. Author manuscript; available in PMC: 2024 Dec 6.

Published in final edited form as: Int J Eat Disord. 2008 Jul;41(5):411–418. doi: 10.1002/eat.20514

TABLE 5.

Comparison of the proposed subtypes on possible validation criteria on which the four groups significantly (p < .05) differ

Measure	Effect Size Estimate (Statistical Significance)
Measure	… Main Effect Depression	… Main Effect Night Eating Pattern	… Depression by Night Eating Pattern Interaction
^***Ever used marijuana	1.48 (^†††)	1.18 (n.s.)	1.28 (n.s.)
^**0 days 5+ drinks	0.811 (n.s.)	0.813 (n.s.)	1.14 (n.s.)
^***Health	−0.17 (^†††)	−0.003 (n.s.)	0.13 (n.s.)
^**Energy (kcal)	−0.025 (n.s.)	0.512 (^†††)	−0.19 (n.s.)
^**% kcal from protein	−0.16 (^†††)	−0.14 (^††)	−0.10 (n.s.)
^*% kcal from carbohydrates	0.13 (^††)	0.11 (^†)	−0.02 (n.s.)
^*Cholesterol	−0.08 (n.s.)	0.39 (^†††)	−0.15 (n.s.)

Notes: Effect size estimates are presented, with statistical significance of the corresponding main effect or interaction in parentheses. The estimates in Table 5 are from analyses that did not take the complex survey design into account, because we are not aware of a validated method for estimating effect sizes for main effects and interactions with complex survey data. Parallel models taking the survey design into account yielded similar parameter estimates and hypothesis test results. For binary measures (marijuana use and drinking), effect size estimates are the exponentiated parameter estimate; for continuous measures (all others), the effect size estimate is the parameter estimate divided by the square root of the model mean squared error.

Overall difference among groups, *p < .05; ** p < .01; *** p < .001.

Significant main effects or interactions, ^† p < 0.05; ^†† p < .01; ^††† p < 0.001; “n.s.” indicates nonsignificant main effects or interactions, p ≥ .05.