Four Uncertain Sampling Methods are Superior to Random Sampling Method in Classification

2021 ◽  
Author(s):  
Zhang Guochen
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods

scholarly journals An investigation of relations between pre-service teachers' metacognition skill levels and their critical thinking situations

2015 ◽  
Vol 5 (1) ◽  
pp. 35-68 ◽  
Author(s):  
Özden Demir ◽  
Halil İbrahim Kaya
Keyword(s):  
Critical Thinking ◽  
Prospective Teachers ◽  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Negative Relationship ◽  
Descriptive Statistics ◽  
Skill Levels ◽  
Screening Model ◽  
Self Confidence

The main purpose of this study is to investigate prospective teachers' critical thinking and metacognition levels. The study is descriptive in nature and based on relational screening model. The participants were selected using stratified sampling method which is one of the random sampling methods, and 293 teachers became the participants of the study. The data collected via "California Critical Thinking Scale" and "Metacognition Scale" were analyzed using independent groups t-test, one-way variance analysis, correlation analysis as well as the descriptive statistics. Results show that although the participants' metacognition perceptions differed according to gender in the evaluation and organization dimensions, no significant differences were found in the other dimensions. The participants' perceived critical thinking levels did not show significant differences according to the departments they attend in the seeking truth, open-mindedness, analyticalness, systematicity, curiousness and total dimensions. Critical thinking total scores were found to display a negative relationship with evaluation, organization, and metacognition total scores while they showed a positive relationship with seeking the truth, open-mindedness, analyticalness, systematicity, self-confidence, and curiousness scores.

scholarly journals SAMPLING METHODS AND NUMBER OF ROUNDWOODS FOR DETERMINING THE MOISTURE CONTENT

2018 ◽  
Vol 42 (4) ◽  
Author(s):  
Danilo Barros Donato ◽  
Renato Vinícius Oliveira Castro ◽  
Angélica de Cássia Oliveira Carneiro ◽  
Ana Márcia Macedo Ladeira Carvalho ◽  
Benedito Rocha Vital ◽  
...  
Keyword(s):  
Moisture Content ◽  
Random Sampling ◽  
Sampling Method ◽  
Optimal Allocation ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Optimum Number ◽  
Average Standard Deviation ◽  
Proportional Allocation ◽  
Allowable Error

ABSTRACT This study had the objective of comparing two methodologies of sampling, Simple Random Sampling (ACS) and Stratified Random Sampling (ACE) to determine the optimum number of roundwood samples to obtain the moisture content of the population. In order to achieve this goal, different percentages of allowable error (5,10,15 and 20%) were considered for each sampling methodology. In the conduction of this study, the samples were randomly taken from a lot of 250 steres of wood, 144 roundwood of three meters of length and distributed in four classes of diameter. Later, the moisture content of these samples was determined. And, from these values, the population estimates (average, standard deviation, variance, coefficient of variation, and standard error) by ACS and ACE methods, helped to determine the optimum number of roundwood (n) to be sampled from different percentages of allowable error adopted in this study at 95% probability. According to the results, the amount of roundwood to be sampled from ACS for each allowable error 5, 10, 15 and 20% was respectively 214, 55, 25 and 14. For the ACE (proportional allocation) the amount of roundwood was 141, 35, 16 and 9 for ACE (optimal allocation) this number was 136, 34, 15 and 8. It was concluded that the most indicated sampling method for this study, considering the allowable error, was the ACE method.

A beginner's Guide to Sampling Methods in Medical Research

2021 ◽  
Author(s):  
Moslem Basti ◽  
Farzan Madadizadeh
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Cluster Sampling ◽  
Stratified Random Sampling ◽  
Systematic Random Sampling ◽  
Probabilistic Sampling ◽  
Multi Stage ◽  
Main Components

Background: Sampling methods are one of the main components of each research. Familiarity with a variety of sampling methods is essential for researchers. Objective: The main purpose of this study was to teach different probabilistic and non-probabilistic sampling methods to improve the knowledge of researchers in conducting more accurate research. Methods: In this tutorial article, useful information about each sampling method, as well as how to properly use each method and its strengths and weaknesses are provided. Results: Five cases of probabilistic sampling methods and four cases of non-probabilistic sampling methods that are common are mentioned. Probabilistic sampling included simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multi-stage random sampling. In addition to introducing each method, its strengths and weaknesses are also mentioned. Conclusion: Probabilistic sampling methods despite limiting assumptions provide more reliable results. Therefore, if it is possible, researchers should use probabilistic sampling methods to increase the accuracy of the study.

scholarly journals Sampling Methods and the Accredited Population in Athletic Training Education Research

2009 ◽  
Vol 4 (2) ◽  
pp. 75-81 ◽  
Author(s):  
W. David Carr ◽  
Jennifer Volberding
Keyword(s):  
Education Research ◽  
Literature Review ◽  
Athletic Training ◽  
Information Search ◽  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Demographic Data ◽  
Athletic Training Education ◽  
Web Based

Context: We describe methods of sampling the widely-studied, yet poorly defined, population of accredited athletic training education programs (ATEPs). Objective: There are two purposes to this study; first to describe the incidence and types of sampling methods used in athletic training education research, and second to clearly define the accredited ATEP population. Design and Setting: Literature review and web-based information search Participants: Accredited programs as of January 2008 Measurements: We conducted a literature review with the following limits: (1) articles with keyword “accreditation,” (2) articles utilizing accredited ATEP population, (3) articles published in the Journal of Athletic Training and the Athletic Training Education Journal, and (4) articles published since 2000. We categorized articles based on their sampling method(s). We conducted a web-based search of all accredited programs as of January 2008 and collected demographic data including: state/private affiliation, university enrollment, cost of attendance, National Athletic Trainers' Association district, and athletic affiliation. Results: Our literature search identified 37 articles. Twenty-seven (73%) articles did not clearly state their sampling methods. Twenty-two (59%) of the articles used some sort of random sampling method. The remaining 15 articles (41%) used some sort of nonrandom sampling method. As of January 2008 there were 360 accredited programs. Conclusions: The following generalizations can be made: (1) The majority of articles used a random sampling method. (2) The vast majority of programs were undergraduate. (3) A majority of programs are affiliated with state institutions.

scholarly journals Field sampling methods for investigating forest-floor bryophytes: Microcoenose vs. random sampling

2018 ◽  
Vol 70 (3) ◽  
pp. 589-598
Author(s):  
Milos Ilic ◽  
Ruzica Igic ◽  
Mirjana Cuk ◽  
Dragana Vukov
Keyword(s):  
Species Richness ◽  
Random Sampling ◽  
Forest Floor ◽  
Sampling Method ◽  
Sampling Methods ◽  
Diversity Index ◽  
Systematic Sampling ◽  
Field Sampling ◽  
Beech Forests ◽  
Quadrat Size

Because of the high importance of bryophytes in forest ecosystems, it is necessary to develop standardized field sampling methodologies. The quadrat method is commonly used for bryophyte diversity and distribution pattern surveys. Quadrat size and the position of quadrats within the studied area have a significant influence on different analyses. The aim of the present study was to define the minimum quadrat size appropriate for sampling ground bryophytes in temperate beech forests, to compare two different field sampling methods for research on ground bryophytes, the random and microcoenose methods; and to test the adequacy of the microcoenose sampling method in temperate beech forests. Research was carried out on Fruska Gora mountain (Serbia) at four different sites. All sites contained temperate broadleaf forest vegetation, predominantly Fagus sylvatica, but also included various other tree species. Systematic sampling based on nested quadrats was used to determine the minimum sampling area. Random sampling was performed using 10 or 20 microplots (minimum area quadrat), randomly located within 10x10 m plots. Microcoenose sampling is a systematic sampling method based on the fact that every bryophyte fragment on the forest floor is a separate microcoenose. These methods were compared using the following criteria: species richness; Shannon?s diversity index and evenness measure; coverage of dominant species, and the time needed for sampling. The microcoenose sampling method has proven to be highly applicable in temperate beech forests in terms of species richness and diversity, in contrast to random sampling, which was not suitable for bryophyte flora with a patchy distribution.

COVARIANCE TRACKING WITH FORGETTING FACTOR AND RANDOM SAMPLING

2011 ◽  
Vol 19 (03) ◽  
pp. 547-558 ◽  
Author(s):  
XUGUANG ZHANG ◽  
XIAOLI LI ◽  
MING LIANG ◽  
YANJIE WANG
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Computing Time ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Forgetting Factor ◽  
Covariance Matching ◽  
Covariance Tracking ◽  
Computing Speed

Covariance matching is an excellent algorithm of target tracking. In this paper, forgetting factor and random sampling methods are proposed to improve the robustness and efficiency of covariance tracking. First, a distance function between covariance matrixes is weighted by using a forgetting factor based on a fuzzy membership function to overcome the disturbances from similar targets. Then a random sampling method is applied to reduce the computing time in covariance matching and to facilitate real-time object tracking. Experiment results show that the algorithm proposed in this paper can effectively mitigate the clutter and occlusion problems at a high computing speed.

scholarly journals Median Double Ranked Set Sampling Method

2018 ◽  
Vol 14 (1) ◽  
pp. 7503-7512
Author(s):  
Nuran Medhat Al-Mawan ◽  
El-Houssainy Rady ◽  
Nasr Rashwan
Keyword(s):  
Random Sampling ◽  
Traditional Method ◽  
Sampling Method ◽  
Sampling Methods ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Median Ranked Set Sampling ◽  
Observational Economy ◽  
The Mean

In environmental monitoring and assessment, the main focus is to achieve observational economy and to collect data with unbiased, efficient and cost-effective sampling methods. Ranked set sampling (RSS) is one traditional method that is mostly used for accomplishing observational economy. In this article, we suggested new sampling method called median double ranked set sampling (MDRSS). The newly suggested sampling method MDRSS is compare to the simple random sampling (SRS), RSS, double ranked set sampling (DRSS), median ranked set sampling (MRSS). When the underlying distributions are symmetric and asymmetric, it is shown that, the variance of the mean estimator under MDRSS is always less than the variance of the mean estimator based on SRS and the other methods.

An improved sampling method of complex network

2014 ◽  
Vol 25 (05) ◽  
pp. 1440007 ◽  
Author(s):  
Qi Gao ◽  
Xintong Ding ◽  
Feng Pan ◽  
Weixing Li
Keyword(s):  
Prior Knowledge ◽  
Complex Network ◽  
Degree Distribution ◽  
Local Structure ◽  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Global Information ◽  
Snowball Sampling ◽  
Original Network

Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

scholarly journals The efficacy of sampling strategies for estimating scabies prevalence

2021 ◽  
Author(s):  
Nefel Tellioglu ◽  
Rebecca H. Chisholm ◽  
Jodie McVernon ◽  
Nicholas Geard ◽  
Patricia T. Campbell
Keyword(s):  
Random Sampling ◽  
Sampling Method ◽  
Sampling Methods ◽  
Indigenous Communities ◽  
Simple Random Sampling ◽  
World Health ◽  
Sampling Strategies ◽  
Specific Component ◽  
Assignment Method ◽  
Wide Range

Background Estimating scabies prevalence in communities is crucial for identifying the communities with high scabies prevalence and guiding interventions. There is no standardisation of sampling strategies to estimate scabies prevalence in communities, and a wide range of sampling sizes and methods have been used. The World Health Organization recommends household sampling or, as an alternative, school sampling to estimate community-level prevalence. Due to varying prevalence across populations, there is a need to understand how sampling strategies for estimating scabies prevalence interact with scabies epidemiology to affect accuracy of prevalence estimates. Methods We used a simulation-based approach to compare the efficacy of different sampling methods and sizes. First, we generate synthetic populations with Australian Indigenous communities' characteristics and then, assign a scabies status to individuals to achieve a specified prevalence using different assumptions about scabies epidemiology (random, age-specific, household-specific, or age-and-household-specific transmissions). Second, we calculate an observed prevalence for different sampling methods (household-based, school-based or random sampling) and sizes. Results The distribution of prevalence in population groups can vary substantially when the underlying scabies assignment method changes. For example, age-specific scabies assignment increases the prevalence among children as well as prevalence in larger households. Household specific assignment approaches introduce higher variance in prevalence among households. Across all of the scabies assignment methods combined, the simple random sampling method produces the narrowest 95% confidence interval for all sampling percentages. The household sampling method introduces higher variance compared to simple random sampling when the assignment of scabies includes a household-specific component. The school sampling method overestimates community prevalence when the assignment of scabies includes an age-specific component. Discussion Our results indicate that there are interactions between transmission assumptions and surveillance strategies, emphasizing the need for understanding scabies transmission dynamics. We suggest using the simple random sampling method for estimating scabies prevalence. Our approach can be adapted to various populations and diseases.

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