ABSTRACT
Introducción: la medición del capital intelectual como activo intangible se ha establecido a partir de una escala de seis dimensiones relacionada con formación, academia, laboral, profesional, sindical y organizativa. La pandemia ha generado estudios que muestran diferencias significativas entre estas dimensiones, abriendo la discusión sobre la validez metaanalítica. Objetivo: realizar una revisión documental, sistemática y metaanalítica, con una muestra de artículos publicados entre 2014 y 2021 en revistas indexadas en repositorios internacionales. Materiales y métodos: se realizó un estudio documental, sistemático y metaanalítico sobre una muestra de artículos publicados en repositorios internacionales en los últimos dos años. Se utilizó la Escala de Capital Intelectual, considerando sus dimensiones reportadas en la literatura. Resultados: se estableció la estructura y los umbrales de los efectos aleatorios, calculados mediante la ecuación para establecer el parámetro delta, considerando sus intervalos de confianza para la corrección de errores de muestreo y estimación, así como las diferencias entre grupos. Conclusión: se recomienda extender la revisión de la literatura hasta agosto de 2021, para poder contrastar ambas revisiones, y establecer así la validez metaanalítica de la escala, y discutir sus implicaciones en la era COVID-19.
Introduction: the measurement of intellectual capital as an intangible asset has been established from a scale of six dimensions related to training, academia; labor, professional, union, and organizational. The pandemic has generated studies that show significant differences between these dimensions, opening the discussion on meta-analytic validity. Objective: to carry out a documentary, systematic and meta-analytical review with a sample of articles published from 2014 to 2021 in journals indexed in international repositories. Materials and methods: a documentary, systematic and meta-analytical study was carried out on a sample of articles published in international repositories in the last two years. The Intellectual Capital Scale was used, considering its dimensions reported in the literature. Results: the structure and thresholds of the random effects were established, calculated by means of the equation to establish the delta parameter, considering their confidence intervals for correction of sampling and estimation errors, as well as differences between groups. Conclusion: it is recommended to extend the review of the literature until August 2021 in order to be able to contrast both reviews to establish the meta-analytic validity of the scale and discuss its implications in the COVID-19 era.
ABSTRACT
The size of the non-sampling error is directly related to the accuracy and reliability of the sampling survey result. This paper studied the non-sampling errors generated during the sampling process of the China National Human Biomonitoring Program(CNBP), mainly including the sampling frame error, non-response error and measurement error. The program reduced the influence of the non-sampling error on the quality of the survey effectively by scientifically designing the sampling scheme and questionnaire, strengthening investigator trainings and standardizing the data review, which could be used to provide reference for the control of non-sampling errors in public health monitoring projects in China.
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Objective To explore the sampling method in China National Human Biomonitoring Program (HBP) and the related errors,so as to calculate and evaluate the study design in sampling.Methods The sampling method of HBP is of multistage nature.Taking the results of sampling method from Guizhou province as an example,results related to sampling error and variation coefficient were calculated,using the multistage unequal probability sampling error method.Results The HBP covered 152 monitoring sites in 31 provinces (autonomous regions and municipalities) and with 21 888 residents selected.The replacement rates at various stages were 5.26％,6.35％ and 40.6％ respectively.The sampling error in Guizhou province was 3 207 594,and the coefficient of variation was 0.097.Conclusions According to the multi-stage unequal probability sampling method,the sampling coefficient variability appeared small with high precision,in Guizhou province.However,this method did not consider the weight adjustment of non-sampling errors such as population missing rate and response rate.Methods related to the calculation on multi-stage sampling error among large-scale public health monitoring projects need to be further studied.
ABSTRACT
Objective To explore the sampling method in China National Human Biomonitoring Program (HBP) and the related errors,so as to calculate and evaluate the study design in sampling.Methods The sampling method of HBP is of multistage nature.Taking the results of sampling method from Guizhou province as an example,results related to sampling error and variation coefficient were calculated,using the multistage unequal probability sampling error method.Results The HBP covered 152 monitoring sites in 31 provinces (autonomous regions and municipalities) and with 21 888 residents selected.The replacement rates at various stages were 5.26％,6.35％ and 40.6％ respectively.The sampling error in Guizhou province was 3 207 594,and the coefficient of variation was 0.097.Conclusions According to the multi-stage unequal probability sampling method,the sampling coefficient variability appeared small with high precision,in Guizhou province.However,this method did not consider the weight adjustment of non-sampling errors such as population missing rate and response rate.Methods related to the calculation on multi-stage sampling error among large-scale public health monitoring projects need to be further studied.
ABSTRACT
Multistage sampling is a frequently-used method in random sampling survey in public health.Clustering or independence between observations often exists in the sampling,often called complex sample,generated by multistage sampling.Sampling error may be underestimated and the probability of type Ⅰ error may be increased if the multistage sample design was not taken into considerationin analysis.As variance (error) estimator in complex sample is often complicated,statistical software usually adopt ultimate cluster variance estimate (UCVE) to approximate the estimation,which simply assume that the sample comes from one-stage sampling.However,with increased sampling fraction of primary sampling unit,contribution from subsequent sampling stages is no more trivial,and the ultimate cluster variance estimate may,therefore,lead to invalid variance estimation.This paper summarize a method of variance estimation considering multistage sampling design.The performances are compared with UCVE and the method considering multistage sampling design by simulating random sampling under different sampling schemes using real world data.Simulation showed that as primary sampling unit (PSU) sampling fraction increased,UCVE tended to generate increasingly biased estimation,whereas accurate estimates were obtained by using the method considering multistage sampling design.
ABSTRACT
Comprehensive evaluation methods are generally used to assess the population data. When we need to estimate the sample data in special situations, the impacts brought by the sampling error should be considered. Due to lack of the accurate measurement for the sensitivity and stability in the comprehensive evaluation methods, sampling errors usually cannot be estimated in the sampling research. Monte Carlo simulation was used in this article to solve the probability of the ordering results, and the matlab programs were presented. Based on the simulated results, we change the conventional “absolute conclusion” of comprehensive evaluation methods to “probability results” for the sample data, and put forward a new sorting and ranking method for the results of comprehensive evaluation.
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To compare the sampling errors from cluster or unequal probability sampling designs and to adopt the unequal probability sampling method to be used for death surveillance.Taking 107 areas from the county level in Shaanxi province as the sampling frame,a set of samples are drawn by equal probability cluster sampling and unequal probability designs methodologies.Sampling error and effect of each design are estimated according to their complex sample plans.Both the sampling errors depend on the sampling plan and the errors of equal probability in stratified cluster sampling appeares to be less than simple cluster sampling.The design effects of unequal probability stratified cluster sampling,such as πPS design,are slightly lower than those of equal probability stratified cluster sampling,but the unequal probability stratified cluster sampling can cover a wider scope of monitoring population.Conclusions:Results from the analysis of sampling data can not be conducted without consideration of the sampling plan when the sampling frame is finite and a given sampling plan and parameters,such as sampling proportion and population weights,are assigned in advance.Unequal probability cluster sampling designs seems to be more appropriate in selecting the national death surveillance sites since more available monitoring data can be obtained and having more weight in estimating the mortality for the whole province or the municipality to be selected.