56  Addressing Validity in Research

56.1 The role of validity

It should be clear by this point that validity in research is critical, for a whole range of reasons. In the rush to undertake research or analysis, it’s sometimes easy to forget how important the validity of our data is in the overall research process.

Validity is not just a statistical ‘concern’, but a vital element in drawing reliable conclusions. When data lacks validity, the conclusions drawn may be fundamentally flawed, potentially leading to false assertions and misguided insights or recommendations.

This is particularly important in research where accuracy is paramount for the credibility of the findings. In an environment (such as sport) where stakeholders rely on these conclusions, the validity of data becomes central to trust.

For example, decisions about in-game strategies, player selection, and training programs can depend on the validity of our data analysis. Inaccurate or invalid data can lead to misguided strategies that may adversely affect the performance of athletes.

The stakes are high; a single flawed decision based on poor data analysis can misdirect resources, increase risks, and even tarnish careers. This can also lead to mistrust of data, and data analysts.

56.2 Validity in data cleaning and transformation

At the heart of valid data analysis lies the imperative of data integrity. This means ensuring that the data remains accurate and consistent throughout the cleaning and transformation processes.

To avoid the risk of data corruption, it’s essential that analysts implement rigorous validation checks and procedures. These steps are not mere ‘formalities’, but are crucial in preserving the authenticity and reliability of the data.

  • As you know, missing data is a common challenge in data analysis and can significantly impact the validity of the results if not handled correctly.

  • Similarly, data transformation (standardisation and normalisation) can be a critical step in preparing data for analysis. However, it’s vital to ensure that these transformations do not distort the inherent relationships within the data.

Documentation plays a pivotal role in ensuring the validity of data cleaning and transformation processes. By recording every step we take in the analytical process, from initial data handling to the final analysis, we can provide a transparent and replicable account of our methodology.