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. 2020 Aug 3;15(8):e0236987. doi: 10.1371/journal.pone.0236987

Table 1. Descriptive statistics and correlations among situational and individual factors.

M SD Range Skew. Kurt. S_ori S_use PA NA RAT AUT_FLU AUT_FLE AUT_ORI
S_ori 45.62 23.93 0–100 0.16 -1.01 0.29** 0.43** -0.12**
S_use 57.17 23.26 0–100 -0.36 -0.97 0.31* 0.17** -0.12**
PA 55.79 15.93 9.0–100.0 -0.07 -0.21 0.54*** 0.34* -0.57**
NA 34.89 15.74 0.0–94.0 0.34 -0.15 0.02 -0.23 -0.48***
RAT 10.33 2.12 4–14 -0.83 0.77 0.19 -0.01 0.27* 0.00
AUT_FLU 0.36 1.68 -2.42–5.14 0.59 0.32 0.08 0.09 -0.12 0.11 -0.05
AUT_FLE 0.24 1.35 -3.50–3.10 -0.18 0.28 -0.02 0.07 -0.04 0.20 -0.13 0.45***
AUT_ORI 0.45 1.85 -2.06–5.57 0.73 -0.08 -0.01 -0.06 -0.19 0.09 -0.10 0.91*** 0.29*
RAPM 27.93 5.05 8–35 -1.35 3.34 0.01 -0.08 0.00 -0.02 0.37** 0.20 0.15 0.22

S_ori = state originality; S_use = state usefulness. Means (M), standard deviations (SD), and ranges of situational variables are calculated by directly aggregating all records. Correlation coefficients below the diagonal are calculated at the between-participant level, in which the state-level variables (S_ori, S_use, PA, NA) were averaged within participants and the correlated with the trait-level variables across all participants (N = 54 for the correlation analyses). Cross-sectional correlations are presented above the diagonal, in which the state-level data were pooled together across participants (N = 1317 for the correlation analyses). Pearson’s correlation was used.

* p < .05

** p < .01

***p < .001.