Daniel M McNeish

Biographic Data

ID5742595
NAMEDaniel M McNeish
GIVEN NAMESDaniel M
FAMILY NAMEMcNeish
SIGNATUREMCNEISH D M
AFFILIATIONSUniversity of Maryland, College Park, MD, USA
VERIFIEDNo
TOTAL WORKS5
TOTAL CITATIONS3
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2015
LATEST PUBLICATION YEAR2018
H-INDEX1

  • Increasing the Consequential Validity of Reading Assessment Using Dynamic Measurement Modeling

    Open AccessDominique Dumas, Denis G Dumas et al.ARTICLEEducational Researcher2018References: 1

    Dynamic measurement modeling (DMM) has been shown to improve the consequential validity of longitudinal mathematics assessment in the Early Childhood Longitudinal Study-Kindergarten (ECLS-K) database. Here, the authors demonstrate the capability of DMM to similarly improve the consequential validity of ECLS-K reading assessment through the estimation of student-specific learning capacities and growth parameters for reading

  • Dynamic Measurement Modeling

    Open AccessDominique Dumas, Denis G Dumas et al.ARTICLEEducational Researcher2017Cited by: 3References: 47

    Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate op…

  • The Effect of Small Sample Size on Two-Level Model Estimates

    Open AccessDaniel M McNeish, Daniel McNeish et al.ARTICLEEducational Psychology Review2016

  • The Role of Measurement Quality on Practical Guidelines for Assessing Measurement and Structural Invariance

    Open AccessYoonjeong Kang, Daniel M McNeish et al.ARTICLEEducational and Psychological…2016

    Although differences in goodness-of-fit indices (ΔGOFs) have been advocated for assessing measurement invariance, studies that advanced recommended differential cutoffs for adjudicating invariance actually utilized a very limited range of values representing the quality of indicator variables (i.e., magnitude of loadings). Because quality of measurement has been found to be relevant in the context of assessing data-model fit in single-group model…

  • Using Lasso for Predictor Selection and to Assuage Overfitting

    Daniel M McNeish, Daniel McNeishARTICLEMultivariate Behavioral Research2015

    Ordinary least squares and stepwise selection are widespread in behavioral science research; however, these methods are well known to encounter overfitting problems such that R(2) and regression coefficients may be inflated while standard errors and p values may be deflated, ultimately reducing both the parsimony of the model and the generalizability of conclusions. More optimal methods for selecting predictors and estimating regression coefficie…