Sampling Strategy. The implementer of the study, DelAgua, wanted a household and main source water sample collected along with the household survey in all 30 districts of Rwanda. Figure 3 displays the geographic hierarchy in Rwanda. The implementer outlined the sampling process as randomly selecting 2 villages within each of the 30 districts, with these villages being from non-adjacent cells to increase geographic distribution and representation across a district. In addition, for each village already selected, another village in each cell was randomly selected in order to include the number of water sources sampled. There was a total of 4 villages in each of the districts with approximately 3-5 household surveyed within each village and 12-20 per district. Households were randomly selected from a master list of households under ubudehe categories 1 and 2 (2012 version), the two poorest wealth categories according to the Government of Rwanda. The unit of analysis for this study is the household. This sampling strategy was unconventional and lead to convenient sampling based on which households had a respondent home and who wanted to participate. Enumerators had to work through the randomized list until enough households had been interviewed for each village. Sample weights were not developed so this study is not generalizable on a national level due to the inability to find parameter estimates. Household Village Cell Sector District Provience
Sampling Strategy. I firstly wanted to provide background on how my three organisations and the participants were identified and engaged to be a part of my research. This starts off with the identification and recruitment of my three conservation organisations and the shared project, before moving onto the identification and recruitment of the participants across all three organisations. I then provide an overview of each of the organisations and their shared project in as much detail as possible given the confidentiality around the research and finally, I present a timeline of the data collection and analysis process to provide some context for the remainder of this chapter.
Sampling Strategy. To gain insights about more- and less-effective MDA strategies, the sampling strategy was developed with the goal of identifying differences in practice that may have impacted coverage between high-distributing and low-distributing zones, each within one high-coverage commune and one low-coverage commune. To determine which of metropolitan Port-au-Prince’s five communes still participating in MDA should be targeted, epidemiological coverage data from 2011/12 through 2018 was averaged to identify the most consistently high-coverage and low-coverage communes. Tabarre and Carrefour were identified as having the most consistently high coverage (90%) and low coverage (54.5%), respectively (Table 1). 10 From the French arrondir (“to encircle”), arrondissements are sometimes referred to as “districts.” Subdivisions of Haiti’s ten departments, the 42 arrondissements are further divided into 145 communes and 571 communal sections. Table 1. Average epidemiological coverage by five communes still participating in annual MDA. Commune Average Epidemiological Coverage, 2011/12 - 2018 Tabarre 90.0% Cite Soleil 73.6% Xxxxxx 65.6% Port-au-Prince 56.4% Carrefour 54.5% Zone performance data were reviewed to identify a high- and low-distribution zone in each commune. Distribution data by post was only available for 2018, and denominator data were unavailable, preventing calculation of true epidemiological coverage. However, each distribution post was expected to serve approximately 1,000 people [75], and thus, zones were selected based on comparing the average number of people treated per distribution post in each zone to the median number of people treated per post in those zones (Table 2). In 2018, the median number of people treated per post in Tabarre and Carrefour were 807 (range: 250-1,726) and 819 (range: 140-2,579), respectively.
Sampling Strategy. Each station was planned a few days or a week in advance by a team of physical and biological oceanographers, together with the chief scientist on board. This short term planning was chosen to take advantage of the latest oceanographic information available based on processed and analyzed satellite data (Chl, SST and altimetry). Near real-time updates of the satellite images were sent to the chief scientist on board. Furthermore, continuous surface measurements (Temperature, Salinity, Fluorescence) were used to fine tune the sampling locations across fronts or filaments for example. When needed, a preliminary CTD transect was performed to characterize station at meso-scale. Finally, on board analysis of sensor readings from the rosette (e.g. CTD, Oxygen, Nutrients, UVP) was used to identify and target features of special interest in water column, such as DCMs, Oxygen Minimum layers, mesopelagic features, etc. In addition to the general sampling strategy outlined above, some topical studies addressed specific scientific questions and required additional sampling approaches and instruments (i. e. state of the art oceanographic instruments such as gliders, biogeochemical autonomous floats, ARGO floats with drogues and LADCPs) were deployed to improve the success of the survey of the oceanographic feature. This study of the world oceans microorganisms biodiversity combined classical analysis methods and genomics and is then particularly relevant for the Micro B3 project. The environmental data and the registry of samples collected during the Xxxx Oceans expedition are archived and managed centrally at PANGAEA. As the data management for the Xxxx Oceans cruise is planned to be supported by the Micro B3 project (see Deliverable
Sampling Strategy. In selecting the AWCs in the first stage of the two-stage clustered sampling procedure, the complete list of AWCs in both the intervention districts was used as the sampling frame. A systematic random sampling technique was adopted to select 31 intervention AWCs in each district, making a total of 62 AWCs in both the intervention districts. Another 31 AWCs were selected in the intermediate area as well. To select AWCs in the control district, the list of AWCs in the two selected blocks was provided and 36 AWCs were selected in each block through a systematic random sampling procedure. Furthermore, stratification took place by intervention and control areas to select AWCs in each stratum. This method of stratification by intervention and control areas reduces the bias associated with clustering around Anganwadi Centers. All eligible children aged 3-3.5 years and 5-5.5 years and their caregivers were selected in the second stage of sampling. Systematic random sampling was employed and resulted in a sample size of 62 AWCs from each intervention and control area. The second stage of sampling resulted in 1248 children between the ages of 5-5.5 years. All eligible children aged 3-3.5 years and 5-5.5 years within the catchment area were covered but the caregivers of children aged 5-5.5 years were the respondents included in this analysis. Therefore, the sample size used in this analysis is 1248.
Sampling Strategy a. The flow chart interfered with first line of the following paragraph on my printout.
Sampling Strategy. 1. The Authenticator Selects the sample of assessment evidence, applying the SOLAS sampling strategy, they must ensure a spread across the different grade bands and at the borderline between grades.
Sampling Strategy. It is crucial for the sampling strategy to reflect the ontology and study design and be relevant to the research question (Xxxxx, 2002; Xxxxxxxx, 2013). The sample which had the characteristics of the populations of interest and was relevant to the ontological and epistemological positions of the study was therefore selected in order to generate meaningful data to address the research question (Xxxxx, 2002; Xxxxxxxx, 2013). For example all the service user participants had acute mental health conditions which reflected the service user population on acute mental health wards; the carer participants were all the designated carers for the service users and provided substantial care to the services when in the community whilst all the RNs and HCAs had an experience of working on the acute mental health settings. The sample was consistent with the theoretical or purposive sample, which involves the selection of groups or criteria for investigation based on their relevance to the research question and the theoretical position being developed (Xxxxx, 2002). The sample included adult service users receiving care on the acute xxxx, carers defined as relatives, family members or significant others providing substantial care to the service user, RNs and HCAs working on the acute wards from January 2013 to July 2013.
Sampling Strategy. I intended to send the questionnaire to manufacturing businesses in both the UK and Germany. Additionally, I conducted a small-scale pilot in other European countries to test the understandability and user-friendliness of the questionnaire. For this purpose, I constituted the sampling frame from different sources (Xxxx and Barnbeck, 2017).
Sampling Strategy. The sampling strategy is part of the experimental setup and describes when and how samples for the functional genomics analysis are collected. It embraces two main issues, namely, collecting the sample at a time point where the biological response relevant to the biological question is present and ensuring that levels of biomolecules remain unchanged from the moment of sampling. Concerning this first issue, if it is unknown beforehand which phases during the cultivation contain information related to the biological question, the sampling protocol should cover all possibly relevant growth phases and phase transitions (Xxxxx et al., 2007). At the same time, practical matters have to be considered as well. For instance, the sampling volumes can limit the number of obtainable samples, or the costs of sample analysis can influence the sampling strategy. In the case of continuous cultures, time issues are of no importance, but due to technical difficulties this fermentation technique is not as commonly applied in fungal research as it is in research involving other microorganisms. Besides, with the application of continuous cultures the approach is quite different, as time is no longer a factor, excluding longitudinal effects (e.g., induction or other perturbations during the fermentation process). In addition, it should be noted that although the process conditions are fixed during continuous cultures, changes in the production organism are frequently observed (Xxxxx et al., 1998; Xxxxxxx et al., 1995), making continuous cultures prone to transitions, albeit of a different kind. The second issue relates to the high turnover of mRNA and metabolites (for proteins this is not so much of an issue), risking the introduction of unwanted changes in RNA or metabolite levels during sample harvesting or work-up. In order to obtain samples that reflect the state of the cell under the environmental conditions at the time of harvesting, rapid sampling (Nasution et al., 2006) and immediate inactivation (quenching) of the cellular metabolism are a necessity. In the literature, the quenching methods used for filamentous fungi mainly include rapid filtration followed by immediate freezing of the cells (mostly used for transcriptomics samples) (Xxxxx et al., 2006) or dilution of the cells in a methanol solution of -45 °C (more often used for metabolomics samples) (Xxxxxxx & Xxxxxx, 1996; Xxxxxxxx et al., 2006; Xxxxxxxxxxxxxx et al., 2008). After quenching the cells, conditions s...
