Distinguishing between conduct problem and emotionally disturbed students in secondary school: a five-instrument discriminant analysis

Five instruments, the Differential Test of Conduct and Emotional Problems, Louisville Behavior Checklist, Personality Inventory for Children, 16 PF Test, and Tennessee Self-concept Scale, were given to differentiate between 50 conduct problem and 48 emotionally disturbed students assigned in public school to secondary level classes for the severely emotionally handicapped. Discriminant analysis of each test's rate of classification germane to both a pre-assessment categorization as conduct problem vs emotionally disturbed and a post-assessment classification as no problem vs conduct problem vs emotionally disturbed vs combined problem is presented. The analysis indicated that the Differential Test of Conduct and Emotional Problems, Louisville Behavior Checklist, and Personality Inventory for Children can, with variable effectiveness, significantly classify adolescents into groups using such pre- and post-assessment information.     

The knowledge content of statistical data

An information-theoretic framework is used to analyze the knowledge content in multivariate cross classified data. Several related measures based directly on the information concept are proposed: the knowledge content (S) of a cross classification, its terseness (Zeta), and the separability (Gamma _X ) of one variable, given all others. Exemplary applications are presented which illustrate the solutions obtained where classical analysis is unsatisfactory, such as optimal grouping, the analysis of very skew tables, or the interpretation of well-known paradoxes. Further, the separability suggests a solution for the classic problem of inductive inference which is independent of sample size.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

Distinguishing between conduct-problem and emotionally disturbed students in elementary school: a five-instrument discriminant analysis.

Five instruments, the Children's Manifest Anxiety Scale, Differential Test of Conduct and Emotional Problems, Personality Inventory for Children, Piers-Harris Children's Self-concept Scale, and Revised Behavior Problem Checklist, were employed to effect differentiations between 44 conduct-problem and 31 emotionally disturbed students placed in public elementary school classes for the severely emotionally handicapped. Discriminant analysis of each test's rate of classification germane to both a preassessment categorization as conduct-problem vs emotionally disturbed and a postassessment classification as no problem vs conduct-problem vs emotionally disturbed vs combined problem is presented. The analysis indicated that the Differential Test of Conduct and Emotional Problems, Personality Inventory for Children, and Revised Behavior Problem Checklist can, with variable effectiveness, significantly classify groups using such pre- and postassessment information.     

Canonical analysis of two convex polyhedral cones and applications

Canonical analysis of two convex polyhedral cones consists in looking for two vectors (one in each cone) whose square cosine is a maximum. This paper presents new results about the properties of the optimal solution to this problem, and also discusses in detail the convergence of an alternating least squares algorithm. The set of scalings of an ordinal variable is a convex polyhedral cone, which thus plays an important role in optimal scaling methods for the analysis of ordinal data. Monotone analysis of variance, and correspondence analysis subject to an ordinal constraint on one of the factors are both canonical analyses of a convex polyhedral cone and a subspace. Optimal multiple regression of a dependent ordinal variable on a set of independent ordinal variables is a canonical analysis of two convex polyhedral cones as long as the signs of the regression coefficients are given. We discuss these three situations and illustrate them by examples.     

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     

Correction for range restriction: an expanded typology

A common research problem is the estimation of the population correlation between x and y from an observed correlation rxy obtained from a sample that has been restricted because of some sample selection process. Methods of correcting sample correlations for range restriction in a limited set of conditions are well-known. An expanded classification scheme for range-restriction scenarios is developed that conceptualizes range-restriction scenarios from various combinations of the following facets: (a) the variable(s) on which selection occurs (x, y and/or a 3rd variable z), (b) whether unrestricted variances for the relevant variables are known, and (c) whether a 3rd variable, if involved, is measured or unmeasured. On the basis of these facets, the authors describe potential solutions for 11 different range-restriction scenarios and summarize research to date on these techniques.     

四字谜问题解决中原型启发的时间抵消与位置效应

根据顿悟的原型启发理论,通过两个实验考察原型启发的时间抵消和位置效应。实验1采用2（字谜类型）×2（原型呈现时间）混合设计,结果发现：松组块字谜的正确率显著高于紧组块字谜;原型谜面消失的正确率显著大于谜面存在的正确率。实验2采用2（原型位置）×2（字谜类型）×2（原型呈现时间）混合设计,结果不仅证明了实验1,且原型位置、字谜类型、原型呈现时间的交互作用显著,原型在靶字谜之前时,紧组块字谜上谜面消失的正确率显著大于谜面存在的正确率;原型在靶字谜之后时,松组块字谜上谜面消失的正确率显著大于谜面存在的正确率。表明字谜顿悟问题解决中存在原型的时间抵消和位置效应,二者连同任务难度共同影响问题解决。     

多级评分的认知诊断计算机化适应测验

本文在0-1评分的CD-CAT基础上, 拓展出了适合多级评分CD-CAT (psCD-CAT)的认知诊断模型及选题策略, 为实现多级评分CD-CAT提供了方法支持。Monte Carlo模拟实验结果表明：本文拓展的多级评分CD-CAT具有较理想的属性诊断正确率及测验效率和题库安全性, 可以用于多级评分数据的CD-CAT; 模拟实验还表明, 整体来看PS-PWKL和PS-HKL两种选题策略具有较高属性判准率、题库安全性和高测验效率, 且均优于PS-KL选题策略。总之, 本研究对于进一步拓展CD-CAT在实践中的应用提供了认知诊断模型与选题策略等。     

The effects on classifier performance of 2D discrete wavelet transform analysis and whale optimization algorithm for recognition of power quality disturbances

Power Quality (PQ) is becoming more and more important day by day in the electric network. Signal processing, pattern recognition and machine learning are increasingly being studied for the automatic recognition of any disturbances that may occur during the generation, transmission, and distribution of electricity. There are three main steps to identify the PQ disturbances. These include the use of signal processing methods to calculate the features representing the disturbances, the selection of those that are more useful than these feature sets to prevent the creation of a complex classification model, the creating a classification model that recognizes multiple classes using the selected feature subsets. In this study, one-dimensional (1D) PQ disturbances signals are transformed into two-dimensional (2D) signals, 2D discrete wavelet transforms (2D-DWT) are used to extract the features. The features are extracted by using the wavelet families such as Daubechies, Biorthogonal, Symlets, Coiflets and Fejer-Korovkin in 2D-DWT to analyze PQ disturbances. Whale Optimization Algorithm (WOA) and k-nearest neighbor (KNN) classifier determine the feature subsets. Then, WOA and k nearest neighbor (KNN) classifier are used to determine the feature group. By using KNN and Support Vector Machines (SVM) classification methods, Classifier models that distinguish PQ disturbances are formed. The main aim of the study is to determine the features derived from 2D wavelet coefficients for different wavelet families and to determine which of them has a better classification performance to distinguish PQ disturbances signals. At the same time, different classification methods are simulated and a model which can classify PQ disturbances signals with high performance is created. Also, the generated models are analysed for their performance in terms of different noise levels (40 dB, 30 dB, 20 dB). The result of this simulation study shows that the model developed to classify PQ disturbances is superior to conventional models and other 2D signal processing methods in the literature. In addition, it was concluded that the proposed method can cope better with noisy signals by low computational complexity and higher classification rate.     

The design and analysis of state-trace experiments

State-trace analysis (Bamber, 1979) addresses a question of interest in many areas of psychological research: Does 1 or more than 1 latent (i.e., not directly observed) variable mediate an interaction between 2 experimental manipulations? There is little guidance available on how to design an experiment suited to state-trace analysis, despite its increasing use, and existing statistical methods for state-trace analysis are problematic. We provide a framework for designing and refining a state-trace experiment and statistical procedures for the analysis of accuracy data using Klugkist, Kato, and Hoijtink's (2005) method of estimating Bayes factors. The statistical procedures provide estimates of the evidence favoring 1 versus more than 1 latent variable, as well as evidence that can be used to refine experimental methodology.     

Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task.     

Categorical versus dimensional status of borderline personality disorder

One of the many controversies concerning the borderline personality disorder (BPD) diagnosis is whether the construct refers to a categorical or dimensional variable. The current study used Meehl's (1973) maximum covariance analysis to investigate this issue. The charts of 409 psychiatric inpatients were systematically reviewed for the presence of BPD and dysthymic symptoms. Charts of 244 inpatients were also reviewed to assess the presence of indicators of male sex, a categorical variable. The results for BPD and dysthymia were consistent with a dimensional model, whereas those for male sex were consistent with a categorical model. A dimensional model of classification of BPD is recommended, and suggestions for future research are provided.     

A graphical representation of the mediated effect

Mediation analysis is widely used in the social sciences. Despite the popularity of mediation models, few researchers have used graphical methods, other than structural path diagrams, to represent their models. Plots of the mediated effect can help a researcher better understand the results of the analysis and convey these results to others. This article presents a method for creating and interpreting plots of the mediated effect for a variety of mediation models, including models with (1) a dichotomous independent variable, (2) a continuous independent variable, and (3) an interaction between an independent variable and the mediating variable. An empirical example is then presented to illustrate these plots. Sample code for creating plots of the mediated effect in R and SAS is also included, and may be downloaded from www.psychonomic.org/archive.     

Orthogonal inter-battery factor analysis

It is the purpose of this paper to present a method of analysis for obtaining (i) inter-battery factors and (ii) battery specific factors for two sets of tests when the complete correlation matrix including communalities is given. In particular, the procedure amounts to constructing an orthogonal transformation such that its application to an orthogonal factor solution of the combined sets of tests results in a factor matrix of a certain desired form. The factors isolated are orthogonal but may be subjected to any suitable final rotation, provided the above classification of factors into (i) and (ii) is preserved. The general coordinate-free solution of the problem is obtained with the help of methods pertaining to the theory of linear spaces. The actual numerical analysis determined by the coordinate-free solution turns out to be a generalization of the formalism of canonical correlation analysis for two sets of variables. A numerical example is provided.This investigation has been supported by the U.S. Office of Naval Research under Contract Nonr-2752(00).     

Solution of the personnel classification problem with the method of optimal regions

The personnel classification problem is identified mathematically with other problems in the social and biological sciences. This mathematical problem is shown to be a special case of the general mathematical problem of linear programming. It is proposed here that the personnel classification problem may be solved directly by methods particularly appropriate to it as well as by the simplex method, which is a standard method for solving the general linear programming problem. The method of optimal regions is derived and illustrated in this paper.Much of the basic research covered in this paper was carried out while the author was working on the problem of personnel classification in his capacity as Consultant, Personnel Research Branch, Adjutant General's Office, Department of the Army. Some of the material was presented in a conference which was held at Personnel Research Branch in August, 1952. The author wishes to express his appreciation to the Department of the Army for permission to use these materials in this paper. The opinions expressed are those of the author and are not to be construed as official or as those of the Department of the Army.     

The prediction of intelligence in preschool children using alternative models to regression

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford–Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression treesprovided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.     

两类常见的类别变量调节效应分析

类别变量在心理学和其他社科研究领域经常遇到,当自变量或调节变量为类别变量时,应当如何分析调节效应呢？详述了多类别变量的被试间设计和两水平被试内设计（因变量重复测量2次）的调节效应分析方法,并给出了分析流程。先进行调节效应的显著性检验,后用选点法或Johnson-Neyman法进行简单斜率检验。多类别变量被试间设计的简单斜率检验是先进行整体检验,后进行配对检验。用两个实际例子演示如何进行类别变量的调节效应分析,最后展望了两类设计的类别变量的调节研究的拓展方向,例如更复杂的类别变量的调节模型等。     

大学生进食障碍症状的潜在剖面分析及其与抑郁的关系

为了解大学生进食障碍症状的类别特征及其与抑郁和性别的关系,使用进食障碍检查自评问卷6.0和9项患者健康问卷对575名大学生施测,采用带预测变量（抑郁）和分类结果变量（性别）的潜在剖面法进行数据分析。结果表明：大学生进食障碍症状可划分为体形关注组（62.4%）、体像顾虑组（28.0%）和进食障碍风险组（9.6%）;抑郁得分越高,个体被归为体像顾虑组或进食障碍风险组的可能性越大;女性属于体像顾虑组和进食障碍风险组的可能性比男性高。     

On the interpretation of removable interactions: A survey of the field 33 years after Loftus

In a classic 1978 Memory & Cognition article, Geoff Loftus explained why noncrossover interactions are removable. These removable interactions are tied to the scale of measurement for the dependent variable and therefore do not allow unambiguous conclusions about latent psychological processes. In the present article, we present concrete examples of how this insight helps prevent experimental psychologists from drawing incorrect conclusions about the effects of forgetting and aging. In addition, we extend the Loftus classification scheme for interactions to include those on the cusp between removable and nonremovable. Finally, we use various methods (i.e., a study of citation histories, a questionnaire for psychology students and faculty members, an analysis of statistical textbooks, and a review of articles published in the 2008 issue of Psychology and Aging) to show that experimental psychologists have remained generally unaware of the concept of removable interactions. We conclude that there is more to interactions in a 2 × 2 design than meets the eye.