Relative precision of stratified sampling, sampling with probability of selection proportional to size, and simple random sampling with ratio estimation

1991 ◽  
Vol 2 (4) ◽  
pp. 475-486 ◽  
Author(s):  
A. L. Jensen
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Ratio Estimation ◽  
Relative Precision

scholarly journals Analysis of the Impact of the Type of Sampling of Representative Properties on the Results of Mass Appraisal

2020 ◽  
Vol 20 (2) ◽  
pp. 152-167
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Multiple Regression Model ◽  
Stratified Sampling ◽  
Sampling Errors ◽  
Real Estate Valuation ◽  
Simple Sampling ◽  
The Impact ◽  
Mean Square Errors

Abstract Research background: Mass valuation is a process in which many properties are valued simultaneously with a uniform approach. An example of a procedure used for mass real estate valuation is the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA), which can be developed into a multiple regression model. The algorithm is based on a set of drawn representative properties. This set determines, inter alia, the quality of obtained valuations. Purpose: The objective of the study is to verify the hypothesis whether changing the method of sampling representative properties from the originally used simple random sampling to stratified sampling improves the results of the SAREMA econometric variant. Research methodology: The article presents a study that uses two methods of representative properties sampling – simple random sampling and stratified sampling. Errors of the models of valuation created taking into account both methods of sampling and different number of representative properties are compared. A key aspect of the survey is the choice of a better sampling method. Results: The study has shown that stratified sampling improves valuation results and, more specifically, allows for lower root mean square errors. Stratified sampling yielded better results in the initial phase of the study with more observations, but reducing the percentage of strata participating in the draws, despite the increase in RMSE, guaranteed lower errors than the corresponding results based on simple sampling in all variants of the study. Novelty: The article confirms the possibility of improving the results of mass property valuation by changing the scheme of representative properties sampling. The results allowed for the conclusion that stratified sampling is a better way of creating a set of representative properties.

Geographically Stratified Importance Sampling for the Calibration of Aggregated Destination Choice Models for Trip Distribution

2005 ◽  
Vol 1935 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Min-Tang Li ◽  
Lee-Fang Chow ◽  
Fang Zhao ◽  
Shi-Chiang Li
Keyword(s):  
Importance Sampling ◽  
Random Sampling ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Choice Models ◽  
Stratified Sampling ◽  
Destination Choice ◽  
Selection Probabilities ◽  
Choice Probabilities ◽  
Choice Set

A key feature in estimating and applying destination choice models with aggregate alternatives is to sample a set of nonchosen traffic analysis zones (TAZs), plus the one a trip maker chose, to construct a destination choice set. Computational complexity is reduced because the choice set would be too large if all study area TAZs were included in the calibration. Commonly, two types of sampling strategies are applied to draw subsets of alternatives from the universal choice set. The first, and simplest, approach is to select randomly a subset of nonchosen alternatives with uniform selection probabilities and then add the chosen alternative if it is not otherwise included. The approach, however, is not an efficient sampling scheme because most alternatives for a given trip maker may have small choice probabilities. The second approach, stratified importance sampling, draws samples with unequal selection probabilities determined on the basis of preliminary estimates of choice probabilities for every alternative in the universal choice set. The stratified sampling method assigns different selection probabilities to alternatives in different strata. Simple random sampling is applied to draw alternatives in each stratum. However, it is unclear how to divide the study area so that destination TAZs may be sampled effectively. The process of and findings from implementing a stratified sampling strategy in selecting alternative TAZs for calibrating aggregate destination choice models in a geographic information system (GIS) environment are described. In this stratified sampling analysis, stratum regions varied by spatial location and employment size in the adjacent area were defined for each study area TAZ. The sampling strategy is more effective than simple random sampling in regard to maximum log likelihood and goodness-of-fit values.

scholarly journals Survey design and analysis considerations when utilizing misclassified sampling strata

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aya A. Mitani ◽  
Nathaniel D. Mercaldo ◽  
Sebastien Haneuse ◽  
Jonathan S. Schildcrout
Keyword(s):  
Ethnic Minorities ◽  
Random Sampling ◽  
Survey Design ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Simple Random Sample ◽  
Parameter Estimates ◽  
Complex Survey ◽  
Racial And Ethnic Minorities ◽  
Complex Survey Design

Abstract Background A large multi-center survey was conducted to understand patients’ perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, disproportionate stratified sampling was implemented with strata defined by electronic health records (EHR) that are known to be inaccurate. We investigate the effect of sampling strata misclassification in complex survey design. Methods Under non-differential and differential misclassification in the sampling strata, we compare the validity and precision of three simple and common analysis approaches for settings in which the primary exposure is used to define the sampling strata. We also compare the precision gains/losses observed from using a disproportionate stratified sampling scheme compared to using a simple random sample under varying degrees of strata misclassification. Results Disproportionate stratified sampling can result in more efficient parameter estimates of the rare subgroups (race/ethnic minorities) in the sampling strata compared to simple random sampling. When sampling strata misclassification is non-differential with respect to the outcome, a design-agnostic analysis was preferred over model-based and design-based analyses. All methods yielded unbiased parameter estimates but standard error estimates were lowest from the design-agnostic analysis. However, when misclassification is differential, only the design-based method produced valid parameter estimates of the variables included in the sampling strata. Conclusions In complex survey design, when the interest is in making inference on rare subgroups, we recommend implementing disproportionate stratified sampling over simple random sampling even if the sampling strata are misclassified. If the misclassification is non-differential, we recommend a design-agnostic analysis. However, if the misclassification is differential, we recommend using design-based analyses.

scholarly journals PERILAKU REMAJA DALAM HAL PERUBAHAN FISIOLOGIS PADA MASA PUBERTAS DI SMP YAYASAN PENDIDIKAN SHAFIYYATUL AMALIYYAH MEDAN TAHUN 2013

2019 ◽  
Vol 9 (1) ◽  
pp. 8-13
Author(s):  
Dina Indarsita ◽  
Mariaty S ◽  
Ravina Primursanti
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Cross Sectional

Latar belakang: Masa pubertas adalah terjadinya perubahan biologis yang meliputi morfologi dan fisiologi yang terjadi dengan pesat dari masa anak kemasa dewasa, terutama kapasitas reproduksi yaitu perubahan alat kelamin dari tahap anak kedewasa. berdasarkan persentase terkecil aspek fisik pada perilaku remaja mengenai keadaan fisik diperoleh 48,4%. Hal ini mengindikasikan bahwa masih banyak siswa yang memiliki pengetahuan, penilaian serta pengharapan yang belum baik tentang perubahan fisik. Hasil penelitian lain menunjukkan Remaja pada masa pubertas memiliki penerimaan yang positif terhadap perubahan fisik, yaitu sebanyak 78,63% dan penerimaan negatif terhadap perubahan fisik, yaitu sebanyak 21,37%. Tujuan penelitian : ini adalah untuk mengetahui perilaku remaja dalam hal perubahan fisiologis pada masa pubertas di SMP Yayasan Pendidikan Shafiyyatul Amaliyyah Medan Tahun 2013. Metodologi : Desain penelitian yang digunakan dalam penelitian ini adalah deskriptif dengan pendekatan cross sectional. Jumlah sampel dalam penelitian ini adalah 173 orang dengan tehnik pengambilan sampel adalah secara proporsi bertingkat (proportional stratified sampling) dan acak sederhana (simple random sampling). Penelitian ini dilakukan pada bulan April 2013. Hasil : Hasil penelitian diperoleh pengetahuan remaja berpengetahuan baik sebanyak 134 orang (77,5 %), berpengetahuan cukup sebanyak 36 orang (20,8 %), dan berpengetahuan kurang sebanyak 3 orang (1,7 %), sikap remaja mayoritas memiliki sikap positif sebanyak 162 orang (93,6 %) dan minoritas memiliki sikap negatif sebanyak 11 orang (6,4 %), tindakan remaja diperoleh tindakan baik sebanyak 157 orang ( 90,8 %) dan tindakan kurang sebanyak 16 orang ( 9,2 %). Dari hasil penelitian ini diketahui bahwa perilaku remaja awal dalam hal perubahan fisiologis di SMP Yayasan Pendidikan Shafiyyatul Amaliyyah Medan Tahun 2013 baik.

Application of Spatial Sampling to Remote Sensing Monitoring of Forest Cover Area

2012 ◽  
Vol 610-613 ◽  
pp. 3732-3737 ◽  
Author(s):  
Ji Ping Zhang ◽  
Lin Bo Zhang ◽  
Bin Gong
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Random Sampling ◽  
Vegetation Index ◽  
Forest Cover ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Spatial Sampling ◽  
Remote Sensing Monitoring ◽  
Study Results

This study combines the sampling technique, geographic information system and remote sensing technique to conduct a sampling survey on forest cover area of Jinggangshan National Nature Reserve in China on the basis of TM remote sensing image. The spatial simple random sampling, spatial stratified sampling and sandwich sampling model are respectively utilized to establish the sampling design. For the spatial simple random sampling model, the spatial autocorrelation analysis method is adopted to determine the spatial autocorrelation coefficient through calculating Moran's I index, while in the spatial stratified sampling and sandwich sampling model, the yearly maximum NDVI (Normalized Difference Vegetation Index) is utilized to conduct the spatial stratification. Through comparison of the sampling accuracy of three sampling models, a higher precision and more reasonable sampling method and sampling model is provided for remote sensing monitoring of forest cover area. The study results show that: sandwich sampling model is featured as the highest sampling accuracy, followed by the spatial stratified sampling and simple random sampling. Under the requirement of same precision, sandwich spatial sampling model can reduce quantity of the sampling points, and create all kinds of report units according to demands of different spatial area, so it is featured as the better suitability.

KID CROP DAN MORTALITAS ANAK KAMBING KACANG DI DAERAH DARATAN DAN KEPULAUAN KABUPATEN BUTON

2015 ◽  
Vol 2 (1) ◽  
pp. 39
Author(s):  
Basman Basman ◽  
Takdir Saili ◽  
La Ode Ba'a
Keyword(s):  
Litter Size ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Purposive Sampling

Penelitian ini bertujuan untuk mengetahui produktivitas ternak Kambing Kacang berdasarkan nilai kid crop dan mortalitas anak Kambing Kacang baik di wilayah kepulauan maupun wilayah daratan Kabupaten Buton. Penelitian ini dilaksanakan di Kecamatan Siompu (mewakili wilayah kepulauan) dan di Kecamatan Lapandewa (mewakili wilayah daratan) Kabupaten Buton. Metode penentuan lokasi penelitian dilakukan secara purposive sampling, stratified sampling dan simple random sampling dan penentuan responden di setiap desa dilakukan secara sensus. Data penelitian dianalisis secara deskriptif. Hasil penelitian menunjukkan bahwa kid crop Kambing Kacang di Kecamatan Siompu sebesar 150,98% dan Kecamatan Lapandewa sebesar 159,84%. Kidding Interval Kambing Kacang di Kecamatan Siompu sebesar 8,2 bulan dan di Kecamatan Lapandewa sebesar 8,19 bulan. Di Kecamatan Siompu diperoleh rataan litter size sebesar 1,77 dan di Kecamatan Lapandewa sebesar 1,53. Jumlah cempe Kambing Kacang yang lahir di Kecamatan Siompu sebanyak 84 ekor (38 ekor jantan dan 46 ekor betina). Di Kecamatan Lapandewa jumlah cempe Kambing Kacang yang lahir sebanyak 68 ekor (37 ekor jantan dan 31 ekor betina). Persentase mortalitas cempe kambing Kacang di Kecamatan Siompu sebesar 22,61% dan di Kecamatan Lapandewa sebesar 11,76%. Dapat disimpulkan bahwa produktivitas dan reprodutivitas ternak Kambing Kacang baik di wilayah kepulauan maupun di wilayah daratan Kabupaten Buton masih sangat baik, namun, tingkat mortalitas cempe di wilayah kepulauan masih relatif tinggi.Kata Kunci: Kambing Kacang, Performans, Kid Crop, Mortalitas, Lapandewa, Siompu

scholarly journals Comparison of quota sampling and stratified random sampling

2021 ◽  
Vol 10 (1) ◽  
pp. 24-27
Author(s):  
Rufai Iliyasu ◽  
Ilker Etikan
Keyword(s):  
Random Sampling ◽  
Sampling Error ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Target Population ◽  
Stratified Sampling ◽  
Sampling Frame ◽  
Probability Sampling ◽  
Group Research ◽  
Quota Sampling

The possibility that researchers should be able to obtain data from all cases is questionable. There is a need; therefore, this article provides a probability and non-probability sampling. In this paper we studied the differences and similarities of the two with approach that is more of fritter away time, cost sufficient with energy required throughout the sample observed. The pair shows the differences and similarities between them, different articles were reviewed to compare the two. Quota sampling and Stratified sampling are close to each other. Both require the division into groups of the target population. The main goal of both methods is to select a representative sample and facilitate sub-group research. There are major variations, however. Stratified sampling uses simple random sampling when the categories are generated; sampling of the quota uses sampling of availability. For stratified sampling, a sampling frame is necessary, but not needed for quota sampling. More specifically, stratified sampling is a method of probability sampling which enables the calculation of the sampling error. For quota samples, this is not possible. Quota sampling is therefore primarily used by market analysts rather than stratified sampling, as it is mostly cost-effective and easy to conduct and has the appealing equity of satisfying population reach. However, it disguises potentially significant bias.

scholarly journals Accuracy of Subsampling for Height Measurements in Loblolly Pine Plots

2009 ◽  
Vol 33 (3) ◽  
pp. 145-149 ◽  
Author(s):  
Colleen A. Carlson ◽  
Thomas R. Fox ◽  
Harold E. Burkhart ◽  
H. Lee Allen ◽  
Timothy J. Albaugh
Keyword(s):  
Loblolly Pine ◽  
Random Sampling ◽  
Southeastern United States ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
The Other ◽  
Diameter Distribution ◽  
Systematic Sampling ◽  
Sampling Strategies ◽  
Sampling Schemes

Abstract Estimating heights in research and inventory plots is costly. We examined the feasibility of subsampling tree heights as opposed to measuring all trees. Four sampling intensities (75, 50, 25, and 10%) and four sampling strategies (systematic sampling, simple random sampling without replacement, stratified sampling across the diameter distribution, and sampling the first trees in each plot) were investigated. Data from 600 loblolly pine plots in fertilizer trials in the southeastern United States were used. The application of a height–dbh regression to predict the heights of unmeasured trees was also investigated. Sampling the first trees generally resulted in poorer estimates than the other sampling schemes. Systematic and simple random sampling performed similarly. A 50% sampling intensity with either systematic or simple random sampling and a height–dbh regression predicting the heights of unmeasured trees estimated more than 90% of plots to within 2.2% of the observed plot height and more than 94% of plots to within 2.5% of the observed volume, and they were more accurate than the stratified sampling at the same intensity. Systematic sampling is easy to implement, requiring no prior plot knowledge. We conclude that a 50% systematic sampling combined with a height–dbh regression will reduce costs without compromising accuracy.

scholarly journals A Jackknife Estimator of Variance for a Random Tessellated Stratified Sampling Design

2019 ◽  
Vol 65 (5) ◽  
pp. 543-547 ◽  
Author(s):  
Steen Magnussen ◽  
Thomas Nord-Larsen
Keyword(s):  
Standard Error ◽  
Forest Inventory ◽  
Random Sampling ◽  
Sampling Design ◽  
Simple Random Sampling ◽  
National Forest Inventory ◽  
Stratified Sampling ◽  
National Forest ◽  
Estimation Of Variance ◽  
Jackknife Estimator

Abstract Semisystematic sampling designs—in which a population area frame is tessellated into cells, and a randomly located sample is taken from each cell—affords random tessellated stratified (RTS) Horvitz–Thompson-type estimators. Forest inventory applications with RTS estimators are rare, possibly because of computational complexities with the estimation of variance. To reduce this challenge, we propose a jackknife estimator of variance for RTS designs. We demonstrate an application with a model-assisted ratio of totals estimator and data from the Danish National Forest Inventory. RTS estimators of standard error were, as a rule, smaller than comparable estimates obtained under the assumption of simple random sampling. The proposed jackknife estimator performed well.

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