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As highlighted earlier (section 1.1.1.1), resilience has been variously described as a trait (Block & Block, 1980), a state-like construct (Luthans et al., 2007), a process (Friborg et al., 2003), and an outcome (Smith et al., 2008). There is also a lack of agreement on the referent for the term, standards for its application, or agreement on its role in models and theories (Glantz & Sloboda, 1999). Variation in defining and measuring resilience has led to an inability to compare the results of research findings due to methodological and definitional differences (Davydov, Stewart, Ritchie, & Chaudieu, 2010). There is, therefore, a need for greater clarity in the operationalisation of resilience to facilitate greater scientific rigour in this area of investigation (Cicchetti & Garmezy, 1993; Kumpfer, 1999; Luthar et al., 2000a). Therefore, the aim of the next section is to highlight some of the main methodological issues associated with the measurement of resilience. In doing so, four general issues will be discussed: trait measurement, measuring adversity, the measurement of outcomes, and item selection and sampling.

1.1.4.1 Measuring resilience as a trait

Three general observations can be made regarding the measurement of resilience as a trait. First, Rutter claims (2006) that the assumption that it is possible to measure resilience as an observed trait is flawed because resilience is not a static quantifiable entity. For example, an individual may be resilient in relation to some type of adversities but not others. Equally, because context is crucial, individuals may be resilient at one time period in their life but not

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at others (Windle et al., 2011). Compounding this problem, those using existing trait resilience measures (e.g. Connor & Davidson, 2003; Friborg, Hjemdal, Rosenvinge, & Martinussen, 2003; Wagnild & Young, 1993) have assumed it is possible to measure a resilient type of person and employ resilience scales as a proxy for resilient outcomes (i.e., resilience is measured as variations on the trait resilience scale) (e.g. Davidson, Connor, & Lee, 2005). In some cases, resilience scales have been used in the absence of an actual acute stressor event (e.g. Montross et al., 2006), thus narrowing the research to the personality variable alone, divorced from the context. The main limitation of measuring resilience as a trait is that personality rarely explains more than a small portion of the actual variance in people’s behaviour across situations (Bonanno, Brewin, Kaniasty, & La Greca, 2010). For example, Weems and colleagues (2010) examined neuroticism in a small sample of adolescents before and after Hurricane Katrina. Controlling for pre-disaster mental health, gender, and number of hurricane-related stressors, they found that pre-disaster neuroticism predicted only a small amount of variance associated with post-disaster symptoms such as anxiety and depression. Thus, the notion of a resilient type of person at best addresses only a piece of the overall puzzle of determining who will or will not be resilient.

Second, despite a number of studies that report associations between personality traits and positive outcomes such as subjective well-being and the absence of psychopathology (Bonanno et al., 2011; Bonanno, 2004), in many studies, personality variables were measured concurrently with the outcome (i.e., after the adversity). Given that personality is not impervious to situational and environmental influences (McCrae & Costa, 1999), it is plausible that the adverse event may inform the personality variable as much as the other way around (Mancini & Bonanno, 2009). The point made here does not refute the notion that personality is a relatively stable disposition, rather it highlights the notion of characteristic adaptation put forward by McCrae and colleagues (2000). Characteristic adaptation refers to environmental variables such as learned skills, habits, beliefs, roles, and relationships that have a direct effect on personality traits. As such, characteristic adaptations are always involved in the expression of personality (McCrae et al., 2000). Returning to the earlier point made that personality is not impervious to situational influences in relation to resilience assessment, a small body of research suggests that traumatic stress may contribute to atrophy in the hippocampus and affect personality through its effects on the brain (Bremner, 1999). There is also cross-sectional evidence that the experience of acculturation (e.g. adapting to new cultures) can change personality profiles (McCrae, Yik, Trapnell, Bond, & Paulhus, 1998), and some longitudinal research has shown that personality change is associated with life events (Agronick & Duncan, 1998). All of these findings are useful reminders that in the

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assessment of positive outcomes following adverse events, theoretical generalisations about personality are not immune to environmental influences. Therefore, when drawing conclusions about the role of trait resilience in positive adaptations to adversity, it necessary to acknowledge the influence of characteristic adaptations on the expression of trait resilience.

Third, resilience measures have predominantly focussed on assessing a constellation of characteristics that enable individuals to adapt to situational demands they encounter. The problem with this approach is that the majority of measures focus on resilient qualities at the level of the individual only (Ahern, Kiehl, Sole, & Byers, 2006; Naglieri, Goldstein, & LeBuffe, 2010; Windle, Bennett, & Noyes, 2011). Whereas features of the individual are undoubtedly important for understanding positive adaptation in the face of adversity, the availability of resources from family (e.g., close bonds with at least one parent) and the community (e.g., support from peers) are also invaluable (see Horton & Wallander, 2001).

1.1.4.2 Measuring adversity

Much of the research investigating resilience antecedents such as adverse events or stressors focus on single events such as reactions to a divorce or prior combat experience. However, adversities often co-occur ( Green et al., 2010), making it difficult to isolate the impact associated with any single event. Seery (2010) suggests that current measures of cumulative adversity commonly assess a small range of stressors. In turn, the fewer stressors measured, the more difficult it is to identify the critical differences between individuals that have limited exposure to stressors versus those that have been exposed to a wide range of stressors. Thus, obscuring the true effects of the adversity in question and limiting conclusions that can be drawn about the inference of resilience itself. For example, two individuals that have high ratings on a daily hassles measure may provide information about how well an individual responds to daily hassles but says nothing about the wider stressors that may be impacting on their resilience.

A further consideration when measuring stressors relates to the heterogeneity of events sampled. There is a need to differentiate between chronic circumstances and acute events since the effects associated with each of these categories can differ (Masten, Neemann, & Andenas, 1994). Different properties of stressors need to be accounted for, such as, the duration (chronic vs. acute), frequency (rare vs. common occurrence) and intensity (high vs. low demand). Thus, it is inappropriate to treat events that vary in intensity, such as, bereavement or financial difficulty as comparable to one another (Luthar & Cushing, 1999).

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1.1.4.3 Measurement of outcomes

Positive outcomes differ widely depending on sample demographics, number of risks, and the number and type of outcomes—in general, studies utilising predominantly white middle class samples and single risk factors found higher rates of positive outcomes than studies using ethnically diverse low-income samples and multiple risk factors. Thus caution is warranted when results from one study are generalised to other samples, so that resilience rates are not overestimated (Bonanno, Galea, Bucciarelli, & Vlahov, 2007).

Another methodological issue worthy of note is to question the stage at which resilient functioning should be measured. Using data obtained years after the occurrence of an aversive event (Wingo, Fani, Bradley, & Ressler, 2010) makes it impossible to retrospectively determine the sustainability of resilient functioning relative to a specific adverse event. For example, data obtained two years after the onset of a traumatic event might show a person to be symptom free and show signs of positive adaption. At the same time, this individual may have suffered Posttraumatic Stress Disorder (PTSD) for a significant portion of time after the event and have only experienced symptom remission two years later.

Relatedly, the assessment of resilience at a single point in time may only capture state characteristics as opposed to assessing an individual’s thoughts, feelings and behaviour throughout the entire process of dealing with adversity (Hoge, Austin, & Pollack, 2007). Therefore, longitudinal studies are important in determining the stability (or lack of stability) of resilience across an individual’s lifespan (Luthar, 2006; Windle, 1999). Moreover, utilising longitudinal designs when researching resilience represents a useful approach that is consistent with the conceptualisation of resilience as a dynamic process of positive adaptation to adversity (Luthar, 2006).

1.1.4.4 Item selection and sampling

There is a limited evidence base for the selection of items within current measures of resilience (Atkinson, Martin, & Rankin, 2009; Davydov et al., 2010). For example, the Brief Resilient Coping Scale (BRCS; Sinclair & Wallston, 2004) was developed solely using Polk’s (1997) classification of resilience phenomenon. Authors did not provide a justification as to why this particular perspective was prioritised over others. Furthermore, although the content of the Connor-Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003) was drawn from a number of different peer-reviewed sources (e.g. Kobasa, 1979; Lyons, 1991; Rutter, 1985), scale authors also used resilience factors that were not based on empirical evidence such as

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the memoirs of Sir Edward Shackleton’s expedition in the Antarctic in 1912 (Alexander, 1998). Similarly, items from the Brief Resilience Scale (BRS; Smith et al., 2008) were solely derived from a dictionary definition of resilience (the ability to “bounce back” or recover from stress) favoured by the lead author. It is critical that in instrument development, item sampling is clearly justified, perhaps starting with a review of the vast empirical knowledge of resilience-related research.

Current measures of resilience predominantly sample items that are implicitly assumed to be associated with positive adaptation in the face of adversity (Olsson, Bond, Burns, Vella- Brodrick, & Sawyer, 2003). However, without the simultaneous measurement of context- specific stressors this connection cannot be corroborated. Rutter (2006) argued that resilience is an interactive concept that can only be studied if there is a thorough measurement of factors relative to the adversity in question. To help reduce ambiguities in item development alternative paradigms that adopt a person-in-context unit of analysis (Little, 2000) such as qualitative methods may be a valuable addition to the item development process.

To summarise, three pivotal components influence the degree to which resilience can be successfully measured—adversity, protective factors, and positive adaptation. As such, Bonanno (2012b) suggests the following methodological criteria should be met in the assessment of resilience: (1) the temporal bounds of adversity should be defined; (2) positive adaptation must be explicitly defined; and (3) measurements need to be obtained at multiple points in time. Without a clear operationalisation of these components it becomes difficult to compare findings across studies (Schoon, 2006) and clarify to what extent an individual displays resilience compared with another (Silver & Wortman, 1980). The issues raised here are critical for the refinement of future measures of resilience.

While diversity in research approaches can be valuable, the result of variation in defining resilience (or failing to) and the measurement of adversity and positive outcomes has led to contradictory findings and in some cases, an inability to compare results due to irreconcilable methodological and definitional differences. There is an obvious need for greater uniformity and clarity in the definition, terminology and operationalisation of resilience to capitalise on current knowledge and to facilitate greater scientific rigour in this body of work (Kumpfer, 1999; Luthar et al., 2000). As there have now been many identified factors associated with resilient outcomes, ongoing development of the concept of resilience will depend in part on greater standardisation of definition and research approaches. An integral part of this process is the development of a reliable and valid means of measurement.

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The aim of this introduction has been to identify and present a critical overview of conceptual and methodological issues associated with the theoretical construct of resilience. The final part of this introduction summarises these findings and presents the research questions guiding this thesis.