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Preregistration for reported change in life satisfaction and memory of past life satisfaction study.

Pre-registration for study on initial elevation phenomenon and evaluative bias on personality items.

This is a pre-registration for calculating variables and conducting correlation analyses between measures of intraindividual variability in positive and negative affect, personality, self-esteem, and variability in ratings for images. There is a .docx document ("EMA variability preregistration") that's attached which contains all details in navigable form (i.e., can use navigation pane to see the headings). I've copy and pasted from that document to here but the document has better formatting. Research Question The research questions examine (1) the extent to which Neuroticism and other scales designed to measure variability in mood and personality actually capture variability in mood and personality, respectively, as measured by daily ratings of mood and personality; and (2) whether intraindividual variability in repeated responses to self-report items is capturing some sort of systematic measurement error (e.g., a general tendency to vary in all things, or a general tendency to give more variable responses to the same questions when asked repeatedly). Sample and Methods/Procedure The target sample size is based on the data collection procedure is N = 700 for the baseline survey and N = 400 for the experience sampling study, or ecological momentary assessment study (EMA). The EMA sample would give two-sided correlation tests 95% power to detect r = .179, 90% power to detect r = .161, and 80% power to detect r = .14. It would also give equivalence tests (or the 90% CI) 98% power to reject (or exclude) r >= |.2|, 92.4% power to reject r >= |.17|, and 88% power to reject r >= |.16|. There is a pre-registration for the data collection procedure and methods, including all measures that will be collected, on the Open Science Framework: https://osf.io/m9dz5. Hypotheses Hypothesis 1 If (i) the self-reported measures of variability in positive (PA) and negative affect (NA) and (ii) variability in multiple self-reports of positive and negative affect taken across time, both capture variability in the constructs of positive and negative affect, then there should be a positive correlation between: a) Negative Emotionality (i.e., Neuroticism) domain from BFI-2 and the adjusted index of variability in NA; b) Emotional Volatility facet from BFI-2 and the adjusted index of variability in NA; c) Volatility subscale from BFAS and the adjusted index of variability in NA; d) Affect Lability Short Scale (18-item) and the adjusted index of variability in NA; e) Affect Lability Short Scale (18-item) and the adjusted index of variability in PA. Hypothesis 2 If (i) the self-reported measures of variability in personality and (ii) variability in multiple self-reports of personality taken across time and contexts, both capture variability in personality, then there should be a positive correlation between: a) Self-Concept Clarity Scale and the adjusted index of variability in contextual personality; b) Self-Concept Clarity Scale and the adjusted index of variability in daily personality; c) Stability of Self Scale and the adjusted index of variability in contextual personality; d) Stability of Self Scale and the adjusted index of variability in daily personality; e) Self-Pluralism Scale and the adjusted index of variability in contextual personality; f) Self-Pluralism Scale and the adjusted index of variability in daily personality. Hypothesis 3 If (i) self-reported measures of variability in self-esteem and (ii) variability in multiple self-reports of self-esteem, both capture variability in self-esteem, then there should be a positive correlation between: Stability of Self Scale and the adjusted index of variability in daily self-esteem. Hypothesis 4 If variability in multiple self-reports taken across time and contexts capture some sort of systematic measurement error (e.g., a tendency to give variable responses to repeated self-reports; or a general tendency to be variable), then there should be a positive correlation between: a) The adjusted index of variability in NA and the adjusted index of variability in image ratings at baseline; b) The adjusted index of variability in PA and the adjusted index of variability in image ratings at baseline; c) The adjusted index of variability in contextual personality and the adjusted index of variability in image ratings at baseline; d) The adjusted index of variability in daily personality and the adjusted index of variability in image ratings at baseline; e) The adjusted index of variability in daily self-esteem and the adjusted index of variability in image ratings at baseline; f) The adjusted index of variability in NA and the adjusted index of variability in daily image ratings; g) The adjusted index of variability in PA and the adjusted index of variability in daily image ratings; h) The adjusted index of variability in contextual personality and the adjusted index of variability in daily image ratings; i) The adjusted index of variability in daily personality and the adjusted index of variability in daily image ratings; j) The adjusted index of variability in daily self-esteem and the adjusted index of variability in daily image ratings; In addition, there would also be a positive correlation between: k) The adjusted index of variability in NA and the adjusted index of variability in contextual personality; l) The adjusted index of variability in PA and the adjusted index of variability in contextual personality; m) The adjusted index of variability in NA and the adjusted index of variability in daily personality; n) The adjusted index of variability in PA and the adjusted index of variability in daily personality. o) The adjusted index of variability in NA and the adjusted index of variability in daily self-esteem; p) The adjusted index of variability in PA and the adjusted index of variability in daily self-esteem. q) The adjusted index of variability in daily self-esteem and the adjusted index of variability in contextual personality; r) The adjusted index of variability in daily self-esteem and the adjusted index of variability in daily personality. Analyses The variables to test each of the above hypotheses are described below. Each of the above hypotheses will be tested with two-sided, pairwise, Pearson’s correlation coefficient tests against a null hypothesis of zero. (Although the predictions are one-sided, two-sided tests can also falsify the predictions.) Therefore, for each hypothesis above, the corresponding measures listed below will be used. Items and Measures All items taken at the baseline survey and in the surveys during the mobile phone app study can be found in the preregistration of the data collection protocol (https://osf.io/m9dz5). Nonetheless, the items used for this particular study are as follows. Items in Baseline Survey Negative Emotionality domain from BFI-2 The items are rated on scale where 1=Disagree strongly, 2=Disagree a little, 3=Neutral; no opinion, 4=Agree a little, 5=Agree strongly. The items with ‘r’ next to the number are reverse scored and scores for the items on the domain are averaged. Negative Emotionality Anxiety 19. Can be tense. 34. Worries a lot. 4r. Is relaxed, handles stress well. 49r. Rarely feels anxious or afraid. Depression 39. Often feels sad. 54. Tends to feel depressed, blue. 9r. Stays optimistic after experiencing a setback. 24r. Feels secure, comfortable with self. Emotional Volatility 14. Is moody, has up and down mood swings. 59. Is temperamental, gets emotional easily. 29r. Is emotionally stable, not easily upset. 44r. Keeps their emotions under control. Emotional Volatility facet from BFI-2 (see above for items) Scores for the Emotional Volatility facet items are averaged after reverse scoring the relevant items. Volatility Subscale from BFAS Because a couple of the items (7 and 4) are virtually the same as the items on the BFI-2 (14 and 44, respectively), I included the BFAS items in the same block of questions as the BFI-2 on Qualtrics with slight adjustments to the wording on the items to fit with the sentence stem of the BFI-2. The wording I used for each item is presented in brackets next to the item on the below list. The items with the strikethrough are those that are already in the BFI-2 and the numbers in the brackets after the item reflect the position of the item in the BFI-2 as presented in Qualtrics BFI-2 question export tags. The items below are thus rated on the same 5-point scale as the BFI-2: 1=Disagree strongly, 2=Disagree a little, 3=Neither agree nor disagree, 4=Agree a little, 5=Agree strongly (numbers aren’t presented on the scales). Ratings on these items are averaged, after reverse scoring the items with (R) next to them. 1. Get angry easily. (66; Gets angry easily.) 2. Rarely get irritated. (R) (67; Rarely gets irritated.) 3. Get upset easily. (68; Gets upset easily.) 4. Keep my emotions under control. (R) (44; Keeps their emotions under control.) 5. Change my mood a lot. (69; Changes their mood a lot.) 6. Rarely lose my composure. (R) (70; Rarely loses their composure.) 7. Am a person whose moods go up and down easily. (14; Is moody, has up and down mood swings.) 8. Am not easily annoyed. (R) (71; Is not easily annoyed.) 9. Get easily agitated. (72; Gets easily upset.) 10. Can be stirred up easily. (73; Can be stirred up easily.) Affect Lability Short Scale 18-item Affect Lability Scale (Oliver & Simons, 2004; see also Look et al., 2010): “How descriptive of you are the following statements?” from 1 = very undescriptive, to 4 = very descriptive. The total scale is summed for a total score on affect lability. 1. At times I feel just as relaxed as everyone else and then within minutes I become so nervous that I feel light-headed and dizzy. 2. There are times when I have very little energy and then just afterwards I have about the same energy level as most people. 3. One minute I can be feeling OK and then the next minute I’m tense, jittery, and nervous. 4. I frequently switch from being able to control my temper very well to not being able to control it very well at all. 5. Many times I feel nervous and tense and then I suddenly feel very sad and down. 6. Sometimes I go from feeling extremely anxious about something to feeling very down about it. 7. I shift back and forth from feeling perfectly calm to feeling uptight and nervous. 8. There are times when I feel perfectly calm one minute and then the next minute the least little thing makes me furious. 9. Frequently, I will be feeling OK but then I suddenly get so mad that I could hit something. 10. Sometimes I can think clearly and concentrate well one minute and then the next minute I have a great deal of difficulty concentrating and thinking clearly. 11. There are times when I am so mad that I can barely stop yelling and other times shortly afterwards when I wouldn’t think of yelling at all. 12. I switch back and forth between being extremely energetic and having so little energy that it’s a huge effort just to get where I am going. 13. There are times when I feel absolutely wonderful about myself but soon afterwards I often feel that I am just about the same as everyone else. 14. There are times when I’m so mad that my heart starts pounding and/or I start shaking and then shortly afterwards I feel quite relaxed. 15. I shift back and forth between being very unproductive and being just as productive as everyone else. 16. Sometimes I feel extremely energetic one minute and then the next minute I might have so little energy that I can barely do a thing. 17. There are times when I have more energy than usual and more than most people and then soon afterwards I have about the same energy level as everyone else. 18. At times I feel that I’m doing everything at a very slow pace but then soon afterwards I feel that I’m no more slowed down than anyone else. Self-Concept Clarity Scale 12-item Self-Concept Clarity Scale (Campbell et al. (1996): “How much do you agree or disagree with each of the following statements?” 1=Disagree strongly, 2=Disagree a little, 3=Neither agree nor disagree, 4=Agree a little, 5=Agree strongly (numbers aren’t presented on the scales). The items are presented in randomized order. The ratings are summed, after reverse scoring the items without a *. 1. My beliefs about myself often conflict with one another.* 2. On one day I might have one opinion of myself and on another day I might have a different opinion.* 3. I spend a lot of time wondering about what kind of person I really am.* 4. Sometimes I feel that I am not really the person that I appear to be.* 5. When I think about the kind of person I have been in the past, I’m not sure what I was really like.* 6. I seldom experience conflict between the different aspects of my personality. 7. Sometimes I think I know other people better than I know myself.* 8. My beliefs about myself seem to change very frequently.* 9. If I were asked to describe my personality, my description might end up being different from one day to another day.* 10. Even if I wanted to, I don’t think I would tell someone what I’m really like.* 11. In general, I have a clear sense of who I am and what I am. 12. It is often hard for me to make up my mind about things because I don’t really know what I want.* The items with * are typically reverse scored. However, for ease of interpretability of correlation coefficients, we reverse score the items that don’t have a *, since then higher scores on this scale would mean greater variability in personality. Stability of Self Scale 5-item Stability of Self Scale (Marsh, 1993): The original scale was on 5-point scale from never true, seldom true, sometimes true, often true, to almost always true. But we adapted it to be presented in the same block of questions as the Self-Concept Clarity Scale and therefore it has the same sentence stem and 5-point rating scales as above (numbers aren’t presented on the scales). The items are presented in randomized order within that block. The ratings are averaged after reverse scoring the items without a *. The numbers in the brackets to the right of each item reflects the position of that item in the Qualtrics export tag from the Self-Concept clarity block of items. 1. My opinion of myself tends to change a good deal instead of always remaining the same.* (13) 2. I find that on one day I have one opinion of myself and on another day I have a different opinion.* (2; see item 2 from self-concept clarity scale) 3. I often change from a very good opinion of myself to a very poor opinion of myself.* (14) 4. I have noticed that my ideas about myself seem to change very quickly.* (8; see item 8 from self-concept clarity scale) 5. I feel that nothing can change the opinion I currently hold of myself. (15) The items with * are typically reverse scored. However, for ease of interpretability of correlation coefficients, we reverse score the items that don’t have a *, since then higher scores on this scale would mean greater variability in personality. Note that for item 3 the word “often” was added because the original scale was about how often this statement was true but with the disagree-agree scale the item required some sort of frequency for the statement to make sense. Self-Pluralism Scale The Self-concept Clarity and Stability of Self scales may seem to tap a little more into self-esteem than into personality, assuming the two concepts are distinct. Therefore, we’ll also include the Brief Self-Pluralism Scale (SPS-10; McReynolds et al., 2000) with 10 items presented in random order. Participants are told to “Please indicate whether each of the following statements is true or false.”. False is coded as 0 and True is coded as 1 and the items are summed, after reverse scoring the items with a star, to produce a score with higher scores reflecting greater variability in personality: 1. I am the same kind of person in every way, day in and day out.* 2. People who know me well would say I act quite differently at different times. 3. Though I vary somewhat from time to time, in general I always feel much the same.* 4. People who know me well would say I’m pretty predictable.* 5. There have been times when I felt like a completely different person from what I was the day before. 6. I act and feel essentially the same way whether at home, at work, or with friends.* 7. I get along best when I act and feel like a totally different person at different times. 8. I sometimes have conflicts over whether to be one kind of person or a different kind. 9. I’m the same sort of person regardless of whom I’m with.* 10. People who know me would say that my behaviour changes from situation to situation. Adjusted Index of Variability in Contextual Personality Using the BFI-10 (Rammstedt & John, 2007). “How much do you agree or disagree that each of the following statements describes your personality as a friend / family member / romantic partner / stranger?”. The following 10 items are then presented such that participants are asked to respond to them regarding how they are in the 4 different contexts. Each context is presented as part of a separate block of questions so that there are 4 blocks of questions. Among friends/ Among family/ When with a romantic partner/ Among strangers, I am someone who … 1. … is reserved 2. … is generally trusting 3. … tends to be lazy 4. … is relaxed, handles stress well 5. … has few artistic interests 6. … is outgoing, sociable 7. … tends to find fault with others 8. … does a thorough job 9. … gets nervous easily 10. … has an active imagination The items are presented in randomized order. Each of the items are rated on a 5-point scale: 1=Disagree strongly, 2= Disagree a little, 3=Neither agree nor disagree, 4=Agree a little, 5=Agree strongly (numbers aren’t presented on the scales). To calculate the index of variability in contextual personality, I’ll use the contextual personality measure following Baird et al. (2017): For each item, calculate the means and standard deviations across the different roles/contexts for each participant. You then regress each of these standard deviations for the different roles/contexts onto the corresponding mean and the square of the mean. You save the residuals from these regressions for each item, which can then be combined to create variability scores for each of the Big Five Traits and for overall variability. See Baird et al. (2018, p. 8). Therefore, for each item separately, assuming each item in the dataset has a similar suffix across the different roles/contexts: data$itemM = .16/.17/.2 for these correlations, then our conclusion would be that if the variability measures are capturing some sort of systematic measurement error, it's likely to be less than .16/etc.. We can further conclude that this should be further investigated. However, the results would suggest that at this stage we shouldn't be overly worried about systematic measurement error in the intraindividual variability measures. On the other hand, if we find a significant correlation between (i) and every one of (ii)-(v), then we'd conclude that the variability measures are capturing some sort of systematic measurement error, such as response styles or a general tendency to be more variable in all things. This would be a problem for those measures of within-person variability. Finally, if we find a significant correlation between (i) and any one of (ii)-(v) we'd draw a similar conclusion to the preceding paragraph but a bit more tentatively. If, on the other hand, the intraindividual variability in contextual and EMA personality measures capture systematic measurement error, then they should positively correlate with intraindividual variability in image ratings and those correlations should be stronger than the correlation between the intraindividual variability in contextual/EMA personality and self-reported variability in personality. Finally, look at how the volatility subscale of the BFI is linked to variability, and whether that link is larger than the anxiety and depression subscales. So can compare the correlation of the self-reported measures of variability in negative emotions with the corrected SD in daily NA, and compare these to the correlation of the depression and anxiety facets from BFI-II Neuroticism domain with the corrected SD in daily NA. Is the former stronger than the latter? If the intraindividual variability measures are capturing systematic measurement error in the form of response styles or global variation, then we expect: 1. Variability in daily PA and NA should be positively correlated with variability in contextual personality and variability in image ratings; 2. Variability in contextual personality, variability in daily personality, and variability in daily self-esteem should be positively correlated with variability in image ratings; 3. The relationships in (1) should be stronger than the correlation between variability in daily mood ratings and self-reported variability in mood; 4. The relationships in (2) should be stronger than the correlation of variability in contextual and daily personality with self-reported variability in personality; 5. Variability in daily PA and NA should show equivalence in relationship with self-reported variability in NA and self-reported variability in mood; and 6. Variability in contextual personality and variability in daily personality should show equivalence in relationship with self-reported variability in personality. If we observe that variability in daily PA and NA ratings are positively correlated with self-reported variability in mood then we can say that the different measures of variability in mood have some convergent validity. Similarly, if we observe that variability in contextual and daily personality ratings are positively correlated with self-reported variability in personality then we can say that the different measures of variability in personality have some convergent validity. If we observe that there is: 1. no correlation of variability in daily NA and PA with variability in image ratings AND 2. no correlation of variability in daily NA and PA with self-reported variability in mood then we conclude that the different measures of variability in mood don’t have convergent validity and that the measures of variability in daily mood ratings may not be capturing a type of response styles, or that if they are then that relationship is likely to be smaller than xxx (based on the CIs). Similarly, if we observe that there is: 1. no correlation of variability in contextual and daily personality ratings with variability in image ratings AND 2. no correlation of variability in contextual and daily personality ratings with self-reported variability in personality then we conclude that the different measures of variability in personality don’t have convergent validity and that the measures of variability in daily personality ratings may not be capturing a type of response styles, or that if they are then that relationship is likely to be smaller than xxx (based on the CIs). A note from Rich Lucas: And although I had forgotten that we did this study, we did in fact use a version of the color task you suggested, but we did it kind of haphazardly, and didn't think carefully about the colors we used. We also did not do it over time, we asked them about multiple colors (that varied from red to purple to blue and they had to rate how red versus blue they were) all at once. We also included some other measures, including rating how happy various faces were, the neutral objects questionnaire that we had in the other study, and then personality variability measure where people rate their personality in different roles. There was a consistent association among the measures, but it wasn't strong: r.faces r.colors r.neutral r.others r.personality r.faces 1.0000000 0.2487348 0.2662130 0.6710672 0.2093353 r.colors 0.2487348 1.0000000 0.1775679 0.6002129 0.0856562 r.neutral 0.2662130 0.1775679 1.0000000 0.7958955 0.3984736 r.others 0.6710672 0.6002129 0.7958955 1.0000000 0.3652643 r.personality 0.2093353 0.0856562 0.3984736 0.3652643 1.0000000 r.faces = residual of sd controlling for mean of rating of happiness; r.colors = residual of sd controlling for mean of colors; r.neutral = residual of neutral objects sd controlling for mean; r.others = average of the other three; r.personality = residual of personality variability controlling for mean. Thus, currently, it’s not at all clear whether the problem is with the self-reported variability in NA (i.e., Neuroticism) or with the measure of variability in NA (i.e., the corrected intraindividual variability in NA ratings). Kalokerinos et al. (2020) and others suggest the problem is with measures of Neuroticism. But this assumes that the truth criterion is the in the moment ratings. Baird et al. (2006, 2018) would suggest that there might also be a problem with the variability in the momentary ratings, since they might be capturing something much broader than only variability in that construct. If we find that corrected intraindividual variability in NA is correlated with corrected intraindividual variability in contextual personality and corrected intraindividual variability in image ratings, then we can argue that we don’t know whether the problem is with Neuroticism as a measure of emotional volatility or whether the problem is that intraindividual variability in NA ratings is capturing something much more global such that it is a general variability in people or response styles. Some evidence supports this already. The following Table is from an email from Rich Lucas based off of the data used in one of his papers (Baird et al., 2006, or 2018): pa_res = adjusted daily PA ratings; na_res = adjusted daily NA ratings, unadjPA = standard deviation in daily PA; unadjNA = standard deviation in daily NA; genn_i = neuroticism items from the contextual personality measures; neur_i = a measure of Neuroticism; neur_ii = another measure of Neuroticism. pa_res na_res unadjPA unadjNA genn_i pa_res 1.00000000 0.54614260 0.9962609 0.633356976 0.20500909 na_res 0.54614260 1.00000000 0.5445449 0.783666792 0.01478801 unadjPA 0.99626093 0.54454488 1.0000000 0.620855768 0.19890447 unadjNA 0.63335698 0.78366679 0.6208558 1.000000000 0.14278036 genn_i 0.20500909 0.01478801 0.1989045 0.142780362 1.00000000 neur_i 0.29292520 0.03817321 0.2835682 0.214554380 0.66520155 neur_ii 0.26572122 0.06294902 0.2513075 0.264493507 0.63570413 Although the standard deviation of daily NA ratings is correlated with N the adjusted intraindividual variability in daily NA ratings is NOT correlated with N. This replicates the findings of Kalokerinos et al. (2020) and others. (Note that both the standard deviation in daily PA and the adjusted intraindividual variability in daily PA are correlated with N.) However, Baird et al. (2018) found that the adjusted intraindividual variability in daily NA was correlated with intraindividual variability in contextual personality ratings, intraindividual variability in daily personality ratings, intraindividual variability in ratings of Neutral objects, and in daily weather ratings: This is preliminary evidence supporting my arguments above. But one could still argue that variability in daily NA ratings may be expected to be related to daily variability in weather ratings (and in weather actually), variability in satisfaction with neutral objects, variability in ratings of a close friend’s personality, and variability in daily ratings of own personality. In my study, the same expectation shouldn’t hold as strongly for variability in ratings for how white-black and how fuzzy-sharp images are. One could argue that variability might be a very global thing, so that variability in NA could be related to variability in everything else, because people may be globally variable. But such a finding would bring into question the claims of Kalokerinos et al. that N is a poor measure and future research would need to be done about whether variability in daily NA is part of global variability in people or whether it is measuring something like response styles. Exploratory Items and Measures In the evening surveys of the EMA study, participants will also be asked to report how much they experienced each of the emotions in the positive affect (positive, happy, proud, joyful, enthusiastic, and calm) and negative affect scales (negative, sad, ashamed, angry, anxious, and bored) during the whole day. Therefore, I can also calculate an index of adjusted variability in PA and NA using these items (see below). But the momentary ratings taken throughout the day, in real-time, don’t rely on memory, whereas these end-of-day reports on the whole day do. Therefore, the adjusted index of variability in end-of-day reports will be used for exploratory purposes only. Intraindividual variability in daily retrospective end-of-day reports Using the ratings for how people felt throughout the day taken each evening from the EMA phase, I’ll calculate an adjusted index of variability in daily retrospective PA reports and daily retrospective NA reports: data$HapDayM

This is a time-stamped preregistration of a registered report protocol that has received stage 1 in principle acceptance at Nature Communications. One file is the approved stage 1 manuscript (317965_2_art_file_7047800_rkpv4w.pdf). The other file is the stage 1 supplemental (317965_2_supp_7047801_rkpv4w.pdf). Both files are the PDFs from the journal portal.

The main aim of the study is to test whether retrospective reports of mood, and mental health symptoms, are prone to the initial elevation bias (as found by Shrout et al., 2018). We focus specifically on a widely used scale measuring symptoms of generalized anxiety disorder (GAD-7) and another widely used scale measuring symptoms of depression (PHQ-8). The two scale are typically used to screen for the disorders and for diagnosis. In addition, we attempt to directly replicate Shrout et al.’s findings for retrospective frequency reports of physical symptoms.

This is a preregistration of additional data collection. To test our hypotheses, we have collected the data for the three task difficulty levels, i.e. 1-back, 2-back, and 3-back. 274 individuals participated in the study (139 women and 135 men). However, the results were inconclusive. Therefore, we intend to collect additional data for an extra difficulty level - 5-back, which, as we expect, will clarify the results.

Study 2, attempting to replicate Study 1 with higher power and better question wording for more clarity.

Study 3 following reviews.

This study sets out to investigate how the magnitude of task difficulty affects challenge and hindrance appraisals and performance in a laboratory setting.

Behavioural Study

This is a preregistration of the data collection protocol and the measures that are included as part of the protocol. All details are contained in the attached microsoft word document with navigable headings. The preregistration here has been copy and pasted from that document.

This is a preregistration for a study. I wrote it in word, and am attaching it as a PDF. I'll also copy and paste the preregistration into the open field that follows.

preregistration updated following approval from handling editor regarding inclusion criteria (previously required that minimum of 30 words be found for inclusion but now 20 words). no data have been collected yet.

This study has two main aims. First, it's designed to examine a potential mechanism behind the initial elevation bias; namely, whether the elevation bias is caused by changes in how people feel between the first and second time they take a survey. Second, the study is designed to examine the breadth of the impact of the initial elevation bias. Measures of affective well-being, personality, and satisfaction with political leadership are included.

We are investigating the robustness of the initial elevation bias. We will be measuring anxiety using a 3-item, 5-point rating scale, as originally used by Shrout et al. (2018). If the initial elevation bias is robust, then we expect to see mean ratings on this scale to be higher for people who are responding to the scale for the first time (the Later Start group) than for people who are responding to the scale for the second time (the Earlier Start group).

© The Author(s), under exclusive licence to Springer Nature Limited 2021, corrected publication 2022, The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world., This project was supported by funds from: the Amazon Web Services (AWS) Imagine Grant (to E.M.B.); the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (JSPS KAKENHI; 16h03079, 17h00875, 18k12015, and 20h04581 to Y.Y.); the research programme Dipartimenti di Eccellenza from the Ministry of Education University and Research (MIUR to N. Cellini and G.M. and the Department of General Psychology of the University of Padua); statutory funds of the University of Wroclaw (to A. Sorokowska); the Charles University Research Programme PROGRES (Q18 to M. Vranka); the Knut and Alice Wallenberg Foundation (2016:0229 to J.K.O.); the Rubicon Grant (019.183sg.007 to K.v.S.) from the Netherlands Organisation for Scientific Research; the Australian Research Council (dp180102384 to R.M.R.); the US National Institutes of Health (NIMH111640 to M.N.-D.), the Huo Family Foundation to N.J.; the NSF Directorate for Social, Behavioral and Economic Sciences, Division of Social and Economic Sciences (1559511 to J.S.L.); the US National Institutes of Health (RO1-CA-224545 to J.S.L.); Eesti Teadusagentuur–Estonian Research Council (PSG525 to A. Uusberg); the J. William Fulbright Program (to F. Azevedo); the HSE Basic Research Program (to D. Dubrov); Dominican University (a Faculty Development Grant to A. Krafnick); and the French National Research Agency Investissements d’avenir supporting PSF (ANR-15-IDEX-02 to H.I.); the Slovak Research and Development Agency (project no. APVV-20-0319 to M. Adamkovič); the programme FUTURE LEADER of Lorraine Université d’Excellence within the French National Research Agency Investissements d’avenir (ANR-15-IDEX-04-LUE to S.M.). Computation for this research was assisted by: the Harvard Business School compute cluster (HBSGrid); and the Open Science Grid. The Open Science Grid is supported by the National Science Foundation award 1148698 and the US Department of Energy’s Office of Science, as well as by the compute resources and assistance of the UW-Madison Center For High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW-Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation, and is an active member of the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy’s Office of Science.

Supplemental material, sj-docx-1-pps-10.1177_17456916221091565 for Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters by Farid Anvari, Rogier Kievit, Daniël Lakens, Charlotte R. Pennington, Andrew K. Przybylski, Leo Tiokhin, Brenton M. Wiernik and Amy Orben in Perspectives on Psychological Science

Inspired by field-specific theoretical developments, scholars from different scientific disciplines have devised various methods to measure how people explore new options and environments. There are therefore many ways to quantify and measure exploratory behaviour. However, it remains unclear whether the different measures (i) have convergent validity relative to one another, (ii) capture a domain general tendency to explore, and (iii) capture a tendency that is stable across time. To answer these questions, we designed a study with a representative sample where more than 700 participants will complete five behavioural tasks and four self-report scales developed to measure exploratory behaviour, most of which examine exploratory behaviour in the context of exploration-exploitation trade-offs. Up to 350 of the participants will complete the same measures again one month later. This design allows us to investigate how much people’s exploratory behaviour as summarized by the different measures is correlated, revealing whether the measures capture the same general tendency to explore, and whether this tendency is stable across time. ITEMS: Stage 1 Registered Report Protocol, Stage 1 Registered Report Supplementary Information

Supplemental material, sj-docx-1-spp-10.1177_19485506221129160 for Bias in Self-Reports: An Initial Elevation Phenomenon by Farid Anvari, Emir Efendić, Jerome Olsen, Ruben C. Arslan, Malte Elson and Iris K. Schneider in Social Psychological and Personality Science