scholarly journals A Weighted Residual Bootstrap Method for Multilevel Modeling with Sampling Weights

2021 ◽  
Author(s):  
Wen Luo ◽  
Hok Chio Lai
Keyword(s):  
Sample Size ◽  
Multilevel Modeling ◽  
Bootstrap Method ◽  
Intraclass Correlation ◽  
R Package ◽  
Sampling Weights ◽  
Multistage Sampling ◽  
The Impact ◽  
Level 2 ◽  
The Bootstrap Method

Multilevel modeling is often used to analyze survey data collected with a multistage sampling design. When the selection is informative, sampling weights need to be incorporated in the estimation. We propose a weighted residual bootstrap method as an alternative to the multilevel pseudo-maximum likelihood (MPML) estimators. In a Monte Carlo simulation using two-level linear mixed effects models, the bootstrap method showed advantages over MPML for the estimates and the statistical inferences of the intercept, the slope of the level-2 predictor, and the variance components at level-2. The impact of sample size, selection mechanism, intraclass correlation (ICC), and distributional assumptions on the performance of the methods were examined. The performance of MPML was suboptimal when sample size and ICC were small and when the normality assumption was violated. The bootstrap estimates performed generally well across all the simulation conditions, but had notably suboptimal performance in estimating the covariance component in a random slopes model when sample size and ICCs were large. As an illustration, the bootstrap method is applied to the American data of the OECD’s Program for International Students Assessment (PISA) survey on math achievement using the R package bootmlm.

scholarly journals Using Bootstrap Method to Evaluate the Power of Tests for Non-Linearity in Asymmetric Price Relationship

2013 ◽  
Vol 5 (4) ◽  
pp. 237-241
Author(s):  
Henry De-Graft Acquah
Keyword(s):  
Sample Size ◽  
Bootstrap Method ◽  
Complex Model ◽  
Model Complexity ◽  
Bootstrap Sample ◽  
Power Of The Test ◽  
Data Generating Process ◽  
Price Relationship ◽  
False Null Hypothesis ◽  
The Bootstrap Method

This paper introduces and applies the bootstrap method to compare the power of the test for asymmetry in the Granger and Lee (1989) and Von Cramon-Taubadel and Loy (1996) models. The results of the bootstrap simulations indicate that the power of the test for asymmetry depends on various conditions such as the bootstrap sample size, model complexity, difference in adjustment speeds and the amount of noise in the data generating process used in the application. The true model achieves greater power when compared with the complex model. With small bootstrap sample size or large noise, both models display low power in rejecting the (false) null hypothesis of symmetry.

scholarly journals Estimation of sensitivity and specificity and calculation of sample size for a validation study with stratified sampling

2020 ◽  
Author(s):  
Kiyoshi Kubota ◽  
Masao Iwagami ◽  
Takuhiro Yamaguchi
Keyword(s):  
Sample Size ◽  
Sensitivity And Specificity ◽  
Validation Study ◽  
Predictive Value ◽  
Validation Studies ◽  
Bootstrap Method ◽  
Stratified Sampling ◽  
Delta Method ◽  
Estimate Sample Size ◽  
The Bootstrap Method

Abstract Background:We propose and evaluate the approximation formulae for the 95% confidence intervals (CIs) of the sensitivity and specificity and a formula to estimate sample size in a validation study with stratified sampling where positive samples satisfying the outcome definition and negative samples that do not are selected with different extraction fractions. Methods:We used the delta method to derive the approximation formulae for estimating the sensitivity and specificity and their CIs. From those formulae, we derived the formula to estimate the size of negative samples required to achieve the intended precision and the formula to estimate the precision for a negative sample size arbitrarily selected by the investigator. We conducted simulation studies in a population where 4% were outcome definition positive, the positive predictive value (PPV)=0.8, and the negative predictive value (NPV)=0.96, 0.98 and 0.99. The size of negative samples, n0, was either selected to make the 95% CI fall within ± 0.1, 0.15 and 0.2 or set arbitrarily as 150, 300 and 600. We assumed a binomial distribution for the positive and negative samples. The coverage of the 95% CIs of the sensitivity and specificity was calculated as the proportion of CIs including the sensitivity and specificity in the population, respectively. For selected studies, the coverage was also estimated by the bootstrap method. The sample size was evaluated by examining whether the observed precision was within the pre-specified value.Results:For the sensitivity, the coverage of the approximated 95% CIs was larger than 0.95 in most studies but in 9 of 18 selected studies derived by the bootstrap method. For the specificity, the coverage of the approximated 95% CIs was approximately 0.93 in most studies, but the coverage was more than 0.95 in all 18 studies derived by the bootstrap method. The calculated size of negative samples yielded precisions within the pre-specified values in most of the studies.Conclusion:The approximation formulae for the 95% CIs of the sensitivity and specificity for stratified validation studies are presented. These formulae will help in conducting and analysing validation studies with stratified sampling.

scholarly journals Influence Function and Bootstrap Methods of Estimating the Standard Errors of the Estimators of Mixture Exponential Distribution Parameter

2021 ◽  
pp. 113-136
Author(s):  
J. I. Udobi ◽  
G. A. Osuji ◽  
S. I. Onyeagu ◽  
H. O. Obiora-Ilouno
Keyword(s):  
Sample Size ◽  
Standard Error ◽  
Influence Function ◽  
Bootstrap Method ◽  
Standard Errors ◽  
Sample Sizes ◽  
Trimmed Mean ◽  
Contamination Levels ◽  
M Estimate ◽  
The Bootstrap Method

This work estimated the standard error of the maximum likelihood estimator (MLE) and the robust estimators of the exponential mixture parameter (θ) using the influence function and the bootstrap approaches. Mixture exponential random samples of sizes 10, 15, 20, 25, 50, and 100 were generated using 3 mixture exponential models at 2%, 5% and 10% contamination levels. The selected estimators namely: mean, median, alpha-trimmed mean, Huber M-estimate and their standard errors (Tn ) were estimated using the two approaches at the indicated sample sizes and contamination levels. The results were compared using the coefficient of variation, confidence interval and the asymptotic relative efficiency of Tn in order to find out which approach yields the more reliable, precise and efficient estimate of Tn. The results of the analysis show that the two approaches do not equally perform at all conditions. From the results, the bootstrap method was found to be more reliable and efficient method of estimating the standard error of the arithmetic mean at all sample sizes and contamination levels. In estimating the standard error of the median, the influence function method was found to be more effective especially when the sample size is small and yet contamination is high. The influence function based approach yielded more reliable, precise and efficient estimates of the standard errors of the alpha-trimmed mean and the Huber M-estimate for all sample sizes and levels of contamination although the reliability of the bootstrap method improved better as sample size increased to 50 and above. All simulations and analysis were carried out in R programming language.

scholarly journals Effect of plot size and plant spatial arrangement on the efficiency of family selection in sugarcane

2021 ◽  
Vol 53 (2) ◽  
pp. 1-10
Author(s):  
Aparecido De Moraes ◽  
Matheus Henrique Silveira Mendes ◽  
Mauro Sérgio de Oliveira Leite ◽  
Regis De Castro Carvalho ◽  
Flávia Maria Avelar Gonçalves
Keyword(s):  
Sample Size ◽  
Bootstrap Method ◽  
Spatial Arrangement ◽  
Family Selection ◽  
Plot Size ◽  
Stalk Diameter ◽  
New Varieties ◽  
Selection Of ◽  
The Bootstrap Method ◽  
The Ideal

The purpose of this study was to identify the ideal sample size representing a family in its potential, to identify superior families and, in parallel, determine in which spatial arrangement they may have a better accuracy in the selection of new varieties of sugarcane. For such purpose, five families of full-sibs were evaluated, each with 360 individuals, in the randomized blocks design, with three replications in three different spacing among plants in the row (50 cm, 75 cm, and 100 cm) and 150 cm between the rows. To determine the ideal sample size, as well as the better spacing for evaluation, the bootstrap method was adopted. It was observed that 100 cm spacings provided the best average for the stalk numbers, stalk diameter and for estimated weight of stalks in the stool. The spacing of 75 cm between the plants allowed a better power of discrimination among the families for all characters evaluated. At this 75 cm spacing  was also possible to identify superior families with a sample of 30 plants each plot and 3 reps in the trial. Highlights The bootstrap method was efficient to determine the ideal sample size, as well as the best spacing for evaluation. The 75-cm spacing had the highest power of discrimination among families, indicating that this spacing is the most efficient in evaluating sugarcane families for selection purposes. From all the results and considering selective accuracy as the guiding parameter for decision making, the highest values obtained considering the number of stalks and weight of stalks in the stools were found at the 75-cm spacing.

scholarly journals Pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling: studi dengan simulasi Monte Carlo

2017 ◽  
Vol 21 (1) ◽  
pp. 34-50
Author(s):  
Muhammad Dwirifqi Kharisma Putra ◽  
Jahja Umar ◽  
Bahrul Hayat ◽  
Agung Priyo Utomo
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sample Size ◽  
Latent Variable ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Latent Variable Modeling ◽  
Intraclass Correlation Coefficients ◽  
The Impact

Studi ini menggunakan simulasi Monte Carlo dilakukan untuk melihat pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling. Kondisi simulasi diciptakan dengan beberapa faktor yang ditetapkan yaitu lima kondisi ICC (0.05, 0.10, 0.15, 0.20, 0.25), jumlah kelompok (30, 50, 100 dan 150), jumlah observasi dalam kelompok (10, 20 dan 50) dan diestimasi menggunakan lima metode estimasi: ML, MLF, MLR, WLSMV dan BAYES. Jumlah kondisi keseluruhan sebanyak 300 kondisi dimana tiap kondisi direplikasi sebanyak 1000 kali dan dianalisis menggunakan software Mplus 7.4. Kriteria bias yang masih dapat diterima adalah < 10%. Hasil penelitian ini menunjukkan bahwa bias yang terjadi dipengaruhi oleh ukuran sampel dan ICC, penelitian ini juga menujukkan bahwa metode estimasi WLSMV dan BAYES berfungsi lebih baik pada berbagai kondisi dibandingkan dengan metode estimasi berbasis ML.Kata kunci: multilevel latent variable modeling, intraclass correlation coefficients, Metode Markov Chain Monte Carlo THE IMPACT OF SAMPLE SIZE AND INTRACLASS CORRELATION COEFFICIENTS (ICC) ON THE BIAS OF PARAMETER ESTIMATION IN MULTILEVEL LATENT VARIABLE MODELING: A MONTE CARLO STUDYAbstractA monte carlo study was conducted to investigate the effect of sample size and intraclass correlation coefficients (ICC) on the bias of parameter estimates in multilevel latent variable modeling. The design factors included (ICC: 0.05, 0.10, 0.15, 0.20, 0.25), number of groups in between level model (NG: 30, 50, 100 and 150), cluster size (CS: 10, 20 and 50) to be estimated with five different estimator: ML, MLF, MLR, WLSMV and BAYES. Factors were interegated into 300 conditions (4 NG  3 CS  5 ICC  5 Estimator). For each condition, replications with convergence problems were exclude until at least 1.000 replications were generated and analyzed using Mplus 7.4, we also consider absolute percent bias <10% to represent an acceptable level of bias. We find that the degree of bias depends on sample size and ICC. We also show that WLSMV and BAYES estimator performed better than ML-based estimator across varying sample sizes and ICC’s conditions.Keywords: multilevel latent variable modeling, intraclass correlation coefficients, Markov Chain Monte Carlo method

scholarly journals Prediction in Multilevel Models

2005 ◽  
Vol 30 (2) ◽  
pp. 109-139 ◽  
Author(s):  
David Afshartous ◽  
Jan de Leeuw
Keyword(s):  
Sample Size ◽  
Parameter Space ◽  
Multilevel Models ◽  
Intraclass Correlation ◽  
Prediction Rule ◽  
Hierarchical Data ◽  
Data Set ◽  
Prediction Rules ◽  
Level 1 ◽  
Level 2

Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2 (group) sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, prior, and multilevel estimators for the Level 1 coefficientsβj The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups, J, increases. Finally, this article investigates the robustness of the multilevel prediction rule to misspecifications of the Level 2 model.

Exploring the Benefits of Early Contractor Involvement (ECI) and the Mechanism for Automated 5D BIM Quantification Process in Offsite Modular Construction

2016 ◽  
Author(s):  
Tochukwu Moses ◽  
David Heesom ◽  
David Oloke ◽  
Martin Crouch
Keyword(s):  
Effective Means ◽  
Cost Effective ◽  
Modular Construction ◽  
Automated Quantification ◽  
Building Information ◽  
Project Risks ◽  
Value Add ◽  
The Uk ◽  
The Impact ◽  
Level 2

The UK Construction Industry through its Government Construction Strategy has recently been mandated to implement Level 2 Building Information Modelling (BIM) on public sector projects. This move, along with other initiatives is key to driving a requirement for 25% cost reduction (establishing the most cost-effective means) on. Other key deliverables within the strategy include reduction in overall project time, early contractor involvement, improved sustainability and enhanced product quality. Collaboration and integrated project delivery is central to the level 2 implementation strategy yet the key protocols or standards relative to cost within BIM processes is not well defined. As offsite construction becomes more prolific within the UK construction sector, this construction approach coupled with BIM, particularly 5D automated quantification process, and early contractor involvement provides significant opportunities for the sector to meet government targets. Early contractor involvement is supported by both the industry and the successive Governments as a credible means to avoid and manage project risks, encourage innovation and value add, making cost and project time predictable, and improving outcomes. The contractor is seen as an expert in construction and could be counter intuitive to exclude such valuable expertise from the pre-construction phase especially with the BIM intent of äóÖbuild it twiceäó», once virtually and once physically. In particular when offsite construction is used, the contractoräó»s construction expertise should be leveraged for the virtual build in BIM-designed projects to ensure a fully streamlined process. Building in a layer of automated costing through 5D BIM will bring about a more robust method of quantification and can help to deliver the 25% reduction in overall cost of a project. Using a literature review and a case study, this paper will look into the benefits of Early Contractor Involvement (ECI) and the impact of 5D BIM on the offsite construction process.

The Impact of School Environment and Grade Level on Student Delinquency: A Multilevel Modeling Approach

2020 ◽  
Author(s):  
Celia C. Lo ◽  
Young S. Kim ◽  
Thomas Allen ◽  
Andrea Allen ◽  
P. Allison Minugh ◽  
...  
Keyword(s):  
Multilevel Modeling ◽  
School Environment ◽  
Grade Level ◽  
Modeling Approach ◽  
The Impact

MHealth Applications for People with Cognitive Impairment and Their Caregivers: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Qing Zhao ◽  
Pei Chen ◽  
Yu Zhang ◽  
Haining Liu ◽  
Xianwen Li
Keyword(s):  
Cognitive Impairment ◽  
Sample Size ◽  
Research Design ◽  
Scoping Review ◽  
Life Support ◽  
Small Sample ◽  
Mobile Technologies ◽  
Control Group ◽  
Caregiver Support ◽  
The Impact

BACKGROUND Mobile health application has become an important tool for healthcare systems. One such tool is the delivery of assisting in people with cognitive impairment and their caregivers. OBJECTIVE This scoping review aims to explore and evaluate the existing evidence and challenges on the use of mHealth applications that assisting in people with cognitive impairment and their caregivers. METHODS Nine databases, including PubMed, EMBASE, Cochrane, PsycARTICLES, CINAHL, Web of Science, Applied Science & Technology Source, IEEE Xplore and the ACM Digital Library were searched from inception through June 2020 for the studies of mHealth applications on people with cognitive impairment and their caregivers. Two reviewers independently extracted, checked synthesized data independently. RESULTS Of the 6101 studies retrieved, 64 studies met the inclusion criteria. Three categories emerged from this scoping review. These categories are ‘application functionality’, ‘evaluation strategies’, ‘barriers and challenges’. All the included studies were categorized into 7 groups based on functionality: (1) cognitive assessment; (2) cognitive training; (3) life support; (4) caregiver support; (5) symptom management; (6) reminiscence therapy; (7) exercise intervention. The included studies were broadly categorized into four types: (1) Usability testing; (2) Pilot and feasibility studies; (3) Validation studies; and (4) Efficacy or Effectiveness design. These studies had many defects in research design such as: (1) small sample size; (2) deficiency in active control group; (3) deficiency in analyzing the effectiveness of intervention components; (4) lack of adverse reactions and economic evaluation; (5) lack of consideration about the education level, electronic health literacy and smartphone proficiency of the participants; (6) deficiency in assessment tool; (7) lack of rating the quality of mHealth application. Some progress should be improved in the design of smartphone application functionality, such as: (1) the design of cognitive measurements and training game need to be differentiated; (2) reduce the impact of the learning effect. Besides this, few studies used health behavior theory and performed with standardized reporting. CONCLUSIONS Preliminary results show that mobile technologies facilitate the assistance in people with cognitive impairment and their caregivers. The majority of mHealth application interventions incorporated usability outcome and health outcomes. However, these studies have many defects in research design that limit the extrapolation of research. The content of mHealth application is urgently improved to adapt to demonstrate the real effect. In addition, further research with strong methodological rigor and adequate sample size are needed to examine the feasibility, effectiveness, and cost-effectiveness of mHealth applications for people with cognitive impairment and their caregivers.

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