scholarly journals Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1445
Author(s):  
Mauro Giammarino ◽  
Silvana Mattiello ◽  
Monica Battini ◽  
Piero Quatto ◽  
Luca Maria Battaglini ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Dairy Goat ◽  
Microsoft Excel ◽  
Bootstrap Methods ◽  
Welfare Indicators ◽  
Observer Reliability ◽  
High Concordance ◽  
Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

scholarly journals Parametric Bootstrap Methods for Estimating Model Parameters of Non-homogeneous Gamma Process

2018 ◽  
Vol 3 (2) ◽  
pp. 167-176 ◽  
Author(s):  
Yasuhiro Saito ◽  
Tadashi Dohi
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Gamma Process ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Trend Function ◽  
Bootstrap Methods ◽  
Parametric Bootstrapping ◽  
Simulation Based

Non-Homogeneous Gamma Process (NHGP) is characterized by an arbitrary trend function and a gamma renewal distribution. In this paper, we estimate the confidence intervals of model parameters of NHGP via two parametric bootstrap methods: simulation-based approach and re-sampling-based approach. For each bootstrap method, we apply three methods to construct the confidence intervals. Through simulation experiments, we investigate each parametric bootstrapping and each construction method of confidence intervals in terms of the estimation accuracy. Finally, we find the best combination to estimate the model parameters in trend function and gamma renewal distribution in NHGP.

scholarly journals Inter- and intra-observer reliability of animal welfare indicators for the on-farm self-assessment of fattening pigs

animal ◽  
2019 ◽  
Vol 13 (8) ◽  
pp. 1712-1720 ◽  
Author(s):  
M. Pfeifer ◽  
L. Eggemann ◽  
J. Kransmann ◽  
A.O. Schmitt ◽  
E.F. Hessel
Keyword(s):  
Animal Welfare ◽  
Self Assessment ◽  
Welfare Indicators ◽  
Observer Reliability ◽  
On Farm ◽  
Fattening Pigs

scholarly journals Animal welfare indicators for sheep during sea transport: the effect of voyage day and time of day

2021 ◽  
pp. 105304
Author(s):  
Renee S. Willis ◽  
Patricia A. Fleming ◽  
Emma J. Dunston-Clarke ◽  
Anne L. Barnes ◽  
David W. Miller ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Time Of Day ◽  
Sea Transport ◽  
Welfare Indicators

scholarly journals Which particles to select, and if yes, how many?

2021 ◽  
Author(s):  
Christian Schwaferts ◽  
Patrick Schwaferts ◽  
Elisabeth von der Esch ◽  
Martin Elsner ◽  
Natalia P. Ivleva
Keyword(s):  
Confidence Intervals ◽  
Bootstrap Method ◽  
Analytical Techniques ◽  
Single Particles ◽  
Minimal Sample ◽  
Error Quantification ◽  
Measurement Algorithm ◽  
Reliable Quantification ◽  
Window Selection ◽  
Number Of Particles

AbstractMicro- and nanoplastic contamination is becoming a growing concern for environmental protection and food safety. Therefore, analytical techniques need to produce reliable quantification to ensure proper risk assessment. Raman microspectroscopy (RM) offers identification of single particles, but to ensure that the results are reliable, a certain number of particles has to be analyzed. For larger MP, all particles on the Raman filter can be detected, errors can be quantified, and the minimal sample size can be calculated easily by random sampling. In contrast, very small particles might not all be detected, demanding a window-based analysis of the filter. A bootstrap method is presented to provide an error quantification with confidence intervals from the available window data. In this context, different window selection schemes are evaluated and there is a clear recommendation to employ random (rather than systematically placed) window locations with many small rather than few larger windows. Ultimately, these results are united in a proposed RM measurement algorithm that computes confidence intervals on-the-fly during the analysis and, by checking whether given precision requirements are already met, automatically stops if an appropriate number of particles are identified, thus improving efficiency.

scholarly journals PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI

2014 ◽  
Vol 3 (4) ◽  
pp. 130
Author(s):  
NI MADE METTA ASTARI ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Least Square ◽  
Prediction Tool ◽  
Analysis Method ◽  
Ordinary Least Square ◽  
Independent Variable ◽  
Parameter Estimators

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ??OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.

scholarly journals Variance additivity of genetic populational parameter estimates obtained through bootstrapping

2003 ◽  
Vol 60 (1) ◽  
pp. 97-103 ◽  
Author(s):  
Luciana Aparecida Carlini-Garcia ◽  
Roland Vencovsky ◽  
Alexandre Siqueira Guedes Coelho
Keyword(s):  
Bootstrap Method ◽  
Natural Populations ◽  
Molecular Data ◽  
Fixation Index ◽  
Parameter Estimates ◽  
Genetic Population ◽  
Relative Contribution ◽  
Population Structure Analysis ◽  
Resampling Procedure ◽  
Variance Estimates

Studying the genetic structure of natural populations is very important for conservation and use of the genetic variability available in nature. This research is related to genetic population structure analysis using real and simulated molecular data. To obtain variance estimates of pertinent parameters, the bootstrap resampling procedure was applied over different sampling units, namely: individuals within populations (I), populations (P), and individuals and populations simultaneously (I, P). The considered parameters were: the total fixation index (F or F IT), the fixation index within populations (f or F IS) and the divergence among populations or intrapopulation coancestry (theta or F ST). The aim of this research was to verify if the variance estimates of <IMG SRC="/img/fbpe/sa/v60n1/14549x09.gif">, <IMG SRC="/img/fbpe/sa/v60n1/14549x10.gif">and <IMG SRC="/img/fbpe/sa/v60n1/14549x11.gif">, found through the resampling over individuals and populations simultaneously (I, P), correspond to the sum of the respective variance estimates obtained from separated resampling over individuals and populations (I+P). This equivalence was verified in all cases, showing that the total variance estimate of <IMG SRC="/img/fbpe/sa/v60n1/14549x09.gif">, <IMG SRC="/img/fbpe/sa/v60n1/14549x10.gif">and <IMG SRC="/img/fbpe/sa/v60n1/14549x11.gif">can be obtained summing up the variances estimated for each source of variation separately. Results also showed that this facilitates the use of the bootstrap method on data with hierarchical structure and opens the possibility of obtaining the relative contribution of each source of variation to the total variation of estimated parameters.

Sign in / Sign up

Export Citation Format

Share Document