Skip to main content

Table 6 Sequential comparison of multinomial logistic models for the prediction of physical activity frequency in boys (n = 3 225)

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

  Statistics for individual predictors Model statistics
Model   Age Pubic hair stage Voice change Pubertal timing BDS Migback × pubic hair stage BDS × age Correct classification Pseudo -2
Log-
Likelihood
Wald χ2 (df) corr. for model Nagelkerke's pseudo R2
0 # Wald χ2 (df) corrected         36.5% 9431.11 39.09 (13.62) .018
  p-value*           < .001  
1 Wald χ2 (df) corrected 68.82 (2.92)        37.6% 9319.74 104.58 (16.06) .050
  p-value* < .001          < .001  
2 Wald χ2 (df) corrected 22.77 (2.95) 4.81 (5.56) 1.55 (5.76)      37.7% 9310.25 104.20 (24.40) .053
  p-value* < .001 .836 .990        < .001  
3 Wald χ2 (df) corrected 34.45 (2.92) 7.58 (5.52) 5.31 (5.75 18.80 (5.54)     38.2% 9282.33 112.61 (27.93) .060
  p-value* < .001 .068 .284 .007       < .001  
4 Wald χ2 (df) corrected 33.06 (2.91) 7.14 (5.51) 5.42 (5.74) 18.70 (5.56) 9.90 (8.44)    38.5% 9267.46 115.69 (33.30) .065
  p-value* < .001 .084 .299 .008 .022      < .001  
5 Wald χ2 (df) corrected 28.01
(2.90)
12.70 (5.67) 6.20 (5.75) 19.01 (5.54) 13.86 (8.13) 20.50 (5.75) 14.73 (8.15) 38.3% 9217.06 126.10 (40.51) .078
  p-value* < .001 .013 .258 .007 .017 .024 .008    < .001  
  1. * Adjustment for multiple tests: Šidák sequential
  2. # model 0 = baseline model including body fat percentage and sociodemographic variables: region, SES, migrant background migback = migrant background
  3. 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.
  4. 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.
  5. Correct classification rate is the proportion of participants for whom the tested model could correctly predict the category of the dependent variable (PA frequency).
  6. 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.
  7. Nagelkerke's pseudo R2 is a measure of explained variation in the dependent variable that emulates R2 from linear regression.