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Using Multivariate Statistics

This survey of sexual practices in the United States has been combed by the media for items of interest to the public: monogamous sex is much more widespread in this country than has been thought; infidelity is less frequent than presumed; vaginal intercourse is the defining experience of heterosexual behavior; watching one's partner undress is stimulating to many people; married couples have more sex than single people (unmarried, cohabiting couples have the most sex of all); the majority of couples experience sex twice a week to several times a month; 2.8% of men identify themselves as homosexual and 1.4% of women do so, but a higher percentage of people consider a same-gender experience to have some appeal; 75% of men always experience orgasm compared with 28.6% of women, but more nearly equal numbers of men and women declare themselves satisfied with their sexual experiences. The book is, in fact, a

The Social Organization of Sexuality: Sexual Practices in the United States

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

EDITORIAL BOARD Robert Davison Aviles, Bradley University Harley E. Baker, California State University–Channel Islands Jean-Guy Blais, Universite de Montreal, Canada Catherine Y. Chang, Georgia State University Robert C. Chope, San Francisco State University Kevin O. Cokley, University of Missouri, Columbia Patricia B. Elmore, Southern Illinois University Shawn Fitzgerald, Kent State University John J. Fremer, Educational Testing Service Vicente Ponsoda Gil, Universidad Autonoma de Madrid, Spain Jo-Ida C. Hansen, University of Minnesota Charles C. Healy, University of California at Los Angeles Robin K. Henson, University of North Texas Flaviu Adrian Hodis, Victoria University of Wellington, New Zealand Janet K. Holt, Northern Illinois University David A. Jepsen, The University of Iowa Gregory Arief D. Liem, National Institute of Education, Nanyang Technological University Wei-Cheng J. Mau, Wichita State University Larry Maucieri, Governors State College Patricia Jo McDivitt, Data Recognition Corporation Peter F. Merenda, University of Rhode Island Matthew J. Miller, University of Maryland Ralph O. Mueller, University of Hartford Jane E. Myers, The University of North Carolina at Greensboro Philip D. Parker, University of Western Sydney Ralph L. Piedmont, Loyola College in Maryland Alex L. Pieterse, University at Albany, SUNY Nicholas J. Ruiz, Winona State University James P. Sampson, Jr., Florida State University William D. Schafer, University of Maryland, College Park William E. Sedlacek, University of Maryland, College Park Marie F. Shoffner, University of Virginia Len Sperry, Florida Atlantic University Kevin Stoltz, University of Mississippi Jody L. Swartz-Kulstad, Seton Hall University Bruce Thompson, Texas A&M University Timothy R. Vansickle, Minnesota Department of Education Steve Vensel, Palm Beach Atlantic University Dan Williamson, Lindsey Wilson College F. Robert Wilson, University of Cincinnati

measurement and evaluation in counseling and Development

/pdf/beck-depression-inventory-iinituitenoyi-kao-cha-3143h0pleh.pdf

Beck Depression Inventory-IIについての一考察

Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.

Bridge Centrality: A Network Approach to Understanding Comorbidity.

American Physical Therapy Association

A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder.

Networks have emerged as a popular method for studying mental disorders Psychopathology networks consist of aspects (eg, symptoms) of mental disorders (nodes) and the connections between those aspects (edges) Unfortunately, the visual presentation of networks can occasionally be misleading For instance, researchers may be tempted to conclude that nodes that appear close together are highly related, and that nodes that are far apart are less related Yet this is not always the case In networks plotted with force-directed algorithms, the most popular approach, the spatial arrangement of nodes is not easily interpretable However, other plotting approaches can render node positioning interpretable We provide a brief tutorial on several methods including multidimensional scaling, principal components plotting, and eigenmodel networks We compare the strengths and weaknesses of each method, noting how to properly interpret each type of plotting approach

/pdf/visualizing-psychological-networks-a-tutorial-in-r-19ixw5f08m.pdf

Visualizing Psychological Networks: A Tutorial in R.

Network analysis can be used to identify central symptoms of eating disorders such as anorexia nervosa (AN), but the validity of this approach has been questioned. Using network analysis, in the pr...

https://journals.sagepub.com/doi/pdf/10.1177/2167702619865958

Payton J. Jones

Papers

Bridge Centrality: A Network Approach to Understanding Comorbidity.

American Physical Therapy Association

A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder.

Visualizing Psychological Networks: A Tutorial in R.

Central Symptoms Predict Posttreatment Outcomes and Clinical Impairment in Anorexia Nervosa: A Network Analysis: