Experience Sampling

Experience Sampling refers to the repeated sampling of momentary experiences in the individual’s natural environment. Methodological advantages include the minimization of retrospective response biases and the maximization of the validity of the assessment. Conceptual benefits include the provision of insights into shortterm processes and into the daily-life contexts of the phenomena under study. Making use of the benefits of Experience Sampling while taking its methodological challenges into consideration allows addressing important research questions in the social and behavioral sciences with much precision and clarity. Despite this, Experience Sampling information is still rare in the data infrastructure that is publicly available to researchers. This stands in contrast to a current thriving of the methodology in research producing datasets that are not publicly available, as is the case in many psychological investigations. Following a discussion of the benefits and challenges of Experience Sampling, this report outlines its potential uses in social science and economic research and characterizes the status quo of Experience Sampling applications in currently available datasets, focusing primarily on household surveys conducted after 2001. Recommendations are given on how an intensified use of Experience Sampling in large-scale data collections can be facilitated in the future.Experience Sampling in the social and behavioural sciences

Experience sampling

"Experience Sampling refers to the repeated sampling of momentary experiences in the individual's natural environment. Methodological advantages include the minimization of retrospective response biases and the maximization of the validity of the assessment. Conceptual benefits include the provision of insights into short-term processes and into the daily-life contexts of the phenomena under study. Making use of the benefits of Experience Sampling while taking its methodological challenges into consideration allows addressing important research questions in the social and behavioral sciences with much precision and clarity. Despite this, Experience Sampling information is still rare in the data infrastructure that is publicly available to researchers. This stands in contrast to a current thriving of the methodology in research producing datasets that are not publicly available, as is the case in many psychological investigations. Following a discussion of the benefits and challenges of Experience Sampling, this report outlines its potential uses in social science and economic research and characterizes the status quo of Experience Sampling applications in currently available datasets, focusing primarily on household surveys conducted after 2001. Recommendations are given on how an intensified use of Experience Sampling in large-scale data collections can be facilitated in the future." (author's abstract

Measuring Time Use in Surveys: How Valid Are Time Use Questions in Surveys? Concordance of Survey and Experience Sampling Measures

Since it is still unclear to what extent time allocation retrospectively reported in questionnaires, reflects people's actual behavior, examining the accuracy of responses to time use survey questions is of crucial importance. We analyze the congruence of time use information assessed through retrospective questionnaires and through experience sampling methodology. The sample comprised 433 individuals ranging in age from 14 to 86 years. Participants completed standard survey questions on time allocation. In addition, a mobile-phone based experience sampling technology was used over a period of three weeks to obtain snapshots of, on average, 54 momentary activities in which participants participated while pursuing their normal daily routines. Experience sampling assessments were scheduled six times a day over at least nine days, including workdays, Saturdays, andSundays. Results indicate that the congruence between time allocation assessed with survey questions (i.e. in SOEP) and time allocation assessed with experience sampling methodology depends on the characteristics of the respective activities. Associations between standard survey questions and experience sampling methods are quite substantial for long-lasting and externally structured activities, such as paid work on workdays. Incontrast, associations between survey and experience sampling methods are somewhat weaker, though highly statistically significant, for less externally structured, short-term and infrequent activities, such as errands, housework, and leisure. These moderate and relatively small correlations may indicate either an error-prone estimation of the prevalence of shortterm and infrequent activities by experience sampling or respondents' overrating of sporadic and short activities in survey questions. We conclude that activities with a long duration, such as paid work, can be measured in a satisfactory manner using short survey questions. Futureresearch is necessary to elucidate which method (experience sampling method or survey questions) delivers more reliable and valid measures for shortterm and sporadic activities.Day Reconstruction Methods (DRM) should be included in this future methodological research.Survey methods, experience sampling method, validity, time use, market work, housework, leisure, German Socio-Economic Panel Study, MMAA, SOEP

When and How to Regulate: Everyday Emotion-Regulation Strategy Use and Stressor Intensity

Contextual factors shape emotion regulation (ER). The intensity of emotional stimuli may be such a contextual factor that influences the selection and moderates the effectiveness of ER strategies in reducing negative affect (NA). Prior research has shown that, on average, when emotional stimuli were more intense, distraction was selected over reappraisal (and vice versa). This pattern was previously shown to be adaptive as the preferred strategies were more efficient in the respective contexts. Here, we investigated whether stressor intensity predicted strategy use and effectiveness in similar ways in daily life. We examined five ER strategies (reappraisal, reflection, acceptance, distraction, and rumination) in relation to the intensity of everyday stressors, using two waves of experience-sampling data (N = 156). In accordance with our hypotheses, reappraisal, reflection, and acceptance were used less, and rumination was used more, when stressors were more intense. Moreover, results suggested that distraction was more effective, and rumination more detrimental the higher the stressor intensity. Against our hypotheses, distraction did not covary with stressor intensity, and there was no evidence that reappraisal, reflection, and acceptance were more effective at lower levels of stressor intensity. Instead, when examined individually, reflection and reappraisal (like distraction) were more effective at higher levels of stressor intensity. In sum, stressor intensity predicted ER selection and moderated strategy effectiveness, but the results also point to a more complex ER strategy use in daily life than in the laboratory

Focusing and restricting: Two aspects of motivational selectivity in adulthood

Using a short-term longitudinal design, the authors investigated implications of 2 facets of motivational selectivity-restricting (to few goals) and focusing (on central and similar goals)-for goal-pursuit investment. Participants were 20 -69 years old (Time 1, N ϭ 177; Time 2, N ϭ 160). Results show that motivational selectivity in terms of focusing (but not in terms of restricting) is associated with an enhanced involvement in goal pursuit (assessed 3 months later), irrespective of age. Structural equation models demonstrated that this association is completely mediated by the degree of mutual facilitation among goals. Furthermore, motivational selectivity increases from middle to older adulthood. This contributes to the maintenance of high goal involvement into later adulthood, despite aging-related increases in resource limitations. Keywords: motivational selectivity, adult development, goal-pursuit involvement, intergoal facilitation "Don't put all your eggs in one basket," or "Those who follow every path never reach any destination"-folk wisdom offers contradictory advice. Is it better to diversify and have many goals or to focus one's resources on a limited number of options? Or is it not an "either-or" question, and selectivity is advisable in some situations but not others? We propose that a person's age can be seen as a proxy variable for a group of factors determining selectivity. In this article, we present an empirical study on the development and function of two forms of motivational selectivity, restricting (to few goals) and focusing (on central and similar goals). This study tested whether people react to age-associated decreases in goal-relevant resources by becoming increasingly selective in their future-oriented motivations and whether this increase in selectivity contributes to the maintenance of high behavioral involvement in goal pursuit in a reality of increasingly limited resources. A key feature of human evolution is the emergence of a behavioral repertoire that is both vast and flexible and thus opens a multitude of potential developmental pathways in a person's life Regulatory processes that address this challenge occur on both the societal and the personal level Our aim in this research was to address the phenomenon of selectivity as it is evident in developmental-regulatory processes on the part of the individual Previous research on aspects of motivational selectivity-as indexed, for example, by the concepts of elective selection (e.g., Moreover, different from the aforementioned studies that have addressed the association between selectivity and psychological well-being, we targeted a more proximal outcome of selectivity in the present study, namely, goal-directed behavioral investment. Choosing goals is only a first step in eventually achieving desired outcomes. Shaping one's life course in aspired directions also requires goal-directed action. Selecting a goal, however, does not necessarily imply that the individual will also engage in behaviors directed at goal realization. Many goals remain just that: goals. We posit that the two facets of motivational selectivity-restricting (the number of goals) and focusing (the content of goals)-foster, on a behavioral level, a high involvement in goal pursuit. This should be the case because selecting few goals that address subjectively central life domains and are similar to each other might result in a high degree of mutual facilitation among the selected goals as indexed by instrumental goal relations and overlapping goal-attainment strategies Age-Related Changes in Motivational Selectivity Taking a life span developmental perspective, we further predicted that motivational selectivity increases from early to late adulthood. This increase might accelerate beginning with the transition from middle to later adulthood. Goal-relevant resourcesthat is, means to realize aspired outcomes-are limited in all phases of life. These resource limitations become increasingly pronounced throughout life (Baltes, 1997), and particularly so in later adulthood There is some evidence that the first facet of motivational selectivity-restricting the number of goals-increases throughout adulthood. For example, Cross and Markus (1991) observed in a cross-sectional sample that adults reported fewer hoped-for and feared possible selves the older they were. A similar finding was reported by There is also some empirical evidence that is consistent with the view that motivational selectivity in terms of focusing on subjectively important life domains might increase in older adulthood. For example, in the domain of social relations, research emanating from socioemotional selectivity theory The purpose of this study was to investigate the implications of older adults' increased selectivity for their goal-directed behavioral investment. We predicted that motivational selectivity might serve an important behavioral function in older adulthood, namely, the maintenance of high levels of active involvement in goal pursuit, despite age-associated declines in available resources, and that a high extent of intergoal facilitation plays a mediating role in this association. Overview of the Present Study To investigate our predictions, which are summarized in Method Sample At the first measurement session, the sample comprised 177 participants ranging in age from 20.10 to 69.43 years (M ϭ 44.69, SD ϭ 14.55). About equal numbers of participants belonged to each of five age groups (20 -29 years, 30 -39 years, 40 -49 years, 50 -59 years, 60 -69 years; see A survey company contacted Berlin residents by means of a random dialing procedure and asked for their willingness to participate in this study. Participants were recruited on a first-come basis until prescribed cell sizes of the sample composition were reached. Procedure The study procedure comprised two assessment sessions with an average interval of about 3 months (M ϭ 88.2 days, SD ϭ 10.5). At each of these measurement sessions, participants completed a set of questionnaires in small groups. At Time 1 (T1), participants reported their current personal goals and we obtained information on their motivational selectivity and on the extent of mutual facilitation among their goals. At Time 2 (T2), we assessed how intensively participants had engaged in goal-pursuit behaviors during the study interval. Respective instruments are described in the Instruments section. At both measurement sessions, participants completed a number of additional instruments that are not relevant here. Participants received a 15-Euro (approximately $20) reimbursement for each measurement session. MOTIVATIONAL SELECTIVITY Of the 177 participants at T1, 160 (90.4%) also participated at T2 (20 -29 years: n ϭ 29, 82.9%; 30 -39 years: n ϭ 29, 82.9%; 40 -49 years: n ϭ 32, 94.1%; 50 -59 years: n ϭ 38, 97.4%; 60 -69 years: n ϭ 32, 94.1%). Participants who only participated at T1 did not differ from those who took part in both measurement occasions in any of the variables that are relevant here and were assessed at T1 (i.e., three facets of motivational selectivity and intergoal facilitation; discussed later). 1 Instruments Personal Goals (T1) At T1, participants were asked to describe their current goals in an open response format. They were instructed to report goals that they had for the near future (i.e., coming weeks, months, or years), currently judged to be important, and that they expected would still be important in the coming weeks or months. The instructions included a brief explanation of the concept of goals as well as sample life domains and sample goals. 2 The number of goals to be reported was not specified. Following this free self-report, participants were asked to select the three goals they considered to be most important out of their list of reported goals. These three goals were then rated on a number of dimensions (as described later in this article). The decision to focus on the participants' three most important goals was based on our expectation from previous research observations that a high percentage of participants would report at least three goals when the number of to-be-reported goals is not specified, whereas a considerably lower percentage would report four or more goals. This expectation was confirmed in the present study: 173 participants (97.7%) reported at least three goals. More than three goals were reported by 130 participants (73.4%). Motivational Selectivity (T1) At T1, we assessed two facets of motivational selectivity. One facet pertained to the quantity of reported goals (i.e., restricting to few vs. many goals). The other facet pertained to the quality (or content) of the reported goals (i.e., focusing on similar vs. diverse goals and on central vs. marginal goals). Selectivity in terms of restricting. We used the number of current goals reported at T1 as an indicator of the quantity aspect of motivational selectivity. One univariate within-age-group outlier was identified in the oldest subsample (z score ϭ 3.39, raw score ϭ 10) and adjusted to the closest raw value in the within-age-group distribution (raw score ϭ 9; total sample: M ϭ 5.15, SD ϭ 2.08). 3 Selectivity in terms of focusing. We assessed two content-related aspects of motivational selectivity, namely, the similarity (vs. variability) and the centrality (vs. marginality) of personal goals reported at T1. Respective indicators were derived with respect to 14 content (life) domains. Previous research has shown that life domains addressed in people's goals vary substantially according to age. On the basis of these findings (e.g., Heckhausen, 1997; Nurmi, 1992), we aimed at compiling a selection of life domains that includes in equal parts domains with high relevance for different adult age groups. Assembling such an "age-fair" selection of life domains was a prerequisite for investigating potential age-associated differences in the two content-related selectivity indicators (see later). Our selection included the following domains: (a) friends-acquaintances, (b) family circle-children, (c) profession-work, (d) health-physical wellbeing, (e) education, (f) recreational activities, (g) financial situation, (h) material belongings, (i) partnership, (j) personal characteristics, (k) mental health, (l) physical capability, (m) enjoyment of life, and (n) appearance. Three different approaches empirically supported the assumption that the prerequisite of age-fairness in the selection of life domains was met. First, we asked participants to indicate how important they considered each of the 14 life domains to be for their life satisfaction. Response options ranged from 1 (not at all important) to 7 (very important). As expected, participants in the five age groups differed in the subjective importance they ascribed to single life domains. When averaged across all domains, however, the five age groups did not differ significantly with respect to (a) the mean importance they ascribed to the 14 life domains (M ϭ 5.49, SD ϭ .64) and (b) the within-person standard deviation of these 14 importance ratings (M ϭ 1.31, SD ϭ .40). There also were no age-group differences in (c) the number of life domains participants rated as highly important (i.e., a rating of 6 or 7; M ϭ 8.17, SD ϭ 2.82), as moderately important (i.e., a rating of 3, 4, or 5; M ϭ 5.27, SD ϭ 2.73), or as unimportant for their life satisfaction (i.e., a rating of 1 or 2; M ϭ .67, SD ϭ 1.09; all ps Ͼ .65). Second, we asked participants to evaluate how relevant each of the 14 life domains was for each of their three most important goals. Response options ranged from 1 (not at all relevant) to 5 (very relevant). The five age groups did not differ with respect to (a) the mean goal relevance they ascribed to these 14 life domains across all three goals (averaged across all life domains and goals; M ϭ 3.43, SD ϭ .50), (b) the overall number of life domains participants rated as highly relevant (i.e., a rating of 4 or 5) for at least one of their goals (M ϭ 11.27, SD ϭ 1.91), and (c) the average number of life domains they rated as highly relevant for each of their three most important goals (averaged across all three goals; M ϭ 7.36, SD ϭ 2.05; all ps Ͼ .25). Third, using a dichotomous (yes-no) item, we asked participants to indicate, for each of their three most important goals, whether there are life domains other than the 14 included in the list that are relevant for this goal. Again, the five age groups did not differ in the number of participants who affirmed this item for one or more of the three goals (M ϭ 6.80, SD ϭ 1,48), 2 (4, N ϭ 175) ϭ 1.03, p ϭ .91. In short, the empirical basis is strong for assuming that the prerequisite of age fairness in the compilation of life domains was met. This warrants the derivation of the two selectivity indicators described next and the interpretation of respective age-group differences. First, as an indicator of the extent to which participants were selective in terms of selecting similar (vs. diverse) goals, we determined the variability of the life-domain relevance of the participants' three most important goals (following a rationale used by 2 Examples of goals reported by a 64-year-old female participant are (a) "to continue living a quiet life," (b) "to broaden my knowledge," (c) "to engage in social-welfare activities, that is, to look for an opportunity to work with foreign children," (d) "to become more courageous, take sides when injustice occurs." 3 To avoid distortions of statistical analyses, we tested all variables for univariate within-age-group outliers, which we defined-in accordance with 176 RIEDIGER AND FREUND determined, separately for each of the 14 life domains, the within-person standard deviation of these ratings across the three goals. This information indicates how distinctively (vs. similarly) participants evaluated the relevance of this particular life domain for their three most important goals. Averaging this information across all 14 life domains yielded an indicator of the overall dissimilarity of the participants' three most important goals in terms of their life-domain relevance (M ϭ 1.00, SD ϭ 0.31). 4 Second, as an indicator of the degree to which participants were selective in terms of selecting central (vs. marginal) goals, we determined the extent to which they addressed with their goals those life domains they regarded as central to their life satisfaction. As described earlier, participants rated on 7-point rating scales how important they considered each of 14 life domains to be to their life satisfaction. Ratings of 6 or 7, the two highest points on the rating scale, indicated that a person regarded this particular life domain as central to his or her life satisfaction. Participants also indicated, on 5-point rating scales, how relevant each of these 14 life domains was for each of their three most important personal goals. Again, ratings of the two highest points on the rating scale, 4 or 5, indicated that a particular life domain was highly relevant for a given goal. From these ratings, we determined, separately for each of the three reported goals, which percentage of those life domains that the participant considered highly important for his or her life satisfaction (i.e., importance ratings 6 or 7) were addressed by this goal (i.e., goal relevance rating 5 or 6). Averaging this index across the three most important goals yielded an indicator of the average percentage to which a participant's goals addressed those life domains he or she considered to be central to his or her life satisfaction (M ϭ 64.07%, SD ϭ 18.03). 5 Consider the following illustrating example: A person rated 8 of the 14 life domains as highly important for her life satisfaction. Of these 8 life domains, she rated 6 domains (75%) as highly relevant for her first goal, 4 domains (50%) as highly relevant for her second goal, and 5 domains (62.5%) as highly relevant for her third goal. This person would receive a centrality score of 62.5%, which is the average percentage to which this person's goals addressed those life domains she considered to be central to her life satisfaction. Intergoal Facilitation (T1) To assess the extent of mutual facilitation among the participants' three most important personal goals reported at T1, participants responded, for each of the 6 pairwise combinations of these goals, to the facilitation scale of the Intergoal Relations Questionnaire (IRQ; 6 Goal Pursuit (T2) At T2, participants responded, for each of the three most important goals they had reported at T1, to the following items: "In the past three months since you first participated in this study, on average . . . Results The description of results is organized as follows. We first present results regarding age-group mean differences in the various measures of motivational selectivity, intergoal facilitation, and intensity of goal pursuit. Then, we present results of structural equation models testing our predictions regarding associations between the study variables in the investigated sample. Age-Group Mean Differences in Study Variables A multivariate analysis of variance on the three indicators of motivational selectivity (assessed at T1), the indicator of intergoal facilitation (assessed at T1), and the indicator of goal-pursuit intensity (assessed at T2) yielded a significant multivariate agegroup effect according to Wilks's Lambda, F(20, 471.911) ϭ 2.33, p ϭ .001 (partial 2 ϭ .08). Means and standard deviations of each of these constructs in the five investigated age groups as well as results of univariate follow-up analyses are summarized in 5 This information was not available for 5 participants (2.8% of the total sample: 1 male participant, 34.85 years; 4 female participants, 59.56, 62.31, 64.25, and 67.89 years, respectively) who rated none of the 14 life domains as highly important for their life satisfaction. 6 This information was not available for 5 participants (2.8% of the total sample: 1 male participant, 35.14 years; 4 female participants, 41.21, 49.31, 52.63, and 62.31 years, respectively) who provided incomplete responses to the IRQ items. 177 MOTIVATIONAL SELECTIVITY related to those life domains they regarded as highly important for their life satisfaction. 7 The remaining four age groups of young and middle-aged adults (covering the age range from 20 to 59 years old) were largely comparable in the extent of intergoal facilitation and in the three facets of motivational selectivity (for pairwise tests of mean differences between age groups, see As a first step toward exploring factors underlying the observed pattern of age-group differences, we examined whether these age effects are due to the fact that, in contrast to all other age groups, most of the participants above age 60 were no longer involved in work or study (see In two of the four analyses, occupational status explained significant amounts of variance in the dependent variable: Nonworking persons (i.e., retirees and homemakers) reported significantly fewer goals than both participants who were employed or in training and participants with unspecified occupational status of "other" (7% of variance explained). In addition, nonworking participants showed significantly less dissimilarity among their goals than all other occupational status categories (10% of variance 7 Please note that there were no age-related differences in the overall percentage of subjectively central life domains participants addressed with at least one of their goals (M ϭ 91.92, SD ϭ 12.70, p ϭ .73). What this result indicates is that participants in the oldest age group addressed with each single of their three most important goals, on average, a higher percentage of subjectively central life domains than did younger and middle-aged adults

Emotion regulation strategies and psychological health across cultures

Emotion regulation is important for psychological health and can be achieved by implementing various strategies. How one regulates emotions is critical for maximizing psychological health. Few studies, however, tested the psychological correlates of different emotion regulation strategies across multiple cultures. In a preregistered cross-cultural study (N = 3,960, 19 countries), conducted during the COVID-19 pandemic, we assessed associations between the use of seven emotion regulation strategies (situation selection, distraction, rumination, cognitive reappraisal, acceptance, expressive suppression, and emotional support seeking) and four indices of psychological health (life satisfaction, depressive symptoms, perceived stress, and loneliness). Model comparisons based on Bayesian information criteria provided support for cultural differences in 36% of associations, with very strong support for differences in 18% of associations. Strategies that were linked to worse psychological health in individualist countries (e.g., rumination, expressive suppression) were unrelated or linked to better psychological health in collectivist countries. Cultural differences in associations with psychological health were most prominent for expressive suppression and rumination and also found for distraction and acceptance. In addition, we found evidence for cultural similarities in 46% of associations between strategies and psychological health, but none of this evidence was very strong. Cultural similarities were most prominent in associations of psychological health with emotional support seeking. These findings highlight the importance of considering the cultural context to understand how individuals from diverse backgrounds manage unpleasant emotions. (PsycInfo Database Record (c) 2023 APA, all rights reserved

30. Vorlesung (14.07.2020): Altersstereotype - Teil B

Vorlesungsinhalt: Aus der Sicht älterer Menschen; Interpersonale und Intrapersonale Auswirkungen von Altersstereotype; Stereotype Embodiment Theor

22. Vorlesung (23.06.2020): Meditation und Altern - Teil C

Vorlesungsinhalt: Kognition; Einfluss von Meditationstraining auf Kognition; Auswirkungen von Meditation allgemein; Gehirnaktivitätsänderungen durch Meditation; Einfluss von Meditation auf Emotionen und Wohlbefinde

33. Vorlesung (14.07.2020): Prüfungsvorbereitung

Vorlesungsinhalt: Lernziele; Klausur; Wichtige Regeln; Was ist prüfungsrelevant?; Prüfungsaufgaben; Aufgabenbeispiel