Age, puberty, body dissatisfaction, and physical activity decline in adolescents. Results of the German Health Interview and Examination Survey (KiGGS)

Table 5 Sequential comparison of multinomial logistic models for the prediction of physical activity frequency in girls (n = 3 119)

	*Statistics for individual predictors*								*Model statistics*
Model		Age	Pubic hair stage	Menarche	Pubertal timing	BDS	Region × body fat	SES × body fat	Correct classification	Pseudo -2 Log-Likelihood	Wald χ² (df) corr. for model	Nagelkerke's pseudo R²
0^#	Wald χ² (df) corrected								36.2%	9393.16	92.77 (12.99)	.045
	p-value*‚										< .001
1	Wald χ² (df) corrected	112.22 (2.90)							36.7%	9222.36	178.55 (15.38)	.093
	p-value *	< .001									< .001
2	Wald χ² (df) corrected	41.32 (2.80)	6.23 (5.74)	8.71 (5.60)					37.5%	9201.17	181.48 (24.41)	.099
	p-value*	< .001	.506	.152							< .001
3	Wald χ² (df) corrected	40.28 (2.88)	5.98 (5.75)	6.94 (5.61)	16.70 (5.72)				38.1%	9177.39	185.18 (27.99)	.106
	p-value*	< .001	.588	.177	.480						< .001
4	Wald χ² (df) corrected	39.54 (2.90)	5.65 (5.76)	7.85 (5.61)	17.12 (5.71)	21.38 (8.45)			38.2%	9146.57	189.62 (33.27)	.114
	p-value*	< .001	.635	.135	.414	.014					< .001
5	Wald χ² (df) corrected	38.57 (2.90)	5.79 (5.75)	7.70 (5.60)	17.07 (5.70)	21.40 (8.43)	5.75 (2.92)	8.24 (5.68)	38.1%	9128.14	194.32 (38.08)	.119
	p-value*	< .001	.616	.147	.372	.012	.048	.017			< .001

* Adjustment for multiple tests: Šidák sequential
^# model 0 = baseline model including body fat percentage and sociodemographic variables: region, SES, migrant background
Each row of the table shows the results of one tested model. Left-hand the test statistics for the independent variables are given while right-hand information on model fit is displayed.
The corrected Wald chi-square test tests if an individual independent variable (individual predictors) or all independent variables together (model statistics) significantly contribute to the prediction of the dependent variable; it is corrected for the sampling plan.
Correct classification rate is the proportion of participants for whom the tested model could correctly predict the category of the dependent variable (PA frequency).
Pseudo -2 Log-Likelihood: In logistic regression models are compared due to their -2 log-likelihood; since for complex samples no likelihood ratio test is available the values are only descriptive; better fitting models have smaller values.
Nagelkerke's pseudo R² is a measure of explained variation in the dependent variable that emulates R² from linear regression.

ISSN: 1479-5868