This section talks about the statistical methods used to analyse the survey data which is the Samoan schools dropout rates for this research. We used the statistical computer software SAS Enteprise Guide 4.3 for all the statistical analysis.
83 Statistical Analysis
This study used the Logistic Regression models to analyse the Samoan schools dropout rates. The given dropout rates were for year level only therefore estimates for the numbers of dropouts were calculated by using the formula:
Size Level Year Dropout Percentage Number Dropout 100 (4.20) The year level dropout rates are measured every year by the Ministry of Education, Sports and Culture (MESC) of Samoa through Census forms from the same schools. Notice that the year level refers to Year 1, Year 2,.…,Year 13. For each calendar year (from 1995 to 2007), we use logistic regression models to find the relationship between the dropout rate and possible explanatory variables (such as region, year level, school level, and school status). Because the dropout rates are correlated within each school across different year levels, we use the GEE method to analyse such clustered data.
Among all the calendar years in this study (1995-2007), the interaction terms between region, year level, school level, school status, etc were not significant except for some few years. Therefore, we mostly focused on the main effects.
We used the backward elimination method to choose the best model by dropping insignificant explanatory variables based on the chi-square statistics. There is one thing we need to keep in mind. Some explanatory variables (predictors) were removed because of non-significance at the 5% significance level. It does not mean that these variables were not related to the school dropout or dropout rate. It could be the result of multi-collinearity. This means that when these variables were highly correlated with other explanatory variables, it seemed that these variables were not important when all the others were in the model. This process was repeated until we found the simpler model whereby all the remaining variables were significantly associated with the response variable.
The effects of the explanatory variables on the response variable of selected models were explained or interpreted in terms of odds ratios. The effect of one variable was explained while keeping others fixed. The odds ratio was calculated using the formula (4.15) which was the exponential of the estimate of a particular variable.
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Chapter 5
Results
The chapter 3 shows the summarised dropout information across different explanatory variables. This chapter reports on findings about the association of the response variable (dropping out of school), and the selected explanatory variables based on models, where the models take the correlations into account and give the statistical inference on the effects of these explanatory variables. All models reported here were analysed separately by calendar year (1995 – 2006) for section 5.1 to 5.5 and 2007 for 5.6 to 5.8 due to reasons explained in chapters 1 and 3.
Section 5.1 will talk about the association of the response variable and the school year level and region. Again, the school year level is referring to Year 2 (denoted by Yr2) up to Year 13 (denoted by Yr13) and region is referring to Apia Urban, Rest of Upolu and Savaii. The Year 9 level was dropped from the analysis as we treated it being the first grade (year level) at Secondary school just like the Year 1 for Primary schools. There is no dropout information for the first year/grade in the school. This section will also report on which one of the three regions where students are more likely to give up schools.
In section 5.2, we will present the results of the association of dropping out of school and school year level and school level. The school level is comprised of Primary, Primary/Secondary and Secondary schools. There are 11 school year levels used in this study which started from Year 2 (Yr 2) up to Year 13 (Yr 13). The year levels Year 2 (Yr 2) to Year 8 (Yr 8) are for Primary Schools while Year 10 (Yr 10) to Year 13 (Yr 13) (an equivalent to Form 6) are for Secondary Schools as explained in chapter one.
Section 5.3 will display the association of dropping out of school and school year level and school status. Like the region and school level, the school status has three levels - Government, Mission and Private.
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For each calendar year, we employed the GEE-approach to model both the main effects and interaction effects of the given explanatory variables. If the interaction effect is not significant then we drop it from the model.
The model selection was done using the backward elimination method, based on the type 3 Chi-Square statistics. An explanatory variable with a p-value of less than 0.05 (or 5% significance level) will be kept otherwise it will be removed from the model.
In section 5.4, the study presents findings about the association of the response variable and the student-teacher ratio. The larger the school size in terms of student enrolments and fewer teachers, the higher this ratio is.
In section 5.5, the school size as in total school enrolment (TotEnrol) effects on the dropout will be covered here.
Section 5.6 reports on findings of the association of the secondary school teacher variables and the school dropouts for 2007 only. The secondary school teacher variables consisted of the proportion of female teachers; the proportion of Samoan teachers; the number of teachers with certificates and the number of teachers with degrees. The interpretation of the parameters is also done by odds ratio, which is the exponential of these parameter estimates. The effect of school building variables on the dropout will be the focus of section 5.7. The findings in this section were based on 2007 secondary schools data only.
The last section (5.8) of this chapter reports on findings about the effects of the school facility variables on the school dropout or dropout rates for all the secondary schools in 2007.