Major Life Events and Changes in Prosocialness and Empathy
Creators: name: Fassbender, Ina
name: Haehner, Peter
name: Buecker, Susanne
name: Luhmann, Maike
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Anonymous preregistration for the study “Changes in prosocialness and empathy associated with characteristics of major life events” Research Questions Prosocialness is described as intentional and voluntary actions aimed at benefitting others (Caprara, Steca, Zelli, &amp; Capanna, 2005). It combines different behaviours such as caring, comforting, donating, helping, and sharing (e.g. Eisenberg, Fabes, &amp; Spinrad, 2006). In young adulthood, independence from parents increases and (in Western societies) individuals depend more strongly on their peer relations. Peer relations are more supportive, the more empathetic and socially competent individuals are and the more prosocial goals they have (Eisenberg, Morris, McDaniel, &amp; Spinrad, 2009). Thus, acting prosocially (which can be costly) results in social advances for an individual. Empathy is described as the ability to perceive and to feel others’ thoughts and feelings (Hoffman, 2010). Some authors consider empathy to motivate prosocialness (A. N. Davis, Martin-Cuellar, &amp; Luce, 2019, Grühn, Rebucal, Diehl, Lumley, &amp; Labouvie-Vief, 2008, Sze, Gyurak, Goodkind, &amp; Levenson, 2012), others consider empathy to have various prosocial correlates (Allemand, Steiger, &amp; Fend, 2015), or to form an integral part of prosocialness (Caprara et al., 2005). Either way, prosocialness and empathy are interwoven elements of personality. From childhood to adulthood, and especially in adolescence and young adulthood, both prosocialness and empathy are increasing, and women score higher in both than men (Luengo Kanacri, Pastorelli, Eisenberg, Zuffianò, &amp; Caprara, 2013, O'Brien, Konrath, Grühn, &amp; Hagen, 2013, Oh, Chopik, Konrath, &amp; Grimm, 2019, Silke, Brady, Boylan, &amp; Dolan, 2018). There is also evidence for prosocial behaviour to increase shortly after traumatic MLEs (Frazier et al., 2013) and for empathy to be a potential growth outcome after traumatic MLEs (Staub &amp; Vollhardt, 2008). An open question is whether MLEs need to be traumatic to have an impact on prosocialness and empathy. Indeed, a recent meta-analysis suggests that positive personality changes are also possible after positive events (Mangelsdorf et al., 2019). Furthermore, other characteristics of the MLE, for example whether it was predictable or controllable, might also affect the impact of an MLE on prosocial behaviours and empathy. In this study, we examine whether and which characteristics of MLEs are associated with changes in prosocial behaviours and empathy. We explore changes in prosocialness and empathy up to nine months after an MLE. The MLE is assessed with the Event Characteristics Questionnaire (Luhmann, Fassbender, Alcock, &amp; Haehner, 2020) in which participants describe a recently experienced MLE on nine continuous dimensions: challenge, emotional significance, extraordinariness, impact, external control, valence, social status change, predictability and change in world views. Research Question 1: How is the perception of an MLE’s valence associated with changes in prosocialness? Based on findings from the posttraumatic growth literature (e.g. A. N. Davis et al., 2019, Jayawickreme &amp; Blackie, 2014), we may assume that experiencing an adverse MLE and recovering from this adversity increases an individual’s disposition to help others in a similar situation. Luhmann et al. (2020) found life satisfaction and mood to drop after an adverse MLE and to constantly increase over the following 40 weeks, whereas for positive MLEs, both life satisfaction and mood were at a higher level and also generally increasing. We analyse possible changes in prosocialness in young adults after experiencing adverse and positive MLEs. Research Question 2: How is the perception of an MLE’s valence associated with changes in empathy? Lim and DeSteno (2016) found the severity of an event’s adversity to be related to an increase in empathy (dispositional compassion). The authors also assessed the time since the event (from 1 to ‘more than 12 months’) but did not find time-related changes in empathy. We expect similar changes in empathy after an adverse MLE (low valence) thereby exploring the effect of time (between zero days and nine months) since the event. Regarding positive events (high valence), we plan to explore changes in empathy between zero days and nine months after having experienced an MLE. Research Question 3: Are the event characteristics assessed with the other ECQ dimensions associated with changes in prosocialness? As there is no theory regarding the other eight ECQ dimension’s influence on prosocialness, we explore possible prosocialness changes for the other eight ECQ-dimensions challenge, change in world views, emotional significance, external control, extraordinariness, impact, predictability and social status change. Our aim is to identify the characteristics of MLEs which provoke changes in young adults’ prosocialness. Research Question 4: Are the event characteristics assessed with the other ECQ dimensions associated with changes in empathy? Similarly to Research Question 3, we explore possible changes in empathy for the other eight ECQ-dimensions. Our aim is to identify the characteristics of MLEs which provoke changes in young adults’ empathy between zero days and nine months since an MLE. Gender differences A lot of studies found empathy to be higher in females than in males from adolescence on. Allemand et al. (2015), for instance, report that at twelve years of age, girls are more empathetic than boys (intercept differences) and that these differences seem to be stable until adulthood. Slope differences in empathy were related to higher perceived social integration and higher communication skills. In their review, Silke et al. (2018) found empathy and prosocial behaviour to be higher in female than male adolescents in 115 out of 168 studies. Caprara et al. (2005) report on gender differences in empathy: Women scored higher on items of the Prosocialness Scale (Caprara et al., 2005) referring to empathy, while men scored higher on items assessing immediate assistance or action. To account for possible gender differences in changes in empathy and prosocialness after MLEs, we include participant gender in our analyses (for details, see Analyses section). Hypotheses and/or Estimates As there is no theory regarding the association between event characteristics and prosocialness and empathy, particularly with respect to the nine dimensions provided in the ECQ, the analyses for all four research questions are exploratory. For our planned statistical procedures, see Analyses section. Data description Brief description of data set: Data were collected for the project “What are the Ingredients of Adversity? Examining Characteristics of Adverse Events as Predictors of Character Growth”, which is part of the Pathways to Character Project (www.pathwaystocharacter.org). The longitudinal study entitled “What’s NEXT? Dein Leben in einer neuen Phase” assessed different facets of young adults’ character at five measurement points within one year (i.e. every three months), together with description of an MLE experienced zero days to three months ago at each measurement point. The MLEs were rated using the ECQ (Luhmann et al., 2020). The study design preregistration can be found at https://osf.io/kvf5g/. The ECQ items are listed on pp. 9-11. We use ECQ data collected at T3 (for an MLE that occurred between T2 and T3). For our empathy measure, the subscales Empathic Concern and Perspective Taking of the Interpersonal Reactivity Index (IRI-EC and IRI-PT) (M. H. Davis, 1983; Koller &amp; Lamm, 2015) as well for our prosocialness measure, the Prosocialness Scale (PS) (Caprara et al., 2005, German short form by Fassbender &amp; Luhmann, 2020), we use data from all five measurement points, thus obtaining two pre-event measures of the outcomes and three post-event measures (prospective longitudinal data). Are this data open or publicly available? No, the data are not publicly available, but a dataset will be made available to the reviewers through an anonymized link. With acceptance of the manuscript for publication, the data used for the analyses along with a codebook and the R-scripts used will be made publicly available. How can the data be accessed? Before publication of a manuscript, the dataset can only be assessed through members of the project team “What’s NEXT? Dein Leben in einer neuen Phase”. Upon manuscript submission, data, a codebook and R-scripts will be provided to reviewers through an anonymized link. Date of access: Data collection started on July 24th, 2018 and finished on October 28th, 2019. Data collection was implemented in Qualtrics. The data from the five measurement points were merged into longitudinal data sets (wide format and long format) in November 2019. Data source: The data were collected by Fassbender and Luhmann, Ruhr-University Bochum. Codebook: For the variables used in this study, a codebook will be made publicly available in an online repository for review and permanently once the manuscript is published. Sampling and data collection procedures: The sampling and data collection procedures are described in the study design preregistration (https://osf.io/kvf5g/). The data collection periods were longer than originally planned (T1 from July 2018 until October 2018 – with the respective repeated measures every three months). Possibly because of summer vacations, recruitment had been slower than expected in July and August 2018. Consequently, the registration for the study was open until October 2018, leading to an (unproblematic) overlap of assessments of different measurement points (e.g. in October 2018, Person A was assessed for the second time and Person B for the first time). In all other aspects, including the raffles and the individual feedback, the procedures were maintained as described in the study design preregistration. Knowledge of data Prior work based on the dataset: The longitudinal data set was not prepared before November 2019. For data scoring, two student assistants categorized all events we had asked the participants to name in an open answer field (listed on p. 9 of the study design preregistration at https://osf.io/kvf5g/) before filling out the ECQ for the event named. Although the event categories found from the open answers are not part of the analyses preregistered here, they were found by working with the dataset on a qualitative level. Partial data from waves 1 to 3 were used to present some preliminary results from the ongoing project at a conference: BLINDED FOR ANONYMIZATION Only participants with complete data sets on the following variables were included; all items with references to the original scales can be found in the study design preregistration (https://osf.io/kvf5g/): - Mean scores of gratitude from waves 1 to 3 - Mean scores of PS from waves 1 to 3 - Mean scores of the ECQ scales valence and impact from wave 2. - Life domains the participants had checked for the event described An overview on the life domains most frequently mentioned was given, and the development of gratitude and prosocialness as functions of time, time x valence, time x impact and time x impact x valence were calculated using multilevel models and the R packages psych (Revelle, 2018), lmerTest (Kuznetsova, Brockhoff, &amp; Christensen, 2017) and car (Fox &amp; Weisberg, 2019a). The following data were used for manuscripts submitted to journals or published: Data of the six prosocialness items from wave 1 (only if participants had answered all items) and demographic information (gender, age and education) were used in BLINDED FOR ANONYMIZATION. The following analyses were calculated: - classical test theory item analyses using the R packages psych (Revelle, 2018) and sjPlot to gain information on item difficulty and item discrimination characteristics - internal consistencies - confirmatory factor analyses using the R-package lavaan (Rosseel, 2012) In BLINDED FOR ANONYMIZATION, besides demographic information for sample description (age, gender and education), the following data were used: - Mean scores of the nine ECQ scales from wave 2. - Data on subjective wellbeing (Satisfaction with Life Scale and Scale of Positive and Negative Experiences, references see https://osf.io/kvf5g/) from all five waves. The analyses were based on multilevel growth curve models to explore changes in subjective well-being after a major life event described on the nine ECQ dimensions. Prior research activity: Some of these variables were used before, namely data from waves 1 to 3 out of 5 waves (see previous section). Prior knowledge of the current dataset: Apart from the analyses mentioned above, we have no prior knowledge of the dataset we will analyse here. Moment of preregistration: Registration after data cleaning, but before any main analyses Variables Manipulated variables: not applicable. Measured variables: All variables assessed in the study “what’s NEXT?” are listed in the design preregistration (https://osf.io/kvf5g/). Apart from demographic background variables (age, gender, education), we use the items and answer options as listed at https://osf.io/kvf5g/ for the following questionnaires: - all nine subscales of the ECQ (Luhmann et al., 2020) at T3 - Prosocialness Scale (Caprara et al., 2005, German Short form by Fassbender &amp; Luhmann, 2020) from T1 to T5 - IRI-EC and IRI-PT (Koller &amp; Lamm, 2015) from T1 to T5 We will use mean scores as scale scores. Items: We provide a detailed overview in the items used for our analyses in the pdf-version of this preregistration (https://osf.io/u6x93/?view_only=54c9daf5ba1b446990c760729463bfbf) which we uploaded separately due to limitations in the formatting options in this preregistration page. Indices: Not Applicable Transformations: We will use grand-mean centring for the ECQ-subscales and standardisation of the dependent variables on the T2 mean and SD (for details see statistical models). Data inclusion/exclusion: All participants who responded correctly on two instructed response items and who answered all items of the ECQ at T3 and all items of PS and IRI-EC and IRI-PT at least at T2 and T3 (to obtain one pre-event measure and one post-event measure) will be included. Scale scores will only be calculated if all items of a (sub-)scale were answered. We will use data from all five waves to explore changes in empathy and prosocialness before and after a life event that occurred between the second and the third assessment. Note that participants first registered for the study and could either subsequently start with the T1 questionnaire or decide to receive an email reminder to fill out the questionnaire within a week. Independently of whether they had participated at a previous assessment, registered participants were invited to subsequent assessments unless they unsubscribed from the invitation emails. Consequently, there can be participants at T3 who did not answer the T1 or the T2 questionnaire, or neither. Thus, although we plan to use data from all waves, we will restrict the sample to those who participated at both T2 and T3 to ensure they reported an MLE no longer than 3 months ago at T3 (because we asked to report “since your last participation three months ago”). Participants who report earlier events will be excluded. Outliers: Not Applicable Weights: Not Applicable Sample size: The sample sizes after exclusion of participants with wrong answers on the instructed response items are N = 706 at T1, N = 542 at T2, N = 469 at T3, N = 425 at T4 and N = 411 at T5. We do not yet know the final sample sizes after applying the exclusion criteria mentioned in the previous point. Given that the drop from T2 to T3 is about 10% and assuming that another 10% might have left out single relevant items or might not have participated at both T2 and T3, we expect sample sizes of about 420 participants for our analyses. Missing data We will calculate (sub-)scale scores only if all items of the relevant scale were answered. All our research questions analyse changes in PS, IRI-PT and IRI-EC after an MLE reported at T3, i.e. experienced between T2 and T3. Analyses Statistical models All our research questions will be addressed exploratively. We have no hypothesis regarding the (longevity of) changes in prosocialness and empathy after experiencing MLEs assessed with the nine ECQ dimensions. We will analyse changes in PS, IRI-EC and IRI-PT with separate multilevel growth curve models with assessments nested within participants. Similarly to Luhmann et al. (2020), time will be represented with (1) a dummy variable coded 1 at T2 and 0 in all other occasions and (2) a linear variable reflecting the number of weeks since the MLE. With that, the intercept of the models represents the level of all three dependent variables (PS, IRI-EC and IRI-PT) when the MLE occurred. The fixed effect for the dummy variable will reflect sudden mean-level changes in the dependent variables between T2 and the MLE occurrence, and the fixed effect for the linear variable will reflect average linear weekly changes in the dependent variable after the MLE. We will try to model these two time variables with random slopes. If models do not converge, we will use fixed slopes. In addition, we will explore whether including a quadratic time variable (linear time variable ^2) improves the model fit significantly. If it does not or if the model including the quadratic variable does not converge, this variable will not be included in further analyses. For all ECQ subscales, we will estimate simple models with the two time variables as Level-1-predictors, the respective ECQ subscale (valence for Research Questions 1 and 2; all other eight subscales for Research Questions 3 and 4) as Level-2-predictors, and cross-level interactions between each time variable and the respective ECQ subscale. The models will be expanded adding gender, age and the remaining ECQ subscales as covariates. Model fits will be compared using χ² deviance tests. Fixed effects will be tested using t-tests with Satterthwaite-approximated degrees of freedom implemented in the R package lmerTest (Kuznetsova et al., 2017). Significant interactions will be followed up using the emmeans package (Lenth, 2020). The dependent variables (PS, IRI-EC and IRI-PT) will be standardized on the T2 (immediate pre-event measure) mean and standard deviation. The ECQ subscales (level-2 predictors) will be standardised on the grand mean and standard deviation, unless the mean of an ECQ subscale deviates more than 0.5 scale points from the scale midpoint (i.e. less than 2.5 or more than 3.5 on the PS and IRI five-point scales). In this case, we will centre the respective subscale on the scale midpoint and the T2 standard deviation to facilitate interpretability of the simple slopes estimated to follow up significant cross-level interactions. We will fit the model using the R packages lmerTest (Kuznetsova et al., 2017), test interaction effects with the R package car (Fox &amp; Weisberg, 2019b) and estimate simple slopes using the R package emmeans (Lenth, 2020). To explore whether women’s and men’s PS, IRI-PT and IRI-EC change differently after an MLE, we will test cross-level interactions between the time variables and gender with age as a covariate. Follow-up analyses: not applicable. 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