Running head: GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 1
1 I don´t fit, so I blame you? - Influence of Regulatory Focus and Fit on Emotion Generation and
2 Regulation in Single- and Group-Context
Christopher M. Jones1,2 & Daniel Memmert2 1 Institute of Public Health and Nursing Research, University of Bremen, Bremen, Germany 2 Institute of Exercise Training and Sport Informatics, German Sport University Cologne, Cologne, Germany
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8 Author note
9 Correspondence concerning this article should be addressed to Christopher M. Jones,
10 Institute of Public Health and Nursing Research, University of Bremen, Grazer Str. 4, 28359
11 Bremen, Germany. E-mail: [email protected] GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 2
12 Abstract
13 Person-environment-interactions play a main role in the process of emotional experience.
14 While Regulatory Focus Theory has been adopted to illustrate how some goal-oriented parts of
15 this process might shape by proposing a regulatory fit between individual and environmental
16 characteristics, whether this fit not only implies feeling “right” but feeling “good” or at least
17 cope better, has not been tested empirically. In this study, we extend earlier research on the
18 influence of regulatory fit to the generation and regulation of emotions. We additionally
19 emphasize the role of the context, by integrating current work on group-based emotion regulation
20 in comparing single- and group-environments. We used a within-subjects design, with 2
21 (situational focus) x 2 (single/group environment) levels. Thirty-two male football players
22 participated in one football-specific task per level. Emotional experience and cognitive
23 regulation strategies were measured after each. Multilevel regression showed, that a regulatory
24 fit predicted more passive-negative emotions in both and more active-negative emotions in the
25 group-environments. The Regulatory fit predicted stronger use of functional regulation strategies
26 in the single- but less in the group-environment. Group-membership predicted stronger use of
27 group-based regulation strategies and weaker use of other strategies – thus indicating further
28 constraints and new ways to cope. We discuss the counter-intuitive results regarding emotional
29 experience in the light of the athletic context as well as theoretical accounts of regulatory fit and
30 its role in moderating motivational intensity and value assignment. Results regarding influence
31 of group-membership are integrated into current research and we highlight directions for future
32 research.
33 Keywords: group-based emotion, emotion regulation, self-regulation, regulatory focus GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 3
34 I don´t fit, so I blame you? - Influence of Regulatory Focus and Fit on Emotion Generation and
35 Regulation in Single- and Group-Context
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37 While being the focal part of a highly favoured but hugely underperforming Argentinian
38 team during their deciding World Cup qualifier against Chile, Lionel Messi did not adapt too
39 well to the demands and constraints of the situation as he severly insulted the referee, ultimately
40 receiving not only a red card but also a ten-thousand dollar fine and four-game ban. As this scene
41 encapsulated, even when fight, flight, laughter, or tears are provoked as first action-oriented
42 response to an emotional experience, they do not necessarily lead to the best adaption to a given
43 situation. This becomes all the more obvious when looking at today’s highly complex
44 interpersonal relationships and goal attainment processes that leave individuals with only a
45 restricted range of ways to act and cope, thus making a dynamic and adaptive regulation process
46 inevitable (Gross, 2014).
47 Regulatory focus theory (RFT) proposes a framework to describe the relationship of goal-
48 related environmental and individual characteristics and assesses a degree of fit between both.
49 Fitting with one´s individual mode of self-regulation into a situation with corresponding features
50 is assumed to make an individual “feel right”, increase the subjective value of the action and,
51 according to recent research, facilitate better performance (Keller & Bless, 2006; Spiegel, Grant-
52 Pillow, & Higgins, 2004). The subjective experience of “fit” has been shown to influence
53 information processing, motivational intensity and different aspects of evaluations of decisions
54 and assigned values (Higgins, 2000). What has been lacking to date, is an answer to the question
55 if feeling “right” also leads to feeling “good” - or at least the higher ability to cope and thus feel
56 “good”. While many aspects and effects of the subjective experience of a regulatory fit have GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 4
57 been examined, a possible connection on an individual´s emotional experience remains to be
58 tested empirically.
59 However, as our introduction shows, many situations offer additional constraints to an
60 individual´s adaption as it is framed within RFT. These restrictions are especially important
61 when teams in general and team sports in particular are considered. Concerned with both goal
62 directed self- and emotion regulation, the individual is not only faced with personal but team
63 aims, obligations and resources as well as further situational characteristics. We thus additionally
64 assume, that connecting RFT and regulatory fit with the process of emotion regulation falls well
65 short without further extending this connection to group-based emotions and their regulation.
66 Only this extension may shed light on the specific adaptive functioning. By integrating recently
67 introduced approaches of group-based emotion regulation theory (Goldenberg, Halperin, van
68 Zomeren, & Gross, 2016), we add to existing work that neglects group-membership as an
69 important factor in emotion generation as well as regulation.
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71 Group-based emotion regulation
72 To date, work in the emerging field of emotion regulation has mostly focused on the role
73 of either hedonistic (i.e. feeling better in a given situation) or instrumental goals as well as
74 different types of regulation strategies and their effectiveness in different situations (Koole,
75 2009). However, as in Messi’s case, group-membership based on one´s self-categorization and
76 perceived relevance for the group play a key role in emotion generation as well as regulation.
77 Goldenberg et al. (2016) propose the process model of group-based emotion regulation by
78 integrating self-categorization- (Tajfel & Turner, 2004) and inter-group emotion theory (Mackie,
79 Maimer, & Smith, 2009) into Gross´ (2014) process model. This allows them to broaden the GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 5
80 framework of regulation processes, as strategies for regulating non-group-based emotions (e.g.
81 situation selection, situation modification, attentional deployment, cognitive change, response
82 modulation) are adapted for group-based emotions (i.e. appraising “anthems do not belong to
83 sports events”) and extended by the possibility of changing the state of self-categorization and
84 perceived importance to the group. Because of the aforementioned constraints in many group-
85 related situations (i.e. difficulties in avoiding emotion arousing situations) or potentially higher
86 resource-costs to do so (Khawaja, 1993), special emphasis is put on cognitive strategies
87 (Halperin, Porat, Tamir, & Gross, 2012).
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89 Regulatory focus theory
90 RFT proposes two modes of an individual´s self-regulation as an interplay of personal
91 and environmental factors, framing dimensions such as information processing and emotional
92 experience (Higgins et al., 1997). Individuals with a chronic promotion focus are attributed a
93 tendency to orientate towards positive outcomes, accomplishments and aspirations, whereas a
94 prevention focus is attributed to an orientation towards possible negative outcomes, safety and
95 responsibilities. Looking back at Lionel Messi´s tricky situation, one can easily adopt the given
96 distinction in order to understand why regulatory focus theory also proposes a situational focus.
97 In this case, the Argentinian team was trying to prevent a crushing blow to their World Cup
98 hopes and their compatriots´ expectations (situational prevention focus), whereas the Chileans
99 were chasing high hopes and aspirations of beating the favorite (situational promotion focus).
100 There is no superior mode per se, but based on the assumption of higher performance- and goal
101 attainment motivation as well as positive affect, several works have shown a positive effect on
102 cognitive (Förster, Higgins, & Idson, 1998; Keller & Bless, 2006; Shah, Higgins, & Friedman, GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 6
103 1998; Spiegel, Grant-Pillow, & Higgins, 2004) and athletic (Memmert, Hüttermann, & Orliczek,
104 2013; Plessner, Unkelbach, Memmert, Baltes, & Kolb, 2009; Vogel & Genschow, 2013)
105 performance measures when situational and chronic focus match – the so-called regulatory fit
106 between individual and environmental characteristics. It has been theorized, that a regulatory fit
107 not only influences the nature of one´s emotional experience (e.g. positive feedback raises
108 cheerfulness, negative feedback raises disappointment in case of chronic promotion focus and
109 calmness opposed to agitation in case of chronic prevention focus), but its degree as well
110 (Brockner & Higgins, 2001). Research on emotion regulation within an RFT-framework is
111 lacking to date.
112
113 Aims of the Present Research
114 The connection between RFT, conceptualizing the link between person and environment,
115 and an individual´s emotional experience is well-established theoretically. Yet, the nature and
116 degree have been only brought into focus and not been tested empirically yet. As regulatory fit
117 has been shown to improve different aspects of (cognitive) performance, we also include
118 cognitive emotion regulation as a main feature of an individual´s adaption to situation specific
119 demands and the subjective, transactional experience. We further distinguish between single- and
120 group-environments to accommodate different constraints and possibilities this might bring for
121 an individual.
122 We thus assume that (a) the emotional experience and (b) the use of cognitive emotion
123 regulation strategies as part of the transactional process are influenced by the interaction of
124 chronic and situational focus and a potential regulatory fit – resulting in a cross-over interaction
125 effect. To broaden the understanding and accommodate specific constraints and resources of a GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 7
126 given situation, we integrate the substantial work of Goldenberg et al. (2016). We (c) consistently
127 assume that being part of a group not only changes emotion generation but also the strategies
128 used to regulate these emotions accordingly. Thus, in the present study we examine the influence
129 of chronic regulatory focus, situational regulatory focus and their interaction in single as well as
130 group-context. As our introduction shows, the many different facets of team-sports offer a unique
131 setup to probe complex relationships between different theoretical constructs. For the present
132 research we therefore utilize individual- and group-tasks in football, to change the respective
133 environmental characteristics according to the underlying theoretical assumptions.
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135 Method
136 Participants
137 Thirty-two male football players with a mean age of 17.12 years (�� = 1.03)
138 participated in the present study. Another two players had to be omitted from all analysis as they
139 only completed questionnaires for three of the four experimental conditions. No incentives were
140 offered. Players actively played in the third and second tier of the German youth football system
141 and accordingly had several years of expertise (� = 8.75, �� = 2.22). Several players had been
142 training in a football academy setting before joining their respective teams. Both teams trained
143 three or four times a week on a regular basis. Written consent was obtained from all participants
144 and parents prior to testing according to the Declaration of Helsinki in 1975.
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146 Football tasks
147 The four (two for each context) football-specific tasks we used as experimental
148 conditions had been adopted from the coaches´ monthly journal of the German Football GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 8
149 Federation (DFB) as part of a set of twelve different exercises (set up as 1 v 1-drills or small-
150 sided games)(Höner & Votteler, 2016). The first selection was based on our assumptions about
151 the respective task´s inherent focus (i.e. not losing the ball as a team vs. scoring a goal while
152 being outnumbered), the chance of success for each player / team, and each task´s set of rules.
153 We then cast the experts´ vote, asking six highly qualified coaches from the DFB (� = 36.00)
154 by showing them a graphic illustration and rule-set for each of the twelve exercises. The experts
155 were asked to rate each exercise presented in randomized order on a 100-point scale, resembling
156 the work of Plessner et al. (2009), as either “focused on maximal gains” or “focused on safety
157 and preventing losses”. Two-way ANOVA showed a significant main effect regarding the
158 situational regulatory focus (�(1,69) = 455.39, ��� = 69.72, � < .001, � = .868), but no
159 effect regarding context (�(1,69) = 2.07, ��� = 69.72, � = .155, � = .029). In accordance
160 with our assumptions, the exercises could be separated into two different sets (one promotion
161 oriented, the other one prevention oriented). Within each set, no significant mean differences
162 were found. Thus, we were able to select exercises containing two types of situational focus, one
163 for each team or opponent, streamlining procedures and promoting compliance.
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165 Measures
166 Before the first trial, all participants a priori completed an adapted German seven-point
167 scale version of the regulatory focus questionnaire (Keller & Bless, 2006; Lockwood, Jordan, &
168 Kunda, 2002), adjusted to the study´s specific context by changing the items description of
169 “academic goals” to more appropriate “athletic goals”. The questionnaire contained nine
170 promotion and nine prevention related items. After each trial participants completed two more
171 questionnaires. First, they answered an adjectively worded 13-item scale regarding their GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 9
172 emotional experience taken from the EER (Benecke, Vogt, Bock, Koschier, & Peham, 2008).
173 Based on the authors´ factorial analysis, results were merged into three main factors (“passive-
174 negative”, “active-negative”, “positive” emotional experience). In accordance with the
175 assumptions stated above, we excluded the EER´s part focusing on emotion regulation. Instead
176 participants completed an adapted German version of the cognitive emotion regulation
177 questionnaire (CERQ) based on Gross and Thompson´s (2014) conceptualization of emotion
178 regulation (Garnefski & Kraaij, 2006) that focuses strongly on the cognitive parts of the
179 regulative process more accessible to self-report measures. Based on guidelines by Goldenberg et
180 al. (2016), we added four similiarly phrased questions, regarding the use of group-based
181 regulation strategies (e.g. weaker and stronger self-categorization, up- and devalueing
182 teammates). Instructions on both questionnaires put the focus upon the respective given task
183 participants had just accomplished. No further demographic variables were collected.
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185 Design and Procedure
186 The present study follows a two-factor within-subjects design, yielding 2 (situational
187 prevention focus / situational promotion focus, experimental) x 2 (single or group context,
188 experimental) factor levels. The whole study was conducted as part of one regular training
189 session per team. All participants were informed by their respective coach that a study on
190 motivation and emotion in football would be part of the next training session, so players not
191 willing to participate were allowed to stay at home. On arrival, players were handed the
192 regulatory focus questionnaire and a randomly assigned identification number by a researcher.
193 After getting dressed and going through their usual warm-up routines, all players gathered to
194 receive basic organizational guidelines from their coaches, who had been instructed beforehand. GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 10
195 Players were then randomly assigned into two groups and order for the first trial. Players first
196 completed both single-environment tasks (the order if a promotion or prevention oriented task
197 was to be completed first, was randomly assigned beforehand), before completing the two group-
198 environment tasks. Because of the above described choice of football tasks no further framing
199 was needed and each trial connected a situational prevention focus for one player with a
200 situational promotion focus for the other. All tasks were introduced to the players by the coaches
201 focusing only on organization and rules. During the trials no further instructions or comments -
202 except for remaining game time - were given. After each trial the players were handed the two
203 questionnaires on emotional experience and cognitive emotion regulation during the task, both to
204 be tagged with their identification number. Once all players had done their first single-context
205 trial (e.g. 1v1 setup), the earlier assigned groups switched (e.g. regarding experimental factor
206 situational focus) and players were again randomly ordered within, so they would not face the
207 same opponent again. After completing the second single-context trial and both questionnaires,
208 the same principles of random assignment and group switch were used again for the two group-
209 context trials (e.g. small-sided games). All players and coaches gathered once the last sequence
210 of trials had finished and were debriefed and thanked by the researcher as the training session
211 continued right after.
212
213 Analysis
214 To be able to explicitly model the interaction between continuous chronic focus and
215 categorical design factors situational focus and single-/group environment, we used a multilevel
216 modelling approach (Hoffman & Rovine, 2007). Thus, we treated observations (level-1) as
217 crossed within participants (level-2), with chronic regulatory focus as a level-2 variable and GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 11
218 situational focus, single- or group-environment as well as emotional experience and used
219 regulation strategies as level-1 variables. Using this approach also made it possible to
220 accommodate the non-independent nature of observations. Prior to analysis, chronic regulatory
221 focus was centered at the grand mean, while situational focus (prevention focus as −1,
222 promotion focus as +1) as well as single- or group-context (single context as −1, group context
223 as +1) were effect-coded. As the main question of this study can only be answered by the
224 inclusion of a combined (three-way) interaction-term, we used the following modelling
225 approach. Instead of building models stepwise and adding random effects as well as interaction
226 terms separately, we started each procedure with a nearly full model (Model 1) (Barr et al.,
227 2013). Model 1 included all main fixed effects as well as two- and three-way interaction terms.
228 As observations were treated as nested within participants, we allowed intercepts to vary
229 between participants. In order to be able to test interactions and still yield an acceptable Type I-
230 error performance, we conformed to the approach suggested by Barr (2013) and allowed slopes
231 to vary for the interaction of single- / group-environment and situational regulatory focus. We
232 compared this Model 1 to two reduced versions. Model 2 allowed intercepts to vary as the full
233 model and slopes for single- / group-environment and situational regulatory focus to vary each
234 but did not include any interaction terms. Model 3 allowed only intercepts to vary between
235 participants and did not include interaction terms either. We compared respective model fit by
236 using likelihood ratio tests and used an unstructured covariance matrix as well as the between-
237 within method of estimating degrees of freedom. Explained variance and effect sizes were
238 estimated with Conditional � (Nakagawa & Schielzeth, 2013) and semipartial � (Edwards,
239 Muller, Wolfinger, Qaqish, & Schabenberger, 2008). To further explore interactions between
240 single-/group-environment and a possible regulatory fit of chronic and situational regulatory GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 12
241 focus, we used simple slopes analysis. To account for risks of multiple comparisons only values
242 below significance levels of � = .0045 were deemed significant (Bonferroni correction).
243 For all analysis and production of this reproducible manuscript, we used R (Version 3.5.1;
244 R Core Team, 2018) and the R-packages lme4 (Version 1.1.19; Bates, Mächler, Bolker, &
245 Walker, 2015), multilevel (Version 2.6; Bliese, 2016), papaja (Version 0.1.0.9842; Aust & Barth,
246 2018), psych (Version 1.8.10; Revelle, 2018).
247
248 Results
249 Scales
250 Internal consistencies were checked before any further analysis, yielding questionable
251 results for both regulatory focus subscales (prevention subscale: � = 4.06, �� = 0.84, � = .61,
252 promotion subscale: � = 5.68, �� = 0.66, � = .67). In spite of their orthogonal theoretical
253 nature (Haws, Dholakia, & Bearden, 2010) both scales correlated moderately (� = .54, � <
254 .001) with one another. Following the proposed procedures by Keller and Bless (2006) as well as
255 Plessner et al. (2009), we thus computed a difference score (� = 0.00, �� = 0.96), subtracting
256 the z-standaradized prevention score from the z-standaradized promotion score, representing
257 relative focus strength - also avoiding a possible loss of power. Two of the three subscales of the
258 EER yielded good results regarding their internal consistency (passive-negative: � = 1.40,
259 �� = 1.22, � = .89, active-negative: � = 1.80, �� = 1.31, � = .77), replicating the findings
260 of Benecke et al. (2008). The subscale spanning all parts of one´s positive emotional experience
261 had to be adjusted because of negative correlations between one specific item and the others,
262 which resulted in this item being deleted and therefore an increased alpha (� = 3.11, �� = GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 13
263 0.78, � = .65). As the CERQ-items range from 1 to 5, we subtracted 1 from each answer, in
264 order to give intercepts a meaningful zero.
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266 Emotion generation
267 To verify whether the hypothesized change in emotion generation needed for any further
268 analysis, was de facto induced by the experimental conditions, we used the modelling procedure
269 described above for each subscale of the EER. Intraclass correlation (‘active negative’: ��� =
270 0.16, ‘passive negative’: ��� = 0.18, ‘positive’: ��� = 0.07) was higher than . 1 in two of the
271 three cases, so we used the described modelling approach. Conditional � ranged from .25
272 (‘positive’) to .27 (‘passive negative’) and .45 (‘active negative’). As Table 3 shows, players
273 generally reported a more positive than active or passive negative emotional experience.
274 However, conforming to the lower level of significance, this stronger positive experience was not
275 predicted by any of the factors we included into our model (Context: � = −.81, �� = .40,
276 �(87) = −2.05, � = .04, semi-partial � = 0.05). In contrast, looking at the fixed effects
277 regarding passive-negative emotional experience, there was a significant small interaction effect
278 of situational regulatory focus and chronic regulatory focus (� = 0.28, �� = .13, �(84) = 2.11,
279 � = .04, semi-partial � = 0.11). Simple slopes analysis (Figure 1) reveal a cross-over
280 interaction effect suggesting, that participants experience stronger passive-negative emotions in
281 case of a situational and chronic regulatory focus fit. A possible three-way interaction including
282 single- / group-environment did not predict passive-negative experience significantly and thus
283 did not influence this interaction. On the other hand, there was a significant small three-way
284 interaction effect of situational regulatory focus, chronic regulatory focus and context for the
285 active-negative subscale (� = 0.93, �� = .37, �(76) = 2.55, � = .01, semi-partial � = 0.08). GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 14
286 Simple slopes analysis (Figure 2), illustrating the interaction of situational and chronic regulatory
287 focus for single- / and group-environment, show that participants only report marginal
288 differences regarding active-negative experience during the single-tasks, but a cross-over
289 interaction with major differences during group-tasks. When in a promotion focused situation,
290 participants in a regulatory fit experienced far stronger active-negative emotions than their
291 counterparts. None of the three factors alone, nor any of their two-way interactions significantly
292 predicted active-negative emotional experience.
293
294 Use of Regulation Strategies
295 Intraclass correlation ranged from 0.01 (‘stronger self-categorization’) to 0.46
296 (‘acceptance’) and was higher than . 1 in most cases, thus showing additional support for the use
297 of multilevel models. Conditional � for the model including all interaction terms varied widely
298 and ranged from medium (‘upvalue teammates’ Cond. � = .23) to large (‘refocus on planning’
299 Cond. � = .71) (Tables 4-15). As Tables 4 to 15 show, players in general used a variety of
300 emotion regulation strategies to cope with the given situation. Conforming to the adjusted level
301 of significance, several fixed main effects of environmental factors situational focus (‘self-
302 blame’) and single-/group-context (‘refocus on planning’) as well as individual level chronic
303 regulatory focus (‘refocus on planning’, ‘positive reappraisal’, ‘blaming others’) cannot be
304 deemed significant. In addition, several two-way (Sit. Focus ´ Chronic Focus on ‘refocus on
305 planning’, Sit. Focus ´ Chronic Focus on ‘positive reappraisal’, Context ´ Sit. Focus on ‘putting
306 into perspective’, Context ´ Chronic Focus on ‘blaming others’, Context ´ Chronic Focus and
307 Sit. Focus ´ Chronic Focus on ‘stronger self-categorization’, Context ´ Chronic Focus on
308 ‘devalue teammates’) and three-way interaction effects (‘devalue teammates’, ‘stronger self- GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 15
309 categorization’) fell between a-levels of p < 0.05 and the corrected threshold of p < 0.0045
310 (Tables 4-15). Most effect sizes were smaller than semi-partial � = .10 and thus appeared to be
311 practicably irrelevant. In some cases, estimation yielded effect sizes that might be of practical
312 relevance and should be reported within the exploratory focus of this research. Of the fixed main
313 effects, Situational Focus predicted ‘self-blame’ (� = .66, �� = .27, �(63) = −2.42, � = .02,
314 semi-partial � = .11), while the interaction of Context ´ Chronic Focus predicted ‘blaming
315 others’ (� = 1.04, �� = .39, �(60) = 2.69, � = .009, semi-partial � = .11) and ‘stronger self-
316 categorization’ (� = −0.99, �� = .39, �(60) = 6.46, � = .01, semi-partial � = .10). Simple
317 slopes analysis show, that while participants blamed others less in their respective single-
318 environment fit, they all blamed others more in a promotion-oriented situation when being part
319 of a group (Figure 3). Figure 4 (‘stronger self-categorization’) depicts another type of change as
320 group-membership not only cancels out the cross-over interaction seen in the single-
321 environment, but also increases the general frequency of strategy use. Below the adjusted level
322 of significance, single-/group-environment predicted ‘putting into perspective’ (� = −1.31,
323 �� = .45, �(59) = −2.92, � = .005, semi-partial � = 0.13) and ‘stronger self-categorization’
324 (� = 1.56, �� = .39, �(60) = 4.01, � < .001, semi-partial � = 0.21), as participants
325 relativized less but categorized themselves as more important parts of the team when in a group-
326 environment. Analysis showed no significant fixed main effects for situational or chronic
327 regulatory focus. However, single-/group-environment interacted with participants´ chronic foci
328 to predict ‘refocus on planning’ (� = 1.21, �� = .36, �(58) = 3.37, � = .001, semi-partial
329 � = 0.16) and ‘positive reappraisal’ (� = 1.57, �� = .46, �(60) = 3.39, � = .001, semi-
330 partial � = 0.16). In both cases a three-way interaction, now also including situational
331 regulatory focus, predicted ‘refocus on planning’ (� = −0.71, �� = .24, �(82) = −2.96, � = GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 16
332 .004, semi-partial � = 0.10) and ‘positive reappraisal’ (� = −1.11, �� = .29, �(69) = −3.80,
333 � < .001, semi-partial � = 0.17). Simple slopes analysis revealed, that while performing on
334 their own, participants refocused on planning more in case of a fit scenario – although no cross-
335 over interaction can be reported. When they became part of a group, participants instead showed
336 a cross-over interaction with less use of this strategy in case of a regulatory fit (Figure 5).
337 Analysis depicted in Figure 6 (‘positive reappraisal’) show a cross-over interaction effect going
338 in the exact opposite direction. While participants reappraised more positively when in a single-
339 environment regulatory fit compared to a non-fit, they did way less compared to their respective
340 non-fit scenario when being part of a group. Effect sizes suggested practical relevance for all of
341 the reported fixed effects.
342
343 Discussion
344 Earlier, we assumed that (�) emotional experience and (b) also the use of emotion
345 regulation strategies as part of the transactional process are influenced by the interaction of
346 chronic and situational focus, thus a potential regulatory fit. Additionally, we assumed that (�)
347 being part of a group not only changes emotion generation but also the strategies used to regulate
348 these emotions accordingly.
349 Our results support the hypothesized influence of regulatory fit on emotional experience.
350 While neither an individual´s chronic focus nor a situational focus show a direct influence on
351 emotional experience, their interaction well does. But as reported above, there are several
352 necessary differentiations to be made. On the one hand, only active- and passive-negative parts
353 of participants´ emotional experience changed in case of a regulatory fit, but not the positive
354 aspects. This finding is especially surprising as participants in general showed a stronger positive GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 17
355 than negative experience (i.e. more potential variance could be expected) and all theoretical
356 accounts of regulatory focus include positively valenced parts of one´s emotional experience. A
357 potentially fundamental problem might have resulted from a measurement choice, as the scale
358 we used to measure the positive experience of participants yielded a considerably worse
359 performance as the two scales measuring negative experience did - additionally to the much
360 smaller and less differentiated number of items representing positively as opposed to negatively
361 valanced experience. This adds to the difficulties arising from the transfer of items from one
362 context to another - an approach Turner and Haslam (2001) have already criticized and which
363 needs to be addressed in future research. Results also indicate the notion of a more active
364 emotional experience for participants with a higher promotional focus as well as a more passive
365 experience for participants with a higher preventional focus. These observations resemble the
366 distinction made by Werth and Foerster (2007). They describe affective experience as between
367 joy and disappointment, thus more active and externalizing, in case of a promotion focus and
368 between ease and tenseness, thus more passive and internalizing, in case of a preventional focus.
369 On the other hand, regulatory fit exclusively predicted passive-negative experience, while
370 this relationship was only significant when also including single- / group-environment in case of
371 active-negative experience. As simple slopes analysis revealed, this three-way interaction
372 resulted from participants only reporting the hypothesized cross-over interaction for promotion
373 and prevention regulatory fit when being part of a group. For the two single-tasks, this cross-over
374 interaction cannot be deemed significant. To add to this finding, being in a group-environment
375 also exhibits a main fixed effect on positive emotional experience, with players reporting fewer
376 positive emotions. These effects may, although team-sports are often associated with societal
377 motives (Recours, Souville, & Griffet, 2004), have resulted from the added pressures when GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 18
378 competing within a team and its specific goals. Players may also have experienced more
379 constraints regarding their freedom to express emotions as well as reduced opportunities to cope
380 with them, consequently resulting in a less positive experience and influencing the role
381 regulatory fit might play. Combining these two results, we support the important role we had
382 hypothesized a group-environment might play the generation of one´s emotional experience.
383 Results suggest, that this role might be important in different aspects of the transactional process,
384 as not only nature and degree of emotional experience change with being part of a group, but it
385 may also enhance or attenuate the influence a potential regulatory fit plays. This connection
386 seems to justify the complex experimental approach we used to empirically test our assumptions
387 and adds further support for the integration of theoretical accounts of group-based emotion
388 generation (Goldenberg et al., 2016).
389 What is most surprising about the influence regulatory fit (and its interaction with single-
390 / group environment) exhibits on emotion generation, is the counter-intuitive direction of the
391 associated change in experience. While participants in a fit-scenario experience more negative
392 emotions, their respective counterparts in a non-fit-scenario experience quite the opposite. At
393 first glance, these findings do not fit the hypothesized cross-over interaction effect of a
394 regulatory fit, as feeling “right” might have resulted in feeling “good”, too (Cesario et al., 2004).
395 However, one might look at the different aspects a regulatory fit is associated with, before
396 making any further conclusions. As described above, a regulatory fit is associated with higher
397 motivational intensity and higher value assigned to the task at hand (Higgins, 2000). As we did
398 not control for success within the tasks or the players´ subjective evaluations, it can be
399 hypothesized, that the added value and importance might have resulted in negative emotional
400 experience, when not being successful. But even without the players´ evaluations of success, GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 19
401 there remain two important points, influencing the valence of experience. A higher value of the
402 task and higher motivational intensity might themselves result in more negative experiences
403 because of the added pressure to succeed and importance in relation to players´ self-concepts in a
404 given fit-scenario. This relation might be emphasized, when players become part of a group, as
405 this adds additional importance to both aspects as well as further constraints to ways of coping.
406 To further add to this line of thought, negative emotional experience has played an important part
407 as a driver to success in many aspects of goal-striving, especially in sports, where athletes use
408 negative feeling such as anger to fuel their competitive fire (Lazarus, 2000). This is in line with
409 the inevitable ambiguity when trying to rate emotional experience as good or bad: As several
410 authors have pointed out (Robazza, Pellizzari, & Hanin, 2004; Tamir & Ford, 2009),
411 instrumentally using negative emotional experience to reach a goal might make sense in many
412 cases - even when going against the hedonic principle. Recapitulating these different aspects that
413 might play a role in deciding whether players experience stronger positive or negative emotions,
414 it might be less of a surprise to see regulatory fit predict emotional experience, but not in the
415 hypothesized direction.
416 Goldenberg et al. (2016) suggest, that being part of a group changes the ways an
417 individual can regulate their emotions as constraints and new opportunities arise. Our results
418 support both of these assumptions. On the one hand, participants used ‘putting into perspective’
419 less, when part of group, which might be due to added constraints as relativizing can be limited
420 by group-based evaluations as well as norms and thus not as flexible as when on one´s own.
421 Turner and Haslam (2001) have questioned the transferability of strategies used in individual
422 contexts to those with group-membership, a notion Goldenberg et al. (2016) have rejected and
423 we can do so, too, based on the reported findings. On the other hand, not only constraints GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 20
424 become apparent. Being part of a group may also add new ways to cope. Our results support this
425 suggestion as participants emphasized their self-categorization as members of a group when part
426 of one. However, single- / group-environment showed no further main effects on use of group-
427 based regulation strategies. While it has to be emphasized, that the items we created for group-
428 based emotion regulation were heavily based on the work of Goldenberg et al. (2016), but not at
429 all further validated, this not significant influence might also be due to a more complex
430 connection between the factors we tested. When leaving out all interaction terms, single- / group-
431 environment strongly predicts ‘self-blame’, ‘devalue teammates’ and ‘weaker self-
432 categorization’. This influence appears much more complex, when interaction terms are added.
433 While in the case of ‘weaker self-categorization’ no effects remain significant, for both other
434 strategies Context ´ Chronic Focus as well as Context ´ Situational Focus ´ Chronic Focus are
435 significant fixed effects with effect sizes ranging from semi-partial � = 0.10 to 0.17.
436 Regulatory fit yields only significant results above the corrected a-level. The complex three-
437 way-interaction becomes all the more interesting, when looking at the simple slope plots (Figures
438 5 and 6). For ‘refocus on planning’ as well as ‘positive reappraisal’, participants used these
439 potentially functional regulation strategies more when in a regulatory fit and on their own.
440 However, this relationship was inverted when participants became part of a group. As the overall
441 strategy use did not diminish, these effects cannot be interpreted as group-membership
442 constraining participants´ ways to regulate their emotions but inverting the role a regulatory fit
443 might play. So, while participants were able to emphasize their belonging and importance to the
444 group - whether to distribute the load to more shoulders or to stake a claim in the case of success
445 goes beyond the scope of this research – group-membership also changed the way regulatory fit
446 could work. As this change resulted in less use of functional strategies, we have to not only point GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 21
447 out a counter-intuitive emotional experience in a regulatory fit but also in some cases a less
448 functional regulation of the experiences. This makes differentiating the type of environment an
449 individual is in all the more important, as the expected functionally of feeling “right” and a
450 person-environment-fit – based on a regulatory fit – seems to work in single-context but not in
451 group-context. This becomes especially troubling as the stronger negative experiences and the
452 reduced positive experiences participants reported, called for a better and more functional use of
453 regulation strategies. In this case it appears to be crucial, that besides adding further pressures
454 and constraints, being part of a group also adds new opportunities to cope.
455 On a cautionary note, we have to point out, that the scale we used to assess participants
456 chronic regulatory focus (Lockwood, Jordan, & Kunda, 2002) has not shown acceptable or better
457 reliability ratings in several studies (e.g. Plessner et al., 2009; Vogel & Genschow, 2013). Hence,
458 effects based on this measure and its interactions might be over- or underestimated. Still, on the
459 one hand we found this measure to be the most adoptable to the given situation within the
460 training setup of the teams as the more reliable and implicit Regulatory Strength Measure (Shah,
461 Higgins, & Friedman, 1998) can only be used in an extremely controlled environment and
462 administered via computer. On the other hand the Regulatory Focus Questionnaire (Higgins et
463 al., 2001) does relate way less to current attitudes, actions and habits which might have made it
464 much more difficult for the young participants to complete and additionally reduced its potential
465 specification to the athletic environment and its specific aims and ambitions. Based on these
466 considerations, we decided to use the approach advocated by Plessner et al. (2009) and used an
467 adopted version of the scale developed by Lockwood et al. (2002), thus making it comparable to
468 other studies in the context of sports. GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 22
469 Future research might aim to address generalizability and replicability of the findings in
470 different contexts and with different measures, especially of chronic regulatory focus. Higher
471 ratings of internal consistency could add credibility to the findings and help evaluate the strength
472 of the effects. Another aspect future research should address is, whether the reported interaction
473 effects can be shown to work in explicitly induced situational foci, thus making comparisons
474 with past research more viable.
475 Nonetheless, the findings reported above support the hypothesized complex dynamics
476 and interplay between a possible regulatory fit and a single- or group-environment regarding not
477 only emotional experience but also its regulation. We can thus add further evidence for the
478 explanatory role both theories play within stages of the transactional process of emotion
479 generation and regulation, implicating a broader integrating view of underlying processes in not
480 only individuals, but groups – making sympathizing with poor Lionel Messi all the more easy. GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 23
481 References
482 Aust, F., & Barth, M. (2018). papaja: Create APA manuscripts with R Markdown. Retrieved
483 December 8, 2018, from https://github.com/crsh/papaja
484 Barr, D.J. (2013). Random effects structure for testing interactions in linear mixed-effects
485 models. Frontiers in Psychology 4(328). doi:10.3389/fpsyg.2013.00328
486 Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for
487 confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language,
488 68(3), 255-278. doi:10.1016/j.jml.2012.11.001
489 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models
490 using lme4. Journal of Statistical Software, 67(1), 1–48. doi:10.18637/jss.v067.i01
491 Benecke, C., Vogt, T., Bock, A., Koschier, A., & Peham, D. (2008). A Self Report Questionnaire
492 for the Assessment of Emotional Experience and Emotion Regulation. Psychotherapie
493 Psychosomatik Medizinische Psychologie, 58(09/10), 366–370. doi:10.1055/s-2007-
494 986319
495 Bliese, P. (2016). Multilevel: Multilevel functions. Retrieved December 8, 2018, from
496 https://CRAN.R-project.org/package=multilevel
497 Brockner, J., & Higgins, E. (2001). Regulatory Focus Theory: Implications for the Study of
498 Emotions at Work. Organizational Behavior and Human Decision Processes, 86(1), 35–66.
499 doi:10.1006/obhd.2001.2972
500 Cesario, J., Grant, H., & Higgins, E. T. (2004). Regulatory fit and persuasion: Transfer from"
501 feeling right.". Journal of Personality and Social Psychology, 86(3), 388.
502 doi:10.1037/0022-3514.86.3.388 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 24
503 Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., & Schabenberger, O. (2008). An
504 R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine, 27(29),
505 6137–6157. doi:10.1002/sim.3429
506 Förster, J., Higgins, E. T., & Idson, L. C. (1998). Approach and avoidance strength during goal
507 attainment: Regulatory focus and the "goal looms larger" effect. Journal of Personality and
508 Social Psychology, 75(5), 1115–1131. doi:10.1037/0022-3514.75.5.1115
509 Garnefski, N., & Kraaij, V. (2006). Cognitive emotion regulation questionnaire development of a
510 short 18-item version (CERQ-short). Personality and Individual Differences, 41(6), 1045–
511 1053. doi:10.1016/j.paid.2006.04.010
512 Goldenberg, A., Halperin, E., van Zomeren, M., & Gross, J. J. (2016). The Process Model of
513 Group-Based Emotion: Integrating Intergroup Emotion and Emotion Regulation
514 Perspectives. Personality and Social Psychology Review, 20(2), 118–141.
515 doi:10.1177/1088868315581263
516 Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations. In J. J. Gross
517 (Ed.) Handbook of emotion regulation (pp. 3–20). New York: Guilford Press.
518 Halperin, E., Porat, R., Tamir, M., & Gross, J. J. (2012). Can Emotion Regulation Change
519 Political Attitudes in Intractable Conflicts? From the Laboratory to the Field.
520 Psychological Science, 24(1), 106–111. doi:10.1177/0956797612452572
521 Haws, K. L., Dholakia, U. M., & Bearden, W. O. (2010). An Assessment of Chronic Regulatory
522 Focus Measures. Journal of Marketing Research, 47(5), 967–982.
523 doi:10.1509/jmkr.47.5.967 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 25
524 Higgins, E. T., Shah, J., & Friedman, R. (1997). Emotional responses to goal attainment:
525 Strength of regulatory focus as moderator. Journal of Personality and Social Psychology,
526 72(3), 515–525. doi:10.1037/0022-3514.72.3.515
527 Higgins, E. T. (2000). Making a good decision: Value from fit. American Psychologist, 55(11),
528 1217-1230. doi:10.1037/0003-066X.55.11.1217
529 Höner, O., & Votteler, A. (2016). Prognostic relevance of motor talent predictors in early
530 adolescence: A group- and individual-based evaluation considering different levels of
531 achievement in youth football. Journal of Sports Sciences, 34(24), 2269–2278.
532 doi:10.1080/02640414.2016.1177658
533 Keller, J., & Bless, H. (2006). Regulatory fit and cognitive performance: The interactive effect of
534 chronic and situationally induced self-regulatory mechanisms on test performance.
535 European Journal of Social Psychology, 36(3), 393–405. doi:10.1002/ejsp.307
536 Khawaja, M. (1993). Repression and popular collective action: Evidence from the West Bank.
537 Sociological Forum, 8(1), 47–71. doi:10.1007/BF01112330
538 Koole, S. L. (2009). The psychology of emotion regulation: An integrative review. Cognition and
539 Emotion, 23(1), 4–41. doi:10.1080/02699930802619031
540 Lazarus, R. S. (2000). How emotions influence performance in competitive sports. The Sport
541 Psychologist, 14(3), 229-252.
542 Lockwood, P., Jordan, C. H., & Kunda, Z. (2002). Motivation by positive or negative role
543 models: Regulatory focus determines who will best inspire us. Journal of Personality and
544 Social Psychology, 83(4), 854–864. doi:10.1037/0022-3514.83.4.854 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 26
545 Mackie, D. M., Maimer, A. T., & Smith, E. R. (2009). Intergroup emotions theory. In T.D. (Ed.),
546 Handbook of prejudice, stereotyping, and discrimination (pp. 285–307). New York:
547 Psychology Press.
548 Memmert, D., Hüttermann, S., & Orliczek, J. (2013). Decide like Lionel Messi! The impact of
549 regulatory focus on divergent thinking in sports. Journal of Applied Social Psychology,
550 43(10), 2163–2167. doi:10.1111/jasp.12159
551 Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from
552 generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–
553 142. doi:10.1111/j.2041-210x.2012.00261.x
554 Plessner, H., Unkelbach, C., Memmert, D., Baltes, A., & Kolb, A. (2009). Regulatory fit as a
555 determinant of sport performance: How to succeed in a soccer penalty-shooting.
556 Psychology of Sport and Exercise, 10(1), 108–115. doi:10.1016/j.psychsport.2008.02.001
557 R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria:
558 R Foundation for Statistical Computing. Retrieved December 8, 2018, from https://www.R-
559 project.org/
560 Recours, R. A., Souville, M., & Griffet, J. (2004). Expressed Motives for Informal and
561 Club/Association-based Sports Participation. Journal of Leisure Research, 36(1), 1-22.
562 doi:10.1080/00222216.2004.11950008
563 Revelle, W. (2018). Psych: Procedures for psychological, psychometric, and personality
564 research. Evanston, Il: Northwestern University. Retrieved December 8, 2018, from
565 https://CRAN.R-project.org/package=psych GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 27
566 Robazza, C., Pellizzari, M., & Hanin, Y. (2004). Emotion self-regulation and athletic
567 performance: An application of the IZOF model. Psychology of Sport and Exercise, 5(4),
568 379–404. doi:10.1016/S1469-0292(03)00034-7
569 Shah, J., Higgins, E., & Friedman, R. (1998). Performance incentives and means: How
570 regulatory focus influences goal attainment. Journal of Personality and Social Psychology,
571 74(2), 285–293. doi:10.1037//0022-3514.74.2.285
572 Spiegel, S., Grant-Pillow, H., & Higgins, E. T. (2004). How regulatory fit enhances motivational
573 strength during goal pursuit. European Journal of Social Psychology, 34(1), 39–54.
574 doi:10.1002/ejsp.180
575 Tajfel, H., & Turner, J. C. (2004). The Social Identity Theory of Intergroup Behavior. New York:
576 Psychology Press.
577 Tamir, M., & Ford, B. Q. (2009). Choosing to be afraid: Preferences for fear as a function of goal
578 pursuit. Emotion, 9(4), 488–497. doi:10.1037/a0015882
579 Turner, J. C., & Haslam, S. A. (2001). Social identity, organizations, and leadership. In M.E.
580 Turner (Ed.), Groups at work: Theory and research (pp. 25–65). Mahwah, NJ: Lawrence
581 Erlbaum Associates Publishers.
582 Vogel, T., & Genschow, O. (2013). When Do Chronic Differences in Self-Regulation Count?:
583 Regulatory Focus Effects in Easy and Difficult Soccer Tasks. Journal of Sport and
584 Exercise Psychology, 35(2), 216–220. doi:10.1123/jsep.35.2.216
585 Werth, L., & Foerster, J. (2007). How regulatory focus influences consumer behavior. European
586 Journal of Social Psychology, 37(1), 33–51. doi:10.1002/ejsp.343 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 28
587 Tables 588 Table 1 589 Multilevel Model Parameters and Standard Errors for Passive-Negative Emotional Experience
590 591 592 Parameters Model 1 Model 2 Model 3
593 594 595 Fixed Effects Coefficients Estimates (SE) 596 Intercept 1.40*** (0.17) 1.41*** (0.21) 1.41*** (0.17) 597 Context -0.01 (0.19) -0.02 (0.24) -0.03 (0.20) 598 Situational Focus 0.04 (0.14) -0.05 (0.10) -0.05 (0.10) 599 Chronic Focus -0.14 (0.17) 0.12 (0.13) -0.02 (0.14) 600 Context ´ Situational Focus -0.19 (0.19) 601 Context ´ Chronic Focus 0.20 (0.19) 602 Situational Focus ´ Chronic Focus 0.28* (0.13) 603 Context ´ Sit. Focus ´ Chron. Focus 0.07 (0.19)
604 605 Random Effects 606 Residual 1.14 0.83 1.25 607 Intercept 0.30 1.04 0.29 608 Sit. Focus x Single Context Slope 0.00 609 Sit. Focus x Group Context Slope 0.00 610 Intercept – Sit. F. ´ Single Covariance 0.01 611 Intercept – Sit. F. ´ Group Covariance -0.01 612 Context Slope 0.94 613 Sit. Focus Slope 0.08 614 Intercept - Context Covariance -0.87 615 Intercept - Sit. Focus Covariance 0.03 616 Context - Sit. Focus Covariance -0.15
617 618 Fit Statistics 619 Conditional R2 (number of parameters) 0.27 (15) 0.47 (11) 0.19 (6) 620 AIC; BIC 422.47; 464.53 409.12; 439.96 410.09; 426.92 621 Log Likelihood -196.23 -193.56 -199.05
622 623 624 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 625 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 626 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.017 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 29
627 Table 2 628 Multilevel Model Parameters and Standard Errors for Active-Negative Emotional Experience
629 630 631 632 Parameters Model 1 Model 2 Model 3
633 634 635 Fixed Effects Coefficients Estimates (SE) 636 Intercept 1.92*** (0.42) 1.71*** (0.41) 1.72*** (0.37) 637 Context -0.32 (0.56) 0.09 (0.24) 0.08 (0.22) 638 Situational Focus -0.11 (0.25) 0.03 (0.21) 0.02 (0.22) 639 Chronic Focus -0.49 (0.41) 0.22 (0.14) 0.16 (0.14) 640 Context ´ Situational Focus 0.27 (0.37) 641 Context ´ Chronic Focus -0.95 (0.56) 642 Situational Focus ´ Chronic Focus 0.29 (0.25) 643 Context ´ Sit. Focus ´ Chron. Focus 0.93* (0.37)
644 645 Random Effects 646 Residual 1.00 1.22 1.49 647 Intercept 0.39 1.81 0.26 648 Sit. Focus x Single Context Slope 0.03 649 Sit. Focus x Group Context Slope 0.09 650 Intercept – Sit. F. ´ Single Covariance 0.04 651 Intercept – Sit. F. ´ Group Covariance -0.05 652 Context Slope 0.61 653 Sit. Focus Slope 0.19 654 Intercept - Context Covariance -1.02 655 Intercept - Sit. Focus Covariance -0.51 656 Context - Sit. Focus Covariance 0.33
657 658 Fit Statistics 659 Conditional R2 (number of parameters) 0.45 (15) 0.32 (11) 0.16 (6) 660 AIC; BIC 433.27; 475.81 445.10; 476.30 438.90; 455.92 661 Log Likelihood -201.63 -211.55 -213.45
662 663 664 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 665 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 666 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.017 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 30
667 Table 3 668 Multilevel Model Parameters and Standard Errors for Positive Emotional Experience
669 670 671 672 Parameters Model 1 Model 2 Model 3
673 674 675 Fixed Effects Coefficients Estimates (SE) 676 Intercept 3.49*** (0.26) 3.29*** (0.20) 3.29*** (0.22) 677 Context -0.81* (0.40) -0.40** (0.15) -0.40** (0.13) 678 Situational Focus -0.13 (0.18) 0.01 (0.12) 0.01 (0.13) 679 Chronic Focus 0.38 (0.28) 0.00 (0.08) -0.05 (0.08) 680 Context ´ Situational Focus 0.27 (0.25) 681 Context ´ Chronic Focus 0.11 (0.40) 682 Situational Focus ´ Chronic Focus -0.25 (0.18) 683 Context ´ Sit. Focus ´ Chron. Focus -0.15 (0.25)
684 685 Random Effects 686 Residual 0.49 0.41 0.53 687 Intercept 0.00 0.17 0.06 688 Sit. Focus x Single Context Slope 0.04 689 Sit. Focus x Group Context Slope 0.03 690 Intercept – Sit. F. ´ Single Covariance 0.00 691 Intercept – Sit. F. ´ Group Covariance 0.00 692 Context Slope 0.27 693 Sit. Focus Slope 0.07 694 Intercept - Context Covariance -0.21 695 Intercept - Sit. Focus Covariance -0.06 696 Context - Sit. Focus Covariance 0.06
697 698 Fit Statistics 699 Conditional R2 (number of parameters) 0.25 (15) 0.35 (11) 0.16 (6) 700 AIC; BIC 318.25; 360.56 309.33; 340.35 303.70; 320.62 701 Log Likelihood -144.13 -143.66 -145.85
702 703 704 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 705 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 706 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.017 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 31
707 Table 4 708 Multilevel Model Parameters and Standard Errors for Self-Blame
709 710 711 712 Parameters Model 1 Model 2 Model 3
713 714 715 Fixed Effects Coefficients Estimates (SE) 716 Intercept 1.88*** (0.42) 2.20*** (0.31) 2.20*** (0.34) 717 Context -0.13 (0.56) -0.78** (0.24) -0.78** (0.20) 718 Situational Focus 0.66* (0.27) 0.44* (0.18) 0.44* (0.20) 719 Chronic Focus -0.54 (0.42) -0.18 (0.12) -0.16 (0.12) 720 Context ´ Situational Focus -0.43 (0.36) 721 Context ´ Chronic Focus 0.31 (0.56) 722 Situational Focus ´ Chronic Focus 0.33 (0.27) 723 Context ´ Sit. Focus ´ Chron. Focus -0.36 (0.37)
724 725 Random Effects 726 Residual 0.98 0.88 1.26 727 Intercept 0.61 0.64 0.15 728 Sit. Focus x Single Context Slope 0.40 729 Sit. Focus x Group Context Slope 0.05 730 Intercept – Sit. F. ´ Single Covariance -0.32 731 Intercept – Sit. F. ´ Group Covariance -0.18 732 Context Slope 0.97 733 Sit. Focus Slope 0.18 734 Intercept - Context Covariance -0.17 735 Intercept - Sit. Focus Covariance -0.11 736 Context - Sit. Focus Covariance -0.36
737 738 Fit Statistics 739 Conditional R2 (number of parameters) 0.41 (15) 0.47 (11) 0.23 (6) 740 AIC; BIC 429.77; 472.43 418.82; 450.10 417.83; 434.90 741 Log Likelihood -199.88 -198.41 -202.92
742 743 744 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 745 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 746 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 32
747 Table 5 748 Multilevel Model Parameters and Standard Errors for Rumination
749 750 751 752 Parameters Model 1 Model 2 Model 3
753 754 755 Fixed Effects Coefficients Estimates (SE) 756 Intercept 2.19*** (0.27) 2.12*** (0.24) 2.12*** (0.23) 757 Context -0.34 (0.33) -0.20 (0.17) -0.20 (0.13) 758 Situational Focus 0.06 (0.16) 0.11 (0.11) 0.11 (0.13) 759 Chronic Focus -0.25 (0.27) -0.03 (0.09) -0.02 (0.11) 760 Context ´ Situational Focus 0.09 (0.22) 761 Context ´ Chronic Focus -0.14 (0.33) 762 Situational Focus ´ Chronic Focus 0.20 (0.16) 763 Context ´ Sit. Focus ´ Chron. Focus -0.01 (0.22)
764 765 Random Effects 766 Residual 0.35 0.29 0.53 767 Intercept 0.61 1.00 0.28 768 Sit. Focus x Single Context Slope 0.15 769 Sit. Focus x Group Context Slope 0.07 770 Intercept – Sit. F. ´ Single Covariance -0.07 771 Intercept – Sit. F. ´ Group Covariance -0.21 772 Context Slope 0.63 773 Sit. Focus Slope 0.11 774 Intercept - Context Covariance -0.42 775 Intercept - Sit. Focus Covariance -0.15 776 Context - Sit. Focus Covariance -0.14
777 778 Fit Statistics 779 Conditional R2 (number of parameters) 0.58 (15) 0.65 (11) 0.35 (6) 780 AIC; BIC 338.38; 381.16 320.29;351.67 336.04;353.15 781 Log Likelihood -154.19 -149.15 -162.02
782 783 784 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 785 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 786 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 33
787 Table 6 788 Multilevel Model Parameters and Standard Errors for Refocus on Planning
789 790 791 792 Parameters Model 1 Model 2 Model 3
793 794 795 Fixed Effects Coefficients Estimates (SE) 796 Intercept 3.22*** (0.31) 3.39*** (0.29) 3.42*** (0.27) 797 Context 0.74* (0.35) 0.36* (0.17) 0.35* (0.14) 798 Situational Focus -0.17 (0.17) -0.29* (0.13) -0.31* (0.14) 799 Chronic Focus -0.79* (0.32) -0.16 (0.15) -0.19 (0.15) 800 Context ´ Situational Focus -0.26 (0.24) 801 Context ´ Chronic Focus 1.21** (0.36) 802 Situational Focus ´ Chronic Focus 0.36* (0.18) 803 Context ´ Sit. Focus ´ Chron. Focus -0.72** (0.24)
804 805 Random Effects 806 Residual 0.39 0.50 0.65 807 Intercept 1.02 1.20 0.58 808 Sit. Focus x Single Context Slope 0.17 809 Sit. Focus x Group Context Slope 0.04 810 Intercept – Sit. F. ´ Single Covariance -0.15 811 Intercept – Sit. F. ´ Group Covariance -0.17 812 Context Slope 0.45 813 Sit. Focus Slope 0.01 814 Intercept - Context Covariance -0.48 815 Intercept - Sit. Focus Covariance -0.09 816 Context - Sit. Focus Covariance 0.04
817 818 Fit Statistics 819 Conditional R2 (number of parameters) 0.71 (15) 0.62 (11) 0.51 (6) 820 AIC; BIC 369.90; 412.45 370.73; 401.93 367.14; 384.16 821 Log Likelihood -169.95 -174.37 -177.57
822 823 824 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 825 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 826 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 34
827 Table 7 828 Multilevel Model Parameters and Standard Errors for Positive Reappraisal
829 830 831 832 Parameters Model 1 Model 2 Model 3
833 834 835 Fixed Effects Coefficients Estimates (SE) 836 Intercept 3.00*** (0.38) 3.01*** (0.33) 3.00*** (0.29) 837 Context -0.00 (0.46) 0.02 (0.16) 0.02 (0.16) 838 Situational Focus -0.02 (0.21) -0.01 (0.16) -0.01 (0.16) 839 Chronic Focus -0.96* (0.39) -0.30 (0.15) -0.27 (0.16) 840 Context ´ Situational Focus 0.02 (0.29) 841 Context ´ Chronic Focus 1.57** (0.46) 842 Situational Focus ´ Chronic Focus 0.49* (0.21) 843 Context ´ Sit. Focus ´ Chron. Focus -1.11*** (0.29)
844 845 Random Effects 846 Residual 0.66 0.75 0.77 847 Intercept 1.20 1.31 0.61 848 Sit. Focus x Single Context Slope 0.02 849 Sit. Focus x Group Context Slope 0.07 850 Intercept – Sit. F. ´ Single Covariance -0.15 851 Intercept – Sit. F. ´ Group Covariance -0.29 852 Context Slope 0.02 853 Sit. Focus Slope 0.04 854 Intercept - Context Covariance -0.16 855 Intercept - Sit. Focus Covariance -0.22 856 Context - Sit. Focus Covariance 0.03
857 858 Fit Statistics 859 Conditional R2 (number of parameters) 0.55 (15) 0.49 (11) 0.47 (6) 860 AIC; BIC 393.83; 436.50 395.84; 427.12 387.70; 404.76 861 Log Likelihood -181.92 -186.92 -187.85
862 863 864 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 865 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 866 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 35
867 Table 8 868 Multilevel Model Parameters and Standard Errors for Putting into Perspective
869 870 871 872 Parameters Model 1 Model 2 Model 3
873 874 875 Fixed Effects Coefficients Estimates (SE) 876 Intercept 3.38*** (0.37) 2.83*** (0.33) 2.83*** (0.30) 877 Context -1.31** (0.45) -0.20 (0.18) -0.21 (0.16) 878 Situational Focus -0.38 (0.20) -0.01 (0.15) -0.01 (0.16) 879 Chronic Focus -0.18 (0.37) -0.16 (0.15) -0.08 (0.16) 880 Context ´ Situational Focus 0.73* (0.29) 881 Context ´ Chronic Focus 0.44 (0.46) 882 Situational Focus ´ Chronic Focus 0.14 (0.20) 883 Context ´ Sit. Focus ´ Chron. Focus -0.44 (0.29)
884 885 Random Effects 886 Residual 0.63 0.64 0.77 887 Intercept 1.20 1.74 0.64 888 Sit. Focus x Single Context Slope 0.00 889 Sit. Focus x Group Context Slope 0.13 890 Intercept – Sit. F. ´ Single Covariance -0.06 891 Intercept – Sit. F. ´ Group Covariance -0.35 892 Context Slope 0.35 893 Sit. Focus Slope 0.04 894 Intercept - Context Covariance -0.62 895 Intercept - Sit. Focus Covariance -0.25 896 Context - Sit. Focus Covariance 0.09
897 898 Fit Statistics 899 Conditional R2 (number of parameters) 0.57 (15) 0.56 (11) 0.46 (6) 900 AIC; BIC 396.31; 438.97 393.05; 424.34 389.69;406.75 901 Log Likelihood -183.16 -185.53 -188.84
902 903 904 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 905 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 906 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 36
907 Table 9 908 Multilevel Model Parameters and Standard Errors for Catastrophizing
909 910 911 912 Parameters Model 1 Model 2 Model 3
913 914 915 Fixed Effects Coefficients Estimates (SE) 916 Intercept 1.81*** (0.29) 1.72*** (0.24) 1.72*** (0.24) 917 Context -0.31 (0.35) -0.12 (0.16) -0.12 (0.13) 918 Situational Focus -0.03 (0.19) 0.03 (0.13) 0.03 (0.13) 919 Chronic Focus -0.13 (0.29) -0.06 (0.10) -0.10 (0.12) 920 Context ´ Situational Focus 0.13 (0.23) 921 Context ´ Chronic Focus 0.17 (0.35) 922 Situational Focus ´ Chronic Focus -0.02 (0.18) 923 Context ´ Sit. Focus ´ Chron. Focus -0.04 (0.2)
924 925 Random Effects 926 Residual 0.38 0.29 0.54 927 Intercept 0.80 1.03 0.32 928 Sit. Focus x Single Context Slope 0.30 929 Sit. Focus x Group Context Slope 0.14 930 Intercept – Sit. F. ´ Single Covariance -0.25 931 Intercept – Sit. F. ´ Group Covariance -0.32 932 Context Slope 0.50 933 Sit. Focus Slope 0.26 934 Intercept - Context Covariance -0.18 935 Intercept - Sit. Focus Covariance -0.27 936 Context - Sit. Focus Covariance -0.25
937 938 Fit Statistics 939 Conditional R2 (number of parameters) 0.57 (15) 0.66 (11) 0.38 (6) 940 AIC; BIC 347.13; 389.79 324.79; 356.08 338.45; 355.51 941 Log Likelihood -158.56 -151.40 -163.22
942 943 944 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 945 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 946 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 37
947 Table 10 948 Multilevel Model Parameters and Standard Errors for Blaming Others
949 950 951 952 Parameters Model 1 Model 2 Model 3
953 954 955 Fixed Effects Coefficients Estimates (SE) 956 Intercept 2.03*** (0.28) 1.73*** (0.24) 1.73*** (0.24) 957 Context -0.59 (0.38) 0.02 (0.18) 0.02 (0.14) 958 Situational Focus -0.06 (0.18) 0.14 (0.12) 0.14 (0.14) 959 Chronic Focus -0.61* (0.29) 0.16* (0.08) 0.07 (0.09) 960 Context ´ Situational Focus 0.41 (0.25) 961 Context ´ Chronic Focus 1.04** (0.39) 962 Situational Focus ´ Chronic Focus 0.29 (0.18) 963 Context ´ Sit. Focus ´ Chron. Focus -0.38 (0.25)
964 965 Random Effects 966 Residual 0.47 0.45 0.64 967 Intercept 0.22 0.54 0.08 968 Sit. Focus x Single Context Slope 0.08 969 Sit. Focus x Group Context Slope 0.02 970 Intercept – Sit. F. ´ Single Covariance -0.04 971 Intercept – Sit. F. ´ Group Covariance -0.07 972 Context Slope 0.58 973 Sit. Focus Slope 0.02 974 Intercept - Context Covariance -0.43 975 Intercept - Sit. Focus Covariance -0.04 976 Context - Sit. Focus Covariance -0.03
977 978 Fit Statistics 979 Conditional R2 (number of parameters) 0.37 (15) 0.42 (11) 0.12 (6) 980 AIC; BIC 341.09; 383.87 336.60; 367.97 338.03; 355.15 981 Log Likelihood -155.54 -157.30 -163.02
982 983 984 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 985 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 986 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 38
987 Table 11 988 Multilevel Model Parameters and Standard Errors for Acceptance
989 990 991 992 Parameters Model 1 Model 2 Model 3
993 994 995 Fixed Effects Coefficients Estimates (SE) 996 Intercept 2.95*** (0.38) 3.16*** (0.31) 3.17*** (0.27) 997 Context 0.52 (0.44) 0.08 (0.14) 0.08 (0.15) 998 Situational Focus 0.23 (0.22) 0.09 (0.16) 0.08 (0.15) 999 Chronic Focus -0.31 (0.37) -0.40** (0.13) -0.38** (0.14) 1000 Context ´ Situational Focus -0.29 (0.28) 1001 Context ´ Chronic Focus 0.20 (0.44) 1002 Situational Focus ´ Chronic Focus -0.06 (0.22) 1003 Context ´ Sit. Focus ´ Chron. Focus -0.11 (0.28)
1004 1005 Random Effects 1006 Residual 0.60 0.59 0.68 1007 Intercept 1.39 1.38 0.43 1008 Sit. Focus x Single Context Slope 0.29 1009 Sit. Focus x Group Context Slope 0.19 1010 Intercept – Sit. F. ´ Single Covariance -0.52 1011 Intercept – Sit. F. ´ Group Covariance -0.47 1012 Context Slope 0.02 1013 Sit. Focus Slope 0.24 1014 Intercept - Context Covariance 0.02 1015 Intercept - Sit. Focus Covariance -0.48 1016 Context - Sit. Focus Covariance -0.03
1017 1018 Fit Statistics 1019 Conditional R2 (number of parameters) 0.53 (15) 0.53 (11) 0.46 (6) 1020 AIC; BIC 383.28; 425.83 372.10; 403.30 364.53; 381.55 1021 Log Likelihood -176.64 -175.05 -176.27
1022 1023 1024 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 1025 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 1026 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 39
1027 Table 12 1028 Multilevel Model Parameters and Standard Errors for Upvalue Teammates
1029 1030 1031 1032 Parameters Model 1 Model 2 Model 3
1033 1034 1035 Fixed Effects Coefficients Estimates (SE) 1036 Intercept 1.59*** (0.30) 1.36*** (0.24) 1.36*** (0.24) 1037 Context -0.56 (0.42) -0.09 (0.17) -0.09 (0.15) 1038 Situational Focus -0.00 (0.19) 0.16 (0.13) 0.16 (0.15) 1039 Chronic Focus -0.05 (0.30) -0.04 (0.08) -0.04 (0.08) 1040 Context ´ Situational Focus 0.31 (0.28) 1041 Context ´ Chronic Focus 0.40 (0.43) 1042 Situational Focus ´ Chronic Focus -0.01 (0.19) 1043 Context ´ Sit. Focus ´ Chron. Focus -0.25 (0.28)
1044 1045 Random Effects 1046 Residual 0.58 0.54 0.68 1047 Intercept 0.05 0.32 0.03 1048 Sit. Focus x Single Context Slope 0.03 1049 Sit. Focus x Group Context Slope 0.05 1050 Intercept – Sit. F. ´ Single Covariance 0.04 1051 Intercept – Sit. F. ´ Group Covariance -0.04 1052 Context Slope 0.42 1053 Sit. Focus Slope 0.00 1054 Intercept - Context Covariance -0.33 1055 Intercept - Sit. Focus Covariance -0.02 1056 Context - Sit. Focus Covariance 0.02
1057 1058 Fit Statistics 1059 Conditional R2 (number of parameters) 0.23 (15) 0.25 (11) 0.06 (6) 1060 AIC; BIC 355.21; 397.99 343.08; 374.45 338.23; 355.34 1061 Log Likelihood -162.61 -160.54 -163.11
1062 1063 1064 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 1065 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 1066 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 40
1067 Table 13 1068 Multilevel Model Parameters and Standard Errors for Devalue Teammates
1069 1070 1071 1072 Parameters Model 1 Model 2 Model 3
1073 1074 1075 Fixed Effects Coefficients Estimates (SE) 1076 Intercept 1.66*** (0.31) 1.47*** (0.24) 1.47*** (0.28) 1077 Context 0.44 (0.43) 0.81*** (0.21) 0.81*** (0.16) 1078 Situational Focus 0.03 (0.20) 0.16 (0.14) 0.16 (0.16) 1079 Chronic Focus -0.36 (0.31) 0.00 (0.11) -0.02 (0.11) 1080 Context ´ Situational Focus 0.25 (0.29) 1081 Context ´ Chronic Focus 1.00* (0.44) 1082 Situational Focus ´ Chronic Focus 0.22 (0.20) 1083 Context ´ Sit. Focus ´ Chron. Focus -0.65* (0.29)
1084 1085 Random Effects 1086 Residual 0.60 0.55 0.87 1087 Intercept 0.07 0.34 0.17 1088 Sit. Focus x Single Context Slope 0.10 1089 Sit. Focus x Group Context Slope 0.17 1090 Intercept – Sit. F. ´ Single Covariance 0.07 1091 Intercept – Sit. F. ´ Group Covariance -0.05 1092 Context Slope 0.92 1093 Sit. Focus Slope 0.06 1094 Intercept - Context Covariance -0.52 1095 Intercept - Sit. Focus Covariance 0.05 1096 Context - Sit. Focus Covariance -0.05
1097 1098 Fit Statistics 1099 Conditional R2 (number of parameters) 0.51 (15) 0.55 (11) 0.29 (6) 1100 AIC; BIC 389.96; 432.74 378.07; 409.44 380.65; 397.76 1101 Log Likelihood -179.98 -178.03 -184.32
1102 1103 1104 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 1105 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 1106 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 41
1107 Table 14 1108 Multilevel Model Parameters and Standard Errors for Stronger Self-Categorization
1109 1110 1111 1112 Parameters Model 1 Model 2 Model 3
1113 1114 1115 Fixed Effects Coefficients Estimates (SE) 1116 Intercept 1.97*** (0.29) 2.02*** (0.24) 2.02*** (0.26) 1117 Context 1.56*** (0.39) 1.47*** (0.20) 1.47*** (0.15) 1118 Situational Focus -0.09 (0.17) -0.12 (0.13) -0.13 (0.15) 1119 Chronic Focus 0.44 (0.29) -0.15 (0.10) -0.16 (0.11) 1120 Context ´ Situational Focus -0.06 (0.26) 1121 Context ´ Chronic Focus -0.99* (0.39) 1122 Situational Focus ´ Chronic Focus -0.34* (0.18) 1123 Context ´ Sit. Focus ´ Chron. Focus 0.53* (0.26)
1124 1125 Random Effects 1126 Residual 0.49 0.39 0.74 1127 Intercept 0.22 0.83 0.17 1128 Sit. Focus x Single Context Slope 0.00 1129 Sit. Focus x Group Context Slope 0.23 1130 Intercept – Sit. F. ´ Single Covariance 0.03 1131 Intercept – Sit. F. ´ Group Covariance -0.09 1132 Context Slope 0.95 1133 Sit. Focus Slope 0.13 1134 Intercept - Context Covariance -0.64 1135 Intercept - Sit. Focus Covariance -0.23 1136 Context - Sit. Focus Covariance 0.16
1137 1138 Fit Statistics 1139 Conditional R2 (number of parameters) 0.68 (15) 0.74 (11) 0.50 (6) 1140 AIC; BIC 371.36; 414.14 357.92; 389.29 363.31; 380.43 1141 Log Likelihood -170.68 -167.96 -175.66
1142 1143 1144 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 1145 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 1146 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 42
1147 Table 15 1148 Multilevel Model Parameters and Standard Errors for Weaker Self-Categorization
1149 1150 1151 1152 Parameters Model 1 Model 2 Model 3
1153 1154 1155 Fixed Effects Coefficients Estimates (SE) 1156 Intercept 1.40*** (0.33) 1.17*** (0.25) 1.16*** (0.28) 1157 Context -0.06 (0.43) 0.40* (0.20) 0.39* (0.17) 1158 Situational Focus 0.16 (0.22) 0.32* (0.16) 0.33* (0.17) 1159 Chronic Focus -0.22 (0.33) -0.02 (0.09) -0.00 (0.10) 1160 Context ´ Situational Focus 0.31 (0.29) 1161 Context ´ Chronic Focus -0.14 (0.43) 1162 Situational Focus ´ Chronic Focus 0.11 (0.22) 1163 Context ´ Sit. Focus ´ Chron. Focus 0.17 (0.29)
1164 1165 Random Effects 1166 Residual 0.58 0.63 0.88 1167 Intercept 0.63 0.26 0.08 1168 Sit. Focus x Single Context Slope 0.42 1169 Sit. Focus x Group Context Slope 0.17 1170 Intercept – Sit. F. ´ Single Covariance -0.45 1171 Intercept – Sit. F. ´ Group Covariance -0.20 1172 Context Slope 0.61 1173 Sit. Focus Slope 0.17 1174 Intercept - Context Covariance 0.09 1175 Intercept - Sit. Focus Covariance -0.16 1176 Context - Sit. Focus Covariance -0.18
1177 1178 Fit Statistics 1179 Conditional R2 (number of parameters) 0.45 (15) 0.38 (11) 0.14 (6) 1180 AIC; BIC 384.07; 426.73 375.44; 406.72 371.36; 388.42 1181 Log Likelihood -177.03 -176.72 -179.68
1182 1183 1184 Note – AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; Contrast Coded: Sit. 1185 Prevention Focus ‘-1’, Sit. Promotion Focus ‘+1’; Single Context ‘-1’, Group Context ‘+1’. 1186 * p < 0.05, ** p < 0.01, *** p < 0.001, bold: p < 0.0045 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 43
1187 Figures
1.8
1.6
Chro nic
1.4 Regulatory Focus + 1 SD
Mean
− 1 SD − negative Experience Passive
1.2
1.0
Prevention Promotion 1188 Situational Regulatory Focus
1189 Figure 1. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1190 2) and Situational Focus (Level 1) in predicting Passive-negative Emotional Experience. Note –
1191 Chronic Regulatory Focus is z-standardized.
1192 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 44
Single Context Group Context 3.0
2.5
2.0 Active − negative Experience
1.5
1.0
Prevention Promotion Prevention Promotion Situational Regulatory Focus
Chronic Regulatory Focus − 1 SD Mean + 1 SD 1193
1194 Figure 2. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1195 2), Situational Focus (Level 1) and Single- or Group-Context (Level 1) in predicting Active-
1196 negative Emotional Experience. Note – Chronic Regulatory Focus is z-standardized.
1197 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 45
Single Context Group Context
2.4
2.2
2.0 Blaming Others
1.8
1.6
1.4
Prevention Promotion Prevention Promotion Situational Regulatory Focus
Chronic Regulatory Focus − 1 SD Mean + 1 SD 1198
1199 Figure 3. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1200 2), Situational Focus (Level 1) and Single- or Group-Context (Level 1) in predicting Blaming
1201 Others. Note – Chronic Regulatory Focus is z-standardized.
1202
1203
1204 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 46
Single Context Group Context
3.5
3.0
2.5 Stronger Self − Categorization Stronger
2.0
1.5
Prevention Promotion Prevention Promotion Situational Regu latory Focu s
Chronic Regulatory Focus − 1 SD Mean + 1 SD 1205
1206 Figure 4. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1207 2), Situational Focus (Level 1) and Single- or Group-Context (Level 1) in predicting Stronger
1208 Self-Categorization. Note – Chronic Regulatory Focus is z-standardized.
1209 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 47
Single Context Group Context
3.6
3.4
3.2 Refocus on Planning Refocus 3.0
2.8
2.6
Prevention Promotion Prevention Promotion Situational Regulatory Focus
Chronic Regulatory Focus − 1 SD Mean + 1 SD 1210
1211 Figure 5. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1212 2), Situational Focus (Level 1) and Single- or Group-Context (Level 1) in predicting Refocus on
1213 Planning. Note – Chronic Regulatory Focus is z-standardized.
1214
1215 GROUP-BASED EMOTION REGULATION AND REGULATORY FOCUS 48
Single Context Group Context
3.6
3.2 Positive Reappraisal Positive
2.8
2.4
Prevention Promotion Prevention Promotion Situational Regulatory Focus
Chronic Regulatory Focus − 1 SD Mean + 1 SD 1216
1217 Figure 6. Simple slopes analysis of cross-level interaction of Chronic Regulatory Focus (Level
1218 2), Situational Focus (Level 1) and Single- or Group-Context (Level 1) in predicting Positive
1219 Reappraisal. Note – Chronic Regulatory Focus is z-standardized.