N. | Description | Rationale |

1 | Data screening | To detect outliers with Mahalanobis distance critical value. |

2 | Univariate normality evaluation with multiple tests | To test for skewness, kurtosis and the univariate normality assumption with Kolmogorov-Smirnov (Lilliefors), Shapiro-Wilk, Shapiro-Francia, and Anderson-Darling tests. |

3 | Multivariate normality evaluation with multiple tests | To test for the multivariate normality assumption with Mardia’s multivariate kurtosis and multivariate skewness tests, Henze-Zirkler’s consistent test, Doornik-Hansen omnibus test, Energy test and Royston test. |

4 | Sample-splitting (20%, 40%, 40%) | To carry out EFA (20%), an initial CFA1 (40%) and cross-validating CFA2 (40%) in three different subsamples, the sample was randomly divided into three parts (20%, 40%, 40%). The two CFA subsamples (40%) were of equal power (3-Faced |

5 | Exploratory Factor Analysis (EFA) | To establish a structure for CD-RISC10. The number of factors to retain was examined with Parallel Analysis |

6 | Confirm the EFA results with a Confirmatory Factor Analysis (CFA 1) | The EFA structure of CD-RISC10 was confirmed with an initial CFA, to test alternative models including a bifactor model. |

7 | Tests of fit difference | To compare the model fit of all the alternative CFA1 models with the likelihood ratio test (−2ΔLL MLR rescaled version; |

8 | Evaluating the Bifactor model | To evaluate the bifactor model, using bifactor ancillary model fit measures |

9 | Evaluating the influence of outliers on CFA1 | To test if outliers influenced CFA1 model fit with 2 trial CFAs (in a subsample with vs without multivariate outliers; |

10 | Cross-validating the CFA1 optimal model with CFA2 | To cross-validate the optimal CFA1 in a different subsample of equal power (CFA2). |

11 | A priori & post hoc power analysis of the CFA2 model | To evaluate the sample required for achieving a power of 80% to reject a wrong model. An alpha level of .05 was assumed with an RMSEA misspecification of .05 |

12 | Measurement invariance across gender to the strict level of the CFA2 model | To test if the cross-validated CFA2 model was invariant factors, factor loadings, intercepts, and error variances across gender. |

13 | Internal Consistency Reliability, Model-Based Reliability and Model-based Convergent Validity after testing tau-equivalency of the optimal model | To evaluate Cronbach’s alpha [95% CI], and the greatest lower bound estimate ( |