Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 4 |
Since 2016 (last 10 years) | 8 |
Since 2006 (last 20 years) | 12 |
Descriptor
Source
Grantee Submission | 12 |
Author
Zhang, Zhiyong | 3 |
Bottge, Brian A. | 2 |
Cho, Sun-Joo | 2 |
Lockwood, J. R. | 2 |
Yuan, Ke-Hai | 2 |
Amy K. Nuttall | 1 |
Avi Feller | 1 |
Castellano, Katherine E. | 1 |
Dan Soriano | 1 |
Dyer, W. Justin | 1 |
Eli Ben-Michael | 1 |
More ▼ |
Publication Type
Reports - Research | 10 |
Journal Articles | 7 |
Reports - Descriptive | 1 |
Reports - Evaluative | 1 |
Education Level
Middle Schools | 4 |
Elementary Education | 2 |
Grade 7 | 2 |
Junior High Schools | 2 |
Secondary Education | 2 |
Grade 5 | 1 |
Grade 6 | 1 |
Intermediate Grades | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Dan Soriano; Eli Ben-Michael; Peter Bickel; Avi Feller; Samuel D. Pimentel – Grantee Submission, 2023
Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to…
Descriptors: Statistical Analysis, Computation, Mathematical Formulas, Monte Carlo Methods
Qinyun Lin; Amy K. Nuttall; Qian Zhang; Kenneth A. Frank – Grantee Submission, 2023
Empirical studies often demonstrate multiple causal mechanisms potentially involving simultaneous or causally related mediators. However, researchers often use simple mediation models to understand the processes because they do not or cannot measure other theoretically relevant mediators. In such cases, another potentially relevant but unobserved…
Descriptors: Causal Models, Mediation Theory, Error of Measurement, Statistical Inference
Ke-Hai Yuan; Yong Wen; Jiashan Tang – Grantee Submission, 2022
Structural equation modeling (SEM) and path analysis using composite-scores are distinct classes of methods for modeling the relationship of theoretical constructs. The two classes of methods are integrated in the partial-least-squares approach to structural equation modeling (PLS-SEM), which systematically generates weighted composites and uses…
Descriptors: Statistical Analysis, Weighted Scores, Least Squares Statistics, Structural Equation Models
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
Testing Autocorrelation and Partial Autocorrelation: Asymptotic Methods versus Resampling Techniques
Ke, Zijun; Zhang, Zhiyong – Grantee Submission, 2018
Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such…
Descriptors: Correlation, Mathematical Formulas, Sampling, Monte Carlo Methods
Zhang, Zhiyong; Yuan, Ke-Hai – Grantee Submission, 2016
Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation…
Descriptors: Computation, Error of Measurement, Robustness (Statistics), Statistical Analysis
Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun – Grantee Submission, 2017
The normal-distribution-based likelihood ratio statistic T[subscript ml] = nF[subscript ml] is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that T[subscript ml] follows a central chi-square distribution under H[subscript 0] and a noncentral chi-square…
Descriptors: Statistical Analysis, Evaluation Methods, Structural Equation Models, Reliability
Lockwood, J. R.; Castellano, Katherine E. – Grantee Submission, 2015
This article suggests two alternative statistical approaches for estimating student growth percentiles (SGP). The first is to estimate percentile ranks of current test scores conditional on past test scores directly, by modeling the conditional cumulative distribution functions, rather than indirectly through quantile regressions. This would…
Descriptors: Statistical Analysis, Achievement Gains, Academic Achievement, Computation
Cho, Sun-Joo; Bottge, Brian A. – Grantee Submission, 2015
In a pretest-posttest cluster-randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre-test scores and other related covariates while estimating an intervention effect at post-test. In many applications in education, the total post-test and pre-test scores that ignores measurement error in the…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Pretests Posttests, Scores
Cho, Sun-Joo; Preacher, Kristopher J.; Bottge, Brian A. – Grantee Submission, 2015
Multilevel modeling (MLM) is frequently used to detect group differences, such as an intervention effect in a pre-test--post-test cluster-randomized design. Group differences on the post-test scores are detected by controlling for pre-test scores as a proxy variable for unobserved factors that predict future attributes. The pre-test and post-test…
Descriptors: Structural Equation Models, Hierarchical Linear Modeling, Intervention, Program Effectiveness
Lockwood, J. R.; McCaffrey, Daniel F. – Grantee Submission, 2015
Regression, weighting and related approaches to estimating a population mean from a sample with nonrandom missing data often rely on the assumption that conditional on covariates, observed samples can be treated as random. Standard methods using this assumption generally will fail to yield consistent estimators when covariates are measured with…
Descriptors: Simulation, Computation, Statistical Analysis, Statistical Bias
Kern, Justin L.; McBride, Brent A.; Laxman, Daniel J.; Dyer, W. Justin; Santos, Rosa M.; Jeans, Laurie M. – Grantee Submission, 2016
Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well.…
Descriptors: Psychology, Measurement, Error of Measurement, Measurement Objectives