Baseline data on coordinated school health programs in the state of Ohio

American Journal of Health Studies, Wntr, 2001 by Jann Greenberg, Randall Cottrell, Amy L. Bernard

Significant differences (p < .05) were found in total scores on the survey between those districts describing themselves as rural and those describing themselves as suburban. The mean score on the survey for rural districts was 57.42, while the mean score for suburban districts was 66.03 (Table 2). These data indicated that suburban districts had stronger coordinated school health programs than rural districts. No significant differences existed between urban and rural school districts or between urban and suburban school districts. The mean for urban school districts was 59.11.

The data were then analyzed to determine if the size of the district had an effect on the total score of the survey. The smallest responding district had a total of 347 students while the largest had 38,000 students. Seven districts failed to report this data.

The districts were ranked ordered according to size and then divided into thirds. Data were then analyzed to look for a relationship between size of the district and the total score on the survey. The Levine test for homogeneity of variance was .872 which indicated that the three rank ordered groups had equal variances. The one-way ANOVA was statistically significant at the .05 level of significance, p = .0175, F (2, 113) = 4.1914. This indicated that differences existed between the groups. Because a positive relationship existed between the size of the district and the total score on the survey, post-hoc analyses were run.

A Tukey-HSD test with a significance level of .05 was run to determine where the differences were located. Significant differences were found between the largest third of the districts and the other two groups (Table 2). Based on these data, the largest districts had better coordinated school health programming than the middle or small sized districts. It is important to note that outliers may have affected these results. The means (64.92, 57.24, and 56.86 respectively) of all the groups were close together, however group two has a district reporting a very low level of coordinated school health programming while group three had two districts with coordinated school health programming in place.

To examine the relationship between per pupil expenditure and the total score on the survey, a correlation coefficient was run. One hundred and two districts provided their annual per pupil expenditure. Fourteen districts declined to provide this data. The range of spending was $2,800 to $10,000, with a mean of $4,962.

The correlation between per pupil expenditure and total score was statistically significant (alpha = .05, = .006). The correlation between per pupil expenditure and total score was .2682.

Higher per pupil expenditure was related to higher levels of coordinated school health programming in the district.

CONCLUSIONS

Results of this study may be limited by the number of surveys returned and the demographics of the responding districts. Findings may also be limited by the honesty of the respondents and their personal perceptions and biases regarding coordinated school health education. The effects of these limitations on the study's outcomes must be considered when interpreting results.