Conceptual and measurement issues of the complementarity dimension of the coach–athlete relationship across cultures

ObjectivesGrounded in the 3Cs model of the coach–athlete relationship, this multi-study outlined the development of two scales that measure coaches' dominant behaviors and athletes' submissive behaviors.Method & resultsIn study 1, a pool of items was generated based on relevant literatures and interviews. The item pool was then assessed by an expert panel including academics, athletes and coaches. In Study 2 and Study 3, construct and criterion validity, as wells as internal reliability of the refined items were tested with a sample of elite coaches and athletes. Analyses revealed that Coach Dominant Behavior Scale (CDB-S) and Athlete Submissive Behavior Scale (ASB-S) possessed sound psychometric properties. In Study 4, the measurement invariance of the 10-item ASB-S was assessed across gender and five countries: Britain, China, Greece, Spain, and Sweden. Results supported the full structural invariance of the ASB-S.ConclusionOverall, the items of both CDB-S and ASB-S were found to be psychometrically sound. The discussion highlights the contributions these findings make on both conceptual and measurement levels as well as the opportunities it opens up for research with practical relevance.     

Bias in Estimation of Misclassification Rates

When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the independent variable. In typical cases, this increased misclassification rate due to sampling is remarkably small relative to other increases in expected measures of prediction accuracy due to samplings that are typically encountered in statistical analysis. This issue is particularly striking if a polytomous variable predicts a polytomous variable, for the excess misclassification rate due to estimation approaches 0 at an exponential rate as n increases. Even with a continuous real predictor and with simple nonparametric methods, it is typically not difficult to achieve an excess misclassification rate on the order of n ⁻¹. Although reduced excess error is normally desirable, it may reasonably be argued that, in the case of classification, the reduction in bias is related to a more fundamental lack of sensitivity of misclassification error to the quality of the prediction. This lack of sensitivity is not an issue if criteria based on probability prediction such as logarithmic penalty or least squares are employed, but the latter measures typically involve more substantial issues of bias. With polytomous predictors, excess expected errors due to sampling are typically of order n ⁻¹. For a continuous real predictor, the increase in expected error is typically of order n ^−2/3     

The Problem with Having Two Watches: Assessment of Fit When RMSEA and CFI Disagree

The root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are two widely applied indices to assess fit of structural equation models. Because these two indices are viewed positively by researchers, one might presume that their values would yield comparable qualitative assessments of model fit for any data set. When RMSEA and CFI offer different evaluations of model fit, we argue that researchers are likely to be confused and potentially make incorrect research conclusions. We derive the necessary as well as the sufficient conditions for inconsistent interpretations of these indices. We also study inconsistency in results for RMSEA and CFI at the sample level. Rather than indicating that the model is misspecified in a particular manner or that there are any flaws in the data, the two indices can disagree because (a) they evaluate, by design, the magnitude of the model's fit function value from different perspectives; (b) the cutoff values for these indices are arbitrary; and (c) the meaning of “good” fit and its relationship with fit indices are not well understood. In the context of inconsistent judgments of fit using RMSEA and CFI, we discuss the implications of using cutoff values to evaluate model fit in practice and to design SEM studies.     

Approximations to the distribution of a test statistic in covariance structure analysis: A comprehensive study

In structural equation modelling (SEM), a robust adjustment to the test statistic or to its reference distribution is needed when its null distribution deviates from a χ² distribution, which usually arises when data do not follow a multivariate normal distribution. Unfortunately, existing studies on this issue typically focus on only a few methods and neglect the majority of alternative methods in statistics. Existing simulation studies typically consider only non-normal distributions of data that either satisfy asymptotic robustness or lead to an asymptotic scaled χ² distribution. In this work we conduct a comprehensive study that involves both typical methods in SEM and less well-known methods from the statistics literature. We also propose the use of several novel non-normal data distributions that are qualitatively different from the non-normal distributions widely used in existing studies. We found that several under-studied methods give the best performance under specific conditions, but the Satorra–Bentler method remains the most viable method for most situations.     

Learning phonetic categories by tracking movements

We explore in this study how infants may derive phonetic categories from adult input that are highly variable. Neural networks in the form of self-organizing maps (SOMs; ) were used to simulate unsupervised learning of Mandarin tones. In Simulation 1, we trained the SOMs with syllable-sized continuous F(0) contours, produced by multiple speakers in connected speech, and with the corresponding velocity profiles (D1). No attempt was made to reduce the large amount of variability in the input or to add to the input any abstract features such as height and slope of the F(0) contours. In the testing phase, reasonably high categorization rate was achieved with F(0) profiles, but D1 profiles yielded almost perfect categorization of the four tones. Close inspection of the learned prototypical D1 profile clusters revealed that they had effectively eliminated surface variability and directly reflected articulatory movements toward the underlying targets of the four tones as proposed by . Additional simulations indicated that a further learning step was possible through which D1 prototypes with one-to-one correspondence to the tones were derived from the prototype clusters learned in Simulation 1. Implications of these findings for theories of language acquisition, speech perception and speech production are discussed.     

语言理解的体验观

随着体验哲学和体验认知的蓬勃发展,在语言理解中出现了强调经验作用的新观点,这就是语言理解的体验观。其基本观点是语言根植于知觉和运动,语言理解就是对语言所描述的情境建构心理模拟。这种模拟是以理解者的身体的、情绪的和社会的经验为基础的,因此语言理解实质上是对语言所指代的情境的心理上的经验重演。在语言理解的体验观中,索引假设、浸入式经历者框架和语言的神经理论具有一定的代表性     

When words do not mean what they say. Self-agency and the coercive use of language

Abstract :  This paper explores the relationship between language and the development of self-agency. I suggest that a child discovers that he or she actually exists as a person with a mind and desires through the mirroring response he or she creates in the parent. This developmental stage of 'teleological' level of self-agency is related to Terrence Deacon's concept of indexical communication. The coercive effect this has on others is explored and the way language may become subverted from its symbolic function and used in the service of indexical communication is discussed, illustrated by examples from the plays of Harold Pinter.     

Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses

Informative hypotheses are increasingly being used in psychological sciences because they adequately capture researchers’ theories and expectations. In the Bayesian framework, the evaluation of informative hypotheses often makes use of default Bayes factors such as the fractional Bayes factor. This paper approximates and adjusts the fractional Bayes factor such that it can be used to evaluate informative hypotheses in general statistical models. In the fractional Bayes factor a fraction parameter must be specified which controls the amount of information in the data used for specifying an implicit prior. The remaining fraction is used for testing the informative hypotheses. We discuss different choices of this parameter and present a scheme for setting it. Furthermore, a software package is described which computes the approximated adjusted fractional Bayes factor. Using this software package, psychological researchers can evaluate informative hypotheses by means of Bayes factors in an easy manner. Two empirical examples are used to illustrate the procedure.     

Think Before You Speak: Utterances and the Logic of Indexicals

This essay discusses some aspects of the logical behaviour of sentences in languages containing indexical and demonstrative expressions. After some preliminary remarks in section one, sections two and three focus on instances of logically true sentences that may be uttered falsely, and on cases of logically equivalent sentences whose utterances may have distinct truth-values. The logical and semantic problems taken into consideration include the validity of a Principle of Translation, the so-called puzzle of addressing, and examples related to measurement and approximation. Section four discusses the complementary phenomenon of sentences that may presumably always be uttered truly, but that do not qualify as logically true. In particular, I argue against the widespread tendency to consider I am here now or I exist as truths of the logic of indexicals.     

Graph-theoretic representations for proximity matrices through strongly-anti-Robinson or circular strongly-anti-Robinson matrices

There are various optimization strategies for approximating, through the minimization of a least-squares loss function, a given symmetric proximity matrix by a sum of matrices each subject to some collection of order constraints on its entries. We extend these approaches to include components in the approximating sum that satisfy what are called the strongly-anti-Robinson (SAR) or circular strongly-anti-Robinson (CSAR) restrictions. A matrix that is SAR or CSAR is representable by a particular graph-theoretic structure, where each matrix entry is reproducible from certain minimum path lengths in the graph. One published proximity matrix is used extensively to illustrate the types of approximation that ensue when the SAR or CSAR constraints are imposed.The authors are indebted to Boris Mirkin who first noted in a personal communication to one of us (LH, April 22, 1996) that the optimization method for fitting anti-Robinson matrices in Hubert and Arabie (1994) should be extendable to the fitting of strongly anti-Robinson matrices as well.