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Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a critical review of the state of sampling in recent, high-quality software engineering research. The key findings are: (1) random sampling is rare; (2) sophisticated sampling strategies are very rare; (3) sampling, representativeness and randomness often appear misunderstood. These findings suggest that software engineering research has a generalizability crisis. To address these problems, this paper synthesizes existing knowledge of sampling into a succinct primer and proposes extensive guidelines for improving the conduct, presentation and evaluation of sampling in software engineering research. It is further recommended that while researchers should strive for more representative samples, disparaging non-probability sampling is generally capricious and particularly misguided for predominately qualitative research.

Environmental DNA (eDNA) analysis is a rapid, non-invasive, cost-efficient biodiversity monitoring tool with enormous potential to inform aquatic conservation and management. Development is ongoing, with strong commercial interest, and new uses are continually being discovered. General applications of eDNA and guidelines for best practice in freshwater systems have been established, but habitat-specific assessments are lacking. Ponds are highly diverse, yet understudied systems that could benefit from eDNA monitoring. However, eDNA applications in ponds and methodological constraints specific to these environments remain unaddressed. Following a stakeholder workshop in 2017, researchers combined knowledge and expertise to review these applications and challenges that must be addressed for the future and consistency of eDNA monitoring in ponds. The greatest challenges for pond eDNA surveys are representative sampling, eDNA capture, and potential PCR inhibition. We provide recommendations for sampling, eDNA capture, inhibition testing, and laboratory practice, which should aid new and ongoing eDNA projects in ponds. If implemented, these recommendations will contribute towards an eventual broad standardisation of eDNA research and practice, with room to tailor workflows for optimal analysis and different applications. Such standardisation will provide more robust, comparable, and ecologically meaningful data to enable effective conservation and management of pond biodiversity.

RDS is not a variant of either usage of snowball sampling, nor is the reverse true. Because of the confusion surrounding this term, in Gile and Handcock (2010) we prefer, and use throughout that paper, the more precise broad category \"link-tracing sampling\" while paying homage to the intellectual descent of the methods from snowball sampling.

We give a formal definition of a representative sample, but roughly speaking, it is a scaled-down version of the population, capturing its characteristics. New methods for selecting representative probability samples in the presence of auxiliary variables are introduced. Representative samples are needed for multipurpose surveys, when several target variables are of interest. Such samples also enable estimation of parameters in subspaces and improved estimation of target variable distributions. We describe how two recently proposed sampling designs can be used to produce representative samples. Both designs use distance between population units when producing a sample. We propose a distance function that can calculate distances between units in general auxiliary spaces. We also propose a variance estimator for the commonly used Horvitz-Thompson estimator. Real data as well as illustrative examples show that representative samples are obtained and that the variance of the Horvitz-Thompson estimator is reduced compared with simple random sampling.

With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample. Causal effects estimated via multiple regression differentially weight each unit's contribution. The \"effective sample\" that regression uses to generate the estimate may bear little resemblance to the population of interest, and the results may be nonrepresentative in a manner similar to what quasi-experimental methods or experiments with convenience samples produce. There is no general external validity basis for preferring multiple regression on representative samples over quasi-experimental or experimental methods. We show how to estimate the \"multiple regression weights\" that allow one to study the effective sample. We discuss alternative approaches that, under certain conditions, recover representative average causal effects. The requisite conditions cannot always be met.

To promote digital transformation, equal emphasis needs to be placed on digital skills development as to infrastructure development. Integral to investment in digital skills development is the subsequent management and evaluation of digital training programmes. This paper assesses mechanisms to ensure digital training programmes are adequately managed using a standardized data collection framework to measure an internationally accepted digital literacy index. Such an index requires an agile definition of digital literacy, responsive to the fluid nature of the digital economy. The paper also explores the extent to which a G20 advisory body may inform a nationally representative data collection strategy within the context of a data collection process that is cognizant of the evolving demands of businesses and users alike.

Background and aims Belowground carbon (C) inputs are a major source of soil organic carbon (SOC) in terrestrial ecosystems, and substrate C:N ratios drive SOC stabilisation. In perennial systems, quantitative information on seasonal dynamics of belowground biomass is scarce, but necessary, e.g. to improve SOC modelling and representative sampling. Methods Seasonal dynamics and depth distribution of belowground biomass C and N of extensive grassland and Miscanthus on sandy soil were estimated. Core samples (1 m depth) were taken six times in 1 year. Miscanthus -derived SOC was quantified using 13 C natural abundance. Results Grassland and Miscanthus differed strongly in belowground biomass C (2.5 ± 0.3 vs. 7.3 ± 1.1 Mg ha −1 ) and C:N ratios (28.6 ± 0.5 vs. 60 ± 3.3). Peak grassland belowground biomass C and N stocks occurred in summer, while those of rhizomatous Miscanthus were in winter due to different strategies of resource allocation. Grassland roots showed a strong seasonal pattern of C:N ratios, indicating N remobilisation. Miscanthus -derived topsoil SOC was low relative to the high belowground biomass, indicating a slow transfer of rhizome carbon to bulk SOC. Conclusions Representative belowground biomass sampling of perennials should take seasonal dynamics into account, especially in system comparisons. Furthermore, C inputs from rhizome and roots should be estimated separately owing to likely differences in turnover times.

With the International Maritime Organization’s (IMO) International Convention for the Control and Management of Ships’ Ballast Water and Sediments now in force, determining abundance and distribution of phytoplankton inside ballast tanks is critical for successful ballast water management, particularly when assessing compliance. The relationship between the abundance and distribution of cells was examined to obtain the best representative sample of the entire phytoplankton community in ballast tanks, comparing three ballast water sampling techniques including in-line, in-tank, and Van Dorn bottle methods. Lloyd’s index, Dy, and Gini index were applied to compare methods of sample collection and determine representativeness of samples and performance of sampling methods. Phytoplankton abundance trends from live microscopy counts using fluorescein diacetate (FDA) were also compared to those using a FlowCAM on preserved samples. The phytoplankton community showed a patchy distribution inside the ballast tank and this trend was observed across all voyages. The estimated marginal mean analysis showed that in hypothetical conditions (e.g., 702 m3 of water in ballast tank and phytoplankton whole-tank abundance of 19,522 cells), the difference among the three methods was small. Conversely, statistical analysis performed on empiric abundances using a negative binomial regression model determined that the volume discharged during sampling of ballast water has an effect on the number of cells collected on a given voyage. Results of this study also confirmed that the in-line method may be a better method at collecting phytoplankton samples from ballast tanks than the in-tank or Van Dorn method, regardless of the time at which samples are collected. Finally, the number of living cells and the number of preserved cells showed similar trends for most of the voyages, despite fewer samples analyzed using FDA.

The chemical analysis of an urolith is often interpreted as “stone’s composition”. However, it must be taken into consideration, that in most cases, only a fragment of the stone has been sent to the laboratory. In some recurrent patients, stone compositions either vary considerably between episodes or the analytical result obtained from the stone fragment does not fit with the data of e.g. current 24 h-urinalysis or urinary pH-records. The question arises, whether this outcome may be the result of an improper stone sampling scheme. On a simple layered 2D-stone model composed of two mineral phases it is shown, how the choice of a stone fragment process may influence the result of “stone composition”. Depending on the initial position of fragment within the whole stone, the respective calculated analyses can relevantly differ from the whole stone composition as well as strongly between two fragments. Even under the simplified conditions of a 2D-2-component-model “grown” under defined conditions, the differences between the analyses of the different specimens taken from a stone are in part remarkable. The more it can be argued that these differences increase if a real 3D-urolith is investigated. Further sampling biases may evolve and increase the problem of proper sampling:, e.g., if an urolith’s more resistant parts remain intact while ESWL or laser-based stone fragmentation (“dusting”), the weak parts became fully disintegrated and removed from the body as fine-grained sludge—the stone’s fine fraction is lost although its composition may carry important information on the stone’s pathogenesis. Consequently, a “stone analysis” only obtained from the harder remains reveals an incomplete result, a fact that in principle limits its clinical interpretation. Choice of stone fragment is crucial. The extent of the uncertainty of an analysis resulting from potential selection biases should not be underestimated. Thus, sampling should be considered as an important part of the processes of quality assurance and management. Errors made at this early stage of diagnosis finding will affect the analytical result and thus influence the clarification of the underlying pathomechanism. This can lead to an improper metaphylactic strategy potentially causing recurrent stone formation which otherwise would have been prevented. A decision scheme for analysis of urinary stones removed using endoscopic methods is suggested.

Background Within the last decades, a substantial number of reports have established bullying behaviours as a severe risk to the health and safety of workers. However, in Hungary, the severity of this issue remains largely unknown. Therefore, the current study aimed to 1) determine the prevalence of offensive workplace behaviours in the Hungarian working population and 2) examine the relationship between exposure to these offensive behaviours and certain mental health indicators. Methods The cross-sectional analyses of the present study are based on a sample of 13,104 active workers being representative of the Hungarian working population according to gender, age, educational level, and 18 occupational sectors. The mid-length version of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) was used to measure workplace offensive behaviours (bullying, sexual harassment, threats of violence, and physical violence) in the 12 months preceding the survey. Examined mental health correlates included depressive symptomatology (Beck Depression Inventory), functional somatic symptoms (PHQ-15), perceived stress (Perceived Stress Scale), and general well-being (WHO Well-being Index). Results Almost half (48.7%) of the sample reported exposure to some form of offensive behaviour; 37.6% of participants reported occasional-, while 11.1% reported weekly or daily exposure. More women than men were exposed to offensive workplace behaviours, and those targeted the most were individuals aged 18–29 and in companies employing 20–49 employees. Top managers reported the lowest amount of bullying, while unskilled labourers reported the most frequent exposure. A moderately strong relationship was discovered between exposure to workplace offensive behaviours and all indicators of mental health. Conclusion Workplace bullying was revealed to be a significant public health concern according to this large, representative data set from Hungary. Strategies to reduce the occurrence and impact of these behaviours on employee health should be a priority for occupational health and safety interventions.