Statistical Analyses

Data analysis in the dissertation proceeds in four stages. First, summary statistics of the study variables are used to describe the demographic characteristics of study participants as well as their reported activities and attitudes. Second, the demographic characteristics of producers participating in different nutrient management activities are compared. Activity participation among producers with different sized farms is

investigated to generate a measure of general levels of compliance with the Neuse and Tar-Pamlico Nutrient Management Rules in the study counties. ANOVA test results are also presented to identify significant demographic differences among producers in the six activity groups. The ANOVA results are intended to help program managers more effectively target future outreach and education efforts.

Third, bivariate relationships between the study variables are tested. Correlations are tested among the capacity variables and among the mediating variables.

Relationships between the dependent and independent variables are tested with the same types of regression analyses utilized in the multivariate models, with the type of analysis depending on the measurement scale of the dependent variable. Results of the bivariate regression models are provided in Appendix C.

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Fourth, the dissertation evaluates a series of multivariate statistical models to test the study’s main hypotheses. Models with continuous dependent variables are tested using OLS regression, whereas those with categorical dependent variables are tested with either logistic, ordered logistic, or multinomial logistic regression analysis. Models with dichotomous dependent variables utilize logistic regression, those with dependent

variables that consist of ordered categories use ordered logistic regression, and those that do not meet the parallel slopes assumption of the ordered logistic regression model utilize multinomial regression analysis. These approaches overcome problems associated with using OLS regression for noncontinuous dependent variables, including violations of basic model assumptions (Long, 1997).

In all of the models tested, several dummy variables are included. To test the influence of education, both college graduate and some college are compared to the base case of high school. To test the influence of unknown factors associated with farming in a particular county, dummy variables for the five counties are included. Edgecombe is used as the base case in the main models. For nutrient management activity participation, intend to train, both activities, intend to do both, nutrient plan, and no activities are compared to the base case of train only. Using train only as the base case allows for direct comparisons to be made between those who trained only and those who intended to train only, which lends this aspect of the dissertation a quasi-experimental structure. For each set of dummy variables, any additional statistically significant comparisons are described in the discussion section for each model.

The following model tests Hypotheses 1, 2, 3, 4, and 5. This model uses logistic regression analysis to identify factors that have a significant direct relationship with the

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adoption of RYEs, cover crops, and soil testing.5 The specific variables included in each vector are listed in Table 4.3.

Ln (PRYEs/1-PRYEs) = B0 + B1-12CAPACITY + B13-22MEDIATORS + B23-26COUNTIES + B27-31ACTIVITIES

The following model tests Hypotheses 7, 8, 9, 10, and 11, identifying factors that have a significant relationship with the study’s potential mediators. Factors associated with fear of penalties, fear of inspection, and income impact are all tested utilizing logistic regression:

Ln (PMEDIATOR/1 - PMEDIATOR) = B0 + B1-12CAPACITY + B13AWARE + B23- 26COUNTIES + B27-31ACTIVITIES

Tests of the factors associated with the potential mediators: attitude, norm, external, denial, and perceived control utilize OLS regression:

MEDIATOR = B0 + B1-12CAPACITY + B13AWARE + B23-26COUNTIES + B27- 31ACTIVITIES + e

Factors associated with fear of stricter regulations are tested with multinomial logistic regression.6 This model uses those who responded strongly disagree, disagree, or neither agree nor disagree (coded “2”) as the base case.

Ln[Pr(4|x)/Pr(2|x)] = B0, 4|2 + B1-12, 4|2CAPACITY + B13, 4|2AWARE + B23-26, 4|2COUNTIES + B27-31, 4|2ACTIVITIES

Ln[Pr(5|x)/Pr(2|x)] = B0, 5|2 + B1-12, 5|2CAPACITY + B13, 5|2AWARE + B23-26, 5|2COUNTIES + B27-31, 5|2ACTIVITIES

5_{When testing cover crops, the left side of the equation is Ln (P}

CVRCROP/1-PCVRCROP) and when

testing soil testing, it is Ln (PSOILTEST/1-PSOILTEST).

6_{Multinomial logistic regression analysis is used because the model does not meet the parallel}

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Table 4.3. Variables Planned for Inclusion in Study Models.

Model Numbera,b

Variablesc 1 2 3 4 5 6 7 8 9 10 Capacity: 1. Rent x x x x x x x x x x 2. Age x x x x x x x x x x 3. Farm size (ln) x x x x x x x x x x 4. Farm size sq. (ln) x x x x x x x x x x 5. Income x x x x x x x x x x 6. Farm income x x x x x x x x x x 7. Experience x x x x x x x x x x 8. Some college x x x x x x x x x x 9. College graduate x x x x x x x x x x 10. Innovativeness x x x x x x x x x x 11. Cost share cover crops x x x x x x x x x 12. Cost share nutrient mngt. x x x x x x x x x Mediators: 13. Rule awareness x x x x x x x x x

14. Income impact x x x 15. Attitude x x x 16. Norm x x x 17. External x x x 18. Denial x x x 19. Fear of penalties x x x 20. Fear of inspection x x x 21. Fear of stricter regulation x x x 22. Perceived control x x x

Counties:d _{23. Johnston x x x x x x x x x x}

24. Lenoir x x x x x x x x x x

25. Nash x x x x x x x x x x

26. Wayne x x x x x x x x x x

Activities:e _{27. Intend to train x x x x x x x x x x}

28. Both activities x x x x x x x x x x 29. Intend to do both x x x x x x x x x x 30. Nutrient plan x x x x x x x x x x 31. No activities x x x x x x x x x x Notes: a Dependent Variables: 1= RYEs, 2 = Cover Crops, 3 = Soil Tests, 4 = Fear of

Inspections, 5 = Fear of Stricter Regulations, 6 = Rule Awareness, 7 = External, 8 = Denial, 9 = Perceived Control, 10 = Income Impact. b Models for Attitude, Norm, and Fear of Penalties ultimately were not included in the analysis because they were found to be statistically

nonsignificant. c The variables rent, income impact, norm, attitude, external, denial, and perceived control ultimately were excluded from the tested models due to a lack of statistical significance. d _{County dummy variables are compared to the base: Edgecombe County.} e _{Activity group dummy variables are compared to the base: Train.}

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Hypotheses 6 and 12 are tested with the following model, using ordered logistic regression to identify factors significantly associated with producers’ awareness of the nutrient management rules. This variable has four categories, and thus the model estimates three equations:

Ln (PAWARE_2/1-PAWARE_3+4+5) = B0 + B1-10CAPACITY + B23-26COUNTIES + B27- 31ACTIVITIES

Ln (PAWARE_2+3/1-PAWARE_4+5) = B0 + B1-10CAPACITY + B23-26COUNTIES + B27- 31ACTIVITIES

Ln (PAWARE_2+3+4/1-PAWARE_5) = B0 + B1-10CAPACITY + B23-26COUNTIES + B27- 31ACTIVITIES

In order to test for the mediation effects predicted in Hypothesis 13, the dissertation utilizes Mplus statistical software. Tests of mediation effects rely on the products of coefficients approach, which is found to be the most accurate for models with categorical outcomes (MacKinnon, 2008). The effect estimate generated through this approach indicates how much a one unit change in X affects Y through its influence on the mediator of interest. Standard errors and confidence limits for identified mediation effects are obtained using bootstrapping.

Based on the results of the preceding models, additional models will test

Hypotheses 14 and 15, which predict interactions among key variables. If any capacity factors and motivational factors are found to have a significant relationship with practice adoption, the following general model will test for interactions among the significant factors:

Ln (PRYEs/1-PRYEs) = B0 + CAPACITY + MOTIVATIONS +

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Similarly, if any of the deterrence motivations (fear of inspection, fear of stricter regulation, or fear of penalties) and any of the normative motivations (attitude, norm, external, or denial) are found to have significant relationships with practice adoption, the following general model will test for interactions among these factors:

Ln (PRYEs/1-PRYEs) = B0 + B1-12CAPACITY + B13AWARE + NORMATIVE + DETERRENCE + NORMATIVE*DETERRENCE + B23-26COUNTIES + B27- 31ACTIVITIES