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The file drawer problem—often operationalized in terms of statistically significant results being published and statistically insignificant not being published—is widely documented in the social sciences. We extend Franco’s et al. [Science345, 1502–1505(2014)] seminal study of the file drawer problem in survey experiments submitted to the Time-sharing Experiments for the Social Sciences (TESS) data collection program. We examine projects begun after Franco et al. The updated period coincides with the contemporary open science movement. We find evidence of the problem, stemming from scholars opting to not write up insignificant results. However, that tendency is substantially smaller than it was in the prior decade. This suggests increased recognition of the importance of null results, even if the problem remains in the domain of survey experiments. 

ImportanceEfforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety. ObjectiveTo characterize the association between self-reported use of individual social media platforms and irritability among US adults. Design, Setting, and ParticipantsThis survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability. Survey respondents were aged 18 years and older. ExposureSelf-reported social media use. Main Outcomes and MeasuresThe primary outcome was score on the Brief Irritability Test (range, 5-30), with higher scores indicating greater irritability. ResultsAcross the 2 survey waves, there were 42 597 unique participants, with mean (SD) age 46.0 (17.0) years; 24 919 (58.5%) identified as women, 17 222 (40.4%) as men, and 456 (1.1%) as nonbinary. In the full sample, 1216 (2.9%) identified as Asian American, 5939 (13.9%) as Black, 5322 (12.5%) as Hispanic, 624 (1.5%) as Native American, 515 (1.2%) as Pacific Islander, 28 354 (66.6%) as White, and 627 (1.5%) as other (ie, selecting the other option prompted the opportunity to provide a free-text self-description). In total, 33 325 (78.2%) of the survey respondents reported daily use of at least 1 social media platform, including 6037 (14.2%) using once a day, 16 678 (39.2%) using multiple times a day, and 10 610 (24.9%) using most of the day. Frequent use of social media was associated with significantly greater irritability in univariate regression models (for more than once a day vs never, 1.43 points [95% CI, 1.22-1.63 points]; for most of the day vs never, 3.37 points [95% CI, 3.15-3.60 points]) and adjusted models (for more than once a day, 0.38 points [95% CI, 0.18-0.58 points]; for most of the day, 1.55 points [95% CI, 1.32-1.78 points]). These associations persisted after incorporating measures of political engagement. Conclusions and RelevanceIn this survey study of 42 597 US adults, irritability represented another correlate of social media use that merits further characterization, in light of known associations with depression and suicidality. 

Spectroscopic ellipsometry and ultraviolet-visible (UV-VIS) spectrometry were utilized to study the optical properties of ferroelectric lead lanthanum zirconate titanate (PLZT) films. These films were deposited on platinized silicon [Si(100)/ SiO2/TiO2/Pt(111)] substrates using the chemical solution deposition method. Films were annealed at two different temperatures (650 and 750 °C) using rapid thermal annealing. Shimadzu UV-1800 UV-VIS spectrophotometer with a resolution of 1 nm was used to measure the reflectance data in the spectral range of 300–1000 nm with a step size of 1 nm. The bandgap values were determined from the reflectance spectra using appropriate equations. A J.A. Woollam RC2 small spot spectroscopic ellipsometer was used to obtain the change in amplitude (Ψ) and phase (Δ) of polarized light upon reflection from the film surface. The spectra were recorded in the wavelength range of 210–1500 nm at an incident angle of 65°. Refractive index (n) and extinction coefficient (k) were obtained by fitting the spectra (Ψ, Δ) with the appropriate models. No significant changes were observed in the optical constants of PLZT films annealed at 650 and 750 °C. The optical transparency and the strong absorption in the ultraviolet (UV) region of PLZT films make them an attractive material for optoelectronic and UV sensing applications. 

We present complete results for the hadronic vacuum polarization (HVP) contribution to the muon anomalous magnetic moment $a_{\mu }$in the short- and intermediate-distance window regions, which account for roughly 10% and 35% of the total HVP contribution to $a_{\mu }$, respectively. In particular, we perform lattice-QCD calculations for the isospin-symmetric connected and disconnected contributions, as well as corrections due to strong-isospin breaking. For the short-distance window observables, we investigate the so-called log-enhancement effects as well as the significant oscillations associated with staggered quarks in this region. For the dominant, isospin-symmetric light-quark-connected contribution, we obtain $a_{\mu }^{ll,\mathrm{SD}}\left(\mathrm{conn}\right)=48.139\left(11{)}_{\mathrm{stat}}\right(91{)}_{\mathrm{syst}}[92{]}_{\text{total}}\times {10}^{-10}$and $a_{\mu }^{ll,\mathrm{W}}\left(\mathrm{conn}\right)=206.90\left(14{)}_{\mathrm{stat}}\right(61{)}_{\mathrm{syst}}[63{]}_{\text{total}}\times {10}^{-10}$. We use Bayesian model averaging to fully estimate the covariance matrix between the individual contributions. Our determinations of the complete window contributions are $a_{\mu }^{\mathrm{SD}}=69.05\left(1{)}_{\mathrm{stat}}\right(21{)}_{\mathrm{syst}}[21{]}_{\text{total}}\times {10}^{-10}$and $a_{\mu }^{\mathrm{W}}=236.45\left(17{)}_{\mathrm{stat}}\right(83{)}_{\mathrm{syst}}[85{]}_{\text{total}}\times {10}^{-10}$. This work is part of our ongoing effort to compute all contributions to HVP with an overall uncertainty at the few-permille level. Published by the American Physical Society2025 

ImportanceIdentifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic’s effects, yet it remains a challenging task. ObjectiveTo characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. Design, Setting, and ParticipantsInternet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium—the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution. Main Outcomes and MeasuresThe main outcomes were (1) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023 and (2) estimates of uncounted test-confirmed cases from February 1, 2022, to January 1, 2023. These estimates were compared with institutionally reported COVID-19 infections collected by Johns Hopkins University and wastewater viral concentrations for SARS-CoV-2 from Biobot Analytics. ResultsThe survey spanned 17 waves deployed from June 1, 2020, to January 31, 2023, with a total of 408 515 responses from 306 799 respondents (mean [SD] age, 42.8 [13.0] years; 202 416 women [66.0%]). Overall, 64 946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation,r = 0.96;P < .001) from April 2020 to January 2022 (50-state correlation mean [SD] value,r = 0.88 [0.07]). This was before the government-led mass distribution of at-home rapid tests. After January 2022, correlation was diminished and no longer statistically significant (r = 0.55;P = .08; 50-state correlation mean [SD] value,r = 0.48 [0.23]). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r = 0.92;P < .001) and after (r = 0.89;P < .001) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79;P < .001) with wastewater viral concentrations before January 2022, but poorly (r = 0.31;P = .35) after, suggesting that both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were likely unaccounted for in official records between January 2022 and January 2023. Conclusions and RelevanceThis study suggests that nonprobability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and health care officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future.