Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2 results

  • Chapter 1 - Machine Learning Algorithms and Measurement

  • from Part I - Foundations
  • Edited by Louis Tay, Purdue University, Indiana, Sang Eun Woo, Purdue University, Indiana, Tara Behrend, Purdue University, Indiana
  • Book:
    Technology and Measurement around the Globe
    Published online:
    08 November 2023
    Print publication:
    09 November 2023, pp 11-45

  • Summary

    This chapter provides an overview of the common machine learning algorithms used in psychological measurement (to measure human attributes). They include algorithms used to measure personality from interview videos; job satisfaction from open-ended text responses; and group-level emotions from social media posts and internet search trends. These algorithms enable effective and scalable measures of human psychology and behavior, driving technological advancements in measurement. The chapter consists of three parts. We first discuss machine learning and its unique contribution to measurement. We then provide an overview of the common machine learning algorithms used in measurement and their example applications. Finally, we provide recommendations and resources for using machine learning algorithms in measurement.

  • Chapter twenty-four - Missing Data Analysis

  • from Part three - Data Analytic Strategies
  • Edited by Harry T. Reis, University of Rochester, New York, Charles M. Judd, University of Colorado Boulder
  • Book:
    Handbook of Research Methods in Social and Personality Psychology
    Published online:
    05 June 2014
    Print publication:
    24 February 2014, pp 627-652

  • Summary

    This chapter focuses on what has become known as the psychometric or nonrepresentational approach to measurement. It considers the issues traditionally discussed under the heading of reliability, and reviews several still persistent definitions or types of reliability coefficients. The chapter discusses the problems and misuses of coefficient alpha, the most commonly used psychometric index in social-personality psychology. It suggests the generalizability theory as a broader and more heuristic perspective. The chapter examines issues related to construct validation. It also considers the construct validation as the crucial issue in psychological measurement and includes a broad range of validity evidence, focusing on convergent and discriminant aspects. The chapter describes model testing in construct validation and scale construction. It also focuses on the measurement models in structural equation modeling. The chapter reviews three classical strategies (external criterion, rational-intuitive, and internal-factor analytic) and also suggests an integrated model adopting the construct-oriented approach.