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      The Incremental Validity of Average State Self-Reports Over Global Self-Reports of Personality.

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          Abstract

          Personality traits are most often assessed using global self-reports of one's general patterns of thoughts, feelings, and behavior. However, recent theories have challenged the idea that global self-reports are the best way to assess traits. Whole Trait Theory postulates that repeated measures of a person's self-reported personality states (i.e., the average of many state self-reports) can be an alternative and potentially superior way of measuring a person's trait level (Fleeson & Jayawickreme, 2015). Our goal is to examine the validity of average state self-reports of personality for measuring between-person differences in what people are typically like. In order to validate average states as a measure of personality, we examine whether they are incrementally valid in predicting informant reports above and beyond global self-reports. In 2 samples, we find that average state self-reports tend to correlate with informant reports, although this relationship is weaker than the relationship between global self-reports and informant reports. Further, using structural equation modeling, we find that average state self-reports do not significantly predict informant reports independently of global self-reports. Our results suggest that average state self-reports may not contain information about between-person differences in personality traits beyond what is captured by global self-reports, and that average state self-reports may contain more self-bias than is commonly believed. We discuss the implications of these findings for research on daily manifestations of personality and the accuracy of self-reports. (PsycINFO Database Record

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          Author and article information

          Journal
          J Pers Soc Psychol
          Journal of personality and social psychology
          American Psychological Association (APA)
          1939-1315
          0022-3514
          Mar 09 2017
          Article
          2017-10511-001
          10.1037/pspp0000136
          28277717
          5bf6fc61-5efa-478c-b23e-c4975e90405d
          History

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