Advances in Methods and Practices in Psychological Science心理科学方法与进展
Advances in Methods and Practices in Psychological Science(英文缩写 ADV METH PRACT PSYCH),ISSN 2515-2459,eISSN 2515-2467,中文译名:心理科学方法与进展 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 15.817 | Q1 |
| 2022 | 13.600 | Q1 |
| 2023 | 15.600 | Q1 |
| 2024 | 13.400 | Q1 |
| 2025 | 14.600 | Q1 |
Advances in Methods and Practices in Psychological Science 最新收录文献
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1. Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment.
PMID:日期:2026-03-05Ecological momentary assessments (EMAs) and wearable devices afford opportunities to collect real-time data on events experienced in daily life. Examples of event-based data in the psychological and behavioral sciences include smoking a cigarette, experiencing a stressor, having a disruption to sleep, experiencing a depressive or manic episode, drinking an alcoholic beverage, or engaging in a bout of exercise. The increasing availability of dense sampling approaches allows for the measurement of such events at relatively fast timescales (e.g., occurring across minutes, hours, days, or weeks), expanding the possibilities for how time can be conceptualized and modeled. Survival analysis is a modeling approach that allows researchers to address scientific questions regarding whether and when events occur in time. Although not often applied to EMA data, there are myriad research questions relevant to psychosocial and behavioral scientists that can be addressed using survival analysis. In this article, we provide an overview of survival analysis, describe several time-based considerations for modeling event-based EMA data using survival analysis, and provide several illustrative examples of the different time-based considerations. Altogether, the goals of this article are to enhance knowledge of the types of research questions that can be examined using survival analysis, illustrate nuances of applying the method to EMA data, and spark ideas for future empirical and methodological research.
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2. When Do Interaction/Moderation Effects Stabilize in Linear Regression?
PMID:日期:2026-02-19Two-way interaction effects in linear regression occur when the relation between two variables changes depending on the level of a third. Despite their frequent use, interactions are notoriously difficult to estimate accurately and test for statistical significance because of small effect sizes and low reliability. In this study, we used Monte Carlo simulations to establish stability thresholds for two-way interactions between continuous variables across combinations of reliability (0.7-1.0), main effect size (0.1-0.5), collinearity (0.1-0.5), and interaction effect size (0.05-0.2). Stability was defined as the consistency of estimated effect sizes across repeated samples of the same size from the same population and operationalized using modified definitions of the corridor of stability and point of stability from Schönbrodt and Perugini. Results show that the stability of interaction estimates is primarily determined by sample size and predictor reliability. The case representing a realistic psychology field study, in which researchers have limited control over variables, stabilized at , requiring 72% statistical power. At , 11% to 45% of the estimates were incorrectly signed (i.e., negative when the true effect was positive). Most psychology studies enroll far fewer than 500 participants, and our results indicate many published interactions may be unstable. Analyses involving highly reliable predictors, such as group assignment in experimental designs, may stabilize at lower sample sizes because they attenuate the expected effect size less than variables with more measurement error. Researchers are encouraged to avoid routine tests of two-way interactions unless sample size and reliability are adequate and hypotheses are specified a priori.
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3. A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models.
PMID:日期:2024-03-28In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/. Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.
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4. A Delphi Study to Strengthen Research-Methods Training in Undergraduate Psychology Programs.
PMID:日期:2024-01-29Psychology programs often emphasize inferential statistical tests over a solid understanding of data and research design. This imbalance may leave graduates underequipped to effectively interpret research and employ data to answer questions. We conducted a two-round modified Delphi to identify the research-methods skills that the UK psychology community deems essential for undergraduates to learn. Participants included 103 research-methods instructors, academics, students, and nonacademic psychologists. Of 78 items included in the consensus process, 34 reached consensus. We coupled these results with a qualitative analysis of 707 open-ended text responses to develop nine recommendations for organizations that accredit undergraduate psychology programs-such as the British Psychological Society. We recommend that accreditation standards emphasize (1) data skills, (2) research design, (3) descriptive statistics, (4) critical analysis, (5) qualitative methods, and (6) both parameter estimation and significance testing; as well as (7) give precedence to foundational skills, (8) promote transferable skills, and (9) create space in curricula to enable these recommendations. Our data and findings can inform modernized accreditation standards to include clearly defined, assessable, and widely encouraged skills that foster a competent graduate body for the contemporary world.
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5. Tutorial: Power analyses for interaction effects in cross-sectional regressions.
PMID:日期:2023-09-26Interaction analyses (also termed 'moderation' analyses or 'moderated multiple regression') are a form of linear regression analysis designed to test whether the association between two variables changes when conditioned on a third variable. It can be challenging to perform a power analysis for interactions with existing software, particularly when variables are correlated and continuous. Moreover, while power is impacted by main effects, their correlation, and variable reliability, it can be unclear how to incorporate these effects into a power analysis. The R package and associated Shiny apps allow researchers with minimal or no programming experience to perform analytic and simulation-based power analyses for interactions. At minimum, these analyses require the Pearson's correlation between variables and sample size, and additional parameters including reliability and the number of discrete levels that a variable takes (e.g., binary or likert scale) can optionally be specified. In this Tutorial we demonstrate how to perform power analyses using our package and give examples of how power can be impacted by main effects, correlations between main effects, reliability, and variable distributions. We also include a brief discussion of how researchers may select an appropriate interaction effect size when performing a power analysis.
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6. iCatcher+: Robust and Automated Annotation of Infants' and Young Children's Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies.
PMID:日期:2023-04-18Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months-3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing "LEFT" versus "RIGHT" and "ON" versus "OFF" looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.
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7. Hybrid Experimental Designs for Intervention Development: What, Why, and How.
PMID:日期:2022-09-07Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)-a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).
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8. Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction.
PMID:日期:2021-09-23Consensus on standards for evaluating models and theories is an integral part of every science. Nonetheless, in psychology, relatively little focus has been placed on defining reliable communal metrics to assess model performance. Evaluation practices are often idiosyncratic and are affected by a number of shortcomings (e.g., failure to assess models' ability to generalize to unseen data) that make it difficult to discriminate between good and bad models. Drawing inspiration from fields such as machine learning and statistical genetics, we argue in favor of introducing common benchmarks as a means of overcoming the lack of reliable model evaluation criteria currently observed in psychology. We discuss a number of principles benchmarks should satisfy to achieve maximal utility, identify concrete steps the community could take to promote the development of such benchmarks, and address a number of potential pitfalls and concerns that may arise in the course of implementation. We argue that reaching consensus on common evaluation benchmarks will foster cumulative progress in psychology and encourage researchers to place heavier emphasis on the practical utility of scientific models.
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9. Multilab Direct Replication of Flavell, Beach, and Chinsky (1966): Spontaneous Verbal Rehearsal in a Memory Task as a Function of Age.
PMID:日期:2021-06-24Work by Flavell, Beach, and Chinsky (1966) indicated a change in the spontaneous production of overt verbalization behaviors when comparing young children (age 5) to older children (age 10). Despite the critical role that this evidence of a change in verbalization behaviors plays in modern theories of cognitive development and working memory, there has only been one other published near-replication of this work (Keeney, Cannizzo, & Flavell, 1967). This Registered Replication Report relied upon 17 labs who contributed their results to a larger and more comprehensive sample of children. We assessed memory performance and the presence or absence of verbalization behaviors of young children at different ages, and determined that the original pattern of findings was largely upheld: older children were more likely to verbalize, and their memory spans improved. We confirmed that 5- and 6-year-old children who verbalized recalled more than children who did not verbalize. However, unlike Flavell et al., substantial proportions of our 5- and 6-year-old samples overtly verbalized at least sometimes during the picture memory task. Also, continuous increase in overt verbalization from 7- to 10-years-old was not consistently evident in our samples. These robust findings should be weighed when considering theories of cognitive development, particularly theories concerning when verbal rehearsal emerges and relations between speech and memory.
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10. Evaluating Response Shift in Statistical Mediation Analysis.
PMID:日期:2021-05-13Researchers and prevention scientists often develop interventions to target intermediate variables (known as ) that are thought to be related to an outcome. When researchers target a mediating construct measured by self-report, the meaning of self-report measure could change from pretest to posttest for the individuals who received the intervention - which is a phenomenon referred to as . As a result, any observed changes on the mediator measure across groups or across time might reflect a combination of true change on the construct and response shift. Although previous studies have focused on identifying the source and type of response shift in measures after an intervention, there has been limited research on how using sum scores in the presence of response shift affects the estimation of mediated effects via statistical mediation analysis, which is critical for explaining how the intervention worked. In this paper, we focus on recalibration response shift, which is a change in internal standards of measurement, which affects how respondents interpret the response scale. We provide background on the theory of response shift and the methodology used to detect response shift (i.e., tests of measurement invariance). Additionally, we use simulated datasets to provide an illustration of how recalibration in the mediator can bias estimates of the mediated effect and also impact type I error and power.