The current study tested three mediation hypotheses predicted by the original TMIM and subsequent applications of the model (Afifi & Weiner, 2004; Wong, 2014). The first mediation hypothesis (H8) predicted anxiety would mediate the relationship between uncertainty
discrepancy and efficacy. The second mediation hypothesis (H9) predicted anxiety would mediate the relationship between uncertainty discrepancy and outcome expectancies. The third mediation hypothesis (H10) predicted anxiety would mediate the relationship between
uncertainty discrepancy and information seeking intentions.
All mediation hypotheses were tested using Baron and Kenny’s (1986) causal steps approach for performing mediation analyses were used. The recommendations are as follows: (1) the independent variable has a significant zero-order correlation with both the dependent variable and the mediator variable, respectively, (2) the mediator variable has a significant zero-order correlation with the dependent variable, and (3) when the mediator is added as a predictor, the effect of the independent variable on the dependent is reduced to zero (in cases of full mediation) or reduced but not to zero (in cases of partial mediation). If Baron & Kenny’s (1986) mediation requirements were met, bootstrapping (Preacher & Hayes, 2004) was performed to directly test indirect effects.
Bootstrapping provides a nonparametric method to estimate effect size and makes no assumptions about the distributions of indirect effects (Preacher & Hayes, 2004). This is possible because bootstrapping takes a large number of samples of the original sample size (with
replacement) and computes the indirect effect in each sample (Preacher & Hayes, 2004). Bootstrapping is a “more rigorous and powerful” approach to test indirect effects than the oft- used Sobel test (Zhao et al., 2010, p. 205), which generally accompanies the Baron and Kenny
(1986) approach to mediation testing and examines whether the mediator is responsible for the influence of the independent variable on the dependent variable. Unlike the Sobel test,
bootstrapping does not make assumptions about the sampling distribution of the indirect effect (Hayes, 2009). In the current study, bootstrapping tested indirect effects based on 1,000 bootstrap resamples. The PROCESS macro (Preacher & Hayes, 2004) was used for all bootstrapping analyses.
4.6.1 Uncertainty Discrepancy, Anxiety, and Efficacy (H8)
The TMIM predicts anxiety mediates the relationship between uncertainty discrepancy and efficacy (Afifi & Weiner, 2004). Thus, H8 predicted that anxiety would mediate the effect of uncertainty discrepancy about the sexual health of one’s romantic partner on efficacy to obtain sexual health information from the individual and to cope with related outcomes. As displayed in Table 17, the zero-order correlations for uncertainty discrepancy, anxiety, and efficacy were all significant.
Table 17. Correlations for Uncertainty Discrepancy, Anxiety, and Efficacy
Variable 1 2 3
1. Uncertainty Discrepancy (IV) 2. Anxiety (M) - .360** - 3. Efficacy (DV) -.131* -.447** - N = 301. * p < .05, ** p < .01.
Following Baron and Kenny (1986), in Step 1 of the mediation model, the regression of anxiety on uncertainty discrepancy, was significant, B = .245, t(308) = 6.746, p < .01. In Step 2, the regression of efficacy on the mediator, anxiety, was also significant, B = -.372, t(301) = - 8.652, p < .01. In Step 3, when controlling for the mediator, anxiety, uncertainty discrepancy no longer accounted for a significant portion of the variance in efficacy, B = .032, SE = .026, t(300) = .471, p > .05. Bootstrapping procedures were used to test the significance of the indirect effect.
Unstandardized indirect effects were computed for 1,000 bootstrapped samples. The
bootstrapped unstandardized effect was -.05, and the 95% confidence interval ranged from -.09 to -.03. Because zero lies outside the confidence interval, the indirect effect was statistically significant. Thus, H8 was supported, as anxiety was found to mediate the relationship between uncertainty discrepancy and efficacy (Figure 9).
Figure 9. Anxiety mediating the relationship between uncertainty discrepancy and efficacy. Note. Unstandardized regression coefficients for the relationship between uncertainty discrepancy and efficacy as mediated by anxiety. The standardized regression coefficient between uncertainty discrepancy and efficacy controlling for anxiety is in parentheses. ** p < .01.
4.6.2 Uncertainty Discrepancy, Anxiety, and Outcome Expectancies (H9)
The TMIM predicts anxiety mediates the relationship between uncertainty discrepancy and outcome expectancies (Afifi & Weiner, 2004). H9 proposed anxiety would mediate the effect of uncertainty discrepancy about the sexual health of one’s romantic partner on outcome expectancies of seeking sexual health information from romantic partners. The zero-order correlations for uncertainty discrepancy (IV), anxiety (mediator), and outcome expectancies (DV) were calculated and appear in Table 18.
Table 18. Correlations for Uncertainty Discrepancy, Anxiety, and Outcome Expectancies
Variable 1 2 3
1. Uncertainty Discrepancy (IV) 2. Anxiety (M) - .360* - 3. Outcome Expectancies (DV) -.06 -.182** - N = 308. * p < .01.
The first criterion of Baron and Kenny’s (1986) mediation test was not met. Specifically, the independent variable (uncertainty discrepancy) and the dependent variable (outcome
expectancies) did not significantly correlate (p > .05). Thus, the hypothesis was not supported. 4.6.3 Uncertainty Discrepancy, Anxiety, Information Seeking Intent (H10)
Whereas most applications of the TMIM examine actual information management behaviors, Wong (2014) recently tested the ability of the TMIM to predict information seeking intentions and found partial support for his hypothesis that anxiety would mediate the
relationship between uncertainty discrepancy about the HPV vaccination and information seeking intentions. The current study tested the ability of the TMIM to predict information seeking intentions within the context of seeking sexual health information from romantic
partners. Specifically, H10 predicted anxiety would mediate the relationship between uncertainty discrepancy and intent to seek sexual health information from romantic partners. As reported in Table 19, all variables had significant zero-order correlations amongst them.
Table 19. Correlations for Uncertainty Discrepancy, Anxiety, and Information Seeking Intent
Variable 1 2 3
1. Uncertainty Discrepancy (IV) 2. Anxiety (M)
-
.360** -
3. Information Seeking Intent (DV) .107* .239** -
N = 306.
* p < .05, ** p < .01.
Following Baron and Kenny (1986), in Step 1 of the model, the regression of anxiety on uncertainty discrepancy was significant (B = .245, SE = .036, t(307) = 6.75, p < .01). In Step 2,
the regression of information seeking intent on the mediator, anxiety, was statistically
significantly (B = .318, SE = .074, t(307) = 4.31, p < .01). In Step 3, controlling for anxiety, the direct effect of uncertainty discrepancy on information seeking intent was not statistically significant (B = .020, SE = .022, t(306) = .36, p > .05). Next, bootstrapping was performed, as unstandardized indirect effects were computed for 1,000 resamples. The bootstrapped
unstandardized effect was .07, and the 95% confidence interval ranged from .02 to .13. Because zero lies outside the confidence interval, the indirect effect was statistically significant. Thus, H10 was supported, as anxiety was found to mediate the relationship between uncertainty discrepancy and information seeking intentions (Figure 10).
Figure 10. Anxiety mediating the relationship between uncertainty discrepancy and information seeking intentions
Note. Unstandardized regression coefficients for the relationship between uncertainty discrepancy and information seeking intentions as mediated by anxiety. The standardized regression coefficient between uncertainty discrepancy and information seeking controlling for anxiety is in parentheses.