Psychological methods最新文献

{"title":"Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method.","authors":"Miriam K Forbes","doi":"10.1037/met0000546","DOIUrl":"10.1037/met0000546","url":null,"abstract":"<p><p>Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among <i>all</i> levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1062-1073"},"PeriodicalIF":7.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10696269","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":"Ubiquitous bias and false discovery due to model misspecification in analysis of statistical interactions: The role of the outcome's distribution and metric properties.","authors":"Benjamin W Domingue, Klint Kanopka, Sam Trejo, Mijke Rhemtulla, Elliot M Tucker-Drob","doi":"10.1037/met0000532","DOIUrl":"10.1037/met0000532","url":null,"abstract":"<p><p>Studies of interaction effects are of great interest because they identify crucial interplay between predictors in explaining outcomes. Previous work has considered several potential sources of statistical bias and substantive misinterpretation in the study of interactions, but less attention has been devoted to the role of the outcome variable in such research. Here, we consider bias and false discovery associated with estimates of interaction parameters as a function of the distributional and metric properties of the outcome variable. We begin by illustrating that, for a variety of noncontinuously distributed outcomes (i.e., binary and count outcomes), attempts to use the linear model for recovery leads to catastrophic levels of bias and false discovery. Next, focusing on transformations of normally distributed variables (i.e., censoring and noninterval scaling), we show that linear models again produce spurious interaction effects. We provide explanations offering geometric and algebraic intuition as to why interactions are a challenge for these incorrectly specified models. In light of these findings, we make two specific recommendations. First, a careful consideration of the outcome's distributional properties should be a standard component of interaction studies. Second, researchers should approach research focusing on interactions with heightened levels of scrutiny. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1164-1179"},"PeriodicalIF":7.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9862990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why multiple hypothesis test corrections provide poor control of false positives in the real world.","authors":"Stanley E Lazic","doi":"10.1037/met0000678","DOIUrl":"10.1037/met0000678","url":null,"abstract":"<p><p>Most scientific disciplines use significance testing to draw conclusions about experimental or observational data. This classical approach provides a theoretical guarantee for controlling the number of false positives across a set of hypothesis tests, making it an appealing framework for scientists seeking to limit the number of false effects or associations that they claim to observe. Unfortunately, this theoretical guarantee applies to few experiments, and the true false positive rate (FPR) is much higher. Scientists have plenty of freedom to choose the error rate to control, the tests to include in the adjustment, and the method of correction, making strong error control difficult to attain. In addition, hypotheses are often tested after finding unexpected relationships or patterns, the data are analyzed in several ways, and analyses may be run repeatedly as data accumulate. As a result, adjusted <i>p</i> values are too small, incorrect conclusions are often reached, and results are harder to reproduce. In the following, I argue why the FPR is rarely controlled meaningfully and why shrinking parameter estimates is preferable to <i>p</i> value adjustments. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142688594","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":"Simulation studies for methodological research in psychology: A standardized template for planning, preregistration, and reporting.","authors":"Björn S Siepe, František Bartoš, Tim P Morris, Anne-Laure Boulesteix, Daniel W Heck, Samuel Pawel","doi":"10.1037/met0000695","DOIUrl":"10.1037/met0000695","url":null,"abstract":"<p><p>Simulation studies are widely used for evaluating the performance of statistical methods in psychology. However, the quality of simulation studies can vary widely in terms of their design, execution, and reporting. In order to assess the quality of typical simulation studies in psychology, we reviewed 321 articles published in <i>Psychological Methods, Behavior Research Methods, and Multivariate Behavioral Research</i> in 2021 and 2022, among which 100/321 = 31.2% report a simulation study. We find that many articles do not provide complete and transparent information about key aspects of the study, such as justifications for the number of simulation repetitions, Monte Carlo uncertainty estimates, or code and data to reproduce the simulation studies. To address this problem, we provide a summary of the ADEMP (aims, data-generating mechanism, estimands and other targets, methods, performance measures) design and reporting framework from Morris et al. (2019) adapted to simulation studies in psychology. Based on this framework, we provide ADEMP-PreReg, a step-by-step template for researchers to use when designing, potentially preregistering, and reporting their simulation studies. We give formulae for estimating common performance measures, their Monte Carlo standard errors, and for calculating the number of simulation repetitions to achieve a desired Monte Carlo standard error. Finally, we give a detailed tutorial on how to apply the ADEMP framework in practice using an example simulation study on the evaluation of methods for the analysis of pre-post measurement experiments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Item response theory-based continuous test norming.","authors":"Hannah M Heister,Casper J Albers,Marie Wiberg,Marieke E Timmerman","doi":"10.1037/met0000686","DOIUrl":"https://doi.org/10.1037/met0000686","url":null,"abstract":"In norm-referenced psychological testing, an individual's performance is expressed in relation to a reference population using a standardized score, like an intelligence quotient score. The reference population can depend on a continuous variable, like age. Current continuous norming methods transform the raw score into an age-dependent standardized score. Such methods have the shortcoming to solely rely on the raw test scores, ignoring valuable information from individual item responses. Instead of modeling the raw test scores, we propose modeling the item scores with a Bayesian two-parameter logistic (2PL) item response theory model with age-dependent mean and variance of the latent trait distribution, 2PL-norm for short. Norms are then derived using the estimated latent trait score and the age-dependent distribution parameters. Simulations show that 2PL-norms are overall more accurate than those from the most popular raw score-based norming methods cNORM and generalized additive models for location, scale, and shape (GAMLSS). Furthermore, the credible intervals of 2PL-norm exhibit clearly superior coverage over the confidence intervals of the raw score-based methods. The only issue of 2PL-norm is its slightly lower performance at the tails of the norms. Among the raw score-based norming methods, GAMLSS outperforms cNORM. For empirical practice this suggests the use of 2PL-norm, if the model assumptions hold. If not, or the interest is solely in the point estimates of the extreme trait positions, GAMLSS-based norming is a better alternative. The use of the 2PL-norm is illustrated and compared with GAMLSS and cNORM using empirical data, and code is provided, so that users can readily apply 2PL-norm to their normative data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439230","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":"Comments on the measurement of effect sizes for indirect effects in Bayesian analysis of variance.","authors":"Sang-June Park,Youjae Yi","doi":"10.1037/met0000706","DOIUrl":"https://doi.org/10.1037/met0000706","url":null,"abstract":"Bayesian analysis of variance (BANOVA), implemented through R packages, offers a Bayesian approach to analyzing experimental data. A tutorial in Psychological Methods extensively documents BANOVA. This note critically examines a method for evaluating mediation using partial eta-squared as an effect size measure within the BANOVA framework. We first identify an error in the formula for partial eta-squared and propose a corrected version. Subsequently, we discuss limitations in the interpretability of this effect size measure, drawing on previous research, and argue for its potential unsuitability in assessing indirect effects in mediation analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"106 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436375","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":"The potential of preregistration in psychology: Assessing preregistration producibility and preregistration-study consistency.","authors":"Olmo R van den Akker,Marjan Bakker,Marcel A L M van Assen,Charlotte R Pennington,Leone Verweij,Mahmoud M Elsherif,Aline Claesen,Stefan D M Gaillard,Siu Kit Yeung,Jan-Luca Frankenberger,Kai Krautter,Jamie P Cockcroft,Katharina S Kreuer,Thomas Rhys Evans,Frédérique M Heppel,Sarah F Schoch,Max Korbmacher,Yuki Yamada,Nihan Albayrak-Aydemir,Shilaan Alzahawi,Alexandra Sarafoglou,Maksim M Sitnikov,Filip Děchtěrenko,Sophia Wingen,Sandra Grinschgl,Helena Hartmann,Suzanne L K Stewart,Cátia M F de Oliveira,Sarah Ashcroft-Jones,Bradley J Baker,Jelte M Wicherts","doi":"10.1037/met0000687","DOIUrl":"https://doi.org/10.1037/met0000687","url":null,"abstract":"Study preregistration has become increasingly popular in psychology, but its potential to restrict researcher degrees of freedom has not yet been empirically verified. We used an extensive protocol to assess the producibility (i.e., the degree to which a study can be properly conducted based on the available information) of preregistrations and the consistency between preregistrations and their corresponding papers for 300 psychology studies. We found that preregistrations often lack methodological details and that undisclosed deviations from preregistered plans are frequent. These results highlight that biases due to researcher degrees of freedom remain possible in many preregistered studies. More comprehensive registration templates typically yielded more producible preregistrations. We did not find that the producibility and consistency of preregistrations differed over time or between original and replication studies. Furthermore, we found that operationalizations of variables were generally preregistered more producible and consistently than other study parts. Inconsistencies between preregistrations and published studies were mainly encountered for data collection procedures, statistical models, and exclusion criteria. Our results indicate that, to unlock the full potential of preregistration, researchers in psychology should aim to write more producible preregistrations, adhere to these preregistrations more faithfully, and more transparently report any deviations from their preregistrations. This could be facilitated by training and education to improve preregistration skills, as well as the development of more comprehensive templates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"124 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436379","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":"Lagged multidimensional recurrence quantification analysis for determining leader-follower relationships within multidimensional time series.","authors":"Alon Tomashin,Ilanit Gordon,Giuseppe Leonardi,Yair Berson,Nir Milstein,Matthias Ziegler,Ursula Hess,Sebastian Wallot","doi":"10.1037/met0000691","DOIUrl":"https://doi.org/10.1037/met0000691","url":null,"abstract":"The current article introduces lagged multidimensional recurrence quantification analysis. The method is an extension of multidimensional recurrence quantification analysis and allows to quantify the joint dynamics of multivariate time series and to investigate leader-follower relationships in behavioral and physiological data. Moreover, the method enables the quantification of the joint dynamics of a group, when such leader-follower relationships are taken into account. We first provide a formal presentation of the method, and then apply it to synthetic data, as well as data sets from joint action research, investigating the shared dynamics of facial expression and beats-per-minute recordings within different groups. A wrapper function is included, for applying the method together with the \"crqa\" package in R. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"85 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436376","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":"Harvesting heterogeneity: Selective expertise versus machine learning.","authors":"Rumen Iliev,Alex Filipowicz,Francine Chen,Nikos Arechiga,Scott Carter,Emily Sumner,Totte Harinen,Kate Sieck,Kent Lyons,Charlene Wu","doi":"10.1037/met0000640","DOIUrl":"https://doi.org/10.1037/met0000640","url":null,"abstract":"The heterogeneity of outcomes in behavioral research has long been perceived as a challenge for the validity of various theoretical models. More recently, however, researchers have started perceiving heterogeneity as something that needs to be not only acknowledged but also actively addressed, particularly in applied research. A serious challenge, however, is that classical psychological methods are not well suited for making practical recommendations when heterogeneous outcomes are expected. In this article, we argue that heterogeneity requires a separation between basic and applied behavioral methods, and between different types of behavioral expertise. We propose a novel framework for evaluating behavioral expertise and suggest that selective expertise can easily be automated via various machine learning methods. We illustrate the value of our framework via an empirical study of the preferences towards battery electric vehicles. Our results suggest that a basic multiarm bandit algorithm vastly outperforms human expertise in selecting the best interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"23 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386319","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":"How to conduct an integrative mixed methods meta-analysis: A tutorial for the systematic review of quantitative and qualitative evidence.","authors":"Heidi M Levitt","doi":"10.1037/met0000675","DOIUrl":"10.1037/met0000675","url":null,"abstract":"<p><p>This article is a guide on how to conduct mixed methods meta-analyses (sometimes called mixed methods systematic reviews, integrative meta-analyses, or integrative meta-syntheses), using an integrative approach. These aggregative methods allow researchers to synthesize qualitative and quantitative findings from a research literature in order to benefit from the strengths of both forms of analysis. The article articulates distinctions in how qualitative and quantitative methodologies work with variation to develop a coherent theoretical basis for their integration. In advancing this methodological approach to integrative mixed methods meta-analysis (IMMMA), I provide rationales for procedural decisions that support methodological integrity and address prior misconceptions that may explain why these methods have not been as commonly used as might be expected. Features of questions and subject matters that lead them to be amenable to this research approach are considered. The steps to conducting an IMMMA then are described, with illustrative examples, and in a manner open to the use of a range of qualitative and quantitative meta-analytic approaches. These steps include the development of research aims, the selection of primary research articles, the generation of units for analysis, and the development of themes and findings. The tutorial provides guidance on how to develop IMMMA findings that have methodological integrity and are based upon the appreciation of the distinctive approaches to modeling variation in quantitative and qualitative methodologies. The article concludes with guidance for report writing and developing principles for practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142366352","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}