Overview ... 45 Purpose ... 47 Method ... 47 Interviews ... 47 Subjective Measures ... 49 Analytical Software ... 50 Coding Procedure ... 50 ‘Self’ Codes ... 51 Self-‐as-‐Process ... 51 [SP-‐hedge] Self-‐as-‐Process Hedge ... 51 [SP-‐now] Self-‐as-‐Process Now ... 52 [SP-‐then] Self-‐as-‐Process Then ... 53 Self-‐as-‐Story ... 56 [SS] Self-‐as-‐Story (Positive, Negative & Neutral) ... 56 Self-‐Rules ... 68 [COR] Control Oriented Self-‐Rule ... 68 [VOR] Value Oriented Self-‐Rule ... 71 Self-‐as-‐Perspective ... 75 [SX1] Self-‐as-‐Perspective 1 ... 76 [SX2] Self-‐as-‐Perspective 2 ... 81 Statements Not Coded ... 86 NUL sentences ... 86 Inheriting meaning from the interviewer’s question ... 87
Results ... 87 Pilot Study ... 88 Pilot Study Method ... 88 Pilot Study Results ... 89 Discussion (Pilot Study Coding) ... 90 Round One ... 91 Round One Method ... 91 Round One Results ... 92 Discussion (Round One Coding) ... 94
Round Two ... 94 Round Two Method ... 94 Round Two Results ... 94 Discussion (Round Two Coding) ... 95 Round Three ... 97 Round Three Method ... 97 Round Three Results ... 98 Discussion (Round Three Coding) ... 102
Overview
Broadly, the aim of this study was to develop a coding scheme for natural language, the Functional Self-‐Discrimination Measure (FSDM), that could predict wellbeing. This involved examining the relationship between different types of self-‐
discrimination statements (detailed below) and a set of subjective wellbeing measures. In previous research a colleague and I had shown that the number of literal self-‐conceptualisation statements a person made decreased and the number of perspective-‐taking statements a person made increased in frequency following a mindfulness course (Atkins & Styles in press). These findings were consistent with how ACT interventions work to help people respond more flexibly to their inner experience (Hayes et al. 2012b). Based on this research I aimed to further test these previous findings; that the number of statements uttered by a person indicating they knew themselves as the context of their experience [SX], less the number of rigid statements about their own identity [SS], would correlate positively with a set of wellbeing measures. I took a grounded approach and after several rounds of analysis I found that the number of statements uttered by a person indicating they knew themselves as the context of their experience [SX], plus the number of value oriented self-‐rule statements [VOR] they uttered, correlated positively with the measures of wellbeing. The final results lead me to formulate a measure of Psychological
Flexibility [FLEX].
The measures of wellbeing were along two dimensions – hedonic and eudemonic. Hedonic wellbeing was measured by the Positive & Negative Affect Scale (PANAS) (Watson et al. 1988) and symptoms of depression, anxiety and stress by the Depression, Anxiety and Stress Scale (DASS: Antony et al. 1998). The PANAS measured the presence of positive and negative mood and emotional states
experienced by the individual over a period of 3 months prior to taking the measure. Positive Affect (PA) reflected the extent to which people felt enthusiastic, active and alert. A high PA state reflected high energy, full concentration, and pleasurable engagement. In contrast Negative Affect (NA) reflected a general dimension of subjective distress and unpleasant engagement that subsumed a variety of aversive mood states including anger, disgust, guilt, fear, and nervousness. Low NA reflected a state of calmness and serenity. In general, research on PA & NA indicate that the two
mood states relate to self-‐reported stress, poor coping, health complaints, and frequency of unpleasant events (Watson et al. 1988). Similarly, levels of depression, anxiety and stress (measures by the DASS) have been show to relate to physical arousal, psychological tension, panic attacks, fear, agitation, tension, irritability, and a tendency to overreact to stressful events in clinical and nonclinical groups (Antony et al. 1998).
Eudemonic forms of wellbeing were evaluated in terms of Psychological Wellbeing (Ryff & Keyes 1995) and Satisfaction With Life (Diener et al. 1985). Psychological Wellbeing has been conceived as a multidimensional model that includes six distinct components of positive psychological functioning (Ryff & Keyes 1995). These
components, derived from multiple theoretical frameworks, have been combined as a valid measure of wellness. These six dimensions include: positive evaluations of oneself and one’s past life (Self-‐Acceptance); a sense of continued growth and development as a person (Personal Growth); the belief that one's life is purposeful and meaningful (Purpose in Life); the possession of quality relations with others (Positive Relations With Others); the capacity to manage effectively one's life and surrounding world (Environmental Mastery); and, a sense of self-‐determination (Autonomy). The other measure, Satisfaction With Life, is understood to be the cognitive, judgmental process, of assessing the quality of one’s life according to
personally chosen criteria (Diener et al. 1985). This involves comparing one’s present circumstance and state of affairs with a set of standards that have been personally chosen, not externally imposed.
Through a process of coding and correlating the frequency of various categories of self-‐discrimination statements with this set of wellbeing measures I sought to
validate my approach to coding natural language as a functional assessment of verbal operant behaviour that allowed for the prediction of wellbeing along these
dimensions. This work is the topic of this chapter.
Purpose
Taking a grounded approach, I aimed to evolve and refine the Functional Self-‐ Discrimination Measure (FSDM) based previous findings (Atkins & Styles in press); beginning with the assumptions that:
• The number of SX statements uttered by a person less the number SS
statements would correlate positively with wellbeing measures and that this measure is a valid measure of Psychological Flexibility FLEX = SX – SS.
Method
To test if coded measures of self-‐discrimination predicted wellbeing, I coded a set of transcribed interviews and correlated code frequencies with the set of subjective measures taken at the time of interview then six and twelve months later. I
completed three rounds of coding during which code definitions were clarified and expanded, and calculations of Psychological Flexibility [FLEX] were refined. I present this work below in three broad sections. First, in this section I discuss information about the interviews, subjective measures and analytical software. Second, in the Coding Procedure section, I provide the definition and description of each ‘Self’ code, with explanations of how each code evolved over the three rounds of coding. Then finally in the Results section, I provide the results from the various rounds of coding.
Interviews
The thirty-‐four interviews used to validate the coding scheme in this study were a subset of a larger database of over 100 interviews conducted as part of study done by Paul Atkins, ANU, with Michael Cavanagh and colleagues, University of Sydney. Their study was designed to evaluate developing leadership in health services and law firms: improving well-‐being, engagement, and staff retention. Participants were all professionals or para-‐professionals and all had received between 2 and 9 years of tertiary education. Those from the law firm were all practicing lawyers ranging in seniority from senior associate to senior partners. Participants from the hospitals
were doctors, nurses and administrative managers. The legal sample was mostly men and the hospital sample was mostly women (Table 3.1 below).
Men Women % Men
Years of Tertiary Study M (SD) Age M (SD) Legal Firm 11 8 58% 6.4 (1.9) 43 (8.6) Hospital Network 3 12 20% 4.8 (1.9) 45 (6.7)
Table 3.1: Demographic characteristics of the sample (n=34).
The interviews were focused on the recollection of a critical incident by interviewees. The interviews were semi-‐structured, conducted over the phone for 35-‐60 minutes and based upon Kegan’s subject-‐object interview procedure (Lahey et al. 1988). At the beginning of the interview, participants were read six key phrases sequentially, each describing an affective experience: 1) delight, 2) anxious or stressed, 3) angry, 4) torn (in conflict about something), 5) strong stand or conviction and 6) important to me. After participants had noted experiences consistent with those affective states that had occurred in the past few weeks or months, they were asked to pick one and then tell their story. Participants were told the interviewer’s primary purpose was to understand the participants experience from their own point of view (“to see the world through your eyes”). Participants were told that they could choose which stories to discuss and how much detail to present.
The interviews were semi-‐structured in order to gather rich data about individuals’ lived experience. The role of the interviewers was to listen reflectively and ask open questions such as “What is the hardest/most challenging part of this for you?” “How would you decide if you had been successful?” and “What did that situation tell you about yourself?” Although the subject-‐object interview was originally designed to measure stages of adult development (Kegan 1994; Kegan et al. 1982), it is similar to a typical functional interview in that it explores the perceived antecedents and personal consequences of various responses to situations (Ramnero & Torneke 2008). The interviews were all transcribed for coding.
Subjective Measures
In addition to subject-‐object interviews, the set of self-‐report measures discussed above were administered at three time points, at the same time as the interviews, then six and twelve months later.
Hedonic affect was measured using two scales. The first was the Positive and Negative Affect scale (Watson et al. 1988) with participants asked to rate the
frequency of 10 different emotions over the past three months: Happy, Angry, Joyful, Depressed/Blue, Enjoyment/Fun, Anxious, Pleased, Frustrated, Enthusiastic,
Unhappy. The second measure of affect was the 21-‐item version of the Depression, Anxiety, Stress scale (Antony et al. 1998).
Eudemonic forms of wellbeing were measured using two scales: the Psychological Well-‐Being Scale (Ryff & Keyes 1995) consisting of six subscales: Autonomy, Positive Relations, Self-‐Acceptance, Environmental Mastery, Purpose in Life, Personal Growth. Although subscales for Autonomy, Positive Relations and Self-‐Acceptance were initially measured with nine-‐items drawn from the original corpus of twenty, the results in this study are based entirely upon the version of the measure reported by Ryff and Keyes (1995) with three-‐items per subscale. Participants were also given the five-‐item Satisfaction with Life scale (Diener et al. 1985).
In addition, the International Personality Item Pool measures (Goldberg et al. 2006) for: Openness, Neuroticism, Agreeableness, Extroversion and Conscientiousness were also administered at the time of interview.
The subjective measures were correlated with the frequency of self-‐discrimination and self-‐rule codes. Then a series of regression analyses were conducted to assess how well measures of self-‐discrimination and self-‐rules compared with the
International Personality Item Pool measures to predict hedonic (affective) and eudemonic (meaning and satisfaction in life) wellbeing.
Analytical Software
The software QDA Miner published by Provalis Research was used to code the interviews and SPSS was used for the statistical analysis.