ed. 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 Being Away factors were sub-titled Physical (instead of Escape) and Psychological (instead of Novelty), to simplify interpretation.
It should be noted that results are reported here (see Table 1) for Factor Analysis via Maximum Likelihood as the focus is on the theoretical structure of the data/measure. This provides a more stringent test than PCA as both error and communality variance have been removed from results.
Library data yielded three factors comprising the same items revealed in the playground data– Being Away- Physical, Being Away- Psychological and Fascination. Compatibility, however, was split over two factors and an Extent factor was not revealed (a priori Extent items were either not loading at all, or loading onto the Compatibility or Being Away– Physical factors).
I determined that factors for both environments would be based on the 15 items retained from the Playground data as they met both theoretical and statistical criteria. Although the library data yielded a similar factor structure, it did not exactly replicate the structure revealed within the playground data; that is, no Extent factor was revealed.
Cronbach’s alpha was calculated for the five factors for both the playground and the library data to determine internal consistency. As shown in Table 2, highest values were yielded with the four-item factor Fascination and, as would be expected, the lowest values were yielded with the two items of Extent.
Correlations between the five factors were conducted and are reported in Table 3. All associations across both environments were positive and significant except for Fascination and Being Away– Physical for the playground. Generally the relationships between the factors were stronger for the library environment.
In order to evaluate whether the PRCS-C would distinguish between environments, results for the playground and the library environments were compared. A mean item score for each of the five factors was computed for both environments. As shown in Table 4, means for the five factors an