J. Arthur Woodward: Early Career Awards for 1977.

J. Arthur Woodward received an Early Career Award for his outstanding contributions to psychometric theory and the application of methodology to meaningful psychological problems. He has made important extensions of generalizability theory into domains having multiple dependent measures and has developed interval estimates of reliability. Woodward has contributed to the study of variance heterogeneity in complex factorial designs, provided an analysis of appropriate uses for the analysis of covariance, clarified the multivariate analysis of variance through multiple regression, and reintroduced a useful rank-order approach to factor analysis. He has also made substantial contributions to clinical and socially relevant research, for example, in developing methodology to determine the severity of the national heroin problem. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pupillographic assessment of sleepiness in sleep-deprived healthy subjects

Medical research frequently involves the statistical comparison of >2 groups, often using data obtained through the application of complex experimental designs. Fortunately, inferential statistical methodologies exist to address these situations. Analysis of variance (ANOVA) in its many forms is used to simultaneously test the equality of all groups in a study. One-way (with 1 independent variable), 2-way (with 2 independent variables), and repeated-measures (patients serve as their own controls) ANOVAs are forms of this technique. Each form has been developed to analyze data from a specific experimental design. Analysis of covariance (ANCOVA) allows the researcher to control for confounding variables that may influence the response of the dependent variable. Finally, multivariate analysis of variance (MANOVA) evaluates the simultaneous responses of multiple dependent variables to > or = 1 independent variable. Whereas ANOVA is the correct alternative to statistically inappropriate multiple t-tests, MANOVA is the correct alternative to statistically inappropriate multiple univariate ANOVA calculations. Use of each of these statistical methods requires an appropriate experimental design and data meeting a number of assumptions. When used properly, each of these methods provides a powerful statistical analysis technique.     

Work values: Genetic and environmental influences.

The Minnesota Importance Questionnaire (MIQ), measuring work values, was administered to 23 monozygotic and 20 dizygotic reared-apart twin pairs to test the hypothesis that genetic factors are associated with work values. Both univariate and multivariate analyses were performed. In the univariate analysis, intraclass correlations were computed to estimate the proportion of variability in work values associated with genetic factors for each of the 20 MIQ scales and for the 6 higher-order work value scales. The multivariate analysis used maximum likelihood estimation to separate the genetic and environmental factors for the correlated higher-order scales. Results from both analyses indicated that, on average, 40% of the variance in measured work values was related to genetic factors, whereas approximately 60% of the variance was associated with environmental factors and error variance. Implications for job enrichment and motivation theories are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Inference using complex data from surveys and experiments.

Examines methods for analyzing complex data (i.e., data that do not conform to the assumptions of independence and homoscedasticity on which many classical procedures are based). Primary attention is given to regression analysis, with ANOVA as a special case, though reference to related work on loglinear models and logit analysis is also made. The problems associated with using standard methods and software on complex data are discussed. Much of the work on alternative strategies for complex data analysis is based on an inferential framework that is fundamentally different from the model-based inference familiar to most psychologists. Though model-based inference is the most popular approach to analyzing experiments in psychology, the randomization approach is increasingly being advocated as an alternative. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multivariate analysis versus multiple univariate analyses.

The argument for preceding multiple analysis of variance ({anovas}) with a multivariate analysis of variance ({manova}) to control for Type I error is challenged. Several situations are discussed in which multiple {anovas} might be conducted without the necessity of a preliminary {manova}. Three reasons for considering multivariate analysis are discussed: to identify outcome variable system constructs, to select variable subsets, and to determine variable relative worth. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The contribution of individual variables to Hotelling's T2, Wilks' lambda, and R2

We examine the effect of each variable on the following statistics: the one-sample and two-sample Hotelling's T2, Wilks' lambda for multivariate analysis of variance, and R2 in multiple regression. For T2, the net effect of each variable is an increase in the multivariate statistic, and the particular factors determining the amount of increase are (i) the multiple correlation of the variable with all other variables, and (ii) how well the variable's contribution to falsifying the hypothesis can be linearly predicted from the other variables. The effect of each predictor variable on R2 is similar to the effect of each variable on T2. For Wilks' lambda, each variable induces a decrease, due to (i) the F for that variable alone, and (ii) the change in multiple correlation from within-sample to total-sample.     

Stimuli related to stress, neuroticism, excitation, and anxiety response patterns: Illustrating a new multivariate experimental design.

The present study serves both as a vehicle for the demonstration of a new research design which combines analysis of variance and factor analytic techniques, as well as to experimentally demonstrate that an affect assumed to be characteristic of a stimulus, e.g., anxiety, may be distinguished from the affect as experienced by a person. Moreover, anxiety is seen as a multivariate, not univariate, complex. The present findings are related to previous research by Cattell and his associates as well as what meaning this bears on other research, e.g., the Taylor Anxiety Scale. 23 refs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Precursor frequency analysis of bryostatin activated lymphocytes

The purpose of this paper is to demonstrate the application of generalized estimating equations to assess an exposure effect using multiple birth outcomes. This multivariate approach provides the flexibility of regression modeling and improved power, as compared to series of univariate analyses or collapsing the multiple end-points to a single indicator of affectedness. Motivating the discussion will be a large cohort study designed to assess the effects of anticonvulsant medications on a variety of birth outcomes, including major malformations, and growth and weight parameters, as well as a broad spectrum of minor physical anomalies. Because the study is still in progress, the aim here is not to present a definitive analysis, but to present and describe the application of these recently developed statistical methods to analyze studies with multiple outcomes. For simplicity, we will focus on the control and drug-exposed groups only from that study (ignoring the seizure history group), and we will concentrate on an analysis of minor physical anomalies.     

激光诱导击穿光谱结合多变量回归法对石灰岩中主量元素的定量分析

石灰岩由于其优异的矿物学特性,在近年来已广泛用于冶金、制造、化学、建筑等领域。其中包含的碳酸盐与非碳酸盐成分如CaO、SiO₂、Fe₂O₃和MgO等对其工业应用起着重要作用。因此,为了实现对石灰岩中以上成分的准确定量,从而最大程度开发其商业价值,基于便携式激光诱导击穿光谱仪(LIBS-Tracer),分别结合单变量分析、偏最小二乘回归(PLSR)和主成分回归(PCR)对石灰岩样品中Ca、Si、Fe、Mg 4种主量元素进行定量分析。以交叉验证的结果作为多元回归模型参数寻优的标准,并以预测决定系数、预测均方根误差和测试集的相对标准偏差作为指标分别评估了上述3种回归模型的定量精度和稳定性。结果表明,多变量回归方法显著改善了传统单变量分析的定量效果。其中主成分回归表现最佳,4种目标元素分别达到了0.999 8、0.999 6、0.999 6和0.999 0的预测决定系数和0.066 6%、0.089 3%、0.014 8%和0.038 9%的预测均方根误差,测试样品中4种目标元素分别达到了1.00%、5.04%、5.03%和13.18%的相对标准偏差。研究表明,多变量回归模型不仅可以修正传统单变量分析由于基质效应、谱线干扰等影响所造成的定量精度偏差,还可以校正由于环境、硬件系统、样品等因素所导致的检测不稳定性。此外,主成分回归也可成为该便携式LIBS对于石灰岩样品中主量元素定量的可靠分析方法。     

Multivariate analysis of variance.

We provide an expository presentation of multivariate analysis of variance (MANOVA) for both consumers of research and investigators by capitalizing on its relation to univariate analysis of variance models. We address several questions: (a) Why should one use MANOVA? (b) What is the structure of MANOVA? (c) How are MANOVA test statistics obtained and interpreted? (d) How are MANOVA follow-up tests obtained and interpreted? (e) How is strength of association assessed in MANOVA? (f) How should the results of MANOVA be presented? (g) Are there any alternatives to MANOVA? We use an example data set throughout the article to illustrate these points. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Parametric statistics and levels of measurement: Factorial designs and multiple regression.

Let Y be a continuous, ordinal measure of a latent variable Θ. In general, for factorial designs, an analysis of variance of the observed variable Y cannot be used to draw inferences about main effects and interactions on the latent variable Θ even when the standard normality and equality of variance assumptions hold. If Y is a continuous, ordinal measure of a latent variable Θ; X?,…, Xn are continuous, ordinal measures of latent variables Φ?,…, Φn; and the observed measures have a multivariate normal distribution, then a multiple regression analysis of the observed criterion measure Y and predictors X?,…, Xn can be used to test hypotheses about multivariate associations among the latent variables. Furthermore, the predicted values Y′ are unbiased estimates of quantities that are monotonically related to predicted values on the latent criterion variable Θ. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Regression models for multiple outcomes in large epidemiologic studies

In situations in which one cannot specify a single primary outcome, epidemiologic analyses often examine multiple associations between outcomes and explanatory covariates or risk factors. To compare alternative approaches to the analysis of multiple outcomes in regression models, I used generalized estimating equations (GEE) models, a multivariate extension of generalized linear models, to incorporate the dependence among the outcomes from the same subject and to provide robust variance estimates of the regression coefficients. I applied the methods in a hospital-population-based study of complications of surgical anaesthesia, using GEE model fitting and quasi-likelihood score and Wald tests. In one GEE model specification, I allowed the associations between each of the outcomes and a covariate to differ, yielding a regression coefficient for each of the outcome and covariate combinations; I obtained the covariances among the set of outcome-specific regression coefficients for each covariate from the robust 'sandwich' variance estimator. To address the problem of multiple inference, I used simultaneous methods that make adjustments to the test statistic p-values and the confidence interval widths, to control type I error and simultaneous coverage, respectively. In a second model specification, for each of the covariates I assumed a common association between the outcomes and the covariate, which eliminates the problem of multiplicity by use of a global test of association. In an alternative approach to multiplicity, I used empirical Bayes methods to shrink the outcome-specific coefficients toward a pooled mean that is similar to the common effect coefficient. GEE regression models can provide a flexible framework for estimation and testing of multiple outcomes.     

Response variable hypotheses in the multivariate analysis of variance.

Es using multivariate analysis of variance (MANOVA) usually want to analyze effects separately for each response variable after rejecting a null hypothesis of multivariate dispersion. From the standpoint of the multivariate general linear model, 4 measures of importance for response variables are discussed: univariate F statistic for each response, standardized canonical coefficient for each response, contribution to the MANOVA test criterion by each response, and simultaneous confidence intervals on estimates of treatment effects on each response. Artificial data are presented to illustrate problems in using these measures. (17 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multivariate statistics for family psychology research.

The application of selected multivariate statistics is illustrated for use in family psychology research. The use of multivariate analysis of variance (MANOVA) and discriminant analysis in factorial designs and profile analysis is discussed. Profile analysis provides a method for dealing with unit of analysis issues in family psychology research. Applications of confirmatory factor analysis are also discussed as useful methods for researchers examining multiple components of families and handling multiple perspectives of various family members. Limitations and applications of these methods in family psychology research are reviewed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Demand Forecasting for Irrigation Water Distribution Systems

One of the main problems in the management of large water supply and distribution systems is the forecasting of daily demand in order to schedule pumping effort and minimize costs. This paper examines methodologies for consumer demand modeling and prediction in a real-time environment for an on-demand irrigation water distribution system. Approaches based on linear multiple regression, univariate time series models (exponential smoothing and ARIMA models), and computational neural networks (CNNs) are developed to predict the total daily volume demand. A set of templates is then applied to the daily demand to produce the diurnal demand profile. The models are established using actual data from an irrigation water distribution system in southern Spain. The input variables used in various CNN and multiple regression models are (1) water demands from previous days; (2) climatic data from previous days (maximum temperature, minimum temperature, average temperature, precipitation, relative humidity, wind speed, and sunshine duration); (3) crop data (surfaces and crop coefficients); and (4) water demands and climatic and crop data. In CNN models, the training method used is a standard back-propagation variation known as extended-delta-bar-delta. Different neural architectures are compared whose learning is carried out by controlling several threshold determination coefficients. The nonlinear CNN model approach is shown to provide a better prediction of daily water demand than linear multiple regression and univariate time series analysis. The best results were obtained when water demand and maximum temperature variables from the two previous days were used as input data.     

Equivalence of orthogonal and nonorthogonal analysis of variance.

Considers that the controversy surrounding dummy variate multiple regression approaches to nonorthogonal analysis of variance would be cleared up if a criterion could be accepted for deciding what constitutes a proper generalization of the classical analysis of variance for orthogonal factorial designs. It is proposed that a general multiple regression solution be interpreted as testing analysis of variance effects only if it results in an estimation of the same parameters and tests of the same hypotheses that might otherwise be estimated and tested in an orthogonal design involving the same factors. A method which satisfies this criterion is identified, and a simple procedure for examining equivalence in orthogonal and nonorthogonal cases is suggested. (19 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Some multivariate displays for NAEP results.

The principal goal of graphic display is to ease access to complex information. Simple univariate displays are easy to understand but usually do not have the capability to transmit accurately the often complex structure of multivariate data. Multivariate displays were specifically designed for exactly this purpose. The National Assessment of Educational Progress (NAEP) generates data of a multivariate richness and complexity that defies accurate univariate transmission. The broad use and understanding of the information NAEP provides can be aided through the use of more suitable and evocative data displays. In this article, we demonstrate the limitations of univariate displays and suggest some multivariate displays that may enable us to understand, and thence communicate, what is contained in NAEP more fully. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Risk factors for multiple intracranial aneurysms

OBJECTIVE: Risk factors that predispose to the formation of multiple intracranial aneurysms, which are present in up to 34% of patients with intracranial aneurysms, are not well defined. In this study, we examined the association between known risk factors for cerebrovascular disease and presence of multiple intracranial aneurysms. METHODS: We reviewed the medical records and results of conventional angiography in all patients with a diagnosis of intracranial aneurysms admitted to the Johns Hopkins University hospital between January 1990 and June 1997. We determined the independent association between various cerebrovascular risk factors and the presence of multiple aneurysms using logistic regression analysis. RESULTS: Of 419 patients admitted with intracranial aneurysms (298 ruptured and 121 unruptured), 127 (30%) had multiple intracranial aneurysms. In univariate analysis, female gender (odds ratio [OR] = 1.9; 95% confidence interval [CI], 1.1-3.3) and cigarette smoking at any time (OR = 1.8; 95% CI, 1.1-3.0) were significantly associated with presence of multiple aneurysms. In the multivariate analysis, cigarette smoking at any time (OR = 1.7; 95% CI, 1.1-2.8) and female gender (OR = 2.1; 95% CI 1.2-3.5) remained significantly associated with multiple aneurysms. Hypertension, diabetes mellitus, and alcohol and illicit drug use were not significantly associated with presence of multiple aneurysms. CONCLUSION: Cigarette smoking and female gender seem to increase the risk for multiple aneurysms in patients predisposed to intracranial aneurysm formation. Further studies are required to investigate the mechanism underlying the association between cigarette smoking and intracranial aneurysm formation.     

Dyadic social interactions.

Kraemer and Jacklin (1979) proposed a method of analysis of univariate dyadic social interactions or relational data, and Mendoza and Graziano (1982) extended this method to multivariate relations. Their approach is based on an analysis-of-variance-type model that contains parameters characterizing the behavior of actors and partners and their interactions on each relation. The techniques presented in this article offer an alternative approach to the multivariate analysis of social interactions by realizing that many relations yield discrete-valued data and thus are better modeled by using methods designed for categorical data. This alternative approach is also more general because it allows more types of models to be fit. We illustrate, using the same data analyzed by the earlier methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What to do with significant multivariate effects in multivariate analyses of variance.

Significant multivariate tests in the multivariate analysis of variance are often followed by analyses of the contributions of individual dependent variables to those significant effects. There has been little agreement, however, as to which specific analyses should be performed. The use of the 2 most common techniques, analysis of variance for each dependent variable and discriminant analysis, are discussed and then illustrated in a computer study. It is suggested that the purpose of the user should determine the technique chosen as the 2 methods are not alternative approaches to the same problem. Analysis of variance can be used for hypothesis testing of individual variables and is appropriate for research. The value of discriminant analysis is in prediction and classification, although it can indicate complex relationships between measures in hypothesis testing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)