The Associations between Affect and Internalizing Symptoms in the Reporting of Stressors in Daily Life.

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Learn more: PMC Disclaimer | PMC Copyright Notice Affect Sci . 2025 May 22;6(2):285–295. doi: 10.1007/s42761-025-00306-z The Associations between Affect and Internalizing Symptoms in the Reporting of Stressors in Daily Life Jessica P Lougheed 1, ✉ , Justin Chanut 1 , Alyssa K Truong 2 Author information Article notes Copyright and License information 1 Department of Psychology, University of British Columbia Okanagan, Kelowna, Canada 2 University of Calgary, Calgary, Canada Handling Editor: Amitai Shenhav ✉ Corresponding author. Received 2024 Oct 2; Accepted 2025 Apr 28; Collection date 2025 Jun. © The Society for Affective Science 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12209166  PMID: 40605940 Abstract Stressor reactivity, the association between the experience of stressors and same-day changes in affect (Almeida et al., 2023), has been examined extensively. Much less research has examined the complementary process: the extent to which affect may influence the perception of stressors. We examined whether affect in daily life was related to the likelihood that individuals report stressors, and whether internalizing symptoms (depressive and anxious symptoms) were associated with these links. Participants ( N = 609) were recruited from an undergraduate participant pool and reported daily on their positive and negative affect, and the types of stressors they experienced, in a 14-day daily diary design. We used multilevel survival analysis (MSA) to estimate the likelihood of reporting recurring stressors from mean levels of positive and negative affect, the previous day’s positive and negative affect, internalizing symptoms (depressive and anxious symptoms), and interactions between previous day’s affect and internalizing symptoms. Previous day’s affect was not associated with the risk of reporting stressors. Greater negative affect at the between-person level was associated with a greater risk of all types of stressors we examined, whereas lower between-person positive affect was only associated with the risk of reporting work/school and any stressors. Depressive and anxious symptoms were related in different ways to the likelihood of reporting stressors. This study demonstrates the utility of daily diary methods in combination with MSA to examine directional associations from affect to stressors and their temporal links. Keywords: Stress, Affect, Daily diary, Depression, Anxiety Affect and stress play important roles in mental health. A large body of work examines the links between stressors in daily life and their concomitant affect (Almeida, 2005 ; Sin et al., 2020 ). Research on stressor reactivity (i.e., within-day correlations between the experience of stressors and changes in affect) has received much needed attention (Charles et al., 2013 ; Klaiber et al., 2021 ). However, this method typically involves examining same-day associations between the reporting of stressors and affect, so the direction of the effect is assumed rather than modeled statistically. There are several reasons to also examine whether affect may influence how likely we are to experience/report stressors as, in lab studies, inducing negative affect and positive affect has been shown to influence perceptions and attentional processes (e.g., Lundh et al., 2001 ). We aimed to examine whether positive and negative affect on a given day were associated with the likelihood of reporting stressors the following day, and whether individual differences in internalizing symptoms were associated with these links. Affect, Internalizing Symptoms, and the Perceptions of Experiences Affect contributes to the perception of experiences and can bias attentional processes, memory, and interpretation (Lundh et al., 2001 ). For example, fear may lead to a bias for threat-related information, and sadness can lead to memory bias for negative information (Huntsinger et al., 2014 ; Lundh et al., 2001 ). Positive affect can lead to more global processing styles (Fredrickson, 2013 ) and can prompt urges play or savor (Fredrickson, 2004 ). In lab studies, inducing negative affect is associated with more negative social perceptions (Forgas et al., 1984 ) and pessimistic views of the future (Hepburn et al., 2009 ), whereas inducing positive affect is associated with attentional bias to reward (Tamir & Robinson, 2007 ). Internalizing problems (depression, anxiety) also contribute to how individuals perceive their experiences. Individuals with depressogenic tendencies tend to view experiences negatively, including depressogenic beliefs about the self, the world, and the future (Beck & Bredemeier, 2016 ). Individuals experiencing depressive symptoms may be more likely to interpret events through a negative lens, thereby increasing the salience of perceived stressors, especially when negative affect is experienced (Li et al., 2017 ). Individuals experiencing anxiety are primed to interpret experiences according to perceived threats (Beck & Clark, 1997 ), which reinforce maladaptive thought patterns (Leung et al., 2022 ). They are more vigilant to threats and have difficulty disengaging from threatening stimuli. They also have tendencies to interpret ambiguous information in a negative way and/or in line with meanings that are congruent with affect (Klein et al., 2018 ). Both depression and anxiety are closely related to affective experiences, including how they are processed and regulated. Depression has been linked to decreased reactivity of positive and negative affect (Bylsma, 2021 ), and anxiety has been linked to greater negative affect reactivity (Herres et al., 2018 ). Thus, it will be valuable to understand the potential contributions of depressive and anxious symptoms, and positive and negative affect, together and separately to the experience of stressors. For example, if affect contributes to the likelihood that individuals perceive daily events as stressful, and if an individual possesses traits (e.g., internalizing symptoms) that are associated with greater stressor reactivity, there may be a positive feedback loop between affect and stressor perception whereby both processes amplify each other. Knowledge of this type of dynamic would inform how we understand and treat internalizing symptoms and affect regulation. The Current Study We examined whether previous day’s affect was associated with the likelihood that individuals reported stressful events in daily life, and whether these associations varied by individual differences in depressive and anxious symptoms. We used a daily diary design where participants reported on positive and negative affect and the experience of stressors over 14 days. As specific stressor types may be differently associated with affect, we examined these associations for multiple common stressors (Almeida et al., 2002 ): Argument, conflict, or disagreement; family or home stress; work or school stress; financial problem; traffic or transportation stress; health problem or accident; a stressful event that happened to close friends or family; and whether any of these events occurred on a given day. We did not have specific hypotheses about the associations between affect, internalizing symptoms, and specific stressor types given the lack of research to date on these associations. Our research questions were: Are depressive and anxious symptoms associated with the likelihood of reporting stressors? We expected that individuals with greater depressive or anxious symptoms would show a greater risk of reporting stressors. Are overall levels of positive and negative affect (between-person differences) associated with the likelihood of reporting stressors? We expected that individuals who report greater negative affect and lower positive affect would be more likely to report stressors. Are positive and negative affect on a given day associated with the likelihood of reporting stressors on the following day (within-person effects)? We expected that greater negative affect and lower positive affect on a given day would be associated with a greater likelihood of reporting stressors the following day. Do the associations between positive and negative affect on a given day and the likelihood of reporting stressors the following day vary by depressive and anxious symptoms? We expected that the associations between depressive and anxious symptoms and the likelihood of reporting stressors would be stronger when participants reported greater than average negative affect and lower than average positive affect on a given day. Method Participants and Procedure The sample consisted of 930 undergraduate students at a university in western Canada who participated through the Department of Psychology’s participant research pool. Participants indicated how old they were in one-year categories ranging from 17 to 29 years old and 30 + , given concerns from the institutional research ethics board regarding the collection of personally identifying information such as birthdays. Participants’ ages ranged from 17 to 30 + years old, with the mode being 20 years old. Data were collected over four consecutive semesters between February 2021 and March 2022. Participants identified predominantly as Straight/heterosexual (81%), as women (81%), and White (59%, see Table 1 for full demographic information). Table 1. Sample Demographics n (%) Sexual orientation Asexual 23 (4%) Bisexual 60 (10%) Gay 3 (< 1%) Lesbian 9 (1%) Pansexual 12 (2%) Straight/Heterosexual 494 (81%) An identity not listed 10 (2%) Gender identity Man 101 (17%) Woman 496 (81%) Gender non-conforming/ Genderfluid 6 (1%) Genderqueer 5 (1%) Prefer to self-describe 3 (< 1%) Ethnicity Asian 117 (19%) Black 21 (3%) East Asian 48 (8%) Hispanic 12 (2%) Indigenous 8 (1%) Multiple ethnicities 42 (7%) White 362 (59%) Student status Domestic 517 (85%) International 93 (15%) N 609 Open in a new tab This study was conducted in two parts. First, in Part 1, participants provided their informed consent to participate and then filled out questionnaires via Qualtrics pertaining to demographic information, mental health symptoms, and emotional tendencies. Participants received 0.5 course credit through the Department of Psychology Sona Systems site (the website for the participant research pool) once they completed Part 1, which was followed up with an email detailing how to complete Part 2 of the survey. In Part 2, participants completed a daily diary survey once per day for 14 days. A free mobile app called ExpiWell (Tay & Pineda, 2020 ) was used. This daily survey included questions about daily experiences (experience of stressors and positive events, positive and negative affect). A notification was sent to participants via ExpiWell each day at 8:00 pm, and participants received a reminder 20 min later if they had not yet completed the daily report, with daily surveys remaining open until 11:59 pm. Compensation for Part 2 was also in the form of credit upon completion of the 14 days (0.5 credit for completing 7 or fewer daily diaries, and 1.0 credit for completing 8 or more daily diaries). Although 930 individuals participated in this study, 49 participants did not provide the needed ID variable required to match data from both parts of the study and were therefore excluded. Of the remaining 881 participants, 265 only completed Part 1 and did not move on to Part 2. In addition, 7 participants were excluded due to missing data on individual differences variables. The final sample used in the current study consisted of 609 participants. The completion rate was high for Part 2 of the study, with 39% of participants having completed all 14 days’ surveys, and 89% having completed 10 days’ surveys or more. The mean number of days completed was 12.22 ( SD = 2.57). There was no minimum number of completed days required to be included in the analyses of the current study. Measures Depressive Symptoms In Part 1, participants completed the National Institutes of Health Sadness scale (Slotkin et al., 2012 ), which consists of 8 items related to the affective symptoms of depression. Participants respond to questions with how often they felt a certain way (e.g., “I felt worthless”, “I felt depressed”) over the last 7 days on a 5-point scale (1 = never , 2 = rarely , 3 = sometimes , 4 = often , 5 = always ). In analyses, participants’ mean scores on these items represented depressive symptoms, with higher values indicating greater depressive symptoms. These scores were grand-mean centered for use in analyses. The reliability for this measure was good (α = .93). Anxious Symptoms In Part 1, participants completed the Emotional Distress (Anxiety) scale from the PROMIS item bank (short form; Cella et al., 2010 ). This measure consists of 8 items to which participants respond how often they felt a certain way in the last 7 days (e.g., “ I felt fearful ”, “ My worries overwhelmed me ”) on a 5-point scale (1 = never , 2 = rarely , 3 = sometimes , 4 = often , 5 = always ). In analyses, participants’ mean scores on these items represented anxious symptoms, with higher values indicating greater anxious symptoms. These scores were grand-mean centered for use in analyses. The reliability for this measure was good (α = .93). Daily Stressors In Part 2, participants completed the Today’s Events questionnaire (Scott et al., 2015 ; Sin & Almeida, 2018 ; Sin et al., 2017 ), which asked for details regarding stressful experiences each day. The question prompt was, “ Did any of these stressful events occur today? A stressful event is any event, even a minor one, which negatively affected you. (If more than one happened, please select the categories for each event) .” Participants then selected which of the following stressful events they experienced: Argument, conflict, or disagreement; Family or home stress; Work or school stress; Financial problem; Traffic or transportation stress; Health problem or accident; Stressful event that happened to close friends or family; Other stressful event (please describe); No stressful event happened today. For use in analyses, daily stressor occurrence (separately for each stressor type) was coded as 1 (event occurred) or 0 (event did not occur) for each day. An “any stressor” variable, which indicated days on which any of the aforementioned stressors occurred, was also derived and was coded as 1 (any stressful event occurred) or 0 (no stressful events occurred). Daily Negative Affect In Part 2, the Your Feelings questionnaire, adapted from the National Study of Daily Experience (Charles et al., 2019 ), was used to measure participants’ negative affect each day. This questionnaire includes 16 emotion-word items that participants rate each day on a scale from 0 to 4 (0 = None of the time , 1 = A little of the time , 2 = Some of the time , 3 = Most of the time , 4 = All of the time ). Each day, participants were asked, “ Below is a list of words describing different feelings. How well does each word describe how you felt today? ”. Negative affect items were: Anxious, sad, angry, frustrated, disgusted, lonely, and ashamed. The mean of each participants’ responses on all negative affect items was calculated for each day for use in analysis as a time-varying predictor, which was person-mean centered. We also calculated each person’s mean across days for use as a between-person predictor, which was grand-mean centered. Within-person and between-person reliability of daily negative affect was calculated based on guidelines for intensive longitudinal data (Bolger & Laurenceau, 2013 ). Negative affect showed good within-person (ranging from .73 to .77) and between-person (ranging from .74 to .83) reliability across all semesters of participation. Daily Positive Affect Daily positive affect was also measured with the Your Feelings questionnaire (Charles et al., 2019 ) in Part 2. Positive affect items were enthusiastic, happy, satisfied, confident, calm, like you belong, close to others, proud, and full of life, which participants rated on a scale from 0 to 4 (0 = None of the time , 1 = A little of the time , 2 = Some of the time , 3 = Most of the time , 4 = All of the time ) in response to the prompt. The mean of each participants’ responses on all positive affect items was calculated for each day for use in analysis as a time-varying (within-person) predictor, which was person-mean centered. We also calculated each person’s mean across days for use as a between-person predictor, which was grand-mean centered. Within-person reliability of positive affect was acceptable within the range of .86 to .88, and between-person reliability was acceptable within the range of .89 to .92 across semesters of participation. Data Analysis Plan When estimating the likelihood of event occurrence, such as stressors, an important feature of the data includes right censored cases, which refers to cases who did not report any events during the observation period (Mills, 2011 ). When such events are estimated as dependent variables in statistical models, the presence of right censored data can lead to biased results if appropriate methods are not used to account for right censoring (e.g., Lougheed et al., 2023 ), such as assuming that events never occurred at all for these individuals. This assumption is untenable when events are common daily stressors. Therefore, we used multilevel survival analysis (MSA), which accounts for right censoring, to estimate the likelihood of reporting recurring stressors based on time-varying (e.g., changing daily affect) and time-invariant (e.g., anxious and depressive symptoms) predictors (Lougheed et al., 2019 ). We used MSA models to test our research questions regarding whether depressive and anxious symptoms (Research Question 1), overall levels of positive and negative affect (Research Question 2), previous day’s positive and negative affect (Research Question 3), and interactions between previous day’s positive and negative affect with depressive symptoms (Research Question 4) and between previous day’s positive and negative affect with anxious symptoms (Research Question 5) predicted the likelihood of reporting stressful events. We estimated the likelihood of participants reporting daily stressors in separate models by stressor type using the Cox semiparametric approach (Cox, 1972 ). In line with recommendations for this method (Griffin, 1993 ), we ran models only for dependent variables that showed no more than 50% of cases with right censored data to prevent high levels of right censoring leading to untrustworthy results. Equation 1 shows the general form for each of the MSA models we ran: Equation 1 : h ij t = h 0 t exp v i exp ( β 1 P ositiveAffect t - 1 + β 2 N egativeAffect t - 1 + β 3 PositiveAffect i + β 4 NegativeAffect i + β 5 Depression i + β 6 Anxiety i + β 7 Depression i X P o s i t i v e A f f e c t t - 1 + β 8 Depression i X N e g a t i v e A f f e c t t - 1 + β 9 Anxiety i X P o s i t i v e A f f e c t t - 1 + β 10 Anxiety i X N e g a t i v e A f f e c t t - 1 ) 1 This equation shows that the hazard of the j th episode of a stressor at a given time interval ( t ) for a given individual ( i ) is the product of the baseline hazard h 0 (t) , an exponentiated random effect for individuals ( v i ), and an exponentiated linear function of the predictors. Our predictors included both time-varying and time-invariant predictors. Specifically, predictors were the previous day’s positive affect (β 1 ), the previous day’s negative affect (β 2 ), mean levels of positive affect (β 3 ), mean levels of negative affect (β 4 ), the main effect of depressive (β 5 ) and anxious (β 6 ) symptoms, interaction terms between depressive symptoms and the previous day’s positive (β 7 ) and negative affect (β 8 ), and interaction terms between anxious symptoms and the previous day’s positive (β 9 ) and negative affect (β 10 ). We used the coxph() function of the survival package to fit all models (Therneau, 2015 ) in R. First, we fit a baseline model of the hazard function (no independent variables included) so we could examine the shape of the baseline survival function (Lougheed et al., 2019 ). Then, we added all predictors into the next model and evaluated the goodness of model fit using the likelihood ratio test to determine if the model including all predictors fits significantly better than the baseline model (Mills, 2011 ). Results Data Description Descriptive statistics for daily stressors are shown in Table 2 . Event types that had more than 50% right censoring were financial problem, traffic or transportation stress, health problem or accident, stressful event to friends or family, and other stressor. Thus, we proceeded to the next step of data analysis for argument, conflict, disagreement; family or home stress; work or school stress, and any stressor. Descriptive statistics and bivariate correlations among predictor variables are shown in Table 3 . Table 2. Descriptive Statistics for Stressor Events Number of Events Right Censored Cases Stressor event M ( SD ) Range n (%) Argument, conflict, disagreement 1.70 (1.90) 1–11 215 (35%) Family or home stress 2.01 (2.48) 0–14 209 (34%) Work or school stress 6.81 (3.98) 0–17 27 (4%) Financial problem 1.13 (2.34) 0–14 392 (64%) Traffic or transportation stress 0.91 (1.49) 0–10 353 (58%) Health problem or accident 0.87 (1.75) 0–14 379 (62%) Stressful event to friends or family 0.59 (1.16) 0–8 409 (67%) Any stressor 8.97 (3.58) 0–17 4 (< 1%) Other stressor 0.43 (1.06) 0–12 456 (75%) Open in a new tab N = 609 individuals for the full sample Table 3. Descriptive Statistics and Bivariate Correlations for Predictor Variables Variable M ( SD ) Range Correlations 1 2 3 4 1. Positive affect 1.73 (0.66) 1.00–3.87 — 2. Negative affect 0.85 (0.47) 0.00–3.86 −.44* — 3. Depressive symptoms 2.62 (0.93) 1.00–5.00 −.53* .55* — 4. Anxious symptoms 2.83 (0.91) 1.00–5.00 −.39* .54* .74* — Open in a new tab * p < 0.05. Positive and negative affect variables represent the aggregated person means Model Fitting and Results The results of all models are shown in Table 4 . We interpreted the hazard ratios (HRs), which can be interpreted similarly to odds ratios, where values of 1 indicate no effect of the predictor on the likelihood of reporting a stressor, values greater than 1 indicate an increased likelihood of reporting a stressor at greater levels of the predictor, and values less than 1 indicate a lower likelihood of reporting a stressor at greater levels of the predictor (Mills, 2011 ). HRs can also be interpreted as the percent change in hazard, calculated as 100 x (HR-1; Mills, 2011 ). Table 4. Results of Multilevel Survival Analysis Models predicting Risk of Stressor Reports Predictor Estimate (coef) SE p HR exp(coef) 95% CI Model 1: Predicting Argument, Conflict, or Disagreement Positive affect t-1 (β 1 ) 0.15 0.08 .06 1.16 0.99–1.35 Negative affect t-1 (β 2 ) 0.14 0.10 .13 1.16 0.96–1.39 Positive affect mean (β 3 ) 0.15 0.09 .10 1.16 0.97–1.39 Negative affect mean (β 4 ) 0.78 0.13 .00 2.19 1.70–2.81 Depression (β 5 ) −0.22 0.09 .01 0.80 0.67–0.95 Anxiety (β 6 ) 0.13 0.08 .11 1.14 0.97–1.34 Depression x Positive affect t-1 (β 7 ) −0.08 0.12 .50 0.92 0.72–1.17 Depression x Negative affect t-1 (β 8 ) −0.03 0.15 .85 0.97 0.73–1.29 Anxiety x Positive affect t-1 (β 9 ) 0.07 0.13 .56 1.08 0.84–1.38 Anxiety x Negative affect t-1 (β 10 ) −0.03 0.16 .85 0.97 0.72–1.32 Random effect variance ( v i ) 0.58 Log-likelihood (fitted) −4,969.03 Log-likelihood (unconditional) −5,330.48 AIC 10,469.11 Model 2: Predicting Family or Home Stress Estimate (coef) SE p HR exp(coef) 95% CI Positive affect t-1 (β 1 ) 0.02 0.07 .79 1.02 0.89–1.18 Negative affect t-1 (β 2 ) 0.10 0.09 .28 1.10 0.92–1.32 Positive affect mean (β 3 ) −0.14 0.09 .15 0.87 0.73–1.05 Negative affect mean (β 4 ) 0.62 0.13 .00 1.86 1.43–2.41 Depression (β 5 ) −0.20 0.09 .03 0.82 0.69–0.98 Anxiety (β 6 ) 0.31 0.08 .00 1.36 1.15–1.60 Depression x Positive affect t-1 (β 7 ) 0.14 0.11 .20 1.15 0.93–1.41 Depression x Negative affect t-1 (β 8 ) 0.03 0.12 .81 1.03 0.81–1.31 Anxiety x Positive affect t-1 (β 9 ) −0.03 0.11 .76 0.97 0.78–1.20 Anxiety x Negative affect t-1 (β 10 ) 0.07 0.13 .56 1.08 0.84–1.39 Random effect variance ( v i ) 0.79 Log-likelihood (fitted) −5,863.00 Log-likelihood (unconditional) −6,419.99 AIC 12,384.54 Model 3: Predicting Work or School Stress Estimate (coef) SE p HR exp(coef) 95% CI Positive affect t-1 (β 1 ) −0.05 0.04 .17 0.95 0.88–1.02 Negative affect t-1 (β 2 ) 0.04 0.04 .45 1.05 0.94–1.14 Positive affect mean (β 3 ) −0.17 0.05 < .001 0.84 0.77–0.92 Negative affect mean (β 4 ) 0.25 0.07 < .001 1.28 1.13–1.46 Depression (β 5 ) −0.09 0.04 .05 0.92 0.84–1.00 Anxiety (β 6 ) 0.05 0.04 .25 1.05 0.97–1.14 Depression x Positive affect t-1 (β 7 ) 0.00 0.07 .99 1.00 0.89–1.13 Depression x Negative affect t-1 (β 8 ) 0.05 0.07 .50 1.05 0.91–1.20 Anxiety x Positive affect t-1 (β 9 ) 0.04 0.06 .48 1.04 0.93–1.18 Anxiety x Negative affect t-1 (β 10 ) −0.06 0.07 .44 0.94 0.82–1.09 Random effect variance ( v i ) 0.18 Log-likelihood (fitted) −21,411.39 Log-likelihood (unconditional) −21,886.03 AIC 43,432.56 Model 4: Predicting Any Stress Estimate (coef) SE p HR exp(coef) 95% CI Positive affect t-1 (β 1 ) −0.01 0.03 .82 0.99 0.93–1.06 Negative affect t-1 (β 2 ) 0.03 0.04 .44 1.03 0.95–1.12 Positive affect mean (β 3 ) −0.07 0.03 .01 0.93 0.88–0.98 Negative affect mean (β 4 ) 0.26 0.04 < .001 1.30 1.21–1.40 Depression (β 5 ) −0.07 0.03 .01 0.93 0.88–0.98 Anxiety (β 6 ) 0.03 0.02 .16 1.03 0.99–1.09 Depression x Positive affect t-1 (β 7 ) 0.01 0.05 .85 1.01 0.91–1.12 Depression x Negative affect t-1 (β 8 ) 0.01 0.06 .83 1.01 0.90–1.14 Anxiety x Positive affect t-1 (β 9 ) −0.01 0.05 .91 0.99 0.90–1.10 Anxiety x Negative affect t-1 (β 10 ) −0.03 0.06 .66 0.97 0.86–1.10 Random effect variance ( v i ) < 0.001 Log-likelihood (fitted) −29,037.70 Log-likelihood (unconditional) −29,081.39 AIC 58,095.40 Open in a new tab HR = hazard ratio; CI = confidence interval; AIC = Akaike information criterion Model Fit The results of all four models are shown in Table 4 . All four models fit the data well as indicated by the likelihood ratio tests. The results of these tests were significant for all four models, indicating that each model including the predictors fit the data better than the unconditional model (for Model 1, testing the likelihood of reporting arguments, conflicts, or disagreements, X 2 [265.50, 859 events] = 772.90, p < .001; for Model 2, testing the likelihood of reporting family or home stress, X 2 [329.30, 1036 events] = 1,114, p < .001; for Model 3, testing the likelihood of reporting work or school stress, X 2 [304.90, 3,536 events] = 949.30, p < .001; for Model 4, testing the likelihood of reporting any stress, X 2 [10, 4,694 events] = 87.39, p < .001). Research Question 1: Depressive and Anxious Symptoms For Model 1 (arguments, conflicts, disagreements; see Table 4 ), the main effect of depressive symptoms (β 5 ) was significant, indicating that individuals with greater depressive symptoms were less likely to report arguments, conflicts, or disagreements (HR = 0.80, p = .01), with each unit increase in depressive symptoms being associated with a 20% decrease in the risk of reporting this stressor (percent change = 100 x [0.80–1.00]). Contrary to expectations, the main effect of anxious symptoms (β 6 ) was not significant on the likelihood of reporting arguments, conflicts, and disagreements. For Model 2 (family or home stress; see Table 4 ), the main effect of depressive symptoms (β 5 ) was significant, indicating that individuals with greater depressive symptoms were less likely to report family or home stress (HR = 0.82, p = .03), with each unit increase in depressive symptoms being associated with an 18% decrease (percent change = 100 x [0.82–1.00]) in the likelihood of reporting this stressor. In contrast, the significant main effect of anxious symptoms (β 6 ) indicated that individuals with greater anxious symptoms were more likely to report family or home stress (HR = 1.36, p < 0.001), with a one-unit increase in anxious symptoms being associated with a 36% increase (percent change = 100 x [1.36–1.00]) in the likelihood of reporting this stressor. For Model 3 (work or school stress; see Table 4 ), both main effects of depressive (β 5 ) and anxious symptoms (β 6 ) were not associated with the likelihood of reporting work or school stress, which was contrary to expectations. For Model 4 (any stress; see Table 4 ), contrary to expectations, the significant main effect of depressive symptoms (β 5 ) indicated that greater levels of depressive symptoms were associated with a lower likelihood of reporting any stressor (HR = 0.93), specifically that a one-unit increase in depressive symptoms was associated with a 7% lower likelihood (percent change = 100 x [0.93–1.00]) of reporting any stress. Contrary to expectations, the main effect of anxious symptoms (β 6 ) was not associated with the likelihood of reporting any stress. Research Question 2: Between-Person Differences in Positive and Negative Affect For Model 1 (arguments, conflicts, disagreements; see Table 4 ), the main effect of mean level of positive affect was not significant (β 3 ). The main effect of mean level of negative affect significantly predicted the likelihood that participants reported arguments, conflicts, or disagreements (HR = 2.19, p < .001), indicating that each unit increase in negative affect was associated with 119% increase (percent change = 100 x [2.19–1.00]) in the risk of reporting this stressor. For Model 2 (family or home stress; see Table 4 ), the main effect of mean level of positive affect was not significant (β 3 ). The main effect of mean level of negative affect significantly predicted the likelihood that participants reported family or home stress (HR = 1.86, p < .001), indicating that each unit increase in mean levels of negative affect was associated with an 86% increase (percent change = 100 x [1.86–1.00]) in the likelihood of reporting this stressor. For Model 3 (work or school stress; see Table 4 ), in line with expectations, greater mean levels of positive affect (β 3 ) were associated with a lower risk of reporting work or school stress (HR = 0.84). Specifically, a one unit increase in mean positive affect was associated with a 16% lower likelihood (percent change = 100 x [0.84–1.00]) of reporting work or school stress. Also in line with expectations, greater levels of mean negative affect (β 4 ) were associated with a greater risk in reporting work or school stress (HR = 1.25), with a one unit increase in negative affect being associated with a 28% increase (percent change = 100 x [1.28–1.00]) in the likelihood of reporting work or school stress. For Model 4 (any stress; see Table 4 ), in line with expectations, greater mean levels of positive affect (β 3 ) were associated with a lower risk of reporting any stress (HR = 0.97). Specifically, a one unit increase in mean positive affect was associated with a 3% lower (percent change = 100 x [0.97–1.00]) likelihood of reporting any stress. Also in line with expectations, greater levels of mean negative affect (β 4 ) were associated with a greater risk in reporting any stress (HR = 1.30), with a one unit increase in negative affect being associated with a 30% increase (percent change = 100 x [1.30–1.00]) in the likelihood of reporting work or school stress. Research Question 3: Previous Days’ Positive and Negative Affect Contrary to expectations, the main effects of previous days’ positive and negative affect did not significantly predict the likelihood of reporting any of the stressors we examined in Models 1 through 4 (see β 1 and β 2 parameters for all models in Table 4 ). Research Question 4: Interactions between Depressive Symptoms and Previous Days’ Affect Contrary to expectations, the interactions between depressive symptoms and previous day’s positive (β 7 ) and negative affect (β 8 ) were not significant in any of the four models (see Table 4 ). Research Question 5: Interactions between Anxious Symptoms and Previous Days’ Affect Contrary to expectations, the interactions between anxious symptoms and previous day’s positive (β 9 ) and negative affect (β 10 ) were not significant in any of the four models (see Table 4 ). Discussion Our use of MSA demonstrates its promise in examining whether affect is related to the reporting of stressors in daily life. Previous research has mostly used statistical approaches that do not account for right censoring (e.g., Wardecker et al., 2022 ), which leads to inaccurate estimates of event occurrence (Lougheed et al., 2019 , 2023 ). It is time to move towards greater understanding of the temporal links in how affect may be related to the experience and reporting stressors, and MSA is one way to do that. Positive and Negative Affect Although previous days’ affect was not associated with the risk of reporting stressors, greater mean levels of negative affect were associated with greater risk of reporting all stressors. Lower mean levels of positive affect were only associated with a greater risk of reporting work or school stress, and any stress. These between-person variables were derived from the full observation period, so it could be that participants who experienced more stressors experienced greater negative affect (or lesser positive affect) as a result, that participants who experienced greater negative affect (or lesser positive affect) tended to perceive and thus report more stressors, or some combination of both. The time scale of our design may have been too coarse to pick up within-person effects of affect on the perception of stressors. Given that this study was among the first examinations of this directional effect, and that more work is needed to determine the timescale at which various affective dynamics operate (see Bülow et al., 2025 for fulsome discussion), future work could use experience sampling methods (ESM) for an analysis of within-day associations on the order of hours, rather than days. Depressive and Anxious Symptoms Depressive symptoms were associated with a lower risk of reporting argument, conflict, or disagreement stressors; family or home stressors; and any stressor. These negative associations were surprising, as depressive symptoms can include greater irritability and thus potentially more interpersonal conflict (e.g., Joiner et al., 1999 ). However, individuals experiencing greater depressive symptoms may be more withdrawn from social interactions and daily activities, which could lead to fewer opportunities to experience conflicts and other common stressors in daily life (Allen & Badcock, 2003 ). Anxious symptoms were not associated with the risk of reporting argument, conflict, or disagreement stressors; work/school stressors; or any stressor. These null results may be a true null effect, but they could also be related to the low range of anxious symptoms in our non-clinical sample. In contrast, greater anxious symptoms were associated with a greater risk of reporting family or home stressors. Individuals experiencing anxiety may be avoidant of interpersonal conflict, yet also highly attuned to other potential interpersonal threats (e.g., Zahlquist et al., 2023 ), which may explain why anxious symptoms were only associated with the risk of reporting home/family stressors. It is also possible that individuals experiencing anxious symptoms are anxious in part due to stressors experienced at home or in the family. Neither depressive nor anxious symptoms interacted with previous days’ affect in likelihood of reporting stressors. While we caution against overinterpreting null effects, we believe it would be worthwhile, as mentioned above, for future work to explore these associations at different time scales to rule out whether our null results were potentially related to our study design versus a true null effect. Limitations and Future Directions Our results should be interpreted with respect to several limitations. First, growing literature emphasizes the need to disentangle within-person effects from between-person effects via multilevel modeling approaches (e.g., Keskin et al., 2023 ; Lougheed et al., 2022 ). Although our design allowed us to do so for positive and negative affect, we measured internalizing symptoms via a one-time survey. Future work could measure internalizing symptoms daily to fully disentangle both within- and between-person. Moreover, dynamic structural equation modeling (DSEM; Asparouhov et al., 2018 ) is innovating the modeling of intensive longitudinal data by decomposing within- and between-person effects in a latent variable approach. MSA has been incorporated into the traditional SEM framework through a joint modeling approach as demonstrated by Stoolmiller and Snyder ( 2014 ) for use with traditional panel longitudinal data. However, to our knowledge, no joint modeling approaches have been developed specifically to incorporate the prediction of hazard rates (and therefore the appropriate treatment of right-censored dependent variables) within the DSEM framework. Researchers using the MSA approach as used in the current study could consider controlling for both previous days’ experience of stressors and between-person differences in stressors to more closely align with the DSEM approach until a joint modeling MSA/DSEM approach has been developed. Third, research and theoretical perspectives (Christensen et al., 2019 ; Holmes & Rahe, 1967 ; Lazarus, 1984 ) distinguish between objective and subjective reports of stressors. Objective stressors are typically measured by participants’ responses to a checklist of events determined to be “objectively” stressful, whereas subjective stressors are measured by participants’ reports of the experience of stress, regardless of the event type being determined to be “objectively” stressful. This ongoing dialogue is informative to current and future research given our proposal that individuals’ reports of common stressors in daily life may, on the one hand, be objective indicators of the experience of stressors, but may also result from affective processes (positive and negative affect, internalizing problems) that may make individuals more likely to interpret experiences as stressful. Accordingly, another fruitful research direction would be to disentangle whether affect and internalizing symptoms are associated with reports of stressors in terms of stressor severity and appraisal. Finally, our sample was from an undergraduate Psychology research pool, and our results may not generalize to other populations. Moreover, our sample identified predominately as White, cisgender female, and heterosexual. Given that experiences related to affect, internalizing problems, and stress may vary according to ethnicity, gender identity, and sexual orientation (e.g., Cichy et al., 2012 ; Salerno et al., 2020 ), it is important for future research to examine samples that have a greater representation of diverse identities. Conclusion It is an exciting time for research on the psychological processes in daily life that are associated with stress. The uptake of daily diary and ESM methods over the last several decades (Almeida et al., 2023 ) has provided a strong base of research on stressor reactivity and its associations with individual differences. We hope that work in this area will continue to explore the temporal and directional associations between affect and the perceptions of stressors, thereby leading to new insights in our understandings of these processes. Acknowledgements We thank Alyssa Truong, Summer Fulford, Keaghan Forster, and Sierra Adamow-Boudreau for their assistance with data collection. Additional Information Funding This work was supported by funding awarded to Jessica P. Lougheed from the Aspire/Start Up Funding from the UBCO Irving K. Barber Faculty of Arts and Social Sciences (F20-03321) and the Michael Smith Health Research BC Scholar award (SCH-2023–3044). Competing Interests The authors declare no competing interests. Data Availability All data analysis scripts and data files are available here: https://osf.io/ufr2y . Author Contributions JPL contributed to study conception and design, JPL and AKT contributed to acquisition of data, JPL and AKT contributed to the analysis of data, JPL and JC contributed to the interpretation of data, JPL and JC contributed to drafting and revising the article, and all authors approved the submitted version for publication. Ethics Approval This study was approved by the University of British Columbia Okanagan Behavioural Research Ethics Board (#H20-03001). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Consent to Participate All participants provided informed consent prior to taking part in the study. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Allen, N. B., & Badcock, P. B. T. (2003). The social risk hypothesis of depressed mood: Evolutionary, psychosocial, and neurobiological perspectives. Psychological Bulletin, 129 (6), 887–913. 10.1037/0033-2909.129.6.887 [ DOI ] [ PubMed ] [ Google Scholar ] Almeida, D. M. (2005). Resilience and vulnerability to daily stressors assessed via diary methods. Current Directions in Psychological Science, 14 (2), 64–68. 10.1111/j.0963-7214.2005.00336.x [ Google Scholar ] Almeida, D. M., Rush, J., Mogle, J., Piazza, J. R., Cerino, E., & Charles, S. T. (2023). Longitudinal change in daily stress across 20 years of adulthood: Results from the national study of daily experiences. Developmental Psychology, 59 , 515–523. 10.1037/dev0001469 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Almeida, D. M., Wethington, E., & Kessler, R. C. (2002). The daily inventory of stressful events: An interview-based approach for measuring daily stressors. Assessment, 9 (1), 41–55. [ DOI ] [ PubMed ] [ Google Scholar ] Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25 (3), 359–388. 10.1080/10705511.2017.1406803 [ Google Scholar ] Beck, A. T., & Bredemeier, K. (2016). A unified model of depression: Integrating clinical, cognitive, biological, and evolutionary perspectives. Clinical Psychological Science, 4 (4), 596–619. 10.1177/2167702616628523 [ Google Scholar ] Beck, A. T., & Clark, D. A. (1997). An information processing model of anxiety: Automatic and strategic processes. Behaviour Research and Therapy, 35 (1), 49–58. 10.1016/S0005-7967(96)00069-1 [ DOI ] [ PubMed ] [ Google Scholar ] Bolger, N., & Laurenceau, J. P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research . The Guilford Press. Bülow, A., Boele, S., Lougheed, J. P., Denissen, J. J. A., van Roekel, E., & Keijsers, L. (2025). A matter of timing? Effects of parent–adolescent conflict on adolescent negative affect and depressive symptoms on six timescales. Journal of Psychopathology and Clinical Science, 134 (4), 414–426. 10.1037/abn0000987 [ DOI ] [ PubMed ] [ Google Scholar ] Bylsma, L. M. (2021). Emotion context insensitivity in depression: Toward an integrated and contextualized approach. Psychophysiology, 58 (2), e13715. 10.1111/psyp.13715 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J. F., Gershon, R., Hahn, E. A., Lai, J.-S., Pilkonis, P., Revicki, D., … PROMIS Cooperative Group. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology , 63 (11), 1179–1194. 10.1016/j.jclinepi.2010.04.011 [ DOI ] [ PMC free article ] [ PubMed ] Charles, S. T., Mogle, J., Leger, K. A., & Almeida, D. M. (2019). Age and the factor structure of emotional experience in adulthood. The Journals of Gerontology Series B, Psychological Sciences and Social Sciences, 74 (3), 419–429. 10.1093/geronb/gbx116 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Charles, S. T., Piazza, J. R., Mogle, J., Sliwinski, M. J., & Almeida, D. M. (2013). The wear and tear of daily stressors on mental health. Psychological Science, 24 (5), 733–741. 10.1177/0956797612462222 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Christensen, D. S., Dich, N., Flensborg-Madsen, T., Garde, E., Hansen, Å. M., & Mortensen, E. L. (2019). Objective and subjective stress, personality, and allostatic load. Brain and Behavior, 9 (9), e01386. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cichy, K. E., Stawski, R. S., & Almeida, D. M. (2012). Racial differences in exposure and reactivity to daily family stressors. Journal of Marriage and Family, 74 (3), 572–586. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series B (Methodological), 34 (2), 187–220. [ Google Scholar ] Forgas, J. P., Bower, G. H., & Krantz, S. E. (1984). The influence of mood on perceptions of social interactions. Journal of Experimental Social Psychology, 20 (6), 497–513. 10.1016/0022-1031(84)90040-4 [ Google Scholar ] Fredrickson, B. L. (2004). The broaden–and–build theory of positive emotions. Philosophical transactions of the royal society of London. Series B. Biological Sciences, 359 (1449), 1367–1377. 10.1098/rstb.2004.1512 [ DOI ] [ PMC free article ] [ PubMed ] Fredrickson, B. L. (2013). Positive emotions broaden and build. In Advances in experimental social psychology (Vol. 47, p. 53). Elsevier. 10.1016/b978-0-12-407236-7.00001-2 Griffin, W. A. (1993). Event history analysis of marital and family interaction: A practical introduction. Journal of Family Psychology, 6 (3), 211–229. 10.1037/0893-3200.6.3.211 [ Google Scholar ] Hepburn, S. R., Barnhofer, T., & Williams, J. M. G. (2009). The future is bright? Effects of mood on perception of the future. Journal of Happiness Studies, 10 (4), 483–496. 10.1007/s10902-008-9102-9 [ Google Scholar ] Herres, J., Caporino, N. E., Cummings, C. M., & Kendall, P. C. (2018). Emotional reactivity to daily events in youth with anxiety disorders. Anxiety, Stress, & Coping, 31 (4), 387–401. 10.1080/10615806.2018.1472492 [ DOI ] [ PubMed ] [ Google Scholar ] Holmes, T. H., & Rahe, R. H. (1967). The social readjustment rating scale. Journal of Psychosomatic Research, 11 (2), 213–218. 10.1016/0022-3999(67)90010-4 [ DOI ] [ PubMed ] Huntsinger, J. R., Isbell, L. M., & Clore, G. L. (2014). The affective control of thought: Malleable, not fixed. Psychological Review, 121 (4), 600–618. 10.1037/a0037669 [ DOI ] [ PubMed ] [ Google Scholar ] Joiner, T., Coyne, J. C., & Blalock, J. (1999). On the interpersonal nature of depression: Overview and synthesis. In T. Joiner & J. C. Coyne (Eds.), The interactional nature of depression: Advances in interpersonal approaches. (pp. 3–19). American Psychological Association. 10.1037/10311-013 Keskin, G., Lougheed, J. P., & Duncan, R. (2023). Within- and between-person associations among internalizing and externalizing problems during middle childhood. Social Development, 32 (3), 889–904. 10.1111/sode.12660 [ Google Scholar ] Klaiber, P., Wen, J. H., DeLongis, A., & Sin, N. L. (2021). The ups and downs of daily life during COVID-19: Age differences in affect, stress, and positive events. The Journals of Gerontology Series B, Psychological Sciences and Social Sciences, 76 (2), e30–e37. 10.1093/geronb/gbaa096 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Klein, A. M., de Voogd, L., Wiers, R. W., & Salemink, E. (2018). Biases in attention and interpretation in adolescents with varying levels of anxiety and depression. Cognition and Emotion, 32 (7), 1478–1486. 10.1080/02699931.2017.1304359 [ DOI ] [ PubMed ] [ Google Scholar ] Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer. Leung, C. J., Yiend, J., Trotta, A., & Lee, T. M. C. (2022). The combined cognitive bias hypothesis in anxiety: A systematic review and meta-analysis. Journal of Anxiety Disorders, 89 , 102575. 10.1016/j.janxdis.2022.102575 [ DOI ] [ PubMed ] [ Google Scholar ] Li, Y. I., Starr, L. R., & Hershenberg, R. (2017). Responses to positive affect in daily life: Positive rumination and dampening moderate the association between daily events and depressive symptoms. Journal of Psychopathology and Behavioral Assessment, 39 (3), 412–425. 10.1007/s10862-017-9593-y Lougheed, J. P., Benson, L., Ram, N., & Cole, P. M. (2019). Multilevel survival analysis: Studying the timing of children’s recurring behaviors. Developmental Psychology, 55 (1), 53–65. 10.1037/dev0000619 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lougheed, J. P., Duncan, R. J., Keskin, G., & Marceau, K. (2022). Longitudinal associations between mother–child conflict and child internalizing problems in mid-childhood. Development and Psychopathology, 34 (1), 263–272. 10.1017/S0954579420000863 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lougheed, J. P., Keskin, G., & Morgan, S. (2023). The hazards of daily stressors: Comparing the experiences of sexual and gender minority young adults to cisgender heterosexual young adults during the covid-19 pandemic. Collabra: Psychology, 9 (1), 73649. 10.1525/collabra.73649 [ Google Scholar ] Lundh, L.-G., Wikström, J., & Westerlund, J. (2001). Cognitive bias, emotion, and somatic complaints in a normal sample. Cognition and Emotion, 15 (3), 249–277. 10.1080/02699930126255 [ Google Scholar ] Mills, M. (2011). Introducing survival and event history analysis . Sage Publications Ltd. 10.4135/9781446268360.n1 Salerno, J. P., Devadas, J., Fish, J. N., Pease, M., Nketia, B., Williams, N. D., & Rinderknecht. (2020). Sexual and gender minority stress amid the COVID-19 pandemic: Implications for LGBTQ young persons’ mental health and well-being. Public Health Reports , 135 (6), 721–727. 10.1177/0033354920954511 [ DOI ] [ PMC free article ] [ PubMed ] Scott, S. B., Graham-Engeland, J. E., Engeland, C. G., Smyth, J. M., Almeida, D. M., Katz, M. J., Lipton, R. B., Mogle, J. A., Munoz, E., Ram, N., & Sliwinski, M. J. (2015). The effects of stress on cognitive aging, physiology and emotion (ESCAPE) project. BMC Psychiatry, 15 (1), 146. 10.1186/s12888-015-0497-7 [ DOI ] [ PMC free article ] [ PubMed ] Sin, N. L., & Almeida, D. M. (2018). Daily positive experiences and health: Biobehavioral pathways and resilience to daily stress. In C. D. Ryff & R. F. Krueger (Eds.), Oxford handbook of integrative health science . Oxford University Press. 10.1093/oxfordhb/9780190676384.001.0001 Sin, N., Almeida, D. M., Crain, T. L., Kossek, E. E., Berkman, L. F., & Buxton, O. M. (2017). Bidirectional, temporal associations of sleep with positive events, affect, and stressors in daily life across a week. Annals of Behavioral Medicine., 51 (3), 402–415. 10.1007/s12160-016-9864-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sin, N. L., Wen, J. H., Klaiber, P., Buxton, O. M., & Almeida, D. M. (2020). Sleep duration and affective reactivity to stressors and positive events in daily life. Health Psychology, 39 (12), 1078–1088. 10.1037/hea0001033 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Slotkin, J., Nowinski, C., Hays, R., Beaumont, J., Griffith, J., Magasi, S., Salsman, J., Gershon, R. (2012). NIH Toolbox scoring and interpretation guide. NIDDK Central Repository. https://repository.niddk.nih.gov/media/studies/look-ahead/Forms/Look_AHEAD_Cognitive_Function/NIH%20Toolbox%20Scoring%20and%20Interpretation%20Manual%209-27-12.pdf Stoolmiller, M., & Snyder, J. (2014). Embedding multilevel survival analysis of dyadic social interaction in structural equation models: Hazard rates as both outcomes and predictors. Journal of Pediatric Psychology, 39 (2), 222–232. 10.1093/jpepsy/jst076 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tamir, M., & Robinson, M. D. (2007). The happy spotlight: Positive mood and selective attention to rewarding information. Personality & Social Psychology Bulletin, 33 (8), 1124–1136. 10.1177/0146167207301030 [ DOI ] [ PubMed ] [ Google Scholar ] Tay, L., & Pineda, O. (2020). ExpiWell [Computer software]. https://www.expiwell.com . Accessed 20 Sept 2020. Therneau, T. M. (2015). A package for survival analysis in S (Version 2.38) [Computer software]. https://CRAN.R-project.org/package=survival . Accessed 20 Sept 2020. Wardecker, B. M., Surachman, A., Matsick, J. L., & Almeida, D. M. (2022). Daily stressor exposure and daily well-being among sexual minority and heterosexual adults in the United States: Results from the National Study of Daily Experiences (NSDE). Annals of Behavioral Medicine, 56 (6), 536–550. 10.1093/abm/kaab062 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zahlquist, L., Hetland, J., Einarsen, S. V., Bakker, A. B., Hoprekstad, Ø. L., Espevik, R., & Olsen, O. K. (2023). Daily interpersonal conflicts and daily exposure to bullying behaviors at work: The moderating roles of trait anger and trait anxiety. Applied Psychology, 72 (3), 893–914. [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement All data analysis scripts and data files are available here: https://osf.io/ufr2y . 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