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Table 6 Latent construct correlations (upper off-diagonal values), square root of AVE (bold, diagonal) and scale intercorrelations (lower off-diagonal values)

From: Analyzing the effects of barriers to and facilitators of medication adherence among patients with cardiometabolic diseases: a structural equation modeling approach

Nr Factors 1 2 3 4 5 6 7 8 9
1 Physician–patient relationship .73 -.42e a -.29e -.42e -.34e -.39e -.18c -.25e -.24d
2 Insecurity -.36d b .89 .37e .50e .35e .44e .13 .13 .19c
3 Falsified patient information -.32d .36d .89 .39e .27e .35e .35e .33e .15c
4 Reservations -.37d .44d .31d .75 .67e .34e .15 .08 .39e
5 Fear of side effects -.28d .29d .25d .50d .82 .27d .10 .02 .22d
6 Individual decisions -.30d .35d .34d .26d .21c .74 .51e .29e .29e
7 Avoidance of drug side effects -.14c .04 .35d .13 .08 .36d .79 .18c .17c
8 Carelessness -.19d .10 .29d .07 -.01 .20d .12 .79 .12
9 Drug intake in public -.22d .16c .11 .32d .15c .18d .13 .07 .81
  1. aFornell-Larcker-criterion of discriminant validity: each latent correlation must be lower than both the corresponding row and column value (square root of AVE of each construct)
  2. bInterpretation according to product-moment correlation: > .1 weak effect; > .3 moderate effect; > .5 strong effect
  3. cCorrelations are significant at the level of .05 (2-tailed)
  4. dCorrelations are significant at the level of .01 (2-tailed)
  5. eCorrelations are significant at the level of .001 (2-tailed)