Weekend is where the magic happens: How romantic relationship variables change across the weekend
Creators: name: Weidmann, Rebekka
name: Wünsche, Jenna
name: Buehler, Janina
name: Burriss, Robert
name: Grob, Alexander
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Personality and Social Contexts
subject: Social Psychology
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology

description: Summary People wish each other a “happy weekend” on Fridays or complain about the “Monday Blues” on Mondays. Affective changes across the weekend have been studied in previous research with small weekend effects suggesting that on the weekend, people feel better compared to weekdays (e.g., Areni et al., 2011; Egloff et al., 1995; Gnambs, 2021; Helliwell &amp; Wang, 2015; Larson &amp; Richards, 1998; Ryan et al., 2010; Stone et al., 2012; Tsai, 2019). Given that weekdays are qualitatively different from weekend days when people on average enjoy more leisure time, do usually not need to work, and spend more time with their loved ones, we want to examine if this qualitative difference is also reflected in people’s perception of their romantic relationship across the week. If this is the case, the findings of this study will contribute to researchers’ understanding of romantic relationship experiences across the week and have implications for the time points researchers choose to assess and measure couple members’ relationship experiences. The present project examines how relationship variables (i.e., relationship satisfaction, sexual satisfaction, closeness, partner perception, self-disclosure) change across 2 weeks (including a weekend) and which variables may moderate these changes. Our main research question is: How do relationship variables change before, during, and after the weekend? We expect to find that the ‘weekend is where the magic happens’ such that people’s relationship satisfaction, sexual satisfaction, closeness, partner perception, and self-disclosure increase towards the weekend, reaches a peak plateau on weekend days, and decrease after the weekend. We also explore whether living situation (i.e., living with a partner, living with offspring), employment status, relationship duration, and personality traits moderate these changes. Finally, we test whether daily positive and negative affect account for the changes across the week. The effects are tested considering participants’ gender and relationship duration. Research Questions and Hypotheses The present study aims to address three research questions: 1) Do relationship variables change across the week? 2) Are changes across the week moderated by living situation, employment status, relationship duration, and personality traits? 3) Do changes across the week in relationship variables still hold when controlling for changes across the week in positive and negative affect? Regarding the first research question, we expect that people will be generally more satisfied with their relationship (Berli et al., 2021), more satisfied with their sexuality, feel closer to their partner, perceive their partner more positively, and disclose more to their partner on weekend days compared to weekdays. In conjunction with this weekend effect, we also expect anticipation effects (i.e., pre-weekend increases) and depletion effects (i.e., post-weekend decreases). Given that this is expected to be a cyclical experience, which happens every week, we expect these changes to be non-linear. Regarding the second research question, we expect that people’s living situation moderates the weekend changes such that the changes are more pronounced in people who do not live with their partner (because they might not see their partner across the week but might see them over the weekend, which might give them a boost in their relationship variables) and in people who do not live with their offspring (because the parenting responsibilities and accompanied additional household chores continue across the weekend, which might dampen some of the weekend positivity). We also expect that there is a stronger weekend effect in people who work (because they enjoy a more pronounced change in the amount of leisure time during the weekend). We expect people who started their relationship more recently to have a stronger weekend effect compared to people who have been in their relationship longer (due to processes of accommodation). And we expect more agreeable, extraverted, and more emotionally stable individuals to experience more pronounced changes across the weekend (because combined with the possibility that they spent a larger amount of time with their partner, this augmented time spent together is likely to be more pleasurable, positive, and less stressful if they are agreeable, extraverted, and emotionally stable). We have no expectations regarding conscientiousness and openness to experience. Regarding the third research question, we expect that the weekend changes are not as pronounced when including positive and negative affect. In other words, we expect that daily affect accounts for at least some of the weekend changes. Method The data for this study have been collected within the SNSF funded project Processes in Romantic Relationships and Their Impact on Relationship and Personal Outcomes (CouPers Study) between March 2016 and November 2018. The data include four 14-days waves (waves 1, 2, 3, and 4). At the start of the CouPers Study, 1,313 couples consented for surveys to be emailed to both partners. During the study, 10 new partners began participating. Thirteen participants asked that their data be deleted, and a further 427 participants withdrew. During wave 1, 2,317 participants at least partially responded to surveys; during wave 2, 1,760; during wave 3, 1,481; and during wave 4, 1,265. Daily relationship variables were only assessed if participants were currently in a romantic relationship (vs. those who had broken up during the study). Thus, we will only include data of participants who are currently in a relationship and exclude data of participants who are single. This means, if a person became single between wave 2 and 3, we would include data of that person from wave 1 and 2 when they reported daily relationship variables. Measures Relationship Satisfaction Relationship satisfaction was measured as self-report with a single item (“How satisfied were you today all together with your relationship to your partner?”) every evening during waves 1, 2, 3, and 4. The item was rated on a 5-point Likert scale: 1 (very dissatisfied), 2 (rather dissatisfied), 3 (so, so), 4 (rather satisfied), 5 (very satisfied). Sexual Satisfaction Sexual satisfaction was measured as self-report with a single item (“How satisfied were you today with your sexual satisfaction with your partner?”) every evening during waves 1, 2, 3, and 4 (Gadassi et al., 2016). The item was rated on a 5-point Likert scale ranging from 1 (extremely unhappy) to 5 (extremely happy). Closeness Closeness will be included as a composite score of two daily scales: perceived responsiveness and partner closeness. Compositing these variables is justified based on their high statistical and conceptual overlap (see Reitz et al., 2021). Partner closeness was measured as self-report with 4 items (example item: “How close did you feel to your partner today?”) every evening during waves 1, 2, 3, and 4 (Schoebi et al., 2012). The items were rated on a 7-point Likert scale ranging from 1 (hardly) to 7 (very much). Perceived responsiveness was measured as self-report with 4 items each (example item: “I felt understood by my partner today.”) every evening during waves 1, 2, 3, and 4 (Laurenceau et al., 2005). The items were rated on a 5-point Likert scale ranging from 1 (very little) to 5 (a great deal). The composite score of closeness will be created by averaging the mean scores of partner closeness and responsiveness. Partner Perception Partner perception, a self-generated scale, was introduced with the question: “How did you perceive your partner today in relation to the following attributes?”. Participants rated six positive attributes (e.g., generous, affectionate, attentive) and six negative attributes (e.g., cold, impatient, selfish) every evening during waves 1, 2, 3, and 4 on a 5-point Likert scale: 1 (not at all true), 2 (rather not true), 3 (neither), 4 (rather true), 5 (totally true). We recoded the negative-attributes ratings and created a mean score across all 12 items, with higher scores indicating more positive partner perception. Self-Disclosure Self-disclosure was measured as self-report with three items each (example items: “Today I have shared my thoughts with my partner”) every evening during waves 1, 2, 3, and 4 using a 5-point Likert scale ranging from 1 (very little) to 5 (a great deal) (Laurenceau et al., 2005). We will create a mean score with higher values indicating more self-disclosure. Control Variables We will use gender and relationship duration as control variables. We will not control for age given that we expect a high correlation (i.e., r ≥ .80) between age and relationship duration. Relationship duration is chosen as it is a proxy of other processes that might go on in the couple (e.g., “honeymoon effect”) and because we include moderator variables that are age-related (e.g., living with child[ren]). The control variables’ assessments at wave 1 (or the first assessment of the participant) will be included in the models as time-invariant control variables. To examine whether positive and negative affect account for the changes in relationship variables, we use positive and negative affect, which were measured daily. Positive and negative affect were measured with the brief 10-item measure of the Positive and Negative Affect Schedule (PANAS; Krohne et al., 1996; Thompson, 2007; Watson et al., 1988) at waves 2, 3, and 4 on a daily basis. Moderators As moderators, we will examine how the people’s living situation, employment status, relationship duration, and personality traits moderate the changes across the weekend. We will use two different types of living situations (i.e., living with partner and living with offspring) as well as employment status (i.e., working [full- or part-time] vs. not-working [in training, not active on the labor market, unemployed]) as moderators. More specifically, we test whether living with the partner, living with (a) child(ren), and employment status moderates the changes across the weekend. Living situation and employment status were assessed in waves 1, 3, and 4. Thus we will use wave 1 information (or the person’s first response to these questions) for the moderation analyses for waves 1 and 2. We will also test relationship duration as moderator (without entering it as control variable) to examine whether the effects across the weekend are different for couples who started their relationship more recently compared to those that have been together for a longer while. In addition, we examine how the Big Five personality traits act as moderators. These were measured with the Big Five Inventory (John &amp; Srivastava, 1999) using 45 items at the beginning and end of wave 1, and at the beginning of waves 2, 3, and 4. We will use the first available measure of each person’s personality traits as a time-invariant moderator in the analyses (given the high stability of the Big Five traits). Data-Analytic Plan To evaluate if the data suggests a weekend effect and if we can proceed to model the trajectories towards and after a weekend with multilevel models, we will depict the average trajectory with ggplot2 (version 3.3.3; Wickham &amp; Wickham, 2007) using the “gam” method (Wood, 2006) in R (R Core Team, 2021). The code below illustrates how this will be done: relsat &lt;- ggplot(data=data,aes(x=day, y=relsatT)) plot_relsat &lt;- relsat + geom_smooth(method = "gam", formula = y ~ s(x), size = 1) + scale_x_discrete(limits = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14")) + labs(title = "Relationship Satisfaction", x = "Days", y = "Relationship Satisfaction (T-Scores)") + coord_cartesian(xlim = c(1,14), ylim = c(45,55)) plot_relsat If these data-driven plots indicate a flat line or a linear flat line (i.e., increases or decreases) across the entire 2-week diary phase, we will only examine whether the variance of the PreLin, PreSqua, PostLin, PostSqua, and Weekend coefficients is significant (without testing their fixed effects) and examine the moderation analyses (see below for a detailed descriptions of these coefficients). If the plots suggest that there is change across the weekend on average, we will proceed with non-linear multilevel models to test our research questions. For that purpose, we use the lme4 package (version 1.1-26; Bates et al., 2015) and the lmerTest package (for testing the significance of fixed effects, version 3.1-3; Kuznetsova et al., 2017). A three-level approach will be applied because we have repeated person observations (i.e., days) nested in participants, nested in romantic dyads. Thus, daily observations (Level 1) are nested in participants (Level 2), which are nested in couples (Level 3). Because there is no theoretical reason to expect significant variation across waves and because we were not interested in examining such variation, we will control for assessment wave (instead of using it as an additional level which would further complicate the models). To answer our first research question, we will test a set of multilevel models that will include a linear (preLin) and quadratic (preSqua) anticipation effect, and a linear (postLin) and quadratic (postSqua) depletion or post-weekend effect as well as a weekend-day effect (Weekend; see also Buecker et al., 2021 for an example with life events). The preLin effect is the linear anticipation effect which reflects the time before the weekend in days. We coded this value with negative values (-5 = 5 days ahead of the weekend). Similarly, the preSqua effect is the quadratic anticipation effect (preLin * preLin). The PreLin and PreSqua effects reflect the rate of increase towards the weekend. The postLin effect and the postSqua effects are the effects after the weekend, with positive values after the weekend days. Finally, the Weekend variable signifies whether Saturday and Sunday (the weekend days, coded as 1) are different from the weekdays (coded as 0). These variable codings are illustrated in Table 1. If there is an anticipation and depletion effect across the week it is likely that it reflects a cyclical effect, in that the anticipation or pre-weekend effect is similar to the depletion or post-weekend effect of the next week. To analyze the data, we will predict variation in the relationship variables (i.e., relationship satisfaction, sexual satisfaction, closeness, partner perception, self-disclosure) by the preLin, preSqua, postLin, postSqua, and Weekend variables. We will also estimate the inter-individual variance of these effects while controlling for gender and relationship duration. The code below illustrates our analytic plan: model_relsat &lt;- lmerTest::lmer(relsatT ~ 1 + PreLin + PreSqua + PostLin + PostSqua + Weekend + gender + reldur + wave + (1 + PreLin + PreSqua + PostLin + PostSqua + Weekend|personID) + (1|coupleID) Regarding our expectations for the first research question, if the Weekend coefficient is significant and positive, then we conclude that weekend days are different from weekdays in terms of people’s relationship variables. If the PreLin and PreSqua effects are significant, we conclude that there are significant (nonlinear) changes towards the weekend. If the PostLin and PostSqua effects are significant, we conclude that there are significant nonlinear changes after the weekend. Likewise, if only the PreLin and/or the PostLin effects are significant but not the respective PreSqua or PostSqua, we conclude that the increases and/or decreases are linear. We expect both the pre-weekend and the post-weekend effect to show u-shaped curvilinear changes with increases towards and decreases away from the weekend. If we find that there is significant variance in at least one of the PreLin, PreSqua, PostLin, PostSqua, and Weekend coefficients, we will conduct the moderation analyses for these variables. For example, if only the Weekend effect shows significant variation across people, we will only test moderation effects for that specific effect. But if, for example, the PreLin, PreSqua, and Weekend effects are significant, we will test moderation effects for these three effects. Significance of random effects will be derived from the 95% confidence intervals of the random effects. The planned moderation tests will then include examining in different models if a person’s living situation (e.g., living with partner or living with offspring), employment status, relationship duration, and their Big Five personality traits moderate the pre-weekend anticipation, weekend, and post-weekend depletion effects (9 moderation models for each relationship variable, 45 models in total). The code below illustrates an example model for living with partner as moderator. model_relsat_livingpart &lt;- lmerTest::lmer(relsatT ~ 1 + PreLin*livingwithpartner + PreSqua*livingwithpartner + PostLin*livingwithpartner + PostSqua*livingwithpartner + Weekend*livingwithpartner + gender + reldur + wave + (1 + PreLin + PreSqua + PostLin + PostSqua + Weekend|personID) + (1|coupleID) Lastly, to examine whether positive and negative affect account for the weekend changes, we use data of wave 2, 3, and 4 and examine changes in relationship variables while controlling for positive and negative affect of each day. The code below illustrates an example model: model_relsat &lt;- lmerTest::lmer(relsatT ~ 1 + PreLin + PreSqua + PostLin + PostSqua + Weekend + gender + reldur + wave + PA + NA + (1 + PreLin + PreSqua + PostLin + PostSqua + Weekend|personID) + (1|coupleID) We will compare whether effects become non-significant after including the positive and negative affect variables to conclude whether the changes in positive and negative affect account for the weekend changes in relationship variables. We plan to analyze data of all four waves. Some participants will have provided data of four two-week periods, while others will have only provided data of three, two, or one two-week period(s). Collapsing the waves has the advantage that possible effects of outlier weekends (e.g., weekend apart, vacation, weekend shift) are averaged and the weekend reports are more representative of the person’s general weekend experience. We will analyze data of all participants who have taken part in at least one wave (with or without their partner), and who participated once each week to provide at least one assessment before and one assessment after the weekend days (see also Yap et al., 2012). Participants who only took part in the study on a Saturday and/or Sunday will be excluded from the present analysis. We will T-standardize the dependent variables (i.e., relationship satisfaction, sexual satisfaction, closeness, partner perception, self-disclosure), we will center the relationship duration variable and the positive and negative affect mean scores, and we will set gender to 1 = female and 0 = male. Knowledge of Data In preparing this pre-registration, the first author tested the above code lines with pilot study data that was conducted prior to the CouPers Study with individuals from 79 couples for the time frame of one 14-days period (not controlling for waves). For a conference poster and an oral conference presentation in May and September 2017, respectively, the first author has analyzed the changes in sexual satisfaction separately for female and male partners in wave 1 of the CouPers Study, while data collection of later waves was still ongoing. The first author used structural equation modeling with quadratic changes for both weeks separately to depict the increases in sexual satisfaction towards the weekend and tested whether neuroticism of both partners predicted the intercept and change in sexual satisfaction across the week. The CouPers Study data have been used for several other publications, which are listed below. None of these tested weekend effects in daily relationship variables. Avilés, T. G., Burriss, R. P., Weidmann, R., Bühler, J. L., Wünsche, J., &amp; Grob, A. (2021). Committing to a romantic partner: Does attractiveness matter? A dyadic approach. Personality and Individual Differences, 176, 110765. https://doi.org/10.1016/j.paid.2021.110765 Barbaro, N., Weidmann, R., Burriss, R. P., Wünsche, J., Bühler, J. L., Shackelford, T. K., &amp; Grob, A. (2021). The (bidirectional) associations between romantic attachment orientations and mate retention behavior in male-female romantic couples. Evolution and Human Behavior, 42, 497-506. https://doi.org/10.1016/j.evolhumbehav.2021.04.005 Bühler, J. L., Weidmann, R., Wünsche, J., Burriss, R. P., &amp; Grob, A. (2020). Daily responsiveness, expectations, and self–disclosure: How the average levels and within–person variability of three relationship components mediate personality–relationship transactions in romantic couples. European Journal of Personality, 34, 367-392. https://doi.org/10.1002/per.2255 Bühler, J. L., Wrzus, C., Weidmann, R., Wünsche, J., Burriss, R. P., &amp; Grob, A. (2021). Hard-working in general but lazy at home? Generalized Big Five traits and relationship-specific traits in romantic couples over time. Journal of Research in Personality, 92, 104087. https://doi.org/10.1016/j.jrp.2021.104087 Johnson, M. D., Lavner, J. A., Mund, M., Zemp, M., Stanley, S. M., Neyer, F. J., ... &amp; Grob, A. (2022). Within-couple associations between communication and relationship satisfaction over time. Personality and Social Psychology Bulletin, 48, 534-549. https://doi.org/10.1177/01461672211016920 Mund, M., Weidmann, R., Wrzus, C., Johnson, M. D., Bühler, J. L., Burriss, R. P., ... &amp; Grob, A. (2022). Loneliness is associated with the subjective evaluation of but not daily dynamics in partner relationships. International Journal of Behavioral Development, 46, 28-38. https://doi.org/10.1177/0165025420951246 Nikitin, J., Wünsche, J., Bühler, J. L., Weidmann, R., Burriss, R. P., &amp; Grob, A. (2021). Interdependence of approach and avoidance goals in romantic couples over days and months. The Journals of Gerontology: Series B, 76, 1251-1263. https://doi.org/10.1093/geronb/gbaa149 Reitz, A. K., Weidmann, R., Wünsche, J., Bühler, J. L., Burriss, R. P., &amp; Grob, A. (2021). In good times and in bad: A longitudinal analysis of the impact of bereavement on self-esteem and life satisfaction in couples. European Journal of Personality, 08902070211054896. https://doi.org/10.1177/08902070211054896 References Bates, D., Mächler, M., Bolker, B., &amp; Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1-48. https://doi.org/10.18637/jss.v067.i01 Areni, C. S., Burger, M., &amp; Zlatevska, N. (2011). Factors affecting the extent of Monday Blues: Evidence from a meta-analysis. Psychological Reports, 109, 723–733. https://doi.org/10.2466/13.20.PR0.109.6.723-733 Berli, C., Schwaninger, P., &amp; Scholz, U. (2021). “We feel good”: Daily support provision, health behavior, and well-being in romantic couples. Frontiers in Psychology, 11, 622492. https://doi.org/10.3389/fpsyg.2020.622492 Egloff, B., Tausch, A., Kohlmann, C.-W., &amp; Krohne, H. W. (1995). Relationships between time of day, day of the week, and positive mood: Exploring the role of the mood measure. Motivation and Emotion, 19, 99–110. https://doi.org/10.1007/BF02250565 Gadassi, R., Bar-Nahum, L. E., Newhouse, S., Anderson, R., Heiman, J. R., Rafaeli, E., &amp; Janssen, E. (2016). Perceived Partner Responsiveness Mediates the Association Between Sexual and Marital Satisfaction: A Daily Diary Study in Newlywed Couples. Archives of Sexual Behavior, 45, 109–120. https://doi.org/10.1007/s10508-014-0448-2 Gnambs, T. (2021). The Day of the Week Effect on subjective well-being in the European Social Survey: An individual-participant meta-analysis. Zeitschrift Für Psychologie, 229, 38–47. https://doi.org/10.1027/2151-2604/a000436 Helliwell, J. F., &amp; Wang, S. (2015). How was the weekend? How the social context underlies weekend effects in happiness and other emotions for US workers. PLOS ONE, 10, e0145123. https://doi.org/10.1371/journal.pone.0145123 John, O. P., &amp; Srivastava, S. (1999). The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. In Handbook of personality: Theory and research (1999-04371-004; 2nd ed., pp. 102–138). Guilford Press. Krohne, H. W., Egloff, B., Kohlmann, C. W., &amp; Tausch, A. (1996). Investigations with a German version of the positive and negative affect schedule (PANAS). Diagnostica, 42(2), 139–156. Larson, R., &amp; Richards, M. (1998). Waiting for the weekend: Friday and Saturday night as the emotional climax of the week. New Directions for Child and Adolescent Development, 1998, 37–52. https://doi.org/10.1002/cd.23219988204 Laurenceau, J. P., Barrett, L. F., &amp; Rovine, M. J. (2005). The interpersonal process model of Intimacy in marriage: A daily-diary and multilevel modeling approach. Journal of Family Psychology, 19, 314–323. https://doi.org/10.1037/0893-3200.19.2.314 Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., &amp; Team, R. C. (2019). nlme: Linear and nonlinear mixed effects models (version 3.1-141). Ryan, R. M., Bernstein, J. H., &amp; Brown, K. W. (2010). Weekends, work, and well-being: Psychological need satisfactions and day of the week effects on mood, vitality, and physical symptoms. Journal of Social and Clinical Psychology, 29, 95–122. https://doi.org/10.1521/jscp.2010.29.1.95 Schoebi, D., Perrez, M., &amp; Bradbury, T. N. (2012). Expectancy effects on marital interaction: Rejection sensitivity as a critical moderator. Journal of Family Psychology, 26, 709–718. https://doi.org/10.1037/a0029444 Stone, A. A., Schneider, S., &amp; Harter, J. K. (2012). Day-of-week mood patterns in the United States: On the existence of ‘Blue Monday’, ‘Thank God it’s Friday’ and weekend effects. The Journal of Positive Psychology, 7, 306–314. https://doi.org/10.1080/17439760.2012.691980 Thompson, E. R. (2007). Development and validation of an internationally reliable short-form of the Positive and Negative Affect Schedule (PANAS). Journal of Cross-Cultural Psychology, 38, 227–242. https://doi.org/10.1177/0022022106297301 Tsai, M.-C. (2019). The good, the bad, and the ordinary: The Day-of-the-Week Effect on mood across the globe. Journal of Happiness Studies, 20, 2101–2124. https://doi.org/10.1007/s10902-018-0035-7 Watson, D., Clark, L. A., &amp; Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. https://doi.org/10.1037//0022-3514.54.6.1063