Intraindividual variability, how do I measure thee? Let me count the ways

<b>Objective:</b> The present study aimed to better understand key conceptualizations and operationalizations of intraindividual variability (IIV). We expected that differing types and metrics of IIV would relate to one another and predict outcomes (academic achievement) similarly. <b>Method:</b> The sample comprised 238 young adults. IIV was computed within and across six measures - three related to math and three more generally cognitive; in each case, score was separated from response time. We computed three types of IIV (inconsistency, dispersion, and dispersion of inconsistency), across several metrics (standard deviation, coefficient of variability, residualized standard deviation), and assessed their interrelations, and their prediction of academic achievement. <b>Results:</b> Differing metrics of variability were related to one another, but variably so. For prediction, whether or not inconsistency IIV metrics were significant was highly dependent on the measure they were derived from, with or without the primary score for a given measure also included. For dispersion of inconsistency and dispersion, variability metrics were often significant, though this was eliminated in most cases when score was also included in models. <b>Conclusions:</b> By concurrently examining multiple metrics and types of IIV within the same set of measures, this study highlights the need to (a) clarify the type of IIV utilized and why; (b) clarify the rationale for the kinds of measures used to compute IIV, particularly dispersion; and (c) include score alongside timing. Doing so will likely improve the generalizability of IIV findings, and prompt future research avenues, both psychometric- (e.g. simulations) and clinical-related (e.g. across ages and populations).