An Innovative Class of Chain Ratio-Type Estimators Using Auxiliary Information in Sample Survey

2021 ◽  
Vol 20 (1) ◽  
pp. 15-20
Author(s):  
Khalid Ul Islam R ◽  
Tanveer Ahmad Tarr
Keyword(s):  
Auxiliary Information ◽  
Sample Survey

scholarly journals Estimating poverty in the Italian provinces using small area estimation models

2007 ◽  
Vol 4 (1) ◽  
Author(s):  
Claudio Quintano ◽  
Rosalia Castellano ◽  
Gennaro Punzo
Keyword(s):  
Small Area ◽  
Random Effect ◽  
National Level ◽  
Random Effect Model ◽  
Auxiliary Information ◽  
Sample Survey ◽  
Income Poverty ◽  
Estimation Process ◽  
Domain Specific ◽  
Italian Provinces

Sample survey data are broadly used to provide direct estimates of poverty for the whole population and large areas or domains. That is one of the main deficiencies of poverty analysis at a sub-national level (i.e., related either to regions, or provinces). As they are considered very small geographical areas, since the domain-specific sample is not large enough to support direct estimates of adequate precision, they are likely to produce large standard errors, due to the unduly small size of the sample in that area (Ghosh & Rao, 1994). The aim of our paper is to improve the estimation process quality, in terms of efficiency, of some poverty measures for Italian provinces (NUTS3). The adopted approach deals with Area Level Random Effect Model (Fay & Herriot, 1979) which relates small area direct estimators to domain specific covariates, considering the random area effects as independent. Under that model, the Empirical Best Linear Unbiased Predictor (EBLUP) is obtained. We extend the analysis beyond the conventional measures of income poverty that simply dichotomise the population into the "poor" and the "non poor" by a threshold value and we also consider a fuzzy monetary measure treating poverty as a matter of degree (Cheli & Lemmi, 1995; Cheli, 1995). Through such an analysis, we determine some of the socio-economic factors contributing to poverty levels and living standards, and we investigate in depth the territorial perspective. In order to evaluate the performance of the estimation process through small area models and, consequently, the contribution of auxiliary information to composite poverty estimates, we have defined some outcome measures and some quality indicators (Rao, 2003) have been computed. They allow us to test the extent to which the modelling modifies the input direct estimates and the degree of improvement in the accuracy level of the estimates provided by modelling and, more generally, to evaluate the performance of small area estimators.

scholarly journals Regional soil erosion assessment based on sample survey and geostatistics

2016 ◽  
Author(s):  
Shuiqing Yin ◽  
Zhengyuan Zhu ◽  
Li Wang ◽  
Baoyuan Liu ◽  
Yun Xie ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Auxiliary Information ◽  
Sample Survey ◽  
Shaanxi Province ◽  
Engineering Practice ◽  
Rainfall Erosivity ◽  
Soil Erodibility ◽  
Soil Erosion Assessment

Abstract. Soil erosion is one of the major environmental problems in China. From 2010–2012 in China, the fourth national census for soil erosion sampled 32 364 Primary Sampling Units (PSUs, micro watersheds) with the areas of 0.2–3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and soil loss rate for each land use in the PSUs were estimated using an empirical model Chinese Soil Loss Equation (CSLE). Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale, the problem of estimating soil erosion condition on a regional scale has not been well addressed. The aim of this study is to introduce a new spatial interpolation method based on Bivariate Penalized Spline over Triangulation (BPST) for the estimation of regional soil erosion. We compared five interpolation models based on BPST to generate a regional soil erosion assessment from the PSUs. Land use, rainfall erosivity, and soil erodibility at the resolution of 250 × 250 m pixels for the entire domain were used as the auxiliary information. Shaanxi province (3116 PSUs) in China was used to conduct the comparison and assessment as it is one of the most serious erosion areas. The results showed that three models with land use as the auxiliary information generated much lower mean squared errors (MSE) than the other two models without land use. The model assisted by the land use, rainfall erosivity factor (R), and soil erodibility factor (K) is the best one, which has MSE less than half that of the model smoothing soil loss in the PSUs directly. 56.5 % of total land in Shaanxi province has annual soil loss greater than 5 t ha−1 y−1. High (20–40 t ha−1 y−1),severe (40–80 t ha−1 y−1) and extreme (>80 t ha−1 y−1) erosion occupied 14.3 % of the total land. The farmland, forest, shrub land and grassland in Shaanxi province had mean soil loss rates of 19.00, 3.50, 10.00, 7.20 t ha−1 y−1, respectively. Annual soil loss was about 198.7 Mt in Shaanxi province, with 67.8 % of soil loss originating from the farmlands and grasslands in Yan'an and Yulin districts in the northern Loess Plateau region and Ankang and Hanzhong districts in the southern Qingba mountainous region. This methodology provides a more accurate regional soil erosion assessment and can help policy-makers to take effective measures to mediate soil erosion risks.

Standardized calibration adjustment index (SCAI): A new measure of survey data quality

2021 ◽  
pp. 1-8
Author(s):  
Burton Levine ◽  
Taylor Lewis ◽  
Jim Nonnemaker ◽  
Matthew Farrelly
Keyword(s):  
Data Quality ◽  
Survey Data ◽  
Auxiliary Information ◽  
Target Population ◽  
Sample Survey ◽  
Dual Frame ◽  
Sampling Frame ◽  
Quality Metric ◽  
Nonprobability Sample ◽  
The Difference

In this article, we introduce a new indicator of survey data quality called the standardized calibration adjustment index (SCAI). The SCAI quantifies the difference in the distributions of variables used in the calibration of survey respondents to the target population, accounting for the study design. It does so by a function of the change in respondent-level weights developed to calibrate the survey data to known population totals. A key feature of the SCAI is that it does not require auxiliary information to exist on the sampling frame. The SCAI can be used as a survey data quality metric in both probability and nonprobability sample settings, which we show through example applications with an outbound dual-frame random digit dialing telephone survey, an address-based sample survey, and a redirected inbound call sampling survey.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

On the Causes of Socio-Economic Inequality Reproduction: What Does Resources Approach Show?

2014 ◽  
pp. 144-160
Author(s):  
E. Avraamova ◽  
T. Maleva
Keyword(s):  
Social Dynamics ◽  
Economic Inequality ◽  
Poverty Rate ◽  
Economic Behavior ◽  
Sample Survey ◽  
Family Welfare ◽  
Rate Reduction ◽  
Rational Models ◽  
Information Database ◽  
Vertical Mobility

This paper presents an attempt at answering the question of why the scope of socio-economic inequality stays the same in Russia despite the poverty rate reduction. The authors are looking for the causes of this phenomenon in the domain of social dynamics, i.e., in the nature of current vertical mobility mechanisms. To study these mechanisms the authors use resources approach. The information database of the research is the representative sample survey carried by the Institute for Social Analysis and Forecasting at RANEPA in 2013. The majority of the respondents have, in fact, vague idea of general parameters of the economic development of the country and of their personal prospects to adapt to possible changes. This state of things hinders the development of rational models of socio-economic behavior directed towards the growth of personal and family welfare and productive in terms of national economy development - these, eventually, would advance the reduction of socio-economic inequality. Various groups of population are predominantly oriented towards converting social capital viewed not in terms of trust and solidarity, but in terms of ties or connections and of personal loyalty.

Use of Collaborative Technologies for Knowledge Management in Psychiatric Hospitals in Southwest Nigeria

Mousaion ◽  
2019 ◽  
Vol 37 (3) ◽  
Author(s):  
Oluyemi Folorunso Ayanbode ◽  
Williams Ezinwa Nwagwu
Keyword(s):  
Knowledge Management ◽  
Psychiatric Hospitals ◽  
Task Interdependence ◽  
Sample Survey ◽  
Team Members ◽  
Collaborative Technologies ◽  
Southwest Nigeria ◽  
Transfer And Retention ◽  
Retention Of Knowledge ◽  
Therapy Team

This article concerns the study examining the use of collaborative technologies (CTs) for the acquisition, creation, sharing, transfer, and retention of knowledge by therapy team members (TTMs) in psychiatric hospitals, and the determinants of the use of CTs as well as how they relate to knowledge management (KM) practices. The skills of the TTMs in the use of CTs were also investigated. Carried out within the positivist and constructivist paradigms, a sample survey was conducted among TTMs from two purposively selected psychiatric hospitals in Southwest Nigeria. Quantitative data was collected from self-administered questionnaires completed by 283 TTMs and was analysed using the Statistical Package for Social Sciences (SPSS) 22. Qualitative data was collected from interviews conducted with four heads of departments. The study showed that the TTMs in the relevant hospitals used 26 different CTs for knowledge acquisition, creation, sharing, transfer, and retention. The largest proportion (84.5%) of the respondents confirmed that skill in the use of CTs determined the use of CTs for KM practices. More than half (54.3%) of the TTMs were highly skilled in the use of CTs for KM practices. The findings suggested that the respondents were positively inclined towards the use of CTs and that they were involved in the use of various CTs to facilitate KM practices and processes. It was found that task interdependence was characteristic of the TTMs’ work in the selected psychiatric hospitals, and that, to benefit from the potential advantages of task interdependence and to effectively employ CTs in operations and processes, TTMs’ skills in the use of CTs should be developed. In addition, professional ties among experts in different fields of specialisation should be encouraged.

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