A comparison of maximum covariance and k-means cluster analysis in classifying cases into known taxon groups.

Maximum covariance (MAXCOV) is a method for determining whether a group of 3 or more indicators marks 1 continuous or 2 discrete latent distributions of individuals. Although the circumstances under which MAXCOV is effective in detecting latent taxa have been specified, its efficiency in classifying cases into groups has not been assessed, and few studies have compared its performance with that of cluster analysis. In the present Monte Carlo study, the classification efficiencies of MAXCOV and the k-means algorithm were compared across ranges of sample size, effect size, indicator number, taxon base rate, and within-groups covariance. When the impact of these parameters was minimized, k-means classified more data points correctly than MAXCOV. However, when the effects of all parameters were increased concurrently, MAXCOV outperformed k-means. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A tetrad test for causal indicators.

The authors propose a confirmatory tetrad analysis test to distinguish causal from effect indicators in structural equation models. The test uses "nested" vanishing tetrads that are often implied when comparing causal and effect indicator models. The authors present typical models that researchers can use to determine the vanishing tetrads for 4 or more variables. They also provide the vanishing tetrads for mixtures of causal and effect indicators, for models with fewer than 4 indicators per latent variable, or for cases with correlated errors. The authors illustrate the test results for several simulation and empirical examples and emphasize that their technique is a theory-testing rather than a model-generating approach. They also review limitations of the procedure including the indistinguishable tetrad equivalent models, the largely unknown finite sample behavior of the test statistic, and the inability of any procedure to fully validate a model specification. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

When constraints interact: A caution about reference variables, identification constraints and scale dependencies in structural equation modeling.

In traditional approaches to structural equations modeling, variances of latent endogenous variables cannot be specified or constrained directly and, consequently, are not identified, unless certain precautions are taken. The usual method for achieving identification has been to fix one factor loading for each endogenous latent variable at unity. An alternative approach is to fix variances using newer constrained estimation algorithms. This article examines the philosophy behind such constraints and shows how their appropriate use is neither as straightforward nor as noncontroversial as portrayed in textbooks and computer manuals. The constraints on latent variable variances can interact with other model constraints to interfere with the testing of certain kinds of hypotheses and can yield incorrect standardized solutions with some popular software. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The latent structure of childhood aggression: A taxometric analysis of self-reported and teacher-rated aggression in Israeli schoolchildren.

The purpose of this study was to determine whether the latent structure of childhood aggression, like the latent structure of adult antisocial behavior, is dimensional. One thousand and five Israeli schoolchildren completed a translation of the Buss-Perry Aggression Questionnaire (AQ; Buss & Perry, 1992) and were rated by their homeroom teachers on the Eyberg Child Behavior Inventory (ECBI; Robinson, Eyberg, & Ross, 1980). The AQ Physical Aggression and Verbal Aggression scales were combined to form the 1st indicator, the AQ Anger and Hostility scales were combined to form the 2nd indicator, the 10-item ECBI Oppositional Defiant Behavior Toward Adults scale composed the 3rd indicator, and the 8-item ECBI Conduct Problem Behavior scale composed the 4th indicator. Subjecting these indicators to taxometric analysis revealed consistent support for dimensional latent structure in the full sample as well as in 5 of the 6 subsamples. Childhood aggression, it would seem, differs quantitatively along a dimension (degree of aggression) rather than bifurcating into qualitatively distinct categories (aggressive vs. nonaggressive). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Three Cs in measurement models: Causal indicators, composite indicators, and covariates.

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “Three Cs”). Causal indicators have conceptual unity, and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variables. Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects, and composites are a matter of convenience. The failure to distinguish the Three Cs has led to confusion and questions, such as, Are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Knowledge-Assisted Retrieval of Online Product Information in Architectural/Engineering/Construction

With a better understanding about the product market, industry practitioners can make more informed decisions for product specification, selection, and procurement. However, current approaches that support Architectural/Engineering/Construction (A/E/C) information acquisition do not provide a comprehensive market scan. While the Internet provides an ever-growing resource for product information, existing approaches have limited capabilities to make use of this virtual market. Therefore, the writers have developed a knowledge-assisted approach to specifically address the potential of the virtual market—help retrieve A/E/C product information, particularly information about different manufacturers, from the Internet. The paper introduces the building blocks of the developed approach, which includes domain knowledge utilization, information retrieval (IR) techniques, and strategies that incorporate domain knowledge into knowledge-supported IR approaches. Specifically, domain knowledge represented in the form of a thesaurus was used with query expansion strategies under the framework of an adaptation of the extended IR Boolean model. It was concluded from the prototype validation that domain knowledge can be extremely helpful when acquiring A/E/C product information from online resources using the developed approach.     

Using Taxometric Analysis to Distinguish a Small Latent Taxon From a Latent Dimension With Positively Skewed Indicators: The Case of Involuntary Defeat Syndrome.

Joining the debate on the structure of depression, S. R. H. Bearh and N. Amir (2003) analyzed college students' responses to 6 Beck Depression Inventory (BDI) items with predominantly somatic content and concluded that they identified a small latent taxon corresponding to involuntary defeat syndrome. An exact replication of these analyses yielded virtually identical taxometric results, but parallel analyses of simulated taxonic and dimensional comparison data matching the intercorrelations and skewed distributions of the BDI items showed the results to be more consistent with dimensional than with taxonic latent structure. Analyses in a clinical sample with nonskewed indicators further supported a dimensional interpretation. The authors discuss methodological strategies for conducting and interpreting taxometric analyses under the adverse conditions commonly encountered in psychopathology research, including skewed indicators and small putative taxa. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Interpretational confounding is due to misspecification, not to type of indicator: Comment on Howell, Breivik, and Wilcox (2007).

R. D. Howell, E. Breivik, and J. B. Wilcox (2007; see record 2007-07830-006) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal (formative) indicators rests on several claims: (a) A latent variable exists apart from the model when there are effect (reflective) indicators but not when there are causal (formative) indicators, (b) causal (formative) indicators need not have the same consequences, (c) causal (formative) indicators are inherently subject to interpretational confounding, and (d) a researcher cannot detect interpretational confounding when using causal (formative) indicators. This article shows that each claim is false. Rather, interpretational confounding is more a problem of structural misspecification of a model combined with an underidentified model that leaves these misspecifications undetected. Interpretational confounding does not occur if the model is correctly specified whether a researcher has causal (formative) or effect (reflective) indicators. It is the validity of a model not the type of indicator that determines the potential for interpretational confounding. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Integrated Fuzzy Ranking Analysis for Assessing the Quality of Composting Products

An integrated fuzzy ranking analysis (IFRA) method was developed to help evaluate the quality of waste composts under a variety of system complexities and uncertainties. It combined the latent component analysis and ameliorated G-index fuzzy relationship analysis into a general framework. The IFRA could handle different types of uncertainties, investigate the complex interrelationships among multiple indicators, and determine the best-quality product among a variety of alternatives. The IFRA was then applied to assess eight types of composts. An example standard was established in relation to potting grade and soil amendment. Two main policy scenarios were investigated. The obtained results could help decision makers distinguish the compost products by different usages, and distribute the products to appropriate markets. Moreover, the IFRA method could help promote new policies that would ultimately improve the marketing and development strategies for the composting industry.     

冷轧商品卷表面质量控制及工艺优化

随着钢材市场竞争的日趋激烈,对于冷轧商品卷,用户不仅要求钢板的性能、板形,对钢板的表面质量也提出了更高的要求。表面质量品级率是冷轧商品卷非常重要的一个指标,可以说在一定程度上比性能指标都要重要。该指标的高低不仅直接反映冷轧厂生产能力和全厂的管理技术水平,还直接影响到最终产品的价格。由于冷轧产品的表面质量具有遗传性,所以从酸轧线原料缺陷情况、酸轧线、罩式退火炉以及平整机机组等各自的生产工艺特点,对各自机组产生产品表面质量问题进行分析研究,调整各自的工艺控制参数,并对执行结果进行数据跟踪统计,不断对其进行优化。     

Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.

The current study replicated, in a sample of 2,300 outpatients seeking psychiatric treatment, a previous study (R. F. Krueger & M. S. Finger, 2001) that implemented an item response theory approach for modeling the comorbidity of common mood and anxiety disorders as indicators along the continuum of a shared latent factor (internalizing). The 5 disorders examined were major depressive disorder, social phobia, panic disorder/agoraphobia, specific phobia, and generalized anxiety disorder. The findings were consistent with the prior research. First, a confirmatory factor analysis yielded sufficient evidence for a nonspecific factor underlying the 5 diagnostic indicators. Second, a 2-parameter logistic item response model showed that the diagnoses were represented in the upper half of the internalizing continuum, and each was a strongly discriminating indicator of the factor. Third, the internalizing factor was significantly associated with 3 indexes of social burden: poorer social functioning, time missed from work, and lifetime hospitalizations. Rather than the categorical system of presumably discrete disorders presented in DSM-IV, these 5 mood and anxiety disorders may be alternatively viewed as higher end indicators of a common factor associated with social cost. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Indicators of human health in ecosystems: what do we measure?

Increasingly, scientists are being called upon to assist in the development of indicators for monitoring ecosystem health. For human health indicators, they may draw on environmental exposure, human morbidity/mortality or well-being and sustainability approaches. To improve the rigour of indicators, we propose six scientific criteria for indicator selection: (1) data availability, suitability and representativeness (of populations), (2) indicator validity (face, construct, predictive and convergent) and reliability; (3) indicator responsiveness to change; (4) indicator desegregation capability (across personal and community characteristics); (5) indicator comparability (across populations and jurisdictions); and (6) indicator representativeness (across important dimensions of concern). We comment on our current capacity to adhere to such criteria with examples of measures of environmental exposure, human health and sustainability. We recognize the considerable work still required on documenting environment-human health relationships and on monitoring potential indicators in similar ways over time. Yet we argue that such work is essential in order for science to inform policy decisions which affect the health of ecosystems and human health.     

Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches.

Unreliability of measures produces bias in regression coefficients. Such measurement error is particularly problematic with the use of product terms in multiple regression because the reliability of the product terms is generally quite low relative to its component parts. The use of confirmatory factor analysis as a means of dealing with the problem of unreliability was explored in a simulation study. The design compared traditional regression analysis (which ignores measurement error) with approaches based on latent variable structural equation models that used maximum-likelihood and weighted least squares estimation criteria. The results showed that the latent variable approach coupled with maximum-likelihood estimation methods did a satisfactory job of interaction analysis in the presence of measurement error in terms of Type I and Type II errors. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

建立新一代钢厂精准设计评价指标体系的研究

新一代钢厂的设计水平对钢厂的实际运行水平具有决定性的影响.为了提高新一代钢厂的精准设计水平,按照系统性、层次性等原则建立了新一代钢厂精准设计评价指标体系.指标体系包含49个末端指标和16个中间指标,分为经济投资水平、工艺设备水平、流程运行高效化、生态化水平四大类指标.该指标体系能反映新一代钢铁制造流程装备、工艺先进、流...     

Bootstraps taxometrics: Solving the classification problem in psychopathology.

Classification in psychopathology is a problem in applied mathematics; it answers the empirical question "Is the latent structure of these phenotypic indicator correlations taxonic (categories) or nontaxonic (dimensions, factors)?" It is not a matter of convention or preference. The taxometric procedures, MAMBAC and MAXCOV-HITMAX, provide independent tests of the taxonic conjecture and satisfactorily accurate estimates of the taxon base rate, the latent means, and the valid and false-positive rates achievable by various Cuts. The method requires no gold standard criterion, applying crude fallible diagnostic "criteria" only in the phase of discovery to identify plausible candidate indicators. Confidence in the inference to taxonic structure and numerical accuracy of latent values is provided by multiple consistency tests, hence the term coherent cut kinetics for the general approach. Further revision of diagnostic systems should be based on taxometric analysis rather than on committee decisions based on clinical impressions and nontaxometric research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Construct validity: Construct representation versus nomothetic span.

Presents a new approach to construct validation research: construct modeling. A paradigm shift from functionalism to structuralism in psychology permits 2 types of research to be separated. Construct representation is concerned with identifying the theoretical mechanisms that underlie responses, such as information processes, strategies, and knowledge stores. Three approaches to assessing construct representation are presented: (1) mathematical modeling, particularly as used in cognitive psychology; (2) psychometric modeling, as exemplified by latent trait modeling; and (3) multicomponent latent trait modeling. Nomothetic span is concerned with the network of relationships of a test score with other variables. These 2 types of construct validation research address different issues and require different types of data. For each type of construct validation research, appropriate methods and quantitative models are presented to test a priori hypotheses about construct validity. Examples are presented, and the construct modeling approach is compared with both the traditional psychometric approach and the information-processing approach to establishing theoretical mechanisms in performance. (41 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Biochemical characteristics of blood from patients with febrile attacks of schizophrenia

AIMS: This study describes development and field testing of a set of indicators for drug and therapeutics committees (DTCs) in hospitals. It was intended that these indicators should be accessible, useful and relevant in the Australian setting. METHODS: Candidate indicators were written following consultation and data collection. A framework of outcome, impact and process indicators was based on DTC goals, objectives and strategies. The candidate indicators were field tested over a 2 month period in teaching, city non-teaching, rural and private hospitals. The field tests provided response data for each indicator and evaluation of the indicators against criteria for accessibility, relevance, usefulness, clarity and resource utilisation. Consensus on which indicators to accept, modify or reject was reached at a workshop of stakeholders and experts, taking account of the field test results. RESULTS: Thirty-five candidate indicators were tested in 16 hospitals. Twenty-two had a response from >80% of sites, 23 had a mean relevance rating >3.5, 19 had a mean usefulness rating >3.5, 27 were correctly interpreted by > 90% of sites and 25 could be collected in an acceptable time. The most acceptable indicators required least data collection or provided data deemed useful for purposes other than the field test. At the consensus workshop 13 indicators were accepted with no or minor change, nine were accepted after major modification and eight were discarded. It was recommended that a further five indicators should be merged or subsumed into one indicator. CONCLUSIONS: This study has developed and field tested a set of indicators for DTCs in Australia. The indicators have been taken up enthusiastically as a first attempt to monitor DTC performance but require ongoing validation and development to ensure continuing relevance and usefulness.     

Interpretation of simulation studies for efficient estimation of population pharmacokinetic parameters

OBJECTIVE: To develop new approaches for evaluating results obtained from simulation studies used to determine sampling strategies for efficient estimation of population pharmacokinetic parameters. METHODS: One-compartment kinetics with intravenous bolus injection was assumed and the simulated data (one observation made on each experimental unit [human subject or animal]), were analyzed using NONMEM. Several approaches were used to judge the efficiency of parameter estimation. These included: (1) individual and joint confidence intervals (CIs) coverage for parameter estimates that were computed in a manner that would reveal the influence of bias and standard error (SE) on interval estimates; (2) percent prediction error (%PE) approach; (3) the incidence of high pair-wise correlations; and (4) a design number approach. The design number (phi) is a new statistic that provides a composite measure of accuracy and precision (using SE). RESULTS: The %PE approach is useful only in examining the efficiency of estimation of a parameter considered independently. The joint CI coverage approach permitted assessment of the accuracy and reliability of all model parameter estimates. The phi approach is an efficient method of achieving an accurate estimate of parameter(s) with good precision. Both the phi for individual parameter estimation and the overall phi for the estimation of model parameters led to optimal experimental design. CONCLUSIONS: Application of these approaches to the analyses of the results of the study was found useful in determining the best sampling design (from a series of two sampling times designs within a study) for efficient estimation of population pharmacokinetic parameters.     

The theoretical status of latent variables.

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Child and adolescent conduct disorder substantially shares genetic influences with three socioemotional dispositions.

In a representative sample of twin children and adolescents, we tested the hypothesis that a substantial proportion of the genetic and environmental influences underlying conduct disorder (CD) are shared with three socioemotional dispositions: Prosociality, Negative Emotionality, and Daring. Caretaker ratings of each dispositional dimension were uniquely associated with a latent CD dimension that included both caretaker- and youth-reports of CD as indicators. Behavior genetic analyses indicated that moderate-to-high additive genetic and moderate nonshared environmental influences underlie all three dispositions and CD, with modest shared environmental influences on Prosociality. Forty percent of the additive genetic influences and all of the nonshared environmental influences on the latent CD dimension were shared in common with the three socioemotional dispositions. The finding that CD shares a substantial proportion of its genetic influences with three distinct socioemotional dispositions suggests new perspectives on the heterogeneous etiology of CD and new approaches to exploring its specific etiological mechanisms. (PsycINFO Database Record (c) 2010 APA, all rights reserved)