The quantitative methods for psychology最新文献_第6页

Point and interval estimates for a standardized mean difference in paired-samples designs using a pooled standard deviation. 使用合并标准差的配对样本设计中标准化平均差的点和区间估计。

The quantitative methods for psychology Pub Date : 2022-07-01 DOI: 10.20982/tqmp.18.2.p207

D. A. Fitts

{"title":"Point and interval estimates for a standardized mean difference in paired-samples designs using a pooled standard deviation.","authors":"D. A. Fitts","doi":"10.20982/tqmp.18.2.p207","DOIUrl":"https://doi.org/10.20982/tqmp.18.2.p207","url":null,"abstract":"A standardized mean difference using a pooled standard deviation with paired samples ( d p ; paired-pooled design) can be compared directly to a d p from an independent samples design, but the unbiased point estimate g p and conﬁdence interval (CI) for d p cannot unless the population correlation ρ between the scores is known in the paired-pooled design, which it rarely is. The ρ is required to calculate the degrees of freedom ν for the design, and ν is necessary to calculate the g p and CI. If a variable sample correlation is substituted for ρ the ν is only approximate and the sampling distribution for d p is unknown. This article uses simulations to compare the characteristics of the unknown distribution to the noncentral t distribution as an approximation and provides empirically-derived regression equations to compensate for the bias in the approximated CI computed using the noncentral t distribution. The result is an approximate but much more accurate coverage of the CI than previously available. Tables are supplied to assist sample size planning and computer programs are provided for computations. These results are experimental and tentative until the actual distribution can be discovered. The regularity of the deviation in coverage that allows the compensation to work encourages that search.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43229003","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

Power Analysis for Regression Coefficients: The Role of Multiple Predictors and Power to Detect all Coefficients Simultaneously 回归系数的幂分析：多个预测器的作用和同时检测所有系数的幂

The quantitative methods for psychology Pub Date : 2022-07-01 DOI: 10.20982/tqmp.18.2.p142

C. Aberson, Josue E. Rodriguez, Danielle Siegel

引用次数: 0

Illustrations of serial mediation using PROCESS, Mplus and R 使用PROCESS, Mplus和R进行串行中介的实例

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p066

Laura Lemardelet, Pier-Olivier Caron

引用次数: 2

Histogram lies about distribution shape and Pearson's coefficient of variation lies about relative variability 直方图与分布形状有关，皮尔逊变异系数与相对变异性有关

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p091

P. Silveira, J. O. Siqueira

{"title":"Histogram lies about distribution shape and Pearson's coefficient of variation lies about relative variability","authors":"P. Silveira, J. O. Siqueira","doi":"10.20982/tqmp.18.1.p091","DOIUrl":"https://doi.org/10.20982/tqmp.18.1.p091","url":null,"abstract":"aDepartment of Pathology (LIM01-HCFMUSP), Medical School, University of Sao Paulo, SP, Brazil bDepartment of Legal Medicine, Medical Ethics, Work and Social Medicine, Medical School, University of Sao Paulo, SP, Brazil Abstract Histograms and Pearson’s coefficient of variation are among the most popular summary statistics. Researchers use histograms to judge the shape of quantitative data distribution by visual inspection. The coefficient of variation is taken as an estimator of relative variability of these data. We explore properties of histograms and coefficient of variation by examples in R, thus offering better alternatives: density plots and Eisenhauer’s relative dispersion coefficient. Hypothetical examples developed in R are applied to create histograms and density plots, and to compute coefficient of variation and relative dispersion coefficient. These hypothetical examples clearly show that these two traditional approaches are flawed. Histograms do not necessarily reflect the distribution of probabilities and the Pearson’s coefficient of variation is not invariant with linear transformations and is not a measure of relative variability, for it is a ratio between a measure of absolute variability (standard deviation) and a measure of central position (mean). Potential alternatives are explained and applied for contrast. With the use of modern computers and R language it is easy to apply density plots, which are able to approximate the theoretical probability distribution. In addition, Eisenhauer’s relative dispersion coefficient is suggested as a suitable estimator of relative variability, including sample size correction for lower and upper bounds.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45559000","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: 2

A bivariate longitudinal cluster model with application to the {C}ognitive {R}eflection {T}est 基于{C}认知{R}反射{T}检验的二元纵向聚类模型

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p021

M. Berkowitz, R. Altman

{"title":"A bivariate longitudinal cluster model with application to the {C}ognitive {R}eflection {T}est","authors":"M. Berkowitz, R. Altman","doi":"10.20982/tqmp.18.1.p021","DOIUrl":"https://doi.org/10.20982/tqmp.18.1.p021","url":null,"abstract":"The Cognitive Reflection Test (CRT) is a test designed to assess subjects’ ability to override intuitively appealing but incorrect responses. Psychologists are concerned with whether subjects improve their scores on the test with repeated exposure, in which case, the test’s predictive validity may be threatened. In this paper, we take a novel approach to modelling data recorded on subjects who took the CRT multiple times. We develop bivariate, longitudinal models to describe the responses, CRT score and time taken to complete the CRT. These responses serve as a proxy for the underlying latent variables “numeracy” and “reflectiveness”, respectively—two components of “rationality”. Our models allow for subpopulations of individuals whose responses exhibit similar patterns. We assess the reasonableness of our models via new visualizations of the data. We estimate their parameters by modifying the method of adaptive Gaussian quadrature. We then use our fitted models to address a range of subject-specific questions in a formal way. We find evidence of at least three subpopulations, which we interpret as representing individuals with differing combinations of numeracy and reflectiveness, and determine that, in some subpopulations, test exposure has a greater estimated effect on test scores than previously reported.","PeriodicalId":93055,"journal":{"name":"The quantitative methods for psychology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41785201","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

Bayesian Bootstrapped Correlation Coefficients 贝叶斯自举相关系数

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p039

Josue E. Rodriguez, Donald R Williams

引用次数: 1

Exploring the role of Honesty-Humility in replicating "The relationships between behavioral addictions and the five-factor model of personality" 探索诚实-谦卑在复制“行为成瘾与人格五因素模型的关系”中的作用

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.r001

Anjali Singh, G. Durand

引用次数: 0

Review of the partially overlapping samples framework: Paired observations and independent observations in two samples 部分重叠样本框架的审查：两个样本中的成对观察和独立观察

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p055

Ben Derrick, Paul White

引用次数: 5

Les distributions multinomiales, leur mesure par les tests khi2 et G, leur approximation par la loi khi-carré et l'analyse des tableaux de fréquences par le test $G$ 多项分布，用khi2和G检验测量它们，用khi-平方定律近似它们，用G检验分析频率表

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p001

L. Laurencelle

引用次数: 1

Corrigendum à La loi de Pascal restreinte et ses cas particuliers 限制帕斯卡定律及其特殊情况的更正

The quantitative methods for psychology Pub Date : 2022-03-01 DOI: 10.20982/tqmp.18.1.p128

L. Laurencelle

引用次数: 0