Additionally it is possible to give an intra correlation value in the intra correlation box. This value will be considered in a number of calculations.
Options. There are 4 options.
The split option splits the data after each n-th user specified cluster. For this option to work the user needs to specify an integer value at which to split the data which is larger than one and smaller than the total number of clusters. If the file is split the proportion positive in the each of the sub samples will be compared with the proportion positive in the previous sub sample by way of a t-test and considering the design effects.
The decimals options sets how many decimals are used in numbers in the output
Change the Confidence Interval for all appropriate statistical procedures by choosing CI in the options menu and then ticking the level you want to select, or use the fill in box under the other options. The default setting of the confidence interval is 95%. Confidence intervals smaller than one percent or greater than 99.9998 percent are not allowed
Activate the more output option if you want more output
Intra correlation in clustered studies.The intra correlation is used as a measure of clustering in data that is collected using a multistage, multilevel, procedure of data collection. This is the case if, for example, a number of schools are selected randomly and in each school a number of pupils is sampled for study. The analysis is taking place at the pupil level. If there is intracorrelation the standard errors of statistics at the pupil level are estimated too narrow, tests become statistically significant too quickly and confidence intervals will be too small. The intra correlation coefficient gives you an insight as to what extent the observations at the individual level are influenced by clustering of observations in higher level groups, which is the case if, for example, schools are particularly good, and others particularly bad, at the trait measured with the dependent variable. If the intracorrelation coefficient equals 0 the schools are not different with regard to the independent variable, all the pupils could have been sampled from a single school and the result of the analysis would have been the same. The number of pupils is the correct sample size. If the intra correlation coefficient equals 1 the schools are totally different and pupil performance is totally influenced by the school, effective sample size is the number of schools, and not the number of pupils. The example in the table above concerns the proportion of pupils that passed a test. In the table the data concerns 6 schools with 1865 pupils, 158 were tested positive.
The design effect, design factor and the effective n.The intra correlation coefficient can be calculated into the design effect. The design effect (DEFF) is the factor by which the variance of an estimated mean increases after considering intra correlation caused by a clustered design. DEFF can be translated into the effective n^, which is an estimate of the n after considering the extra variance caused by the clustering in the data. The effective n^ is the observed sample n divided by the design effect, n^=n/DEFF. Lastly, the design factor (DEFFT) is the amount by which the standard error of an estimated mean increases after considering the clustering in the data. One minus the design factor times 100 (1-DEFFT*100) is the percentage by which a confidence interval around a mean increases due to the clustering. The design factor is the square root of the design effect, DEFFT=√DEFF.
