ROM (T1 and T2). At T1 and T2, we examined whether students prioritized time or money by implementing the ROM. This measure requires respondents to read a short paragraph describing two individuals and then presents respondents with a binary choice where they are asked to choose which individual is most like themselves (13). The choices are presented as follows:

Tina values her time more than her money. She is willing to sacrifice her money to have more time. For example, Tina would rather work fewer hours and make less money, than work more hours and make more money.

Maggie values her money more than her time. She is willing to sacrifice her time to have more money. For example, Maggie would rather work more hours and make more money, than work fewer hours and have more time.

The identifiers of the characters and the pronouns that are used in these vignettes are matched to the participants’ gender (Tina/Tom and Maggie/Michael); for people who did not report gender, the names and pronouns used in the vignettes are displayed as gender neutral (Morgan/Taylor). We chose a binary response format based on the precedent set by previous research (13), as well as for pragmatic and theoretical reasons. Conceptually, we chose this response format because we were interested in assessing people’s broad preferences related to prioritizing time over money, as opposed to assessing people’s domain-specific preferences. Practically, there is an increased awareness about the importance of conducting research with large representative samples (28). Thus, it is necessary to design short measures that minimize participant burden while maximizing reliability (29), and implementing a simple measure allowed us to efficiently collect a large number of college students as they were undergoing a major life event.

SWB (T1 and T2). To capture SWB, respondents reported on their overall life satisfaction by answering the question, “Taking all things together, how happy would you say you are?” on a scale from 0 = not at all to 10 = extremely (30). Next, participants completed the Cantril Ladder (31), reporting where they currently stand in life on a ladder spanning from the worst possible to the best possible life imaginable (from 0 = bottom rung to 10 = top rung). We selected these questions because they are brief measures that are used extensively in large-scale survey research to capture the cognitive component of SWB. To capture the affective component of SWB, we asked participants to rate their positive and negative affect in the past 4 weeks using the Schedule for Positive and Negative Affect [SPANE; (38); positive affect, α = 0.84; negative affect, α = 0.86].

We preregistered that we would combine the cognitive component (satisfaction with life) and affective components (positive affect and reverse-scored negative affect) into a single SWB composite if we observed a correlation above 0.50 between these measures. The correlations were more than 0.50 (r > 0.56); thus, we standardized and combined these measures to create an SWB composite. For most of the participants recruited through ongoing laboratory studies, we were able to collect the same measures of SWB at T1. As described above, we only preregistered analyses for which we expected to collect data from all our data collection opportunities. We therefore reported our results that include T1 SWB in the main text while noting that the full results that include T1 as a covariate were not preregistered.

Activity (T2). After reporting well-being, participants selected their one current primary activity from a list we provided. We created this list based on research from our university, showing that graduates most commonly engage in full- or part-time employment, graduate or professional school, service or volunteer activities, internship, travel, or gap years (32). We also allowed participants to report engaging in “other” activities.

Activity motivation (T2). Participants were then asked to report on their primary motivation for engaging in their primary activity. To assess activity motivation, students completed two items adapted from Sheldon et al. (33). Students responded to the question of “why are you engaged in these behaviors” on two sliding scales ranging from 0 = “Because someone told me to” to 100 = “Because I really identify with the activity” and 0 = “Because you would feel guilty if you didn’t” to 100 = “Because of the enjoyment this activity gives you.” Consistent with our preregistered analytic plan, we combined participants’ responses to these two items to form a composite measure indicating intrinsic activity motivation (α = 0.84).

Control variables (T2). Consistent with our preregistered analysis plan and with other recent research on this topic (6), we repeated our main analyses controlling for gender (1 = female), family SES, and materialism. We asked students to report their parents’ education based on research showing that parental education is a more reliable predictor of family SES compared to students’ reports of their parents’ occupation or income (34). We assessed materialism by asking participants to complete the three highest loading items from the Material Values Survey [α = 0.76; (35)]. While previous research has shown that the ROM is distinct from materialism (13), we included a short measure of materialism to ensure that this was the case. Because this measure was of subsidiary interest, we originally planned to ask only a subset of our sample to complete it, but given the brevity of our final questionnaire, we were able to ask all participants to complete the materialism items. In our preregistration, we also indicated that we would include age as a covariate in our analyses. However, because of a programming error, we failed to collect age data from the first 410 respondents who completed the T2 survey. Because the age range in this sample was highly restricted (more than 90% of the sample was between the ages of 21 and 25 at T2), the models we report in text exclude age as a covariate to maximize power. Analyses that include age as a covariate yield statistically equivalent results (see Tables 4C and 5C).

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