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Dongho Shin; Yongyun Shin; Nao Hagiwara – Grantee Submission, 2025
We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates C includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Multivariate Analysis, Data Analysis
William R. Dardick; Jeffrey R. Harring – Journal of Educational and Behavioral Statistics, 2025
Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…
Descriptors: Statistics, Statistics Education, Problem Solving, Multivariate Analysis
Zsuzsa Bakk; Roberto Di Mari; Jennifer Oser; Jouni Kuha – Structural Equation Modeling: A Multidisciplinary Journal, 2022
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Computation, Measurement
E. C. Hedberg – American Journal of Evaluation, 2023
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
Atilgan, Hakan – Eurasian Journal of Educational Research, 2013
Problem Statement: Reliability, which refers to the degree to which measurement results are free from measurement errors, as well as its estimation, is an important issue in psychometrics. Several methods for estimating reliability have been suggested by various theories in the field of psychometrics. One of these theories is the generalizability…
Descriptors: Sample Size, Generalizability Theory, Mathematical Formulas, Measurement Techniques
Wiedmann, Michael; Leach, Ryan C.; Rummel, Nikol; Wiley, Jennifer – Instructional Science: An International Journal of the Learning Sciences, 2012
Schwartz and Martin ("Cogn Instr" 22:129-184, 2004) as well as Kapur ("Instr Sci", this issue, 2012) have found that students can be better prepared to learn about mathematical formulas when they try to invent them in small groups before receiving the canonical formula from a lesson. The purpose of the present research was to investigate how the…
Descriptors: Mathematical Formulas, Intellectual Property, Learning, Multivariate Analysis
Kim, Rae-Seon; Becker, Betsy Jane – Multivariate Behavioral Research, 2010
We examined the degree of dependence between standardized-mean-difference effect sizes in multiple-treatment studies in meta-analysis in terms of the correlation formula provided by Gleser and Olkin (1994). To explore the impact of group size and the values of the true multiple-treatment effect sizes, we simplified the formula for the correlation…
Descriptors: Effect Size, Meta Analysis, Correlation, Control Groups
Peer reviewedDawson-Sunders, Beth K. – Educational and Psychological Measurement, 1982
The canonical redundancy statistic, an estimate of the amount of shared variance between two sets of variables, exhibits an amount of bias similar to that of the first squared canonical correlation coefficient. Two formulae, Wherry and Olkin-Pratt, adequately correct the bias of the redundancy statistic. (Author/BW)
Descriptors: Correlation, Mathematical Formulas, Multivariate Analysis, Statistical Bias
Peer reviewedde Leeuw, Jan – Psychometrika, 1982
Recent work (EJ 208 813) showing that generalized eigenvalue problems in which both matrices are singular can be solved by reducing them to similar problems of smaller order is discussed. Possible extensions of the work are indicated. (Author/JKS)
Descriptors: Mathematical Formulas, Matrices, Multivariate Analysis, Scaling
Peer reviewedStavig, Gordon R.; Acock, Alan C. – Multivariate Behavioral Research, 1981
Examples are given to show how the semistrandardized (SS) regression coefficient provides information not given by the conventional standardized regression coefficients used in factor, canonical, and path analysis. (Author/RL)
Descriptors: Factor Analysis, Mathematical Formulas, Multivariate Analysis, Path Analysis
Peer reviewedLutz, J. Gary; Cundari, Leigh A. – Journal of Educational Statistics, 1989
Means of identifying sources of rejection of hypotheses regarding linear multivariate statistical models are discussed. Problems with the use of a global test using Roy's largest root criterion and means of solving them are presented, along with a practical application of the techniques. (TJH)
Descriptors: Hypothesis Testing, Mathematical Formulas, Mathematical Models, Multivariate Analysis
Computational Formulas for Multivariate Strength of Association from Approximate "F" and "X2" Tests.
Peer reviewedHaase, Richard F. – Multivariate Behavioral Research, 1991
Computational formulas are developed for recovering measures of strength of association from approximate "F" tests and chi-square tests associated with four multivariate test statistics. The four statistics include Wilke's Lambda; Pillai's Trace "V"; Hotelling's Trace "T"; and Roy's greatest characteristic root…
Descriptors: Chi Square, Estimation (Mathematics), Mathematical Formulas, Mathematical Models
Peer reviewedSkinner, C. J. – Psychometrika, 1984
Multivariate selection can be represented as a linear transformation in a geometric framework. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation. (Author/BW)
Descriptors: Factor Analysis, Geometric Concepts, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedEveritt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis
Rupp, Andre A.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2006
One theoretical feature that makes item response theory (IRT) models those of choice for many psychometric data analysts is parameter invariance, the equality of item and examinee parameters from different examinee populations or measurement conditions. In this article, using the well-known fact that item and examinee parameters are identical only…
Descriptors: Psychometrics, Probability, Simulation, Item Response Theory
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