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The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
  • Volume 1: Building a Program of Research
  • Edited by Austin Lee Nichols, John Edlund
  • Published online: 25 May 2023
  • Print publication: 08 June 2023
  • The first of three volumes, the five sections of this book cover a variety of issues important in developing, designing, and analyzing data to produce high-quality research efforts and cultivate a productive research career. First, leading scholars from around the world provide a step-by-step guide to doing research in the social and behavioral sciences. After discussing some of the basics, the various authors next focus on the important building blocks of any study. In section three, various types of quantitative and qualitative research designs are discussed, and advice is provided regarding best practices of each. The volume then provides an introduction to a variety of important and cutting-edge statistical analyses. In the last section of the volume, nine chapters provide information related to what it takes to have a long and successful research career. Throughout the book, example and real-world research efforts from dozens of different disciplines are discussed.

  • Examining the effect of mindfulness on well-being: self-connection as a mediator

  • Kristine Klussman, Nicola Curtin, Julia Langer, Austin Lee Nichols
  • Journal: Journal of Pacific Rim Psychology / Volume 14 / 2020
  • Published online by Cambridge University Press: 21 January 2020, e5
  • Print publication: 2020
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  • The current research sought to better understand the effect of mindfulness on well-being by examining self-connection as a potential mediator. We define self-connection as: (1) an awareness of oneself, (2) an acceptance of oneself based on this awareness, and (3) an alignment of one’s behavior with this awareness. Based on this definition, we measured self-connection, mindfulness and well-being using two distinct samples and two different operationalizations of well-being. In Study 1, we recruited 101 people from Amazon’s Mechanical Turk (MTurk) and asked them about their connection to themselves, mindfulness and flourishing. In Study 2, we surveyed an additional 104 people from MTurk, again measuring mindfulness and self-connection. However, this time we operationalized well-being as satisfaction with life. As expected, mindfulness predicted self-connection and well-being in both studies. Self-connection also predicted well-being and partially mediated the relationship between mindfulness and well-being. These results suggest that mindfulness bolsters self-connection, which in turn increases people’s well-being.

  • 19 - Development and Applications of Item Response Theory

  • from Part Five - New Statistical Trends in the Social and Behavioral Sciences
  • Edited by John E. Edlund, Rochester Institute of Technology, New York, Austin Lee Nichols
  • Book: Advanced Research Methods for the Social and Behavioral Sciences
  • Published online: 22 February 2019
  • Print publication: 14 March 2019, pp 309-327

  • Summary

    Item response theory (IRT) represents an alternative measurement approach to Classical test theory (CTT) that has been developed to address some of the key limitations in CTT. IRT utilizes a logistic function to jointly scale both person characteristics (e.g. ability) and task characteristics (e.g. difficulty) along a common metric, and is grounded upon the notion that different item sets should not result in different scaling solutions. This provides IRT with a number of advantages over CTT; namely that the performances of individuals who are administered different sets of tasks may still be justifiably compared. From this basis, the IRT approach has established its utility through its applications in such varied contexts as adaptive testing, cognitive diagnostic modeling, item difficulty modeling, and latent class analyses. The current chapter focuses on applied issues in measurement with IRT, with emphasis on its distinct advantages over traditional approaches.

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