Modern Analytics in Industrial-Organizational Psychology: Are Masters Graduate Programs Falling Behind?

Industrial-Organizational Psychology is a field of study that has been around for many years and has never been as popular as it is today. This demand creates a need for new I-O psychologists with the latest skills in data science, statistics, and computing (King et al., 2016). There has been a clear shift from traditional I-O research approaches to those that integrate more data analytics, yet the lack of sufficient training in modern analytic approaches potentially puts I-O graduate students at a disadvantage (Putka & Oswald, 2016). As such, there is a need for graduate programs to be aware of this shift and prepare their students accordingly.

The Business Dilemma

Data analytics has become the new language of the modern day business. More and more organizations and HR departments are realizing the value of data and shifting toward using it to make strategic data-driven decisions. Consequently, organizations are increasingly in need of professionals with computer science and data analytical skills in order to compete in this digital era.

I-O psychology professionals are in a unique position to leverage data analytics. One of the reasons is that they often conduct research or provide consulting for organizations with large volumes of employee data. These professionals are also uniquely qualified to interpret statistical analyses because their training includes the human side of statistics–the importance of interpreting results in ways that make sense for people outside the field–and this helps them communicate findings more effectively.

However, I-O professionals can find themselves at a disadvantage when lacking the skills in emerging data science tools and software, such as R, Python, SQL, and data visualization tools ( Oswald et al., 2020). These tools have become immensely popular and widely used in business settings. Some of the biggest companies in the world use R for data science and research. I-O professionals possessing these skills can help organizations maximize their talent potential by using predictive analytics and other data-driven methods to help businesses make evidence-based decisions.

The Gap

The gap in the analytical skills of I-O psychology graduates is a major problem. This gap can be seen in both graduate programs and applied settings, caused by an imbalanced focus on theory rather than practical application. This puts the discipline in a position where new researchers and practitioners in I-O psychology may have obsolete statistics and methods training before completing graduate school, restricting the field’s advancement, value, and multidisciplinary potential (Putka & Oswald, 2016).

The problem with this gap is that it does not seem to be getting any better, especially as it becomes more and more difficult for students to find jobs in their field. The clash between theoretical teachings and practical application will need to be resolved or at least acknowledged by people working within the field of I-O Psychology.

Awareness of the Need For Change

I-O master’s programs provide a sufficient amount of basic statistical training on traditional techniques (e.g., regression, ANOVA) using SPSS, the most commonly used statistical software in graduate-level education. However, many programs are failing to teach traditional statistical procedures using analytical software and tools commonly found in applied business settings. Although the use of modern analytical tools are still absent from many programs, the importance of having a programming or machine learning background has been gaining traction within the I-O community.

The Society of Industrial and Organizational Psychology (SIOP) publishes annual Top 10 Workplace Trends voted on by SIOP members. Artificial Intelligence & Machine Learning and Big Data as listed among the top 3 trends for 2019 and 2020. In Addition, SIOP recently hosted a machine learning competition at the 2021 SIOP Annual Conference in which cross-disciplinary teams built an algorithm to predict job performance. Having a basic understanding of machine learning, R, and Python will assist I-O professionals in bridging the gap between organizational objectives and their big data and analytical resources, ensuring that the correct methods and metrics are used and properly interpreted (Oswald et al., 2020).

Despite SIOP’s increasing emphasis on the value of machine learning and more robust data analytical techniques, progress has been slow in changing the way statistics is being taught to I-O graduate students. Of course, redesigning I-O master’s programs to incorporate R and Python programming is easier said than done. Programs have a considerable number of challenges and barriers to overcome. However, a critical step first step is to recognize the growing skills gap and then work to close it.

Ways I-O Professors Can Help

There are various ways that I-O professors can introduce programming to their graduate students.

1. Let students know about the current trends in data analytics software, such as Tableau and Power BI, and encourage them to seek ways to develop their skills in using these tools.
2. Professors utilizing SPSS in their statistical and research courses could assign SPSS homework, and then challenge their students to try and obtain the same results by running the analyses in R.
3. Offer data science, data analytics, or computer programming as electives. Many universities offer courses that teach these topics. For example, the University of Texas at Austin offers an Introduction to Data Science course with an emphasis on Python and R.

The above examples highlight just a few of the many ways I-O professors can help graduate students build essential data analytical competencies while limiting the additional burden of a curriculum overhaul for professors that are not programming experts themselves.

Conclusion

I-O professionals bring unique and valuable expertise to organizations, especially within the business and industry sector. However, I-O professionals can have a greater organizational impact by increasing their understanding of modern analytical techniques and tools commonly used in applied settings. I-O graduate students would benefit from programs that strive to teach statistics using computer programming languages R and/or Python instead of, or in conjunction with, SPSS. Rather than only knowing how to use one analytical tool, graduates will have an expanded knowledge of performing statistical analyses using multiple tools. Having a more comprehensive scope of experience using various analytical tools can provide students with more options for analyzing and visualizing data, boost their ability to adapt to organizational needs, and ultimately increase their value as I-O professionals.

References

King, E. B., Tonidandel, S., & Cortina, J. M. (Eds.). (2016). Building understanding of the data science revolution and I-O psychology. In S. Tonidandel, E. B. King, & J. M. Cortina (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 1–15). Routledge/Taylor & Francis Group.

Oswald, F. L., Behrend, T. S., Putka, D. J., & Sinar, E. (2020). Big data in Industrial-Organizational Psychology and Human Resource Management: Forward progress for organizational research and practice. Annual Review or Organizational Psychology and Organizational Behavior, 7, 505-533. https://doi.org/10.1146/annurev-orgpsych-032117-104553

Putka, D. J., & Oswald, F. L. (2016). Implications of the big data movement for the advancement of I-O science and practice. In S. Tonidandel, E. B. King, & J. M. Cortina (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 181–212). Routledge/Taylor & Francis Group.