Capítulo 2. Marco Teórico
2.4. Control por Rechazo Activo de Perturbaciones
The tripartite attitude model was tested on the sample of farmers without native forest fragments on their property. The same protocol as described in the Methods section of this chapter is followed. Briefly, the distinction between the affect and cognition dimensions is first investigated. Theconation dimension is then added to the model and the fit of the tripartite model is assessed. The analyses included 291 respondents.
a. Affective and cognitive response toward native forest of farmers without native forest fragments
i. Analysis of the attitudinal variables
All variables were positively correlated with one another, except ChildEnjoyForest and ForestBenefit, whose association was almost nonexistent (Table A9.8). The strong correlations betweenForestValueandForestUsefulandForestBenefit reflected the parceling of the two latter variables into ForestValue. The relationships
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
between the attitudinal observed variables were very similar to the whole sample correlations, with the exception of ForestUseful and ForestBenefit (or the parcel ForestValue) with the affect variables. Here, the associations were much weaker, especially regarding ForestBenefit. The hypothesized affect and cognition variables were strongly correlated within each attitudinal group.
The five analysis strategies (section III.A.2.b) were performed on a one-, and two-factor model. The affect-cognition distinction is validated if the indicators of each of the hypothesized attitudinal dimensions load more strongly together on one factor each.
As for the whole sample, there was no support for the effect of a specific method factor represented by the mailing sequences, nor for a general, unspecified method factor affecting all the attitudinal indicators. None of the five strategies gave satisfactory results with the one-factor model. Three strategies fitted the answers of farmers without native forest well when a two-factor model was specified (Table A9.9): only ForestBenefit retained (strategy 1), ForestValue replacing ForestUseful and ForestBenefit (strategy 2), and ForestUseful and ForestBenefit’s residual correlated (strategy 5).
The three models produced similar estimates and attitudinal structure. The indicator variables expected to represent the affect or cognition factor significantly loaded with similar strength on their respective hypothesized factor. However, ForestBenefit (or the related ForestValue) also significantly and negatively loaded on the affect factor. Nevertheless, compared to its loading on its own cognition factor as well as the loadings of the hypothesized affect indicators on the affect factor, ForestBenefit’s loading on the affect factor was of a lesser magnitude. The two latent factors were significantly correlated, as well as the residuals of ForestUseful and ForestBenefit in model-strategy 5. The standardized estimate of EnjoyForest on the affect factor was very high and highly significant. Its unstandardized estimate was significant at least at p = 0.075, except in model-strategy 2. Collapsing Enjoyforest categories did not improve the results.
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
ii. Attitudinal model choice
According to indices of fit, all three attitudinal models fitted the data well. But, were they describing the data equally well?
The models were not nested in one another as they used different sets of variables.18 Brown (2006, p. 175) suggests comparing non-nested models “with regard
to the three major aspects of model acceptability: overall goodness of fit, focal areas of ill fit, and interpretability/strength of parameter estimates”.
Comparing the models’ overall fit values, the strategy (1) model (ForestBenefit) fitted the dataset the best. Although estimates were similar in each model, the models using strategies (1) and (5) (ForestUsefulandForestBenefit’s residuals correlated) gave the most reliable estimates overall. Finally, although it has been argued that parceling (strategy 2, ForestValue) could be used to absorb method bias, Little and colleagues (2002, p. 169) warn: “The largest threats to the validity of parceling are model misspecification in general and a specific form of misspecification, multidimensionality”. The strategy (1) model showed thatForestBenefitplayed a role in both the underlying cognition and affect dimensions. The loading of the parcel ForestValue on only the cognition factor therefore hid the multidimensionality of ForestBenefit. Because the interest ultimately lies in the relationship among items, and between items and latent factors, models including the most information should be preferred – as long as they fit the data.
Based on the overall goodness-of-fit, the reliability of the estimates and the amount of information contained in the model, the model from strategy (1) (only ForestBenefit) was retained for the rest of the analyses.
iii. Conclusions
Three attitudinal models presented acceptable and similar solutions. The attitude towards New Zealand indigenous forest of farmers without native forest fragments on their property revealed overall distinct, yet related, emotional and cognitive dimensions,
18As these models are non-nested, the model choice cannot rely on the Chi-square test difference. Non-
nested models can be compared via the Aikaike information criterion (AIC) or the Bayesian information criterion (BIC) (Brown 2006, p. 175). However, these criteria are maximum-likelihood-based, and are not provided with WLSMV estimation (Muthén 1998-2004, p. 22; Muthén 2006). Mplus provides a robust maximum likelihood (MLR) estimator, which can be used for EFA or CFA with ordinal variables and produces AIC and BIC measures of model fit; yet such an option is not available for ESEM. One could get around this issue for model strategies 1 and 2, as they are actually equivalent to simple EFA models, but model strategy 5 is not, due to the correlated residuals.
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
with these three models displaying the hypothesized correlated affect and cognition factors. Nevertheless, the positive affective response of farmers without native forest was also directly influenced by how non-beneficial for the farm farmers without fragments perceive native forest to be. Such attitudes would suggest that these respondents see two types of native fragments: those whose features were fit for farming purposes, and those that respondents enjoyed but were not appropriate for farming. Such a dichotomy in perceptions towards native forest could indicate an inherent difficulty for such farmers to have indigenous forest on their farm.
Based on the overall goodness of fit, focal areas of ill fit, interpretability and strength of the parameter estimates, and amount of information in the model, the model keeping ForestBenefit and discarding ForestUseful (strategy 1) gave the most satisfactory results. Next, the conation dimension was added to the affect-cognition model.
b. Affective, cognitive and conative responses toward native forest of farmers without native forest fragments
The conation latent dimension was defined by the three variables measuring intentions of behaviour common to the whole sample of farmers: MoreForest, ConservationandTreePlanting.19As the conationstructure was just-identified20, it was directly added to the affect-cognition strategy-1-model. Affect and cognition latent factors were assumed to directly influence the latentconation.
As a large number of missing values affected the variable MoreForest (50.3%), models were computed both with and without this variable.
The intention variables fitted well with the other attitudinal variables as the moderate and consistent correlations show (Table A9.14). On the other hand, the correlations between the intention variables themselves displayed less coherence.
19See Chapter 5 for a description of the variables.
20The number of observations, that is, variances and covariances, was equal to the number of parameters
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
i. Analysis of the affective, cognitive and conative response of farmers without native forest: three
conationoutcomes
The tripartite attitude model (affect,cognitionandconationlatent factors), based on theaffect-cognition structure found previously (strategy 1 model), was compared to two alternatives: one- and two-factor models (see Methods section). Because of the number of missing values onMoreForest, results from these three models are tentative. Incidentally, the presence of a general method bias affecting all the attitudinal variables was tested.21
The responses of farmers without fragments supported the three-factor model (Table A9.15). The one- and two-factor models did not generate sufficiently good fit to the data. No effect of general method bias was found to affect the sample of farmers without fragments. As in the affect-cognition model previously found, the loading estimates supported the hypothesized affect-cognition distinction, with ForestBenefit loading moderately and negatively on theaffect dimension (Table A9.16). EnjoyForest standardized loading was large and highly significant, while its unstandardized loading was greater than 1 and non-significant.22 The conation outcomes significantly and
positively loaded on their hypothesized latent variable. Both affect and cognition positively influenced conation, although the influence of affecton conationwas half as much as that of cognition, and the unstandardized effect had a p value of 0.107. This model explained a large amount ofconationtotal variance (63.7%) (Table A9.17).
Additionally, the affect and cognition dimensions showed good reliability, with the respective Cronbach alpha equal to 0.77 and 0.75 and the average inter-item correlations 0.63 and 0.42 (Clark and Watson 1995, p. 315-6; Field 2005, p. 668;
21Strategies 3 and 4, see section III.A.2.b. of this chapter.
22Collapsing more categories inEnjoyForestwas considered, as it had some degree of skew, but this did
not improve the results. The fact that theaffectfactor is only composed of two outcomes, instead of three as it is often recommended, may play a role in the discrepancy between the p values of unstandardized and standardized estimates. Unfortunately, the issue of non-significant unstandardized estimates but highly significant standardized values in a well fitting model has not yet received much attention in the literature (McIntosh 2009). As a consequence these results should be taken with a certain caution. However, the fact that both the unstandardized and standardized estimates ofEnjoyForestdescribed the same loading patterns with respect to the other indicators’ loadings gives one more confidence in the estimates.
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
Mueller 1986 in Milfont 2007, p. 84).23However, affect average inter-item correlations
were beyond the threshold advised by Clark and Watson (0.50, Clark and Watson 1995). The conation dimension showed less internal consistency than the other two scales: Cronbach alpha was equal to 0.49 and the average inter-item correlation was 0.25.
ii. Analysis of the affective, cognitive and conative response of farmers without native forest: two
conationoutcomes
As mentioned at the beginning of this section, the behavioural intention variable MoreForestsuffered from a large number of missing values, which weakened the model reliability. Although the model generated good results, a model without the variable MoreForest may yield different results. Nevertheless, a latent factor only defined by two indicators has a high chance of generating estimation problems (i.e., empirical underidentification, negative residual variance; Rindskopf 1984). Two alternatives were therefore considered: (1) a conation latent factor represented by the two remaining outcome variables; (2) no conation latent factor, where affect and cognition directly predicted the two remaining intention variables. When MoreForest was removed the covariance coverage24ranged between 76.7% and 99.3%.
Alternative 1: Conation factor defined by two indicators
When the relationships between the conation latent factor and its indicators (Conservation and TreePlanting) were specified in ESEM the model was empirically underidentified. The scale of the conationlatent factor was then set by fixing one of its indicators unstandardized loading to one, as in CFA. Nevertheless, the model generated a negative residual variance for the conation latent factor. None of these solutions was acceptable.
23 Usually, an alpha of 0.7 and above indicates good scale reliability (Field 2005, p. 668). However,
Mueller (1986 in Milfont 2007, p. 84) suggested that “for samples larger than 100, Cronbach’s alpha coefficients greater than 0.40 are acceptable for research purposes”. The average inter-item correlation should be between 0.15 and 0.50, depending on the specificity of the concept the scale is meant to measure (the greater the specificity, the greater the value) (Clark and Watson 1995, p. 316).
24The covariance coverage over the whole sample represents the percentage of data available per pair of
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
Alternative 2: no Conation factor, two independent intention observed variables
For this second alternative, noconationlatent factor was specified as underlying the two intention outcomes, Conservation and TreePlanting. Instead, affect and cognition directly predicted the two intention variables. The model converged to an acceptable solution. However, as suggested by the correlation table (Table A9.14), the two conation variables did not significantly correlate with one another. This lack of correlation is most probably responsible for the non-proper solutions generated by the previous model including aconationlatent variable defined by these two outcomes.
The present model produced very similar results to the initial model including MoreForestand a latentconationfactor (section III.B.2.a) in terms of overall-goodness- of-fit (Table A9.18) and parameter estimates (Table A9.19). The factor structure was unchanged and the affect and cognition indicators’ loadings had similar strength. Only farmers’ beliefs (cognition) significantly influenced both kinds of intentions towards native forest. Farmers’ feelings (affect) did not influence TreePlanting, and their influence on Conservation was weak. A larger sample may reveal a more significant effect.25 The ESEM affect-cognition-2-conation-variables (only ForestBenefit) model
accounted for less variance in the intention variableConservation(Table A9.20) than in the initial model (section III.B.2.a).
iii. Conclusion
Because of a large number of missing values on the conation indicator MoreForest, the hypothesized tripartite construct was investigated with and without the conation indicator. Two models converged to acceptable solutions supporting the hypothesized tripartite model, with farmers’ feelings (affect) and beliefs (cognition) towards native forest predicting their intentions to act in an environmentally responsible way towards the forest (conation). The first model was a three-factor model including MoreForest. In the second model, MoreForest was removed, and no conation latent factor was specified, withaffect and cognitionlatent dimensions directly predicting the two remaining intention variables. The two models displayed very similar goodness-of- fit and parameter estimates. However, the second model (excludingMoreForest), based
25The somewhat different relationships betweenaffectand the two intention variables may partly explain
the significant standardized, yet non-significant unstandardized, effect ofaffect on the latentconation
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model
on a more consistent number of cases between all correlations, was more reliable. Furthermore, the non-significant correlation between the two intention variables found in the last model indicated that for farmers without fragments, enhancing native forest outside the farm and on the farm were different issues, opposing the use of a conation latent factor. Finally, as the next goal is to assess the effects of situational variables on the attitudes of the sampled New Zealand farmers, it was necessary to include as many respondents as possible in the analysis. Therefore, the next analyses concerning farmers without native forest would be done on the affect-cognition-2-conation-variables (only ForestBenefit) model (Figure 9.2). In this model, affectandcognitionexplained more of the intention variable Conservation’s variance than TreePlanting, suggesting that the attitudinal model was measuring more an abstract attitude towards a native forest outside the farm area. Next, the attitudinal responses of farmers with native fragments are examined.
Figure 9. 2 ESEMaffect-cognition-2-conation-variables(onlyForestBenefit) model on farmers without native forest, N = 291, with standardized regression and correlation coefficients. In solid line, relationships significant at p ≤ 0.10; in dashed line, relationships significant at 0.10 < p ≤ 0.15.
Chapter 9: Farmers’ specific attitude towards native forest: tripartite model