2.2. Statistical analysis
We used multiple techniques for analysis depending on the data
format. For continuous rank data (Objectives 1, 4e5), we used two
way analysis of variance (ANOVA) to test for the influence of the
two explanatory variables (Objective 1: pest and region; Objective
4: control method and region; Objective 5: attribute and region)
and their interaction. When a model was significant, we used
Fisher’s least significant difference (LSD) post hoc test to determine
which values were different (Zar, 1999).
For nominal responses (Objectives 2e3, 6), we used Fisher’s
exact test (i.e., test of independence; Zar, 1999) when we had two
nominal variables, and the exact multinomial test (i.e., goodnessof-
fit test; McDonald, 2009) when we had one nominal variable.
When these tests indicated a significant difference, we used
multiple Fisher’s exact tests or exact binomial tests (McDonald,
2009) to determine which responses were different. We used
a ¼ 0.05 for all tests.
2.2. Statistical analysisWe used multiple techniques for analysis depending on the dataformat. For continuous rank data (Objectives 1, 4e5), we used twoway analysis of variance (ANOVA) to test for the influence of thetwo explanatory variables (Objective 1: pest and region; Objective4: control method and region; Objective 5: attribute and region)and their interaction. When a model was significant, we usedFisher’s least significant difference (LSD) post hoc test to determinewhich values were different (Zar, 1999).For nominal responses (Objectives 2e3, 6), we used Fisher’sexact test (i.e., test of independence; Zar, 1999) when we had twonominal variables, and the exact multinomial test (i.e., goodnessof-fit test; McDonald, 2009) when we had one nominal variable.When these tests indicated a significant difference, we usedmultiple Fisher’s exact tests or exact binomial tests (McDonald,2009) to determine which responses were different. We useda ¼ 0.05 for all tests.
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