your findings with qualitative feedback and industry benchmarks for a complete picture.

By systematically applying correlational research, you can make more informed decisions that enhance your employer branding, optimize your talent assessment strategies, and ultimately build a more resilient and productive workforce. The key takeaways are to leverage your existing data, focus on relationships that align with your business goals, and always interpret correlations as indicative—not definitive—guides for action.