- Oxford Library of Psychology
- Short Contents
- Oxford Library of Psychology
- About the Editor
- Stress and Immunity in Pregnancy
- The Logic of Developmental Psychoneuroimmunology
- Well-Being, Aging, and Immunity
- Stress and Immune System Aging
- Physiological Correlates of Self-Conscious Emotions
- Positive Emotions and Immunity
- Emotional Expression and Disclosure
- Temperament/Animal Personality
- Personality and Human Immunity
- The Association Between Measures of Inflammation and Psychological Factors Associated with an Increased Risk of Atherosclerotic Cardiovascular Disease: Hostility, Anger and Depressed Mood and Symptoms
- Social Support and Immunity
- Socioeconomic Status, Inflammation, and Immune Function
- Social Regulation of Gene Expression in the Immune System
- Comparative Psychoneuroimmunology/Ecoimmunology: Lessons from Simpler Model Systems
- Seasonal Rhythms in Psychoneuroimmunology
- Psychoneuroimmunology of Fatigue and Sleep Disturbance: The Role of Pro-inflammatory Cytokines
- Psychoneuroimmunology and Cancer: Biobehavioral Influences on Tumor Progression
- Regulation of Target System Sensitivity in Neuroinflammation: Role of GRK2 in Chronic Pain
- Stress Management, PNI, and Disease
- Methods, Variance, and Error in Psychoneuroimmunology Research: The Good, the Bad, and the Ugly
- Looking into the Future: Conclusion to the Oxford Handbook of Psychoneuroimmunology
- Author Index
- Subject Index
Abstract and Keywords
Every researcher deals with error at some level. In psychoneuroimmunology (PNI) research, there may be error due to substantive fluctuations in immune parameters (e.g., as related to stress, time of day, or activity). This error is significant for some parameters, but it can and should be minimized by taking multiple measurements or converted into “good,” substantive variance by measuring variables that can predict the fluctuations. Type I and Type II “bad” errors are of more concern; many PNI studies have far too few subjects for the number of effects they test. Of studies included in a recent meta-analysis of stress and human immunity, several studies actually had fewer subjects than they had statistical tests. Finally, variance due to assay or supply variability contributes to “ugly” error, and it should be addressed by analysis of covariance or partial variance. However, too often, important variance due to factors such as age is designated as “ugly” rather than incorporated into the model. We suggest solutions for addressing “good,” “bad,” and “ugly” error and look into the future of physiometrics.
Suzanne C. Segerstrom is a professor in the Department of Psychology at the University of Kentucky in Lexington, KY.
Gregory T. Smith, University of Kentucky
Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.
If you have purchased a print title that contains an access token, please see the token for information about how to register your code.