Psychological methods最新文献

{"title":"An application of procedural knowledge space theory to the study of strategic planning in board games.","authors":"Andrea Brancaccio, Luca Stefanutti","doi":"10.1037/met0000858","DOIUrl":"10.1037/met0000858","url":null,"abstract":"<p><p>Procedural knowledge space theory (PKST) integrates knowledge space theory and problem space theory to model human problem-solving. This study extends PKST to competitive, two-player games and presents the first empirical application of homomorphisms to derive abstract representations of complex problem spaces. Using tic-tac-toe and its numerical variant pick15 as test cases, the study demonstrates how PKST can model complex competitive scenarios effectively. An empirical study involving 148 participants validated these PKST extensions by analyzing their performance on pick15 tasks. Problem and knowledge spaces were derived at multiple levels of abstraction and tested using the Markov solution process model. Results indicated that abstract representations provided a better fit for participants with prior experience in tic-tac-toe, suggesting that expertise promotes the use of generalizable strategies. This work provides a rigorous proof of concept for applying homomorphisms to test theoretical predictions on problem-solving tasks. By linking abstract representations, like tactics, to problem-solving performance, the proposed methodology offers a concrete approach to evaluating cognitive theories of problem-solving and decision-making. These findings highlight the potential of PKST to model broader cognitive phenomena in interactive contexts. The study emphasizes the practical utility and flexibility of PKST for formalizing and empirically testing hypotheses, extending its relevance to diverse domains of cognitive science. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of the predictive performance of continuous and class-based latent trait models.","authors":"Wanjing Anya Ma, Yiqing Liu, Klint Kanopka, Wenchao Ma, Benjamin W Domingue","doi":"10.1037/met0000860","DOIUrl":"10.1037/met0000860","url":null,"abstract":"<p><p>The ability of a student can be conceptualized as either a continuously varying entity (e.g., conventional analysis using dichotomous item response theory [IRT] models; Lord & Novick, 1968) or a bundle of latent classes (e.g., cognitive diagnostic models [CDMs]; Rupp et al., 2010; von Davier & Lee, 2019). This article builds on recent efforts to focus on predictive differences between measurement models in an attempt to examine the degree to which such approaches, which utilize quite distinctive notions regarding the nature of ability, produce different predictions of response behavior in the real world. We first present two simulation studies in which data are generated from variants of CDMs that differ in sample size, attribute hierarchical structures, and attribute estimation methods. We illustrate that, as we would expect given that they were used to generate data, CDM-based predictions uniformly outperform those of IRT models. We then compare the performance of CDM- and IRT-based approaches across 11 empirical data sets previously analyzed using CDMs. Our findings indicate that overfitting is a pervasive issue across CDM-based predictions, particularly with the generalized deterministic inputs, noisy \"and\" gate model. Furthermore, in no case does the CDM show superior performance when using the maximum a posteriori estimator, and only six out of 11 data sets show improved model fit for CDMs over the two-parameter logistic model when using the marginal mastery probabilities estimator. Researchers and practitioners may need to balance the diagnostic appeal of CDMs with the fact that their complexity can come at the cost of predictive accuracy. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network accuracy across levels of analysis using stochastic block models.","authors":"Alexander P Christensen, Jeongwon Choi","doi":"10.1037/met0000853","DOIUrl":"10.1037/met0000853","url":null,"abstract":"<p><p>Researchers apply network psychometric methods across many levels of analysis-from understanding causal pathways (edges) and variable importance (centrality) to uncovering dimensionality (community) and group differences (network similarity). Valid inferences at each level depends on the accuracy of network estimation methods, yet simulation studies have focused almost exclusively on edge recovery using data generating mechanisms that lack known community structures. In this large-scale simulation study, we evaluated four network estimation methods (EBICglasso variants, ggmModSelect, GGMncv, GGMnonreg) across four levels of analysis using stochastic block models with empirically informed edge weight distributions derived from 293 empirical networks in psychology. For most levels, large samples (≥1,000) were necessary for adequate recovery, echoing concerns about sufficient evidence for inferences drawn by many published studies. The EBICglasso variants outperformed the other methods in smaller samples (≤1,000) and dichotomous data; however, these conditions demonstrated inadequate recovery across methods (including EBICglasso). Most methods achieved adequate accuracy in larger samples (≥2,500) and continuous data. Centrality accuracy varied substantially: Network loadings were reliably recovered across all conditions whereas bridge strength was unreliable even under ideal conditions. For node and bridge strength, we recommend reporting raw values rather than rankings, as rank-order recovery required substantially larger samples. We also established interpretation benchmarks for network similarity metrics (<i>s</i>_F and Jensen-Shannon similarity) and found community detection to be robust except under challenging conditions (small samples and dichotomous data). Collectively, these findings provide concrete, level-specific guidance for method and measure selection based on sample size and data categories. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148472369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supplemental Material for A Comparison of the Predictive Performance of Continuous and Class-Based Latent Trait Models","authors":"","doi":"10.1037/met0000860.supp","DOIUrl":"https://doi.org/10.1037/met0000860.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148589122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supplemental Material for An Application of Procedural Knowledge Space Theory to the Study of Strategic Planning in Board Games","authors":"","doi":"10.1037/met0000858.supp","DOIUrl":"https://doi.org/10.1037/met0000858.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"46 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148589111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post Hoc comparisons in fixed-effect ANOVA: A sequential fusion approach with calibrated error control.","authors":"Yvonnick Noel","doi":"10.1037/met0000859","DOIUrl":"https://doi.org/10.1037/met0000859","url":null,"abstract":"Post hoc comparisons following a significant analysis of variance (ANOVA) are typically conducted using pairwise methods such as Tukey's honestly significant difference or Holm-Bonferroni corrections. Although these procedures control family-wise error rates, they can produce nontransitive patterns of equivalence that do not correspond to any coherent partition of the factor levels. We propose a sequential fusion procedure that takes a modeling perspective: Starting from the saturated ANOVA model, the algorithm iteratively fuses the pair of groups with the most similar means, testing each fusion via a likelihood-ratio test using the ANOVA error term. A calibrated sequence of critical values ensures family-wise error rate control at the nominal level (e.g., 5%). Through extensive Monte Carlo simulations across balanced designs with 3-50 groups and 5-50 observations per group, we derive a simple approximation for the calibration factor as a function of the number of groups and sample size. Comparisons with Tukey's honestly significant difference, Holm-Bonferroni, Benjamini-Hochberg, and Scheffé procedures demonstrate that the fusion cascade achieves comparable or superior power while guaranteeing interpretational coherence: The output is always a single, transitive partition of groups. We illustrate the method's practical utility by reanalyzing data from Moreno and Mayer (2000) on multimedia learning, demonstrating how factorial designs are naturally handled by treating them as one-way ANOVA on cell means, with interaction structure revealed directly as a partition. Simulation studies confirm that the fusion cascade consistently identifies exact partitions under well-separated conditions, whereas pairwise methods produce nontransitive patterns in the majority of cases. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"37 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian marginalized zero-inflated Poisson model with random effects for single-case experimental designs: A simulation study.","authors":"Chendong Li,Wen Luo","doi":"10.1037/met0000857","DOIUrl":"https://doi.org/10.1037/met0000857","url":null,"abstract":"Analyzing zero-inflated count data in single-case experimental designs presents a significant analytical challenge. Although traditional zero-inflated generalized linear mixed models are available, they estimate a conditional treatment effect given an individual coming from the count process, which often mismatches the applied researcher's interest in the overall effect of an intervention for each subject. These conditional models also face interpretational and estimation challenges within the small-sample context of single-case experimental designs. This study introduces and evaluates a Bayesian marginalized zero-inflated Poisson (mZIP) model with random effects. This framework reparameterizes the model to estimate the marginal intervention effect, aligning the statistical estimand with the typical research question. A Monte Carlo simulation study was conducted to evaluate the mZIP model's performance. We compared its performance with other methods that also target the marginal treatment effect: the log response ratio (LRR), a Poisson generalized linear mixed-effects model (GLMM), and a negative binomial GLMM. Simulation results indicate that the Bayesian mZIP model consistently recovers unbiased estimates of the marginal treatment effect and provides reliable statistical inference. The LRR, Poisson GLMM, and negative binomial GLMM produced biased estimates for the marginal effect under conditions with smallest sample size and highest zero-inflation rate. They also suffered from low coverage rate, making their inferential statistics invalid. The LRR indicated lower statistical power than the mZIP model. We apply the mZIP model to two empirical data sets to illustrate its application and interpretation. Finally, we discuss the distinction between conditional and marginal estimands, as well as the limitations and future directions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"146 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying careless responding in ecological momentary assessment: Inconsistent signals from different detection methods in the WARN-D Data.","authors":"Leonie V D E Vogelsmeier,Björn S Siepe,Esther Ulitzsch,Gudrun Eisele,Joran Geeraerts,Adam Klocek,Ricarda K K Proppert,Carlotta L Rieble,Rayyan Tutunji,Egon Dejonckheere,Peter Kuppens,Eiko I Fried","doi":"10.1037/met0000845","DOIUrl":"https://doi.org/10.1037/met0000845","url":null,"abstract":"Collecting intensive longitudinal data via methods like ecological momentary assessment (EMA) is necessary to study dynamics in psychological constructs. However, detecting careless responding is more important than ever, as this methodology burdens participants more than traditional single-survey studies. If undetected, careless responding reduces data quality and can lead to invalid findings by distorting correlations between items and constructs and compromising the psychometric properties of scales. In turn, this hinders progress toward cumulative psychological science. In recent years, several data-driven approaches have been proposed that assess whether an individual was attentive or careless for each time point-specific measurement after data collection. However, the variety and complexity of available methods can be overwhelming for researchers. In this article, we summarized, applied, and compared 13 methods that flag careless responses in a rich EMA data set from the WARN-D study with 206,318 observations (from n = 1,194 participants). Together with our detailed online supplementary tutorials, the article serves as a guide for researchers to evaluate and report careless responding in their own data. At the same time, we show that the development of methods for careless responding detection is still in its early stages, as the studied approaches produced very different results. We provide recommendations for which methods are currently most promising based on our findings and discuss important next steps in validating them empirically. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"447 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sample size planning for studies with multiple measurement trials.","authors":"Douglas G Bonett","doi":"10.1037/met0000856","DOIUrl":"https://doi.org/10.1037/met0000856","url":null,"abstract":"When planning a study that uses multiple measurement trials, both the number of participants and the number of measurement trials are fundamental design elements. In multiple measurement trial studies, each participant is repeatedly exposed to a particular treatment condition, and the dependent variable is the average of multiple measurement trials. Increasing the number of measurement trials will increase the reliability of the dependent variable, which will reduce the sample size needed to obtain a confidence interval with desired precision or a hypothesis test with desired power. In studies where the dependent variable is an average of multiple measurement trials, the investigator may want to compare the required sample size for different numbers of measurement trials. Closed-form sample size formulas are derived to determine the sample size needed for desired confidence interval precision or statistical power, given a specified number of measurement trials. Sample size formulas are derived for the two-group design and the paired-samples design and then extended to the case of general linear contrasts in between-subjects and within-subjects designs. R functions for each sample size formula are given in the online supplemental materials. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"152 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148432722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating local structural-after-measurement and traditional approaches for the estimation of complex nonlinear effects among latent variables.","authors":"Felipe Fontana Vieira,Kjell Solem Slupphaug,Yves Rosseel","doi":"10.1037/met0000851","DOIUrl":"https://doi.org/10.1037/met0000851","url":null,"abstract":"Various methods exist to include interaction or quadratic terms involving latent variables in structural equation models. Most use system-wide estimation, where all parameters are estimated simultaneously and have been investigated in models with few nonlinear effects. Recently, structural-after-measurement approaches have been proposed, where estimation proceeds in two stages: the measurement model first, then the structural model. Rosseel et al. (2025) extended local structural after measurement (LSAM) to handle second-order nonlinear effects among latent variables, although analytical standard errors for this extension had not been derived. In this article, we provide such a two-step standard error formula for LSAM and evaluate its performance, alongside that of LSAM more broadly, in two simulation studies. These varied latent exogenous predictor distributions (normal, right-skewed, and uniform), reliabilities (0.4, 0.6, and 0.8), sample sizes (400 and 1,000), and measurement error and structural disturbance distributions (normal and right-skewed). The first study examined a model with three nonlinear effects. LSAM produced largely unbiased estimates with stable coverage, standard errors, and Type I error rates, although some metrics were less stable under low reliability and with uniform latent exogenous distributions-issues that were mitigated by larger sample sizes. Traditional methods (latent moderated structural equations, quasimaximum likelihood, and unconstrained product indicator) showed results consistent with prior literature. The second study, excluding latent moderated structural equations, tested a more complex model with eight nonlinear effects. LSAM maintained adequate performance despite greater complexity, although similar limitations emerged. Traditional methods yielded more variable results, with generally poorer performance overall. Right-skewed measurement errors affected results more than right-skewed structural disturbances, particularly under low and medium reliability. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"14 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148408585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}