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Anna-Carolina Haensch; Jonathan Bartlett; Bernd Weiß – Sociological Methods & Research, 2024
Discrete-time survival analysis (DTSA) models are a popular way of modeling events in the social sciences. However, the analysis of discrete-time survival data is challenged by missing data in one or more covariates. Negative consequences of missing covariate data include efficiency losses and possible bias. A popular approach to circumventing…
Descriptors: Research Methodology, Research Problems, Social Science Research, Statistical Analysis
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Bom, Pedro R. D.; Rachinger, Heiko – Research Synthesis Methods, 2019
Publication bias distorts the available empirical evidence and misinforms policymaking. Evidence of publication bias is mounting in virtually all fields of empirical research. This paper proposes the endogenous kink (EK) meta-regression model as a novel method of publication bias correction. The EK method fits a piecewise linear meta-regression of…
Descriptors: Bias, Publications, Models, Regression (Statistics)
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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
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Li, Tongyun; Jiao, Hong; Macready, George B. – Educational and Psychological Measurement, 2016
The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo…
Descriptors: Item Response Theory, Psychometrics, Test Construction, Monte Carlo Methods
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Lang, Kyle M.; Little, Todd D. – International Journal of Behavioral Development, 2014
We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model ?2 difference testing. A Monte Carlo simulation study was conducted to…
Descriptors: Hypothesis Testing, Data Analysis, Error of Measurement, Computation
Owens, Corina M. – ProQuest LLC, 2011
Numerous ways to meta-analyze single-case data have been proposed in the literature, however, consensus on the most appropriate method has not been reached. One method that has been proposed involves multilevel modeling. This study used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena's (2008) raw data multilevel…
Descriptors: Monte Carlo Methods, Meta Analysis, Case Studies, Research Design
Kreft, Ita G. G.; Yoon, Bokhee – 1994
The merits of the multilevel model for educational research and its uses for school effectiveness research are considered. The main goal of the paper is to establish what intelligent applications of multilevel models can do, helping researchers decide what they must do to make a rational choice between models. Multilevel models are random line…
Descriptors: Correlation, Data Analysis, Educational Research, Effective Schools Research