Teaching happiness to students - implementation and evaluation of a program aiming at promoting wellbeing in elementary schools.

Since the beginning of the COVID-19 pandemic, the prevalence of mental disorders in children and adolescents has increased significantly. Evidence shows that childhood mental disorders can have serious consequences on psychosocial, cognitive, and physical development. Approaches from Positive Education go further than the urgently needed prevention of mental disorders by aiming directly at promoting subjective, psychological, and social wellbeing. The present study describes the implementation of a brief program to promote wellbeing in 15 elementary schools. For this purpose, in a regular university seminar, students of teaching and educational science were instructed to give 11 "happiness lessons" for fourth graders in a team of two and in the presence of the class teacher over the course of 3 months. Quantitative data were collected from children and parents in the treatment group classes and in the parallel classes serving as the waiting control group at four measurement points (pre, post, 1- and 2-month follow-up). We assessed psychological wellbeing, negative emotions and moods, parent support and home life, perception of the school environment, and self-esteem of the children with established instruments with versions for children and their parents and the frequency of positive and negative emotions of the children in self-report only. Additionally, we applied ad hoc items on subjective perception of the project and open questions in the treatment group. Data were analyzed with EffectLiteR using multigroup structural equation models. Results showed a small significant effect for negative emotions with the children's data and a medium effect for psychological wellbeing in the perception of the parents at the 1-month follow-up. Interaction effects suggest that lower baseline levels in parent support and home life and self-esteem would increase the treatment effect for these constructs. The need for more grounded framework in positive education and the inclusion of more qualitative methods as well as suggestions to improve the program in the sense of a whole school approach are discussed.

The Development of Predictive Gender Processing Strategies During Noun Phrase Decoding: An Eye-Tracking Study With German 5- to 10-Year-Old Children.

PURPOSE: Many models of language comprehension assume that listeners predict the continuation of an incoming linguistic stimulus immediately after its onset, based on only partial linguistic and contextual information. Their related developmental models try to determine which cues (e.g., semantic or morphosyntactic) trigger such prediction, and to which extent, during different periods of language acquisition. One morphosyntactic cue utilized predictively in many languages, inter alia German, is grammatical gender. However, studies of the developmental trajectories of the acquisition of predictive gender processing in German remain a few. METHOD: This study attempts to shed light on such processing strategies used in noun phrase decoding among children acquiring German as their first language by examining their eye movements during a language-picture matching task (N = 78, 5-10 years old). Its aim was to confirm whether the eye movements indicated the presence of age-specific differences in the processing of a gender cue, provided either in isolation or in combination with a semantic cue. RESULTS: The results revealed that German children made use of predictive gender processing strategies from the age of 5 years onward; however, the pace of online gender processing, as well as confidence in the predicted continuation, increased up to the age of 10 years. CONCLUSION: Predictive processing of gender cues plays a role in German language comprehension even in children younger than 8 years.

Evaluation of frequentist test statistics using constrained statistical inference in the context of the generalized linear model.

When faced with a binary or count outcome, informative hypotheses can be tested in the generalized linear model using the distance statistic as well as modified versions of the Wald, the Score and the likelihood-ratio test (LRT). In contrast to classical null hypothesis testing, informative hypotheses allow to directly examine the direction or the order of the regression coefficients. Since knowledge about the practical performance of informative test statistics is missing in the theoretically oriented literature, we aim at closing this gap using simulation studies in the context of logistic and Poisson regression. We examine the effect of the number of constraints as well as the sample size on type I error rates when the hypothesis of interest can be expressed as a linear function of the regression parameters. The LRT shows the best performance in general, followed by the Score test. Furthermore, both the sample size and especially the number of constraints impact the type I error rates considerably more in logistic compared to Poisson regression. We provide an empirical data example together with R code that can be easily adapted by applied researchers. Moreover, we discuss informative hypothesis testing about effects of interest, which are a non-linear function of the regression parameters. We demonstrate this by means of a second empirical data example.

On the Relationship between ANOVA Main Effects and Average Treatment Effects.

We adopt a causal inference perspective to shed light into which ANOVA type of sums of squares (SS) should be used for testing main effects and whether main effects should be considered at all in the presence of interactions. We consider balanced, proportional and nonorthogonal designs, and models with and without interactions. When the design is balanced, we show that the average treatment effect is estimated by the main effects obtained by type I, II, and III sums of squares. In proportional designs, we show that the average treatment effect is estimated by the the type I and type II main effects, whereas type III SS yield biased estimates of the average treatment effect if there are interactions. When the design is nonorthogonal, ANOVA type I is always highly biased and ANOVA type II and III main effects are biased if there are interactions. We include a simulation study to illustrate the magnitude of the bias in estimating the average treatment effect across a variety of conditions, and provide recommendations for applied researchers.

The Jena Voice Learning and Memory Test (JVLMT): A standardized tool for assessing the ability to learn and recognize voices.

The ability to recognize someone's voice spans a broad spectrum with phonagnosia on the low end and super-recognition at the high end. Yet there is no standardized test to measure an individual's ability of learning and recognizing newly learned voices with samples of speech-like phonetic variability. We have developed the Jena Voice Learning and Memory Test (JVLMT), a 22-min test based on item response theory and applicable across languages. The JVLMT consists of three phases in which participants (1) become familiarized with eight speakers, (2) revise the learned voices, and (3) perform a 3AFC recognition task, using pseudo-sentences devoid of semantic content. Acoustic (dis)similarity analyses were used to create items with various levels of difficulty. Test scores are based on 22 items which had been selected and validated based on two online studies with 232 and 454 participants, respectively. Mean accuracy in the JVLMT is 0.51 (SD = .18) with an empirical (marginal) reliability of 0.66. Correlational analyses showed high and moderate convergent validity with the Bangor Voice Matching Test (BVMT) and Glasgow Voice Memory Test (GVMT), respectively, and high discriminant validity with a digit span test. Four participants with potential super recognition abilities and seven participants with potential phonagnosia were identified who performed at least 2 SDs above or below the mean, respectively. The JVLMT is a promising research and diagnostic screening tool to detect both impairments in voice recognition and super-recognition abilities.

A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions.

The a priori calculation of statistical power has become common practice in behavioral and social sciences to calculate the necessary sample size for detecting an expected effect size with a certain probability (i.e., power). In multi-factorial repeated measures ANOVA, these calculations can sometimes be cumbersome, especially for higher-order interactions. For designs that only involve factors with two levels each, the paired t test can be used for power calculations, but some pitfalls need to be avoided. In this tutorial, we provide practical advice on how to express main and interaction effects in repeated measures ANOVA as single difference variables. In particular, we demonstrate how to calculate the effect size Cohen's d of this difference variable either based on means, variances, and covariances of conditions or by transforming [Formula: see text] or [Formula: see text] from the ANOVA framework into d. With the effect size correctly specified, we then show how to use the t test for sample size considerations by means of an empirical example. The relevant R code is provided in an online repository for all example calculations covered in this article.

Subgroup discovery in structural equation models.

Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Unveiling mechanisms of change in digital interventions for depression: Study protocol for a systematic review and individual participant data meta-analysis.

Introduction: The efficacy and effectiveness of digital interventions for depression are both well-established. However, precise effect size estimates for mediators transmitting the effects of digital interventions are not available; and integrative insights on the specific mechanisms of change in internet- and mobile-based interventions (IMIs)-as related to key features like delivery type, accompanying support and theoretical foundation-are largely pending. Objective: We will conduct a systematic review and individual participant data meta-analysis (IPD-MA) evaluating the mediators associated with therapeutic change in various IMIs for depression in adults. Methods: We will use three electronic databases (i.e., Embase, Medline/PubMed, PsycINFO) as well as an already established database of IPD to identify relevant published and unpublished studies. We will include (1) randomized controlled trials that examine (2) mediators of (3) guided and unguided (4) IMIs with (5) various theoretical orientations for (6) adults with (7) clinically relevant symptoms of depression (8) compared to an active or passive control condition (9) with depression symptom severity as primary outcome. Study selection, data extraction, as well as quality and risk of bias (RoB) assessment will be done independently by two reviewers. Corresponding authors of eligible primary studies will be invited to share their IPD for this meta-analytic study. In a 1-stage IPD-MA, mediation analyses (e.g., on potential mediators like self-efficacy, emotion regulation or problem solving) will be performed using a multilevel structural equation modeling approach within a random-effects framework. Indirect effects will be estimated, with multiple imputation for missing data; the overall model fit will be evaluated and statistical heterogeneity will be assessed. Furthermore, we will investigate if indirect effects are moderated by different variables on participant- (e.g., age, sex/gender, symptom severity), study- (e.g., quality, studies evaluating the temporal ordering of changes in mediators and outcomes), and intervention-level (e.g., theoretical foundation, delivery type, guidance). Discussion: This systematic review and IPD-MA will generate comprehensive information on the differential strength of mediators and associated therapeutic processes in digital interventions for depression. The findings might contribute to the empirically-informed advancement of psychotherapeutic interventions, leading to more effective interventions and improved treatment outcomes in digital mental health. Besides, with our novel approach to mediation analyses with IPD-MA, we might also add to a methodological progression of evidence-synthesis in psychotherapy process research. Study registration with Open Science Framework OSF: https://osf.io/md7pq/.

Overview and evaluation of various frequentist test statistics using constrained statistical inference in the context of linear regression.

Within the framework of constrained statistical inference, we can test informative hypotheses, in which, for example, regression coefficients are constrained to have a certain direction or be in a specific order. A large amount of frequentist informative test statistics exist that each come with different versions, strengths and weaknesses. This paper gives an overview about these statistics, including the Wald, the LRT, the Score, the F ¯ - and the D-statistic. Simulation studies are presented that clarify their performance in terms of type I and type II error rates under different conditions. Based on the results, it is recommended to use the Wald and F ¯ -test rather than the LRT and Score test as the former need less computing time. Furthermore, it is favorable to use the degrees of freedom corrected rather than the naive mean squared error when calculating the test statistics as well as using the F ¯ - rather than the χ ¯ 2 -distribution when calculating the p-values.

Comparing revised latent state-trait models including autoregressive effects.

Understanding the longitudinal dynamics of behavior, their stability and change over time, are of great interest in the social and behavioral sciences. Researchers investigate the degree to which an observed measure reflects stable components of the construct, situational fluctuations, method effects, or just random measurement error. An important question in such models is whether autoregressive effects occur between the residuals, as in the trait-state occasion model (TSO model), or between the state variables, as in the latent state-trait model with autoregression (LST-AR model). In this article, we compare the two approaches by applying revised latent state-trait theory (LST-R theory). Similarly to Eid et al. (2017) regarding the TSO model, we show how to formulate the LST-AR model using definitions from LST-R theory, and we discuss the practical implications. We demonstrate that the two models are equivalent when the trait loadings are allowed to vary over time. This is also true for bivariate model versions. The different but same approaches to modeling latent states and traits with autoregressive effects are illustrated with a longitudinal study of cancer-related fatigue in Hodgkin lymphoma patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal effect analysis in nonrandomized data with latent variables and categorical indicators: The implementation and benefits of EffectLiteR.

Instead of using manifest proxies for a latent outcome or latent covariates in a causal effect analysis, the R package EffectLiteR facilitates a direct integration of latent variables based on structural equation models (SEM). The corresponding framework considers latent interactions and provides various effect estimates for evaluating the differential effectiveness of treatments. In addition, a user-friendly graphical interface customizes the implementation of the complex models. We aim to enable applications of EffectLiteR in more contexts, and therefore generalize the framework for incorporating latent variables measured with categorical indicators. This refers, for instance, to achievement tests in educational large-scale assessments (LSAs), which are typically constructed in the tradition of item response theory (IRT). We review different modeling strategies for incorporating latent variables from IRT models in an effect analysis (i.e., individual score estimates, plausible values, SEM for categorical indicators). The strategies differ in the handling of measurement error and, thus, have different implications for the accuracy and efficiency of causal effect estimates. We describe our extensions of EffectLiteR based on SEM for categorical indicators and illustrate the model specification step-by-step. In addition, we present a hands-on example, where we apply EffectLiteR in LSA data. The practical benefit of using latent variables in comparison to proficiency scores is of special interest in the application and discussion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue.

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.

Repeated Measures ANOVA with Latent Variables to Analyze Interindividual Differences in Contrasts.

Repeated measures analysis of variance (RM-ANOVA) is a broadly used statistical method to analyze data from experimental designs. RM-ANOVA aims at investigating effects of experimental conditions (i.e., factors) and predictors that affect the outcome of interest. It mainly considers contrasts that test standard main and interaction effects, even though more complex contrasts can in principle be used. Analyses, however, only focus on drawing conclusions about average effects and do not take into consideration interindividual differences in these effects. We propose an alternative approach to RM-ANOVA for analyzing repeated measures data, termed latent repeated measures analysis of variance (L-RM-ANOVA). The new approach is based on structural equation modeling and extends the latent growth components approach. L-RM-ANOVA enables the researcher to not only consider mean differences between different experimental conditions (i.e., average effects), but also to investigate interindividual differences in effects. Such interindividual differences are considered with regard to standard main and interactions effects and also with regard to customized contrasts that allow for testing specific hypotheses of interest. Furthermore, L-RM-ANOVA can include a measurement model for latent variables and can be used for the analysis of complex multi-factorial repeated measures designs. We conclude the presentation by demonstrating L-RM-ANOVA using a minimal repeated measures example.

Sleep-related parenting self-efficacy and parent-reported sleep in young children: A dyadic analysis of parental actor and partner effects.

OBJECTIVES: The role of positive cognitions, particularly domain-specific sleep-related parenting self-efficacy (SPSE), for young children's sleep has received limited attention so far. The present study investigates possible interdependencies between maternal/paternal SPSE and parent-reported sleep problems in infants and toddlers. DESIGN AND SETTING: Mother-father dyads participated in this cross-sectional German study and filled out questionnaires. PARTICIPANTS: One hundred thirty-one parental dyads with children aged between 2 and 47 months (M = 19.4; standard deviation = 10.6) participated in the study. MEASUREMENTS: Parents answered a questionnaire about their own SPSE and the Children's Sleep Habits Questionnaire-Infant Version about their child's sleep. Paired sample t tests and correlations were used to investigate parental differences and agreement. An actor-partner interdependence model to examine the relationship between maternal/paternal SPSE and parent-reported child sleep problems was estimated, controlling for child age and co-sleeping. RESULTS: The results reveal no differences between parents regarding their own SPSE and between mother- and father-reported child sleep problems. Mothers and fathers showed significant agreement regarding their children's sleep problems. For both parents, significant actor effects between SPSE and child sleep emerged, with higher SPSE being related to fewer child sleep problems. For fathers, also partner effects were significant with higher paternal SPSE being related to fewer child sleep problems in the maternal report. CONCLUSION: This study underlines the importance of considering mothers' and fathers' nonindependence in dyadic data analysis. Parenting self-efficacy might play a specific role in the context of young children's sleep and could be used for early intervention and prevention programs.

Therapeutic processes in digital interventions for anxiety: A systematic review and meta-analytic structural equation modeling of randomized controlled trials.

While the efficacy of Internet- and mobile-based interventions (IMIs) for treating anxiety disorders is well established, there is no comprehensive overview about the underlying therapeutic processes so far. Thus, this systematic review and meta-analysis evaluated research on mediators and mechanisms of change in IMIs for adult anxiety disorders (PROSPERO: CRD42020185545). A systematic literature search was performed in five databases (i.e., CENTRAL, Embase, MEDLINE, PsycINFO and ClinicalTrials.gov). Two reviewers independently screened studies for inclusion, assessed the risk of bias and adherence to quality criteria for process research. Overall, 26 studies (N = 6042) investigating 64 mediators were included. Samples consisted predominantly of participants with clinically relevant symptoms of generalized anxiety disorder and severe health anxiety, as well as of participants with non-clinically relevant anxiety symptoms. The largest group of examined mediators (45%) were cognitive variables, evincing also the second highest proportion of significance (19/29); followed in numbers by skills (examined: 22%; significant: 10/14) and a wide range of other (19%; 7/12), emotional/affective (11%; 2/7) and behavioral mediators (3%; 1/2). Meta-analytical synthesis of mediators, limited by a small number of eligible studies, was conducted by deploying a two-stage structural equation modeling approach, resulting in a significant indirect effect for negative thinking (k = 3 studies) and non-significant indirect effects for combined cognitive variables, both in clinical (k = 5) and non-clinical samples (k = 3). The findings of this review might further the understanding on presumed change mechanisms in IMIs for anxiety, informing intervention development and the concurrent optimization of outcomes. Furthermore, by reviewing eligible mediation studies, we discuss methodological implications and recommendations for future process research, striving for causally robust findings. Future studies should investigate a broader range of variables as potential mediators, as well as to develop and apply original (digital) process and engagement measures to gather qualitative and high-resolution data on therapeutic processes.

Treatment effects on count outcomes with non-normal covariates.

The effects of a treatment or an intervention on a count outcome are often of interest in applied research. When controlling for additional covariates, a negative binomial regression model is usually applied to estimate conditional expectations of the count outcome. The difference in conditional expectations under treatment and under control is then defined as the (conditional) treatment effect. While traditionally aggregates of these conditional treatment effects (e.g., average treatment effects) are computed by averaging over the empirical distribution, a recently proposed moment-based approach allows for computing aggregate effects as a function of distribution parameters. The moment-based approach makes it possible to control for (latent) multivariate normally distributed covariates and provides more reliable inferences under certain conditions. In this paper we propose three different ways to account for non-normally distributed continuous covariates in this approach: an alternative, known non-normal distribution; a plausible factorization of the joint distribution; and an approximation using finite Gaussian mixtures. A saturated model is used for categorical covariates, making a distributional assumption obsolete. We further extend the moment-based approach to allow for multiple treatment conditions and the computation of conditional effects for categorical covariates. An illustrative example highlighting the key features of our extension is provided.

Accounting for Latent Covariates in Average Effects from Count Regressions.

The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.

The interplay between cancer-related fatigue and functional health in Hodgkin lymphoma survivors.

OBJECTIVE: Cancer-related fatigue (CRF) is among the most distressing symptoms reported by cancer survivors as compromising their quality of life. This study investigates the complex interplay between CRF and functional health (FH) in survivors of Hodgkin lymphoma by using longitudinal data to clarify the etiology of CRF. METHODS: Data from N = 3596 survivors (HD13-15) from the German Hodgkin Study Group was analyzed using bivariate latent curve models with structured residuals to model how the interplay between CRF and FH unfolds over time across and within individuals. CRF and FH were measured with the EORTC QLQ-C30. Assessed FH domains were physical, cognitive, emotional, social, and role functioning. Age at diagnosis, gender, country, baseline fatigue, and cancer stage were included as covariates. RESULTS: The latent curve models with structured residuals had an adequate model fit (χ² = 416.63-548.28, df = 114, p < .001, root mean square error of approximation = .03, comparative fit index = .98-.99, Tucker-Lewis index = .97-.98). On the between-person level, CRF and all FH domains were strongly negatively correlated (r = -.72 to r = -.84). On the within-person level, earlier CRF (ρF = -.05 to ρF = -.12) and FH deviations (ρFH = -.05 to ρFH = -.13) negatively predicted subsequent deviations of the respective other variable. Time-specific within-person improvements in physical, cognitive, and emotional functioning reduced CRF more than vice versa, whereas the effect of CRF was stronger for social functioning. Role functioning had a balanced relation with CRF. CONCLUSIONS: This analysis reveals a complex reciprocal relation between CRF and FH with distinct between- and within-person effects. The results contribute to a better understanding of CRF in survivors of Hodgkin lymphoma and could inform the development of much-needed targeted interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Repeated Bereavement Takes Its Toll on Subjective Well-Being.

BACKGROUND AND OBJECTIVES: The bereavement literature has shown that losing close loved ones can lead to sustained declines in quality of life. Research in this area has typically focused on singular bereavement events, such as the loss of a spouse or child. Much less is known regarding the consequences of repeated bereavement or repeated losses in one's social network. RESEARCH DESIGN AND METHODS: We use longitudinal panel survey data from the Household Income and Labour Dynamics of Australia study to examine the effect of repeated bereavement in one's social network on cognitive and affective measures of subjective well-being and whether there are age differences in the magnitude of these effects across young adulthood, midlife, and old age. To address our research questions, we use a multiple-group discontinuous change model with random effects. RESULTS: Repeated deaths in one's social network had a nonlinear effect on life satisfaction and positive affect, suggesting that individuals were able to adapt to two bereavements, but each bereavement beyond two resulted in sustained lower levels. Negative affect did not show increases because of repeated bereavements. Repeated bereavement had the strongest effect for those in young adulthood and old age. DISCUSSION AND IMPLICATIONS: Our findings demonstrate that repeated bereavement has consequences for subjective well-being and that young and older adults are most vulnerable to repeated bereavement. Our discussion focuses on the conceptual and methodological advancements of our study for the examination of major life stressors.

Nail-biters and thrashing wins: Testosterone responses of football fans during World Cup matches.

Two main hypotheses have been formulated to explain short-term testosterone responses to competitions. The challenge hypothesis and the biosocial model of status make different predictions concerning the point of time, direction, and meaning of hormonal changes. This field study investigated whether testosterone reacts to experiences of challenge during the early stages of a competition or to experiences of status change as a consequence of the competition's outcome. Over a period of 28 days, approximately 2000 salivary testosterone samples were collected from 82 football fans (53% men), while they were watching the matches of their favorite national team during the 2014 World Cup. Conducting repeated measurements across seven competitive events (i.e., matches) and over the course of each match allowed us to split vicarious experiences during each competition into phases of challenge and phases of status change. For both sexes, the results revealed discriminable testosterone trajectories depending on whether the fans experienced highly competitive matches or quick victories. By use of a discontinuous change model, maximal testosterone increases were detected during experiences of challenge. In contrast, a return to pre-contest baseline testosterone levels was initiated as soon as a status gain became certain. Testosterone responsiveness was partly moderated by the subjective importance of the competitive event. Thus, this study provides evidence in favor of the challenge hypothesis and emphasizes the value of conducting high-resolution within-subject designs to further explain the adaptive meaning of androgen responses.