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Ratio scale data were collected concerning information about the number of kilns, capacity of largest kilns, annual dried lumber production, or number of employees. In this case, mean values, 95% confidence intervals for means, frequencies, and minimum and maximum values were computed. Responses from the question on the number of employees were divided in 5 groups, following a similar categorization that was conducted in the European study to facilitate regional comparisons. Certain important variables were compared between early and late respondents to examine whether non- response bias was present. Independent samples t-tests and Levene's tests for equality of variance were performed (alpha = 0.05) for 3 variables to detect statistically significant differences between early and late responses. The existence of such differences would indicate the presence of non-response bias. However, no statistically significant differences were found between the two groups for the t-tests. In order to examine whether issues such as dried product quality, drying process, kiln control and kiln drying related knowledge of groups differed between companies with different characteristics (like type of species dried or geographical location), Kruskal-Wallis tests (alpha = 0.05) were conducted. The Kruskal-Wallis test was suitable because data were not normal. Subsequently, the Scheffe test (alpha = 0.05) was used to indicate which groups of companies differed significantly. This test was used only for those cases where the Kruskal-Wallis test was significant. Moreover, the Kruskal-Wallis and Scheffe tests (both at an alpha level of 0.05) were also used to identify significant differences in kiln drying related knowledge between groups of stakeholders. In order to provide a better understanding of key drying variables that affect a company's decision to upgrade its kilns the multivariate technique of Logistic Regression was applied to create a model. This technique is suitable when the dependent variable is dichotomous (i.e., there are only two possible outcomes), while the independent variables are either continuous or categorical (Field, 2002). The dependent variable in this case was whether or not a company would upgrade its kilns. Such a model is 39PDF Image | KILN DRYING PROBLEMS AND ISSUES IN CANADA
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