Spss amos v 29

We used data from the second split-half to conduct a CFA using the SPSS AMOS v.29 software. A previous study suggested that the minimum sample size to conduct a confirmatory factor analysis ranges from 3 to 20 times the number of the scale’s variables [58 (link)]. Therefore, we assumed a minimum sample of 250 participants needed to have enough statistical power based on a ratio of 15 participants per one item of the scale, which was exceeded in this subsample. Parameter estimates were obtained using the robust maximum likelihood method and fit indices. Values ≤ 5 for χ²/df, and ≤ 0.08 for RMSEA, and 0.90 for CFI and TLI indicate good fit of the model to the data [59 (link)]. However, these cut-off values should not be interpreted rigidly (Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011; Perry, Nicholls, Clough, & Crust, 2015); values between 0.08 and 0.10 for RMSEA can indicate acceptable but mediocre fit to the data [60 , 61 (link)].

CFA is used to confirm the factor structure resulting from the EFA. It aims to see how well the data fits the proposed model (Tavakol & Wetzel, 2020 (link)). We used data from the second split-half to conduct a CFA of the model obtained in the EFA, using the SPSS AMOS v.29 software. The recommended minimum sample size required for conducting a confirmatory factor analysis is 3 to 20 times the number of the scale’s items (Mundfrom et al., 2005 (link)). Therefore, we assumed a minimum sample of 80 participants needed to achieve adequate statistical power based on a ratio of 20 participants per one item of the scale, which was exceeded in this subsample. We obtained parameter estimates using the robust maximum likelihood method and fit indices. Additionally, evidence of convergent validity was assessed using the average variance extracted (AVE), with values ≥ 0.50 considered adequate (Malhotra, 2011 ).