Person-oriented profiles of originality and fluency in divergent thinking responses

Researchers often use divergent thinking tasks to assess creative potential and find a positive inter-individual relation between fluency and originality. But are there different within-person patterns of originality and fluency? Study 1: undergraduates completed an alternate uses task and the NEO-FFI. Three profiles emerged: (1) low originality and fluency; (2) above average originality, moderate fluency; and (3) average originality, high fluency. Study 2: high school students completed a divergent thinking task and 10 facets of the IPIP NEO-PI. Four profiles emerged: (1) average originality, moderate fluency; (2) above average originality, high fluency; (3) low originality and fluency; and (4) high originality, low fluency. Profile differences in personality and maximum originality, and implications of these findings are discussed.     

认知控制在发散性思维中的作用

探讨认知控制在发散性思维中的作用是当前创造力研究领域的热点问题。目前一致认为认知控制包括工作记忆、抑制控制和认知灵活性三个核心成分。工作记忆对个体在发散性思维中维持任务目标、提取和操作信息发挥重要作用; 抑制控制, 包括优势反应抑制、分心干扰抑制和对潜在相关信息的低抑制, 分别压制对常规观点的提取、确保对内在注意状态的维持和提供更多观念的组合, 进而促进个体的发散性思维表现。此外, 流体智力作为一种高级认知控制能力, 可通过增强策略使用的灵活性从而利于发散性思维。近来神经科学研究表明, 发散性思维不同阶段需要执行控制网络和默认网络的动态协同加工。未来研究应澄清认知控制各子成分间的关系, 探讨认知控制影响发散性思维是否受到其他因素(如动机和情绪)的调节, 以及探索执行控制网络和默认网络的动态协作是否受到任务要求和个体差异(如人格和智力)的影响。     

Asymptotic identifiability of nonparametric item response models

The identifiability of item response models with nonparametrically specified item characteristic curves is considered. Strict identifiability is achieved, with a fixed latent trait distribution, when only a single set of item characteristic curves can possibly generate the manifest distribution of the item responses. When item characteristic curves belong to a very general class, this property cannot be achieved. However, for assessments with many items, it is shown that all models for the manifest distribution have item characteristic curves that are very near one another and pointwise differences between them converge to zero at all values of the latent trait as the number of items increases. An upper bound for the rate at which this convergence takes place is given. The main result provides theoretical support to the practice of nonparametric item response modeling, by showing that models for long assessments have the property of asymptotic identifiability. The research was partially supported by the National Institute of Health grant R01 CA81068-01.     

Improving precision of ability estimation: Getting more from response times

By considering information about response time (RT) in addition to response accuracy (RA), joint models for RA and RT such as the hierarchical model (van der Linden, 2007) can improve the precision with which ability is estimated over models that only consider RA. The hierarchical model, however, assumes that only the person's speed is informative of ability. This assumption of conditional independence between RT and ability given speed may be violated in practice, and ignores collateral information about ability that may be present in the residual RTs. We propose a posterior predictive check for evaluating the assumption of conditional independence between RT and ability given speed. Furthermore, we propose an extension of the hierarchical model that contains cross-loadings between ability and RT, which enables one to take additional collateral information about ability into account beyond what is possible in the standard hierarchical model. A Bayesian estimation procedure is proposed for the model. Using simulation studies, the performance of the model is evaluated in terms of parameter recovery, and the possible gain in precision over the standard hierarchical model and an RA-only model is considered. The model is applied to data from a high-stakes educational test.     

发散思维的序列位置效应：创新想法动态产生机制的新视角

发散思维的序列位置效应是指随着时间的推移,单位时间内想法生成的数量逐渐降低,但想法的独创性逐渐升高的现象。联想理论和执行控制假说分别侧重对序列位置效应的个体差异和认知加工过程进行解释。神经影像学研究表明,在想法产生的整个阶段序列位置效应依赖于默认网络后部和突显网络的持续增强激活和协同合作;在想法产生的后期阶段,颞-顶区域alpha能量值以及执行控制网络与默认网络的协同模式增强。未来研究需要结合语义和神经动态分析等技术,对序列位置效应的特异性机制、动态加工机制以及影响因素进行深入探究,从而为解构创新思维的动态过程提供新的视角。     

Reliability of test scores in nonparametric item response theory

Three methods for estimating reliability are studied within the context of nonparametric item response theory. Two were proposed originally by Mokken (1971) and a third is developed in this paper. Using a Monte Carlo strategy, these three estimation methods are compared with four classical lower bounds to reliability. Finally, recommendations are given concerning the use of these estimation methods.The authors are grateful for constructive comments from the reviewers and from Charles Lewis.     

Divergent thinking in the grasslands: thinking about object function in the context of a grassland survival scenario elicits more alternate uses than control scenarios

The survival processing effect is a recall advantage for information processed in the context of a grassland survival scenario. The current studies build upon previous research suggesting the effect is due to elaborative encoding and functional thinking. In two experiments, participants completed the “alternate uses test” under five conditions: baseline, grassland survival, Ebola survival, moving to a new home and planning a bank heist. Experiment 1 stimuli were everyday objects. Experiment 2 stimuli were functionally ambiguous “mystery” objects. Number of generated uses was highest in the baseline, but the grassland scenario was consistently highest of the schematic conditions. Recall data lend support to the mnemonic superiority of the grassland condition. Results suggest that grassland scenarios place fewer attenuating constraints on divergent thinking. It is suggested that the survival processing effect might be usefully conceptualised as an effect of creatively thinking about object function in response to broadly defined problems.     

Combining mixture distribution and multidimensional IRTree models for the measurement of extreme response styles

Personality constructs, attitudes and other non-cognitive variables are often measured using rating or Likert-type scales, which does not come without problems. Especially in low-stakes assessments, respondents may produce biased responses due to response styles (RS) that reduce the validity and comparability of the measurement. Detecting and correcting RS is not always straightforward because not all respondents show RS and the ones who do may not do so to the same extent or in the same direction. The present study proposes the combination of a multidimensional IRTree model with a mixture distribution item response theory model and illustrates the application of the approach using data from the Programme for the International Assessment of Adult Competencies (PIAAC). This joint approach allows for the differentiation between different latent classes of respondents who show different RS behaviours and respondents who show RS versus respondents who give (largely) unbiased responses. We illustrate the application of the approach by examining extreme RS and show how the resulting latent classes can be further examined using external variables and process data from computer-based assessments to develop a better understanding of response behaviour and RS.     

Estimating the imputed social cost of errors of measurement

If a loss function is available specifying the social cost of an error of measurement in the score on a unidimensional test, an asymptotic method, based on item response theory, is developed for optimal test design for a specified target population of examinees. Since in the real world such loss functions are not available, it is more useful to reverse this process; thus a method is developed for finding the loss function for which a given test is an optimally designed test for the target population. An illustrative application is presented for one operational test.This work was supported in part by contract N00014-80-C-0402, project designation NR 150-453 between the Office of Naval Research and Educational Testing Service. Reproduction in whole or in part is permitted for any purpose of the United States Government.     

Enhancing measurement validity in diverse populations: Modern approaches to evaluating differential item functioning

When developing and evaluating psychometric measures, a key concern is to ensure that they accurately capture individual differences on the intended construct across the entire population of interest. Inaccurate assessments of individual differences can occur when responses to some items reflect not only the intended construct but also construct-irrelevant characteristics, like a person's race or sex. Unaccounted for, this item bias can lead to apparent differences on the scores that do not reflect true differences, invalidating comparisons between people with different backgrounds. Accordingly, empirically identifying which items manifest bias through the evaluation of differential item functioning (DIF) has been a longstanding focus of much psychometric research. The majority of this work has focused on evaluating DIF across two (or a few) groups. Modern conceptualizations of identity, however, emphasize its multi-determined and intersectional nature, with some aspects better represented as dimensional than categorical. Fortunately, many model-based approaches to modelling DIF now exist that allow for simultaneous evaluation of multiple background variables, including both continuous and categorical variables, and potential interactions among background variables. This paper provides a comparative, integrative review of these new approaches to modelling DIF and clarifies both the opportunities and challenges associated with their application in psychometric research.     

A semi-parametric within-subject mixture approach to the analyses of responses and response times

In item response theory, modelling the item response times in addition to the item responses may improve the detection of possible between- and within-subject differences in the process that resulted in the responses. For instance, if respondents rely on rapid guessing on some items but not on all, the joint distribution of the responses and response times will be a multivariate within-subject mixture distribution. Suitable parametric methods to detect these within-subject differences have been proposed. In these approaches, a distribution needs to be assumed for the within-class response times. In this paper, it is demonstrated that these parametric within-subject approaches may produce false positives and biased parameter estimates if the assumption concerning the response time distribution is violated. A semi-parametric approach is proposed which resorts to categorized response times. This approach is shown to hardly produce false positives and parameter bias. In addition, the semi-parametric approach results in approximately the same power as the parametric approach.     

多维对数正态作答时间模型：对潜在加工速度多维性的探究

在心理与教育测量中,潜在加工速度反映学生运用潜在能力解决问题的效率。为在多维测验中探究潜在加工速度的多维性并实现参数估计,本研究提出多维对数正态作答时间模型。实证数据分析及模拟研究结果表明:(1)潜在加工速度具有与潜在能力相匹配的多维结构;(2)新模型可精确估计个体水平的多维潜在加工速度及与作答时间有关的题目参数;(3)冗余指定潜在加工速度具有多维性带来的负面影响低于忽略其多维性所带来的。     

A Comparison of ML,WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study

Multilevel structural equation models are increasingly applied in psychological research. With increasing model complexity, estimation becomes computationally demanding, and small sample sizes pose further challenges on estimation methods relying on asymptotic theory. Recent developments of Bayesian estimation techniques may help to overcome the shortcomings of classical estimation techniques. The use of potentially inaccurate prior information may, however, have detrimental effects, especially in small samples. The present Monte Carlo simulation study compares the statistical performance of classical estimation techniques with Bayesian estimation using different prior specifications for a two-level SEM with either continuous or ordinal indicators. Using two software programs (Mplus and Stan), differential effects of between- and within-level sample sizes on estimation accuracy were investigated. Moreover, it was tested to which extent inaccurate priors may have detrimental effects on parameter estimates in categorical indicator models. For continuous indicators, Bayesian estimation did not show performance advantages over ML. For categorical indicators, Bayesian estimation outperformed WLSMV solely in case of strongly informative accurate priors. Weakly informative inaccurate priors did not deteriorate performance of the Bayesian approach, while strong informative inaccurate priors led to severely biased estimates even with large sample sizes. With diffuse priors, Stan yielded better results than Mplus in terms of parameter estimates.     

Increasing item complexity: A possible cause of scale shrinkage for unidimensional item response theory

When the three-parameter logistic model is applied to tests covering a broad range of difficulty, there frequently is an increase in mean item discrimination and a decrease in variance of item difficulties and traits as the tests become more difficult. To examine the hypothesis that this unexpected scale shrinkage effect occurs because the items increase in complexity as they increase in difficulty, an approximate relationship is derived between the unidimensional model used in data analysis and a multidimensional model hypothesized to be generating the item responses. Scale shrinkage is successfully predicted for several sets of simulated data.The author is grateful to Robert Mislevy for kindly providing a copy of his computer program, RESOLVE.     

The unique correspondence of the item response function and item category response functions in polytomously scored item response models

The item response function (IRF) for a polytomously scored item is defined as a weighted sum of the item category response functions (ICRF, the probability of getting a particular score for a randomly sampled examinee of ability ). This paper establishes the correspondence between an IRF and a unique set of ICRFs for two of the most commonly used polytomous IRT models (the partial credit models and the graded response model). Specifically, a proof of the following assertion is provided for these models: If two items have the same IRF, then they must have the same number of categories; moreover, they must consist of the same ICRFs. As a corollary, for the Rasch dichotomous model, if two tests have the same test characteristic function (TCF), then they must have the same number of items. Moreover, for each item in one of the tests, an item in the other test with an identical IRF must exist. Theoretical as well as practical implications of these results are discussed.This research was supported by Educational Testing Service Allocation Projects No. 79409 and No. 79413. The authors wish to thank John Donoghue, Ming-Mei Wang, Rebecca Zwick, and Zhiliang Ying for their useful comments and discussions. The authors also wish to thank three anonymous reviewers for their comments.     

An accumulator model for responses and response times in tests based on the proportional hazards model

Latent trait models for responses and response times in tests often lack a substantial interpretation in terms of a cognitive process model. This is a drawback because process models are helpful in clarifying the meaning of the latent traits. In the present paper, a new model for responses and response times in tests is presented. The model is based on the proportional hazards model for competing risks. Two processes are assumed, one reflecting the increase in knowledge and the second the tendency to discontinue. The processes can be characterized by two proportional hazards models whose baseline hazard functions correspond to the temporary increase in knowledge and discouragement. The model can be calibrated with marginal maximum likelihood estimation and an application of the ECM algorithm. Two tests of model fit are proposed. The amenability of the proposed approaches to model calibration and model evaluation is demonstrated in a simulation study. Finally, the model is used for the analysis of two empirical data sets.