or their parents’ review. In addition to parental approval, children’s assent was also obtained prior to data collection (note: two children decided not to participate).
Data collection was conducted during school hours in groups of up to approximately 30 children, based on children’s grade levels (Grades 3 and 4 together, Grades 5 and 6 together). The majority of participants could see a small section of their playground (from the classroom window), but not the library during completion. Items were read aloud to all groups of children, taking approximately 30 minutes, with older children working at their own pace. Children were encouraged to ask questions if they were unsure of the meaning of any words and advised there were no right or wrong answers. Once complete, children were advised of the purpose of the study and thanked for their time.
ResultsData was analysed using SPSS (v. 11.0) and included factor analysis to determine the factor structure of the restorative components measure. Resulting factors were then examined for internal consistency with Cronbach’s alpha, with factor scores being compared across environments and by sex and age via dependent and independent t-tests. Factor analysis was chosen over Principal Components Analysis (PCA), as a theoretical solution was of primary interest.
First, missing value analysis was conducted. Three participants had more than half of their data missing and were therefore deleted. The majority of variables had less than 1 percent of data missing and no variable had more than 1.8 percent missing (i.e., four participants did not answer). Missing values were replaced by estimated values via Expectation Maximisation (EM) method and were deemed to reflect actual values. The data formed part of a larger study where univariate outliers were retracted to ±3.29 standard deviations from the mean (no univariate outliers for the restorative components items) and two multivariate outliers were reviewed and deleted. No major violations of factor analysis assumptions were detected. Sample size was not optimal (i.e., N<300), an issue discussed later, and as expected, some items were not normally distributed. They were however, not transformed due to the sample size (i.e., N>30) and difficulty in interpreting transformed variables (Tabachnick and Fidell 1996). Initial analysis indicated the playground restorativeness data was suitable for factor analysis. Features noted were: presence of correlations over .3, Kaiser-Meyer-Olkin Measure of Sampling Adequacy was .82 and Bartlett’s Test of Sphericity was significant.
A preliminary factor analysis was conducted using Maximum Likelihood extraction with direct oblimin (Delta = 0) rotation, as it was expected the factors would be correlated. There were seven initial eigen values over 1.0 explaining 61 percent of the variance. The scree plot indicated five factors could be extracted, with an initial break between the first two and then the next three. The presence of five factors is also consistent with prior research developing the adults’ RCS, on which this study is based.
A second factor analysis was then conducted requesting five factors to be extracted. Eigen values ranged from 3.38 to .53, explaining 47 percent of the variance. Although three eigen values were less than 1.0 after extraction, examination of the results continued as it is argued a larger sample size would yield five factors with this number of items. Four items were deleted as they did not load onto any factor, one item was deleted as it loaded at .3 on two factors and two items were deleted as they did not load onto theoretically-driven a priori factors. A third factor analysis was then run resulting in an additional item to be deleted as it did not load onto any factor. This resulted in 15 items remaining (see Table 1 for items and factor loadings) comprising five factors, revealing Thurstone’s Simple Structure. (Note: The negative item loadings on the Compatibility factor indicate this factor correlates negatively with others, within the Factor Correlation Matrix.) The two