There has been tremendous progress in statistical software in the field of psychometrics in providing open-source solutions [...]
心理测量学领域的统计软件在提供开源解决方案方面取得了巨大的进步[…]
{"title":"Editorial for the Special Issue “Computational Aspects and Software in Psychometrics II”","authors":"Alexander Robitzsch","doi":"10.3390/psych5030065","DOIUrl":"https://doi.org/10.3390/psych5030065","url":null,"abstract":"There has been tremendous progress in statistical software in the field of psychometrics in providing open-source solutions [...]","PeriodicalId":93139,"journal":{"name":"Psych","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885910","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}
The Special Issue “Feature Papers in Psychometrics and Educational Measurement” (https://www [...]
《心理测量学与教育测量专题论文》特刊(https://www[…]
{"title":"Editorial to the Special Issue “Feature Papers in Psychometrics and Educational Measurement”","authors":"Alexander Robitzsch","doi":"10.3390/psych5030066","DOIUrl":"https://doi.org/10.3390/psych5030066","url":null,"abstract":"The Special Issue “Feature Papers in Psychometrics and Educational Measurement” (https://www [...]","PeriodicalId":93139,"journal":{"name":"Psych","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135886403","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}
In a planned missing design, the nonresponses occur according to the researcher’s will, with the goal of increasing data quality and avoiding overly extensive questionnaires. When adjusting a structural equation model to the data, there are different criteria to evaluate how the theoretical model fits the observed data, with the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI) and Tucker–Lewis index (TLI) being the most common. Here, I explore the effect of the nonresponses due to a specific planned missing design—the three-form design—on the mentioned fit indices when adjusting a structural equation model. A simulation study was conducted with correctly specified model and one model with misspecified correlation between factors. The CFI, TLI and SRMR indices are affected by the nonresponses, particularly with small samples, low factor loadings and numerous observed variables. The existence of nonresponses when considering misspecified models causes unacceptable values for all the four fit indexes under analysis, namely when a strong correlation between factors is considered. The results shown here were performed with the simsem package in R and the full information maximum-likelihood method was used for handling missing data during model fitting.
{"title":"Evaluating the Effect of Planned Missing Designs in Structural Equation Model Fit Measures","authors":"Paula C. R. Vicente","doi":"10.3390/psych5030064","DOIUrl":"https://doi.org/10.3390/psych5030064","url":null,"abstract":"In a planned missing design, the nonresponses occur according to the researcher’s will, with the goal of increasing data quality and avoiding overly extensive questionnaires. When adjusting a structural equation model to the data, there are different criteria to evaluate how the theoretical model fits the observed data, with the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI) and Tucker–Lewis index (TLI) being the most common. Here, I explore the effect of the nonresponses due to a specific planned missing design—the three-form design—on the mentioned fit indices when adjusting a structural equation model. A simulation study was conducted with correctly specified model and one model with misspecified correlation between factors. The CFI, TLI and SRMR indices are affected by the nonresponses, particularly with small samples, low factor loadings and numerous observed variables. The existence of nonresponses when considering misspecified models causes unacceptable values for all the four fit indexes under analysis, namely when a strong correlation between factors is considered. The results shown here were performed with the simsem package in R and the full information maximum-likelihood method was used for handling missing data during model fitting.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47196381","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}
A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s).
{"title":"A SAS Macro for Automated Stopping of Markov Chain Monte Carlo Estimation in Bayesian Modeling with PROC MCMC","authors":"Wolfgang Wagner, Martin Hecht, Steffen Zitzmann","doi":"10.3390/psych5030063","DOIUrl":"https://doi.org/10.3390/psych5030063","url":null,"abstract":"A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s).","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43030696","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}
A constituting feature of item response models is that item and person parameters share a latent scale and are therefore comparable. The Person–Item Map is a useful graphical tool to visualize the alignment of the two parameter sets. However, the “classical” variant has some shortcomings, which are overcome by the new RMX package (Rasch models—eXtended). The package provides the RMX::plotPIccc() function, which creates an extended version of the classical PI Map, termed “PIccc”. It juxtaposes the person parameter distribution to various item-related functions, like category and item characteristic curves and category, item, and test information curves. The function supports many item response models and processes the return objects of five major R packages for IRT analysis. It returns the used parameters in a unified form, thus allowing for their further processing.
{"title":"RMX/PIccc: An Extended Person–Item Map and a Unified IRT Output for eRm, psychotools, ltm, mirt, and TAM","authors":"Milica Kabic, Rainer W. Alexandrowicz","doi":"10.3390/psych5030062","DOIUrl":"https://doi.org/10.3390/psych5030062","url":null,"abstract":"A constituting feature of item response models is that item and person parameters share a latent scale and are therefore comparable. The Person–Item Map is a useful graphical tool to visualize the alignment of the two parameter sets. However, the “classical” variant has some shortcomings, which are overcome by the new RMX package (Rasch models—eXtended). The package provides the RMX::plotPIccc() function, which creates an extended version of the classical PI Map, termed “PIccc”. It juxtaposes the person parameter distribution to various item-related functions, like category and item characteristic curves and category, item, and test information curves. The function supports many item response models and processes the return objects of five major R packages for IRT analysis. It returns the used parameters in a unified form, thus allowing for their further processing.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41910973","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}
The typical state empathy research used perspective-taking instructions and examined the effect of instructions on empathy-related variables. Empathy-arousing processes were generally not measured. The effect of perspective-taking instructions has been questioned recently. Observers could imagine targets’ feelings without such instructions. This study evoked empathy in Japanese undergraduates (N = 157) without instructional procedure, and based on participants’ responses to questionnaires, measured individual differences between antecedent, process, and intrapersonal outcome variables of state empathy, referring to the organizational model and theories of empathy-arousing processes. The purpose of this study was to measure these variables, examine the causal relationship between them using path analysis, and clarify how empathy occurs. In this way, we could suggest through which processes and antecedent factors intrapersonal empathic outcomes are produced. It is probably the first attempt to clarify how empathy occurs using a social psychological study framework and questionnaire method. This research was originally conducted in 2011 based on two similar studies not published internationally, when only some of the variables were used in our analyses. Afterwards, we constructed another analysis method, reanalyzed the data in 2019 and further reanalyzed in 2023 to obtain the final version of the results. Limitations and scientific and practical implications were discussed.
{"title":"Measurement of Individual Differences in State Empathy and Examination of a Model in Japanese University Students","authors":"Maine Tobari, Atsushi Oshio","doi":"10.3390/psych5030061","DOIUrl":"https://doi.org/10.3390/psych5030061","url":null,"abstract":"The typical state empathy research used perspective-taking instructions and examined the effect of instructions on empathy-related variables. Empathy-arousing processes were generally not measured. The effect of perspective-taking instructions has been questioned recently. Observers could imagine targets’ feelings without such instructions. This study evoked empathy in Japanese undergraduates (N = 157) without instructional procedure, and based on participants’ responses to questionnaires, measured individual differences between antecedent, process, and intrapersonal outcome variables of state empathy, referring to the organizational model and theories of empathy-arousing processes. The purpose of this study was to measure these variables, examine the causal relationship between them using path analysis, and clarify how empathy occurs. In this way, we could suggest through which processes and antecedent factors intrapersonal empathic outcomes are produced. It is probably the first attempt to clarify how empathy occurs using a social psychological study framework and questionnaire method. This research was originally conducted in 2011 based on two similar studies not published internationally, when only some of the variables were used in our analyses. Afterwards, we constructed another analysis method, reanalyzed the data in 2019 and further reanalyzed in 2023 to obtain the final version of the results. Limitations and scientific and practical implications were discussed.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43836463","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}
A mantra often repeated in the introductory material to psychometrics and Item Response Theory (IRT) is that a Rasch model is a probabilistic version of a Guttman scale. The idea comes from the observation that a sigmoidal item response function provides a probabilistic version of the characteristic function that models an item response in the Guttman scale. It appears, however, more difficult to reconcile the assumption of local independence, which traditionally accompanies the Rasch model, with the item dependence existing in a Guttman scale. In recent work, an alternative probabilistic version of a Guttman scale was proposed, combining Knowledge Space Theory (KST) with IRT modeling, here referred to as KST-IRT. The present work has, therefore, a two-fold aim. Firstly, the estimation of the parameters involved in KST-IRT models is discussed. More in detail, two estimation methods based on the Expectation Maximization (EM) procedure are suggested, i.e., Marginal Maximum Likelihood (MML) and Gibbs sampling, and are compared on the basis of simulation studies. Secondly, for a Guttman scale, the estimates of the KST-IRT models are compared with those of the traditional combination of the Rasch model plus local independence under the interchange of the data generation processes. Results show that the KST-IRT approach might be more effective in capturing local dependence as it appears to be more robust under misspecification of the data generation process, but it comes with the price of an increased number of parameters.
{"title":"Parameter Estimation of KST-IRT Model under Local Dependence","authors":"Sangbeak Ye, A. Kelava, S. Noventa","doi":"10.3390/psych5030060","DOIUrl":"https://doi.org/10.3390/psych5030060","url":null,"abstract":"A mantra often repeated in the introductory material to psychometrics and Item Response Theory (IRT) is that a Rasch model is a probabilistic version of a Guttman scale. The idea comes from the observation that a sigmoidal item response function provides a probabilistic version of the characteristic function that models an item response in the Guttman scale. It appears, however, more difficult to reconcile the assumption of local independence, which traditionally accompanies the Rasch model, with the item dependence existing in a Guttman scale. In recent work, an alternative probabilistic version of a Guttman scale was proposed, combining Knowledge Space Theory (KST) with IRT modeling, here referred to as KST-IRT. The present work has, therefore, a two-fold aim. Firstly, the estimation of the parameters involved in KST-IRT models is discussed. More in detail, two estimation methods based on the Expectation Maximization (EM) procedure are suggested, i.e., Marginal Maximum Likelihood (MML) and Gibbs sampling, and are compared on the basis of simulation studies. Secondly, for a Guttman scale, the estimates of the KST-IRT models are compared with those of the traditional combination of the Rasch model plus local independence under the interchange of the data generation processes. Results show that the KST-IRT approach might be more effective in capturing local dependence as it appears to be more robust under misspecification of the data generation process, but it comes with the price of an increased number of parameters.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47688432","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}
Akiko Megumi, Jungpil Shin, Yuta Uchida, A. Yasumura
We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters.
{"title":"Increased Activity in the Prefrontal Cortex Related to Planning during a Handwriting Task","authors":"Akiko Megumi, Jungpil Shin, Yuta Uchida, A. Yasumura","doi":"10.3390/psych5030059","DOIUrl":"https://doi.org/10.3390/psych5030059","url":null,"abstract":"We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49379762","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}
The R packages Dire and EdSurvey allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. Dire is distinct from other available software in R in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with EdSurvey, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in Dire and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with Dire.
{"title":"Expanding NAEP and TIMSS Analysis to Include Additional Variables or a New Scoring Model Using the R Package Dire","authors":"P. Bailey, B. Webb","doi":"10.3390/psych5030058","DOIUrl":"https://doi.org/10.3390/psych5030058","url":null,"abstract":"The R packages Dire and EdSurvey allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. Dire is distinct from other available software in R in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with EdSurvey, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in Dire and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with Dire.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48646913","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}
The aim of the present study is to postulate the existence of psychological phenotypes associated with obesity, based on individual history. While metabolic phenotypes have been acknowledged in the field of medicine, the same cannot be affirmed in the realm of psychology. A longstanding tradition in obesity research has sought to identify shared characteristics among individuals affected by obesity, including personality traits. However, research found no adequate empirical evidence to support the existence of a specific psychological and psychopathological profile among individuals with obesity. Recent efforts in the literature have attempted to correlate these findings and ascertain which metabolic phenotype correlates with a diminished quality of life. We propose a novel differentiation between two categories: (1) individuals who affected by obesity since childhood, and (2) individuals who developed obesity following a life event. Further investigations are imperative to amass experimental data that substantiate this hypothesis. Proactively identifying psychological phenotypes is presumed to impact therapeutic outcomes.
{"title":"Obesity and Life History: The Hypothesis of Psychological Phenotypes","authors":"A. Rizzo, A. Sitibondo","doi":"10.3390/psych5030057","DOIUrl":"https://doi.org/10.3390/psych5030057","url":null,"abstract":"The aim of the present study is to postulate the existence of psychological phenotypes associated with obesity, based on individual history. While metabolic phenotypes have been acknowledged in the field of medicine, the same cannot be affirmed in the realm of psychology. A longstanding tradition in obesity research has sought to identify shared characteristics among individuals affected by obesity, including personality traits. However, research found no adequate empirical evidence to support the existence of a specific psychological and psychopathological profile among individuals with obesity. Recent efforts in the literature have attempted to correlate these findings and ascertain which metabolic phenotype correlates with a diminished quality of life. We propose a novel differentiation between two categories: (1) individuals who affected by obesity since childhood, and (2) individuals who developed obesity following a life event. Further investigations are imperative to amass experimental data that substantiate this hypothesis. Proactively identifying psychological phenotypes is presumed to impact therapeutic outcomes.","PeriodicalId":93139,"journal":{"name":"Psych","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46788457","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：