About: Sampling (statistics) is a(n) research topic. Over the lifetime, 65377 publication(s) have been published within this topic receiving 1248808 citation(s).
01 Jan 1980-
TL;DR: History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical Guide
Abstract: History Conceptual Foundations Uses and Kinds of Inference The Logic of Content Analysis Designs Unitizing Sampling Recording Data Languages Constructs for Inference Analytical Techniques The Use of Computers Reliability Validity A Practical Guide
03 Feb 1984-
TL;DR: This paper presents the results of a series of experiments conducted in farmers' fields in the Czech Republic over a period of three years to investigate the effects of agricultural pesticides on animal welfare and human health.
Abstract: Elements of Experimentation. Single-Factor Experiments. Two-Factor Experiments. Three-or More-Factor Experiments. Comparison Between Treatment Means. Analysis of Multiobservation Data. Problem Data. Analysis of Data from a Series of Experiments. Regression and Correlation Analysis. Covariance Analysis. Chi-Square Test. Soil Heterogeneity. Competition Effects. Mechanical Errors. Sampling in Experimental Plots. Experiments in Farmers' Fields. Presentation of Experimental Results. Appendices. Index.
01 Jan 1974-Behaviour
TL;DR: Seven major types of sampling for observational studies of social behavior have been found in the literature and the major strengths and weaknesses of each method are pointed out.
Abstract: Seven major types of sampling for observational studies of social behavior have been found in the literature. These methods differ considerably in their suitability for providing unbiased data of various kinds. Below is a summary of the major recommended uses of each technique: In this paper, I have tried to point out the major strengths and weaknesses of each sampling method. Some methods are intrinsically biased with respect to many variables, others to fewer. In choosing a sampling method the main question is whether the procedure results in a biased sample of the variables under study. A method can produce a biased sample directly, as a result of intrinsic bias with respect to a study variable, or secondarily due to some degree of dependence (correlation) between the study variable and a directly-biased variable. In order to choose a sampling technique, the observer needs to consider carefully the characteristics of behavior and social interactions that are relevant to the study population and the research questions at hand. In most studies one will not have adequate empirical knowledge of the dependencies between relevant variables. Under the circumstances, the observer should avoid intrinsic biases to whatever extent possible, in particular those that direcly affect the variables under study. Finally, it will often be possible to use more than one sampling method in a study. Such samples can be taken successively or, under favorable conditions, even concurrently. For example, we have found it possible to take Instantaneous Samples of the identities and distances of nearest neighbors of a focal individual at five or ten minute intervals during Focal-Animal (behavior) Samples on that individual. Often during Focal-Animal Sampling one can also record All Occurrences of Some Behaviors, for the whole social group, for categories of conspicuous behavior, such as predation, intergroup contact, drinking, and so on. The extent to which concurrent multiple sampling is feasible will depend very much on the behavior categories and rate of occurrence, the observational conditions, etc. Where feasible, such multiple sampling can greatly aid in the efficient use of research time.
21 Sep 1970-Educational and Psychological Measurement
03 Jan 2007-Journal of Chemical Physics
Abstract: The authors present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. The authors illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent of the thermostat parameter also with regard to the dynamic properties.