scholarly journals A Bayesian approach to estimating variance components within a multivariate generalizability theory framework

2017 ◽  
Vol 50 (6) ◽  
pp. 2193-2214 ◽  
Author(s):  
Zhehan Jiang ◽  
William Skorupski
Keyword(s):  
Bayesian Approach ◽  
Variance Components ◽  
Generalizability Theory ◽  
Multivariate Generalizability Theory ◽  
Theory Framework

On Describing an Incidence Sample

1982 ◽  
Vol 7 (4) ◽  
pp. 311-331 ◽  
Author(s):  
Gwyneth M. Boodoo
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Standard Errors ◽  
Eighth Grade ◽  
Eighth Grade Students ◽  
Competency Based ◽  
Mean And Variance ◽  
The Mean ◽  
Using Data ◽  
Variance Estimates

Parameters used to describe an incidence sample are estimated using the theory of generalized symmetric means and generalizability theory. The former is used to compute estimates of the mean and variance components in an ANOVA framework, while the latter is used in obtaining generalizability coefficients. Standard errors of the variance estimates are obtained. The procedure is illustrated using data from two competency-based tests given to eighth grade students in mathematics and reading.

An Alternative Method for Estimating Variance Components in Generalizability Theory

1990 ◽  
Vol 66 (2) ◽  
pp. 379-386 ◽  
Author(s):  
George A. Marcoulides
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Restricted Maximum Likelihood ◽  
Alternative Method ◽  
Sample Data

This study compares, using simulated data, two methods for estimating variance components in generalizability (G) studies. Traditionally variance components are estimated from an analysis of variance of sample data. The alternative method for estimating variance components is restricted maximum likelihood (REML). The results indicate that REML provides estimates for the components in the various designs that are closer to the true parameters than the estimates from analysis of variance.

scholarly journals Measuring motivational relationship processes in experience sampling: A reliability model for moments, days, and persons nested in couples

2021 ◽  
Author(s):  
Felix D. Schönbrodt ◽  
Caroline Zygar-Hoffmann ◽  
Steffen Nestler ◽  
Sebastian Pusch ◽  
Birk Hagemeyer
Keyword(s):  
Variance Components ◽  
Generalizability Theory ◽  
Relative Proportion ◽  
Experience Sampling ◽  
Process Models ◽  
Sampling Studies ◽  
Reliable Assessment ◽  
Relationship Processes ◽  
Level Scale ◽  
Variance Estimates

AbstractThe investigation of within-person process models, often done in experience sampling designs, requires a reliable assessment of within-person change. In this paper, we focus on dyadic intensive longitudinal designs where both partners of a couple are assessed multiple times each day across several days. We introduce a statistical model for variance decomposition based on generalizability theory (extending P. E. Shrout & S. P. Lane, 2012), which can estimate the relative proportion of variability on four hierarchical levels: moments within a day, days, persons, and couples. Based on these variance estimates, four reliability coefficients are derived: between-couples, between-persons, within-persons/between-days, and within-persons/between-moments. We apply the model to two dyadic intensive experience sampling studies (n1 = 130 persons, 5 surveys each day for 14 days, ≥ 7508 unique surveys; n2 = 508 persons, 5 surveys each day for 28 days, ≥ 47764 unique surveys). Five different scales in the domain of motivational processes and relationship quality were assessed with 2 to 5 items: State relationship satisfaction, communal motivation, and agentic motivation; the latter consists of two subscales, namely power and independence motivation. Largest variance components were on the level of persons, moments, couples, and days, where within-day variance was generally larger than between-day variance. Reliabilities ranged from .32 to .76 (couple level), .93 to .98 (person level), .61 to .88 (day level), and .28 to .72 (moment level). Scale intercorrelations reveal differential structures between and within persons, which has consequences for theory building and statistical modeling.

scholarly journals Initial Validation Evidence for Clinical Case Presentations by Student Pharmacists

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Jennifer S Byrd ◽  
Michael J Peeters
Keyword(s):  
Clinical Case ◽  
Rating Scale ◽  
Generalizability Theory ◽  
Rasch Measurement ◽  
Care Plan ◽  
Point Rating Scale ◽  
Performance Based Assessment ◽  
Multiple Case ◽  
Case Presentations ◽  
Multivariate Generalizability Theory

Objective: There is a paucity of validation evidence for assessing clinical case-presentations by Doctor of Pharmacy (PharmD) students.  Within Kane’s Framework for Validation, evidence for inferences of scoring and generalization should be generated first.  Thus, our objectives were to characterize and improve scoring, as well as build initial generalization evidence, in order to provide validation evidence for performance-based assessment of clinical case-presentations. Design: Third-year PharmD students worked up patient-cases from a local hospital.  Students orally presented and defended their therapeutic care-plan to pharmacist preceptors (evaluators) and fellow students.  Evaluators scored each presentation using an 11-item instrument with a 6-point rating-scale.  In addition, evaluators scored a global-item with a 4-point rating-scale.  Rasch Measurement was used for scoring analysis, while Generalizability Theory was used for generalization analysis. Findings: Thirty students each presented five cases that were evaluated by 15 preceptors using an 11-item instrument.  Using Rasch Measurement, the 11-item instrument’s 6-point rating-scale did not work; it only worked once collapsed to a 4-point rating-scale.  This revised 11-item instrument also showed redundancy.  Alternatively, the global-item performed reasonably on its own.  Using multivariate Generalizability Theory, the g-coefficient (reliability) for the series of five case-presentations was 0.76 with the 11-item instrument, and 0.78 with the global-item.  Reliability was largely dependent on multiple case-presentations and, to a lesser extent, the number of evaluators per case-presentation.  Conclusions: Our pilot results confirm that scoring should be simple (scale and instrument).  More specifically, the longer 11-item instrument measured but had redundancy, whereas the single global-item provided measurement over multiple case-presentations.  Further, acceptable reliability can be balanced between more/fewer case-presentations and using more/fewer evaluators.

scholarly journals Updating Knowledge in The Estimation of The Genetics Parameters Multi-trait and Multi-Environment Bayesian Analysis in Rice (Oryza Sativa L.)

2021 ◽  
Author(s):  
Camila Ferreira Azevedo ◽  
Cynthia Barreto ◽  
Matheus Suela ◽  
Moysés Nascimento ◽  
Antônio Carlos Júnior ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Prior Distribution ◽  
Variance Components ◽  
Genetic Correlations ◽  
Bayesian Framework ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Informative Prior Distributions ◽  
The Bayesian Approach

Abstract Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.

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