Adrian J. Tomyn

Quality of Life in Australia

This chapter describes some aspects of life quality in Australia. The data for the initial overview have come from various sources. Following this general introduction, the chapter concentrates on two areas of Australian life that are key to subjective wellbeing. Using the combined data base from 24 surveys using the Australian Unity Wellbeing Index, we identified the happiest and the saddest groups in Australia as defined by their demographic profile. The highest wellbeing groups comprise people who have both a partner and a decent level of wealth. The lowest wellbeing groups are people with no partner, who have a low income and who are unemployed. The chapter then concentrates on the two common, defining elements of the highest and lowest groups as money and relationships. The results are interpreted in terms of data norms, homeostasis theory and possible causal agents. It is concluded that the results generally conform to the predictions of homeostasis and offer signposts as to the kinds of structural changes that influence population wellbeing.

Resilience and Subjective Wellbeing: A Psychometric Evaluation in Young Australian Adults

Objective Resilience is an important and underdeveloped area of research, and there are few studies that describe levels of resilience among youth samples. A major aim of this research is to explore the utility of an adapted form of the 10‐item Connor Davidson Resilience Scale and to clarify the association between this construct and a robust measure of subjective wellbeing. Method A representative sample of 1000 Victorians aged 16–25 years participated in a telephone interview comprising the modified 10‐item Connor Davidson Resilience Scale and the Personal Wellbeing Index. Results The modified 10‐item Connor Davidson Resilience Scale demonstrated adequate inter‐item reliability and factored as intended. A moderate, positive correlation was found between the modified 10‐item Connor Davidson Resilience Scale and the Personal Wellbeing Index. Significance testing revealed group differences for gender, age, and annual household income. The results are also used to establish theoretical “normal” ranges for resilience in Victorias youth population. Conclusion The results from this study support the modified 10‐item Connor Davidson Resilience Scale as a valid and reliable measure of young peoples resilience using traditional psychometric tests. Moreover, this is the first study to describe the levels of resilience among Victorian youths and to evaluate these data alongside a robust measure of subjective wellbeing. The implications of the findings for government policy and service delivery are discussed.

Using dynamic factor analysis to provide insights into data reliability in experience sampling studies.

The past 2 decades have seen increasing use of experience sampling methods (ESMs) to gain insights into the daily experience of affective states (e.g., its variability, as well as antecedents and consequences of temporary shifts in affect). Much less attention has been given to methodological challenges, such as how to ensure reliability of test scores obtained using ESM. The present study demonstrates the use of dynamic factor analysis (DFA) to quantify reliability of test scores in ESM contexts, evaluates the potential impact of unreliable test scores, and seeks to identify characteristics of individuals that may account for their unreliable test scores. One hundred twenty-seven participants completed baseline measures (demographics and personality traits), followed by a 7-day ESM phase in which positive and negative state affect were measured up to 6 times per day. Analyses showed that although at the sample level, scores on these affect measures exhibited adequate levels of reliability, up to one third of participants failed to meet conventional standards of reliability. Where these low reliability estimates were not significantly associated with personality factors, they could—in some cases—be explained by model misspecification where a meaningful alternative structure was available. Despite these potential differences in factor structure across participants, subsequent modeling with and without these “unreliable” cases showed similar substantive results. Hence, the present findings suggest typical analyses based on ESM data may be robust to individual differences in data structure and/or quality. Ways to augment the DFA approach to better understand unreliable cases are discussed.