20240613 - bib update

book.bib

@@ -111444,6 +111444,33 @@ @Article{Myers2002
   url      = {https://www.sciencedirect.com/science/article/pii/S0890856709606535},
 }
 
+
+@Article{Geldhof2014,
+  author   = {Geldhof, G. John and Preacher, Kristopher J. and Zyphur, Michael J.},
+  journal  = {Psychological Methods},
+  title    = {Reliability estimation in a multilevel confirmatory factor analysis framework},
+  year     = {2014},
+  number   = {1},
+  pages    = {72--91},
+  volume   = {19},
+  abstract = {Scales with varying degrees of measurement reliability are often used in the context of multistage sampling, where variance exists at multiple levels of analysis (e.g., individual and group). Because methodological guidance on assessing and reporting reliability at multiple levels of analysis is currently lacking, we discuss the importance of examining level-specific reliability. We present a simulation study and an applied example showing different methods for estimating multilevel reliability using multilevel confirmatory factor analysis and provide supporting Mplus program code. We conclude that (a) single-level estimates will not reflect a scale’s actual reliability unless reliability is identical at each level of analysis, (b) 2-level alpha and composite reliability (omega) perform relatively well in most settings, (c) estimates of maximal reliability (H) were more biased when estimated using multilevel data than either alpha or omega, and (d) small cluster size can lead to overestimates of reliability at the between level of analysis. We also show that Monte Carlo confidence intervals and Bayesian credible intervals closely reflect the sampling distribution of reliability estimates under most conditions. We discuss the estimation of credible intervals using Mplus and provide R code for computing Monte Carlo confidence intervals. (PsycINFO Database Record (c) 2019 APA, all rights reserved)},
+  doi      = {10.1037/a0032138},
+  keywords = {*Confirmatory Factor Analysis *Estimation *Factor Analysis *Statistical Reliability *Structural Equation Modeling Confidence Limits (Statistics)},
+}
+
+@Article{Hove2022,
+  author   = {ten Hove, Debby and Jorgensen, Terrence D. and van der Ark, L. Andries},
+  journal  = {Psychological Methods},
+  title    = {Interrater reliability for multilevel data: A generalizability theory approach},
+  year     = {2022},
+  number   = {4},
+  pages    = {650--666},
+  volume   = {27},
+  abstract = {Current interrater reliability (IRR) coefficients ignore the nested structure of multilevel observational data, resulting in biased estimates of both subject- and cluster-level IRR. We used generalizability theory to provide a conceptualization and estimation method for IRR of continuous multilevel observational data. We explain how generalizability theory decomposes the variance of multilevel observational data into subject-, cluster-, and rater-related components, which can be estimated using Markov chain Monte Carlo (MCMC) estimation. We explain how IRR coefficients for each level can be derived from these variance components, and how they can be estimated as intraclass correlation coefficients (ICC). We assessed the quality of MCMC point and interval estimates with a simulation study, and showed that small numbers of raters were the main source of bias and inefficiency of the ICCs. In a follow-up simulation, we showed that a planned missing data design can diminish most estimation difficulties in these conditions, yielding a useful approach to estimating multilevel interrater reliability for most social and behavioral research. We illustrated the method using data on student–teacher relationships. All software code and data used for this article is available on the Open Science Framework: https://osf.io/bwk5t/. (PsycInfo Database Record (c) 2022 APA, all rights reserved)},
+  doi      = {10.1037/met0000391},
+  keywords = {*Estimation *Experimental Design *Interrater Reliability *Simulation *Bayesian Analysis Markov Chains Statistical Probability},
+}
+
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