Calculating impact factors solely from raw citation counts can be misleading because citation counts vary across disciplines and publication years. Citation analysis is a fundamental bibliometric methodology that aids in determining trends, frequency, and influence; nevertheless, normalisation methods for reducing skewness are still not well understood and are not always used. Selecting an appropriate normalisation technique to map source data to a standardised citation scale effectively is challenging. This study aims to provide new insights into which normalisation methods researchers should use when conducting citation analysis. Normalisation at the author level using Field-Weighted Citation Impact (FWCI) and Log Normalisation Citation Score (Log NCS), and the field level using Mean Normalised Citation Score (MNCS) and Mean Normalised Log Citation Score (MNLCS) are the two components of the comparative analysis presented in this work. Using citation data from six different academic fields, this study assesses how well each approach reduces skewness and creates more equitable comparisons. This study offers four contributions: it provides a structured comparison approach to author and field normalisation; it empirically demonstrates conditions under which log transformation outperforms non-log methods; it provides decision-making guidance for researchers when selecting normalisation approaches; and it validates findings across multiple disciplines. These findings are intended to improve the accuracy and validity of citation-based impact evaluations, thereby facilitating more equal academic benchmarking and collaboration.
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