Article info
- Correspondence to: Professor Ken Judge, Health Promotion Policy Unit, University of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ, UK; K.Judge{at}clinmed.gla.ac.uk
Citation
Publication history
- Received October 4, 2002
- Accepted April 21, 2003
- First published September 4, 2003.
. Web-only Tables
Correlations between dependent and independent variablesTable A1 shows the correlation coefficients between the four dependent variables for both the full sample of 99 health authorities and the reduced sample of 76 used in the modelling. The strength of the correlations varied only slightly with the sample chosen. There was a strong inverse relation between reach and cessation; namely health authorities which reach a high proportion of the smoking population tended to have lower cessation rates and vice versa. Absolute success was very highly positively correlated with reach. However, success tended to be slightly lower when cessation rates were higher. Loss rates were much lower when cessation rates were higher, and were slightly higher when reach was higher. Loss rates were virtually independent of number of successes.
Further information about correlations between outcomes and the predictor variables is shown in Tables A2 to A5.
The tables are available as downloadable PDFs (printer friendly files).
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- [View PDF] - Table A1 Pearson correlation coefficients between the four dependent variables
- [View PDF] - Table A2 Pearson correlation coefficients between dependent and predictor variables in the model for reach
- [View PDF] - Table A3 Pearson correlation coefficients between dependent and predictor variables in the model for absolute success
- [View PDF] - Table A4 Pearson correlation coefficients between dependent and predictor variables in the model for cessation rate
- [View PDF] - Table A5 Pearson correlation coefficients between dependent and predictor variables in the model for loss
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