Quantitative Methods (1~3)

How would you measure these constructs? Is reliability relevant to all of the constructs or variables of interest?

To investigate the proposed hypotheses, it may be appropriate to utilise psychometrically validated scales. For general self-efficacy, the General Self-Efficacy Scale (GSE; Schwarzer & Jerusalem, 1995, pp. 35-37) could be employed to reveal insights into the first hypothesis that postulates a positive association between self-efficacy and resilience. Resilience might be quantified using the Connor-Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003), potentially illuminating age-related differences as suggested in the second hypothesis. Snyder’s Self-Monitoring Scale (SM; Snyder, 1974) may offer a measure of self-monitoring relevant for examining its hypothesised relationship with self-efficacy.

The relevance of reliability across these constructs cannot be overstated, as it ensures the consistency and accuracy of measurements, reducing the likelihood of random error influencing the results (Pallant, 2020, pp. 19-20). Cronbach’s alpha may provide a robust estimate of internal consistency for these scales, which is crucial for validating the hypothesised relationships.

Correlational or regression analyses may be applied to test the first hypothesis, corroborating findings from prior research, such as Benight and Bandura (2004), who reported a significant link between perceived self-efficacy and resilience. The second hypothesis could be explored through a cross-sectional study design, which may confirm that resilience varies with age, as Windle, Bennet, and Noyes (2011) indicated. For the third hypothesis, a correlational study could ascertain whether a relationship exists between self-efficacy and self-monitoring.

Attention must be paid to potential confounding factors that could influence the results. Complementing quantitative data with qualitative insights can also capture the constructs’ complexity. Employing reliable and valid measurements while accounting for potential confounders would enable researchers to examine these relationships more rigorously.

Defining the core tenets of generalisability theory, defining coefficient alpha, and explaining how generalisability theory differs from coefficient alpha.

Generalisability theory (GT) may provide a more comprehensive framework than classical test theory (CTT) for evaluating the reliability of psychological measures, which is potentially beneficial for investigating the proposed hypotheses regarding self-efficacy, resilience, and self-monitoring (Cronbach et al., 1972, pp. 1-14; Brennan, 2011). GT considers multiple sources of measurement error, such as items, occasions, and raters, which could be particularly informative when examining whether constructs like self-efficacy and resilience are consistent across different populations and settings.

Coefficient alpha, also known as Cronbach’s alpha, developed to assess internal consistency, represents the proportion of score variance attributable to true score variance and is widely used in psychological research (Cronbach & Shavelson, 2004). Nonetheless, the underlying assumptions of tau-equivalence and uncorrelated errors might only be tenable in some research contexts (Allen & Yen, 1979, pp. 56-71). This suggests that the reliability of measures tested by alpha could vary with the sample or measurement conditions, potentially influencing the validity of the hypotheses.

GT diverges from alpha by facilitating a detailed analysis of different sources of error variance. This analytical flexibility could be essential for accurately testing Hypothesis 1, which suggests a relationship between self-efficacy and resilience. GT would allow researchers to ascertain if such a relationship is stable across various contexts or is influenced by specific measurement conditions. Similarly, GT could be invaluable for evaluating Hypothesis 2, as it would enable assessing whether the resilience measure is equally reliable among diverse age groups. Regarding Hypothesis 3, GT could elucidate whether the relationship between self-efficacy and self-monitoring is consistent or subject to variations due to individual or item interactions.

Therefore, GT facilitates a deeper understanding of reliability and provides a methodological approach for designing studies to test these hypotheses (Shavelson & Webb, 1991, pp. 12-16). Employing GT could ensure that robust and generalisable findings underpin the practical application of constructs in real-world settings.