Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives

Kyle D. Husmann; Timothy R. Brick; James C. DiPerna

doi:10.1016/j.jsp.2022.04.004

Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives

Research output: Contribution to journal › Article › peer-review

Abstract

The Measurement Model of Derivatives (MMOD; Estabrook, 2015) provides the opportunity to evaluate and refine measurement scales used in longitudinal studies to clarify their theoretical distinctions and relationship to academic achievement. We demonstrate this using three teacher-rated scales of child self-regulatory behavior obtained from the Early Childhood Longitudinal Study Kindergarten Class of 2010–11 (ECLS-K:2011; Tourangeau et al., 2019). Data-driven factor structures were generated using a training sample (N = 2821), then compared using the MMOD to the theoretical measurement structure on a holdout sample (N = 2822). Finally, to externally validate their utility, the best-fitting data-driven measurement structure was compared to the theoretical structure in their ability to predict academic achievement on a validation sample (N = 5643). We discuss theoretical implications for self-regulation, as well as the MMODs applicability to other educational data sets.

Original language	English (US)
Pages (from-to)	360-375
Number of pages	16
Journal	Journal of School Psychology
Volume	92
DOIs	https://doi.org/10.1016/j.jsp.2022.04.004
State	Published - Jun 2022

All Science Journal Classification (ASJC) codes

Education
Developmental and Educational Psychology

Access to Document

10.1016/j.jsp.2022.04.004

Cite this

@article{501232827a46447c99d197f8c1832a2f,

title = "Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives",

abstract = "The Measurement Model of Derivatives (MMOD; Estabrook, 2015) provides the opportunity to evaluate and refine measurement scales used in longitudinal studies to clarify their theoretical distinctions and relationship to academic achievement. We demonstrate this using three teacher-rated scales of child self-regulatory behavior obtained from the Early Childhood Longitudinal Study Kindergarten Class of 2010–11 (ECLS-K:2011; Tourangeau et al., 2019). Data-driven factor structures were generated using a training sample (N = 2821), then compared using the MMOD to the theoretical measurement structure on a holdout sample (N = 2822). Finally, to externally validate their utility, the best-fitting data-driven measurement structure was compared to the theoretical structure in their ability to predict academic achievement on a validation sample (N = 5643). We discuss theoretical implications for self-regulation, as well as the MMODs applicability to other educational data sets.",

author = "Husmann, \{Kyle D.\} and Brick, \{Timothy R.\} and DiPerna, \{James C.\}",

note = "Funding Information: The research reported here was supported, in whole or in part, by the Institute of Education Sciences, U.S. Department of Education, through grant R305B090007 to The Pennsylvania State University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education. Funding Information: In addition to the fit of the MMOD, the distinguishability of the new DI and SF factors was supported by the DI and SF factors' relatively weak correlations to each other. Furthermore, the 3-item SF factor was present in each of the five candidate factor structures, all of which had MMOD fit statistics that outperformed the theoretical structure as measured by AIC, BIC, RMSEA, and CFI/TLI. This finding points to the underlying process represented by the SF factor as a key feature of the dynamic underlying developmental processes related to self-regulation in the classroom. ☆ The research reported here was supported, in whole or in part, by the Institute of Education Sciences, U.S. Department of Education, through grant R305B090007 to The Pennsylvania State University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education. Publisher Copyright: {\textcopyright} 2022 Society for the Study of School Psychology",

year = "2022",

month = jun,

doi = "10.1016/j.jsp.2022.04.004",

language = "English (US)",

volume = "92",

pages = "360--375",

journal = "Journal of School Psychology",

issn = "0022-4405",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives

AU - Husmann, Kyle D.

AU - Brick, Timothy R.

AU - DiPerna, James C.

N1 - Funding Information: The research reported here was supported, in whole or in part, by the Institute of Education Sciences, U.S. Department of Education, through grant R305B090007 to The Pennsylvania State University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education. Funding Information: In addition to the fit of the MMOD, the distinguishability of the new DI and SF factors was supported by the DI and SF factors' relatively weak correlations to each other. Furthermore, the 3-item SF factor was present in each of the five candidate factor structures, all of which had MMOD fit statistics that outperformed the theoretical structure as measured by AIC, BIC, RMSEA, and CFI/TLI. This finding points to the underlying process represented by the SF factor as a key feature of the dynamic underlying developmental processes related to self-regulation in the classroom. ☆ The research reported here was supported, in whole or in part, by the Institute of Education Sciences, U.S. Department of Education, through grant R305B090007 to The Pennsylvania State University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education. Publisher Copyright: © 2022 Society for the Study of School Psychology

PY - 2022/6

Y1 - 2022/6

N2 - The Measurement Model of Derivatives (MMOD; Estabrook, 2015) provides the opportunity to evaluate and refine measurement scales used in longitudinal studies to clarify their theoretical distinctions and relationship to academic achievement. We demonstrate this using three teacher-rated scales of child self-regulatory behavior obtained from the Early Childhood Longitudinal Study Kindergarten Class of 2010–11 (ECLS-K:2011; Tourangeau et al., 2019). Data-driven factor structures were generated using a training sample (N = 2821), then compared using the MMOD to the theoretical measurement structure on a holdout sample (N = 2822). Finally, to externally validate their utility, the best-fitting data-driven measurement structure was compared to the theoretical structure in their ability to predict academic achievement on a validation sample (N = 5643). We discuss theoretical implications for self-regulation, as well as the MMODs applicability to other educational data sets.

AB - The Measurement Model of Derivatives (MMOD; Estabrook, 2015) provides the opportunity to evaluate and refine measurement scales used in longitudinal studies to clarify their theoretical distinctions and relationship to academic achievement. We demonstrate this using three teacher-rated scales of child self-regulatory behavior obtained from the Early Childhood Longitudinal Study Kindergarten Class of 2010–11 (ECLS-K:2011; Tourangeau et al., 2019). Data-driven factor structures were generated using a training sample (N = 2821), then compared using the MMOD to the theoretical measurement structure on a holdout sample (N = 2822). Finally, to externally validate their utility, the best-fitting data-driven measurement structure was compared to the theoretical structure in their ability to predict academic achievement on a validation sample (N = 5643). We discuss theoretical implications for self-regulation, as well as the MMODs applicability to other educational data sets.

UR - https://www.scopus.com/pages/publications/85130526182

UR - https://www.scopus.com/pages/publications/85130526182#tab=citedBy

U2 - 10.1016/j.jsp.2022.04.004

DO - 10.1016/j.jsp.2022.04.004

M3 - Article

C2 - 35618381

AN - SCOPUS:85130526182

SN - 0022-4405

VL - 92

SP - 360

EP - 375

JO - Journal of School Psychology

JF - Journal of School Psychology

ER -

Evaluating measurement of longitudinal education data using the Measurement Model of Derivatives

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this