Closeness in Student–Teacher Relationships and Students’ Psychological Well-Being: The Mediating Role of Hope

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Abstract

A close student–teacher relationship is a protective factor for students’ psychological well-being, and it is associated with students’ internalizing and externalizing symptoms, but the mechanism underlying this association is unclear. To address this issue, this study investigated the role of children’s hope in the relationship between teachers’ perceived closeness in the student–teacher relationship and children’s internalizing and externalizing symptoms. Participants consisted of 562 Italian students ages 4 to 9 years and 48 Italian teachers ages 26 to 60 years. Results indicated that the children’s hope played the mediating role between closeness and children’s internalizing and externalizing symptoms. Findings, limitations, and suggestions for future research were discussed.

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Mediation is said to occur when a causal effect of some variable X on an outcome Y is explained by some intervening variable M. The authors recommend that with small to moderate samples, bootstrap methods (B. Efron & R. Tibshirani, 1993) be used to assess mediation. Bootstrap tests are powerful because they detect that the sampling distribution of the mediated effect is skewed away from 0. They argue that R. M. Baron and D. A. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility. Empirical examples and computer setups for bootstrap analyses are provided.

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Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods.

David P Mackinnon, Chondra M Lockwood, Jason Williams (2004)

The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.