IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v80y2015i3p727-747.html

Predictive Inference Using Latent Variables with Covariates

Author

Listed:
  • Lynne Schofield

  • Brian Junker
  • Lowell Taylor
  • Dan Black

Abstract

Plausible values (PVs) are a standard multiple imputation tool for analysis of large education survey data, which measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations model of Schofield (Modeling measurement error when using cognitive test scores in social science research. Doctoral dissertation. Department of Statistics and Heinz College of Public Policy. Pittsburgh, PA: Carnegie Mellon University, 2008 ). Copyright The Psychometric Society 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:3:p:727-747
    DOI: 10.1007/s11336-014-9415-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-014-9415-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-014-9415-z?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Robert Mislevy, 1991. "Randomization-based inference about latent variables from complex samples," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 177-196, June.
    2. Hua-Hua Chang & William Stout, 1993. "The asymptotic posterior normality of the latent trait in an IRT model," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 37-52, March.
    3. Deping Li & Andreas Oranje & Yanlin Jiang, 2009. "On the Estimation of Hierarchical Latent Regression Models for Large-Scale Assessments," Journal of Educational and Behavioral Statistics, , vol. 34(4), pages 433-463, December.
    4. Kevin Lang & Michael Manove, 2011. "Education and Labor Market Discrimination," American Economic Review, American Economic Association, vol. 101(4), pages 1467-1496, June.
    5. Robert Mislevy, 1993. "Should “multiple imputations” be treated as “multiple indicators”?," Psychometrika, Springer;The Psychometric Society, vol. 58(1), pages 79-85, March.
    6. Neal, Derek A & Johnson, William R, 1996. "The Role of Premarket Factors in Black-White Wage Differences," Journal of Political Economy, University of Chicago Press, vol. 104(5), pages 869-895, October.
    7. Brian Junker & Lynne Schofield & Lowell Taylor, 2012. "The use of cognitive ability measures as explanatory variables in regression analysis," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-19, December.
    8. Jules Ellis & Brian Junker, 1997. "Tail-measurability in monotone latent variable models," Psychometrika, Springer;The Psychometric Society, vol. 62(4), pages 495-523, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

    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. Brian Junker & Lynne Schofield & Lowell Taylor, 2012. "The use of cognitive ability measures as explanatory variables in regression analysis," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-19, December.
    2. Lin, Dajun & Lutter, Randall & Ruhm, Christopher J., 2018. "Cognitive performance and labour market outcomes," Labour Economics, Elsevier, vol. 51(C), pages 121-135.
    3. Schofield, Lynne Steuerle, 2014. "Measurement error in the AFQT in the NLSY79," Economics Letters, Elsevier, vol. 123(3), pages 262-265.
    4. 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.
    5. Javier Cano-Urbina & Patrick L. Mason, 2016. "Acculturation and the labor market in Mexico," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-29, December.
    6. Martin Nordin & Dan‐Olof Rooth, 2009. "The Ethnic Employment and Income Gap in Sweden: Is Skill or Labor Market Discrimination the Explanation?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(3), pages 487-510, September.
    7. Celeste K. Carruthers & Marianne H. Wanamaker, 2017. "Separate and Unequal in the Labor Market: Human Capital and the Jim Crow Wage Gap," Journal of Labor Economics, University of Chicago Press, vol. 35(3), pages 655-696.
    8. Daniel B. Jones & Werner Troesken & Randall Walsh, 2012. "A Poll Tax by any Other Name: The Political Economy of Disenfranchisement," NBER Working Papers 18612, National Bureau of Economic Research, Inc.
    9. Jean-Baptiste Vilain, 2018. "Three essays in applied economics [Trois essais en économie appliquée]," Sciences Po Economics Publications (main) tel-03419493, HAL.
    10. Romain Aeberhardt & Élise Coudin & Roland Rathelot, 2017. "The heterogeneity of ethnic employment gaps," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(1), pages 307-337, January.
    11. Cai, Zhengyu & Maguire, Karen & Winters, John V., 2019. "Who benefits from local oil and gas employment? Labor market composition in the oil and gas industry in Texas and the rest of the United States," Energy Economics, Elsevier, vol. 84(C).
    12. Gil S. Epstein & Dalit Gafni & Erez Siniver, 2014. "Even Education and Experience Has Its Limits: Closing the Wage Gap," Working Papers 2014-14, Bar-Ilan University, Department of Economics.
    13. Jonah B. Gelbach, 2016. "When Do Covariates Matter? And Which Ones, and How Much?," Journal of Labor Economics, University of Chicago Press, vol. 34(2), pages 509-543.
    14. Benjamin Williams, 2019. "Identification of a nonseparable model under endogeneity using binary proxies for unobserved heterogeneity," Quantitative Economics, Econometric Society, vol. 10(2), pages 527-563, May.
    15. Roland G. Fryer, Jr, 2010. "Racial Inequality in the 21st Century: The Declining Significance of Discrimination," NBER Working Papers 16256, National Bureau of Economic Research, Inc.
    16. Andreas Oranje & Andrew Kolstad, 2019. "Research on Psychometric Modeling, Analysis, and Reporting of the National Assessment of Educational Progress," Journal of Educational and Behavioral Statistics, , vol. 44(6), pages 648-670, December.
    17. Nicole Maestas & Kathleen J. Mullen & David Powell & Till von Wachter & Jeffrey B. Wenger, 2023. "The Value of Working Conditions in the United States and the Implications for the Structure of Wages," American Economic Review, American Economic Association, vol. 113(7), pages 2007-2047, July.
    18. Richard Chisik, 2015. "Job market signalling, stereotype threat and counter‐stereotypical behaviour," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 48(1), pages 155-188, February.
    19. Ioannis Kospentaris & Leslie S. Stratton, 2025. "The evolution of labor market disparities between Hispanic and non-Hispanic men: 1970–2019," Journal of Population Economics, Springer;European Society for Population Economics, vol. 38(1), pages 1-21, March.
    20. Germinario, Giuseppe & Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2022. "What can we learn about the effect of mental health on labor market outcomes under weak assumptions? Evidence from the NLSY79," Labour Economics, Elsevier, vol. 79(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:psycho:v:80:y:2015:i:3:p:727-747. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.