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Publication - Research Publication

Early learning and childcare at age five: comparing two cohorts

Published: 22 Dec 2017

A report on early learning and childcare use and provision in Scotland, comparing Growing Up in Scotland data from 2008-09 and 2014.

33 page PDF

586.8kB

33 page PDF

586.8kB

Contents
Early learning and childcare at age five: comparing two cohorts
8.2. Appendix B: Interpreting the Cohort Comparison Tables

33 page PDF

586.8kB

8.2. Appendix B: Interpreting the Cohort Comparison Tables

Many of the tables in Appendix C are presented as 'nested' cross-tabulations. These are cross-tabulations of two variables (e.g. whether child has a longstanding illness by equivalised household income) nested by a third variable: cohort. This approach allows that all of the information of interest is produced as a single table and also permits a statistical test to explore whether the relationship between the two variables has changed between the cohorts.

As standard, the statistical tests carried out for the nested cross-tabulations were based on combined values for both cohorts (not shown in the table) and not on the individual cohort figures. As such, this test does not tell us whether differences by income are statistically significant within each cohort. Furthermore, the test is run across all categories and does not test for differences between each individual category and the next, e.g. between the 4 th quintile and highest quintile.

Statistical significance levels are reported as *, ** or ***, indicating statistical significance at the 95%, 99% and 99.9% levels. Where nothing else is indicated, no statistically significant differences were found.

Running the tables as nested cross-tabulations allows us to test (using interaction analysis) whether the relationship between two variables (e.g. whether child has a longstanding illness by equivalised household income) is statistically significantly different between the two cohorts. Where there is a statistically significant difference in the relationship between the two variables in question across the cohorts, this is indicated by the use of 'a' (e.g. Table 8-2). A difference in relationship may refer to a strengthening of the association, a weakening of the association or some other change - such as moving from a positive relationship (e.g. as income increases likelihood of having a longstanding illness also increase) to a negative relationship (e.g. as income increases likelihood of having a longstanding illness decreases).

In cases where a statistically significant difference in the relationship between the two variables was found (as indicated by an 'a'), separate statistical tests were run for each cohort to test whether the relationship between the two variables held for both cohorts. In these cases, statistical significance is reported for each cohort (e.g. Table 8-2).


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