I checked 15 psychology journals on Saturday, May 30, 2026 using the Crossref API. For the period May 23 to May 29, I found 26 new paper(s) in 9 journal(s).

Behavior Research Methods

Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation
Yongseok Lee, Walter L. Leite
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Researchers using propensity score analysis (PSA) to estimate treatment effects using secondary data may have to handle data that are missing not at random (MNAR). Existing methods for PSA with MNAR data use logistic regression to model the missing data mechanisms, thus requiring manual specification of functional forms, and are difficult to implement with a large number of covariates. To overcome these limitations, this study proposes alternatives to existing methods by replacing logistic regression with a random forest. Also, it introduces the dual-forest proximity imputation method, which leverages two types of proximity matrices of random forest techniques and incorporates missingness pattern information in each matrix. Results from a Monte Carlo simulation show dual-forest proximity imputation’s enhanced bias reduction with various types of MNAR mechanisms as compared to existing and alternative methods. A case study is also provided using data from the National Longitudinal Survey of Youth (Enders, 1979) (NLSY79).
Beyond the overt response: Reaction time modeling in self-report surveys across administration modes and item formats
David Lacko, Tomáš Prošek, Adam Dostál, Anna Lázníčková, Jiří Čeněk, Čeněk Šašinka, Sylvie Graf
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Synthesis of single-case design mediation effects using two-stage multilevel modeling
Mariola Moeyaert, Milica Miočević, Yaosheng Lou, Matthew J. Valente
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Adding volition to word processing: Expected utility norms for 80,000 English words and multiword expressions
Andrew Wang, Marc Brysbaert, Fritz GĂĽnther
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This study examined the concept of word usefulness by analyzing expected utility ratings for over 80,000 English words and multiword expressions. Participants used best–worst ratings to indicate how useful it is to know each word/expression. Our findings show a high level of agreement regarding the usefulness of words and expressions. Stimuli were rated as more useful if they were more frequent, widely known, learned early in life, and central to the semantic network. Concreteness had a substantial negative correlation, indicating that abstract words in general received higher utility scores than concrete words. Positive stimuli received slightly lower utility scores than negative stimuli. Expected utility was a good predictor of which words are known to speakers of English as a first and second language, but did not contribute to predicting response times to known words. These findings suggest that expected utility is a variable affecting which words are likely to be learned, but does not affect word processing times (much). The expected utility scores are freely available for research and education.
Implementing multiple implicit association tests in Qualtrics: A guide and demonstration using balanced identity design theory
Alyssa Ream, Anna Woodcock, Sarah Zlatkovic, Rachelle M. Pedersen, Ashley Bonilla, Hannah Middleton, Paul R. Hernandez, P. Wesley Schultz
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Interest in implicit processes, such as attitudes and identities, has grown in behavioral sciences since Greenwald and colleagues (1998) developed and introduced the Implicit Association Test (IAT). The IAT was developed to measure these processes, which has become more accessible with the popularity of Qualtrics as a data collection tool, especially through methods and code provided by Carpenter et al. (2019). They demonstrated how to construct a single IAT for use on the Qualtrics platform, providing research resources for creating IATs to examine concept-pair associations. However, there is no guidance on constructing multiple IATs on survey software platforms for research that requires two or more IATs in a single study. This paper extends Carpenter et al.’s (2019) work by outlining the process of developing, implementing, and utilizing multiple IATs within a single Qualtrics survey. We provide a tutorial using examples from our research using the Balanced Identity Design (BID) framework, including step-by-step written and visual instructions and templates, instructions for planning and building multiple IATs and dynamically presenting them in a single Qualtrics survey and code for processing IAT data. We also demonstrate the utility of the multiple IAT approach by reporting a short study utilizing BID theory wherein we measure implicit racial/ethnic identity, STEM identity, and race/ethnicity-STEM associations in a single study via three IATs.
Know when to trust: Making AI scoring more reliable for educational assessment
Peter Organisciak, Selcuk Acar
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Robust Bayesian hypothesis testing with the hierarchical EZ-DDM
Adriana F. Chávez De la Peña, Eunice Shin, Joachim Vandekerckhove
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The EZ-diffusion model (EZ-DDM) uses a method of moments to provide closed-form estimators for the three-parameter drift-diffusion model from summary statistics. In previous work, we showed that using the sampling distributions of these statistics enables the implementation of hierarchical EZ-DDM extensions, supporting scalable Bayesian inference in cognitive psychometrics applications. However, the summary statistics used in EZ-DDM implementations (the mean and variance of the response time distribution) are sensitive to contaminant data points, limiting its utility in real-world applications. To address this, we propose a variation on the EZ-DDM implementation in which the summary statistics are replaced with robust alternatives, substituting mean RT with median RT and RT variance with an estimate derived from the interquartile range. We explore and evaluate the effectiveness of this substitution through simulation studies using a within-subject t test design across varying sample sizes and effect sizes. We show that the robust variant matched the diagnostic accuracy of the EZ-DDM implementation on uncontaminated data while maintaining diagnostic accuracy under contamination, unlike the standard model. This extension preserves efficiency while adding robustness in real-world applications. We recommend using the robust EZ-DDM in practical applications.
The benefits of in situ reporting: Experimental evidence from in-the-moment surveys of public transit riders
Christopher Antoun, Vanessa FrĂ­as-MartĂ­nez, Anthony Garove, Naman Awasthi, Saad Mohammad Abrar
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In-the-moment surveys administered when respondents participate in a predetermined event of interest—such as visiting a particular location or completing a specific activity—allow researchers to measure behaviors and experiences as the event occurs, situating data collection within the relevant context and reducing reliance on retrospective self-reports. However, data quality can be compromised if individuals are unwilling to participate or provide low-quality responses. In particular, the timing of survey prompts and the structure of incentives may affect data quality. We evaluated these issues in an experimental study of 610 individuals who used a custom smartphone app to answer a survey about each of their public transit trips over 2 weeks. Participants were randomly assigned to receive either daily or per-survey incentives, with surveys administered either during trips (in situ) or immediately afterward (near-time). Both daily incentives and in situ prompts significantly increased participation. In situ prompts also reduced “speeding” and increased the variation in survey responses across trips, suggesting improved accuracy in capturing event-specific details. These findings demonstrate that prompt timing—specifically, in situ prompts as opposed to near-time prompts—plays a key role in enhancing data quality for in-the-moment surveys.
Making the impossible possible: Leveraging built-in features for non-intrusive and accurate Apple Screen Time tracking through ASTER
Marijn Martens, Kyle Van Gaeveren
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Computers in Human Behavior

Understanding the Efficacy of Online Bystander Intervention when Journalists are the Targets
Kathleen Searles, Rebekah Tromble
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When Conversational AI Personalises Too Much: Refining Privacy Calculus for Bundled Interactional Cues in AI-Mediated Disclosure
Phan Khanh Duy, Bao Quoc Truong-Dinh
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Unraveling the behavioral and autonomic dynamics of defensive states in humans in an unconstrained, immersive virtual world
Alma-Sophia Merscher, Lea K. Hildebrandt, Jasin Sahraoui, Matthias Gamer
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Journal of Experimental Social Psychology

Fairness matters: The impact of leaders' emotional expressions on subordinates' behavior
Adili Abudukadier, Zhikun Zhou, Zhijun Cheng, Xudong Wang, Yangmei Luo, Xuhai Chen
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Journal of Personality and Social Psychology

Within-nation variation in war exposure and psychological and physical adjustment.
Theresa M. Entringer, Christoph Halbmeier, Laura Buchinger, Anne K. Reitz
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Multivariate Behavioral Research

Bayesian Variable Selection via Shrinkage Priors in Growth Mixture Models
Ihnwhi Heo, Fan Jia, Sarah Depaoli
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Random Forest Analyses of Precollege Covariates
N. F. Tennessen, Shinji Katsumoto, Nicholas A. Bowman, Lauren N. Irwin
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Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes
Jihyun Lee, S. Natasha Beretvas, Brian T. Keller
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Personality and Social Psychology Bulletin

Perceiving to Provide: How Partner Attachment Perceptions Inform Reassurance Provision in Romantic Relationships
Elina R. Sun, Xiangjing Kong, Jason A. Mitala, Jeewon Oh, Brett K. Jakubiak
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Romantic partners can play a key role in buffering one another’s attachment insecurities, but how they perceive one another’s attachment and whether perceptions predict behavior remains unclear. This research examined how accurately individuals perceive their partner’s global and relationship-specific attachment anxiety and avoidance, what biases shape these perceptions, and whether perceptions predict reassurance provision, a recommended buffering strategy for anxiously attached partners. Using the Truth and Bias Model, two studies (Study 1: N = 108 couples; Study 2: N = 147 couples) showed that individuals perceive their partner’s attachment with moderate accuracy, though they also overestimate insecurity and are biased to assume both similarity and complementarity in attachment. Moreover, people who perceived their partners to be higher in attachment anxiety provided greater reassurance during personal stressor discussions and in daily life (10-day ecological momentary assessment), suggesting that attachment perceptions may play a key role in initiating and calibrating buffering strategies.
Women Disclosing Experiences of Sexism to Men Partners: Prevalence, Predictors, and Responses
Emily. J. Cross, Alyssa DeBlaere, Tarnpreet Virk, Anya Sharma, Amy Muise
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Although sexism is pervasive, the dynamics of disclosing such experiences within romantic relationships remain under-examined. Across two studies ( N = 1076) involving individuals in woman–man relationships, we found that approximately 75% of women have disclosed an experience of sexism to their men partners. Notably, a general tendency toward disclosure—rather than relationship satisfaction, perceived partner responsiveness, or sexist attitudes—predicted disclosure. Women who recalled their partner as more responsive and autonomy-supportive when they shared an experience of sexism reported greater closeness, satisfaction with partner’s response, and a greater likelihood of future disclosure and confrontation of sexism (Study 1). For men, greater self-reported responsiveness and autonomy support was associated with greater felt closeness, perceived partner satisfaction, and allyship behavior; but was not linked to a greater awareness of gender discrimination (Study 2). Findings highlight romantic relationships as a key, underexamined context in which experiences of sexism are discussed.
Everyone I Don’t Like Is Biased: Affective Evaluations and the Bias Blind Spot
Alexander C. Walker, Robert N. Collins, Heather E. K. Walker, Jonathan A. Fugelsang, David R. Mandel
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People commonly exhibit a bias blind spot (BBS), judging themselves as less susceptible to bias than the “average other.” However, less is known about how people attribute bias to familiar others who evoke strong affect. We examined whether attributions of bias are sensitive to affective impressions of others. In Experiment 1, participants viewed themselves as considerably less biased than the average survey respondent and a personally-known disliked other, but not less biased than a familiar individual whom they liked. Experiments 2 and 3 examined the BBS in politically polarized groups of Democrats and Republicans. While participants judged themselves as somewhat less biased than co-partisans, they viewed themselves as much less biased than their political opponents. In all experiments, the effect of other target selection on the BBS was mediated by affective evaluations. We discuss the theoretical implications of affective evaluations guiding how people attribute bias to familiar others.

Psychological Bulletin

Artificial intelligence as a partner in meta-analysis—Research agenda, user recommendations, and speed–accuracy tradeoffs: Commentary on Jansen et al. (2025).
Brooke N. Macnamara
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The association between reading anxiety and reading achievement: A meta-analysis and systematic review.
Rachelle M. Johnson, Maxine Schaefer, Cynthia U. Norris, Richard K. Wagner, Sara A. Hart
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Relationships between cognition and daily functioning in adults with bipolar disorder: A systematic review and multilevel meta-analysis.
Amanda McCleery, Gerhard Stefan Hellemann, Junghee Lee
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Perfectionism is accelerating over time: A cross-temporal meta-analytic review of 35 years of college student data.
Thomas Curran, Andrew Hill, Pia Marie Pose
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Psychology of Music

Temporal Dynamics of Intraparietal Sulcus Activation in Response to Tonal Uncertainty: An fMRI Study of the Diminished Seventh Chord
Chen-Gia Tsai, Ling-Yao Chien, Joshua Oon Soo Goh
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Tonality and harmony in music provide a structured framework to explore the neural mechanisms underlying uncertainty. The diminished seventh (dim7) chord, characterized by its multiple potential resolutions, serves as an ideal stimulus for examining the neural correlates of tonal uncertainty. This functional magnetic resonance imaging study investigated neural activity elicited by the dim7 chord in listeners with refined music listening skills, focusing on three harmonic progressions: CHANGE, modulating to a distantly related tonality; RETURN, resolving to the original tonality; and PERSIST, sustaining tonal ambiguity through repeated dim7 chords. A finite impulse response model was used to analyze responses in regions associated with uncertainty processing. The intraparietal sulcus (IPS) uniquely exhibited distinct temporal activation patterns across the three conditions. In CHANGE, IPS activity initially decreased, likely reflecting reduced confidence in the outdated tonal model’s predictions, followed by an increase as sensory evidence for a new tonality accumulated. In RETURN, IPS activity showed limited fluctuation. In PERSIST, a progressive increase in IPS activity may reflect heightened cognitive demands for maintaining and evaluating multiple tonalities. These findings deepen our understanding of predictive coding by highlighting the nuanced role of the IPS in processing uncertainty.

Psychology of Popular Media

The impact of fitspiration depicting different racial groups on Asian American women’s body dissatisfaction and appearance comparison.
Ruifei Zhang, Lauren A. Stutts
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