The British journal of mathematical and statistical psychology最新文献_第2页

Confidence interval-based sample size determination formulas and some mathematical properties for hierarchical data. 基于置信区间的样本量确定公式和分层数据的一些数学特性。

The British journal of mathematical and statistical psychology Pub Date : 2020-11-01 DOI: 10.1111/bmsp.12181

S. Usami

{"title":"Confidence interval-based sample size determination formulas and some mathematical properties for hierarchical data.","authors":"S. Usami","doi":"10.1111/bmsp.12181","DOIUrl":"https://doi.org/10.1111/bmsp.12181","url":null,"abstract":"The use of hierarchical data (also called multilevel data or clustered data) is common in behavioural and psychological research when data of lower-level units (e.g., students, clients, repeated measures) are nested within clusters or higher-level units (e.g., classes, hospitals, individuals). Over the past 25 years we have seen great advances in methods for computing the sample sizes needed to obtain the desired statistical properties for such data in experimental evaluations. The present research provides closed-form and iterative formulas for sample size determination that can be used to ensure the desired width of confidence intervals for hierarchical data. Formulas are provided for a four-level hierarchical linear model that assumes slope variances and inclusion of covariates under both balanced and unbalanced designs. In addition, we address several mathematical properties relating to sample size determination for hierarchical data via the standard errors of experimental effect estimates. These include the relative impact of several indices (e.g., random intercept or slope variance at each level) on standard errors, asymptotic standard errors, minimum required values at the highest level, and generalized expressions of standard errors for designs with any-level randomization under any number of levels. In particular, information on the minimum required values will help researchers to minimize the risk of conducting experiments that are statistically unlikely to show the presence of an experimental effect.","PeriodicalId":272649,"journal":{"name":"The British journal of mathematical and statistical psychology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128607599","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

Score‐based measurement invariance checks for Bayesian maximum‐a‐posteriori estimates in item response theory 项目反应理论中贝叶斯最大后验估计的基于分数的测量不变性检验

The British journal of mathematical and statistical psychology Pub Date : 2020-04-08 DOI: 10.31234/osf.io/24a9g

Rudolf Debelak, Samuel Pawel, C. Strobl, E. Merkle

引用次数: 0

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response. 基于猜测和项目层面无反应的考生参与推理的层次潜反应模型。

The British journal of mathematical and statistical psychology Pub Date : 2019-11-10 DOI: 10.1111/bmsp.12188

Esther Ulitzsch, Matthias von Davier, S. Pohl

{"title":"A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response.","authors":"Esther Ulitzsch, Matthias von Davier, S. Pohl","doi":"10.1111/bmsp.12188","DOIUrl":"https://doi.org/10.1111/bmsp.12188","url":null,"abstract":"In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.","PeriodicalId":272649,"journal":{"name":"The British journal of mathematical and statistical psychology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134349927","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: 47

Bayesian power equivalence in latent growth curve models 潜在增长曲线模型中的贝叶斯幂等价

The British journal of mathematical and statistical psychology Pub Date : 2019-11-05 DOI: 10.1111/bmsp.12193

A. Stefan, Timo von Oertzen

引用次数: 0

The use of item scores and response times to detect examinees who may have benefited from item preknowledge. 使用项目得分和反应时间来检测可能受益于项目预知的考生。

The British journal of mathematical and statistical psychology Pub Date : 2019-08-16 DOI: 10.1111/bmsp.12187

S. Sinharay, Matthew S. Johnson

引用次数: 24

Bayesian Gaussian distributional regression models for more efficient norm estimation 贝叶斯高斯分布回归模型更有效的范数估计

The British journal of mathematical and statistical psychology Pub Date : 2019-08-14 DOI: 10.31234/osf.io/7j8ym

Lieke Voncken, T. Kneib, C. Albers, Nikolaus Umlauf, M. Timmerman

{"title":"Bayesian Gaussian distributional regression models for more efficient norm estimation","authors":"Lieke Voncken, T. Kneib, C. Albers, Nikolaus Umlauf, M. Timmerman","doi":"10.31234/osf.io/7j8ym","DOIUrl":"https://doi.org/10.31234/osf.io/7j8ym","url":null,"abstract":"A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost‐efficient high‐quality norms. The method is illustrated using empirical normative data from the IDS‐2 intelligence test.","PeriodicalId":272649,"journal":{"name":"The British journal of mathematical and statistical psychology","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128170495","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: 5

A note on monotonicity of item response functions for ordered polytomous item response theory models. 有序多分项反应理论模型中项反应函数的单调性。

IF 2.6

The British journal of mathematical and statistical psychology Pub Date : 2018-11-01 Epub Date: 2018-03-08 DOI: 10.1111/bmsp.12131

Hyeon-Ah Kang, Ya-Hui Su, Hua-Hua Chang

{"title":"A note on monotonicity of item response functions for ordered polytomous item response theory models.","authors":"Hyeon-Ah Kang, Ya-Hui Su, Hua-Hua Chang","doi":"10.1111/bmsp.12131","DOIUrl":"https://doi.org/10.1111/bmsp.12131","url":null,"abstract":"<p><p>A monotone relationship between a true score (τ) and a latent trait level (θ) has been a key assumption for many psychometric applications. The monotonicity property in dichotomous response models is evident as a result of a transformation via a test characteristic curve. Monotonicity in polytomous models, in contrast, is not immediately obvious because item response functions are determined by a set of response category curves, which are conceivably non-monotonic in θ. The purpose of the present note is to demonstrate strict monotonicity in ordered polytomous item response models. Five models that are widely used in operational assessments are considered for proof: the generalized partial credit model (Muraki, 1992, Applied Psychological Measurement, 16, 159), the nominal model (Bock, 1972, Psychometrika, 37, 29), the partial credit model (Masters, 1982, Psychometrika, 47, 147), the rating scale model (Andrich, 1978, Psychometrika, 43, 561), and the graded response model (Samejima, 1972, A general model for free-response data (Psychometric Monograph no. 18). Psychometric Society, Richmond). The study asserts that the item response functions in these models strictly increase in θ and thus there exists strict monotonicity between τ and θ under certain specified conditions. This conclusion validates the practice of customarily using τ in place of θ in applied settings and provides theoretical grounds for one-to-one transformations between the two scales.</p>","PeriodicalId":272649,"journal":{"name":"The British journal of mathematical and statistical psychology","volume":"71 3","pages":"523-535"},"PeriodicalIF":2.6,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/bmsp.12131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35894261","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: 13

A penalized likelihood method for multi-group structural equation modelling. 多组结构方程建模的惩罚似然法。

IF 2.6

The British journal of mathematical and statistical psychology Pub Date : 2018-11-01 Epub Date: 2018-03-03 DOI: 10.1111/bmsp.12130

Po-Hsien Huang

引用次数: 42

Bias-corrected estimation of the Rudas-Clogg-Lindsay mixture index of fit. Rudas-Clogg-Lindsay混合拟合指数的偏差校正估计。

IF 2.6

The British journal of mathematical and statistical psychology Pub Date : 2018-11-01 Epub Date: 2017-09-12 DOI: 10.1111/bmsp.12118

Jenő Reiczigel, Márton Ispány, Gábor Tusnády, György Michaletzky, Marco Marozzi

引用次数: 1

Numerical approximation of the observed information matrix with Oakes' identity. 用奥克斯恒等式对观测到的信息矩阵进行数值逼近。