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123
Evaluating the use of exploratory factor analysis in psychological research
 Psychological Methods
, 1999
"... Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of ..."
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Cited by 65 (0 self)
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Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed. Since its initial development nearly a century ago (Spearman, 1904, 1927), exploratory factor analysis (EFA) has been one of the most widely used statistical procedures in psychological research. Despite this
From vigilance to violence: Mate retention tactics in married couples
 Journal of Personality and Social Psychology
, 1997
"... Although much research has explored the adaptive problems of mate selection and mate attraction, little research has investigated the adaptive problem of mate retention. We tested several evolutionary psychological hypotheses about the determinants of mate retention in 214 married people. We assesse ..."
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Cited by 52 (21 self)
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Although much research has explored the adaptive problems of mate selection and mate attraction, little research has investigated the adaptive problem of mate retention. We tested several evolutionary psychological hypotheses about the determinants of mate retention in 214 married people. We assessed the usage of 19 mate retention tactics ranging from vigilance to violence. Key hypothesized findings include the following: Men's, but not women's, mate retention positively covaried with partner's youth and physical attractiveness. Women's, but not men's, mate retention positively covaried with partner's income and status striving. Men's mate retention positively covaried with perceived probability of partner's infidelity. Men, more than women, reported using resource display, submission and debasement, and intrasexual threats to retain their mates. Women, more than men, reported using appearance enhancement and verbal signals of possession. Discussion includes an evolutionary psychological analysis of mate retention in married couples. An individual's life, from the perspective of life history theory, consists of the allocation of effort to various adaptive problems (Chamov, 1993; Steams, 1992). At the broadest level of analysis, these problems can be partitioned into survival and growth (somatic effort), mating (reproductive effort), parenting and grandparenting (parental and grandparental effort), and investments in nondescendant genetic relatives (roughly kin effort). Within the domain of reproductive effort, much research has been conducted on effort devoted to the adaptive problems
A method to standardize usability metrics into a single score
, 2005
"... Current methods to represent system or task usability in a single metric do not include all the ANSI and ISO defined usability aspects: effectiveness, efficiency & satisfaction. We propose a method to simplify all the ANSI and ISO aspects of usability into a single, standardized and summated usabili ..."
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Cited by 28 (5 self)
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Current methods to represent system or task usability in a single metric do not include all the ANSI and ISO defined usability aspects: effectiveness, efficiency & satisfaction. We propose a method to simplify all the ANSI and ISO aspects of usability into a single, standardized and summated usability metric (SUM). In four data sets, totaling 1860 task observations, we show that these aspects of usability are correlated and equally weighted and present a quantitative model for usability. Using standardization techniques from Six Sigma, we propose a scalable process for standardizing disparate usability metrics and show how Principal Components Analysis can be used to establish appropriate weighting for a summated model. SUM provides one continuous variable for summative usability evaluations that can be used in regression analysis, hypothesis testing and usability reporting. ACM Classification
Principal Component Analysis
 (IN PRESS, 2010). WILEY INTERDISCIPLINARY REVIEWS: COMPUTATIONAL STATISTICS, 2
, 2010
"... Principal component analysis (pca) is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal var ..."
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Cited by 25 (5 self)
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Principal component analysis (pca) is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the pca model can be evaluated using crossvalidation techniques such as the bootstrap and the jackknife. Pca can be generalized as correspondence analysis (ca) in order to handle qualitative variables and as multiple factor analysis (mfa) in order to handle heterogenous sets of variables. Mathematically, pca depends upon the eigendecomposition of positive semidefinite matrices and upon the singular value decomposition (svd) of rectangular matrices.
Return on marketing: Using customer equity to focus marketing strategy
 Journal of Marketing
, 2004
"... The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm’s customer equity relative to the incremental expenditure necessary to produce the change. ..."
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Cited by 22 (1 self)
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The authors present a unified strategic framework that enables competing marketing strategy options to be traded off on the basis of projected financial return, which is operationalized as the change in a firm’s customer equity relative to the incremental expenditure necessary to produce the change. The change in the firm’s customer equity is the change in its current and future customers ’ lifetime values, summed across all customers in the industry. Each customer’s lifetime value results from the frequency of category purchases, average quantity of purchase, and brandswitching patterns combined with the firm’s contribution margin. The brandswitching matrix can be estimated from either longitudinal panel data or crosssectional survey data, using a logit choice model. Firms can analyze drivers that have the greatest impact, compare the drivers ’ performance with that of competitors ’ drivers, and project return on investment from improvements in the drivers. To demonstrate how the approach can be implemented in a specific corporate setting and to show the methods used to test and validate the model, the authors illustrate a detailed application of the approach by using data from the airline industry. Their framework enables whatif evaluation of marketing return on investment, which can include such criteria as return on quality, return on advertising, return on loyalty programs, and even return on corporate citizenship, given a particular shift in customer perceptions. This enables the firm to focus marketing efforts on strategic initiatives that generate the greatest return.
Minimum spanning trees for gene expression data clustering
 Genome Informatics
, 2001
"... This paper describes a new framework for microarray geneexpression data clustering. The foundation of this framework is a minimum spanning tree (MST) representation of a set of multidimensional gene expression data. A key property of this representation is that each cluster of the expression data c ..."
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Cited by 18 (2 self)
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This paper describes a new framework for microarray geneexpression data clustering. The foundation of this framework is a minimum spanning tree (MST) representation of a set of multidimensional gene expression data. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multidimensional clustering problem to a tree partitioning problem. We have demonstrated that though the interdata relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multidimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MSTbased clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXCAVATOR. To demonstrate its effectiveness, we have tested it on two data sets, i.e., expression data from yeast Saccharomyces cerevisiae, and Arabidopsis expression data in response to chitin elicitation.
High dimensional data clustering
 LMCIMAG, Université J. Fourier Grenoble
, 2006
"... Summary. Clustering in highdimensional spaces is a recurrent problem in many domains, for example in object recognition. Highdimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subs ..."
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Cited by 17 (2 self)
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Summary. Clustering in highdimensional spaces is a recurrent problem in many domains, for example in object recognition. Highdimensional data usually live in different lowdimensional subspaces hidden in the original space. This paper presents a clustering approach which estimates the specific subspace and the intrinsic dimension of each class. Our approach adapts the Gaussian mixture model framework to highdimensional data and estimates the parameters which best fit the data. We obtain a robust clustering method called HighDimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images in a probabilistic framework. Experiments on a recently proposed database demonstrate the effectiveness of our clustering method for category localization. Key words: Modelbased clustering, highdimensional data, dimension reduction, dimension reduction, parsimonious models. 1
High dimensional discriminant analysis
, 2005
"... Abstract. We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and ..."
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Cited by 16 (7 self)
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Abstract. We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt the Gaussian framework to high dimensional data. This regularization is achieved by assuming that classes are spherical in their eigenspace. HDDA is applied to recognize object in real images and its performances are compared to classical classification methods.
Testing Hypotheses About the Number of Factors in Large Factor Models
 Econometrica
"... In this paper we study highdimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k0 factors against the alternative that the number of factors is larger than k0 but no larger than k1> k0. Our test statistic equals maxk0
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Cited by 15 (0 self)
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In this paper we study highdimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k0 factors against the alternative that the number of factors is larger than k0 but no larger than k1> k0. Our test statistic equals maxk0<k≤k1 γk − γk+1 / γk+1 − γk+2, where γi is the ith largest eigenvalue of the smoothed periodogram estimate of the spectral density matrix of data at a prespecified frequency. We describe the asymptotic distribution of the statistic, as the dimensionality and the number of observations rise, as a function of the TracyWidom distribution and tabulate the critical values of the test. As an application, we test different hypotheses about the number of dynamic factors in macroeconomic time series and about the number of dynamic factors driving excess stock returns.
An empirical investigation of the key factors for success in software process improvement
 IEEE Transactions on Software Engineering
, 2005
"... Abstract—Understanding how to implement software process improvement (SPI) successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, the research efforts to da ..."
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Cited by 11 (0 self)
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Abstract—Understanding how to implement software process improvement (SPI) successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, the research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper extends and integrates models from prior research by performing an empirical investigation of the key factors for success in SPI. A quantitative survey of 120 software organizations was designed to test the conceptual model and hypotheses of the study. The results indicate that success depends critically on six organizational factors, which explained more than 50 percent of the variance in the outcome variable. The main contribution of the paper is to increase the understanding of the influence of organizational issues by empirically showing that they are at least as important as technology for succeeding with SPI and, thus, to provide researchers and practitioners with important new insights regarding the critical factors of success in SPI.