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Some Recommendations on the Use of Daily Life Methods in Affective Science

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Abstract

Real-world emotions are often more vivid, personally meaningful, and consequential than those evoked in the lab. Therefore, studying emotions in daily life is essential to test theories, discover new phenomena, and understand healthy emotional functioning; in short, to move affective science forward. The past decades have seen a surge of research using daily diary, experience sampling, or ecological momentary assessment methods to study emotional phenomena in daily life. In this paper, we will share some of the insights we have gained from our collective experience applying such daily life methods to study everyday affective processes. We highlight what we see as important considerations and caveats involved in using these methods and formulate recommendations to improve their use in future research. These insights focus on the importance of (i) theory and hypothesis-testing; (ii) measurement; (iii) timescale; and (iv) context, when studying emotions in their natural habitat.

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References

  • Ariens, S., Ceulemans, E., & Adolf, J. K. (2020). Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview. Journal of Psychosomatic Research, 137, 110191.

    Article  PubMed  Google Scholar 

  • Barker, R. G. (1968). Ecological psychology: Concepts and methods for studying the environment of human behavior. Stanford University Press.

    Google Scholar 

  • Barrett, L. F., Mesquita, B., Ochsner, K. N., & Gross, J. J. (2007). The experience of emotion. Annual Review of Psychology, 58, 373–403.

    Article  PubMed  PubMed Central  Google Scholar 

  • Belisario, J. S. M., Doherty, K., O’Donoghue, J., Ramchandani, P., Majeed, A., Doherty, G., et al. (2017). A bespoke mobile application for the longitudinal assessment of depression and mood during pregnancy: protocol of a feasibility study. BMJ open, 7, e014469.

    Article  Google Scholar 

  • Bennett, D., Davidson, G., & Niv, Y. (2021). A model of mood as integrated advantage. Manuscript submitted for publication. Psychological Review. https://doi.org/10.1037/rev0000294

  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.

    Google Scholar 

  • Borsboom, D., van der Maas, H., Dalege, J., Kievit, R., & Haig, B. (2021). Theory Construction Methodology: A practical framework for theory formation in psychology. https://doi.org/10.31234/osf.io/w5tp8

  • Bos, E. H., de Jonge, P., & Cox, R. F. A. (2019). Affective variability in depression: Revisiting the inertia-instability paradox. British Journal of Psychology, 110, 814–827. https://doi.org/10.1111/bjop.12372

    Article  PubMed  Google Scholar 

  • Brose, A., Schmiedek, F., Gerstorf, D., & Voelkle, M. C. (2020). The measurement of within-person affect variation. Emotion, 20, 677–699.

    Article  PubMed  Google Scholar 

  • Butler, E. A. (2015). Interpersonal affect dynamics: It takes two (and time) to tango. Emotion Review, 7, 336–341.

    Article  Google Scholar 

  • Carlier, C., Niemeijer, K., Mestdagh, M., Bauwens, M., Vanbrabant, P., Geurts, L., van Waterschoot, T., & Kuppens, P. (in press). In search of state and trait emotion markers in mobile-sensed language: A field study. JMIR Mental Health. https://doi.org/10.2196/31724

  • Chow, S.-M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5, 208–225.

    Article  PubMed  Google Scholar 

  • Clark, L. A., Watson, D., & Leeka, J. (1989). Diurnal variation in the positive affects. Motivation and Emotion, 13(3), 205–234.

    Article  Google Scholar 

  • Coan, J. A. (2010). Emergent ghosts of the emotion machine. Emotion Review, 2, 274–285.

    Article  Google Scholar 

  • Coifman, K. G., Bonanno, G. A., & Rafaeli, E. (2007). Affect dynamics, bereavement and resilience to loss. Journal of Happiness Studies, 8, 371–392.

    Article  Google Scholar 

  • Conner, T. S., & Barrett, L. F. (2012). Trends in ambulatory self-report: The role of momentary experience in psychosomatic medicine. Psychosomatic Medicine, 74, 327–337.

    Article  PubMed  PubMed Central  Google Scholar 

  • Cummins, R. (2010). The world in the head. Oxford University Press.

    Book  Google Scholar 

  • Dejonckheere, E., & Mestdagh, M. (2021). On the signal-to-noise ratio in real-life emotional time series. In C. E. Waugh & P. Kuppens (Eds.), Affect dynamics (pp. 131–152). https://doi.org/10.1007/978-3-030-82965-0_7

  • Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamic measures add limited information to the prediction of psychological well-being. Nature Human Behaviour, 3, 478–491.

    Article  PubMed  Google Scholar 

  • Dejonckheere, E., Mestdagh, M., Kuppens, P., & Tuerlinckx, F. (2020). Reply to: Context matters for affective chronometry. Nature Human Behavior, 4, 690–693.

    Article  Google Scholar 

  • Dejonckheere, E., Mestdagh, M., Verdonck, S., Lafit, G., Ceulemans, E., Bastian, B., & Kalokerinos, E. K. (2019). The relation between positive and negative affect becomes more negative in response to personally relevant events. Emotion Advanced online publication.

  • Dickens, Y. L., Van Raalte, J., & Hurlburt, R. T. (2018). On investigating self-talk: A descriptive experience sampling study of inner experience during golf performance. The Sport Psychologist, 32, 66–73.

    Article  Google Scholar 

  • Ebner-Priemer, U. W., & Sawitzki, G. (2007). Ambulatory assessment of affective instability in borderline personality disorder: The effect of the sampling frequency. European Journal of Psychological Assessment, 23, 238–247. https://doi.org/10.1027/1015-5759.23.4.238

    Article  Google Scholar 

  • Eichler, M. (2012). Causal inference in time series analysis. In C. Berzuini, P. Dawid, & L. Bernardinelli (Eds.), Causality: Statistical perspectives and applications (pp. 327–354). John Wiley & Sons. https://doi.org/10.1002/9781119945710.ch22

    Chapter  Google Scholar 

  • Fiedler, K. (2017). What constitutes strong psychological science? The (neglected) role of diagnosticity and a priori theorizing. Perspectives on Psychological Science, 12, 46–61.

    Article  PubMed  Google Scholar 

  • Fried, E. I. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry,31(4), 271–288. https://doi.org/10.1080/1047840X.2020.1853461.

    Article  Google Scholar 

  • Frijda, N. H. (1988). The laws of emotion. American Psychologist, 43, 349–358.

    Article  PubMed  Google Scholar 

  • Greenaway, K. H., Kalokerinos, E. K., & Williams, L. A. (2018). Context is everything (in emotion research). Social and Personality Psychology Compass, 12, e12393.

    Article  Google Scholar 

  • Gollob, H., & Reichardt, C. (1987). Taking account of time lags in causal models. Child Development, 58, 80–92. https://doi.org/10.2307/1130293

    Article  PubMed  Google Scholar 

  • Grahek, I., Schaller, M., & Tackett, J. L. (2021). Anatomy of a psychological theory: Integrating construct-validation and computational-modeling methods to advance theorizing. Perspectives on Psychological Science, 1745691620966794, 16, 803–815.

    Article  PubMed  Google Scholar 

  • Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 37, 424–438.

    Article  Google Scholar 

  • Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26, 1–26.

    Article  Google Scholar 

  • Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26, 10–15. https://doi.org/10.1177/0963721416666518

    Article  Google Scholar 

  • Haslbeck, J. M., & Ryan, O. (2021). Recovering within-person dynamics from psychological time series. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2021.1896353

  • Heininga, V. E., Dejonckheere, E., Houben, M., Obbels, J., Sienaert, P., Leroy, B., van Roy, J., & Kuppens, P. (2019). The dynamical signature of anhedonia in major depressive disorder: Positive emotion dynamics, reactivity, and recovery. BMC Psychiatry, 19, 59.

    Article  PubMed  PubMed Central  Google Scholar 

  • Heller, A. S., Fox, A. S., Wing, E. K., McQuisition, K. M., Vack, N. J., & Davidson, R. J. (2015). The neurodynamics of affect in the laboratory predicts persistence of real-world emotional responses. Journal of Neuroscience, 35(29), 10503–10509.

    Article  PubMed  Google Scholar 

  • Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020). Context-aware experience sampling reveals the scale of variation in affective experience. Scientific Reports, 10(1), 12459.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181–188.

    Article  PubMed  Google Scholar 

  • Horstmann, K. T., & Ziegler, M. (2020). Assessing personality states: What to consider when constructing personality state measures. European Journal of Personality, 34 (6), 037–1059. https://doi.org/10.1002/per.2266

  • Houben, M., Claes, L., Vansteelandt, K., Berens, A., Sleuwaegen, E., & Kuppens, P. (2017). The emotion regulation function of nonsuicidal self-injury: A momentary assessment study in inpatients with borderline personality disorder features. Journal of Abnormal Psychology, 126, 89–95.

    Article  PubMed  Google Scholar 

  • Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141, 901–930.

    Article  PubMed  Google Scholar 

  • Jacobson, N. C., Chow, S. M., & Newman, M. G. (2019). The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data. Behavior Research Methods, 51(1), 295–315.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jacobson, N. C., & Chung, Y. J. (2020). Passive sensing of prediction of moment-to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones. Sensors, 20(12), 3572.

    Article  PubMed Central  Google Scholar 

  • Kalokerinos, E. K., Erbas, Y., Ceulemans, E., & Kuppens, P. (2019). Differentiate to regulate: Low negative emotion differentiation is associated with ineffective use but not selection of emotion-regulation strategies. Psychological Science, 30, 863–879.

    Article  PubMed  Google Scholar 

  • Kalokerinos, E. K., Murphy, S. C., Koval, P., Bailen, N. H., Crombez, G., Hollenstein, T., et al. (2020). Neuroticism may not reflect emotional variability. Proceedings of the National Academy of Sciences, 117, 9270–9276.

    Article  Google Scholar 

  • Kalokerinos, E. K., Résibois, M., Verduyn, P., & Kuppens, P. (2017). The temporal deployment of emotion regulation strategies during negative emotional episodes. Emotion, 17, 450–458. https://doi.org/10.1037/emo0000248

    Article  PubMed  Google Scholar 

  • Kalokerinos, E. K., Tamir, M., & Kuppens, P. (2017). Instrumental motives in negative emotion regulation in daily life: Frequency, consistency, and predictors. Emotion, 17, 648–657. https://doi.org/10.1037/emo0000269

    Article  PubMed  Google Scholar 

  • Koval, P., & Kuppens, P. (2012). Changing emotion dynamics: Individual differences in the effect of anticipatory social stress on emotional inertia. Emotion, 12, 256–267.

    Article  PubMed  Google Scholar 

  • Koval, P., Pe, M. L., Meers, K., & Kuppens, P. (2013). Affect dynamics in relation to depressive symptoms: variable, unstable or inert? Emotion, 13, 1132–1141.

    Article  PubMed  Google Scholar 

  • Kuppens, P. (2015). It’s about time: A special section on affect dynamics. Emotion Review, 7, 297–300.

    Article  Google Scholar 

  • Kuppens, P., & Verduyn, P. (2015). Looking at emotion regulation through the window of emotion dynamics. Psychological Inquiry, 26, 72–79.

    Article  Google Scholar 

  • Kuppens, P., Oravecz, Z., & Tuerlinckx, F. (2010). Feelings change: Accounting for individual differences in the temporal dynamics of affect. Journal of Personality and Social Psychology, 99, 1042–1060.

    Article  PubMed  Google Scholar 

  • Kuppens, P., Tuerlinckx, F., Russell, J. A., & Barrett, L. F. (2013). The relation between valence and arousal in subjective experience. Psychological Bulletin, 139, 917–940.

    Article  PubMed  Google Scholar 

  • Kuppens, P., Van Mechelen, I., Nezlek, J. B., Dossche, D., & Timmermans, T. (2007). Individual differences in core affect variability and their relationship to personality and psychological adjustment. Emotion, 7, 262–274.

    Article  PubMed  Google Scholar 

  • Lapate, R. C., & Heller, A. S. (2020). Context matters for affective chronometry. Nature Human Behaviour, 4, 688–689.

    Article  PubMed  Google Scholar 

  • Laport-López, F., Serrano, E., Bajo, J., & Campbell, A. T. (2020). A review of mobile sensing systems, applications, and opportunities. Knowledge and Information Systems, 62(1), 145–174.

    Article  Google Scholar 

  • Larsen, R. J. (1987). The stability of mood variability: A spectral analytic approach to daily mood assessments. Journal of Personality and Social Psychology, 52, 1195–1204.

    Article  Google Scholar 

  • Larsen, R. J. (2000). Toward a science of mood regulation. Psychological Inquiry, 11, 129–141.

    Article  Google Scholar 

  • Lieberman, M. D. (2019). Boo! The consciousness problem in emotion. Cognition and Emotion, 33, 24–30.

    Article  PubMed  Google Scholar 

  • Liu, Y., & West, S. G. (2016). Weekly cycles in daily report data: An overlooked issue. Journal of Personality, 84, 560–579.

    Article  PubMed  Google Scholar 

  • Loossens, T., Mestdagh, M., Dejonckheere, E., Kuppens, P., Tuerlinckx, F., & Verdonck, S. (2020). The affective ising model: A computational account of human affect dynamics. PLoS Computational Biology,16(e1007860), 1–27. https://doi.org/10.1371/journal.pcbi.1007860.

    Article  Google Scholar 

  • Lord, F. M., & Novick, R. (1968). Statistical theories of mental test scores. AddisonWesley.

    Google Scholar 

  • Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23, 209–237.

    Article  PubMed  Google Scholar 

  • Medland, H., De France, K., Hollenstein, T., Mussoff, D., & Koval, P. (2020). Regulating emotion systems in everyday life. European Journal of Psychological Assessment, 36, 437–446.

    Article  Google Scholar 

  • Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806–834.

    Article  Google Scholar 

  • Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66, 195–244.

    Article  Google Scholar 

  • Mestdagh, M., & Dejonckheere, E. (2021). Ambulatory assessment in psychopathology research: Current achievements and future ambitions. Current Opinion in Psychology, 41, 1–8.

    Article  PubMed  Google Scholar 

  • Metalsky, G. I., Joiner, T. E., Hardin, T. S., & Abramson, L. Y. (1993). Depressive reactions to failure in a naturalistic setting: A test of the hopelessness and self-esteem theories of depression. Journal of Abnormal Psychology, 102, 101–109.

    Article  PubMed  Google Scholar 

  • Moors, A. (2009). Theories of emotion causation: A review. Cognition and Emotion, 23, 625–662.

    Article  Google Scholar 

  • Mõttus, R., Wood, D., Condon, D. M., Back, M. D., Baumert, A., Costantini, G., & Zimmermann, J. (2020). Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the Big Few traits. European Journal of Personality, 34(6), 1175–1201. https://doi.org/10.1002/per.2311

  • Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 3, 221–229.

    Article  PubMed  Google Scholar 

  • Niemeijer, K., & Kuppens, P. (2022). Emotion detection with mobile passive sensing. In Mehl, M., R., Wrzus, C., Eid, M., Harari, G., & Ebner-Priemer, U. (Eds.), Mobile Sensing in Psychology: Methods and Applications. Guilford University Press.

  • Oberauer, K., & Lewandowsky, S. (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review, 26, 1596–1618.

    Article  Google Scholar 

  • Ram, N., Brinberg, M., Pincus, A. L., & Conroy, D. E. (2017). The questionable ecological validity of ecological momentary assessment: Considerations for design and analysis. Research in Human Development, 14, 253–270.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rivera, N. V., Parmelee, P. A., & Smith, D. M. (2020). The impact of social interactions and pain on daily positive and negative affect in adults with osteoarthritis of the knee. Aging and Mental Health, 24, 8–14.

    Article  PubMed  Google Scholar 

  • Robinaugh, D. J., Haslbeck, J. M., Ryan, O., Fried, E. I., & Waldorp, L. J. (2021). Invisible hands and fine calipers: A call to use formal theory as a toolkit for theory construction. Perspectives on Psychological Science, 16(4), 725–743. https://doi.org/10.1177/1745691620974697

  • Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility model of emotional self-report. Psychological Bulletin, 128, 934–960.

    Article  PubMed  Google Scholar 

  • Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57, 493–502.

    Article  Google Scholar 

  • Schimmack, U. (2003). Affect measurement in experience sampling research. Journal of Happiness Studies, 4, 79–106.

    Article  Google Scholar 

  • Schuurman, N. K., & Hamaker, E. L. (2019). Measurement error and person-specific reliability in multilevel autoregressive modeling. Psychological Methods, 24, 70–91.

    Article  PubMed  Google Scholar 

  • Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.

    Article  PubMed  Google Scholar 

  • Shmueli, G. (2010). To explain or to predict? Statistical Science, 25, 289–310.

    Article  Google Scholar 

  • Shrout, P. E., Stadler, G., Lane, S. P., McClure, M. J., Jackson, G. L., Clavél, F. D., Iida, M., Gleason, M. E. J., Xu, J. H., & Bolger, N. (2018). Initial elevation bias in subjective reports. Proceedings of the National Academy of Sciences, 115(1), E15–E23.

    Article  Google Scholar 

  • Smaldino, P. (2019). Better methods can’t make up for mediocre theory [Review of Better methods can’t make up for mediocre theory]. Nature, 575(7781), 9.

    Article  PubMed  Google Scholar 

  • Smaldino, P. E. (2020). How to translate a verbal theory into a formal model. Social Psychology, 51, 207–218.

    Article  Google Scholar 

  • Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine, 16, 199–202.

    Article  Google Scholar 

  • Tag, B., Webber, S., Wadley, G., Bartlett, V., Goncalves, J., Koval, P., & Kostakos, V. (2021). Making sense of emotion-sensing: Workshop on quantifying human emotions. UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 226–229. https://doi.org/10.1145/3460418.3479272

  • Trull, T. J., & Ebner-Priemer, U. W. (2020). Ambulatory assessment in psychopathology research: A review of recommended reporting guidelines and current practices. Journal of Abnormal Psychology, 129, 56–63. https://doi.org/10.1037/abn0000473

    Article  PubMed  Google Scholar 

  • Trull, T. J., Lane, S. P., Koval, P., & Ebner-Priemer, U. W. (2015). Affective dynamics in psychopathology. Emotion Review, 7, 355–361.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tukey, J. W. (1980). We need both exploratory and confirmatory. The American Statistician, 34, 23–25.

    Google Scholar 

  • van de Maat, R., Lataster, J., & Verboon, P. (2020). Why and how to deal with diurnal cyclic patterns in ambulatory assessment of emotions. European Journal of Psychological Assessment, 36(3), 471–481. https://doi.org/10.1027/1015-5759/a000579

  • van Halem, S., van Roekel, E., Kroencke, L., Kuper, N., & Denissen, J. (2020). Moments that matter? On the complexity of using triggers based on skin conductance to sample arousing events within an experience sampling framework. European Journal of Personality, 34, 794–807.

    Article  Google Scholar 

  • van Rooij, I., & Blokpoel, M. (2020). Formalizing verbal theories. Social Psychology, 51, 285–298.

    Article  Google Scholar 

  • Verduyn, P., Delaveau, P., Rotgé, J.-Y., Fossati, P., & Van Mechelen, I. (2015). Determinants of emotion duration and underlying psychological and neural mechanisms. Emotion Review, 7, 330–335. https://doi.org/10.1177/1754073915590618

    Article  Google Scholar 

  • Verduyn, P., Delvaux, E., Van Coillie, H., Tuerlinckx, F., & Van Mechelen, I. (2009). Predicting the duration of emotional experience: Two experience sampling studies. Emotion, 9, 83–91. https://doi.org/10.1037/a0014610

    Article  PubMed  Google Scholar 

  • Waugh, C., & Kuppens, P. (Eds.). (2021). Affect Dynamics. Springer. https://doi.org/10.1007/978-3-030-82965-0

  • Weidman, A. C., Steckler, C. M., & Tracy, J. L. (2017). The jingle and jangle of emotion assessment: Imprecise measurement, casual scale usage, and conceptual fuzziness in emotion research. Emotion, 17, 267–295.

    Article  PubMed  Google Scholar 

  • Wilhelm, F. H., & Grossman, P. (2010). Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biological Psychology, 84, 552–569.

    Article  PubMed  Google Scholar 

  • Wirth, M., Voss, A., Wirth, S., & Rothermund, K. (in press). Affect dynamics and well-being: explanatory power of the model of intraindividual variability in affect. Cognition & Emotion. https://doi.org/10.1080/02699931.2021.1993148

  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12, 1100–1122.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zelenski, J. M., & Larsen, R. J. (2000). The distribution of basic emotions in everyday life: A state and trait perspective from experience sampling data. Journal of Research in Personality, 34, 178–197.

    Article  Google Scholar 

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Acknowledgements

We acknowledge the help of Leonie Cloos for helpful comments on a draft of this manuscript.

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  1. KU Leuven, Leuven, Belgium

    Peter Kuppens, Egon Dejonckheere & Peter Koval

  2. Tilburg University, Tilburg, Netherlands

    Egon Dejonckheere

  3. University of Melbourne, Melbourne, Australia

    Elise K. Kalokerinos & Peter Koval

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Correspondence to Peter Kuppens.

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The writing of this article was supported by a KU Leuven Research Council grant (C14/19/054) and IBOF grant (IBOF/21/090) awarded to P. Kuppens and Discovery Early Career Researcher Awards (DE180100352; DE190100203) awarded to E. Kalokerinos and P. Koval. Egon Dejonckheere is an FWO postdoctoral fellow (1210621N).

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Kuppens, P., Dejonckheere, E., Kalokerinos, E.K. et al. Some Recommendations on the Use of Daily Life Methods in Affective Science. Affec Sci 3, 505–515 (2022). https://doi.org/10.1007/s42761-022-00101-0

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