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Thelwall (2017a, 2017b) proposed a new family of field- and time-normalizedindicators, which is intended for sparse data. These indicators are based onunits of analysis (e.g., institutions) rather than on the paper level. Theycompare the proportion of mentioned papers (e.g., on Twitter) of a unit withthe proportion of mentioned papers in the corresponding fields and publicationyears (the expected values). We propose a new indicator (Mantel-Haenszelquotient, MHq) for the indicator family. The MHq goes back to the MH analysis.This analysis is an established method, which can be used to pool the data fromseveral 2x2 cross tables based on different subgroups. We investigate (usingcitations and assessments by peers, i.e., F1000Prime recommendations) whetherthe indicator family (including the MHq) can distinguish between quality levelsdefined by the assessments of peers. Thus, we test the convergent validity. Wefind that the MHq is able to distinguish between quality levels (in most cases)while other indicators of the family are not. Since our study approves the MHqas a convergent valid indicator, we apply the MHq to four different Twittergroups as defined by the company Altmetric (e.g., science communicators). Ourresults show that there is a weak relationship between all four Twitter groupsand scientific quality, much weaker than between citations and scientificquality. Therefore, our results discourage the use of Twitter counts inresearch evaluation.

Field- and time-normalization of data with many zeros: An empirical analysis using citation and Twitter data