With the advent of online and app-based studies, researchers in psychology are making increasing use of repeated subjective reports. The new methods open up opportunities to study behavior in the field and to map causal processes, but they also pose new challenges. Recent work has added initial elevation bias to the list of common pitfalls; here, higher negative states (i.e., thoughts and feelings) are reported on the first day of assessment than on later days. This article showcases a new approach to addressing this and other measurement reactivity biases. Specifically, we employed a planned missingness design in a daily diary study of more than 1,300 individuals who were assessed over a period of up to 70 days to estimate and adjust for measurement reactivity biases. We found that day of first item presentation, item order, and item number were associated with only negligible bias: items were not answered differently depending on when and where they were shown. Initial elevation bias may thus be more limited than has previously been reported or it may act only at the level of the survey, not at the item level. We encourage researchers to make design choices that will allow them to routinely assess measurement reactivity biases in their studies. Specifically, we advocate the routine randomization of item display and order, as well as of the timing and frequency of measurement. Randomized planned missingness makes it possible to empirically gauge how fatigue, familiarity, and learning interact to bias responses.

How attractive we find ourselves decides who we target as potential partners and influences our reproductive fitness. Self-perceptions on women's fertile days could be particularly important. However, results on how self-perceived attractiveness changes across women's ovulatory cycles are inconsistent and research has seldomly assessed multiple attractiveness-related constructs simultaneously. Here, we give an overview of ovulatory cycle shifts in self-perceived attractiveness, sexual desirability, grooming, self-esteem and positive mood. We addressed previous methodological shortcomings by conducting a large, preregistered online diary study of 872 women (580 naturally cycling) across 70 consecutive days, applying several robustness analyses, and comparing naturally cycling women to women using hormonal contraceptives. As expected, we found robust evidence for ovulatory increases in self-perceived attractiveness and sexual desirability in naturally cycling women. Unexpectedly, we found moderately robust evidence for smaller ovulatory increases in self-esteem and positive mood. Although grooming showed an ovulatory increase descriptively, the effect was small, failed to reach our strict significance level of .01 and was not robust to model variations. We discuss how these results could follow an ovulatory increase in sexual motivation while calling for more theoretical and causally informative research to uncover the nature of ovulatory cycle shifts in the future.

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In Arslan et al. (2018), we reported ovulatory increases in extra-pair sexual desire, in-pair sexual desire, and self-perceived desirability, as well as several moderator analyses related to the good genes ovulatory shift hypothesis, which predicts attenuated ovulatory increases in extra-pair desire for women with attractive partners. Gangestad and Dinh (2021) identified errors in how we aggregated two out of four main moderator variables. We are grateful that their scrutiny uncovered these errors. After corrections, our moderation results are more mixed than we previously reported and depend on the moderator specification. However, we disagree that the evidence for moderation is robust and compelling, as Gangestad and Dinh (2021) claim. Our data are consistent with some previously reported effect sizes, but also with negligible moderator effects. We also show that what Gangestad and Dinh (2021) call an "a priori [...] more comprehensive and valid composite" is poorly justifiable on a priori grounds, and follow-up analyses they report are not robust to a composite specification that we consider at least as reasonable. Psychologists have to become acquainted with techniques such as cross-validation or training and test sets to manage the risks of data-dependent analyses. In doing so, we might learn that we need new data more often than we intuit and should remain uncertain far more often.

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