Search

We wish to explore the relations between countries political situation (in particular whether a given country is in peace or in conflict) and the citizens’ COVID-19 responses, beliefs about one’s own and others’ vulnerability to COVID-19, as well as national identification and conspiracy beliefs about COVID-19. We will perform these analyses using existing datasets.

Background The proposed study is part of a larger project, in which we examine the emotional antecedents of a largely understudied form of intergroup harm in conflict, harm through inaction, which is carried out by ignoring the out-group's existence, needs and contributions, and avoiding cooperation and communication. Our previous findings across five studies suggest that group-based contempt towards the rival in conflict is a key predictor of harmful inaction, compared to other negative intergroup emotions such as anger, disgust, hate, and fear. The proposed study extends the findings of the previous studies in three ways: First, while in our previous studies, group-based emotions were either measured or manipulated indirectly based on the method of imagined responses to criteria-based scenario simulations, the proposed study will directly manipulate a first-hand experience of the examined emotions. Second, while our previous studies consistently demonstrate the unique association between contempt and harmful inaction compared to fear, anger, and hate, the effects of group-based contempt and disgust on harmful inaction were less clearly distinguishable. To provide more evidence for the unique association between group-based contempt and harmful inaction, as compared to disgust, the proposed study will focus on comparing the effects of these two group-based emotions. Third, our previous studies focused on group-based contempt by a higher-powered group towards a lower-powered group, or between groups of equal power. The proposed study will examine the unique association between contempt and harmful inaction (compared to disgust), among members of a lower-powered group (i.e., Jewish-Israeli ultra-orthodox) towards members of a higher-powered group (i.e., Jewish-Israeli seculars). Design In this experimental study, participants will first complete a demographic questionnaire (including a scale of religious conservatism, which will be considered as a potential moderator – see below), and will then be randomly assigned to one of two groups. In the “contempt” condition, they will be presented with the following text: “The feeling of “contempt” towards a certain group is based on the perception of one’s in-group as superior to that group in some domains (for example, modesty, spirituality, investment in children). We ask you to recall and describe an event or situation which made you feel contempt towards seculars”. In the “disgust” condition, they will be presented with the following text: the feeling of “disgust” towards a certain group is based on the perception of that group as repulsive to us, as if it poses it may trigger discomfort for whoever comes in contact with it. We ask you to recall and describe an event or situation which made you feel disgust towards seculars”. After completing manipulation checks (see below), participants will rate their support for harmful action and harmful inaction towards Jewish-Israeli seculars (see below). Finally, participants will rate their agreement on two further moderating items (these moderators will be measured at the end of the questionnaire, to avoid priming beliefs that may influence participants’ ratings of their support for harmful inaction; see below). Sample and Sample Size: Participants will be self-defined Jewish-Israeli ultra-orthodox adults (aged 18 and up), who will be recruited using a professional survey company. Based on our preliminary findings, we calculated the sample size needed for detecting a medium-sized effect in a MANOVA (2 dependent variables, one independent variable with 2 groups), based on standard alpha (.05) and 90% power, yielding an estimate of 252 participants. As participants will be filtered out of the initial sample based on exclusion criteria (see below), we aim to recruit 300 ultra-orthodox participants. Exclusion criteria: Participants who do not self-identify as ultra-orthodox in the demographic questionnaire will be excluded from participation in the study. Furthermore, participants who will not comply with the manipulation instructions (will not describe an event in which they felt contempt/disgust towards Jewish-Israeli seculars, e.g., people who mention other emotions, or describe an event in which the core mentioned appraisals fit more with emotions other than contempt), will be excluded from further analyses. Finally, participants who fail a concentration check, or take an irregular time (+/- 2.5 SD) to complete the survey, will be excluded from data analysis. Measured Variables: Moderators: (1) religious conservatism versus modernism (1=modernism, 7 = conservatism); (2) agreement with the phrase “all of Israel is responsible for one another” [considered to be a basic principle for ultra-Orthodox Jews ]; (3) agreement with the belief that seculars are “captured infants” [A Talmudic concept referring to infants that refers to a Jew who sins inadvertently as a result of having been raised without an appreciation for the thought and practices of Judaism, and therefore should not be blamed for their sins. To this day, the captured infant remains one of the dominant Halakhic paradigms through which observant Jews engage their seculars ]. Moderators 2 and 3 will be measured at the end of the questionnaire, to avoid priming beliefs that may influence participants’ ratings of their support for harmful inaction. Manipulation checks: Participants will rate the extent to which they feel contempt and disgust towards Jewish-Israeli seculars, on a scale ranging from 1 (=strongly disagree) to 7 (“strongly agree”). Dependent variables: Participants will rate their support for harmful action and harmful inaction towards Jewish-Israeli seculars on a scale ranging from 1 (=strongly disagree) to 7 (“strongly agree”). First, participants will rate their support for harmful action/inaction in the general context of orthodox-secular relations, using items similar to the ones we used in our previous studies. Next, participants will rank their support for harmful action/inaction in the context of the COVID pandemic. The specific context of the pandemic is particularly relevant for our study because during the pandemic, the orthodox- secular relations were characterized by increased conflict centering around apparent reluctance of some ultra-orthodox communities to obey the secular government’s COVID regulations. Harmful action/inaction items will be presented in pairs, representing comparable content. The pairs under each topic (general/COVID-related) will be presented in random order: • General harmful inaction/action: 1. “The best way to deal with seculars’ attempts to intervene in matters of our community is to ignore them and continue with our ways, even if it hurts them”/ “The best way to deal with seculars’ attempts to intervene in matters of our community is to respond with force to any intervention attempt” 2. “There is no point in reacting to seculars’ requests to cooperate with us on matters that are not relevant for our community”/“We should invest all efforts to prevent cooperation with seculars on matters that are not relevant for our community” 3. “The best way to prevent secular influence on ultra-orthodox in mixed neighborhoods is to behave with seculars as if they do not exist”/”The best way to prevent secular influence on ultra-orthodox in mixed neighborhoods is to expel the seculars from our communities” 4. “'The best way to prevent personal relations between ultra-Orthodox and seculars from neighboring communities is to simply not react to seculars’ attempts to encourage the forming of such relations”/ “The best way to prevent personal relations between ultra-Orthodox and seculars from neighboring communities is to operate with force and involve Rabbis to prevent this” 5. “When we discuss matters of religion and state in the ultra-Orthodox media, we should refrain from mentioning the secular view point”/” When we discuss matters of religion and state in the ultra-Orthodox media, we should forcefully attack the secular view point” • COVID-related harmful inaction/action: 1. “To deal with the COVID crisis, the ultra-Orthodox community should develop its own regulations that uniquely fit it – it does not need secular ideas”/ To deal with the COVID crisis, the ultra-Orthodox community should fight the secular government to prevent it from enforcing its regulations in our community” 2. “The ultra-Orthodox should ignore the governmental COVID regulations that do not fit our community”/” The ultra-Orthodox should vigorously protest in demonstrations against governmental COVID regulations that do not fit our community” 3. “There is not point or need to invest effort in finding common solutions with the seculars to the pandemic”/ “The ultra-Orthodox should prevent cooperation with the seculars to find common solutions to the pandemic” 4. “When seculars criticize us about observing the COVID regulations, we should ignore them and act as if we don’t hear them”/ When seculars criticize us about observing the COVID regulations, we should attack them back forcefully” Total scores will be calculated by averaging the harmful action items and harmful inaction items separately, for each sub-set (general/COVID-related), resulting in 4 total scores. As an exploratory extension of our previous studies, participants will also be asked to complete bi-polar scales for each pair of harmful action/inaction items, in which harmful action and harmful inaction will be presented at two opposite poles (1=harmful inaction, 7=harmful action). Scores on these bi-polar scales will also be averaged in each sub-set of items (general/COVID-related), resulting in 2 total scores in which higher scores represent support for harmful action over support for harmful inaction. Analyses and hypotheses: Manipulation checks: We hypothesize that perceived contempt will be higher in the contempt compared to the disgust condition, whereas perceived disgust will be higher in the disgust compared to the control condition. To test this hypothesis, we will conduct a MANOVA with perceived disgust and contempt towards seculars as the dependent variables, and the condition (disgust/contempt) as the predictor. Main analyses and predictions: (H1) Harmful action versus inaction: We will conduct a factor analysis for the harmful action and harmful inaction items using principal component analysis with direct oblimin rotation. We hypothesize that harmful action items and harmful inaction items will represent independent constructs. (H2) Effects of emotions on harmful action and inaction: We will conduct two MANOVA analyses, one for each topic (general/COVID-related), with the harmful action and harmful inaction scales as the dependent variables, and the condition (disgust/contempt) as the predictor. In both analyses, we hypothesize that (a) participants in the contempt condition will express higher support for harmful inaction than participants in the disgust condition. With regard to harmful action, we put forth two exploratory alternative hypotheses, based on our previous studies: (b.I) participants in the disgust condition will express higher support for harmful action than participants in the contempt condition; or (b.II) participants in the disgust condition will express similar support for harmful action to participants in the contempt condition. The use of MANOVA analyses is intended to reduce the inflation of type I error. Exploratory analyses and predictions: (H3) Effects of emotions on bi-polar harmful action/inaction scales: We will conduct two ANOVA analysis, one for each topic (general/COVID-related), with the bi-polar scale as the dependent variable, and the condition (disgust/contempt) as the predictor. In both analyses, we hypothesize that participants in the contempt condition will score lower on the bi-polar scales (i.e., prefer harmful inaction over harmful action), compared to participants in the disgust condition. (H4) Moderators: We will conduct multiple regression analyses to examine the interactive effects of condition (contempt/disgust) and each of the three moderators on all our dependent variables. We hypothesize that the effects described in H2a and H3 may be weaker among those who (a) rate themselves as more modern on the religious conservatism versus modernism scale, (b) report stronger agreement with the phrase “all of Israel is responsible for one another”, (c) report stronger agreement with the belief that seculars are “captured infants”

The goal of this study is to better understand the behavioral expressions of mothers’ motivation for their child’s level of intergroup empathy, and how it changes according to their political ideology, as well as its effect on the child. One-hundred and Forty-one Jewish-Israeli mother-child dyads participated in this study. Following a short questionnaire, mothers were asked to read a short empathy-inducing vignette about a woman who lost her house in a fire. In the experimental ”conflictual outgroup” condition, mothers read about an old woman named Jamila from Beit-Jalla. According to the vignette, the Israeli army suspected there are weapons concealed in her house and bombed it, only to later find out that it was a mistake and that there were no weapons in her house. In the “control outgroup” condition, mothers read about a Greek woman whose house was burnt down due to a fault in the heating system. After reading the vignette, mothers reported how much empathy they want their child to feel towards the woman they read about. Then, mothers were asked to sit for 5 minutes with their child for a non-guided videotaped conversation about the story she read. Mothers were instructed they have up to five minutes to talk to their child, and that they can choose what and how to tell their child. Then, mothers reported their feedback regarding the conversation, and children reported how much empathy they feel towards the woman their mother told them about, as well as their willingness to help the woman. Finally, mothers’ behavior and language were coded for up- and down-regulation empathy tactics by coders.

Empirical study

In the current study, we leverage previously collected data showing an increase in positive attitudes towards Burmese minorities in the aftermath of the February 2021 junta (in which the military took over political control of the democratically elected government) to develop a descriptive and trending norms intervention to further improve attitudes towards these groups. We aim to improve majority group members’ support for minority rights with our intervention. While our experiment will be run on different religious groups in Burma, we will focus on the Buddhist sample for our analyses, as they form the majority group in that country (Fagan, 2018). During the descriptive norm part, the receiver starts with estimating the extent to which a certain attitude or behavior is normative in one’s group. Then, the receiver is presented with the actual norm of the group regarding this attitude or behavior, which may be more favorable than estimated by the receiver. In this condition, the gap between one’s perception and the actual norm will attempt to sway tolerance levels and willingness to openly support minority rights. We assume that large parts of the Burmese population underestimate the favorability of other group members’ orientations towards minorities and the extent to which it has improved over the past year. The information on these attitudes and behaviors will be taken from our previously collected data. The gap between the individual’s perception of the group norm and the actual group norm is highlighted, potentially improving one’s orientation towards the outgroup. Besides descriptive norms, trending norms can also be efficacious to promote more favorable attitudes towards minorities (Mortensen et al., 2019). Trending norms show the extent to which attitudes of their group have changed over the recent history (Mortensen et al., 2019), thereby aiming to generate similar increases for the receiver of the intervention. In our intervention, for each of the descriptive norms, they will also be shown how the norm has changed over the past year. The trend is a psychological mechanism that will attempt to sway tolerance levels and willingness to openly support minority rights.

The proposed study is part of a larger project, in which we examine the emotional antecedents of a largely understudied form of intergroup harm in conflict, harm through inaction, which is carried out by ignoring the out-group's existence, needs and contributions, and avoiding cooperation and communication. Our previous findings across six studies suggest that group-based contempt towards the rival in conflict is a key predictor of harmful inaction, compared to other negative intergroup emotions such as anger, disgust, hate, and fear. The proposed study extends the findings of the previous studies in two ways: First, while in most our previous studies, group-based emotions were either measured or manipulated indirectly based on the method of imagined responses to criteria-based scenario simulations, the proposed study will directly manipulate a first-hand experience of contempt. Second, most our previous studies focused on group-based contempt by a higher-powered group towards a lower-powered group, or by a lower-powered group towards a higher-powered group. The proposed study will examine the unique association between contempt and harmful inaction (controlling for other negative emotions), between groups of equal status: leftists and rightists in Israel. This intergroup context is particularly relevant to the study of negative intergroup emotions. In the past two years, the relations between leftists and rightists in Israel have been particularly tense, with Israel facing four national elections in two years. During that period, public discourse was characterized by continuously escalating expressions of negative emotions between citizens who identify as leftists and those who identify as rightists.

1. Title Systematic review and meta-analysis of intergroup interventions to reduce violent intergroup conflict and promote post-conflict reconciliation 2. Keywords Conflict reduction; intergroup conflict; intergroup intervention; personalization parameters; peace; meta-analysis 3. Review & meta-analysis milestones Completed tasks (completion date): Defining main terms & goals of the search (February 1, 2021) Creating a reference set (April 30, 2021) Study inclusion/exclusion criteria and design (June 15, 2021) Creating a list of search terms (July 7, 2021) Systematic Search (July 29 & August 30, 2021) Planned tasks (planned completion date): Screening & data Extraction (November 30, 2021) Quality Assessment of Data; Analysis of Data (December 31, 2021) Project Write-Up (February 28, 2022) 4. Stage of review at time of this submission Preliminary searches have been completed, the main database search has been conducted, and the dataset has been manually de-duplicated. No screening or analysis of the dataset has been conducted. 5. Research team PI: Dr. Anna Stefaniak, anna.stefaniak@carleton.ca, Carleton University Prof. Eran Halperin, eran.halperin@mail.huji.ac.il, Hebrew University of Jerusalem Renana Atia, renana.atia@mail.huji.ac.il, Hebrew University of Jerusalem Boaz Hameiri, bhameiri@tauex.tau.ac.il, Tel Aviv University Shira Hebel, shira.hebel@mail.huji.ac.il, Hebrew University of Jerusalem Nimrod Nir, anisker@gmail.com, Hebrew University of Jerusalem Anat Ruhrman, anat_ruhr@yahoo.com, Hebrew University of Jerusalem Deborah Shulman, dshulman23@gmail.com, Hebrew University of Jerusalem Eric Shuman, eshuman1650@gmail.com, Hebrew University of Jerusalem Meghan M. Testerman, mtesterman@princeton.edu, Princeton University Daan Vandermeule, daan.vandermeule@mail.huji.ac.il, Hebrew University of Jerusalem 6. Conflicts of interest Members of the research team declare that they have no conflict of interests. 7. Review question Main question: Are intergroup interventions, that is evidence-based actions aimed at reducing intergroup conflict and hostility (see Halperin & Schori-Eyal, 2020), effective at promoting peace in ongoing violent intergroup conflict and post-conflict societies? Secondary question: Are there moderators of the effectiveness of intergroup interventions to promote peace? We are particularly interested in moderators that can constitute personalization parameters (as described in Halperin & Schori-Eyal, 2020), that is person-level characteristics that determine for whom a given type of intervention is most (or least) effective. Examples of personalization parameters include political orientation, personality, and dominant emotional reactions to conflict (see Halperin & Schori-Eyal, 2020). 8. Searches Database Searching • We will search the following databases: APA PsycINFO (EBSCO) and Science Citation Index Expanded, Social Sciences Citation Index and Arts and Humanities Citation Index (Web of Science), ProQuest Dissertations & Theses Global • The search will be conducted in the title and abstract fields of the databases. • There will be no limitation on publication date or language in the search query although where appropriate subject categories may be used to limit the search results. • Exact search syntax, number of results, and date of search will be provided. • An additional database search will be conducted before the final analysis in order to capture any recent publications that meet the inclusion criteria. Additional sources of published and unpublished literature • In addition to the database searches, we will include articles identified by the research team as the “reference set” (i.e., a set of articles that meet all search criteria), as well as search the reference lists of those articles. We will also reach out to experts in the field using professional associations’ mailing lists. Specifically, we will reach out to members of the Society of Personality and Social Psychology, International Society of Political Psychology, and European Association of Social Psychology. • We will try to access unpublished studies as well by reaching out to researchers as well as practitioners (e.g., non-profit organizations) working to promote peace. 9. URL to search strategy. Search strategy link: https://drive.google.com/drive/folders/1AD7cGxsNTjsJrBTGF1kv7kFi25zvNe6i 10. Condition or domain being studied. This work focuses on interventions to promote peace, that is on evidence-based actions aimed at promoting intergroup peace and/or reconciliation among social groups that used to be or are currently engaged in a violent conflict. 11. Participants/population. Population: People in societies with ongoing or resolved violent intergroup conflict When reviewing the reference set of articles we realized that in some instances abstracts and/or titles do not mention the word “conflict” (or related keywords) explicitly but rather talk about the specific conflict context (e.g., Israel, Rwanda, etc.) we decided to include a list of known conflict settings in our search to make it more comprehensive. To this effect, we created a list of countries with recorded violent intergroup conflict that occurred between 1946 and 2020. The list was based on two lists of conflicts: 1) the Major Episodes of Political Violence, 1946-2018 database created by Center for Systemic Peace (CSP) and accessible through their website (http://www.systemicpeace.org/inscrdata.html) and (2) the UCDP/PRIO Armed Conflict Dataset version 21.1 accessible from the Uppsala Conflict Data Program (UCDP) & Peace Research Institute Oslo (PRIO) (https://ucdp.uu.se/downloads/). The two datasets were used to provide a more comprehensive coverage as they differ slightly in their definition of violent conflict. The CSP defines armed conflict as “[…] systematic and sustained use of lethal violence by organized groups that result in at least 500 directly-related deaths over the course of the episode.” The definition can be found in the codebook for the Major Episodes of Political Violence, 1946-2018 database available here: http://www.systemicpeace.org/inscrdata.html The UCDP defines armed conflict as “[…] a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in one calendar year.” (https://www.pcr.uu.se/research/ucdp/definitions/#tocjump_8252132865605607_9). 12. Intervention(s), exposure(s). Please note that the following were not included as search terms in our search strategy in order to avoid missing studies that utilized less-established interventions that are not included in the above (extensive) list. Intergroup interventions to promote peace, as defined above (and in Halperin & Schori‐Eyal, 2020), may utilize – but are not limited to – the following methods of intergroup attitude/behavior change: Intergroup contact (direct, extended, imagined, vicarious, para-social; see Pettigrew & Tropp, 2011); Paradoxical thinking (Hameiri et al., 2018); Group malleability (Halperin et al., 2011); Outgroup criticism (McDonald et al., 2018); Social norms/ Peer influence (Perkins et al., 2011); Collective blame (dissonance) (Bruneau et al., 2020); Self-affirmation (Čehajić-Clancy et al., 2011); Moral exemplars (Čehajić-Clancy & Bilewicz, 2021); Empathy/perspective taking (Galinsky & Moskowitz, 2000); Cognitive & emotional training (Halperin et al., 2014); Social categorization (Gaertner et al., 1993); Discussion/dialogue (Gurin et al., 1999); Value consistency and self-worth (Rokeach, 1971); Multicultural/anti-bias education (Lustig, 2003); Entertainment (Bilali & Vollhardt, 2013); Diversity training and cross-cultural/intercultural training (Hanover, & Cellar, 1998); Cooperative learning (Roseth et al., 2008). 13. Comparator(s)/control. We will only include studies that utilize experimental design with a non-exposed control group. 14. Types of study to be included. We will only include experimental studies that randomly assign participants to the treatment (intervention) and control (no exposure to intervention) conditions, specifically, we will only include: • Randomized posttest only with control (POWC) • Pretest–posttest with control (PPWC) Studies conducted in the laboratory, in the field, and online will all be included (though we will code for these characteristics and analyze whether they bear on the effects of the interventions). 15. Main outcome(s). Main outcome variable: support for peace Support for peace is defined as positive changes in attitudes/behavior intentions/behavior towards the relationship with the adversary* (e.g., forgiveness, willingness to reconcile, offering/supporting apologies) and towards the conflict (e.g., support of group-level [macro-level] actions that explicitly promote peaceful resolution and/or reconciliation, support for peace, etc.). Change is defined as the difference between the control (baseline) group and the experimental group(s) and/or as the difference in the size of change observed between pre- and post-treatment level of dependent variable(s) in the experimental as compared to the control condition. Not included are attitudes and behaviors towards the adversary that are not specifically related to the context of conflict (e.g., prejudice and stereotypes of the adversary, which may well exist regardless of the conflict). Additionally, we will analyze personalization parameters (i.e., moderators of the effectiveness of interventions to promote peace) such as political orientation; personality; dominant emotions; group identification (as described in Halperin & Schori‐Eyal, 2020) to establish whether certain interventions are more effective for participants with certain characteristics as well as more typical moderators of effectiveness (e.g., study design, methodological characteristics). 16. Inclusion and exclusion criteria The definition of the outcome measures, the interventions of interest, methodological criteria, and the context of the studies of interest (i.e., violent intergroup conflict ongoing or resolved) are a source of our theoretical and methodological inclusion and exclusion criteria. Included will be: • experimental studies that utilize pretest-posttest with a control group or posttest only with control design (i.e., random allocation to intervention and control group[s]); and • studies that assess at least one dependent variable that meets the outcome variable definition criteria; and • studies conducted in the context of violent intergroup conflict * Should some of the identified studies that meet these criteria report data from clusters (e.g., classrooms) assigned to conditions and the number of clusters is low (i.e., < 10), we may need to exclude these from analyses, due to difficulties in estimating unbiased standard errors and potential bias in estimators (see the Supplement to the Paluck et al., 2021). Excluded will be: • studies that evaluate attitudes towards the adversary in conflict; or • non-experimental studies (e.g., correlational, pretest-posttest only); or • studies of conflict that is not violent (e.g., conflict in organizations) In addition, we will limit our analysis to published and unpublished reports in English. 17. Data extraction (selection and coding). Each article will be coded by two members of the research team (who are all PhD students, post-docs, or faculty members), independently of each other, and blind to the others’ coding. Any discrepancies will be discussed to arrive at an agreed-upon coding. This screening and coding of articles will be done in three stages: (1) Stage 1: screening abstracts & titles Articles will be screened based on the title and abstract to remove non-relevant entries (this will be performed by one person per article, but the coders will be instructed to err on the side of caution and include all possibly relevant articles) (2) Stage 2: screening full-text At this stage full-text screening will be performed (two coders per article) to ensure that only relevant articles that meet all inclusion criteria are left (3) Stage 3: coding Two coders will extract all relevant information from the articles identified at Stage 2. Any discrepancies will be resolved through documented discussion between two coders; if agreement could not be reached, the PI will weigh in on the decision. All extracted data will be recorded in a google doc spreadsheet file shared by the team. Data extraction • Participants’ data: - age (average in a given study); - ethnicity - group belonging (i.e., membership in one of the groups in conflict) - location of the study (e.g., school, workplace, community, university, online) will be recorded for study participants. • Study design data: - # of the study in the paper (e.g., 1/2/3 of 5) - number of participants in the intervention group - number of participants in the control group - type of experiment (laboratory/field/online [or survey]); - type of publication (e.g., peer-reviewed article, book chapter, dissertation/thesis, report); - type of control group: empty (no treatment)/ waitlist/placebo); - type of outcome (attitude/behavior intentions/behavior) - focus of outcome (relationship with the adversary [e.g., offer of forgiveness] vs. conflict related [e.g., policy support]) - type of intervention (coded for all interventions listed in #12 or “other”); - intensity/length of intervention (brief [under 30 minutes] vs. longer [30+ minutes]); - number of intervention sessions; - type of measurement delay (immediate measurement/immediate + delayed measurement/only delayed measurement); - length of delay (in days) - preregistration status (yes/no); - open science status (data/materials available: yes/no) • Effects - effect type (difference in means; difference in difference [the difference between changes in the experimental and control groups]; Cohen’s d; regression coefficient; t-test; F-test; eta-squared; partial eta-squared) (*note that all these are included here because we will be extracting any data that allows to calculate effect size, should the actual effect size not be provided by the authors) - raw statistics (are all raw statistics provided to calculate effect size: yes/no) - personalization parameters (are personalization parameters of interest reported: yes/no; if no – reach out and ask about relevant measures; if yes – reach out and ask for data) - location of relevant information in the article (page number) If relevant information is not provided in a given source, we will contact study author(s) to try and extract it directly from them. 18. Risk of bias (quality) assessment. As recommended by Vevea et al. (2019) we will combine several methods that assess quality and publication bias. Should publication bias be detected the final results will be adjusted accordingly. During data extraction, coders will conduct a critical appraisal of the included studies, drawing on relevant bias domains from ‘The Cochrane Collaboration’s tool for assessing risk of bias’ - randomization of participants (in experimental studies) and assessment of self selection (akin to blinding participants) (Higgins & Altman, 2008). We will create a funnel plot to assess the relation between sample size and effect size, estimate a p-curve (Simonsohn et al., 2014), and conduct Egger’s test of the intercept (should we find a sufficient number of studies to be evaluated). We will also perform subgroup analysis by source (published vs. unpublished source), and study design characteristics (e.g., lab/field/online studies). 19. Strategy for data analysis. In this analysis we will utilize random effects approach and we will compare the control group(s) (treated as baseline) to treatment (i.e., intergroup intervention) group(s) (or, in case of pretest-posttest with control, the change in the treatment vs. in control condition). We expect to see an increase in support for peace (as defined in #15) in the treatment groups that have been exposed to intergroup interventions. We will calculate Hedge’s g coefficient to gauge the effect size in each of the included studies. This effect will be averaged for up to 5 most important outcome variables (that meet the dependent variable definition provided herein) reported at the study level. 20. Analysis of subgroups. We will conduct analyses in subgroups (a subgroup needs to consist of at least 3 studies to be considered) based on the type of outcome measured (attitudes vs. behavioral intentions vs. behavior); type of experiment (lab vs. field vs. online/survey experiment); type of intervention (this will be determined based on the findings as at this point it is not known which intergroup interventions will be represented in the identified literature); and type of setting (e.g., university/community/workplace/online etc. as well as conflict setting: Israel/Northern Ireland/Cyprus/South Africa/etc., depending on how many studies are identified pertaining to different conflict regions). These analyses will be useful in determining characteristics of the intervention and the context most likely to yield significant effects. We will test for heterogeneity in these models using the analyses of variance for effect sizes procedures described in Konstantopoulos and Heghes (2019). In order to analyze the role of personalization parameters in shaping the effectiveness of intergroup interventions to promote peace we will include the relevant personalization parameters (e.g., political orientation, personality, dominant emotions) as predictors of effect sizes in multiple regression for effect sizes models (see Konstantopoulos & Hedges, 2019) and/or conduct subgroup comparisons (where appropriate). 21. References: Bilali, R., & Vollhardt, J. R. (2013). Priming effects of a reconciliation radio drama on historical perspective-taking in the aftermath of mass violence in Rwanda. Journal of Experimental Social Psychology, 49(1), 144-151. https://doi.org/10.1016/j.jesp.2012.08.011 Bruneau, E. G., Kteily, N. S., & Urbiola, A. (2020). A collective blame hypocrisy intervention enduringly reduces hostility towards Muslims. Nature Human Behaviour, 4(1), 45–54. https://doi.org/10.1038/s41562-019-0747-7 Čehajić‐Clancy, S., & Bilewicz, M. (2021). 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Publisher Copyright: © The Author(s) 2022. In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.