Connections Between Graphical Gaussian Models and Factor Analysis

Connections between graphical Gaussian models and classical single-factor models are obtained by parameterizing the single-factor model as a graphical Gaussian model. Models are represented by independence graphs, and associations between each manifest variable and the latent factor are measured by factor partial correlations. Power calculations for the single-factor graphical Gaussian model are facilitated by expressing the manifest partial correlations as functions of the factor partial correlations. The power of selecting a graphical Gaussian model with an association structure between manifest variables compatible with a single-factor model is investigated. The results are illustrated using 2 examples: the 1st is a hypothetical factor model with parallel measures. The 2nd uses data from the British Household Panel Survey on job satisfaction.     

Analysis of multivariate frequency data by graphical models and generalizations of the multidimensional row-column association model

Models used to analyze cross-classifications of counts from psychological experiments must represent associations between multiple discrete variables and take into account attributes of stimuli, experimental conditions, or characteristics of subjects. The models must also lend themselves to psychological interpretations about underlying structures mediating the relationship between stimuli and responses. To meet these needs, the author extends the graphical latent variable models for nominal and/or ordinal data proposed by C. J. Anderson and J. K. Vermunt (2000) to situations in which dependencies between observed variables are not fully accounted for by the latent variables. The graphical models provide a unified framework for studying multivariate associations that include log-linear models and log-multiplicative association models as special cases.     

A noniterative method of joint correspondence analysis

Correspondence analysis leads to a graphical representation of the associations between categories of the row and column variables of a contingency table. Greenacre's (1988) formulation of joint correspondence analysis is a multivariate extension which finds the optimal joint display of contingency tables between all pairs of variables in a set. Greenacre presented a discrepancy function and an alternating least squares algorithm for its minimization. Boik (1996) presented an alternative algorithm, also of the alternating least squares type, for minimizing the same discrepancy function. In this paper, a noniterative procedure, not based on the minimization of any discrepancy function, is described.     

A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation

We discuss external and internal graphical and linguistic representational systems. We argue that a cognitive theory of peoples' reasoning performance must account for (a) the logical equivalence of inferences expressed in graphical and linguistic form, and (b) the implementational differences that affect facility of inference. Our theory proposes that graphical representation limit abstraction and thereby aid “processibility”. We discuss the ideas of specificity and abstraction, and their cognitive relevance. Empirical support both comes from tasks which involve the manipulation of external graphics and tasks that do not. For the former, we take Euler's (1772) circles, provide a novel computational reconstruction, show how it captures abstractions, and contrast it with earlier construals and with Johnson-Laird's (1983) mental models representations. We demonstrate equivalence of the graphical Euler system, and the nongraphical mental models system. For tasks not involving manipulation of external graphics, we discuss text comprehension, and the mental performance of syllogisms. By positing an internal system with the same specificity as Euler's circles, we cover the mental models data, and generate new empirical predictions. Finally, we consider how the architecture of working memory explains why such specific representations are relatively easy to store.     

Relationship Between Graphical Device Comprehension and Overall Text Comprehension for Third-Grade Children

This study examined relationships between reading comprehension, known predictors of reading comprehension (i.e., cognitive flexibility, fluency, reading motivation and attitude, vocabulary), and graphical device comprehension. One-hundred fifty-six third graders completed assessments of known predictor variables and an assessment tapping comprehension of graphical devices commonly found in age-appropriate informational texts: captioned pictures, insets, surface diagrams, cross-sectional diagrams, flow charts, timelines, and tables. Graphical device comprehension was strongly correlated with reading comprehension, r_s = 0.49, p (one-tailed) < 0.01. Regression analyses, including known and new variables, revealed that graphical device comprehension accounted for 12.81% of the resulting standardized model predicting general reading comprehension.     

Rotation in Correspondence Analysis from the Canonical Correlation Perspective

Psychometrika - Correspondence analysis (CA) is a statistical method for depicting the relationship between two categorical variables, and usually places an emphasis on graphical representations....     

风险寻求中的图形表征效应

在负性风险信息沟通中,采用文本表征与图形表征,结果发现图形表征方式更能引起风险回避行为。基于此,本研究以获益方案作为风险沟通信息,分别采用文本表征与图形表征来探索信息表征方式对风险寻求的影响。结果显示:在文本表征方式中,个体决策主要受备择方案之间风险大小的影响;而图形表征方式中,个体不仅受备择方案之间风险大小的影响,而且更受备择方案之间获益大小的影响。结果说明:相对文本表征,图形表征方式更能引起决策者为了获益而冒更大的风险,从而表现出风险寻求偏好,从而证实"图形效应"在风险决策中存在普遍性,为我们风险决策信息提供依据。     

QUALITY CIRCLES AND EMPLOYEE ATTITUDES

Quality circles have repeatedly been suggested as a technique for enhancing employees' involvement in and satisfaction with their work. This study explored the relationship between employees' participation in quality circle (QC) activities and their reactions to their jobs. Specifically, the relationship of participation in QC activities and employees' perceptions of the influence they have on their jobs, the characteristics of their jobs, and their overall job satisfaction were examined. Four hundred and fifty-five (455) QC members and 305 non-QC members, all employees of a large electronics manufacturer, were surveyed. The effect of QC membership as well as tenure in quality circles on the above variables is reported. Involvement in a quality circle was found to have a significant relationship to employees' perception of influence, as well as to some job characteristics. No significant effect of QC membership on job satisfaction was found. The results are discussed in the context of the need for further validation of many arguments heard about quality circles.     

Psychometric network models from time-series and panel data

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

     

A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data

Abstract

Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this article, a latent block distance-association model is proposed to apply block clustering to the outcomes of two categorical variables while the cluster centers are represented in a low dimensional space in terms of a distance-association model. This model is particularly useful for contingency tables in which both the rows and the columns are characterized as profiles of sets of response variables. The parameters are estimated under a Poisson sampling scheme using a generalized EM algorithm. The performance of the model is tested in a Monte Carlo experiment, and an empirical data set is analyzed to illustrate the model.     

Back to the basics: Rethinking partial correlation network methodology

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ₁-based regularization. The glasso was developed and optimized for high-dimensional settings where the number of variables (p) exceeds the number of observations (n), which is uncommon in psychological applications. Here we propose to go ‘back to the basics’, wherein the precision matrix is first estimated with non-regularized maximum likelihood and then Fisher Z transformed confidence intervals are used to determine non-zero relationships. We first show the exact correspondence between the confidence level and specificity, which is due to 1 minus specificity denoting the false positive rate (i.e., α). With simulations in low-dimensional settings (p ≪ n), we then demonstrate superior performance compared to the glasso for detecting the non-zero effects. Further, our results indicate that the glasso is inconsistent for the purpose of model selection and does not control the false discovery rate, whereas the proposed method converges on the true model and directly controls error rates. We end by discussing implications for estimating GGMs in psychology.     

Knowledge of spatial relations: A preliminary investigation

The mechanism underlying our knowledge of spatial relationships was investigated. In the first experiment subjects were asked to examine an array of circles, located in different positions on a background; after removal of the array, they answered questions about the spatial relations between the circles. The array was constructed so that the number of circles and the number of relations between circles could be independently varied. The results showed that the number of correct answers decreased as the number of circles increased, but was unaffected by the number of relations between circles. This suggests that subjects learn the positions of the circles, from which relations between circles can be computed, rather than learning the relations directly. Evidence from the second experiment, using a similar experimental paradigm, suggests that the positions of the circles are learned by organizing them into higher-order units or groups.     

Computer-analyzed measures of characteristics of human locomotion and mobility

Path and speed characteristics of human locomotion were detected by a mechanical sensing system and processed by a digital computer to give graphical and numerical measures of performance. The problems of deriving such indices are discussed, and a number of simple experiments are included to demonstrate that these measures yield dependent variables suitable for use in the study of the perceptual information required for mobility and locomotor control.     

Alternatives to Difference Scores as Dependent Variables in the Study of Congruence in Organizational Research

In organizational research, difference scores and profile similarity indices are often used as dependent variables in studies predicting the congruence (i.e., fit, match, similarity, agreement) between two constructs. Unfortunately, this practice introduces serious conceptual and methodological problems that render results ambiguous and potentially misleading. This article proposes an alternative procedure that avoids these problems but fully captures the effects of one or more independent variables on the congruence between two dependent variables. This procedure is illustrated by reanalyzing data from a study of feedback seeking and rating accuracy (Ashford & Tsui, 1991), and the results of this study are reinterpreted in light of these analyses. Limitations and areas for further development of the procedure are discussed.     

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means—the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.     

A modified procedure for parallel analysis of ordered categorical data

Parallel analysis has been well documented to be an effective and accurate method for determining the number of factors to retain in exploratory factor analysis. The O'Connor (2000) procedure for parallel analysis has many benefits and is widely applied, yet it has a few shortcomings in dealing with missing data and ordinal variables. To address these technical issues, we adapted and modified the O'Connor procedure to provide an alternative method that better approximates the ordinal data by factoring in the frequency distributions of the variables (e.g., the number of response categories and the frequency of each response category per variable). The theoretical and practical differences between the modified procedure and the O'Connor procedure are discussed. The SAS syntax for implementing this modified procedure is also provided.     

A comparative study of three methods of rotation

Three methods of rotation (the graphical, the Thurstone analytical, and the direct-rotational) were applied to the matrix of centroid loadings for 35 variables, to determine which method is the most efficient from theoretical and practical standpoints. The direct-rotational method provided the most information for determining the rank of the configuration and was most economical with respect to time required to reach a rotational solution. The analytical method required the least number of judgmental decisions and was the most objective. The graphical method was the most laborious but had a slight advantage with regard to the number of near-zero loadings in the rotational solution.Systems Development Corp.     

愤怒和恐惧情绪对个体危机决策的影响

危机情景下个体和群体的决策行为已经成为学术界关注的焦点。为探讨愤怒和恐惧情绪对个体危机决策的影响,本研究采用实验和问卷调查相结合的方法,通过观看电影片段的方式有效地诱发了愤怒和恐惧两种情绪,在问卷调查的基础上编制危机决策情景进行研究,结果发现：（1）恐惧情绪状态下,被试的危机决策时间显著长于其在愤怒情绪状态下的危机决策时间,且情绪变量和性别变量在危机决策时间上存在显著的交互作用;（2）恐惧情绪状态下,女性被试的危机决策时间显著长于其在愤怒情绪状态下的危机决策时间,并且也显著地长于男性被试的危机决策时间。同时,男性被试对其危机决策的满意程度显著地高于女性被试,且情绪变量和性别变量在危机决策自信程度指标上存在显著的交互作用;（3）随着方案数量的增加,尤其当方案数量为4时,被试的危机决策时间较其他两种情况显著增长。