What determines creative association? Revealing two factors which separately influence the creative process when solving the Remote Associates Test

The Remote Associates Test is a well‐established measure, frequently used to assess individuals’ creative abilities, as a function of the ability to elicit remote associates. The nature of the involved associative processes is still poorly understood. This hampers a deeper understanding of the creative process, rendering it difficult to determine what factors are controlled for, when the RAT is employed. We report an experiment that sheds further light on the nature of the associative process by manipulating (a) the frequency with which a pair of items are associated as associative strength, and (b) the probability of reaching the answer given the strength and the spread. Experimental results indicate a clear and surprisingly separate influence of frequency and probability on accuracy and response times. Frequency and probability both are thus factors that need to be included in the modeling process and controlled for when using the RAT to assess creativity.     

Applying mixed‐effects modeling to single‐subject designs: An introduction

Behavior analysis and statistical inference have shared a conflicted relationship for over fifty years. However, a significant portion of this conflict is directed toward statistical tests (e.g., t‐tests, ANOVA) that aggregate group and/or temporal variability into means and standard deviations and as a result remove much of the data important to behavior analysts. Mixed‐effects modeling, a more recently developed statistical test, addresses many of the limitations of more basic tests by incorporating random effects. Random effects quantify individual subject variability without eliminating it from the model, hence producing a model that can predict both group and individual behavior. We present the results of a generalized linear mixed‐effects model applied to single‐subject data taken from Ackerlund Brandt, Dozier, Juanico, Laudont, & Mick, 2015, in which children chose from one of three reinforcers for completing a task. Results of the mixed‐effects modeling are consistent with visual analyses and importantly provide a statistical framework to predict individual behavior without requiring aggregation. We conclude by discussing the implications of these results and provide recommendations for further integration of mixed‐effects models in the analyses of single‐subject designs.     

On Standardizing Within-Person Effects: Potential Problems of Global Standardization

Person-mean centering has been recommended for disaggregating between-person and within-person effects when modeling time-varying predictors. Multilevel modeling textbooks recommended global standardization for standardizing fixed effects. An aim of this study is to evaluate whether and when person-mean centering followed by global standardization can accurately estimate fixed-effects within-person relations (the estimand of interest in this study) in multilevel modeling. We analytically derived that global standardization generally yields inconsistent (asymptotically biased) estimates for the estimand when between-person differences in within-person standard deviations exist and the average within-person relation is nonzero. Alternatively, a person-mean-SD standardization (P-S) approach yields consistent estimates. Our simulation results further revealed (1) how misleading the results from global standardization were under various circumstances and (2) the P-S approach had accurate estimates and satisfactory coverage rates of fixed-effects within-person relations when the number of occasions is 30 or more (in many conditions, performance was satisfactory with 10 or 20 occasions). A daily diary data example, focused on emotional complexity, was used to empirically illustrate the approaches. Researchers should choose standardization approaches based on theoretical considerations and should clearly describe the purpose and procedure of standardization in research articles.     

Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement

Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty – distinct from both random error (but encompassing it) and systematic error – is a crucial characteristic of any measurement. This research argues that factor indeterminacy is uncertainty. Factor indeterminacy fundamentally threatens the validity of psychometric measurement, because it blurs the linkage between a common factor and the conceptual variable that the factor represents. Acknowledging and quantifying factor indeterminacy is important for progress in reducing this component of uncertainty in measurement, and thus improving psychological measurement over time. Based on our elaborations, we offer a range of recommendations toward achieving this goal.     

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling

Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.     

Student Explanations in the Context of Computational Science and Engineering Education

Creating explanations is an important process for students, not only to make connections between novel information and background knowledge, but also to be able to communicate their understanding of any given topic. This article explores students’ explanations in the context of computational science and engineering, an important interdisciplinary field that enables scientists and engineers to solve complex problems. Specifically, this study explores: (a) students’ approaches to create written explanations of programing code and (b) the relationship between students’ explanations and their ability to do computer programing. Students wrote in-code comments for 3 MATLAB^® worked-examples, which were qualitatively analyzed using a coding scheme. Different approaches to self-explain were identified using hierarchical cluster analysis, and differences in students’ ability to do computer programing were identified using analysis of variance. The resulting approaches to self-explain were: original solution, mechanistic, principle-based, limited, and goal-based. The findings suggest that experienced students wrote simple in-code comments to self-explain, but students with lower ability to do computer programing wrote more comprehensive explanations, as they may take this as a learning opportunity.     

If it is easy to remember,then it is not secure: Metacognitive beliefs affect password selection

In this research, we applied current theories of metacognition to study computer security and tested the idea that users' password selection is affected by the metacognitive belief that if a password is memorable, then it is not secure. In two experiments, different types of eight‐character passwords and longer, more secure sentences were presented. Participants rated perceived memorability and perceived security of the passwords and indicated whether they would use them in a critical and in a noncritical service. The results confirmed the belief. Sentences that are in fact highly secure and perceived as highly memorable were also perceived as weak passwords. The belief strongly affected password selection for critical services, but it had no effect on noncritical services. In sum, long sentences are a particularly interesting type of password because they meet both security and memorability criteria, but their use is limited by a false belief.     

小学低年级儿童阅读流畅性的发展轨迹及其对阅读理解的预测作用

对149名小学一年级儿童的阅读流畅性进行历时三年五次的追踪测试,采用潜变量增长模型探索了儿童阅读流畅性的发展轨迹,并在控制相关变量后,考察了阅读流畅性的起始水平和发展速度对阅读理解的预测作用。结果发现：（1）小学低年级儿童字词阅读流畅性呈非线性发展,其中一年级快速发展,二、三年级时进一步发展,但发展速度变缓,起始水平低的儿童其后发展速度快,表现出补偿模式;句子阅读流畅性呈线性发展,儿童个体之间的差异随时间逐渐增大,表现出马太效应;（2）控制一般认知能力、家庭社会经济地位及相关语言认知技能后,字词阅读流畅性的起始水平和发展速度均可预测儿童三年级时阅读理解水平,而句子阅读流畅性的起始水平不能预测,但发展速度有显著预测作用。结果说明字词阅读流畅性和句子阅读流畅性有不同的发展轨迹和发展模式,在小学低年级阶段,相比句子阅读流畅性,儿童字词阅读流畅性的起始水平对阅读理解有预测作用,且两者的发展速度均对阅读理解有预测作用。     

小学低年级儿童口语词汇知识的发展轨迹及其影响因素

以149名小学生为研究对象, 对其口语词汇知识进行了历时3年的5次追踪测试, 采用潜变量增长模型探索了小学1~3年级学生汉语口语词汇知识的发展轨迹, 并考察了语音意识、同形语素意识、复合语素意识和家庭社会经济地位对口语词汇知识发展轨迹的影响。结果发现：(1) 1~3年级学生口语词汇知识发展轨迹呈曲线形式, 其中前两年呈线性发展, 三年级时呈加速发展, 发展速度是前期发展的两倍, 起始水平和发展速度均存在显著的个体差异; (2)语音意识、同形语素意识、复合语素意识和家庭社会经济地位均可以正向预测学生口语词汇知识的起始水平; (3)只有同形语素意识和家庭社会经济地位可以正向预测学生口语词汇知识的发展速度。     

场景知觉过程中的动作意图识别

场景即我们生活于其中的真实环境, 社会场景是其重要组成部分。在社会场景知觉的研究中, 动作意图的识别既受场景背景信息的影响, 也与动作的客观对象有关。因此, 研究者可以根据背景-刺激物、刺激物-刺激物关系, 探索动作识别的影响机制; 另一方面, 也可以根据场景的语义约束和物理限制, 依据合理动作原则及其伴随的生理指标检测并识别动作意图。在机器视觉研究领域, 计算机识别模型为社会场景中动作意图的检测和识别提供了新的视角。在未来的研究中, 研究者需要考虑真实场景中动作意图识别能力的发展、动作意图识别的个体差异和文化差异等问题。