A Critique of Six Measures for Assessing Creativity

Tests and measures of creativity are critical instruments guiding educators in identifying highly productive individuals capable of contributing insightful and far reaching solutionsto ourproblem ridden civilization. This paperanalyzesand critiques six popularlyused measures ofcreativity: 1) The Torrance Test of Creative Thinking by E. Paul Torrance (1966); 2) The Creativity Assessment Packet by frank Williams (1980); three subtests of divergent production of the 3) Structure ofthe Intellect Learning Abilities Test by Mary Meeker; 4) Thinking Creatively With Sounds and Words by E. Paul Torrance, Joe Khatena and Bert F. Cunnington (1973); 5) Thinking Creatively In Action and Movement by E. Paul Torrance (1981); 6) Khatena-Torrance Creative Perception Inventory by Joe Khatena & E. Paul Torrance (1976). Reviewed through personal observation and published literature, these tests reveal a capacity as partial measures of divergent and productive thinking. Findings indicate that updating and upgrading of several tests with thought toward key constituent factors of the creative process would be qualitatively beneficial to test design. Harnessing creative potential is a main concern of educators hoping to prepare America's youth for a fast paced and rapidly changing existence. The questions at the heart of this concern: what creativity is, what constitutes creative thinking, and what the creative individual is like, have given birth to many tests and biographical checklists. Understanding the dimensions ofcreativity is difficult at best, and assessing it through tests or biographical checklists is even more problematic. Through direct examination and a review ofthe literature, this paper critiques six such measures for validity, reliability and utility. Those measures included: the Creativity Assessment Packet (CAP), the Torrance Test of Creative Thinking (TICT), Thinking Creatively in Action and Movement, Thinking Creatively with Sound and Words, Khatena-Torrance Creative Perception Inventory, and three divergent thinking subtests ofthe Structure of Intellect Learning Abilities Test (SOl-LA).     

Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical Ordinal Item Response Data

When categorical ordinal item response data are collected over multiple timepoints from a repeated measures design, an item response theory (IRT) modeling approach whose unit of analysis is an item response is suitable. This study proposes a few longitudinal IRT models and illustrates how a popular compensatory multidimensional IRT model can be utilized to formulate such longitudinal IRT models, which permits an investigation of ability growth at both individual and population levels. The equivalence of an existing multidimensional IRT model and those longitudinal IRT models is also elaborated so that one can make use of an existing multidimensional IRT model to implement the longitudinal IRT models.     

Ordinal state-trait regression for intensive longitudinal data

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).     

序数表征及其脑机制

人类天生就具有一些初级的数概念。研究表明,顶内沟是基数表征的脑基础,这一区域受损或发展受阻将导致计算失能等与数有关的认知障碍。尽管序数与基数存在相似的行为效应,但与序数相联系的皮层通路不同于基数,序数表征主要激活前额皮层与颞叶皮层区域。序数是否也存在符号效应以及跨文化差异等问题有待进一步研究     

A Unified Framework for the Comparison of Treatments with Ordinal Responses

Different latent variable models have been used to analyze ordinal categorical data which can be conceptualized as manifestations of an unobserved continuous variable. In this paper, we propose a unified framework based on a general latent variable model for the comparison of treatments with ordinal responses. The latent variable model is built upon the location-scale family and is rich enough to include many important existing models for analyzing ordinal categorical variables, including the proportional odds model, the ordered probit-type model, and the proportional hazards model. A flexible estimation procedure is proposed for the identification and estimation of the general latent variable model, which allows for the location and scale parameters to be freely estimated. The framework advances the existing methods by enabling many other popular models for analyzing continuous variables to be used to analyze ordinal categorical data, thus allowing for important statistical inferences such as location and/or dispersion comparisons among treatments to be conveniently drawn. Analysis on real data sets is used to illustrate the proposed methods.     

Automatic Bayes Factors for Testing Equality- and Inequality-Constrained Hypotheses on Variances

In comparing characteristics of independent populations, researchers frequently expect a certain structure of the population variances. These expectations can be formulated as hypotheses with equality and/or inequality constraints on the variances. In this article, we consider the Bayes factor for testing such (in)equality-constrained hypotheses on variances. Application of Bayes factors requires specification of a prior under every hypothesis to be tested. However, specifying subjective priors for variances based on prior information is a difficult task. We therefore consider so-called automatic or default Bayes factors. These methods avoid the need for the user to specify priors by using information from the sample data. We present three automatic Bayes factors for testing variances. The first is a Bayes factor with equal priors on all variances, where the priors are specified automatically using a small share of the information in the sample data. The second is the fractional Bayes factor, where a fraction of the likelihood is used for automatic prior specification. The third is an adjustment of the fractional Bayes factor such that the parsimony of inequality-constrained hypotheses is properly taken into account. The Bayes factors are evaluated by investigating different properties such as information consistency and large sample consistency. Based on this evaluation, it is concluded that the adjusted fractional Bayes factor is generally recommendable for testing equality- and inequality-constrained hypotheses on variances.     

亲密关系的外显测量与内隐测量及相互关系

为了验证外显与内隐的差序格局,研究者考察了最亲密的家人、恋人以及最亲密的好朋友的外显和内隐亲密程度的差异与联系,以及性别对亲密关系的影响。结果表明,三个目标对象的外显亲密程度之间存在差异,但内隐亲密程度之间不存在差异;外显和内隐亲密程度之间相关不显著;外显测量结果没有显著的性别差异,但在内隐测量中,男性比女性对恋人感觉更为亲密。研究者针对这些结果分别进行了讨论     

A Complete and Consistent Formal System for Sortals

A formal logical system for sortal quantifiers, sortal identity and (second order) quantification over sortal concepts is formulated. The absolute consistency of the system is proved. A completeness proof for the system is also constructed. This proof is relative to a concept of logical validity provided by a semantics, which assumes as its philosophical background an approach to sortals from a modern form of conceptualism.     

Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple classroom measurement event sessions at varied occasions using disjoint intervals within each session and employment of multiple ratings on intervals scored by different raters; a design which instantiates a nesting structure with each level contributing a source of variation in recorded scores. We generally possess little a priori knowledge of the existence or form of a sparse generating structure for the multivariate dimensions at any level in the nesting that would permit collapsing over dimensions as is done under univariate modeling. Our approach composes a Bayesian data augmentation scheme that introduces a latent continuous multivariate response linked to the observed ordinal scores with the latent response mean constructed as an additive multivariate decomposition of nested level means that permits the extraction of de-noised continuous teacher-level scores and the associated correlation matrix. A semi-parametric extension facilitates inference for teacher-level dependence among the dimensions of classroom performance under multi-modality induced by sub-groupings of rater perspectives. We next replace an inverse Wishart prior specified for the teacher covariance matrix over dimensions of instruction with a factor analytic structure to allow the simultaneous assessment of an underlying sparse generating structure. Our formulation for Bayesian factor analysis employs parameter expansion with an accompanying post-processing sign re-labeling step of factor loadings that together reduce posterior correlations among sampled parameters to improve parameter mixing in our Markov chain Monte Carlo (MCMC) scheme. We evaluate the performance of our formulation on simulated data and make an application for the assessment of the teacher covariance structure with a dataset derived from a study of middle and high school algebra teachers.     

An Alternative Interpretation of the Linearly Weighted Kappa Coefficients for Ordinal Data

When two (or more) observers are independently categorizing a set of observations, Cohen’s kappa has become the most notable measure of interobserver agreement. When the categories are ordinal, a weighted form of kappa becomes desirable. The two most popular weighting schemes are the quadratic weights and linear weights. Quadratic weights have been justified by the fact that the corresponding weighted kappa is asymptotically equivalent to an intraclass correlation coefficient. This paper deals with linear weights and shows that the corresponding weighted kappa is equivalent to the unweighted kappa when cumulative probabilities are substituted for probabilities. A numerical example is provided.     

Consistent Partial Least Squares for Nonlinear Structural Equation Models

Partial Least Squares as applied to models with latent variables, measured indirectly by indicators, is well-known to be inconsistent. The linear compounds of indicators that PLS substitutes for the latent variables do not obey the equations that the latter satisfy. We propose simple, non-iterative corrections leading to consistent and asymptotically normal (CAN)-estimators for the loadings and for the correlations between the latent variables. Moreover, we show how to obtain CAN-estimators for the parameters of structural recursive systems of equations, containing linear and interaction terms, without the need to specify a particular joint distribution. If quadratic and higher order terms are included, the approach will produce CAN-estimators as well when predictor variables and error terms are jointly normal. We compare the adjusted PLS, denoted by PLSc, with Latent Moderated Structural Equations (LMS), using Monte Carlo studies and an empirical application.     

Using Two-Level Factor Analysis to Test for Cluster Bias in Ordinal Data

The test for cluster bias is a test of measurement invariance across clusters in 2-level data. This article examines the true positive rates (empirical power) and false positive rates of the test for cluster bias using the likelihood ratio test (LRT) and the Wald test with ordinal data. A simulation study indicates that the scaled version of the LRT that accounts for nonnormality of the data gives untrustworthy results, whereas the unscaled LRT and the Wald test have acceptable false positive rates and perform well in terms of empirical power rate if the amount of cluster bias is large. The test for cluster bias is illustrated with data from research on teacher-student relations.     

A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables

This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of predicting one scale score from another. The examined strategies include imputing using normal data models with naïve rounding/without rounding, using latent variable models, and using categorical data models such as discriminant analysis and binary logistic regression (for dichotomous data only), multinomial and proportional odds logistic regression (for polytomous data only). The result suggests that both the normal model approach without rounding and the latent variable model approach perform well for either dichotomous or polytomous data regardless of sample size, missing data proportion, and asymmetry of item distributions. The discriminant analysis approach also performs well for dichotomous data. Naïvely rounding normal imputations or using logistic regression models to impute ordinal data are not recommended as they can potentially lead to substantial bias in all or some of the parameters.     

Mathematical Proof Theory in the Light of Ordinal Analysis

We give an overview of recent results in ordinal analysis. Therefore,we discuss the different frameworks used in mathematical proof-theory, namely subsystem of analysis including reversemathematics, Kripke–Platek set theory, explicitmathematics, theories of inductive definitions,constructive set theory, and Martin-Löfs typetheory.     

The Consistent Acceleration of Lifted Objects

In examining films of lifting movements in a study of the size-weight illusion (Davis & Roberts, 1976), a consistency was noted in the values obtained for the maximum accelerations of the objects lifted. While at first surprising, this finding can be embedded significantly in theories relating to kinesthetic illusions and the perception of weight and to theories on the control of general physical movement. This study was designed to confirm its existence. Twenty-four subjects were filmed lifting four objects differing in size, shape, substance, color, and weight. The film was analyzed frame-by-frame and the data were subjected to a two-way analysis of variance. Subjects, while differing from one another, were consistent in the maximum accelerations they applied to the three heaviest of the four objects. The accelerations of the lightest object differed significantly from the accelerations of the other three, but it seems likely that this was due to the experimental task itself.     

Consistent egoists and situation managers: two problems for situationism

According to philosophical “situationism”, psychological evidence shows that human action is typically best explained by the influence of situational factors and not by “global” and robust character traits of the agent. As a practical implication of their view, situationists recommend that efforts in moral education be shifted from character development to situation management. Much of the discussion has focused on whether global conceptions of virtue and character, and in particular Aristotelian virtue ethics, can be defended against the situationist challenge. After several rounds of debate, both sides claim victory, and they seem to have reached a stalemate. In this paper, I refocus the debate on the arguments offered in support of situationism itself. I argue that two serious problems have so far gone unnoticed in the literature. First, the argument in support of situationism is unsound. It is based on evidence that agents’ morally relevant behavior reliably covaries with morally irrelevant situational variables. Using the example of egoism, I show that this evidence does not warrant the situationist inference because the evidence permits a plausible alternative interpretation. Second, I argue that the situationists’ advocacy of situation management is at odds with their core thesis because situation management requires exactly the type of agency they reject as unrealistic.     

Six orienting ideas for collaborative counsellors

An enhanced form of collaboration in counselling is made possible by recent developments in social constructionist theory and a new group of ‘postmodern therapies’. Highlighting six orienting ideas that draw from this theory and the postmodern therapies (solution-focused, narrative and collaborative language systems therapy), the author suggests that these ideas can focus counsellors’ listening and creativity broadly. Examples illustrate each of the orienting ideas, with no assumption that the ideas can be narrowed down to specific techniques.     

Estimating the Expected Replicability of a Pattern of Correlations and Other Measures of Association

Replication is at the heart of all empirical sciences. However, there are no standard procedures for establishing the replicability of a pattern of correlations found linking a particular variable to an inventory or battery of other measures. This article introduces 2 statistics for quantifying the expected replicability of a pattern of associations (i.e., correlations, slope coefficients) between a variable of interest and a SET of other variables, items, measures, and so on. Using simulations and real data, we illustrate that these statistics are highly accurate estimates of the expected replicability of an observed pattern of correlations. These statistics can readily be used to indicate the replicability of patterns of association indexed by other statistics (e.g., regression slopes or covariances) and can be applied to other contexts, such as estimating the reliability of profile correlations. It is recommended that these statistics are regularly reported in such studies.