SAGE Journal Articles

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SAGE Journal User Guide

Article 1:  Miller, J.D., Gaughan, E.T., Maples, J., Gentile, B., Lynam, D.R. & Thomas A. Widiger, T.A. (2011).  Examining the Construct Validity of the Elemental Psychopathy Assessment. Assessment, 18(1) 106–114. DOI: 10.1177/1073191110393139

[This study evaluates the construct validity of a new self-report inventory for the assessment of psychopathy, the Elemental Psychopathy Assessment (EPA). Evidence of the EPA’s construct validity was demonstrated based on its correlations with self and stranger ratings on the Five-Factor Model derived from a sample (N=227) of undergraduate students.]

Questions to Consider:

  1. Discuss the key tenets of the Five Factor Model and why it has superseded the Psychopathy Checklist’s two factor approach.
  2. Using the Five-Factor Model correlations for Extroversion (E) obtained from the self-report (S) listed in Table 1 (pg. 106), discuss why Coldness, with a negative correlation of -.37, is included with Dominance, which has a positive correlation of .53.
  3. Describe how the EPA relates to the construct of an antagonistic interpersonal style, based on this study.

 

Article 2: Mvududu, N.H &Sink, C.A, (2013). Factor Analysis in Counseling Research and Practice. Counseling Outcome Research and Evaluation, 4(2) 75-98. DOI: 10.1177/2150137813494766 

[This article provides an overview of the general uses and characteristics of both exploratory and confirmatory factor analysis, with an example demonstrating their application in the development of a counseling-related instrument.]

Questions to Consider:

  1. The authors characterize factor analysis “as a sophisticated correlational method to locate regularity and trends in a large data set.” Describe how factor analysis is then used to create scales from a set of responses to questions on a test.
  2. Discuss why a researcher will elect to conduct an exploratory factor analysis with their data set rather than a confirmatory factor analysis.
  3.  Based on the results of the factor analysis example used in this study (see Table 7), discuss why the authors elected to select a three factor model when their factor extraction resulted with nine components having eigen values over 1.0, explaining 60.3% of the total variance in the intercorrelation matrix.