Multivariate Experimental Clinical Research, v.12 no.3 (2008)

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    Applied Multivariate Research, v.12 no.3 (complete version)
    (University of Windsor, Dept. of Psychology, 2007)
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    Multimorbidity clusters: Clustering binary data from multimorbidity clusters: Clustering binary data from a large administrative medical database
    (Wichita State University, Department of Psychology, 2008) Cornell, John E.; Pugh, Jacqueline A.; Williams, John W.; Kazis, Lewis; Lee, Austin F. S.; Parchman, Michael L.; Zeber, John; Pederson, Thomas; Montgomery, Kelly A.; Hitchcock Noël, Polly
    Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Multimorbidity is the co-occurrence of 2 or more illnesses within a single person, which raises the question whether consistent, clinically useful multimorbidity groups exist among sets of chronic illnesses. Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Application of cluster analysis involves a sequence of critical methodological and analytic decisions that influence the quality and meaning of the clusters produced. We illustrate the application of cluster analysis to identify multimorbidity clusters in a set of 45 chronic illnesses in primary care patients (N = 1,327,328), with 2 or more chronic conditions, served by the Veterans Health Administration. Six clinically useful multimorbidity clusters were identified: a Metabolic Cluster, an Obesity Cluster, a Liver Cluster, a Neurovascular Cluster, a Stress Cluster and a Dual Diagnosis Cluster. Cluster analysis appears to be a useful technique for identifying multiple disease clusters and patterns of multimorbidity.
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    A truly multivariate approach to MANOVA
    (Wichita State University, Department of Psychology, 2008) Grice, James W.; Iwasaki, Michiko
    All too often researchers perform a Multivariate Analysis of Variance (MANOVA) on their data and then fail to fully recognize the true multivariate nature of their effects. The most common error is to follow the MANOVA with univariate analyses of the dependent variables. One reason for the occurrence of such errors is the lack of clear pedagogical materials for identifying and testing the multivariate effects from the analysis. The current paper consequently reviews the fundamental differences between MANOVA and univariate Analysis of Variance and then presents a coherent set of methods for plumbing the multivariate nature of a given data set. A completely worked example using genuine data is given along with estimates of effect sizes and confidence intervals, and an example results section following the technical writing style of the American Psychological Association is presented. A number of issues regarding the current methods are also discussed.
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    Getting published: An editor's perspective
    (Wichita State University, Department of Psychology, 2008) Kirk, Roger E.
    The process of publishing journal articles is examined from the perspective of an editor. Suggestions are given for starting the writing process, producing a good manuscript, and improving your chances of having your manuscript accepted. The manuscript review process is discussed as well as reasons why editors reject manuscripts.