The quantitative methods for psychology最新文献_第4页

Influence of dual-task load on redundant signal processes 双重任务负载对冗余信号处理的影响

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.1.p084

Elizabeth L. Fox, Ashley D. Cook, Cheng-Ta Yang, Hao-Lun Fu, Kanthika Latthirun, Zachary L. Howard

{"title":"Influence of dual-task load on redundant signal processes","authors":"Elizabeth L. Fox, Ashley D. Cook, Cheng-Ta Yang, Hao-Lun Fu, Kanthika Latthirun, Zachary L. Howard","doi":"10.20982/tqmp.19.1.p084","DOIUrl":"https://doi.org/10.20982/tqmp.19.1.p084","url":null,"abstract":"In high demand contexts, uni-or multi-modal signals are used to convey redundant information and improve performance. This is especially the case with improving the detection of discrete peripheral signals. However, how one processes peripheral signals may change depending on the greater environmental context. The underlying cognitive processing of signals is important to determine how they may inﬂuence the degree to which each signal enhances, as opposed to slows down, detection. Until now, it was unclear if i) the introduction of, or increased difﬁculty of, a second task changes how people combine peripheral signals (that is, in a parallel, serial, or coactive fashion) and ii) if processing efﬁciency depends on the salience of the peripheral signals or the presence/difﬁculty of a centrally located and continuous tracking task. This manuscript describes an application of Systems Factorial Technology to investigate the cognitive processing mechanisms of redundant signals in the context of a multiple object tracking (MOT) task. The MOT task load (track 0, 1, or 4 dots) and the salience of peripheral signals (bright, dim) were manipulated. The data indicate peoples’ processing of peripheral signals changed depending on the MOT task load. Under a high MOT task load, most people processed redundant signals in a parallel fashion. Alternatively, nearly half of people processed the signals in a serial fashion when asked to simultaneously track 0 or 1 dot. Implications for the use and design of redundant signals in multi-task contexts that vary in task demands are discussed.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46941228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

From MMPI to MMPI-2-RF/MMPI-3: The abandonment of subterfuge 从MMPI到MMPI-2- rf /MMPI-3:放弃诡计

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.1.p047

Michel Parisien

引用次数: 0

Confidence Intervals for the Coefficient Alpha Difference from Two Independent Samples (Groups) 两个独立样本(组)间Alpha差系数的置信区间

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p194

M. A. Padilla

引用次数: 0

Determining Negligible Associations in Regression 确定回归中可忽略的关联

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.1.p059

U. Alter, A. Counsell

{"title":"Determining Negligible Associations in Regression","authors":"U. Alter, A. Counsell","doi":"10.20982/tqmp.19.1.p059","DOIUrl":"https://doi.org/10.20982/tqmp.19.1.p059","url":null,"abstract":"Psychological research is rife with inappropriately concluding “no effect” between predictors and outcome in regression models following statistically nonsigniﬁcant results. However, this approach is methodologically ﬂawed because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true. Using this approach leads to high rates of incorrect conclusions that ﬂood psychological literature. This paper introduces a novel, methodologically sound alternative. In this paper, we demonstrate how an equivalence testing approach can be applied to multiple regression (which we refer to here as “negligible effect testing”) to evaluate whether a predictor (measured in standardized or unstandardized units) has a negligible association with the outcome. In the ﬁrst part of the paper, we evaluate the performance of two equivalence-based techniques and compare them to the traditional, difference-based test via a Monte Carlo simulation study. In the second part of the paper, we use examples from the literature to illustrate how researchers can implement the recommended negligible effect testing methods in their own work using open-access and user-friendly tools (negligible R package and Shiny app). Finally, we discuss how to report and interpret results from negligible effect testing and provide practical recommendations for best research practices based on the simulation results. All materials, including R code, results, and additional resources, are publicly available on the Open Science Framework (OSF): osf.io/w96xe/.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41940724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Displaying Latent Classes in Figures: Consideration of Practices 用图形显示潜在类：实践思考

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p165

Xiang Zhao

引用次数: 0

Programming an n-Back task in Qualtrics using HTML and JavaScript 使用HTML和JavaScript在Qualtrics中编写n-Back任务

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p136

Meenakshie Bradley-Garcia, Victoria Bolton

引用次数: 0

Implementing multi-session learning studies out of the lab: Tips and tricks using OpenSesame 在实验室外实现多会话学习研究:使用OpenSesame的提示和技巧

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p156

S. Dahm, E. Ort, Christian Büsel, P. Sachse, S. Mathôt

引用次数: 1

How to Generate Missing Data For Simulation Studies 如何生成模拟研究的缺失数据

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p100

Xijuan Zhang

{"title":"How to Generate Missing Data For Simulation Studies","authors":"Xijuan Zhang","doi":"10.20982/tqmp.19.2.p100","DOIUrl":"https://doi.org/10.20982/tqmp.19.2.p100","url":null,"abstract":"Missing data are common in psychological and educational research. With the improvement in computing technology in recent decades, more researchers have begun developing missing data techniques. In their research, they often conduct Monte Carlo simulation studies to compare the performances of different missing data techniques. During such simulation studies, researchers must generate missing data in the simulated dataset by deciding which data values to delete. However, in the current literature, there are limited guidelines on how to generate missing data for simulation studies. Our paper is one of the ﬁrst that examines ways of generating missing data for simulation studies. I emphasize the importance of specifying missing data rules which are statistical models for generating missing data. I begin the paper by reviewing the types of missing data mechanisms and missing data patterns. I then explain how to specify missing data rules to generate missing data with different mechanisms and patterns. I emphasize the advantages and disadvantages of using different missing data rules and algorithms to generate missing data for simulation studies. Next, I discuss other important aspects of simulation studies involving missing data. I end the paper by offering recommendations for generating missing data for simulation studies.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47124271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Treatment-Control Pre-Post-Follow-up Design Data 治疗-控制前后随访设计数据分析

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.1.p025

D. Sharpe, R. Cribbie

引用次数: 0

Analysis of frequency data: The ANOFA framework 频率数据分析：ANOFA框架

The quantitative methods for psychology Pub Date : 2023-02-01 DOI: 10.20982/tqmp.19.2.p173

L. Laurencelle, D. Cousineau

{"title":"Analysis of frequency data: The ANOFA framework","authors":"L. Laurencelle, D. Cousineau","doi":"10.20982/tqmp.19.2.p173","DOIUrl":"https://doi.org/10.20982/tqmp.19.2.p173","url":null,"abstract":"Analyses of frequencies are commonly done using a chi-square test. This test, derived from a normal approximation, is deemed generally efﬁcient (controlling type-I error rates fairly well and having good statistical power). However, in the case of factorial designs, it is difﬁcult to decompose a total test statistic into additive interaction effects and main effects. Herein, we present an alternative test based on the G statistic. The test has similar type-I error rates and power as the former one. However, it is based on a total statistic that is naturally decomposed additively into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVAs. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. We also examine how to render plots of frequencies along with conﬁdence intervals. Finally, quantifying effect sizes and planning statistical power are described under this framework. The ANOFA is a tool that assesses the signiﬁcance of effects instead of the signiﬁcance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42301837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0