Does self-acceptance captured by life narratives and self-report predict mental health? A longitudinal multi-method approach

Journal of Research in Personality 79 (2019) 13–23 Contents lists available at ScienceDirect Journal of Research in Personality journal homepage: www.elsevier.com/locate/jrp Full Length Article Does self-acceptance captured by life narratives and self-report predict mental health? A longitudinal multi-method approach Ana N. Tibubos a,b,⇑, Christin Köber c, Tilmann Habermas b, Sonja Rohrmann b a Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Germany b Department of Psychology, Goethe University Frankfurt, Germany c Psychology Program, Division of Science, New York University Abu Dhabi, United Arab Emirates article info abstract Article history: We aimed to investigate the validity of different self-acceptance measures to predict mental health. Self- Received 29 June 2018 acceptance and negative life events, assessed via self-report and rated from life narratives (N = 149), Revised 23 January 2019 served as predictors of mental health at baseline (T1) and four years later (T2). Path models showed dis- Accepted 29 January 2019 tinguishable, complementary effects of self-reports and other-ratings. A moderate congruence of self- Available online 30 January 2019 and other-ratings of self-acceptance was observed. Exploratory analyses revealed an association of self-acceptance with emotional words in life narratives. Controlling for negative life events, a positive link Keywords: and a moderating effect of self-acceptance on mental health at T1, but no prediction of mental health at Life narratives T2, were found. The study connects research on personality and narratives from a methodological and Self-acceptance health psychological perspective. Mental health Quantitative text analysis Ó 2019 Elsevier Inc. All rights reserved. 1. Introduction resilience identified self-acceptance as a pivotal component relat- ing to and protecting mental health throughout the life span Narrative research points to the incremental validity of personal (Berger, 1952; Carson & Langer, 2006; Chamberlain & Haaga, narratives as a complementary method to self-report for predicting 2001; Davydov et al., 2010; Joseph & Linley, 2006; Kivity, Tamir, well-being (Adler, Lodi-Smith, Philippe, & Houle, 2016). Following & Huppert, 2016; Ryff & Keyes, 1995; Sagone & De Caroli, 2014; the desideratum to integrate personality research and narrative Xu, Oei, Liu, Wang, & Ding, 2016). Cross-sectional research indi- studies (e.g., McAdams et al., 2004), the current study aimed to cates that self-acceptance does not change with age, but is complement research on self-acceptance, life stories, and mental informed by life events and the varying importance of different life health by tying together these three components. Thus, in order domains and experiences throughout the life course. In young to overcome methodological limitations of previous studies on adulthood, self-acceptance is mainly predicted by extracurricular the effect of self-acceptance on mental health (Davydov, Stewart, and social activities, in mid-life by family relationships, and in late Ritchie, & Chaudieu, 2010; Boyd and Pennebaker, 2017), we not life by prior work and educational experiences (Ryff & Heidrich, only used a multi-method approach but also analyzed longitudinal 1997). data. Conceptually, self-acceptance designates the striving to accu- Self-acceptance, usually assessed by self-report measures, is rately perceive our actions, motivations, and feelings, and more- considered a key component of mental health (Davydov et al., over emphasizes the positive acceptance of one’s past life (Ryff & 2010; Joseph & Linley, 2006; Ryff & Keyes, 1995). The ability to Singer, 2008; Ryff, 2014). Accordingly, the ability to uncondition- maintain or regain well-being in the face of stressful life events ally accept oneself appears to be crucial when going through has a large impact on mental health (Beutel et al., 2017; stressful life events (Macinnes, 2006), to recover from negative life Bonanno, 2004; Ong, Bergeman, Bisconti, & Wallace, 2006; Reich, events, or to even grow after traumatic events (e.g., Joseph & Zautra, & Hall, 2010) and depends on individuals’ resilience Linley, 2006; Rogers, 1959). Moreover, self-acceptance appears to (Davydov et al., 2010). Research on the different dimensions of moderate the link between personality traits and psychological distress, with increasing self-acceptance leading to better mental ⇑ Corresponding author at: Department of Psychosomatic Medicine and health (Flett, Besser, Davis, & Hewitt, 2003; Jibeen, 2017). Appar- ently, self-acceptance helps individuals to accept vulnerabilities Psychotherapy, University Medical Center of the Johannes Gutenberg University and limitations as part of their life, fostering mental health in face Mainz, Zahlbacher Str. 8, D-55131 Mainz, Germany. E-mail address: [email protected] (A.N. Tibubos). https://doi.org/10.1016/j.jrp.2019.01.003 0092-6566/Ó 2019 Elsevier Inc. All rights reserved.

14 A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 of distress. To date, most studies assessed self-acceptance by self- Habermas, & Fivush, 2018), narrative themes (Adler et al., 2015; ratings (Linton, Dieppe, & Medina-Lara, 2016; Ryff, 2014). How- Köber, Weihofen, & Rennstich, 2017; McAdams & McLean, 2013; ever, in clinical settings, clients’ self-acceptance can also be judged McAdams, Reynolds, Lewis, Patten, & Bowman, 2001), and personal by verbal information accumulated across several sessions by the meaning (Banks & Salmon, 2018; McLean, 2008; Merrill, Waters, & psychotherapist, who is at the beginning an unacquainted, yet Fivush, 2016; Pasupathi & Mansour, 2006) can be judged by others trained person for recognizing specific symptoms and individual in form of narrative codings (Adler et al., 2017; Syed & Nelson, characteristics. Even though ratings by others are common in per- 2015). Narrative variables coded by others capture how individuals sonality research and conducted in the so-called zero acquaintance story their lives and articulate their sense of purpose and meaning. condition (Hirschmüller, Egloff, Nestler, & Back, 2013; Kenny, In contrast to self-report measures that measure the perception of Albright, Malloy, & Kashy, 1994; Watson, 1989), self-acceptance, the self (e.g., in personality ratings), narrative methods measure to the best of our knowledge, has never been assessed by other- the individual’s creation of the self. Therefore narrative coding ratings. This is why we assessed self-acceptance by both self- done by others is not redundant to self-report measures but rather ratings and other-ratings, implying that self-acceptance can be, represents a complementary approach to assess the subjectivity of like other personality features, perceived and judged by others. personal identity (Panattoni & McLean, 2018). Brunswik (1956) lens model provides a general framework for Most commonly, different narrative variables are measured and the study of self-other agreement of personality judgements in a then examined in regard to personality correlates such as Big Five situation in which people judge the personalities of others without traits (McAdams et al., 2004; Raggatt, 2006; Thomsen, Olesen, having interacted with them before (termed zero acquaintance; Schnieber, & Tønnesvang, 2014) or psychological outcomes such Albright, Kenny, & Malloy, 1988; Kenny & Schellati, 2007). The lens as well-being (Adler et al., 2015, 2016; Adler, 2012; King, Scollon, model assumes that judges, also referred to as observers, per- Ramsey, & Williams, 2000; Pals, 2006). We expand this well- ceivers, or raters, use an array of observable attributes, labeled established narrative approach by measuring self-acceptance via cues, to infer targets’ personality traits. This means that the obser- both self- and other-ratings, and by employing entire life narra- vers’ accuracy of personality judgement relies on cues. The correla- tives which give more room to depict the sense of self than single tion between actual personality (how an individual sees the self) event narratives (Habermas & Reese, 2015). The present paper and a given cue (e.g., a word used by the target person when nar- addresses this, because it is unknown to which extent personal rating her or his life story) is termed cue validity. The correlation narratives allow an accurate perception of personality features between the personality judgment by others (e.g., raters of life sto- such as self-acceptance, and whether both measures (i.e., self- ries) and each cue (e.g., words in life narratives) is called cue utiliza- and other-rated self-acceptance) capture aspects of self- tion. In the context of personality judgements, the lens model has acceptance that promote mental health in the same way. frequently been proven a useful instrument for understanding accuracy of self- and other-ratings (Borkenau & Liebler, 1992; We complement this exploration by measuring the linguistic Borkenau, Brecke, Möttig, & Paelecke, 2009; Gosling, Ko, cues of self-acceptance via quantitative lexical text analysis, Mannarelli, & Morris, 2002; Hirschmüller et al., 2013; Küfner, because ample evidence showed that linguistic cues are associated Back, Nestler, & Egloff, 2010; Nestler, Egloff, Küfner, & Back, 2012). with personality and mental health (Tausczik & Pennebaker, 2010). For instance, function words have consistently been linked to men- Most frequently, the lens model has been applied to broad char- tal health and to positive therapeutic outcomes by Pennebaker and acteristics such as the Big Five personality traits (Borkenau & colleagues. Also verb tense (an indicator of whether a trauma vic- Liebler, 1992, 1995; Küfner et al., 2010; Watson, 1989), but some- tim has successfully processed an event), plural pronouns like ‘we’ times also to intelligence and self-esteem (Beer & Watson, 2008; (which are relevant indicators of social support), and causal lan- Borkenau & Liebler, 1995; Hirschmüller et al., 2013; guage (which can reveal the degree to which a participant has suc- Hirschmüller, Schmukle, Krause, Back, & Egloff, 2017). Meta- cessfully made meaning of an event) are related to mental health analyses indicate moderate observer accuracy for broad personal- (i.e., Boyd & Pennebaker, 2017; Pennebaker, 2011). Since self- ity traits, with fluctuations depending on the observability of their acceptance and mental health are strongly related, we wanted to behavioral correlates (Connelly & Ones, 2010). Less observable explore linguistic correlates of self-acceptance in life stories which traits such as self-acceptance are expected to be more difficult to has not been provided to date. Lexical text analysis allows narra- judge by unacquainted others than traits such as extraversion tive researchers to uncover patterns of word use reflecting thinking which obviously manifest in behavior (Funder, 1995; Vazire, 2010). styles distinguishable from meaning-making processes. For exam- ple, recent research on redemption (Weston, Cox, Condon, & The life story constitutes a layer of personality that is different Jackson, 2016) comparing automated lingustic analysis and human from dispositional traits and contextualized characteristic adapta- narrative coding suggested complementary functions of both tions because it is the most individual domain of personality, methods. Other studies using autobiographical stories to detect answering the question ‘‘Who am I?” to ourselves and to others linguistic correlates of psychological constructs investigated the (McAdams, 1993, 2013). Individuals organize memories and other Big Five personality traits (Fast & Funder, 2008; Hirsh & Peterson, self-relevant information into coherent life narratives (Habermas & 2009) or depression (Tackman et al., 2018). Thus, the present study Bluck, 2000; McAdams, 1993) in order to create a sense of self- offers an integrative evaluation of multiple assessment methods of continuity across time and situations (Habermas & Köber, 2015; self-acceptance based on self-rated questionnaires, other-rated life Lilgendahl, 2015; McLean, 2008). Although personal narratives of narratives and lexical analyses of life narratives, and examines how single events are more common in daily life, the present study these assessments relate to mental health. employs a multi-method approach to entire life narratives which cover entire lives, starting with birth and ending with the present 1.1. The present study or an outlook onto the future (Köber & Habermas, 2017). Entire life narratives are the most comprehensive form for presenting the self Self-acceptance is considered a key component of resilience fos- (Conway, Singer, & Tagini, 2004; Habermas & Bluck, 2000) and may tering mental health. Yet, previous studies of self-acceptance and thus be especially informative for narrators’ self-acceptance. mental health were limited by assessing self-acceptance mainly with self-reports. Aiming to fill this gap we addressed three novel Narrative personality research demonstrates that personal fea- aspects in this study: First, we assessed self-acceptance in life nar- tures such as narrative coherence (Köber, Schmiedek, & ratives of target individuals via ratings by others in addition to self- Habermas, 2015; McLean, Pasupathi, Greenhoot, & Fivush, 2017; Reese et al., 2011; Waters & Fivush, 2015; Waters, Köber, Raby,

A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 15 ratings of self-acceptance by questionnaire. Second, we used a assessed again. Details of data collection and analyses are described computer-based word count allowing to quantitatively asses lexi- below. cal correlates of self-reported and other rated self-acceptance. Third, we expanded current cross-sectional research on the corre- 2.2.1. Questionnaires lation of self-acceptance with mental health by examining its pre- Self-acceptance was assessed by the corresponding subscale of dictive potential longitudinally. the Ryff Scales of Psychological Well-Being (Ryff & Keyes, 1995) Pointing to inconsistencies between data obtained by self- at T1. Self-acceptance means acknowledging and accepting multi- reports and narrative coding, Panattoni and McLean (2018) called ple aspects of the self, both the good and the bad qualities. It also for more studies employing both methods for the same construct. means feeling positive about the past life. High scores on self- Following this call, we applied the idea of the lens model to dissect acceptance imply a positive attitude towards oneself. Low self- the construct of self-acceptance. Furthermore, since automated acceptance implies a feeling of dissatisfaction with oneself and lexical analyses of autobiographical narratives have been shown being disappointed by one’s past life as well as being troubled to complement human coding (Weston et al., 2016) and have con- about certain personal qualities and in the wish to be different sistently been linked with personality traits (Boyd & Pennebaker, (Ryff, 2014). We used the German 18-item version of the question- 2017; Pennebaker, 2011), we explored the relation of lexical corre- naire (Staudinger, Lopez, & Baltes, 1997), including three items for lates of self-acceptance in life narratives with self-reported self- acceptance. Investigating thus self-acceptance by a multi-method the dimension self-acceptance (a = 0.74). Responses were provided approach, we first analyzed the congruence of self-acceptance assessed via self-report and via independent judges’ ratings of life on a 6-point Likert-type scale, ranging from 1 = strongly disagree to narratives. Second, we explored which lexical cues help transmit 6 = strongly agree. self-acceptance in life narratives and how linguistic correlates are linked to self- and other rated self-acceptance. Since previous Critical life events were assessed by a checklist consisting of 30 research using lexical text analysis has not focused on self- items with critical life events for adulthood at T1. Participants indi- acceptance in life narratives (or other text materials), we applied cated whether they had experienced the events in the past (no/ an exploratory approach to investigate the link between self- yes). Based on prior research on stressful life events (Compas, acceptance (self- and other-report) and linguistic correlates. Third, 1987; Compas, Davis, Forsythe, & Wagner, 1987; Dohrenwend, we tested the predictive validity of self- and other-rated self- Dohrenwend, Dodson, & Shrout, 1984; Holmes & Rahe, 1967; acceptance with regard to mental health by analyzing longitudinal Newcomb, Huba, & Bentler, 1981), we identified 17 negative life data and by factoring in the amount of experienced negative life events that lead to crucial change in life or are constant stressors events. This means that the incremental validity of other-ratings affecting life negatively in a long-term perspective: own serious of self-acceptance based on life narratives compared to self- disease or accident, serious disease, or accident of family member, reported self-acceptance can be determined in our study. Also an partner’s death/death of spouse, unemployment, financial prob- interaction effect of self-acceptance and negative life events was lems, or debt, homelessness, miscarriage/abortion, crisis in family tested, assuming that self-acceptance buffers the effect of negative relations, infidelity of partner, early invalidism/occupational dis- life events on mental health. ability, attack/burglary, imprisonment, deployment in war, suicide attempt, suicide, or suicide attempt of family member, transition to 2. Method nursing home. Transitional life events such as marriage, having children, or retirement were not considered negative, because even 2.1. Participants though these life-changing events are associated with temporary stress and adjustment (Holmes & Rahe, 1967) people mostly con- The current study is part of the ongoing longitudinal MainLife sider them as positive (Berntsen, Rubin, & Siegler, 2011). Less study, which started in 2003 and repeated measurements in stressful or ambiguous life events such as job change, minor or 2007, 2011, and 2015. After implementing institutional review temporary conflicts in the family, and relocation were not consid- board, the two last measurements were approved by the Research ered as clearly negative as they do not change life fundamentally or Ethics Committee of the department of Psychology at Goethe can also be experienced as positive. Transition to nursing home, University Frankfurt, Germany. For the purpose of this study, we however, has been considered negative, because research shows present data of a subsample who participated in 2011 and 2015, that this transition elicits first strong negative feelings and because by then participants were at least 16 years old so that depends on various conditions leading to adaptive acceptance we could use valid self-reported personality data. Thus, this sample and positive adjustment (Brandburg, 2007; Wilson, 1997). For consisted of 149 individuals who participated in 2011 (T1), of the purpose of this study, we used the aggregated experienced whom 125 subjects participated again in 2015 (T2). Dropouts negative life events as predictor for mental health. (16.1%) did not differ with regard to age, sex, and education. At T1, participants were between 16 and 69 years old (M = 34.45, Mental health was measured with the German version of the SD = 19.01), 52% female (n = 77), and fluent in German. Each ses- Brief Symptom Inventory (BSI; Franke, 2000) at T1 and T2. It is a sion lasted almost two hours. Participants were informed that they self-report inventory with nine subscales consisting of 53 items: could quit the study at any time and received a remuneration of 40 somatization, obsessive-compulsive, interpersonal sensitivity, Euros per session. depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. The BSI also includes three scales to capture glo- 2.2. Materials bal psychological distress. Participants were asked to rate the extent to which they had been bothered (0 = not at all to 4 = extre- In order to compare different assessment methods of self- mely) in the past week by symptoms referring to the above men- acceptance and test their validity to predict individuals’ mental tioned nine dimensions. The internal consistencies were very health, we used a multi-method approach. At T1, self-report ques- tionnaires to measure self-acceptance, negative life events, and good for both measurement points, T1 (a = 0.93) and T2 mental health were administered and life narratives were (a = 0.94). We used the reversed coded Global Severity Index to collected. At T2, four years later, self-report mental health was assess mental health which essentially represents the mean of all of the subscale scores. 2.2.2. Life narratives Seven most important memories and life narratives. At T1, partic- ipants wrote their seven most important memories on index cards

16 A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 and put them in chronological order on the table in front of them. M = 3.38, SD = 0.66 (range = 3.26–3.56) and for negative life events This had originally served to make sure that life narratives also M = 3.24, SD = 0.85 (range = 3.01–3.41). Hence, ratings of life narra- contained specific events and to reduce the memory load, espe- tives on average showed moderate scores of self-acceptance and a cially for younger participants. Here the task serves as a measure moderate amount of negative life events. of the stability of single important memories. Participants were then asked to narrate their life for about 15 min without being Quantitative Text Analysis. Further, life narratives were analyzed interrupted. Participants were instructed to include the seven most via quantitative text analysis by Linguistic Inquiry and Word Count important memories and to tell their life so as to explain how they (LIWC), in order to explore linguistic correlates with self-reported had become the person they are at present. Interviewers only and other-rated self-acceptance. Applying the latest installment of encouraged to continue, but asked no questions (for verbatim the software LIWC (Pennebaker, Booth, Boyd, & Francis, 2015), the instruction cf. Habermas & de Silveira, 2008). In contrast to other most recent dictionary translated into German (Wolf et al., 2008) is commonly used more interactive interview techniques eliciting a based on LIWC 2001. LIWC analyzes text on a word by word basis collection of specific event narratives (e.g., Life Story Interview, and calculates the percentage of words in the text that match up to McAdams, 1993), the collection of life narratives used by Habermas 72 lexical subcategories. These are summarized to four main cate- and colleagues (e.g., Habermas & de Silveira, 2008; Köber et al., gories including (1) standard linguistic categories (e.g., word count, 2015) elicits entire life narratives for which narrators are required articles), word categories tapping (2) psychological processes (e.g., to convey their development from the beginning to the present day affect, cognition), categories related to (3) relativity (e.g., time, in a clear coherent way to the listener by relating various life motion), as well as (4) personal concern categories (e.g. work, lei- events, episodes, and phases to each other as well as to personal sure activities). The categorization procedure is highly correlated development. Life narratives were tape recorded, transcribed ver- with that of trained judges which serves as an indicator for good batim, and analyzed in several ways: first, they were rated for external validity (Pennebaker, Booth, & Francis, 2007). self-acceptance and negative life events, and second they were analyzed via software for quantitative text analysis. Statistical Analysis. Descriptive, correlational, mediation and moderation analyses were executed with SPSS 23 (IBM Corp, Rating and coding of self-acceptance and negative life events. 2015). Path models were tested with Mplus Version 8 (Muthén & Three trained coders blind to the hypotheses rated the degree of Muthén, 1998, 2017) using maximum likelihood estimator (ML) perceived self-acceptance and the perceived amount of negative and full information maximum likelihood (FIML) estimation for life events in life narratives. They had to rate each life narrative missing data. Path models allow defining a priori the interplay with regard to different personality variables (e.g. self- (covariation or regression) between all variables in the model. acceptance, Big Five) and psychosocial cues (e.g., reported negative Additionally, given a defined model is non-saturated and identi- life events, frequency of social interaction) on 65 items with a fied, model fit can be estimated. A good model fit is shown by a Likert-type scale ranging from 1 (low) to 5 (high). Out of these rat- ings, we only used two items, a single-item to rate self-acceptance non-significant YB-v2 value (p > .01) as well as RMSEA 0.05 and another item for negative life events. The conception of self- and CFI ! 0.97, an acceptable model fit is available if v2-value/ acceptance as suggested by Ryff and Keyes (1995) served as anchor definition, thus being in line with the concept of self-reported self- df 3, RMSEA 0.08 and CFI ! 0.95 (see Schermelleh-Engel, acceptance. Accordingly, a ‘‘high scorer possess a positive attitude Moosbrugger, & Müller, 2003). We report 95% confidence intervals toward the self; acknowledges and accepts multiple aspects of self, for all coefficients. A-priori sample size analyses were performed including good and bad qualities; feels positive about past life/low by using the calculator provided by Soper (2018). A sample size scorer feels dissatisfied with self, is disappointed with what has of 57 to 104 was required to observe an anticipated moderate occurred in past life, is troubled about certain personal qualities, effect size f2 ranging from 0.13 (Flett et al., 2003) to 0.25 (Xu wishes to be different than what he or she is” (Ryff & Keyes, et al., 2016) of self-acceptance on mental health taking all predic- 1995, p. 727). Excerpts from life narratives that exemplify low tors of the hypothesized model (see Figs. 1a–1c) into account. and high self-acceptance ratings are presented in Appendix A. Because subjective ratings are prone to produce measurement 3. Results errors, reliability indices are useful tools to assess the amount of potential error in a given dataset. To account for differences 3.1. Congruence of self- and other-ratings of self-acceptance between items, intraclass correlations (ICC; Shrout & Fleiss, 1979) were obtained separately for each, and Fisher’s r-to-z for- Table 1 displays bivariate correlations of the target variables in mula consequently made coefficients converge to a normal distri- our study. Self- and other-rated measures of self-acceptance bution. By calculating the average of z-transformed correlation (r = 0.39, p < .001) and negative life events (r = 0.37, p < .001), coefficients, a more appropriate measure of interrater reliability respectively, were moderately (Cohen, Cohen, West, & Aiken, was obtained. The ICC (3, 1) estimates the consistency of the rating 2003) correlated indicating a good self-other rating agreement. by single judges, and the ICC (3, k) estimates reliabilities of the While self-acceptance and negative life events correlated nega- average coding by all three judges. This way, rater reliability is cal- tively in others’ ratings of life narratives (r = À0.30, p < .01 and culated more precisely by leveling out random errors (Gosling r = À0.31, p < .001), they did not correlate in the self-reports et al., 2002; Letzring, Wells, & Funder, 2006). We used a two-way (r = À0.09 and À0.06, p = n.s.). Mental health was positively associ- mixed effect model. Consensus in perceived self-acceptance across ated with self-reported (T1: r = 0.50, p < .001; T2: r = 0.31, p < .001) three observers is shown as mean single-rater intraclass correla- and other-rated self-acceptance (T1: r = 0.36, p < .001; T2: r = 0.22, tion, ICC (3,1) = 0.37, and average-rater intraclass correlation, ICC p < .05) at both measurement points. Self-reported (r = À0.24, (3,k) = 0.64. For perceived negative life events higher consensus p < .01) and other-rated negative life events (r = À0.25, p < .01) was reached, ICC (3,1) = 0.58 and ICC (3,k) = 0.80. Indicating differ- showed negative correlations with mental health only at T1. The ent degrees of assessment difficulty for the raters, average-rater correlation patterns between the variables remained similar even interclass for self-acceptance can be interpreted as good, and for after controlling for age effects (Table 1). negative life events as excellent (Back, Schmukle, & Egloff, 2008; Connelly & Ones, 2010; Hirschmüller et al., 2017). Judge’s ratings 3.2. Linguistic correlates of self-acceptance in life narratives averaged across all life narratives for self-acceptance was Table 2 (supplemental material) displays the exploratory results regarding the associations of self-acceptance with LIWC

A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 17 Self-acceptance T1 (S) .37*** [.25; .49] .39*** [.27; .50] .17* [.04; .29] Self-acceptance T1 (O) -.28*** Mental health T1 (S) [-.40; -.17] -.18* -.28*** [-.31; -.06] [-.40; -.17] NegaƟve life events T1 (S) .38*** -.04 [.27; .49] [-.18; .10] NegaƟve life events T1 (O) Fig. 1a. Path analysis of mental health on self-acceptance and negative life events with cross-sectional data (N = 149). S = self-report based on questionnaire; O = rated by others based on life narratives. T1 = first measurement point. Standardized path coefficients are reported, confidence intervals (CI) are displayed in brackets. Grey regression paths were not significant. Two-tailed, ***p < .001; **p < .01; *p < .05. Self-acceptance T1 (S) .26** [.11; .41] .39*** [.27; .50] .12 [-.04; .27] Self-acceptance T1 (O) -.28*** Mental health T2 (S) [-.40; -.17] -.15 -.28*** [-.29; -.00] [-.40; -.17] NegaƟve life events T1 (S) .38*** .00 [.27; .49] [-.15; .16] NegaƟve life events T1 (O) Fig. 1b. Path analysis of mental health on self-acceptance and negative life events with longitudinal data (N = 149). S = self-report based on questionnaire; O = rated by others based on life narratives. T1 = first measurement point; T2 = second measurement point 4 years later. Standardized path coefficients are reported, confidence intervals (CI) are displayed in brackets. Grey regression paths were not significant. Two-tailed, ***p < .001; **p < .01; *p < .05. categories. In terms of cue validity and cue utilization, positive and emotion words showed a positive link with self-acceptance negative emotion words were the only LIWC subcategories which (rS = 0.25, p < .01; rO = 0.17, p < .05), while negative emotion words correlated with self- and other-ratings of self-acceptance. Positive correlated negatively with self-acceptance (rS = À0.15, p < .05;

18 A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 Self-acceptance T1 (S) .39*** Self-acceptance T1 (O) -.02 [.27; .50] [-.16; .11] .44*** .02 [.34; .55] [-.15; .10] .32*** [.20; .44] -.28*** Mental health T1 (S) .68*** Mental health T2 (S) [-.40; -.17] [.58; .79] -.28*** -.01 [-.40; -.17] -.20** [-.13; .11] [-.31; -.08] .01 [-.12; .13] -.26*** NegaƟve life events T1 (S) [-.38; -.14] .38*** [.27; .49] NegaƟve life events T1 (O) Fig. 1c. Path analysis of mental health on self-acceptance and negative life events with longitudinal data controlling for mental health at T1 (N = 149). S = self-report based on questionnaire; O = rated by others based on life narratives. T1 = first measurement point; T2 = second measurement point 4 years later. Standardized path coefficients are reported, confidence intervals (CI) are displayed in brackets. Grey regression paths were not significant. Two-tailed, ***p < .001; **p < .01; *p < .05. Table 1 Correlation of mental health, life-events, and self-acceptance based on self-report questionnaires (S) and ratings of life narratives by others (O). 1. Self-acceptance (S) 1. 2. 3. 4. 5. 6. 2. Self-acceptance (O) 3. Mental health T1 (S) 0.39*** 0.42*** 0.53*** 0.33*** À0.05 À0.28** 4. Mental health T2 (S) 0.50*** 0.35*** 0.22* À0.12 À0.36*** 5. Negative life events (S) 0.31*** 0.36*** 0.68*** À0.31*** À0.30** 6. Negative life events (O) À0.09 0.22* 0.68*** À0.21* À0.18* À0.30** À0.06 À0.24** À0.17 0.32*** À0.31*** À0.25** À0.16 0.37*** Note: N = 149. T2 = second measurement point 4 years later. Correlation coefficients above the diagonal are partial correlation coefficients controlling for age. Two-tailed, ***p < .001; **p < .01; *p < .05. rO = 0.À19, p < .05). Taken together, the sum of positive and nega- between each self-acceptance measure (self- and other-rated) tive emotion words, showed higher correlations with self- and self-reported negative life events at zero. We tested three dif- reported self-acceptance than with other-rated self-acceptance ferent models: Model 1a represents a cross-sectional design (rS = 0.17, p < .05; rO = 0.08, p = n.s.). Other LIWC categories were including only measures at T1, in which self-acceptance and nega- significantly related to one of both assessment methods of self- tive life events were specified as predictors of mental health. Mod- acceptance or to none of both. Longer life narratives (r = 0.29, els 1b and 1c represent a longitudinal design. In model 1b, p < .01), the use of articles (r = 0.18, p < .05), longer words measures of self-acceptance and negative life events at T1 were (r = 0.19, p < .05), fewer negations (r = À0.18, p < .05), and fewer specified as predictors of mental health at T2. In model 1c, we swear words (r = À0.18, p < .05) were linked with higher other- extended model 1b by additionally controlling for mental health rated self-acceptance. Occupation-related words were also linked at T1. with higher other-rated self-acceptance (r = 0.21, p < .01). In terms of social processes, talking about friends was positively linked The overall model fit was excellent for all three models: model (r = 0.17, p < .05) with self-rated self-acceptance, while talking about family showed a negative correlation (r = À0.23, p < .05) 1a v2(2df) = 1.37, p = .51, RMSEA = 0.00, CFI = 1.00, TLI = 1.03, and with self-rated self-acceptance. SRMR = 0.03 with an explained variance of 26% (Fig. 1a); model 3.3. Predictive validity of self-acceptance for mental health 1b v2(2df) = 1.37, p = .51, RMSEA = 0.00, CFI = 1.00, TLI = 1.01, and In order to test the predictive validity of self-acceptance assessed via self- and other ratings for self-rated mental health, SRMR = 0.03 with an explained variance of 12% (Fig. 1b); model taking negative life events via self- and other ratings into account, we used path analysis (see Figs. 1a–1c with detailed statistics). Due 1c v2(2df) = 1.37, p = .51, RMSEA = 0.00, CFI = 1.00, TLI = 1.02, and to the non-significant correlation at a bivariate level (see Table 1; r = À0.09, p = .34 and r = À0.06, p = .50) and in order to gain an SRMR = 0.03 with an explained variance of 45% (Fig. 1c). Cross- identified and non-saturated model, we fixed the covariance sectional analyses depicted in model 1a revealed that both mea- sures of self-acceptance were positively linked with self-rated mental health. Self-rated self-acceptance showed higher path coef- ficients (b = 0.37, p < .001) than other-rated self-acceptance (b = 0.17, p < .05). Both measures were moderately correlated with each other in our path models (r = 0.39, p < .001), emphasizing that they are correlated, but distinct measures of self-acceptance. In model 1b, mental health assessed four years later at T2 was only

A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 19 predicted by self-rated self-acceptance at T1 (b = 0.26, p < .01). The the association of self-acceptance with quantitative linguistic fea- effect of other-rated self-acceptance based on life narratives was tures of life narratives, and (c) the predictive validity of self- no longer significant (b = 0.12, p = .21). When extending model acceptance (self- and other-rated) for mental health while control- 1b by additionally controlling for mental health at T1 as depicted ling for negative life events. in model 1c, only mental health at T1 turned out to be predictive (b = 0.68, p < .001). Neither self-acceptance measure, self-rated 4.1. Congruence of self- and other-ratings of self-acceptance (b = À0.02, p = .77) or other-rated (b = 0.02, p = .77), showed an effect on mental health at T2 four years later any more. Mental Turning first to the observability of self-acceptance in life narra- health at T1 was positively correlated with both measures of tives, correlation analyses revealed a moderate congruence of self- self-acceptance, self-rated (r = 0.44, p < .001), and other-rated reported and other-rated self-acceptance based on life narratives. (r = 0.32, p < .001). Further results with regard to negative life Self-reported self-acceptance showed higher correlations with events are displayed in Figs. 1a–1c. Significant associations at a self-reported mental health compared to other-rated self- bivariate level (cf. Table 1) between mental health at T2 and self- acceptance. Surprisingly, self-acceptance (self- and other-rated) acceptance as well as negative life events diminished in the path was linked with other-rated negative life events based on individ- analysis. The relations between mental health at T2 and level of uals’ life narratives, but not with self-ratings of negative life-events self-acceptance as well as reported negative life events at T1 were based on a check-list. This finding suggests that the plain number fully captured by mental health at T1. of negative life events is not directly linked with self-acceptance. However, the number of negative life events that were integrated Extended models with an interaction term of self-acceptance in an individual’s narrative seems to convey lowered self- with negative life events, separately for self-report (R2 = 0.27, F acceptance to observers. These findings were still true after con- (3,145) = 18.09, p < .05) and other-ratings (R2 = 0.17, F(3,145) trolling for age effects, corresponding to previous findings of no = 9.84, p < .05), corroborate the assumption of a buffering effect. age effects on self-acceptance (Ryff & Heidrich, 1997). Accordingly, self-reported (b = 0.06, CIL = 0.01, CIU = 0.11, b = 0.16, p < .05) and other-rated self-acceptance (b = 0.06, CIL = 0.00, Overall, results emphasize that each method for the assessment CIU = 0.11, b = 0.16, p < .05) turned out to weaken the association of self-acceptance captures specific variance of self-acceptance. between negative life events and mental health. The effect of each Although the manifestation of self-acceptance in behavior is rather interaction term reflecting the buffering influence of self- low, the validity of self-acceptance ratings by others was higher in acceptance was similar for both models, self- and other-ratings. our study compared to findings referring to other personality fea- However, similar to the previous path model, this was only true tures low in observability (Borkenau & Liebler, 1993; Hirschmüller for the prediction of mental health at T1. The significant interaction et al., 2017). Furthermore, the correlation between self- and other for self-report measures and ratings by others is depicted in Fig. 2. rated self-acceptance (r = 0.39) coincides with previous narrative In line with our expectations, high self-acceptance, whether self- studies comparing self-ratings and narrative codings of other vari- reported via questionnaire or other-rated based on life narrative ables (Grysman, Fivush, Merrill, & Graci, 2016; Panattoni & ratings, diminishes the detrimental effect of negative life events McLean, 2018; Waters, Bauer, & Fivush, 2014). This confirms that on mental health. Interaction effects on mental health four years retrieval processes employed for self-ratings and narrative con- later (T2) were not significant when taking mental health at base- struction overlap, which consequently points to narrative coding line (T1) into account. as an appropriate means for detecting personality features inde- pendently from questionnaires and for verifying self-ratings. Fol- 4. Discussion lowing these findings, future research might find an even stronger accuracy of self-acceptance ratings based on personal nar- The present study investigated (a) the congruence of self- ratives with well-acquainted individuals, which might be impor- reported and other-rated self-acceptance in life narratives, (b) tant for therapeutic or counseling settings in which the Fig. 2. Moderation effects of self-rated (S) and other-rated (O) self-acceptance on the association of negative life events and baseline mental health. The reported values are z- standardized. Degree of self-acceptance is categorized in standard deviation (SD): low (À1 SD), middle and high (+1 SD). N = 149.

20 A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 judgement of the target individual´s self-acceptance is highly rele- Further moderation analyses partially corroborated the buffer- vant for the treatment (Kannan & Levitt, 2013). Thus, a replication ing effect of self-acceptance on mental health. Since high self- of the study design with a clinical population as target individuals acceptance buffered the detrimental effect of negative life events and with psychotherapists as raters of patients‘ life narratives on mental health only at the cross-sectional level, as evidenced might be of particular interest for clinical researchers. in moderated regression analyses, the adaptive effect of self- acceptance seemed to apply only concurrently, but not in the long 4.2. Linguistic correlates of self-acceptance in life narratives run. With regard to the methodological assessment of self- acceptance, the explained variance was larger for the regression Besides self- and other-rated self-acceptance, this study more- model including predictors using self-report measure compared over suggests that emotion words are relevant and valid cues of to the model using ratings of self-acceptance based on life narra- self-acceptance. In our study, quantitative text analyses indicated tives. Nevertheless, it is still noteworthy that strangers’ ratings of that self-acceptance was positively associated with using positive self-acceptance and life events solely based on transcribed life nar- emotion words, whereas using negative emotion words was nega- ratives can be linked with a person’s self-rated mental health. It is tively linked to self-acceptance in life narratives. This is in line with also remarkable that the interaction terms (self- research, for instance, on post-traumatic growth or redemption, acceptance  negative life events) in both models, self-ratings which, taking different methods into account, emphasizes the con- and other-ratings, were of comparable size. nection between displays of positive and negative affect in life nar- ratives and self-concept (e.g. Jayawickreme & Blackie, 2014; 4.4. Conclusion Scrignaro, Marini, Magrin, & Borreani, 2018; Weston et al., 2016). Other LIWC categories, however, were only related to neither or Overall, our findings extend prior personality research in sev- only to one of both measures of self-acceptance, pointing to weak eral ways. First, we used a multi-method approach combining cue validity or cue utilization (Brunswik, 1956), respectively. While self-reports and life narratives as data source for determining this study is the first to examine self-acceptance via language anal- self-acceptance. Second, we demonstrated that not only narrative ysis, it remains to future research to replicate and extend our find- features such as coherence (Adler, 2012; Allé et al., 2015; Raffard ings. With progressing technology, there has been a growing et al., 2010), emotional tone (McAdams et al., 2004; Thomsen interest for language based analyses of mental states. A huge et al., 2014), meaning making (Banks & Salmon, 2013; Dunlop & amount of individuals’ data ranging from social media language Tracy, 2013a), narrative themes (Adler et al., 2016; Dunlop & to real-life behavioral data are accessible to researchers allowing Tracy, 2013b), and autobiographical reasoning (Habermas & them to predict features such as personality, socio-demographic Köber, 2015) contribute to mental health but also self-acceptance characteristics, mortality risk, and behavior (Eichstaedt et al., reported in questionnaires by narrators themselves and as observ- 2015; Schwartz et al., 2013) and might also be useful for assessing able in life narratives for others. The complementary effect of self- self-acceptance and mental health. To know linguistic covariates of and other-rated self-acceptance for the prediction of mental health self-acceptance and current mental health could be a helpful tool was larger in the cross-sectional analysis. Observed longitudinal for online-therapy sessions that use chat protocols (Tibubos, effects were weaker. Third, we provided some evidence, albeit Zwerenz, Brähler, & Beutel, 2018; Zwerenz et al., 2017) as commu- weak because of the exploratory nature of analyses, that single nication source in order to provide therapeutic guidance. emotional word cues in life narratives can be linked with individ- uals’ self-reported self-acceptance as well as its perception by 4.3. Predictive validity of self-acceptance for mental health strangers. In line with previous findings on the link between self- Our finding that self-acceptance relates to the way in which acceptance and mental health (Kivity et al., 2016; Xu et al., individuals integrate their negative life events into life narratives, 2016), bivariate analyses showed positive correlations of both which moreover protects against the detrimental effects of nega- self- and other-rated self-acceptance with mental health in terms tive life events on mental health, raises the question why self- of psychological well-being at T1 and four years later at T2. From acceptance has such benefit and if this is related to other narrative a methodological perspective, our study is the first using two dif- processes. One limitation of our study is the lack of control of time ferent methods, self-report and other-rated life narrative content, effects on the coping with negative life events because the timing for quantifying self-acceptance and relating it to mental health. of self-reported negative life events was not taken into account in Further, multivariate analysis via path model corroborated the pos- the current study. This might explain the non-stable correlation itive link of self-acceptance with mental health at T1 controlling between self-acceptance and negative life events. For instance, for negative life-events in a cross-sectional study design. However, only a significant link was found for self-acceptance and negative self-acceptance did not predict mental health at T2 in the specified life events rated by others because time effects could be extracted path model when taking mental health at T1 as control variable from life narratives, while time related information was com- into account. The effect of mental health at T1 on the link between pletely lacking at self-report level. This missing information might self-acceptance at T1 and mental health at T2 emphasizes the sta- have led to the non-significant link between self-report measures bility of mental health status over time. In sum, correlations of self-acceptance and negative life events. between self-report measures were higher than the link between self-rated and other-rated measures (e.g. self-rated self- Prior research shows that severe negative and traumatic life acceptance and self-rated mental health vs. other-rated self- events bear the potential to either impair mental health or to lead acceptance and self-rated mental health). Multivariate analysis in to positive consequences such as growth after adversity (Joseph & terms of path analysis corroborates the assumption that self- Linley, 2006), depending on the extent to which the negative event reports and other-ratings of life narratives are distinct complemen- becomes central to the life story (Boals & Schuettler, 2011). Indeed, tary methods. By integrating both approaches in one model, our when negative life events become very central for personal iden- study points out differential effects of information extracted from tity, individuals suffer more from the sequelae of adversities or self-report questionnaires and from ratings of life narratives by post-traumatic symptoms (Berntsen & Rubin, 2006, 2007; Rubin, strangers (Panattoni & McLean, 2018). Boals, & Berntsen, 2008). This detrimental effect is even intensified when individuals connect negative life events endorsed as central to identity to their selves by negative self-event connections (Banks & Salmon, 2013). Future research may thus further examine

A.N. Tibubos et al. / Journal of Research in Personality 79 (2019) 13–23 21 whether self-acceptance protects against negative meaning mak- came the marriage of my mother and her new man/ I was 10/ ing associated with psychological distress or stimulates positive and I remember an event/ which shows/ why I don’t want to call meaning making reducing psychological distress and instigating him stepfather/ there was a fragile wooden bridge across the river/ post-traumatic growth (Merrill et al., 2016). In the same vein, and it was real hot/ it was the summer of 2006/ I still remember self-acceptance might shield individuals from ruminating and from that/ and he carried me across the bridge/ I will never forget that/ granting negative life events a central place in their life story and ‘cause I had simply wanted to play on the other side of the river./ identity. Thus, future research is called to identify and disentangle He was so good-hearted./ He did everything for me,/ no question this possible interdependence of self-acceptance with event cen- about it” trality, meaning making, and mental health. Appendix B. Supplementary material This study took a first step by providing narrative data on the health-related effect of self-acceptance, thus extending the theo- Supplementary data to this article can be found online at retical framework of the lens model by a narrative dimension https://doi.org/10.1016/j.jrp.2019.01.003. and by additionally taking into account mental health as external criterion. Cross-sectional and bivariate analyses clearly demon- References strated the complementary effect of self-reported self-acceptance via questionnaire and self-acceptance extracted from life narra- Adler, J. M. (2012). Living into the story: Agency and coherence in a longitudinal tives on mental health. 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