Table 3 Robustness measures for the use...

Table 3

Robustness measures for the use of principal component analysis to construct the variable scores

Variable	FL	CA	AVE	χ²
OPTI	0.699–0.801	0.740	0.569	383
SCEP	0.680–0.735	0.750	0.502	456
INNOV	0.685–0.860	0.675	0.612	242
INSEC	0.593–0.792	0.767	0.525	538
ECU	0.710–0.816	0.747	0.578	436
RISK	0.598–0.831	0.722	0.538	354
NREG	0.682–0.872	0.836	0.614	881

Variable	FL	CA	AVE	χ²
OPTI	0.699–0.801	0.740	0.569	383
SCEP	0.680–0.735	0.750	0.502	456
INNOV	0.685–0.860	0.675	0.612	242
INSEC	0.593–0.792	0.767	0.525	538
ECU	0.710–0.816	0.747	0.578	436
RISK	0.598–0.831	0.722	0.538	354
NREG	0.682–0.872	0.836	0.614	881

Note(s): FL = factor loadings; CA = Cronbach's alpha; AVE = average variance extracted; χ² = chi-square statistic of Bartlett's test. In all cases, Bartlett's test rejects the null hypothesis that the correlation matrix is an identity matrix

Source(s): Author’s own work

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