Search

Children undergoing clinical procedures can experience pain and/or anxiety. This may result in them being unwilling to cooperate and being held still by parents or health professionals. This study aimed to capture an international perspective of health professionals’ reported practices of holding children still for clinical procedures. An online questionnaire was distributed through network sampling to health professionals working with children aged under 16 years of age. A total of 872 responses were obtained from Australia ( n = 477), New Zealand ( n = 237) and the United Kingdom ( n = 158). Responses were from nurses ( n = 651), doctors ( n = 159) and other professionals ( n = 53). Health professionals reported children as held still for clinical procedures quite often (48%) or very often (33%). Levels of holding varied significantly according to country of practice, profession, student status, length of time working within a clinical setting, training received and the availability of resources in the workplace. Health professionals who gained permissions (assent from children and/or consent from parents) before procedures were less likely to hold children still for a clinical procedure than those who did not. Holding children still for procedures is an international practice, which is influenced by training, access to guidance, country of practice and profession. Children's permission and parental consent is often not sought before a child is held for a procedure to be completed.

This article describes how the average path length (APL) of a network is an important metric that provides insights on the interconnectivity in a network and how much time and effort would be required for search and navigation on that network. However, the estimation of APL is time-consuming as its computational complexity scales nonlinearly with the network size. In this article, the authors develop a computationally efficient random node pair sampling algorithm that enables the estimation of APL with a specified precision and confidence. The proposed sampling algorithms provide a speed-up factor ranging from 240-750 for networks with more than 100,000 nodes. The authors also find that the computational time required for estimation APL does not necessarily increase with the network size; it shows an inverted U shape instead.

Learning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman (Ann. Math. Stat. 20 (1949) 572–579) and Frank (Scand. J. Stat. 5 (1978) 177–188). We revisit a problem formulated by Frank (Scand. J. Stat. 5 (1978) 177–188) of estimating the number of connected components in a large graph based on the subgraph sampling model, in which we randomly sample a subset of the vertices and observe the induced subgraph. The key question is whether accurate estimation is achievable in the sublinear regime where only a vanishing fraction of the vertices are sampled. We show that it is impossible if the parent graph is allowed to contain high-degree vertices or long induced cycles. For the class of chordal graphs, where induced cycles of length four or above are forbidden, we characterize the optimal sample complexity within constant factors and construct linear-time estimators that provably achieve these bounds. This significantly expands the scope of previous results which have focused on unbiased estimators and special classes of graphs such as forests or cliques. ¶ Both the construction and the analysis of the proposed methodology rely on combinatorial properties of chordal graphs and identities of induced subgraph counts. They, in turn, also play a key role in proving minimax lower bounds based on construction of random instances of graphs with matching structures of small subgraphs.

Respondent-driven sampling (RDS) is a sampling method designed to study hard-to-reach human populations. Beginning with a convenience sample, each participant receives a small number of coupons, which they distribute to their contacts who become eligible. RDS participants are asked to report on their number of contacts in the target population. Also, a set of characteristics is observed for each participant. Current prevalence estimators assume that these attributes are measured accurately. However, ignoring misclassification may lead to biased estimates. ¶ The main contribution of this paper is to discuss two approaches to correct for the bias introduced by the misclassification on nodal attributes for existing RDS estimators. The two approaches leverage misclassification rates assumed to be available from external validation studies. Most importantly, our analysis identifies circumstances for which the performance of the correction methods is impaired in the specific context of RDS. The two methods that are discussed are an analytical correction for estimators of the Hájek estimator style and the Simulation Extrapolation Misclassification (SIMEX MC) approach. Extended methodology to estimate the uncertainty of the corrected estimators is also presented. The performance of the proposed methods is assessed under varying levels of known or uncertain misclassification error across simulated social networks of varying features. Finally, the methods are used to estimate HIV prevalence among people who inject drugs (PWID) and men who have sex with men (MSM) in India.

Respondent-driven sampling (RDS) is a network sampling technique typically employed for hard-to-reach populations when traditional sampling approaches are not feasible (e.g., homeless) or do not work well (e.g., people with HIV). In RDS, seed respondents recruit additional respondents from their network of friends. The recruiting process repeats iteratively, thereby forming long referral chains. RDS is typically implemented face to face in individual cities. In contrast, we conducted Internet-based RDS in the American Life Panel (ALP), a web survey panel, targeting the general US population. We found that when friends are selected at random, as RDS methodology requires, recruiting chains die out. When self-selecting friends, self-selected friends tend to be older than randomly selected friends but share the same demographic characteristics otherwise. Using randomized experiments, we also found that respondents list more friends when the respondent’s number of friends is preloaded from an earlier question. The results suggest that with careful selection of parameters, RDS can be used to select population-wide Internet panels and we discuss a number of elements that are critical for success.

Global climate observations from ground stations require an evaluation of the effectiveness of a station network, which is often an assessment of the geometric distribution of [Formula: see text] points on a sphere. The representativeness of the Angell–Korshover 63-station network (AK-network) is assessed in this paper. It is shown that AK-network can effectively sample the January global average temperature data of the NCEP/NCAR Reanalysis from 1948 to 2015 when estimating inter-decadal variations, but it has large uncertainties for estimating linear trends. This paper describes a method for the assessment, and also includes an iterative numerical algorithm used to search for the locations of 63 uniformly distributed stations, named U63. The results of AK-63 and U63 are compared. The Appendix explains a problem of searching for the optimal distribution of [Formula: see text] points on a unit sphere in three-dimensional space under the condition of the maximum sum of the mutual distances among the points. The core R code for finding U63 is included. The R code can generate various interesting configurations for different [Formula: see text], among which one is particularly surprising: The configuration of 20 points is not a dodecahedron although the configurations for [Formula: see text], and 12 are tetrahedron, octahedron, cube, and icosahedron, respectively.

Personalized PageRank is an algorithm to classify the importance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization, Occupation-Time Personalized PageRank, is based on the proportion of visits to nodes before the restart, whereas the second generalization, Location-of-Restart Personalized PageRank, is based on the proportion of time a node is visited just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks. Keywords: Personalized PageRank; Markov Processes with Restart; Renewal-reward Theorem

This paper describes results from a network survey of Nain – a predominantly Inuit community of ~1200 people located on the northern coast of Labrador. As part of a larger social network research project, we used peer-referral sampling to recruit 330 residents for interviews about food sharing, housing, public health and community traditions. The peer-referral chains were analysed statistically to determine the presence and absence of social divisions in the community. The results of these analyses show that ethnic identification, relocation status and household income were the most significant social divisions in the community, while gender, education level and employment status show little or no effect on patterns of between-group interconnection. We argue that statistical patterns in the presence (and absence) of intergroup links offer novel ways to examine the interrelationship between recent economic development and the historical disruptions caused by Inuit community relocations in the 1950s.

In this chapter, the different methods of collection of data for rare and mobile populations have been provided. The methods of screening, disproportionate sampling, multiplicity or network sampling, multiframe sampling, snowball sampling, location sampling, and adapted sampling have been discussed. Methods of estimation of animal abundances based on capture–recapture methods for closed and open population have also been discussed.

Missing data is an important, but often ignored, aspect of a network study. Measurement validity is affected by missing data, but the level of bias can be difficult to gauge. Here, we describe the effect of missing data on network measurement across widely different circumstances. In Part I of this study (Smith and Moody, 2013), we explored the effect of measurement bias due to randomly missing nodes. Here, we drop the assumption that data are missing at random: what happens to estimates of key network statistics when central nodes are more/less likely to be missing? We answer this question using a wide range of empirical networks and network measures. We find that bias is worse when more central nodes are missing. With respect to network measures, Bonacich centrality is highly sensitive to the loss of central nodes, while closeness centrality is not; distance and bicomponent size are more affected than triad summary measures and behavioral homophily is more robust than degree-homophily. With respect to types of networks, larger, directed networks tend to be more robust, but the relation is weak. We end the paper with a practical application, showing how researchers can use our results (translated into a publically available java application) to gauge the bias in their own data.

The recent emergence of large networks, mainly due to the rise of online social networks, brought out the difficulty to gather a complete picture of a network and it prompted the development of new distributed techniques. In this thesis, we design and analyze algorithms based on random walks and diffusion for sampling, estimation and inference of the network functions, and for approximating the spectrum of graph matrices. The thesis starts with the classical problem of finding the dominant eigenvalues and the eigenvectors of symmetric graph matrices like Laplacian of undirected graphs. Using the fact that the eigenspectrum is associated with a Schrödinger-type differential equation, we develop scalable techniques with diffusion over the graph and with gossiping algorithms. They are also adaptable to a simple algorithm based on quantum computing. Next, we consider sampling and estimation of network functions (sum and average) using random walks on graph. In order to avoid the burn-in time of random walks, with the idea of regeneration at its revisits to a fixed node, we develop an estimator for the aggregate function which is non-asymptotically unbiased and derive an approximation to its Bayesian posterior. An estimator based on reinforcement learning is also developed making use of regeneration. The final part of the thesis deals with the use of extreme value theory to make inference from the stationary samples of the random walks. Extremal events such as first hitting time of a large degree node, order statistics and mean cluster size are well captured in the parameter “extremal index”. We theoretically study and estimate extremal index of different random walk sampling techniques; L’émergence récente de grands réseaux, surtout réseaux sociaux en ligne (OSN), a révélé la difficulté de crawler le réseau complet et a déclenché le développement de nouvelles techniques distribuées. Dans cette thèse, nous concevons et analysons des algorithmes basés sur les marches aléatoires et la diffusion pour l'échantillonnage, l'estimation et l'inférence des fonctions des réseaux. La thèse commence par le problème classique de trouver les valeurs propres dominants et leurs vecteurs propres de matrices de graphe symétriques, comme la matrice Laplacienne de graphes non orientés. En utilisant le fait que le spectre est associé à une équation de type différentiel Schrödinger, nous développons des techniques évolutives à l’aide de la diffusion sur le graphe. Ensuite, nous considérons l’échantillonnage des fonctions de réseau (comme somme et moyenne) en utilisant les marches aléatoires sur le graphe. Afin d'éviter le temps «burn-in» de marche aléatoire, avec l'idée de régénération à un nœud fixe, nous développons un estimateur de la fonction de somme qui est non asymptotiquement non-biaisé et dérivons une approximation à la postérieure Bayésienne. La dernière partie de la thèse étudie l'application de la théorie des valeurs extrêmes pour faire une inférence sur les événements extrêmes à partir des échantillons stationnaires des différentes marches aléatoires pour l’échantillonnage de réseau

Studies of networked systems typically begin by obtaining information about the network structure. In many settings it is impractical or impossible to directly observe the network, and sampling is used. Sampling the structure of offline social networks is especially costly and time-consuming. Respondents are asked to name close friends and acquaintances (strong and weak ties). However, because a person may have a large number of acquaintances, surveys use a fixed-choice design where respondents are asked to name a small, fixed number of their weak ties. Surprisingly, studies based on fixed-choice designs then directly use the network derived from the responses without correcting for the bias introduced by fixed-choice sampling. In this paper we demonstrate how to account for fixed-choice sampling when inferring network characteristics. Our approach is based on the generalized method of moments. We verify the accuracy of our results via simulation and discuss immediate applications and consequences of our work to existing results.

Many real-world phenomena can be intelligently modeled with complex networks. In this study, the most important optimization issues in complex networks are reviewed including network modeling, network sampling, network partitioning, link prediction, and influence maximization. For each issue, some conventional methods are firstly studied. Then, the most important challenges and future directions would be discussed.

The network scale-up method is a promising technique that uses sampled social network data to estimate the sizes of epidemiologically important hidden populations, such as sex workers and people who inject illicit drugs. Although previous scale-up research has focused exclusively on networks of acquaintances, we show that the type of personal network about which survey respondents are asked to report is a potentially crucial parameter that researchers are free to vary. This generalization leads to a method that is more flexible and potentially more accurate. In 2011, we conducted a large, nationally representative survey experiment in Rwanda that randomized respondents to report about one of 2 different personal networks. Our results showed that asking respondents for less information can, somewhat surprisingly, produce more accurate size estimates. We also estimated the sizes of 4 key populations at risk for human immunodeficiency virus infection in Rwanda. Our estimates were higher than earlier estimates from Rwanda but lower than international benchmarks. Finally, in this article we develop a new sensitivity analysis framework and use it to assess the possible biases in our estimates. Our design can be customized and extended for other settings, enabling researchers to continue to improve the network scale-up method.

The aim of this study was to describe middle-aged women's perceptions of lifestyle and how it was experienced in their daily lives. Interviews with 15 women selected using network sampling were analysed using a phenomenographic approach. Lifestyle was perceived as either ‘handle life’, describing a framework of how everyday life should manifest itself in different areas of life, or ‘relate to life’ implying accepting life as it is and making the best of it based on previous experiences. In terms of providing preventive and health-promoting guiding, it is essential that nurses have an insight into people's reasoning to support them to improve health or deal with illness.

Recent years have seen advances in building large Internet-scale index structures, generally known as structured overlays . Early structured overlays realized distributed hash tables (DHTs) which are ill suited for anything but exact queries. The need to support range queries necessitates systems that can handle uneven load distributions. However such systems suffer from practical problems—including poor latency, disproportionate bandwidth usage at participating peers, or unrealistic assumptions on peers' homogeneity, in terms of available storage or bandwidth resources. In this article we consider a system that is not only able to support uneven load distributions but also to operate in heterogeneous environments, where each peer can autonomously decide how much of its resources to contribute to the system. We provide the theoretical foundations of realizing such a network and present a newly proposed system Oscar based on these principles. Oscar can construct efficient overlays given arbitrary load distributions by employing a novel scalable network sampling technique. The simulations of our system validate the theory and evaluate Oscar's performance under typical challenges, encountered in real-life large-scale networked systems, including participant heterogeneity, faults, and skewed and dynamic load-distributions. Thus the Oscar distributed index fills in an important gap in the family of structured overlays, bringing into life a practical Internet-scale index, which can play a crucial role in enabling data-oriented applications distributed over wide-area networks.

Respondent-Driven Sampling (RDS) is a widely adopted link-tracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling frame. RDS estimators rely upon the assumption that each edge (representing a relationship between two individuals) in the underlying network has an equal probability of being sampled. We show that this assumption is violated in even the simplest cases, and that RDS estimators are sensitive to the violation of this assumption.

This paper presents a novel low power bandgap reference circuit, the bandgap reference adopts half a period working mechanism. The operational amplifier doesn't work during the switched capacitor network sampling. And the operational amplifier works after the charge balanced. The bandgap reference runs discontinuously. The design implements in standard 0.18um CMOS process. The simulation result shows that, the circuit consumes 19.8 uAat 3.3V. The noise

The ability to directly record human face-to-face interactions increasingly enables the development of detailed data-driven models for the spread of directly transmitted infectious diseases at the scale of individuals. Complete coverage of the contacts occurring in a population is however generally unattainable, due for instance to limited participation rates or experimental constraints in spatial coverage. Here, we study the impact of spatially constrained sampling on our ability to estimate the epidemic risk in a population using such detailed data-driven models. The epidemic risk is quantified by the epidemic threshold of the susceptible-infectious-recovered-susceptible model for the propagation of communicable diseases, i.e. the critical value of disease transmissibility above which the disease turns endemic. We verify for both synthetic and empirical data of human interactions that the use of incomplete data sets due to spatial sampling leads to the underestimation of the epidemic risk. The bias is however smaller than the one obtained by uniformly sampling the same fraction of contacts: it depends nonlinearly on the fraction of contacts that are recorded and becomes negligible if this fraction is large enough. Moreover, it depends on the interplay between the timescales of population and spreading dynamics., 21 pages, 7 figures

Low densities and wide-ranging behaviour make wolverines Gulo gulo difficult to monitor. We used quadrat sampling of tracks in snow to estimate wolverine populations. We conducted aerial surveys in upper Turnagain Arm and the Kenai Mountains (TAKM) in south-central Alaska and in Old Crow Flats (OCF) in northern Yukon during March 2004 following procedures for the sample-unit probability estimator (SUPE). This technique uses network sampling of tracks in snow in a stratified random system of quadrats or sample units. In TAKM, we sampled 87 (51%) out of 171 quadrats within a survey area of 4,340 km2. The estimated density was 3.0 (± 0.4 SE) wolverines/1,000 km2 with a coefficient of variation (CV) of 12.0%. In OCF, we sampled 96 (71%) out of 135 quadrats within a survey area of 3,375 km2. The estimated density was 9.7 (± 0.6 SE) wolverines/1,000 km2 with a CV of 6.5%. Our results indicated that the SUPE technique is an efficient method of obtaining precise estimates of wolverine population size und...

The National Ambulatory Medical Care Survey (NAMCS) is a nationally representative survey of medical encounters in physician offices in the United States. Data from this survey and its counterpart in hospitals, the National Hospital Ambulatory Medical Care Survey (NHAMCS), have been used to investigate physician treatment and prescribing patterns. A limitation of these data, however, is that they represent visits rather than patients. Starting in 2001, the survey questionnaires began collecting information on the number of past visits the patient had to the sample provider during the one-year period prior to the sampled visit. This information was used to estimate number of patients from the NAMCS and NHAMCS visit data using a multiplicity estimator. The resulting distribution of patients by the number of annual visits is similar to the distribution of persons in the U.S. making ambulatory care visits from a population-based survey. This estimation technique may be useful in estimating patients with clinical characteristics that are difficult to collect from a population-based survey. Published in 2007 by John Wiley & Sons, Ltd.

Respondent-Driven Sampling is a method to sample hard-to-reach human populations by link-tracing over their social networks. Beginning with a convenience sample, each person sampled is given a small number of uniquely identified coupons to distribute to other members of the target population, making them eligible for enrollment in the study. This can be an effective means to collect large diverse samples from many populations. Inference from such data requires specialized techniques for two reasons. Unlike in standard sampling designs, the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights necessary for traditional design-based inference. Any likelihood-based inference requires the modeling of the complex sampling process often beginning with a convenience sample. We introduce a model-assisted approach, resulting in a design-based estimator leveraging a working model for the structure of the population over which sampling is conducted. We demonstrate that the new estimator has improved performance compared to existing estimators and is able to adjust for the bias induced by the selection of the initial sample. We present sensitivity analyses for unknown population sizes and the misspecification of the working network model. We develop a bootstrap procedure to compute measures of uncertainty. We apply the method to the estimation of HIV prevalence in a population of injecting drug users (IDU) in the Ukraine, and show how it can be extended to include application-specific information., Comment: 38 pages, 11 figures, under review. Includes supplemental materials

In recent years, the data used for analysis of social networks become very huge and restrictive so that it can be used an appropriate and small sampled network of original network for analysis goals. Sampling social network is referred to collect a small subgraph of original network with high property similarities between them. Due to important impact of sampling on the social network analyses, many algorithms have been proposed in the field of network sampling. In this paper, we propose a two-phase algorithm for sampling online social networks. At first phase, our algorithm iteratively constructs several set of minimum spanning trees (MST) of network. In the second phase, the proposed algorithm sorts vertices of MSTs and merge them to form a sampled network. Several simulation experiments are conducted to examine the performance of the proposed algorithm on different networks. The obtained results are compared with counterpart algorithms in terms of KS-test and ND-test. From the results, it can be observed that the proposed algorithm outperforms the existing algorithms.

Given an incomplete (i.e., partially-observed) network, which nodes should we actively probe in order to achieve the highest accuracy for a given network feature? For example, consider a cyber-network administrator who observes only a portion of the network at time t and wants to accurately identify the most important (e.g., highest PageRank) nodes in the complete network. She has a limited budget for probing the network. Of all the nodes she has observed, which should she probe in order to most accurately identify the important nodes? We propose a novel and scalable algorithm, MaxOutProbe, and evaluate it w.r.t. four network features (largest connected component, PageRank, core-periphery, and community detection), five network sampling strategies, and seven network datasets from different domains. Across a range of conditions, MaxOutProbe demonstrates consistently high performance relative to several baseline strategies

Respondent-Driven Sampling (RDS) is an approach to sampling design and inference in hard-to-reach human populations. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. Common examples include injecting drug users, men who have sex with men, and female sex workers. Most analysis of RDS data has focused on estimating aggregate characteristics, such as disease prevalence. However, RDS is often conducted in settings where the population size is unknown and of great independent interest. This paper presents an approach to estimating the size of a target population based on data collected through RDS. The proposed approach uses a successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes. The inference uses the Bayesian framework, allowing for the incorporation of prior knowledge. A flexible class of priors for the population size is proposed that aids elicitation. An extensive simulation study provides insight into the performance of the method for estimating population size under a broad range of conditions. A further study shows the approach also improves estimation of aggregate characteristics. A particular choice of the prior produces interval estimates with good frequentist properties. Finally, the method demonstrates sensible results when used to estimate the numbers of sub-populations most at risk for HIV in two cities in El Salvador., Comment: 36 pages, 7 figures, including color figures

We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study extremal properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers, or income of the nodes in online social networks, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like the kth largest value, clusters of exceedances over a threshold, and first hitting time of a large value are investigated. We abstract the dependence and the statistics of extremes into a single parameter that appears in extreme value theory called extremal index (EI). In this work, we derive this parameter analytically and also estimate it empirically. We propose the use of EI as a parameter to compare different sampling procedures. As a specific example, degree correlations between neighboring nodes are studied in detail with three prominent random walks as sampling techniques.

During the past several decades, various Online Social Networks (OSNs) have experienced a huge development and have given rise to many related studies. However, the huge size of OSNs brings many difficulties to researchers and kinds of sampling methods have been used to get a relatively small but representative sample. Although some traditional sampling methods (e.g. Random Walk and its several improved forms) can help us to get a high-quality sample, there is still a lot of space for improvement in convergence rate, efficiency and their performance in directed OSNs. In this paper, we focus on sampling directed OSNs and propose Bi-graph Random Walk sampling (BRW) as a new sampling method. During each iteration of sampling, we treat directed structure as a combined graph of in-graph and out-graph, and then use a two-stage procedure to sample network. By evaluating this method in both synthetic graph and real OSNs, we find that BRW can achieve higher efficiency and faster convergence rate than traditional sampling methods.

Item does not contain fulltext BACKGROUND: Guidelines and training initiatives (G/TIs) available to support communication in cross-cultural primary health care consultations are not routinely used. We need to understand more about levers and barriers to their implementation and identify G/TIs likely to be successfully implemented in practice. OBJECTIVE: To report a mapping process used to identify G/TIs and to prospectively appraise their implementability, using Normalization Process Theory (NPT). METHODS: RESTORE is a 4-year EU FP-7 project. We used purposeful and network sampling to identify experts in statutory and non-statutory agencies across Austria, England, Greece, Ireland, Scotland and the Netherlands who recommended G/TI data from the grey literature. In addition, a peer review of literature was conducted in each country. Resulting data were collated using a standardized Protocol Mapping Document. G/TIs were identified for inclusion by (i) initial elimination of incomplete G/TI material; (ii) application of filtering criteria; and (iii) application of NPT. RESULTS: 20 G/TIs met selection criteria: 8 guidelines and 12 training initiatives. Most G/TIs were identified in the Netherlands (n = 7), followed by Ireland (n = 6) and England (n = 5). Fewer were identified in Scotland (n = 2), and none in Greece or Austria. The majority (n = 13) were generated without the inclusion of migrant service users. All 20 were prospectively appraised for potential implementability by applying NPT. CONCLUSIONS: NPT is useful as a means of prospectively testing G/TIs for implementability. Results indicate a need to initiate meaningful engagement of migrants in the development of G/TIs. A European-based professional standard for development and assessment of cross-cultural communication resources is advised.

This study investigated the communication strategies used by divorced individuals who did not wish their marriages to end (non-initiators). Participants were 270 divorced persons drawn from divorce recovery and support groups as well as network sampling. An adaptation of Buss's (1988) taxonomy of partner retention tactics served to capture the communication strategies of non-initiators during marital dissolution. A factor analysis revealed that four disengagement resistance strategies—commitment, alignment, negativity, and harm—are used by non-initiators during the process of marital dissolution.

Purpose: As part of a larger study on food insecurity and dietary adequacy of low-income lone mothers and their children in Atlantic Canada, we examined diet quality among household members. Methods: Network sampling for ‘difficult to sample’ populations was used to identify mothers living below the poverty line and alone with at least two children under age 14. Trained dietitians administered 24-hour dietary recalls weekly for one month to mothers on the dietary intake of themselves and their children. We calculated Healthy Eating Index category scores for eligible mothers (129) and children (303) using Canada’s Food Guide to Healthy Eating and the Nutrition Recommendations for Canadians. Results: Diet quality of low-income lone mothers was poor (35.5%) or in need of improvement (64.5%), with no mother having a good diet. The diet quality of children varied by age, with 22.7% of children aged one to three having a good diet or needing improvement (74.6%), 2.1% of children aged four to eight and no child aged nine to 14 having a good diet, while the diets of about 85% of older children in both age categories needed improvement. Conclusions: Younger children seem to be protected from poor quality diets in households with limited resources to acquire food.

Few reliable methods exist for estimating population size of large terrestrial carnivores. This is particularly true in forested areas where sightability is low and when radiocollared individuals are unavailable in the target population. We used stratified network sampling to sample wolf (Canis lycaon) tracks in the snow to estimate density in western Algonquin Park, Ontario in February 2002. We partitioned our 3,425-km2 study area into 137 5 × 5-km sample units (SU) and stratified SUs as having a high (n = 61) or low (n = 76) probability of containing detectable wolf tracks based on the relative amount of watercourses and conifer cover within each block. We used a Bell 206B helicopter to survey 28 high (46%) and 17 low (22%) SUs. When fresh tracks were found in a block, we followed the tracks forward to the wolves themselves and then backward until the tracks were no longer considered “fresh.“ We observed 17 “fresh“ track networks within 45 SUs. The average pack size in the area we surveyed was ...

© 2015, The International Biometric Society. The study of hard-to-reach populations presents significant challenges. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. This is especially true of populations at high risk for HIV/AIDS. Respondent-driven sampling (RDS) is often used in such settings with the primary goal of estimating the prevalence of infection. In such populations, the number of people at risk for infection and the number of people infected are of fundamental importance. This article presents a case-study of the estimation of the size of the hard-to-reach population based on data collected through RDS. We study two populations of female sex workers and men-who-have-sex-with-men in El Salvador. The approach is Bayesian and we consider different forms of prior information, including using the UNAIDS population size guidelines for this region. We show that the method is able to quantify the amount of information on population size available in RDS samples. As separate validation, we compare our results to those estimated by extrapolating from a capture-recapture study of El Salvadorian cities. The results of our case-study are largely comparable to those of the capture-recapture study when they differ from the UNAIDS guidelines. Our method is widely applicable to data from RDS studies and we provide a software package to facilitate this.

This article describes a new method of measurement and determination of an average concentration of gaseous components of flue gases at the boiler output, which is based on the use of equipment for continuous network sampling and analysis of gaseous pollutants, developed at the VaB-TU Ostrava for the purpose of diagnostic measurements in the energy sector. The advantage of this method in comparison with the commonly used system of measurements of concentrations of gaseous components in flue gases at one point of flue gas ducting is its higher accuracy in the determination of the average value of concentration at the output from incineration sources, and the corresponding lower uncertainty in determining the efficiency of these devices. Keywords-diagnostic measurement; continuous network sampling; concentration of gaseous component; flue gases; efficiency

Line-intercept sampling (Becker, 1991) and network sampling (Becker et al., 1998) seem to be the most appropriate procedures for estimating animal abundance in a study area on the basis of tracks. The purpose of this paper is to investigate the statistical properties of these alternative procedures by constructing confidence intervals for abundance and comparing the interval performances in terms of width and coverage.

Background: When a daughter perceives rejection from her mother, she is bound to be sensitive to rejection for most if not all of her life. Such an experience influences almost all future relationships. Objectives: The purpose of this research was to explore and describe the life stories of young women who perceived rejection from their mothers and to formulate guidelines to assist them. Method: A phenomenological interpretive method that is explorative, descriptive, and contextual was used to explore everyday life experiences. Network sampling was used. In-depth phenomenological interviews were conducted with the young women so that they could define the most important dimensions of their life stories and elaborate on what is relevant to them. They were asked: ‘Tell me your life story.’ One of the authors also had a life story of perceived maternal rejection; hence an auto-ethnography was critical and was included in the study. Thematic data analysis was applied. Results: Themes that emerged from the data were that the young women: (1) perceive ongoing challenges in forming and sustaining relationships in their lives; (2) experience their lives as conflicted because their relationship with the central core of their existence, their mother, is perceived as tumultuous; and (3) experience fundamental links to be missing in their ‘motherdaughter relationship’. Conclusion: Only a few women were interviewed regarding perceived rejection from their mothers. Further research in this regard is imperative.

La migration internationale d’étudiants est un investissement couteux pour les familles dans beaucoup de pays en voie de développement. Cependant, cet investissement est susceptible de générer des bénéfices financiers et sociaux relativement importants aux investisseurs, tout autant que des externalités pour d’autres membres de la famille. Cette thèse s’intéresse à deux aspects importants de la migration des étudiants internationaux : (i) Qui part? Quels sont les déterminants de la probabilité de migration? (ii) Qui paie? Comment la famille s’organise-t-elle pour couvrir les frais de la migration? (iii) Qui y gagne? Ce flux migratoire est-il au bénéfice du pays d’origine? Entreprendre une telle étude met le chercheur en face de défis importants, notamment, l’absence de données complètes et fiables; la dispersion géographique des étudiants migrants en étant la cause première. La première contribution importante de ce travail est le développement d’une méthode de sondage en « boule de neige » pour des populations difficiles à atteindre, ainsi que d’estimateurs corrigeant les possibles biais de sélection. A partir de cette méthodologie, j’ai collecté des données incluant simultanément des étudiants migrants et non-migrants du Cameroun en utilisant une plateforme internet. Un second défi relativement bien documenté est la présence d’endogénéité du choix d’éducation. Nous tirons avantage des récents développements théoriques dans le traitement des problèmes d’identification dans les modèles de choix discrets pour résoudre cette difficulté, tout en conservant la simplicité des hypothèses nécessaires. Ce travail constitue l’une des premières applications de cette méthodologie à des questions de développement. Le premier chapitre de la thèse étudie la décision prise par la famille d’investir dans la migration étudiante. Il propose un modèle structurel empirique de choix discret qui reflète à la fois le rendement brut de la migration et la contrainte budgétaire liée au problème de choix des agents. Nos résultats démontrent que le choix du niveau final d’éducation, les résultats académiques et l’aide de la famille sont des déterminants importants de la probabilité d’émigrer, au contraire du genre qui ne semble pas affecter très significativement la décision familiale. Le second chapitre s’efforce de comprendre comment les agents décident de leur participation à la décision de migration et comment la famille partage les profits et décourage le phénomène de « passagers clandestins ». D’autres résultats dans la littérature sur l’identification partielle nous permettent de considérer des comportements stratégiques au sein de l’unité familiale. Les premières estimations suggèrent que le modèle « unitaire », où un agent représentatif maximise l’utilité familiale ne convient qu’aux familles composées des parents et de l’enfant. Les aidants extérieurs subissent un cout strictement positif pour leur participation, ce qui décourage leur implication. Les obligations familiales et sociales semblent expliquer les cas de participation d’un aidant, mieux qu’un possible altruisme de ces derniers. Finalement, le troisième chapitre présente le cadre théorique plus général dans lequel s’imbriquent les modèles développés dans les précédents chapitres. Les méthodes d’identification et d’inférence présentées sont spécialisées aux jeux finis avec information complète. Avec mes co-auteurs, nous proposons notamment une procédure combinatoire pour une implémentation efficace du bootstrap aux fins d’inférences dans les modèles cités ci-dessus. Nous en faisons une application sur les déterminants du choix familial de soins à long terme pour des parents âgés., International migration of students is a costly investment for family units in many developing countries. However, it might yield substantial financial and social return for the investors, as well as externalities for other family members. Furthermore, when these family decisions aggregate at the country-level, they affect the stock of human capital available to the origin country. This thesis addresses primarily two aspects of international student migration: (i) Who goes? What are the determinants of the probability of migration? (ii) Who pays? How does the family organize to bear the cost of the migration? Engaging in this study, one faces the challenge of data limitation, a direct consequence of the geographical dispersion of the population of interest. The first important contribution of this work is to provide a new snowball sampling methodology for hard-to-reach population, along with estimators to correct selection-biases. I collected data which include both migrant and non-migrant students from Cameroon, using an online-platform. A second challenge is the well-documented problem of endogeneity of the educational attainment. I take advantage of recent advances in the treatment of identification problems in discrete choice models to solve this issue while keeping assumptions at a low level. In particular, validity of the partial identification methodology does not rest on the existence of an instrument. To the best of my knowledge, this is the first empirical application of this methodology to development related issues. The first chapter studies the decision made by a family to invest in student. I propose an empirical structural decision model which reflects the importance of both the return of the investment and the budgetary constraint in agent choices. Our results show that the choice of level of education, the help of the family and academic results in secondary school are significant determinant of the probability to migrate, unlike the gender which does not seem to play any role in the family decision. The objective of the second chapter is to understand how agents decide to be part of the migration project and how the family organizes itself to share profits and discourage free riding-behavior. Further results on partial identification for games of incomplete information allow us to consider strategic behavior of family. My estimation suggests that models with a representative individual suit only families which consist of parent and child, but are rejected when a significant extended family member is introduced. Helpers incur a non-zero cost of participation that discourages involvement in the migration process. Kinship obligations and not altruism appears as the main reason of participation. Finally, the third chapter presents the more general theoretical framework in which my models are imbedded. The method presented is specialized to infinite games of complete information, but is of interest for application to the empirical analysis of instrumental variable models of discrete choice (Chapter 1), cooperative and non-cooperative games (Chapter 2), as well as revealed preference analysis. With my co-authors, we propose an efficient combinatorial bootstrap procedure for inference in games of complete information that runs in linear computing time and an application to the determinants of long term elderly care choices.