How credible is the experimental evidence on precarious manhood? A z-curve analysis

Precarious manhood theory (PMT) has become one of the most influential frameworks in social psychology for explaining why men sometimes respond to challenges of their masculine status with aggression, risk-taking, dominance, and gender-traditional attitudes. The theory proposes that manhood is a hard-won, socially conferred status that must be continually earned and can be lost when one’s masculinity is questioned. According to this account, masculinity threats elicit anxiety and motivate compensatory responses aimed at restoring status in the eyes of relevant audiences. Over nearly two decades, this premise has guided a large experimental literature spanning affect, aggression, risk-taking, dominance, and social attitudes. But how credible is that published evidence? A new z-curve analysis by Vladislav Krivoshchekov of École Polytechnique Fédérale de Lausanne and Olga Gulevich of HSE University, published in Frontiers in Social Psychology, offers the first field-level credibility audit of the experimental PMT literature, and the results suggest the record is more fragile than its sheer volume implies.

The empirical record on masculinity threat is now large enough to support quantitative synthesis, but it is also heterogeneous enough to warrant careful interpretation. A recent comprehensive meta-analysis of masculinity-threat experiments in heterosexual men found an overall small-to-medium effect, d = 0.45, with effects varying across components of the threat process, including trigger type, consequence type, publicity, and outcome order. Internalized responses such as negative emotions and reduced self-perceived masculinity were stronger than externalized compensatory reactions. Yet at the same time, recent preregistered replication work has failed to reproduce some influential masculinity-threat effects in a large, nationally representative U.S. sample. In particular, a replication and extension of Willer et al.’s highly cited masculine-overcompensation experiments found no consistent evidence that masculinity threat altered men’s political attitudes, whereas the original studies had reported that threat increased support for war, homophobic attitudes, and dominance-related political positions.

This failure need not imply that PMT is false as a theory. It may instead reflect a false-positive original result, changes in historical context, weaker threat salience in an online survey, differences between student and population-based samples, or theoretically important boundary conditions. Such ambiguity motivated the authors to conduct a field-level credibility analysis: the same literature may contain genuine effects, boundary-conditioned effects, inflated estimates, and false-positive findings all at once. Conventional meta-analyses primarily address how large masculinity-threat effects are on average and across moderators. The second question—how credible is the published significant-results record from which those effect-size estimates are derived—is less straightforward. Audits of more than 1,000 meta-analyses suggest that publication-bias assessment and correction are applied unevenly, heterogeneity and dependence among effect sizes are common, and conclusions often rely on diagnostics with limited sensitivity.

To answer this second question, the researchers turned to z-curve, a selection-model-based approach for characterizing the evidentiary value of a published record under the premise that statistically significant results have a higher probability of entering and remaining in the visible literature than non-significant results. Z-curve does not estimate average effect sizes or researchers’ planned statistical power. Instead, it estimates credibility-related properties of a literature based on the observed distribution of test statistics. The method produces several indicators. The Observed Discovery Rate (ODR) is the observed proportion of extracted tests that are statistically significant at a chosen threshold, usually α = 0.05. The Expected Discovery Rate (EDR) is the model-based estimate of the proportion of significant results expected across the underlying set of conducted studies before selection for significance is taken into account. The Expected Replication Rate (ERR) is the estimated average probability of significance among the subset of studies that produced significant results, corresponding to the expected success rate of close replications using the same design, sample size, and threshold. Finally, the False-Discovery Risk (FDR) is an upper-bound estimate of the proportion of statistically significant findings that could be false discoveries, derived from the EDR using Sorić’s logic: if only a small proportion of all conducted studies are expected to produce significant results before publication selection, then even a relatively small number of false positives can constitute a substantial share of the published significant findings.

The analysis was preregistered on the Open Science Framework, and the authors searched five bibliographic databases—APA PsycINFO, ProQuest, Web of Science Core Collection, Scopus, and DOAJ—completing the search in July 2025. After deduplication, 641 unique records underwent title and abstract screening by two reviewers, with 140 proceeding to full-text screening. Citation tracing yielded additional eligible records. The final coded corpus comprised 188 unique samples contributing 452 focal hypothesis tests, drawn from 99 peer-reviewed journal articles, 13 doctoral dissertations, and 11 master’s theses. Publication years ranged from 1979 to 2025, and data were collected in 21 countries, though most samples came from the United States. Sample sizes of men only varied from N = 24 to N = 867, with a median of N = 126. The researchers coded 14 distinct manipulation types, with the most common involving false feedback: bogus gender-knowledge test feedback and false personality feedback based on instruments such as the Bem Sex Role Inventory and the Conformity to Masculine Norms Inventory. Other frequently used manipulations included writing tasks, interpersonal threat, general threat-to-men messages, and failed masculine-task procedures. Only 16 of 188 samples—8.5 percent—were formally preregistered.

Because multiple eligible tests could be extracted from the same study, the authors addressed within-article dependence using a cluster bootstrap with 10,000 iterations, sampling one test per article in each iteration. Each eligible result was represented as a two-tailed p-value converted to a z-score, the standard input for z-curve. The model treats the observed significant z-scores as a mixture of truncated folded-normal distributions, with mixture weights estimated via an expectation–maximization algorithm to best reproduce the observed distribution, after which the model extrapolates to the implied full distribution including non-significant results. Tests based on very small samples (N < 30) were excluded because conversion to z-scores would be unreliable. The authors also manually coded focal hypothesis tests rather than automatically scraping statistics, and did not code manipulation checks or auxiliary analyses.

The headline finding is a marked gap between the observed discovery rate and the expected discovery rate at α = 0.05: ODR ≈ 0.51 versus EDR ≈ 0.10, with non-overlapping confidence intervals. In other words, roughly half of the extracted focal tests were statistically significant, but the fitted selection model implies that only about 10 percent of all conducted studies would have been expected to yield significant results before selection for significance is taken into account. This discrepancy points to substantial selection for statistical significance across the experimental PMT literature. The clustered z-curve estimate of the expected replication rate was approximately 0.38, suggesting that exact replications using similar designs and sample sizes would be expected to succeed less than half the time at the field level. The Sorić FDR at α = 0.05 was non-trivially large, indicating that a sizeable share of statistically significant findings could be false positives, although the confidence interval was imprecisely estimated.

Subgroup analyses revealed informative patterns. Main-effect tests showed a lower ODR than interaction tests but relatively higher EDR and ERR and a lower FDR, whereas interaction tests had the highest ODR in the table but comparatively low EDR and ERR and a high FDR. This pattern suggests that interaction findings in this literature are especially prone to selection bias and less likely to replicate than main-effect findings, consistent with the experimental design reality that interactions typically require larger samples than main effects to be estimated with adequate precision. Preregistered tests showed a higher EDR, higher ERR, and a much lower Sorić FDR than non-preregistered tests, even though confidence intervals were wide due to the smaller number of preregistered studies. Comparing pre-2015 and post-2015 studies pointed to a modest improvement in evidential value over time: earlier studies showed high ODR combined with very low EDR and ERR and a high FDR, while more recent studies displayed a somewhat lower ODR but clearly higher EDR and ERR and a lower FDR, although selection bias remained visible. An exploratory analysis by outcome domain suggested that affective outcomes showed the most successful published record but did not exhibit a clearly stronger underlying evidential profile, whereas behavioral outcomes appeared most vulnerable to false discoveries, with the highest estimated FDR and lowest ERR. Manipulation types also differed: general threat-to-men and interpersonal threat manipulations showed comparatively higher EDR and ERR alongside lower FDR, whereas writing tasks and heterogeneous other-threat categories combined relatively high ODRs with very low EDRs, low ERRs, and high FDRs.

A practically important implication concerns the significance threshold itself. As a retrospective sensitivity analysis, the authors examined how z-curve indicators change when more stringent thresholds are applied to a literature that was overwhelmingly conducted and published under the conventional α = 0.05. Within the existing literature, lowering the analysis threshold from α = 0.05 to α = 0.01 substantially reduced the estimated upper-bound FDR to 0.028 while maintaining a non-trivial ODR of 0.243 and an ERR of 0.433 consistent with meaningful expected replication. In contrast, moving to α = 0.005 or 0.001 restricted the analysis to the extreme tail of the z-distribution, leaving too few studies for stable estimation. The authors emphasize that these retrospective threshold analyses should not be read as a proposal that changing α alone would control false discoveries prospectively, since such a change would likely alter study design, sample-size planning, publication incentives, and researcher behavior. Readers are encouraged to interpret existing single-study PMT claims supported primarily by p-values between 0.01 and 0.05 more cautiously unless they are accompanied by strong design features or successful direct replication.

The authors stress what z-curve does and does not identify. It evaluates whether the distribution of significant results is consistent with a mixture of probabilities of obtaining statistically significant results under selection, but it does not pinpoint which specific questionable research practices, if any, produced that distribution. The observed pattern may reflect publication bias, selective outcome reporting, undisclosed analytic flexibility, incomplete reporting of non-significant results, or some combination of these processes. A further implication is that published effect-size estimates in the PMT literature may be upwardly biased, and this concern extends to quantitative synthesis, because meta-analyses that rely primarily on the published record can inherit inflation when non-significant or ambiguous results are selectively missing. The authors caution against using the pooled estimate from the recent meta-analysis as a benchmark for planning future PMT experiments without accounting for selection-related effect size inflation in the underlying literature. At a theoretical level, the results do not imply that PMT is false or that masculinity threat never produces compensatory behavior. Instead, they suggest that the published experimental record most likely overstates evidential strength, and that a non-trivial share of statistically significant claims—particularly those supported primarily by p-values in the 0.05–0.01 range and interaction tests based on small sample sizes—should be treated as uncertain. Because PMT is used not only to explain laboratory outcomes but also to inform interventions and public-facing claims about men’s health behavior, violence, and responses to gender equality initiatives, a credibility review is a theory-maintenance task: it clarifies which parts of the evidential base can carry interpretive weight and where the field most needs high-power replication.

Looking forward, the authors recommend that researchers replace rule-of-thumb sample-size rationales with explicit justifications that link design, smallest effect size of interest, and error costs; justify α rather than defaulting to 0.05; prioritize wider uptake of preregistration and Registered Reports; report null outcomes rather than selectively emphasizing clean results; and replicate important findings using samples from multiple countries, including non-WEIRD contexts. Replication resources should prioritize widely cited claims, widely used manipulation types, and interaction-heavy hypotheses that have relied on small per-cell sample sizes. The authors also urge greater methodological attention to manipulation construction and validation: what specifically constitutes a threat to manhood, for whom, and under which cultural and situational conditions. Recent work indicates that the content of unmanliness varies across countries, so threat paradigms developed and repeatedly used in U.S. samples may not transfer easily to other cultural settings. The authors acknowledge limitations: z-curve models selection as a function of the probability of obtaining statistically significant results and cannot identify the mechanisms that produced the record; estimates may be biased if selection operates among already significant results; several subgroup subsets remain relatively small, leading to wide uncertainty intervals; and the estimates should be treated as time-bound. Still, the overall conclusion is clear: precarious manhood remains an influential framework, but the published record shows substantial selection for statistical significance and only moderate expected replicability, and improving cumulative inference will require more than increasing sample size—it will require systematic and transparent reporting of all theoretically relevant outcomes, including null findings.

Subject of Research: Psychology

Article Title: How credible is the experimental evidence on precarious manhood? A z-curve analysis

Article References: Krivoshchekov, V., & Gulevich, O. (2026). How credible is the experimental evidence on precarious manhood? A z-curve analysis. Frontiers in Social Psychology, 4, Article 1864827. https://doi.org/10.3389/frsps.2026.1864827

Image Credits: AI Generated

DOI: 10.3389/frsps.2026.1864827

Keywords: precarious manhood, masculinity threat, z-curve, replication crisis, meta-analysis, social psychology, false discovery risk, preregistration, credible, experimental, evidence, precarious