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Moretti, Angelo; Whitworth, Adam – Sociological Methods & Research, 2023
Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE…
Descriptors: Simulation, Geometric Concepts, Computation, Measurement
Weicong Lyu; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across…
Descriptors: Data Analysis, Test Items, Psychometrics, Item Response Theory
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
Kristin Porter; Luke Miratrix; Kristen Hunter – Society for Research on Educational Effectiveness, 2021
Background: Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs)…
Descriptors: Statistical Analysis, Hypothesis Testing, Computer Software, Randomized Controlled Trials
Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2023
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random…
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions
Pashley, Nicole E.; Miratrix, Luke W. – Journal of Educational and Behavioral Statistics, 2021
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Mansolf, Maxwell; Jorgensen, Terrence D.; Enders, Craig K. – Grantee Submission, 2020
Structural equation modeling (SEM) applications routinely employ a trilogy of significance tests that includes the likelihood ratio test, Wald test, and score test or modification index. Researchers use these tests to assess global model fit, evaluate whether individual estimates differ from zero, and identify potential sources of local misfit,…
Descriptors: Structural Equation Models, Computation, Scores, Simulation
Wang, Chun; Xu, Gongjun; Zhang, Xue – Grantee Submission, 2019
When latent variables are used as outcomes in regression analysis, a common approach that is used to solve the ignored measurement error issue is to take a multilevel perspective on item response modeling (IRT). Although recent computational advancement allow efficient and accurate estimation of multilevel IRT models, we argue that a two-stage…
Descriptors: Error of Measurement, Item Response Theory, Regression (Statistics), Evaluation Methods
Pashley, Nicole E.; Miratrix, Luke W. – Grantee Submission, 2019
In the causal inference literature, evaluating blocking from a potential outcomes perspective has two main branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide…
Descriptors: Causal Models, Statistical Inference, Research Methodology, Computation
Chung, Seungwon; Cai, Li – Grantee Submission, 2019
The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation (Rubin, 1987) is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in…
Descriptors: Computation, Statistical Inference, Structural Equation Models, Goodness of Fit
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
Shang, Yi; VanIwaarden, Adam; Betebenner, Damian W. – Educational Measurement: Issues and Practice, 2015
In this study, we examined the impact of covariate measurement error (ME) on the estimation of quantile regression and student growth percentiles (SGPs), and find that SGPs tend to be overestimated among students with higher prior achievement and underestimated among those with lower prior achievement, a problem we describe as ME endogeneity in…
Descriptors: Error of Measurement, Regression (Statistics), Achievement Gains, Students
McCaffrey, Daniel F.; Castellano, Katherine E.; Lockwood, J. R. – Educational Measurement: Issues and Practice, 2015
Student growth percentiles (SGPs) express students' current observed scores as percentile ranks in the distribution of scores among students with the same prior-year scores. A common concern about SGPs at the student level, and mean or median SGPs (MGPs) at the aggregate level, is potential bias due to test measurement error (ME). Shang,…
Descriptors: Error of Measurement, Accuracy, Achievement Gains, Students
Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…
Descriptors: Error of Measurement, Monte Carlo Methods, Data Collection, Simulation
Westlund, Erik; Stuart, Elizabeth A. – American Journal of Evaluation, 2017
This article discusses the nonuse, misuse, and proper use of pilot studies in experimental evaluation research. The authors first show that there is little theoretical, practical, or empirical guidance available to researchers who seek to incorporate pilot studies into experimental evaluation research designs. The authors then discuss how pilot…
Descriptors: Use Studies, Pilot Projects, Evaluation Research, Experiments