Table 4.
Regression analyses predicting mothers' a) actual frequency and b) recommended frequency of behaviour for 'Providing breakfast', 'Cooking from Scratch' and 'Having a proper 'sit-down' meal'
| Breakfast1 | B | SE B | Β | 95% CI for B | p = |
|---|---|---|---|---|---|
| a) Actual frequency (log) | |||||
| INT | -.04 | .01 | -.32 | -.06 to -.03 | .0001 |
| PBC-C | -.11 | .02 | -.35 | -.15 to -.07 | .0001 |
| PBC-SE | .14 | .03 | .36 | .09 to .19 | .0001 |
| Model Adj R2 = .19, F(3,296) = 23.65, p < 0.0001 | |||||
| b) Recommended frequency | |||||
| INT | .39 | .03 | .68 | .34 to .45 | .0001 |
| PBC-C | .12 | .07 | .09 | -.01 to .26 | .07 |
| PBC- SE | -.04 | .09 | -.02 | -.21 to .13 | .65 |
| Model Adj R2 = .49, F(3,296) = 96.30, p < 0.0001 | |||||
|
Cooking from scratch a) Actual frequency |
B | SE B | Β | 95% CI for B | p = |
| INT | .93 | .04 | .76 | .85 to 1.03 | .0001 |
| PBC-C | .02 | .09 | .01 | -.15 to .20 | .83 |
| PBC-SE | .27 | .08 | -.13 | .11 to .43 | 0.001 |
| Model Adj R2 = .66, F(3,296) = 193.63, p < 0.0001 | |||||
| b) Recommended frequency | |||||
| Int | .37 | .03 | .55 | .31 to .43 | .0001 |
| PBC-C | -.05 | .06 | -.04 | -.17 to .07 | .41 |
| PBC- SE | .30 | .06 | .27 | .31 to .43 | .0001 |
| Model Adj R2 = .44, F(3,296) = 78.2, p < 0.0001 | |||||
| Sit down meal | B | SE B | Β | 95% CI for B | p = |
| a) Actual frequency | |||||
| INT | 1.03 | .06 | .76 | .92 to 1.15 | .0001 |
| PBC-C | -.05 | .16 | .02 | -.28 to -.18 | .64 |
| PBC-SE | .20 | .14 | .07 | .07 to .47 | .15 |
| Model Adj R2 = .62, F(3,296) = 161.5, p > 0.0001 | |||||
| b) Recommended frequency | |||||
| Int | .44 | .02 | .73 | .40 to .48 | .0001 |
| PBC-C | .01 | .04 | .01 | -.07 to .09 | .81 |
| PBC- SE | .27 | .05 | .20 | .17 to .37 | .0001 |
| Model Adj R2 = .74, F(3,296) = 283.4, p > 0.0001 | |||||
1Transformation (log) reduced overall skewness in the 'breakfast' actual frequency variable, but since the transformed variable remained skewed we also dichotomised this into 2 categories, 'every day' and 'less than every day', and carried out a logistic regression using the same predictors as a check. The model was significant, Chi Square = 45.71, p < 0.0001; Nagelkerke R2 = .22, with all predictors being highly significant using the Wald test (all p < 0.0001).