Indeterminacy Problems in the Lisrel Model

The latent variables and errors of the Lisrel model are indeterminate even when the parameters of the model are perfectly identified. The reason for the indeterminacy is that the Lisrel model gives a solution in terms of estimation of latent variables by means of observed variables. The indeterminacy is relevant also in practice; the minimum correlation between equivalent latent variables, is often negative in empirical examples. The degree of indeterminacy of the latent variables depends on the data. The average minimum correlation is a linear combination of the eigenvalues of the correlation matrix of solutions and it is always included in weak bounds which depend on the same eigenvalues.     

The effect of affine transformation on redundancy analysis

Canonical redundancy analysis provides an estimate of the amount of shared variance between two sets of variables and provides an alternative to canonical correlation. The proof that the total redundancy is equal to the average squared multiple correlation coefficient obtained by regressing each variable in the criterion set on all variables in the predictor set is generalized to the case in which there are a larger number of criterion than predictor variables. It is then shown that the redundancy for the criterion set of variables is invariant under affine transformation of the predictor variables, but not invariant under transformation of the criterion variables.     

Observed and latent variables: Discussion of a basic LISREL paradigm

The aim of the present paper is to discuss a general paradigm of LISREL: that observed psychologocal variables are causally determined by non-observed, latent variables. An example from the LISREL manual is used to show that this basic assumption is not generally meaningful. The possibility of understanding the theoretical variables of LISREL in an instrumentalist way is also considered. But instrumentalism, it is concluded, does not provide a tenable way of defending the LISREL paradigm. For LISREL conceives latent variables as causes of observed variables, and this is not compatible with an instrumentalist understanding of theoretical terms.     

How to Generate Non-normal Data for Simulation of Structural Equation Models

A procedure for generating non-normal data for simulation of structural equation models is proposed. A simple transformation of univariate random variables is used for the generation of data on latent and error variables under some restrictions for the elements of the covariance matrices for these variables. Data on the observed variables is then computed from latent and error variables according to the model. It is shown that by controlling univariate skewness and kurtosis on pre-specified random latent and error variables, observed variables can be made to have a relatively wide range of univariate skewness and kurtosis characteristics according to the pre-specified model. Univariate distributions are used for the generation of data which enables a user to choose from a large number of different distributions. The use of the proposed procedure is illustrated for two different structural equation models and it is shown how PRELIS can be used to generate the data.     

Bayesian modeling of measurement error in predictor variables using item response theory

It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between the latent variables and dichotomous observed variables, which may be responses to tests or questionnaires. It will be shown that the multilevel model with measurement error in the observed predictor variables can be estimated in a Bayesian framework using Gibbs sampling. In this article, handling measurement error via the normal ogive model is compared with alternative approaches using the classical true score model. Examples using real data are given.This paper is part of the dissertation by Fox (2001) that won the 2002 Psychometric Society Dissertation Award.     

Redundancy analysis for qualitative variables

Redundancy analysis (also called principal components analysis of instrumental variables) is a technique for two sets of variables, one set being dependent of the other. Its aim is maximization of the explained variance of the dependent variables by a linear combination of the explanatory variables. The technique is generalized to qualitative variables; it then gives implicitly a simultaneous optimal scaling of the dependent, qualitative variables. Examples are taken from the Dutch Life Situation Survey 1977, using Satisfaction with Life and Happiness as dependent variables. The analysis leads to one well-being scale, defined by the explanatory variables Marital status, Schooling, Income and Activity.The views expressed in this paper are those of the author and do not necessarily reflect the policies of the Netherlands Central Bureau of Statistics.     

On the optimality of the simultaneous redundancy transformations

The objective of this paper is to introduce and motivate additional properties and interpretations for the redundancy variables. It is shown that these variables can be derived by application of certain invariance arguments and without reference to the index of redundancy. In addition, an optimality property for the variables is presented which is important whenever one restricts attention in a study to a subset of the redundancy variables. This optimality property pertains to the subset rather than to the individual variables.This paper is based in part on the author's doctoral dissertation, Department of Statistics, Princeton, University. Research was conducted under the supervision of Lawrence S. Mayer.     

On the sampling errors of factor loadings

The results of three empirical studies on the sampling fluctuation of centroid factor loadings are reported. The first study is based on data which happened to be available on 8 variables for 700 cases and which were factored to three factors for subsamples. The second study is based on fictitious data for 2500 cases which provided separate analyses on 25 samples for each of three situations: 5 variables, one factor; 5 variables, two factors; and 6 variables, three factors. The third study, based on real data for 9 variables and 7000 cases, involves separate factorization for 35 samples of 200 cases. The three studies agree in showing that the sampling behavior of first centroid factor loadings is much like that of correlation coefficients, whereas the sampling fluctuations for loadings beyond the first are disturbingly large.     

The wellness behavior interaction model

A cognitive-behavioral model for conceptualizing wellness is proposed consisting of situational/antecedent variables, person variables, and consequence variables which, taken individually or together, may serve to describe and explain wellness behavior. The model also is interactional in the sense that different categories of variables or factors within a category may impact on each other in a multidirectional fashion. A selective review of research relating to each variable is provided to demonstrate the need for such a multidimensional conceptual scheme for wellness.     

A Scale to Measure Perceptions of Organizational Maturity

While there are several standard scales in use for measuring various dimensions of human resource management, research is very limited in terms of scale development for variables that form part of the People Capability Maturity Model (PCMM) for organizational maturity. This study covers a perception measurement scale for people governance variables and organizational maturity developed, validated with industry experts and statistically tested. The scale was developed with 113 items grouped into 11 variables covering governance, staffing, performance management, rewards, quality, training, organizational maturity and business outcomes around staff, firm and customer aspects. The reliability analysis was performed for each of the variables based on inputs from 521 respondents across the services industry. The respondents were experienced professionals in functional management and human resources. Detailed analyses of results provide high scores for the variables, indicating good consistency of items. Convergent and discriminant validity analyses were performed for evaluating validation of item dimensions within each of the variables. The overall results indicate that the newly developed scale is a reliable and validated one to measure variables around people, process and organizational maturity, for researchers particularly in the services industry. This fills in a missing gap in terms of a validated scale for measuring people management variables. Further research can be done to extend this scale to other industries and to look at a model fitment across the related variables.     

Demonstration of properties of a suppressor variable

A general logical model of properties of suppressor variables is proposed. Consistent exploration of possible manifestations of suppressor variables within this theoretical framework accounts for extant classifications of suppressor variables into the classical, net, and cooperative categories and suggests existence of new subcategories, not detected previously. The discussed model leads to consistent identification and classification of suppressor variables and facilitates computer simulation.     

Predictive value of cognitive variables for the information utilization process

Sequential predictive behaviour is used to analyse human problem solving. The most predictive and discriminative variables regarding the observable information reducing phases of the problem solving process are variables of cognitive complexity. The same holds for variables of cognitive flexibility regarding the information generating phase. Empirical evidence with respect to the thereby occurring interaction between the heuristic and epistemic part of the cognitive structure is yielded.     

Forgotten variables in memory theory and research

Abstract

Memory research, like other scientific research, disregards many variables in order to bring the full force of the scientific method to bear on clearly important variables. The reasons why memory research attends to certain variables and disregards others emanate largely from theoretical assumptions that distinguish memory systems from other psychological systems, and that distinguish variables intrinsic to memory from those extrinsic to memory. Nevertheless, a number of these ‘forgotten’ variables affect memory performance. Regardless of past practice, it is a mistake for memory research to continue to ignore relevant variables. Doing so introduces measurement error that contaminates memory performance measures, and classification error that precludes the discovery of legitimate memory variables. It is proposed here that if forgotten memory variables are controlled, manipulated, and measured more extensively, then future memory research will have greater power and memory theories will have greater validity.     

我国公务员绩效评估公平性的影响因素研究

本论文以国家公务员为对象,从背景、核心自我评价和组织文化的层面来探讨公务员绩效评估公平性的影响因素。结果表明,在公务员个人属性变量上,性别、职级、收入、工龄和年终绩效等级对绩效评估公平性上存在显著影响;单位组织文化各因子特别是诚信公正因子对公务员绩效评估公平性的影响最大,核心自我评价各因子对公平性的影响不大。     

When can information from ordinal scale variables be integrated?

Many theoretical constructs of interest to psychologists are multidimensional and derive from the integration of several input variables. We show that input variables that are measured on ordinal scales cannot be combined to produce a stable weakly ordered output variable that allows trading off the input variables. Instead a partial order is obtained in which the amount of ordering depends on the number and nature of the input variables and the relationship between them. However, if trade-offs are excluded, it is still possible to obtain a weak order using lexicographic ordering of the input variables. An implication is that psychological processes that integrate information from different input variables and that produce consistent output require that the input variables be measured on more than ordinal scales. A further implication is that the level of measurement of the input variables affects the kind of psychological model that can be applied to the process.     

Learning to visually perceive the relative mass of colliding balls in globally and locally constrained task ecologies

Novice observers differ from each other in the kinematic variables they use for the perception of kinetic properties, but they converge on more useful variables after practice with feedback. The colliding-balls paradigm was used to investigate how the convergence depends on the relations between the candidate variables and the to-be-perceived property, relative mass. Experiment 1 showed that observers do not change in the variables they use if the variables with which they start allow accurate performance. Experiment 2 showed that, at least for some observers, convergence can be facilitated by reducing the correlations between commonly used nonspecifying variables and relative mass but not by keeping those variables constant. Experiments 3a and 3b further demonstrated that observers learn not to rely on a particular nonspecifying variable if the correlation between that variable and relative mass is reduced.     

FOUNDATION OF A COMMUNICATOR STYLE CONSTRUCT

The foundation of a communicator style construct is presented in this paper. The construct is stipulated to include communication variables which reflect the “way one verbally and paraverbally interacts to signal how literal meaning should be taken, interpreted, filtered, or understood.” Nine independent variables (dominant, dramatic, animated, open, contentious, relaxed, friendly, attentive, and impression leaving) and one dependent variable (communicator image) operationally define the construct. Two independent samples, the first with 80 subjects and 102 items and the second with 1,086 subjects and 51 items, are analyzed in terms of (1) how the variables cluster, (2) what dimensionality is embedded in the structure of intercorrelations, and (3) which variables best predict communicator image. Potentially, the construct provides a context for other communication variables, relates to perceptual processes, and explains interpersonal consequents.     

A Turbo Pascal program for the computation of scale-dependent association coefficients

A Turbo Pascal program is described that computes scale-dependent association coefficients belonging to Zeger’s general family of association coefficients. Members of this family can be constructed that are adapted to the scale type of the variables, in that they are insensitive to admissible transformations of the variables but sensitive to inadmissible transformations. The program can compute (chance-corrected) association coefficients between variables of eight different scale types and between variables of mixed scale type.     

An examination of the relation of individual difference variables to loneliness

This study investigated the relation of five individual difference variables (extroversion, depression, self-esteem, neuroticism, and attitude toward others) to loneliness. The relative contributions of two different models that might explain these relations were examined. One model suggests that individual difference variables are related to loneliness through the mediation of social network variables; that is, individual difference variables may reduce people's motivation and/or ability to build and maintain social relationships, which in turn leads to loneliness. The second model, the cognitive bias model, states that both the individual difference variables and loneliness are influenced by the same intrapersonal, cognitive processes. Some people are prone to negative affect and tend to evaluate themselves and their relationships negatively. The relations of self-esteem, neuroticism, and depression to loneliness were hypothesized to reflect the cognitive bias model, while extroversion and attitudes toward others were hypothesized to be related to loneliness through the mediation of social network variables. Eighty-two female and 42 male adults completed measures of loneliness and the five individual difference variables, as well as an instrument assessing their social networks. The results indicated partial support for both models for each of the individual difference variables. Together, the two models did a good job of explaining the correlations of the individual difference variables and loneliness. The implications of these findings, as well as their relation to previous research, are discussed.     

教师背景变量对教师教学效果影响的多层线性分析

用《教师教学效果评价问卷(学生版)》问卷对高校181名教师的教学效果进行调查,用多层次分析法对影响教师教学效果的教师背景变量:教师性别、年龄、学历、职称、所教专业、年级、班级大小以及课程类别对教师教学效果影响程度进行分析,目的在于通过用多层分析法更合理分析教师背景变量对教学效果产生的影响,为客观比较教师教学效果之间的差异提供依据。研究结果表明:(1)多层次分析法得到比传统回归分析和方差分析更合理更准确的结果;(2)教师背景变量教师中性别、学历、专业以及所教班级的大小对于教师教学效果评价效果没有显著影响;(3)教师职称、年龄、所教学生年级、课程类别对教师的教学效果有显著影响;(4)所考察的教师水平背景变量对教学效果各个维度教师水平总变异的解释率介于3351%-4278%之间。