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A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data

Author

Listed:
  • Takashi Yamashita

    (14701University of Maryland, Baltimore County)

  • Thomas J. Smith

    (2848Northern Illinois University)

  • Phyllis A. Cummins

    (6403Miami University)

Abstract

In order to promote the use of increasingly available large-scale assessment data in education and expand the scope of analytic capabilities among applied researchers, this study provides step-by-step guidance, and practical examples of syntax and data analysis using Mplus. Concise overview and key unique aspects of large-scale assessment data from the 2012/2014 Program for International Assessment of Adult Competencies (PIAAC) are described. Using commonly-used statistical software including SAS and R, a simple macro program and syntax are developed to streamline the data preparation process. Then, two examples of structural equation models are demonstrated using Mplus. The suggested data preparation and analytic approaches can be immediately applicable to existing large-scale assessment data.

Suggested Citation

  • Takashi Yamashita & Thomas J. Smith & Phyllis A. Cummins, 2021. "A Practical Guide for Analyzing Large-Scale Assessment Data Using Mplus: A Case Demonstration Using the Program for International Assessment of Adult Competencies Data," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 501-518, August.
    • Handle: RePEc:sae:jedbes:v:46:y:2021:i:4:p:501-518
    DOI: 10.3102/1076998620978554
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    References listed on IDEAS

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    1. Lynne Schofield & Brian Junker & Lowell Taylor & Dan Black, 2015. "Predictive Inference Using Latent Variables with Covariates," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 727-747, September.
    2. Francesco Avvisati & François Keslair, 2014. "REPEST: Stata module to run estimations with weighted replicate samples and plausible values," Statistical Software Components S457918, Boston College Department of Economics, revised 21 Mar 2024.
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