Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change.

Raudenbush, Stephen W.; Liu, Xiaofeng

doi:10.1037/1082-989x.6.4.387

Cited by 392 publications

(362 citation statements)

References 26 publications

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“…Based on the model defined by Equations (2-4), a total of 11 parameters are specified: four fixed effect coefficients (i.e., µ a1 , µ b1 , µ b2 , and γ ) and seven variances and covariance of random effects (i.e., σ 2 , τ π 00 , τ π 10 , τ π 20 , τ π 11 , τ π 21 , τ π 22 ). Table 1 presents the population parameter values specified based on previous studies (see e.g., [39]). …”

Section: Data Generationmentioning

confidence: 99%

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Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions

Ning

Luo

2017

Front. Appl. Math. Stat.

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Piecewise growth curve model (PGCM) is often used when the underlying growth process is not linear and is hypothesized to consist of phasic developments connected by turning points (or knots or change points). When fitting a PGCM, the conventional practice is to specify turning points a priori. However, the true turning points are often unknown and misspecifications of turning points may occur. The study examined the consequences of turning point misspecifications on growth parameter estimates and evaluated the performance of commonly used fit indices in detecting model misspecification due to mis-specified locations of turning points. In addition, this study introduced and evaluated a newly developed PGCM which allows unknown turning points to be freely estimated. The study found that there are severe consequences of turning point misspecification. Commonly used model fit indices have low power in detecting turning point misspecification. On the other hand, the newly developed PGCM with freely estimated unknown turning point performs well in general.

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Section: Data Generationmentioning

confidence: 99%

“…Given that the growth rate in the first piece is 0.6, following Raudenbush and Liu's [39] effect size equation, the growth rate of the second piece is set to be 0.44 for the medium change condition and 0.54 for the small change condition.…”

Section: Magnitude Of Change In the Growth Ratementioning

confidence: 99%

Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions

Ning

Luo

2017

Front. Appl. Math. Stat.

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“…The variances of b 00 j , c 00k , and their covariance depend on the values of ICC and correlation. j0 and c 10k were set to be half of the variances of b 0 j0 and c 00k as the variances of slopes are often smaller than those of intercepts (Raudenbush & Liu, 2001). The covariances between random intercepts and random slopes (b 0 j0 and b 1 j0 ; c 00k and c 10k ) were set to zero.…”

Section: Model Used To Generate Datamentioning

confidence: 99%

Vol. 16, No. 2 (Full Issue)

2017

J. Mod. App. Stat. Meth.

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EDITORIAL ASSISTANCEJMASM (ISSN 1538−9472, http://digitalcommons.wayne.edu/jmasm) is an independent, open access electronic journal, published biannually in May and November by JMASM Inc. (PO Box 48023, Oak Park, MI, 48237) in collaboration with the Wayne State University Library System. JMASM seeks to publish (1) new statistical tests or procedures, or the comparison of existing statistical tests or procedures, using computer-intensive Monte Carlo, bootstrap, jackknife, or resampling methods, (2) the study of nonparametric, robust, permutation, exact, and approximate randomization methods, and (3) applications of computer programming, preferably in Fortran (all other programming environments are welcome), related to statistical algorithms, pseudo-random number generators, simulation techniques, and self-contained executable code to carry out new or interesting statistical methods.Journal correspondence (other than manuscript submissions) and requests for advertising may be forwarded to ea@jmasm.com. See back matter for instructions for authors. IntroductionComputer simulation studies represent an important tool for investigating statistical procedures difficult or impossible to study using mathematical theory or real data. Descriptors of these studies vary (e.g., statistical experiment, Monte Carlo simulation, computer experiment), but the examples of Hoaglin and Andrews (1975) and Hauck and Anderson (1984) are followed here with use of the term simulation studies. Extensive descriptions of simulation studies can be found in Lewis and Orav (1989) and Santner, Williams, and Notz (2003). In the behavioral sciences simulation studies have been used to study a wide array of statistical methods (e.g., Cribbie, Fiksenbaum, & Wilcox, 2012; Depaoli, 2012; Enders, Baraldi, & Cham, 2014; Hu & Bentler, 1999; Tomarken & Serlin, 1986). The general goal of these studies is to provide evidence of the behavior of statistical methods under a variety of data conditions that improves statistical practice and informs future statistical research. The goal here is to encourage methodological researchers to treat these studies as statistical sampling EXPERIMENTAL DESIGN AND DATA ANALYSIS IN SIMULATION 4 experiments subject to established principles of experimental design and data analysis.An underappreciated facet of simulation studies in statistics is their role in enhancing the reproducibility of scientific findings. The importance of reproducibility has gained momentum in numerous scientific arenas because of growing evidence that many findings cannot be replicated (Stodden, 2015). Concerns over reproducibility and the role of statistics were captured in Statistics and science: A report of the London workshop on the future of the statistical sciences (2014) which noted: "The reproducibility problem goes far beyond statistics, of course, because it involves the entire reward structure of the scientific enterprise. Nevertheless, statistics is a very important ingredient in both the problem and the remedy." (p. 27) Simulati...

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“…Presented in Table 1 are the generating distribution of the residuals in random intercepts and slopes (b 0 j0 , b 1 j0 , c 00k , c 10k ) by correlation and ICC values. Using τ as the symbol for variance, the ICC is calculated as (Raudenbush & Liu, 2001). The covariances between random intercepts and random slopes (b 0 j0 and b 1 j0 ; c 00k and c 10k ) were set to zero.…”

Section: Model Used To Generate Datamentioning

confidence: 99%

The Impact of Inappropriate Modeling of Cross-Classified Data Structures on Random-Slope Models

Ye¹,

Daniel²

2017

J. Mod. App. Stat. Meth.

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Previous studies that explored the impact of misspecification of cross-classified data structure as strictly hierarchical are limited to random intercept models. This study examined the effects of misspecification of a two-level, cross-classified, random effect model (CCREM) where both the level-1 intercept and slope were allowed to vary randomly. Results suggest that ignoring one of the crossed factors produced considerably underestimated standard errors for: 1) the regression coefficients of the level-1 predictor; 2) the inappropriately modeled predictor associated with the misspecified crossed factor; and 3) and their interaction. This misspecification also resulted in a significant inflation of the level-1 residual variances and the intercept and slope variance components across the levels of the remaining crossed factor in hierarchical linear model.

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Effects of study duration, frequency of observation, and sample size on power in studies of group differences in polynomial change.

Cited by 392 publications

References 26 publications

Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions

Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions

Vol. 16, No. 2 (Full Issue)

The Impact of Inappropriate Modeling of Cross-Classified Data Structures on Random-Slope Models

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