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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.

One may use information about a random sample of network members to estimate quantities related to the triad census of a network. Various kinds of information about the graph may be observable from the sample; four observation schemes involving the local networks of the sampled vertices are considered here. Unbiased triad count estimators are defined, and their variances (and unbiased estimators of these variances) are derived. A main result is that under one of the observation schemes, the estimator can be written as a sum of vertex attributes; standard estimation formulas for various sampling designs, such as stratified sampling, are therefore effortlessly applied. The estimator properties are compared in a simulation study.

Institutionalization is associated with the loss of informal social ties because isolation in the community increases the risk of admission and/or admission to a facility increases the risk that ties will be severed. The literature on social relationships with institutions has been limited by the use of biased samples, simplistic measures and exclusion of patients unable to respond to verbal or written questionnaires. The methodological aspects of a whole network study of chronic care hospital patients are discussed with a particular emphasis on network sampling and non-response bias. The substantive aspects of the present analyses focus in the different patterns of relationships of patients, family and employees in a hospital setting.

This paper studies the decision made by a family to invest in student migration. We propose an empirical structural decision model which reflects the importance of both the return to the investment and the budgetary constraint in the choice of the family. We circumvent the problem of endogeneity of the educational attainment by deriving sharp bounds and conduct inference for the parameters of interest. The data are collected on students from Cameroon, using a new snowball sampling procedure, which allow the inclusion of both migrants and non-migrants in the sample. We propose bias corrected estimators for this procedure. We study the characteristics of potential candidates to migration that increase or decrease their probability to migrate, accounting for a potential helper in the diaspora. Among the interesting results we find that a choice to complete a Master's degree doubles the odds of migration, there is little evidence of gender preference, students migrants are positively selected on their previous academic results.

Studying structural and functional characteristics of large scale graphs (or networks) has been a challenging task due to the related computational overhead. Hence, most studies consult to sampling to gather necessary information to estimate various features of these big networks. On the other hand, using a best effort approach to graph sampling within the constraints of an application domain may not always produce accurate estimates. In fact, the mismatch between the characteristics of interest and the utilized network sampling methodology may result in incorrect inferences about the studied characteristics of the underlying system. In this study we empirically investigate the sources of information loss in a sampling process; identify the fundamental factors that need to be carefully considered in a sampling design; and use several synthetic and real world graphs to elaborately demonstrate the mismatch between the sampling design and graph characteristics of interest.

Networks are a popular tool for representing elements in a system and their interconnectedness. Many observed networks can be viewed as only samples of some true underlying network. Such is frequently the case, for example, in the monitoring and study of massive, online social networks. We study the problem of how to estimate the degree distribution - an object of fundamental interest - of a true underlying network from its sampled network. In particular, we show that this problem can be formulated as an inverse problem. Playing a key role in this formulation is a matrix relating the expectation of our sampled degree distribution to the true underlying degree distribution. Under many network sampling designs, this matrix can be defined entirely in terms of the design and is found to be ill-conditioned. As a result, our inverse problem frequently is ill-posed. Accordingly, we offer a constrained, penalized weighted least-squares approach to solving this problem. A Monte Carlo variant of Stein's unbiased risk estimation (SURE) is used to select the penalization parameter. We explore the behavior of our resulting estimator of network degree distribution in simulation, using a variety of combinations of network models and sampling regimes. In addition, we demonstrate the ability of our method to accurately reconstruct the degree distributions of various sub-communities within online social networks corresponding to Friendster, Orkut and LiveJournal. Overall, our results show that the true degree distributions from both homogeneous and inhomogeneous networks can be recovered with substantially greater accuracy than reflected in the empirical degree distribution resulting from the original sampling., Published at http://dx.doi.org/10.1214/14-AOAS800 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A number of actions have been undertaken within the National Atmospheric Deposition Program (NADP) to implement a regional mercury deposition network. This paper describes a field test designed to evaluate a collector design and protocol for implementation within a new Hg network. The collector chosen for evaluation is a “dual-orifice” collector, designed to sample precipitation for mercury and other metals simultaneously. The method chosen for Hg analysis was cold vapour atomic fluorescence spectroscopy (CVAFS). The weekly precipitation Hg concentrations range between 4.29 and 17.88 ng l−1, with a volume-weighted mean of 10 ng l−1 comparable to those reported in other ongoing studies in North America and Europe. Calculated deposition flux ranges from 43 to 358 ng m−2 week−1, with a mean of 186 ng m−2 week−1.

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals' perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings. © 2012 Elsevier B.V.

One of the many challenges hindering the global response to the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemic is the difficulty of collecting reliable information about the populations most at risk for the disease. Thus, the authors empirically assessed a promising new method for estimating the sizes of most at-risk populations: the network scale-up method. Using 4 different data sources, 2 of which were from other researchers, the authors produced 5 estimates of the number of heavy drug users in Curitiba, Brazil. The authors found that the network scale-up and generalized network scale-up estimators produced estimates 5-10 times higher than estimates made using standard methods (the multiplier method and the direct estimation method using data from 2004 and 2010). Given that equally plausible methods produced such a wide range of results, the authors recommend that additional studies be undertaken to compare estimates based on the scale-up method with those made using other methods. If scale-up-based methods routinely produce higher estimates, this would suggest that scale-up-based methods are inappropriate for populations most at risk of HIV/AIDS or that standard methods may tend to underestimate the sizes of these populations.

From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in fact, beneficial for many applications, as they "push" the sampling process towards inclusion of desired properties. Finally, we describe how these sampling biases can be exploited in several, real-world applications including disease outbreak detection and market research., 9 pages; KDD 2011: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling, or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses., Comment: Published in at http://dx.doi.org/10.1214/12-AOS1044 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The paper outlines the methodological choices that analysts must make when sampling social networks and assesses the impact of different sampling techniques on the estimation of network parameters. Using data from Galaskiewicz's (1919) study of Towertown and River City, the results show the extent to which sampling percentage, the number of trials/estimates, sampling procedure, and network size and density affect the ability of researchers to estimate the point centrality of organizations in networks of information and money transactions.

Since crime victimizations are statistically rare events, surveys to estimate rates of victimization are difficult and expensive. In this paper, we examine the advantages of network sampling over traditional methods for conducting crime victimization surveys. Network sampling links population households in specified ways, for reporting purposes, in order to increase the probabilities of locating households with particular characteristics. We conducted a reverse record check field experiment to test whether a telephone survey using network sampling is feasible to collect crime victimization data. Three types of crimes-burglary, robbery, and assault-were tested along with two types of networks-relatives and co-workers/close friends. This paper examines the extent to which victims report their victimization experiences in a general crime and victimization interview and the extent to which a randomly selected relative or close friend will report the same victimization incident in an identical interview. A number of multiplicity counting rules are compared in terms of reporting errors and a mean square error analysis.

� Abstract— Currently there are various approaches to the reconstruction of gene regulatory networks from different sources of data. However, none of these methods incorporates explicitly scale-free property, one of the most important features of the targeted network, into their algorithms. In this paper, several network sampling strategies are explored on a set assembled from previous published gene interactions in yeast, expecting to reconstruct regulatory networks that are scale-free.

Summary Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known sampling algorithms in terms of relative error and Kolmogorov–Smirnov test. It is shown that eDLA-based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA-based sampling algorithm in comparison with the DLA-based sampling algorithm has a 26.93% improvement for the average of Kolmogorov–Smirnov value for degree distribution taken over all test networks. Copyright © 2015 John Wiley & Sons, Ltd.

This thesis is a methodological study in the field of social network analysis. It seeks to investigate how certain factors can interfere with the processes of data collection and data analysis, and therefore lead to invalid or unreliable results for network-analytical measures. The discussion is focused on one-mode whole-network designs with data collection by the way of questionnaires. It begins with a short introduction into the methods of network analysis and then discusses the literature on the field of the validity of network analysis in general. Afterwards the possible influencing factors investigated in this study are discussed and the analysis is described.In particular, nonresponse (biased and unbiased), forgetting, attempts of sampling, the omission of unimportant actors, symmetrization, dichotomization, and collapsing actors are investigated. These processes and methods are simulated by comparing the results of network-analytical measures calculated with an unchanged data set to the results of measures calculated with other variants of the same data set in which these processes have been simulated. The network-analytical measures tested are density, degree centralization, eigenvector centralization, the determination of cliques and k-plexes, degree centrality, closeness centrality, eigenvector centrality, and betweenness centrality. Density, centralization and the extent of size reduction are expected to be the main influencing factors for validity and reliability. All combinations of size reduction or transformation processes and network-analytical measures are simulated using a total of seven matrices representing different densities and centralizations. In some cases, different extents of size reduction and different strategies of dealing with the problem are investigated as well.The study comes to the conclusion that size reduction and transformation processes can significantly change the results of an analysis. In most cases, the error introduced into the network-analytical measures is biased in one direction, most often negative. The deviations of the estimates from the real values depend on the extent of size reduction. Density and centralization are also influencing factors in many cases; however, the direction of this influence can change. Certain network-analytical measures like closeness centrality and the determination of subgroups are especially vulnerable. Certain size-reduction and transformation processes are more dangerous than others. These results are presented in detail at the end of the thesis.

In this paper we propose a participative and reflexive approach to social maps. Several social and ideological categories may be applied in the design of sociograms. The concept of “action set” is presented, as well as its consequences in theoretical terms and for sampling purposes., En este trabajo se propone una metodología participativa y reflexiva de desarrollo de mapas sociales. Se apuesta por incorporar en el sociograma, de modo flexible, el grado de afinidad de las relaciones junto a otros ejes sociales e ideológicos. Se presenta el concepto de conjuntos de acción, y se discuten las implicaciones de este enfoque, tanto en lo que hace referencia al muestreo como a aspectos teóricos más generales.

When stand-alone sampling frames list all establishments and size measures, the Hansen–Hurwitz (HH) pps estimator is generally used to estimate the volume of transactions between establishments and households. The network sampling (NS) version of the HH estimator depends on a population-survey-generated establishment frame, which lists establishments that have transactions with households in a population sample survey and the number of transactions that establishments have with survey households. The NS estimator is a competitor of the HH estimator whenever stand-alone frames of good quality are unavailable and, as this article indicates, the NS estimator may be competitive with the HH estimator when stand-alone and population-survey-generated frames are flawless. Keywords: establishment surveys; sampling frames; network sampling; Hansen–Hurwitz estimator

Technical report TR05-08. We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of ``focus'' that specifies a part of the network and the degree to which it needs to be magnified. Visualizing a sample allows our method overcome the scalability issues inherent in traditional visualization methods. We report some characteristics that frequently occur in large networks and the conditions under which they are preserved when sampling from a network. This can be useful in selecting a proper sampling scheme that yields a sample with similar characteristics as the original network. Our method is built on top of a relational database, thus it can be easily and efficiently implemented using any off-the-shelf database software. As a proof of concept, we implement our methods within a system called ALVIN and report some of our experiments over the movie database and the connectivity graph of the Web with 178 million nodes and over 800 million edges. | TRID-ID TR05-08

ISSUES AND PURPOSE. To examine the nature of daily hassles as perceived by African-American adolescent females. DESIGN AND METHODS. As part of a larger, cross-sectional study, nonrandom network sampling technique was usedto survey 178 adolescent girls between the ages of 11 and 19. RESULTS. This study found that the most common hassles were school and academic, followed by family and economic hassles, peer and social hassles, and personal safety hassles. Socioeconomic factors were strongly associated with the level of hassles reported. PRACTICE IMPLICATIONS. Assess African-American girls' perception of daily hassles, specifically school- and family- related hassles, and also examine the interrelationship between the type of hassles and health problems. Search terms: Adolescence, blacks, daily hassles, ethnic groups, females.

This paper reports on an exploratory study undertaken in New South Wales, Australia which sought to identify the positions nurses go on to when they leave nursing and the skills and experience they gained from nursing which they believe enabled them to obtain employment outside the profession. In addition, the reasons why they left nursing were also ascertained. A network sampling technique was used to recruit 17 participants. A tape-recorded semi-structured interview of approximately 1 h was conducted with each participant. Interviews were conducted until no new information emerged (14) and the remaining three interviews were used for validation. While many participants were employed in health-related fields, others were in diverse areas such as business, landscape coordination and market research, to name a few. All participants reported positively on the range of skills they had acquired as a nurse. Reasons provided for leaving the nursing workforce included reaching a ceiling in nursing or wishing to develop themselves in another direction. Respondents had also undertaken a wide range of additional qualifications. © 2002 Elsevier Science Ltd. All rights reserved.

El muestreo de conglomerados con multiplicidad utiliza el concepto de regla de conteo de multiplicidad para incluir en la encuesta individuos no pertenecientes a la muestra. Se derivan las fórmulas para la ganancia de eficiencia y para los componentes de varianza de dos estimadores de multiplicidad del total poblacional. Mediante simulación se ilustra la magnitud de la ganancia de eficiencia con el muestreo con multiplicidad en función del tamaño de muestra y la amplitud de la regla de conteo. Cluster sampling with multiplicity uses the concept of counting rule of multiplicity in order to include in the survey those individuals not belonging to the sample. Additionally, some results to improve the efficiency and for the variance components from multiplicity estimators of the population total are derived. Finally, a simulation exercise to illustrate the magnitude of the gain in the efficiency with the sampling with multiplicity in function of the sample size and the extent of the counting rule is presented.

The men and women who comprise the survey population around which this study is based were obtained through network sampling. Using original material obtained from in-depth interviews this chapter aims to build up a factual profile of this London-based survey population, firstly by looking at the background history of their migration to Britain and secondly, provide an outline of their present position by looking at their occupational position and educational qualifications.