IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v46y2021i4p501-518.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998620978554
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998620978554?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tommaso AGASISTI & Geraint JOHNES & Marco PACCAGNELLA, 2021. "Tasks, occupations and wages in OECD countries," International Labour Review, International Labour Organization, vol. 160(1), pages 85-112, March.
    2. repec:hal:spmain:info:hdl:2441/6vfmfoopnt95qblsf6jj9f6ics is not listed on IDEAS
    3. Pauline Givord, 2021. "How age at school entry affects future educational and socioemotional outcomes: Evidence from PISA," Working Papers hal-03386582, HAL.
    4. John Jerrim & Nikki Shure & Gill Wyness, 2020. "Driven to succeed? Teenagers' drive, ambition and performance on high-stakes examinations," CEPEO Working Paper Series 20-13, UCL Centre for Education Policy and Equalising Opportunities, revised Jul 2020.
    5. Asuyama, Yoko, 2016. "Delegation to workers across countries and industries : social capital and coordination needs matter," IDE Discussion Papers 620, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    6. Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2021. "On the Treatment of Missing Data in Background Questionnaires in Educational Large-Scale Assessments: An Evaluation of Different Procedures," Journal of Educational and Behavioral Statistics, , vol. 46(4), pages 430-465, August.
    7. Brian Jacob & Jesse Rothstein, 2016. "The Measurement of Student Ability in Modern Assessment Systems," Journal of Economic Perspectives, American Economic Association, vol. 30(3), pages 85-108, Summer.
    8. repec:spo:wpmain:info:hdl:2441/6vfmfoopnt95qblsf6jj9f6ics is not listed on IDEAS
    9. Juan Aparicio & Jose M. Cordero & Lidia Ortiz, 2021. "Efficiency Analysis with Educational Data: How to Deal with Plausible Values from International Large-Scale Assessments," Mathematics, MDPI, vol. 9(13), pages 1-16, July.
    10. Manuel Salas‐Velasco, 2020. "Assessing the performance of Spanish secondary education institutions: Distinguishing between transient and persistent inefficiency, separated from heterogeneity," Manchester School, University of Manchester, vol. 88(4), pages 531-555, July.
    11. Paul Gregg & John Jerrim & Lindsey Macmillan & Nikki Shure, 2017. "Children in jobless households across Europe: Evidence on the association with medium- and long-term outcomes," DoQSS Working Papers 17-05, Quantitative Social Science - UCL Social Research Institute, University College London.
    12. Davide Azzolini & Antonio Schizzerotto, 2017. "The second digital divide in Europe. A crossnational study on students’ digital reading and navigation skills," FBK-IRVAPP Working Papers 2017-02, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.
    13. Karl Fritjof Krassel & Kenneth Lykke Sørensen, 2015. "Childhood and Adulthood Skill Acquisition - Importance for Labor Market Outcomes," Economics Working Papers 2015-20, Department of Economics and Business Economics, Aarhus University.
    14. Maarten Marsman & Gunter Maris & Timo Bechger & Cees Glas, 2016. "What can we learn from Plausible Values?," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 274-289, June.
    15. Li Cai & Carrie R. Houts, 2021. "Longitudinal Analysis of Patient-Reported Outcomes in Clinical Trials: Applications of Multilevel and Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 86(3), pages 754-777, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:46:y:2021:i:4:p:501-518. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.