Psychometrika最新文献_第9页

Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach. 随机效应多叉处理树模型：最大似然法。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-05-29 DOI: 10.1007/s11336-023-09921-w

Steffen Nestler, Edgar Erdfelder

{"title":"Random Effects Multinomial Processing Tree Models: A Maximum Likelihood Approach.","authors":"Steffen Nestler, Edgar Erdfelder","doi":"10.1007/s11336-023-09921-w","DOIUrl":"10.1007/s11336-023-09921-w","url":null,"abstract":"The present article proposes and evaluates marginal maximum likelihood (ML) estimation methods for hierarchical multinomial processing tree (MPT) models with random and fixed effects. We assume that an identifiable MPT model with S parameters holds for each participant. Of these S parameters, R parameters are assumed to vary randomly between participants, and the remaining [Formula: see text] parameters are assumed to be fixed. We also propose an extended version of the model that includes effects of covariates on MPT model parameters. Because the likelihood functions of both versions of the model are too complex to be tractable, we propose three numerical methods to approximate the integrals that occur in the likelihood function, namely, the Laplace approximation (LA), adaptive Gauss-Hermite quadrature (AGHQ), and Quasi Monte Carlo (QMC) integration. We compare these three methods in a simulation study and show that AGHQ performs well in terms of both bias and coverage rate. QMC also performs well but the number of responses per participant must be sufficiently large. In contrast, LA fails quite often due to undefined standard errors. We also suggest ML-based methods to test the goodness of fit and to compare models taking model complexity into account. The article closes with an illustrative empirical application and an outlook on possible extensions and future applications of the proposed ML approach.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"809-829"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10644937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Factor Tree Copula Models for Item Response Data. 项目响应数据的因子树 Copula 模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-01 DOI: 10.1007/s11336-023-09917-6

Sayed H Kadhem, Aristidis K Nikoloulopoulos

{"title":"Factor Tree Copula Models for Item Response Data.","authors":"Sayed H Kadhem, Aristidis K Nikoloulopoulos","doi":"10.1007/s11336-023-09917-6","DOIUrl":"10.1007/s11336-023-09917-6","url":null,"abstract":"Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combined model, the so-called factor tree copula model, with individual benefits from each of the two approaches. Rather than adding factors and causing computational problems and difficulties in interpretation and identification, a truncated vine structure is assumed on the residuals conditional on one or two latent variables. This structure can be better explained as a conditional dependence given a few interpretable latent variables. On the one hand, the parsimonious feature of factor models remains intact and any residual dependencies are being taken into account on the other. We discuss estimation along with model selection. In particular, we propose model selection algorithms to choose a plausible factor tree copula model to capture the (residual) dependencies among the item responses. Our general methodology is demonstrated with an extensive simulation study and illustrated by analyzing Post-Traumatic Stress Disorder.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"776-802"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10644945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology. 比较贝叶斯变量选择法和拉索法在心理学中的应用。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-05-23 DOI: 10.1007/s11336-023-09914-9

Sierra A Bainter, Thomas G McCauley, Mahmoud M Fahmy, Zachary T Goodman, Lauren B Kupis, J Sunil Rao

{"title":"Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.","authors":"Sierra A Bainter, Thomas G McCauley, Mahmoud M Fahmy, Zachary T Goodman, Lauren B Kupis, J Sunil Rao","doi":"10.1007/s11336-023-09914-9","DOIUrl":"10.1007/s11336-023-09914-9","url":null,"abstract":"In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"1032-1055"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10278469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating Functional Response Time Effects into a Signal Detection Theory Model. 将功能响应时间效应纳入信号检测理论模型。

IF 3 2区心理学

Psychometrika Pub Date : 2023-09-01 DOI: 10.1007/s11336-023-09906-9

Sun-Joo Cho, Sarah Brown-Schmidt, Paul De Boeck, Matthew Naveiras, Si On Yoon, Aaron Benjamin

{"title":"Incorporating Functional Response Time Effects into a Signal Detection Theory Model.","authors":"Sun-Joo Cho, Sarah Brown-Schmidt, Paul De Boeck, Matthew Naveiras, Si On Yoon, Aaron Benjamin","doi":"10.1007/s11336-023-09906-9","DOIUrl":"https://doi.org/10.1007/s11336-023-09906-9","url":null,"abstract":"Signal detection theory (SDT; Tanner & Swets in Psychological Review 61:401-409, 1954) is a dominant modeling framework used for evaluating the accuracy of diagnostic systems that seek to distinguish signal from noise in psychology. Although the use of response time data in psychometric models has increased in recent years, the incorporation of response time data into SDT models remains a relatively underexplored approach to distinguishing signal from noise. Functional response time effects are hypothesized in SDT models, based on findings from other related psychometric models with response time data. In this study, an SDT model is extended to incorporate functional response time effects using smooth functions and to include all sources of variability in SDT model parameters across trials, participants, and items in the experimental data. The extended SDT model with smooth functions is formulated as a generalized linear mixed-effects model and implemented in the gamm4 R package. The extended model is illustrated using recognition memory data to understand how conversational language is remembered. Accuracy of parameter estimates and the importance of modeling variability in detecting the experimental condition effects and functional response time effects are shown in conditions similar to the empirical data set via a simulation study. In addition, the type 1 error rate of the test for a smooth function of response time is evaluated.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"1056-1086"},"PeriodicalIF":3.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10281168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rejoinder to Commentaries on Lyu, Bolt and Westby's "Exploring the Effects of Item Specific Factors in Sequential and IRTree Models". 对 Lyu、Bolt 和 Westby 的 "探索顺序模型和 IRTree 模型中项目特定因素的影响 "评论的反驳。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-16 DOI: 10.1007/s11336-023-09913-w

Weicong Lyu, Daniel M Bolt

引用次数: 0

Exploring the Effects of Item-Specific Factors in Sequential and IRTree Models. 探索顺序模型和 IRTree 模型中特定项目因素的影响。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-16 DOI: 10.1007/s11336-023-09912-x

Weicong Lyu, Daniel M Bolt, Samuel Westby

{"title":"Exploring the Effects of Item-Specific Factors in Sequential and IRTree Models.","authors":"Weicong Lyu, Daniel M Bolt, Samuel Westby","doi":"10.1007/s11336-023-09912-x","DOIUrl":"10.1007/s11336-023-09912-x","url":null,"abstract":"Test items for which the item score reflects a sequential or IRTree modeling outcome are considered. For such items, we argue that item-specific factors, although not empirically measurable, will often be present across stages of the same item. In this paper, we present a conceptual model that incorporates such factors. We use the model to demonstrate how the varying conditional distributions of item-specific factors across stages become absorbed into the stage-specific item discrimination and difficulty parameters, creating ambiguity in the interpretations of item and person parameters beyond the first stage. We discuss implications in relation to various applications considered in the literature, including methodological studies of (1) repeated attempt items; (2) answer change/review, (3) on-demand item hints; (4) item skipping behavior; and (5) Likert scale items. Our own empirical applications, as well as several examples published in the literature, show patterns of violations of item parameter invariance across stages that are highly suggestive of item-specific factors. For applications using sequential or IRTree models as analytical models, or for which the resulting item score might be viewed as outcomes of such a process, we recommend (1) regular inspection of data or analytic results for empirical evidence (or theoretical expectations) of item-specific factors; and (2) sensitivity analyses to evaluate the implications of item-specific factors for the intended inferences or applications.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"745-775"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10279931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Item-Specific Factors in IRTree Models: When They Matter and When They Don't. IRTree 模型中的项目特定因素：何时重要，何时不重要？

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-16 DOI: 10.1007/s11336-023-09916-7

Thorsten Meiser, Fabiola Reiber

引用次数: 0

A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times. 用潜空间扩散项目反应理论模型探讨反应与反应时间之间的条件依赖关系。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-14 DOI: 10.1007/s11336-023-09920-x

Inhan Kang, Minjeong Jeon, Ivailo Partchev

{"title":"A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times.","authors":"Inhan Kang, Minjeong Jeon, Ivailo Partchev","doi":"10.1007/s11336-023-09920-x","DOIUrl":"10.1007/s11336-023-09920-x","url":null,"abstract":"Traditional measurement models assume that all item responses correlate with each other only through their underlying latent variables. This conditional independence assumption has been extended in joint models of responses and response times (RTs), implying that an item has the same item characteristics fors all respondents regardless of levels of latent ability/trait and speed. However, previous studies have shown that this assumption is violated in various types of tests and questionnaires and there are substantial interactions between respondents and items that cannot be captured by person- and item-effect parameters in psychometric models with the conditional independence assumption. To study the existence and potential cognitive sources of conditional dependence and utilize it to extract diagnostic information for respondents and items, we propose a diffusion item response theory model integrated with the latent space of variations in information processing rate of within-individual measurement processes. Respondents and items are mapped onto the latent space, and their distances represent conditional dependence and unexplained interactions. We provide three empirical applications to illustrate (1) how to use an estimated latent space to inform conditional dependence and its relation to person and item measures, (2) how to derive diagnostic feedback personalized for respondents, and (3) how to validate estimated results with an external measure. We also provide a simulation study to support that the proposed approach can accurately recover its parameters and detect conditional dependence underlying data.","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"830-864"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10332140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses. Dirichlet 双响应模型：连续有界区间反应的项目反应模型。

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-07-20 DOI: 10.1007/s11336-023-09924-7

Matthias Kloft, Raphael Hartmann, Andreas Voss, Daniel W Heck

{"title":"The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses.","authors":"Matthias Kloft, Raphael Hartmann, Andreas Voss, Daniel W Heck","doi":"10.1007/s11336-023-09924-7","DOIUrl":"10.1007/s11336-023-09924-7","url":null,"abstract":"Standard response formats such as rating or visual analogue scales require respondents to condense distributions of latent states or behaviors into a single value. Whereas this is suitable to measure central tendency, it neglects the variance of distributions. As a remedy, variability may be measured using interval-response formats, more specifically the dual-range slider (RS2). Given the lack of an appropriate item response model for the RS2, we develop the Dirichlet dual response model (DDRM), an extension of the beta response model (BRM; Noel & Dauvier in Appl Psychol Meas, 31:47-73, 2007). We evaluate the DDRM's performance by assessing parameter recovery in a simulation study. Results indicate overall good parameter recovery, although parameters concerning interval width (which reflect variability in behavior or states) perform worse than parameters concerning central tendency. We also test the model empirically by jointly fitting the BRM and the DDRM to single-range slider (RS1) and RS2 responses for two Extraversion scales. While the DDRM has an acceptable fit, it shows some misfit regarding the RS2 interval widths. Nonetheless, the model indicates substantial differences between respondents concerning variability in behavior. High correlations between person parameters of the BRM and DDRM suggest convergent validity between the RS1 and the RS2 interval location. Both the simulation and the empirical study demonstrate that the latent parameter space of the DDRM addresses an important issue of the RS2 response format, namely, the scale-inherent interdependence of interval location and interval width (i.e., intervals at the boundaries are necessarily smaller).","PeriodicalId":54534,"journal":{"name":"Psychometrika","volume":"88 3","pages":"888-916"},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10444675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10628954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fitting and Testing Log-Linear Subpopulation Models with Known Support. 利用已知支持度拟合和测试对数线性子群模型

IF 2.9 2区心理学

Psychometrika Pub Date : 2023-09-01 Epub Date: 2023-06-14 DOI: 10.1007/s11336-023-09922-9

David J Hessen

引用次数: 0