兒童青少年之BEAR團體效能研究：成員特性與團體組成

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(1)國立臺灣師範大學教育學院教育心理與輔導學系碩士論文 Department of Educational Psychology and Counseling College of Education. National Taiwan Normal University. Master’s Thesis. 兒童青少年之 BEAR 團體效能研究：成員特性與團體組成 An Investigation of Children and Adolescents’ BEAR Group Effectiveness: Member Characteristics and Group Composition. 郭欣榆 Koay, Evelyn Yan Yi. 指導教授：王麗斐博士 Advisor: Wang, Li-fei, Ph.D.. 中華民國 109 年 7 月 July 2020.

(2) Acknowledgment I would like to express the deepest appreciation to my thesis advisor, Professor Li-fei Wang, for the invaluable guidance, persistent help, continuous support, and encouragement in finishing this thesis, and also for introducing me to practice-based research. I take this opportunity to thank my committee chair, Professor Dennis Martin Kivlighan, Jr., for the patient guide and assistance, especially in helping me not to feel so difficult to learn new statistical methods. I would like to thank my committee member, Professor Meifen Wei, for asking many important questions that help me gain deeper insights. My completion of this thesis could not have been accomplished without the support of my partner, Cheang Kiang. Thank you for being so caring, loving, and understanding. His constant support and encouragement when the going gets tough are much appreciated. My gratitude also extends to my family—my parents, Eric and Betty; my brothers, Edwin and Kelvin. Thank you for supporting me spiritually in every path that I take. They let me know that no matter where I go, they would back me up. Besides, I thank my fellow labmates in Lab 614, especially to Yu Ling, MeiLing, and, Chia-Ting for the stimulating discussions, companionship, and support. Thanks to my housemate, Jasmine, for all the fun we have had in the last four years. My research life would have been dull without her. Special thanks go to my classmates, San San, Leon, and Debbie, for being there whenever I need a friend. My sincere thanks go to the school counselors who joined this research project and Professor Tzu-Yang Chao who taught me how to conduct latent profile analysis. Last but not least, I would like to thank myself for the hard work and persistence along the journey to complete this thesis.. i.

(3) An Investigation of Children and Adolescents’ BEAR Group Effectiveness: Member Characteristics and Group Composition. Koay, Evelyn Yan Yi. Abstract This research aimed to examine who benefits most from the BEAR group interventions—an 8-session emotional regulation group for children and adolescents, and how the interventions can be more effective. Two studies were conducted to explore the emotion and behavioral problem profiles of student clients (Study 1) and to examine whether different types of student clients would progress differently and under what conditions is the BEAR group interventions more effective (Study 2). The data of 307 student clients and 250 teachers were used to analyze in Study 1 and Study 2. In Study 1, a latent profile analysis using four dual emotional regulation strategies from student clients’ perspectives and two behavioral problems from teachers’ perspectives was conducted in Mplus. The analysis uncovered a three-class solution: student clients with internalizing problems, student clients with hidden problems, and student clients with impulsive problems. In Study 2, multilevel modeling was conducted via hierarchical linear modeling to analyze the nested data of 53 groups. Results found that the BEAR group is more effective in (a) decreasing negative affect for student clients with impulsive and internalizing problems, (b) increasing cultivating emotion strategies from. ii.

(4) student clients’ perspectives for student clients with hidden problems, and (c) increasing academic efficacy for student clients with impulsive problems. Furthermore, we found that the BEAR group is more effective in (a) decreasing negative affect from student clients’ perspectives when the group has fewer student clients with hidden problems and also when the group has fewer student clients with impulsive problems, and (b) increasing cultivating emotion strategies from teachers’ perspectives when the group has more student clients with impulsive problems. In conclusion, the BEAR group was effective for all student clients, but different types of student clients can get help in different aspects, and the impacts of student clients’ characteristics and group composition were also discussed. Keywords: children and adolescence, latent profile analysis, group effectiveness, group composition, member characteristics. iii.

(5) 兒童青少年之 BEAR 團體效能研究：成員特性與團體組成. 郭欣榆. 摘要本研究旨於探討在 BEAR 團體介入中，哪一種類型的學生個案受益最大以及如何介入可以更有效能？BEAR 團體乃八次的兒童青少年情緒調節團體。本研究分為兩個部分，第一部分在探索學生個案的情緒與行為問題類型（研究一），第二部分在探討不同類型的學生個案經過團體介入是否會有不同的進展，以及在什麼情況下 BEAR 團體介入會更有效（研究二）。研究一和研究二皆使用 307 位學生個案的資料進行分析。研究一主要使用 Mplus 軟體，針對學生觀點的四個雙元情緒調節策略和導師觀點的兩個行為問題進行潛在剖面分析（latent profile analysis）。該分析顯示學生個案的情緒與行為問題可分為三種類型，包括具有內向行為問題的學生個案、具有隱藏問題的學生個案，以及具有衝動問題的學生個案。研究二使用階層線性模式軟體（hierarchical linear modeling），針對 53 個團體的嵌套資料進行多層次模式（multilevel modeling）分析。結果發現 BEAR 團體的效能如下：（一）降低具有衝動和內向問題的學生個案之負向情緒、（二）從學生個案的觀點提升具有隱藏問題的學生個案之陶冶情緒策略，以及（三）提升具有衝動問題的學生個案之學業效能。此外，研究發現 BEAR 團體在以下情形更更有效能：（一）當團體有較少具有隱藏問題和較少具有衝動問題的學生個案時可降低負向情緒，以及（二）當團體有較多具有衝動問題的學生個案時可提升陶冶情緒策略。總而言之，BEAR 團體對所有學生個案都有效，但是不同類型的學生個案將會在不同的面向獲得幫助。學生個案之特性及團體組成兩者所帶來的影響將在文中進一步討論。關鍵詞：兒童青少年、潛在剖面分析、團體效能、團體組成、成員特性. iv.

(6) Table of Contents Acknowledgment..………………………………………..………………………………i English Abstract…………………………………………..……………………………ii Chinese Abstract………………………………………..………………………………iv List of Tables…………………………………………………………..………………vi List of Figures………………………………………………………………………....vii Introduction……………………………………………………………………………...1 Research Background...………………………………………………………….1 BEAR Group……..………………………………………………………...…….2 The Purpose of Current Research…………………….………………………...3 Study 1: Exploratory of Emotion and Behavioral Problem Profiles…..………………….7 Method…………………………………………………………………………...7 Results………………………………………………………………………….13 Summary………………………………………………………………………..18 Study 2: Multilevel Modeling……………………….………………………………….20 Method………………………………………………………………………….20 Results………………………………………………………………………….31 Summary………………………………………………………………………..47 Discussion……………………………………………………………………………....49 Counseling Implication…………………………………………………………53 Limitations and Future Directions………………………………………………53 Reference…………………………………………………………………………….....55. v.

(7) List of Tables Table 1. Descriptive Statistics and Correlations for Manifest Variables………………14. Table 2. Fit Indices Latent Profile Analyses for Two to Six Class Models (N = 307)……………………………………………………………………….....16. Table 3. Means and Post Hoc Comparisons for Manifest Variables with the Three-Class Model (N = 307)…………………………..…………………………………18. Table 4. Dual Emotion Regulation Model…………………………………………….24. Table 5. Numbers of Waves of Data Collection for Three Semesters…...…………….27. Table 6. Descriptive Statistics for Study Variables………………………...…………32. Table 7. Fixed Effects Estimation of Three-Level Conditional Growth Model for Negative Affect…………………………………………………...…………37. Table 8. Fixed Effects Estimation of Three-Level Conditional Growth Model for Understanding Emotion Connotations from Student Clients’ Perspectives….38. Table 9. Fixed Effects Estimation of Three-Level Conditional Growth Model for Cultivating Emotion Strategies from Student Clients’ Perspectives…………41. Table 10 Fixed Effects Estimation of Three-Level Conditional Growth Model for Understanding Emotion Connotations from Teachers’ Perspectives...………42 Table 11 Fixed Effects Estimation of Three-Level Conditional Growth Model for Cultivating Emotion Strategies from Teachers’ Perspectives..............………45 Table 12 Fixed Effects Estimation of Three-Level Conditional Growth Model for Academic Efficacy…………………………………………..............………47. vi.

(8) List of Figures Figure 1 Emotion and Behavioral Problem Profiles………………………...…………16 Figure 2 Between-Level Interaction for the Percentage of Student Clients with Hidden Problems and Change in Negative Affect…………………………………....34 Figure 3 Between-Level Interaction for the Percentage of Student Clients with Impulsive Problems and Change in Negative Affect....…...…………………35 Figure 4 Between-Level Interaction for the Student Clients with Impulsive Problems and Change in Negative Affect………………..……………………………..36 Figure 5 Between-Level Interaction for the Student Clients with Hidden Problems and Change in Cultivating Emotion Strategies from Student Clients’ Perspective…………………………………………………………………..40 Figure 6 Between-Level Interaction for the Percentage of Student Clients with Impulsive Problems and Change in Cultivating Emotion Strategies from Teachers’ Perspectives.…………………………………………...…………44 Figure 7 Between-Level Interaction for the Student Clients with Impulsive Problems and Change in Academic Efficacy...……………..…………………………..46. vii.

(9) Introduction Research Background Emotional and behavioral problems in children and adolescents have been a major concern in school counseling. Emotional and behavioral problems during childhood have significant negative impacts on the individual, family, and society (Ogundele, 2018). A recent review of the literature (Ogundele, 2018) reported that emotional and behavioral problems in childhood were frequently associated with academic failure, occupational, and psychosocial functioning. Severe behavioral problems (e.g., substance abuse, delinquency, and intention to engage in high-risk behaviors) are negatively associated with life satisfaction among Hong Kong Chinese adolescents, with behavioral problems and life satisfaction had a bidirectional relationship (Sun & Shek, 2010). There were many approaches of psychological interventions for children and adolescents with emotional and behavioral concerns that were used in schools, such as individual-based, small-group approach, classroom-based, web-based, parenting programs, etc. (Arnarson & Craighead, 2011; Gardner et al., 2017; Moeini et al., 2019; Stallard et al., 2014; Tsai, 2018). However, the effectiveness of interventions was not consistent for targeted programs (i.e., interventions for at-risk populations or populations with existing symptoms; Arnarson & Craighead, 2011). For example, when examining the effectiveness of cognitive behavioral therapy intervention for student clients with emotional problems (i.e., FRIENDS program) that were delivered in a small group format, Shortt et al. (2001) found that 69% of children were diagnosis-free after the intervention, compared to 6% of children in the control groups, hence suggested that FRIENDS was an effective treatment for clinically anxious children. On the other hand, Miller et al. (2011) found that there was no intervention effect, with children in both 1.

(10) control and intervention groups had similar patterns of anxiety reduction over time. Moreover, the effectiveness of group intervention has been empirically studied and supported over several decades; however, there remains much to understand regarding the specific factors contributing to effective group intervention. The inconsistent findings demonstrated that the same intervention might benefit certain group members, but not all. Therefore, the understanding of the group intervention works for whom specifically is important. According to Laska et al. (2014), the common factors in all or most psychotherapies, such as alliance, empathy, goal consensus or collaboration, positive regard or affirmation, and therapist differences, accounted for 5.0% to 11.5% of the outcome variance. It should be noted that there was more than 50.0% of the outcome variance remained, which is attributable to other factors. For group psychotherapy, the other factors that affected the group effectiveness could be clients’ characteristics, group process, group composition, etc. The understanding of influences of student clients’ characteristics and group composition could help to provide a better guideline for screening and selecting student clients so that we can match student clients to the most suitable intervention services. Because the decisions for selecting student clients for a group shall consider what is best for all the student clients, not just what is best for one student client (Corey et al., 2010).. BEAR Group The current investigation was conducted with the BEAR group—a culturally appropriate, empirically derived, group-based emotion cultivation intervention. The BEAR group is a targeted program for children and adolescents who have emotional disturbance issues developed by Wang (2010). Throughout this paper, the terms. 2.

(11) “student client” and “member” will be used interchangeably to refer to the children and adolescents who participated in the BEAR group. The BEAR group effectiveness has been examined and got support as an effective intervention to regulate student clients’ emotions, by involving emotion selfcontrol and internal cognitive process (Wang et al., 2012). Previous research on the BEAR group effectiveness has found that compared with the wait list control group, the treatment group had significantly decreased depression; and significantly increased basic psychological need satisfaction, emotional regulation, and emotional cultivation (Wang, Wei, Koay, & Chen, 2016). Although previous research had indicated that the BEAR group was effective for certain student clients, from further observations, we realized that some student clients or their teachers and counselors had reported that the effectiveness was not evident. This aroused our curiosity, and we wanted to conduct more meticulous analyses so that we can clarify the influences of student clients’ characteristics and group composition in the BEAR group.. The Purpose of Current Research The purpose of this research was to examine who benefits most from the BEAR group interventions and how the interventions can be more effective. We posed two research questions: First, who does best when participating in the BEAR group? Second, what is the best group composition in the BEAR group? To address the research questions, we conducted two studies: (a) to identify latent classes of student clients participated in the BEAR group interventions, and (b) to examine whether different types of student clients would progress differently and under what conditions is the BEAR group interventions more effective. The results of the present research aimed to contribute to the practical and theoretical aspects of emotion cultivation group interventions for children and adolescents. 3.

(12) Latent Profile Analysis of Student Clients Study 1 intended to adopt a person-centered approach—latent profile analysis (LPA) to classify the student clients into distinct classes based on student clients’ patterns of dual emotion regulation strategies (e.g., suppression, impulsiveness, expressiveness, and forbearance) and behavioral problems (e.g., internalizing behavioral problems and externalizing behavioral problems). The person-centered approach would focus on the relationships among student clients so that student clients within a class are more similar to the set of variables than between classes. A person-centered approach is closer to the realistic view of the development of individuals’ complex dynamic systems than is a variable-centered approach (Bergman & Magnusson, 1997). Schmit et al. (2019) believed that LPA can help to connect the research-to-practice gap by providing both statistical and visual output to improve understanding and create applicability of research findings for clinical practice. In Study 1, we decided to use these manifest variables after consulting with experienced counselors regarding the criteria that they would concern when selecting student clients for the BEAR group. The experienced counselors indicated that the patterns of emotion regulation strategies and behavioral problems of student clients were the observable criteria in schools. These criteria would be good indicators for counselors to decide whether the BEAR group could match the needs of the student clients. Besides that, other researchers (Kovacs & Devlin, 1998; Ogundele, 2018) also indicated that symptoms of mental health problems in childhood and adolescence are usually classified into two categories, emotional and behavioral problems. Therefore, the student clients’ patterns of dual emotion regulation strategies (e.g., suppression, impulsiveness, expressiveness, and forbearance) and behavioral problems (e.g.,. 4.

(13) internalizing behavioral problems and externalizing behavioral problems) were chosen as the manifest variables in this study. Influences of Student Clients’ Characteristics and Group Composition The Science to Service Task Force of the American Group Psychotherapy Association (AGPA; 2007) pointed out two important issues in group interventions: (a) who is likely to benefit from group intervention—the issue of selection of student clients, and (b) what blending of student clients will produce the most effective group intervention—the issue of group composition. Group composition is usually considered in terms of how individual member characteristics will affect group cohesion or compatibility and subsequently how the group interacts (Fern, 2001). The recruitment of different types of student clients in a group might influence the effectiveness of the intervention. It is important to learn more about the interaction of student client characteristics in an intervention group, because group composition is proposed to be a salient variable impacting the overall effectiveness of an intervention and group outcomes (Stichter et al., 2019). Thus, in Study 2, we examined how student clients’ negative affect, emotion cultivation, and academic efficacy changed across weeks while participating in the BEAR group. Moreover, Study 2 intended to find out whether different types of student clients would progress differently and under what conditions is the BEAR group interventions more effective. Based on the results in Study 1, we examined whether student clients with different types of emotion and behavioral problem (i.e., student clients with internalizing problems, hidden problems, and impulsive problems) would differ in their growth for negative affect, emotion cultivation, and academic efficacy. Finally, we explored the moderating effects of the compositions of types of student. 5.

(14) clients within groups. Because this was exploratory research, we did not propose specific hypotheses due to the lack of theoretical and empirical evidence.. 6.

(15) Study 1: Exploratory of Emotion and Behavioral Problem Profiles The purpose of Study 1 was to identify the latent classes of student clients participated in the BEAR group interventions, and explore their emotion and behavioral problem profiles, based on four dual emotional regulation strategies from student clients’ perspectives (i.e., suppression, impulsiveness, expressiveness, and forbearance) and two behavioral problems from teachers’ perspectives (i.e., internalizing behavioral problems and externalizing behavioral problems).. Method Participants Participants in Study 1 included student clients (N = 307) and their teachers (N = 250) from 28 elementary and 20 middle schools in Northern (55.7%), Eastern (29.3%), and Central (15.0%) Taiwan. The majority teachers only referred one student client, while 38 teachers referred more than one student clients (two students: n = 28; three students: n = 5; four students: n = 3; five students: n = 1; and six students: n = 1) to participate in the BEAR group across one or two semesters. Student Clients. These student clients (177 [57.65%] boys and 130 [42.35%] girls) were third (n = 4 [1.30%]), fourth (n = 28 [9.12%]), fifth (n = 70 [22.80%]), sixth (n = 71 [23.13%]), seventh (n = 45 [14.66%]), eighth (n = 68 [22.15%]), and ninth (n = 21 [6.84%]) graders. Student clients ranged from 9 to 16 years old (M = 11.83, SD = 1.56). These student clients were referred by teachers because of their emotional problems; for instance, they can be easily losing control of emotions, difficult in obeying rules or commands, difficult in accepting accusations and demands of others, bad-tempered, easily picking a quarrel or fight with others, difficult in cooperating with. 7.

(16) others, difficult in expressing emotions, inclined to suppress emotions, easily falling into negative thoughts, etc. Regarding the student clients’ family structure, 201 student clients (65.47%) were from intact families, 102 student clients (33.22%) were from single-parent families, raised by grandparents, or adopted, 3 student clients (0.78%) were in foster care, and 1 (0.03%) unknown. Regarding ethnicity, 20 student clients (6.51%) were children of new immigrants in Taiwan and 42 student clients (13.68%) were the Indigenous Peoples of Taiwan. There were 27 student clients (8.79%) from low-income or middle-to-low-income families. Moreover, there were 9 student clients (2.93%) with disabilities, such as specific learning disorder, intellectual disability, severe emotional disorder, and autism spectrum disorder. Teachers. A total of 250 teachers (73 [29.20%] men and 177 [70.80%] women) rated the questionnaires for 306 student clients. The highest education level of the teachers was bachelor’s (n = 107 [42.80%), master’s (n = 140 [56.00%]), doctoral (n = 2 [0.80%) degree, and unknown (n = 1 [0.40%]). The highest proportion of the teachers was in 35–39 age group (n = 75 [30.00%]). The proportion of the teachers aged 40–44 (n = 58 [23.20%]) was slightly higher compared to aged 45–49 (n = 49 [19.60%]). A small minority of teachers were aged 30–34 (n = 24 [9.60%]), 50–54 (n = 19 [7.60%]), 29 or under (n = 13 [5.20%]), 55 or over (n = 5 [2.00%]), and unknown (n = 7 [2.80%]). As for the experience of teaching (4 unknown), ranged from 2 to 37 years (M = 15.32; SD = 6.31). Measures Dual Emotion Regulation Strategy. Wang, Chang, and Wei (2019) adapted the East-Asian contextual sensitive approach of emotion regulation to develop a scenariobased self-report scale for young adolescents—the Dual Emotion Regulation Scale 8.

(17) (DERS). This approach is based on two dimensions: (a) contextual sensitivity (appropriate vs. inappropriate in an Asian cultural context), and (b) regulating orientations (regulating outwards vs. regulating inwards). Therefore, a two by two framework with each specific emotion regulation strategy has emerged: (a) Expressiveness (appropriate and regulating outwards), (b) Forbearance (appropriate and regulating inwards), (c) Impulsiveness (inappropriate and regulating outwards), and (d) Suppression (inappropriate and regulating inwards). Wang, Chang, and Wei (2019) developed 12 scenario sets based on the frequent child emotional difficulty issues, which included four scenarios in each type of interpersonal conflict: the parent-child conflict, teacher-student conflict, and peer conflict. Within these conflict situations, each scenario consisted of four emotion regulation strategies as responses, including expressiveness, forbearance, impulsiveness, and suppression. The 48 responses of this scale (12 scenarios × 4 emotion regulation strategies) are rated by a 5-point Likert scale (0 = never to 4 = always). The scores with the same emotion regulation strategy were combined to form the score for each subscale (i.e., Expressiveness, Forbearance, Impulsiveness, and Suppression). Higher scores indicated higher frequency to use the emotion regulation strategies. The coefficient αs ranged from .82 to .86 in each subscale (Wang, Chang, & Wei, 2019). Evidence for criterion validity was demonstrated through significant negative associations of Expressiveness and Forbearance with depression and anxiety, as well as significant positive associations of Impulsiveness and Suppression with depression and anxiety among Taiwanese young adolescents (Wang, Chang, & Wei, 2019). The coefficient αs for the current study were .82 for Expressiveness, .87 for Forbearance, .89 for Impulsiveness, and .89 for Suppression. Behavioral Problems. Behavioral problems of student clients from teachers’ perspectives were measured by the 8-item Internalizing and Externalizing Problems 9.

(18) Scale (IEP). The 8-item IEP is a short version adapted from the 12-item IEP (Wang, Wei, & Ku, 2016). The other report version of the 8-item IEP was developed by replacing the word “I” with “he/she” in the scale items, e.g., “I feel uneasy” was changed to “He/she feels uneasy.” The 8-item IEP measures two behavioral problems, internalizing and externalizing behavioral problems. Internalizing Behavioral Problems (IBP; 4 items) assesses the extent to which individuals experience symptoms of internalizing emotional and behavioral problems (e.g., depression, social withdrawal). Sample items were, “He/she feels uneasy,” “He/she feels lonely,” “He/she feels that no one cares about him/her,” and “He/she is not happy”. Externalizing Behavioral Problems (EBP, 4 items) assesses the extent to which individuals manifest outward behavior (e.g., aggression, rule-breaking). Sample items were, “He/she argues with teachers,” “He/she does not follow class orders during classes,” “He/she has an impulsive personality and easily get into trouble,” and “He/she was often punished by teachers”. Teachers rated each item using a 4-point Likert scale (1 = strongly disagree to 4 = strongly agree) to show the extent of what the teacher agreed concerning the student client’s internalizing and externalizing problems. Higher scores indicated teachers perceived the student clients have greater mood disturbance, or conflict with others and violation of social norms in school. For the other report version of 8-item IEP in the current study, the coefficient αs were .85 for IBP and .90 for EBP; a significant negative correlation was found between IBP and EBP (r = −.22, p = .007). Concurrent validity was supported by the following evidence. For the other report version of 8-item and 12-item IEP, the short version of IBP was significantly positively correlated with the original version of IBP (r = .94, p < .001), but no significant correlated with the original version of EBP (r = −.14, p = .103); the short version of EBP was significantly positively correlated with the 10.

(19) original version of EBP (r = .98, p < .001), and significantly negatively correlated with the original version of IBP (r = −.21, p = .012). For the 8-item IEP, significant positive correlations were found between the self-report and other report versions of IBP (r = .19, p < .001), and self-report and other report versions of EBP (r = .54, p < .001). We tested the confirmatory factor analysis (CFA), the result was χ2(19, N = 306) = 36.67, p = .009, comparative fit index (CFI) = .99, root-mean-square error of approximation (RMSEA) = .06, 90% confidence interval (CI) = [0.03, 0.08], and standardized rootmean-square residual (SRMR) = .04, suggested that the model fit was good. Procedure The data used in this study were part of a database from the Dual Emotion Regulation Research Project. General guidelines for research have been followed in the research project. The data collection of the current study has been approved by the Research Ethical Committee. We received permission from the Department of Education in the local governments to conduct the research in four cities. A total of 307 student clients were recruited across three semesters. All student clients in this study voluntarily participated in 53 BEAR groups (30 groups in elementary school and 23 groups in middle schools). Five schools participated in two semesters, resulting in having two groups of student clients from these five schools, but each student client was unique with no prior exposure to the BEAR group. There were four to six (M = 6) student clients per group. The criteria of selected BEAR group members were student clients with (a) emotional disturbance issues referred by their teacher, (b) motivation to participate in BEAR groups to resolve their emotional disturbance concerns, (c) capacity to concentrate on the group activities for at least 10 minutes, and (d) capacity to follow the group leader’s instructions. Their emotional disturbance issues included (a) difficulty in 11.

(20) controlling emotions, (b) impulsivity in expressing emotions, (c) tendency to suppress emotions, and/or (d) difficulty in expressing emotions, etc. After the referral, BEAR group members will be interviewed by group leaders (i.e., school counselors) to confirm their willingness to participate and determine whether the BEAR group is a good fit for them. All student clients were explained the purpose of the study, what they need to do in the study (e.g., participating in every session of the BEAR group, writing questionnaires in the process, etc.), and could opt-out if desired. Assent was obtained from the student clients, and written consent was obtained from the parents and teachers before the study began. Each group was led by one specifically trained counselor who also served as the data collector. A total of 52 counselors from 48 elementary and middle schools participated in the current study, all of whom were native to the schools. Of these, one counselor led two groups across two semesters. Counselors participated in a 5-day group-based training led by the BEAR group developer that included a standardized data collection procedure training, group members’ recruitment procedure training, the BEAR group theoretical framework introduction, and the BEAR group implementation practice. During the practice, the BEAR group developer observed and gave feedback to the counselors, while the counselors also gave feedback to each other. Supervision of group counseling was offered to the counselors as compensation. Data Analysis Data used for Study 1 were collected before the BEAR group intervention. All student clients completed the scale without missing data, but one teacher did not turn in the questionnaire. All the scales used in this study were presented in Traditional Chinese. Statistical analyses were performed using SPSS version 23.0 and Mplus version 7.0, to identify latent classes of student clients who participated in the BEAR 12.

(21) group. First, Pearson correlation analysis was conducted to test the relation between all the manifest variables in Study 1. The continuous manifest variables included: (1) Suppression, (2) Impulsiveness, (3) Expressiveness, (4) Forbearance, (5) IBP, and (6) EBP. Second, LPA was used to explore the emotion and behavioral problem profiles, based on four dual emotional regulation strategies from student clients’ perspectives (i.e., suppression, impulsiveness, expressiveness, and forbearance) and two behavioral problems from teachers’ perspectives (i.e., internalizing behavioral problems and externalizing behavioral problems). Third, analysis of variance (ANOVA) was conducted to examine the differences between the emotion and behavioral problem profiles in the manifest variables. Finally, the post-hoc pairwise comparisons using Tukey’s tests were conducted to examine the differences in each class.. Results Descriptive Analyses In total, 307 student clients and their teachers were recruited in this study. However, one teacher did not return the questionnaire, resulting in 306 responses that were received for the IBP and EBP subscales. Descriptive statistics and correlations for six manifest variables are presented in Table 1. Expressiveness was significantly positively correlated with impulsiveness. Forbearance was significantly positively correlated with suppression and expressiveness, but negatively correlated with impulsiveness. A significant negative correlation was found between internalizing behavioral problems and expressiveness. Externalizing behavioral problems were significantly positively correlated with impulsiveness and expressiveness.. 13.

(22) Table 1 Descriptive Statistics and Correlations for Manifest Variables Variable. n. M. SD. 1. 2. 1. Suppression. 307 1.68 0.94. —. 2. Impulsiveness. 307 1.52 0.97. .05. 3. Expressiveness. 307 1.80 0.78. .09. 4. Forbearance. 307 2.05 0.87. .38** −.35**. 5. IBP. 306 2.37 0.66. .07. 6. EBP. 306 2.07 0.81 −.08. 3. 4. 5. 6. — .61**. −.07 .23**. — .13*. —. −.17**. −.09. —. .17**. −.08. −.09. —. Note. IBP = Internalizing Behavioral Problems; EBP = Externalizing Behavioral Problems. *p < .05. **p < .01.. Latent Profile Analysis In Study 1, we used LPA to examine whether student clients participated in the BEAR group could be classified into meaningful classes, based on four dual emotional regulation strategies from student clients’ perspectives (i.e., suppression, impulsiveness, expressiveness, and forbearance) and two behavioral problems from teachers’ perspectives (i.e., internalizing behavioral problems and externalizing behavioral problems). The analyses began with an assessment of 2–6 class models. The following criteria were evaluated to determine the appropriate number of profiles: (a) model fit, (b) number of cases in each profile, (c) the fewest number of classes that adequately describe the associations among the manifest indicators; Nylund et al., 2007; NylundGibson & Choi, 2018). Specifically, we considered Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted Baysian information criterion (aBIC), 14.

(23) log-likelihood (LL), and Lo-Mendell-Rubin adjusted likelihood ratio test (LMR-LRT) p value. Lower values of AIC, BIC, aBIC, and LL suggested better fitting model. A significant LMR-LRT p value indicated statistically significant improvement in model fit for the k class model compared with k − 1 class model. A higher Entropy value represented better classification. More specifically, Entropy value higher than .80 indicated adequate classification accuracy. As seen in Table 2, the three-class model appeared to be the optimal model. Three latent profiles emerged from the analysis: student clients with internalizing problems, hidden problems, and impulsive problems (see Figure 1). The “student clients with internalizing problems” profile, containing the most cases (n = 137, 44.63%), had the highest mean of the IBP, the lowest means for Impulsiveness, Expressiveness, and EBP. The “student clients with hidden problems” profile (n = 125, 40.72%) demonstrated moderate means of the Impulsiveness, Expressiveness, and Forbearance, but had high means of the IBP and EBP. The “student clients with impulsive problems” profile contained the least cases (n = 45, 14.66%) and had the highest means for Impulsiveness, Expressiveness, and EBP, and the lowest means of the Forbearance and IBP.. 15.

(24) Table 2 Fit Indices Latent Profile Analyses for Two to Six Class Models (N = 307) Model. AIC. BIC. aBIC. LL. LMR-LRT. Entropy. p value 2-class model. 4436.62. 4507.43. 4447.17. −2199.31. < .001. .73. 3-class model. 4375.44. 4472.34. 4389.88. −2161.72. .004. .82. 4-class model. 4347.93. 4470.92. 4366.26. −2140.97. .107. .79. 5-class model. 4309.35. 4458.42. 4331.56. −2114.67. .465. .81. 6-class model. 4287.12. 4462.28. 4313.22. −2096.56. .656. .79. Note. AIC = Akaike information criterion; BIC = Baysian information criterion; aBIC = adjusted Baysian information criterion; LL = log-likelihood; LMR-LRT p value = LoMendell-Rubin adjusted likelihood ratio test p value.. Figure 1 Emotion and Behavioral Problem Profiles 4.0 Internalizing Problems (Class 1) Hidden Problems (Class 2) Impulsive Problems (Class 3). 3.5 3.0. Score. 2.5 2.0 1.5 1.0 0.5 0.0. Manifest Variables 16.

(25) Note. IBP = Internalizing Behavioral Problems; EBP = Externalizing Behavioral Problems.. Between-profile ANOVA and Post Hoc Comparisons The between-profile ANOVA was conducted to determine the relationship of the three latent classes with six manifest variables (i.e., Suppression, Impulsiveness, Expressiveness, Forbearance, IBP, and EBP). The results of the between-profile ANOVAs (Table 3) indicated that most manifest variables used (except Suppression) were significantly different among the profiles. There was no significant difference between the profiles on Suppression, F(2, 304) = 1.48, p = .229. The Tukey’s post hoc test with pairwise comparisons indicated significant differences between the profiles with regard to student clients’ impulsiveness, F(2, 304) = 851.58, p < .001; expressiveness, F(2, 304) = 102.54, p < .001; forbearance, F(2, 304) = 14.75, p < .001; internalizing behavioral problems, F(2, 303) = 4.19, p < .016; and externalizing behavioral problems, F(2, 303) = 9.21, p < .001. These differences suggested that the differences between the profiles are meaningful.. 17.

(26) Table 3 Means and Post Hoc Comparisons for Manifest Variables with the Three-Class Model (N = 307) Manifest variable. Means associated with the three-class model 1. Internalizing problems. 2. Hidden. 3. Impulsive. problems. problems. Between-profile post hoc comparisons. Suppression. 1.61. 1.79. 1.62. —. Impulsiveness. 0.67. 1.86. 3.15. 3>2>1. Expressiveness. 1.30. 2.05. 2.59. 3>2>1. Forbearance. 2.29. 1.98. 1.54. 1>2>3. IBP. 2.41. 2.38. 2.11. 1, 2 > 3. EBP. 1.86. 2.19. 2.46. 3, 2 > 1. Note. IBP = Internalizing Behavioral Problems; EBP = Externalizing Behavioral Problems.. Summary The main goal of Study 1 was to identify profiles of student clients using LPA. We found that student clients could be meaningfully classifies into three classes. Class 1 (student clients with internalizing problems) was generally characterized by significantly higher frequency to use forbearance as emotion regulation strategy, lower tendency to regulate emotions outwards, and had significantly higher level of internalizing behavioral problems but lower level of externalizing behavioral problems. Class 2 (student clients with hidden problems) was characterized by moderate levels of emotion regulation strategies, but had high levels of both internalizing and externalizing behavioral problems. In contrast with Class 1, Class 3 (student clients with impulsive 18.

(27) problems) was characterized by significantly higher frequency to regulate emotions outwards, lower tendency to use forbearance as emotion regulation strategy, and had significantly higher level of externalizing behavioral problems but lower level of internalizing behavioral problems.. 19.

(28) Study 2: Multilevel Modeling The purpose of Study 2 was to examine whether different types of student clients would progress differently and under what conditions is the BEAR group interventions more effective.. Method Participants Participants in Study 2 were the same as those in Study 1 (i.e., 307 student clients and 250 teachers). Measures Negative Affect. Negative affect was assessed by the Negative Affect (NA) subscale in the international Positive Affect and Negative Affect Short Form (IPANAS-SF; Thompson, 2007). The NA (5 items) measured the extent to which individuals feel a range of negative affect (e.g., ashamed and upset). Student clients rated items on a 5-point Likert scale (1 = never to 5 = always). Higher scores reflect higher levels of negative affect. We received permission from the original author and used the translation procedure outlined by Æ gisdóttir et al. (2008). The coefficient α was .65 for NA among Singaporean Chinese (Wong et al., 2011). Convergent validity evidence was shown by a negative association between negative affect with both subjective well-being and happiness (Thompson, 2007). In the present study, coefficient αs for the NA ranged from .65 to .80 (M = .73) across five waves of data. Emotional Cultivation. The self-report and other report versions of the Emotional Cultivation Scale (ECS; 9-item; Wang, Wei, et al., 2019) were used to measure the knowing (e.g., emotional consequence awareness) and doing (e.g., emotional self-control or flexibly using culturally appropriate emotional regulation strategies) components of the student clients’ emotional regulation process from student 20.

(29) clients and teachers’ perspectives. The ECS consisted of two subscales: Understanding Emotion Connotations (UEC; four items) and Cultivating Emotion Strategies (CES; five items). UEC was defined as being aware of current emotions and understanding how one’s thoughts and actions will impact the consequences of emotional responses. CES was defined as developing strategies of regulating emotions through emotional selfcontrol, creating alternative thoughts, and considering the best for self and others. For better readability, the subscales rated by student clients (i.e., self-report version) were labeled UEC-S and CES-S; the subscales rated by their teachers (i.e., other report version) were labeled UEC-T and CES-T. Sample item for UEC-S was, “I can understand that different ways to express emotions will lead to different consequences.” Sample item for CES-S was, “When encountering problems, I can change my thoughts to control my temper.” The other report version of the ECS was developed (Wang, Wei, et al., 2019) by replacing the word “I” to “he/she” in the scale items, e.g., “When something happens, I have ways to calm myself down” was changed to “When something happens, he/she has ways to calm herself/himself down.” Student clients and teachers rated each item using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), to indicate the extent of what student clients and teachers agreed regarding the student client’s emotional cultivation. Higher scores indicated better emotional cultivation. Coefficient αs were ranged from .63 to .71 for UEC-S, .74 to .80 for CES-S, .77 for UEC-T, and .86 for CES-T among Taiwanese elementary and middle school students (Wang, Wei, et al., 2019). Validity was demonstrated by the following evidence (Wang, Wei, et al., 2019). Convergent validity was supported by the positive associations with cognitive flexibility. Discriminant validity was evidenced by a nonsignificant association with suppression. Concurrent validity was revealed by 21.

(30) positive associations with positive affect, basic psychological need satisfaction, gratitude, responsiveness from teachers and parents. In the present study, coefficient αs were ranged from .68 to .87 (M = .79) for UEC-S, .80 to .87 (M = .83) for CES-S, .80 to .83 (M = .82) for UEC-T, and .86 to .91 (M = .89) for CES-T. Academic Efficacy. Academic efficacy was assessed by the Academic Efficacy (AE) subscale in the Patterns of Adaptive Learning Scales (PALS; Midgley et al., 2000). The AE (5 items) subscale measured the student clients’ beliefs in their ability to succeed in mastering the classwork. Sample item was, “Even if the work is hard, I can learn it.” Student clients rated each item on a 5-point Likert scale (1 = not at all true to 5 = very true). Higher scores indicated better academic self-efficacy. The coefficient α was .78 among the 5th-grade students (Midgley et al., 2000) and .86 among Taiwanese children and adolescents (Wang, Wei, et al., 2019). Construct validity was demonstrated by positive associations between academic efficacy and task goals in both math and English among the 5th-grade students (Midgley et al., 2000). In the present study, coefficient αs for the AE ranged from .87 to .92 (M = .90) across three waves of data. Group Intervention The BEAR group was an 8-session emotional regulation group intervention conducted in the elementary and middle school counseling center. Each session was about 90 minutes. The BEAR group was designed based on the emotional cultivation process (Wang et al., 2012) and dual emotion regulation model (Wang, 2010) in accordance with East Asian cultural characteristics. Emotional Cultivation Process. Wang and her colleagues (2012) categorized culturally effective and helpful emotion regulation strategies into four BEAR domains (i.e., B: Belief reframing, E: Emotional consequences awareness, A: Action control, R: Regulating emotion flexibly through culturally appropriate strategies). This process is 22.

(31) called the Emotional Cultivation Process. It was applied to the BEAR group by integrating this theory into the group activities and by converting the four domains of Emotional Cultivation Process into the goals of BEAR group. To be more specific, student clients were trained to develop awareness of consequences of different emotional responses and ability to cultivate emotions. In situations or stressful events that provoke emotions, student clients learned to first control or hold back the actions that may lead to negative consequences, then identify alternative thoughts, and finally flexibly adopt different appropriate emotion regulation strategies for the particular situation (Wang et al., 2012; Wang, Wei, et al., 2019). These strategies embed the meaning of forbearance in the East Asian cultural context (Wang, Wei, et al., 2019). Meanwhile, they were corresponded with the concept to regulate emotions in socially adaptive ways, which is to adaptively select and vary strategies as needed to fit the context (Gross & Cassidy, 2019). Dual Emotion Regulation Model. The society in Taiwan retained the traditional Eastern cultural values, but was also influenced by the Western culture at the same time. Theories and strategies of emotion regulation from the West have long been spread into Taiwan through various means, such as books, lectures, workshops, etc. In results, the development of emotion regulation capabilities of Taiwanese was probably not only affected by the concept of Eastern culture, but also by the concept of Western culture. However, the emotion management framework developed from an individualistic cultural context might not be completely applicable to the Taiwanese society. To develop an emotion regulation theory for Taiwanese, we need to take cultural considerations (i.e., blend of cultural elements from East and West) into account.. 23.

(32) Cultural differences exist in the use of emotion regulation strategies (Liddell & Williams, 2019). Based on the previous studies, expressiveness was most often regarded as adaptive emotion regulation strategy in Western individualism culture (Butler et al., 2009; Kim & Sherman, 2007), while forbearance was taught to be an adaptive emotion regulation strategy in the East Asian culture (Huang et al., 2008; Li & Hsiao, 2008). The maladaptive emotion regulation strategies in Western culture and East Asian culture were suppression (Butler et al., 2007; Soto et al., 2011) and impulsiveness (Eisenberg et al., 2006) respectively. Wang (2013) categorized these four emotion regulation strategies into two dimensions: (a) contextual sensitivity (appropriate vs. inappropriate in an Asian cultural context), and (b) regulating orientations (regulating outwards vs. regulating inwards). As seen in Table 4, a two by two framework (i.e., Dual Emotion Regulation Model) with each specific emotion regulation strategy has emerged: (a) Expressiveness (appropriate and regulating outwards), (b) Forbearance (appropriate and regulating inwards), (c) Impulsiveness (inappropriate and regulating outwards), and (d) Suppression (inappropriate and regulating inwards).. Table 4 Dual Emotion Regulation Model Regulating orientations Contextual sensitivity. Regulating outwards. Regulating inwards. Appropriate. Expressiveness. Forbearance. Inappropriate. Impulsiveness. Suppression. Note. Adapted from Developing and Validation a Nonwestern Perspective of Emotion Management Model for Children in Taiwan [Paper presentation] by L. Wang, 2013, 24.

(33) Society for Psychotherapy Research 44th International Annual Meeting, Brisbane, Australia.. In the BEAR group, the concept of Dual Emotion Regulation Model was introduced to the student clients with four bears: (a) Expressive Bear, (b) Forbearance Bear, (c) Impulsive Bear, and (d) Suppression Bear (Wang, 2013). Expressive Bear is characterized as brave, likes to think, and willing to express. When encountering problems, the Expressive Bear will consider the consequences according to the situation, then appropriately express the feelings and thoughts. The Expressive Bear is colored in orange, symbolized cheerful. Forbearance Bear is characterized as gentle, optimistic, and does not like conflicts. When encountering problems, the Forbearance Bear can think from different perspectives to regulate emotions. The Forbearance Bear is colored in green, symbolized peaceful. The Impulsive Bear is characterized as brave, straight-forward, and energetic. But sometimes because of carelessness, the Impulsive Bear can get into trouble, and thereafter regret easily. The Impulsive Bear is colored in fiery red. The Suppression Bear is characterized as gentle, shy, and afraid of conflicts. Therefore, the Suppression Bear will hide emotions or thoughts, feel wronged, and feel not being understood. The Suppression Bear is colored in blue, symbolized suppression. The difference between the Expressive Bear and the Impulsive Bear lies in whether the consequences after expressing can match according to the expectations of the given social situation, which focused on external consequences. The difference between the Forbearance Bear and the Suppression Bear is the degree of influence on individual mental health after not expressing, which focused on internal consequences. Group Design Principles. There were several design principles of the BEAR group (Wang et al., 2015). Firstly, the beginning stage of BEAR group focused on 25.

(34) building relationships among group members, establishing therapeutic culture in the group, and teaching the student clients about the theories of Emotional Cultivation Process and Dual Emotion Regulation. Secondly, the middle/working and final stage of BEAR group helped the student clients to develop and apply flexible and adaptive emotion regulation skills. Thirdly, to strengthen the effects of situated learning, the opportunities for “here-and-now” experiential learning and practices for actions that can lead to positive consequences were emphasized. These experiences of actual interpersonal conflicts in the BEAR group, can help student clients to develop the capability to confront and solve problems, and also appropriate ways to control emotions. Fourthly, each session will have “Praise and Appreciation Time”, where student clients complement and thank other group members by pointing out a specific part of what they have done or said throughout the session. Finally, the Counseling Communication Log was used as the homework. The implementation of Counseling Communication Log involved a weekly routine where student clients wrote down what they have learned at the end of each session, and counselors (i.e., the group leaders) provided feedback to student clients. Then, student clients will take back the Counseling Communication Log and complete a task that is designed according to the goal of each session. Student clients returned the Counseling Communication Log to counselors before the next group session. Procedure The data of 307 student clients were retrieved from the database of Dual Emotion Regulation Research Project. All of the student clients voluntarily participated in 53 BEAR groups across three semesters (i.e., Fall 2015, Spring 2017, and Fall 2017). Because of the different strategies of data collection in each semester, the number of waves of data collection for each variable (i.e., NA, UEC-S, CES-S, UEC-T, CES-T, 26.

(35) and AE) in each semester were different (see Table 5). For the analysis in Study 2, NA, UEC-S, and CES-S have five waves of data; UEC-T, CES-T, and AE have three waves of data. For the five waves data, the five time points of data collections were: Week 0 (a week before student clients started to join the first session of BEAR group), Week 3 (after the third session), Week 6 (after the sixth session), Week 9 (a week after the eighth session [i.e., the last session of BEAR group]), and Week 13 (five weeks after the last session). For the three waves data, the three time points of data collections were: Week 0 (equivalent to pretest), Week 9 (equivalent to posttest), and Week 13 (equivalent to follow-up). All the scales used in this study were presented in Traditional Chinese.. Table 5 Numbers of Waves of Data Collection for Three Semesters Semester. n. No. of. No. of waves of data collection. groups. NA. UEC-S. CES-S. UEC-T. CES-T. AE. Fall 2015. 159. 28. 5. 3. 3. 3. 3. 3. Spring 2017. 84. 14. 5. 5. 5. 3. 3. 3. Fall 2017. 64. 11. 0. 5. 5. 3. 3. 3. Note. NA = Negative Affect; UEC-S = Understanding Emotion Connotations from Student Clients’ Perspectives; CES-S = Cultivating Emotion Strategies from Student Clients’ Perspectives; UEC-T = Understanding Emotion Connotations from Teachers’ Perspectives; CES-T = Cultivating Emotion Strategies from Teachers’ Perspectives; AE = Academic Efficacy.. 27.

(36) Data Analysis In Study 2, multilevel modeling (MLM; via hierarchical linear modeling [HLM 8.00 Student version]) was used to analyze the data. A 3-level HLM analysis was performed in this study. Level-1 analysis corresponded to between-sessions, Level-2 analysis corresponded to between-student client, and Level-3 analysis corresponded to between-group. As student clients were nested within individual and within groups, using a traditional regression method to analyze data at the individual level would omit the dependence in data within groups (Cheng et al., 2008). Thus, choosing MLM as the analytic strategy was more appropriate for assessing and handling nested data. To make comparisons, dummy variables were created to examine the effects of student client types and group composition. The student clients with internalizing problems was used as the reference group for Level-2. At The percentage of student clients with internalizing problems was used as the reference group for Level-3. Initially, a completely unconditional 3-level HLM analysis was run to partition the variance in NA scores into between-group, between-student client, and betweensession components. For NA, 53% of the variance was between sessions, 47% of the variance was between student clients (intraclass correlation coefficient [ICC] = .47, χ2(df = 201) = 1080.99, p < .001), and 0% of the variance was between groups (ICC = .00, χ2(df = 41) = 43.45, p = .367). Therefore, there was sufficient variance in NA scores at session, student client, but not group levels. A completely unconditional 3-level HLM analysis was run to partition the variance in UEC-S scores into between-group, between-student client, and betweensession components. For UEC-S, 54% of the variance was between sessions, 32% of the variance was between student clients (ICC = .32, χ2(df = 254) = 834.96, p < .001), and 14% of the variance was between groups (ICC = .14, χ2(df = 52) = 141.03, p < .001). 28.

(37) Therefore, there was sufficient variance in UEC-S scores at session, student client, and group levels. A completely unconditional 3-level HLM analysis was run to partition the variance in CES-S scores into between-group, between-student client, and betweensession components. For CES-S, 62% of the variance was between sessions, 33% of the variance was between student clients (ICC = .33, χ2(df = 254) = 786.16, p < .001), and 5% of the variance was between groups (ICC = .05, χ2(df = 52) = 86.56, p = .002). Therefore, there was sufficient variance in ECS-CES scores at session, student client, and group levels. A completely unconditional 3-level HLM analysis was run to partition the variance in UEC-T scores into between-group, between-student client, and betweensession components. For UEC-T, 62% of the variance was between sessions, 33% of the variance was between student clients (ICC = .33, χ2(df = 253) = 643.73, p < .001), and 6% of the variance was between groups (ICC = .06, χ2(df = 52) = 86.28, p = .002). Therefore, there was sufficient variance in UEC-T scores at session, student client, and group levels. A completely unconditional 3-level HLM analysis was run to partition the variance in CES-T scores into between-group, between-student client, and betweensession components. For CES-T, 49% of the variance was between sessions, 43% of the variance was between student clients (ICC = .43, χ2(df = 253) = 906.79, p < .001), and 9% of the variance was between groups (ICC = .09, χ2(df = 52) = 97.98, p < .001). Therefore, there was sufficient variance in CES-T scores at session, student client, and group levels. A completely unconditional 3-level HLM analysis was run to partition the variance in AE scores into between-group, between-student client, and between-session 29.

(38) components. For AE, 44% of the variance was between sessions, 50% of the variance was between student clients (ICC = .50, χ2(df = 254) = 1094.46, p < .001), and 6% of the variance was between groups (ICC = .06, χ2(df = 52) = 82.50, p = .005). Therefore, there was sufficient variance in AE scores at session, student client, and group levels. In Model 1, we examined how NA, UEC-S, CES-S, UEC-T, CES-T, and AE changed across weeks. Specifically, we conducted an unconditional growth model with week predicting NA, UEC-S, CES-S, UEC-T, CES-T, and AE, respectively. Below is an example of one of these unconditional growth models. Level-1 Model. NA = π0 + π1*(WEEK) + e Level-2 Model. π0 = β00 + r0 π1 = β10 + r1 At level 2 and Level-3 Model. β00 = γ000 + u00 β10 = γ100 + u10 In Model 2, conditional growth models, we examined how types of student clients at level-2 and compositions of types of student clients within groups at level-3 related to growth in NA, UEC-S, CES-S, UEC-T, CES-T, and AE, respectively. Below is an example of one of these conditional growth models. Level-1 Model. NA = π0 + π1*(WEEK) + e Level-2 Model. π0 = β00 + β01*(HIDDEN Type) + β02*(IMPULSIVE Type) + r0 π1 = β10 + β11*(HIDDEN Type) + β12*(IMPULSIVE Type) + r1 30.

(39) Level-3 Model. β00 = γ000 + γ001(Percentage of HIDDEN Type) + γ002(Percentage of IMPULSIVE Type) + u00 β01 = γ010 + u01 β02 = γ020 + u02 β10 = γ100 + γ101(Percentage of HIDDEN Type) + γ102(Percentage of IMPULSIVE Type) + u10 β11 = γ110 + u11 β12 = γ120 + u12. Results Descriptive Analyses Table 6 displays the descriptive statistics (mean and standard deviation) for each variable at each time point of data collections. The missing data in Study 2 were handled with pairwise deletion, all available data were used by eliminating any cases with missing data on an analysis-by-analysis basis (Peugh & Enders, 2004). Multilevel data are often incomplete (Grund et al., 2019), the pairwise deletion was implemented to maximize sample size by not requiring complete data on all variables, under the assumption that missing data are missing due to a missing completely at random mechanism in this study.. 31.

(40) Table 6 Descriptive Statistics for Study Variables Variable. Week 0. Week 3. Week 6. Week 9. Week 13. n. M (SD). n. M (SD). n. M (SD). n. M (SD). n. M (SD). NA. 242. 2.02 (0.76). 242. 2.03 (0.79). 239. 2.01 (0.82). 236. 1.91 (0.84). 237. 1.97 (0.86). UEC-S. 306. 3.71 (0.77). 146. 4.16 (0.77). 144. 4.18 (0.78). 301. 4.00 (0.81). 299. 3.95 (0.88). CES-S. 307. 3.24 (0.86). 146. 3.73 (0.81). 144. 3.71 (0.89). 301. 3.66 (0.82). 299. 3.67 (0.85). UEC-T. 305. 3.26 (0.68). —. —. —. —. 300. 3.63 (0.61). 300. 3.74 (0.59). CES-T. 305. 2.77 (0.76). —. —. —. —. 300. 3.26 (0.79). 300. 3.28 (0.78). AE. 307. 3.41 (0.98). —. —. —. —. 301. 3.60 (1.02). 300. 3.63 (1.00). Note. NA = Negative Affect; UEC-S = Understanding Emotion Connotations from Student Clients’ Perspectives; CES-S = Cultivating Emotion Strategies from Student Clients’ Perspectives; UEC-T = Understanding Emotion Connotations from Teachers’ Perspectives; CEST = Cultivating Emotion Strategies from Teachers’ Perspectives; AE = Academic Efficacy.. 32.

(41) Multilevel Analyses Negative Affect. In the unconditional growth model for NA, the fixed effect for weeks was not significant (γ100 = −0.01, p = .115). Therefore, student clients’ negative affect did not change, significantly over time. The random effects for the unconditional growth model for NA showed that the growth coefficient differed significantly between student clients (r1 = .002, p < .001) but not between groups (u10 < .001, p = .395). In the three-level conditional growth model for the final estimation of the fixed effects (see Table 7), the significant intercept for NA is trivial because the scoring of NA does not contain “0”. For the intercept, there is a significant effect on the percentage of impulsive student clients in a group (γ002 = −1.04, p = .003). This shows that student clients, in groups with a larger proportion of student clients with impulsive problems, had a lower initial level of negative affect. For the intercept, there is also a significant effect for student clients with hidden problems (γ010 = 0.34, p = .001). This shows that student clients with hidden problems had a higher, initial level of negative affect. For the intercept, there is also a significant effect for student clients with impulsive problems (γ020 = 0.32, p = .006). This shows that student clients with impulsive problems had a higher, initial level of negative affect. The effect for weeks was significant (γ100 = −0.03, p = .027). This shows that the student clients in the BEAR groups decreased their negative affect by .03 points per week. The between-level interaction for the percentage of student clients with hidden problems and change in NA was significant (γ101 = 0.05, p = .021). This significant interaction is displayed in Figure 2. As seen in Figure 2, when there are more student clients with hidden problems the negative affect of the student clients does not change over time (simple slope = .007, p = .536); however, when there are fewer student clients with hidden problems in a group, negative affect decreased marginally significant over. 33.

(42) time (simple slope = −.016, p = .087). This shows that the BEAR groups are more effective in decreasing the negative affect when the group has fewer student clients with hidden problems.. Figure 2 Between-Level Interaction for the Percentage of Student Clients with Hidden Problems and Change in Negative Affect. 3.0. Negative Affect (NA). 2.5 Low Percentage of Hidden Students. 2.0. High Percentage of Hidden Students. 1.5. 1.0 Pretest. Follow-Up. The between-level interaction for the percentage of student clients with impulsive problems and change in NA was significant (γ102 = 0.09, p = .017). This significant interaction is displayed in Figure 3. As seen in Figure 3, when there are more student clients with impulsive problems the negative affect of the student clients does not change over time (simple slope = −.001, p = .937); however, when there are fewer student clients with impulsive problems in a group, negative affect decreased. 34.

(43) significantly over time (simple slope = −.023, p = .032). This shows that the BEAR groups are more effective in decreasing negative affect when the group has fewer student clients with impulsive problems.. Figure 3 Between-Level Interaction for the Percentage of Student Clients with Impulsive Problems and Change in Negative Affect. 3.0. Negative Affect (NA). 2.5 Low Percentage of Impulsive Students. 2.0. High Percentage of Impulsive Students. 1.5. 1.0 Pretest. Follow-Up. Weeks. The between-level interaction for the student clients with impulsive problems and change in NA was significant (γ120 = −0.05, p < .001). This significant interaction is displayed in Figure 4. As seen in Figure 4, negative affect decreased significantly over time for student clients with impulsive problems (simple slope = −.074, p < .001) and internalizing problems (simple slope = −.025, p = .023). This shows that the BEAR. 35.

(44) groups are more effective in decreasing negative affect for student clients with impulsive and internalizing problems.. Figure 4 Between-Level Interaction for the Student Clients with Impulsive Problems and Change in Negative Affect. 3.0. Negative Affect (NA). 2.5 Internalizing student. 2.0 Impulsive student. 1.5. 1.0 Pretest. Follow-Up. 36.

(45) Table 7 Fixed Effects Estimation of Three-Level Conditional Growth Model for Negative Affect β. SE. p. 2.13. 0.11. < .001. Percentage of HIDDEN type. −0.36. 0.21. .098. Percentage of IMPULSIVE type. −1.04. 0.34. .003. HIDDEN type. 0.34. 0.10. .001. IMPULSIVE type. 0.32. 0.11. .006. −0.03. 0.01. .027. Percentage of HIDDEN type. 0.05. 0.02. .021. Percentage of IMPULSIVE type. 0.09. 0.04. .017. HIDDEN type. −0.02. 0.01. .061. IMPULSIVE type. −0.05. 0.01. < .001. Parameter For intercept Intercept. For week slope Intercept. Understanding Emotion Connotations from Student Clients’ Perspectives. In the unconditional growth model for UEC-S, the fixed effect for weeks was significant (γ100 = 0.02, p < .001). Therefore, understanding emotion connotations from student clients’ perspectives changed significantly over time. The random effects for the unconditional growth model for UEC-S showed that the growth coefficient differed significantly between student clients (r1 = .001, p < .001) but not between groups (u10 < .001, p > .500). In the three-level conditional growth model for the final estimation of the fixed effects (see Table 8), the significant intercept for UEC-S is trivial because the scoring of. 37.

(46) UEC-S does not contain “0”. For the intercept, there is a significant effect for student clients with hidden problems (γ010 = −0.20, p = .016). This shows that student clients with hidden problems had a lower, initial level of understanding emotion connotations from student clients’ perspectives. The effect for weeks was not significant (γ100 = 0.01, p = .182). This shows that from student clients’ perspectives, their understanding of emotion connotations increased by .01 points per week, but not significantly.. Table 8 Fixed Effects Estimation of Three-Level Conditional Growth Model for Understanding Emotion Connotations from Student Clients’ Perspectives β. SE. p. 3.84. 0.15. < .001. −0.03. 0.27. .908. 0.30. 0.46. .517. HIDDEN type. −0.20. 0.08. .016. IMPULSIVE type. −0.04. 0.12. .713. 0.01. 0.01. .182. Percentage of HIDDEN type. −0.01. 0.02. .662. Percentage of IMPULSIVE type. −0.01. 0.03. .729. HIDDEN type. 0.02. 0.01. .063. IMPULSIVE type. 0.02. 0.02. .283. Parameter For intercept Intercept Percentage of HIDDEN type Percentage of IMPULSIVE type. For week slope Intercept. 38.

(47) Cultivating Emotion Strategies from Student Clients’ Perspectives. In the unconditional growth model for CES-S, the fixed effect for weeks was significant (γ100 = 0.03, p < .001). Therefore, cultivating emotion strategies from student clients’ perspectives changed significantly over time. The random effects for the unconditional growth model for CES-S showed that the growth coefficient differed significantly between student clients (r1 = .001, p = .006) but not between groups (u10 < .001, p > .500). In the three-level conditional growth model for the final estimation of the fixed effects (see Table 9), the significant intercept for CES-S is trivial because the scoring of CES-S does not contain “0”. For the intercept, there is a significant effect for student clients with hidden problems (γ010 = −0.43, p < .001). This shows that student clients with hidden problems had a lower, initial level of cultivating emotion strategies from student clients’ perspectives. For the intercept, there is also a significant effect for student clients with impulsive problems (γ020 = −0.35, p = .029). This shows that student clients with impulsive problems had a lower, initial level of cultivating emotion strategies from student clients’ perspectives. The effect for weeks was not significant (γ100 = 0.01, p = .306). This shows that from student clients’ perspectives, their cultivating emotion strategies increased by .01 points per week, but not significantly. The between-level interaction for the student clients with hidden problems and change in CES-S was significant (γ120 = 0.02, p = .010). This significant interaction is displayed in Figure 5. As seen in Figure 5, cultivating emotion strategies from student clients’ perspectives does not change over time for student clients with internalizing problems (simple slope = .008, p = .302); however, cultivating emotion strategies from student clients’ perspectives increased significantly over time for student clients with hidden problems (simple slope = .033, p. 39.