Do behavioral measures of self-control assess construct-level variance?
Section snippets
Power analysis
Based on research by Duckworth and Kern (2010), Saunders et al., 2017, Reynolds et al., 2006, Lane et al., 2003, Schmeichel and Zell, 2007, White et al., 1994, correlations between different indicators ranged from small and non-significant up to as high as r = 0.35. Based on this level of inconsistency, we calculated the sample size needed to detect a significant correlation of r = 0.20 between any two given indicators. The necessary sample size needed to detect correlations of 0.20 at 80%
Indicator intercorrelations
Table 4 displays intercorrelations between behavioral indicators of self-control. We used the Benjamini-Hochberg correction for multiple comparisons (Benjamini & Hochberg, 1995), which ranks correlations based on p-values and is less penalizing to lower p-values, thus increasing statistical power. An excel spreadsheet with functions for calculating corrected p-values was retrieved online (McDonald, 2014). Notably, only the six strongest correlations remained significant after controlling for
General discussion
The goal of the current research was to add to the body of literature regarding the convergent and predictive validity of behavioral measures of self-control and, in doing so, provide insight on whether these measures are assessing variance at the construct level. By and large, we replicated previous research by finding that that indicator intercorrelations among behavioral measures are low (Duckworth & Kern, 2010; Lane et al., 2003, Reynolds et al., 2006, Saunders et al., 2017, Schmeichel and
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