Хі Міжнародна конференція студентів та аспірантів «Соціологія в (пост)сучасності»

Харківська соціологічна школа, спадкоємець та продовжувач найкращих традицій слобожанської наукової думки, бере початок у XIX ст. Станом на початок XX ст. вона мала і традиції, і серйозні, відомі не тільки в Російській імперії, імена теоретиків всесвітнього масштабу (М.М. Ковалевський, О.О. Богданов та ін.), і напрацювання в емпіричній царині (В.В. Святловський, Ф.Р. Дунаєвський). Сучасна Харківська соціологічна школа, що бере початок з 1960-х років ХХ ст., є одним з лідерів української соціології. Завдяки прикладу справжніх Вчених, Діячів, Людей формуються наступні покоління, здатні підтримувати могутні традиції та гідно репрезентувати наукову школу, вже у студентські роки організовуючи заходи та проводячи самостійні дослідження.

Наступним проектом, що надає таку можливість, є Міжнародна студентська та аспірантська конференція, проведення якої є одним із пріоритетних напрямків діяльності та одним з предметів гордості Студентського наукового товариства соціологічного факультету.

Вперше конференція відбулася у 2002 р. на соціологічному факультеті ХНУ імені В.Н. Каразіна як цілком внутрішньофакультетський науковий захід. На даний момент цей соціологічний молодіжний форум працює за десятками різних напрямків: від теоретико-методологічних проблем розвитку соціологічного знання до практики соціальної роботи, від соціології культури до політичної соціології; розглядаючи проблеми всіх рівнів – від глобалізації і постмодерну до самоідентифікації й особистості, – в рамках різноманітних методологічних підходів. Специфічною його рисою є надання можливостей для самовираження та пошуку молодими вченими свого місця під сонцем соціогуманітарного знання.

Сучасна наука принципово неможлива без комунікації, без взаємозбагачення шляхом постійного обміну думками. Конференція, виконуючи цю функцію, не може остаточно вичерпати комунікативні потреби, і високі технології у вигляді сайта http://snosf.at.ua/ та e-mail sno_sf@mail.ru допоможуть ефективній співпраці.

Запрошуємо до співробітництва! Оргкомітет вітає учасників хi Міжнародної наукової конференції студентів та аспірантів «Соціологія у (пост)сучасності»!

Bushuieva Elyzaveta

V.N. Karazin Kharkiv National University

(Kharkiv, Ukraine)

Korostii Denys

Université Nice Sophia Antipolis

(Nice, France)

Respondent-Driven Sampling in Social Work

Social work is a social discipline essential for the proper development of contemporary society in the sense that it helps identify and solve issues that are responsible for social exclusion of certain people. And whilst lack of resources needed for successful and continuing improvement of the quality of life is nothing more than a purely economical issue, imperfection of the methodology and limitations of existing methods of social work may prove themselves an everyday obstruction on the way to better tomorrow.

Nevertheless, there are other difficulties that make some goals of the social work hard to achieve using classical methods. Most users of the professional social workers’ service are those who experience social exclusion for different reasons. These people form so-called hidden populations: social groups that are hard to access due to lack of communications with the rest of the society. And the tricky part lies in the fact that significant share of members of hidden populations doesn’t reach out for help by themselves. And, obviously, social workers need a way to find persons with a need to resocialization.

Classical methods for reaching hidden populations include snowball-sampling, key informant sampling and targeted sampling. Snowball-sampling is a method, widely used by sociologists for various purposes and consists of a number of stages (or “waves”) of recruitment. On each stage respondents after participating are supposed to give the researcher a list of other hidden population members, which will be used as a sample for the next stage.

The main issue here is that the sample will depend on connections of participants, so some parts of target population may remain untouched, while persons with larger list of contacts will be oversampled. Also, as well as other chain-referral methods, this one has other major biases: first one that comes from “masking” (respondent tend to protect their peers by not referring them), and two others are that initial sample usually consists of volunteers that means it is biased towards more cooperative respondents and that connections in personals networks generally are non-random which may affect the resulting sample significantly.

Key informant sampling method can overcome biases caused by unwilling of some respondents to disclose information related to their own behavior and membership in the hidden populations by using respondents that do not necessarily belong to hidden population, but have knowledge about hidden population, its members and their behavior due to nature of their profession. While removing mentioned biases, this method may introduce other specific problems.

The idea of targeted sampling is to perform an ethnographic mapping of hidden population first and then to choose few specific locations where fixed numbers of respondents are recruited in order to represent all different subgroups of the population. This method allows us to overcome biases of chain-referral methods but it is hard to implement because it highly depends on adequacy of the mapping.

Respondent-Driven Sampling (RDS) is a sampling method that uses chain-referral process to reach hidden populations and a mathematical model to provide an unbiased and representative sample. RDS was created and developed by Douglas D. Heckathorn. Later works have also confirmed effectiveness of this method. Nowadays, RDS is used worldwide in various researches of hidden populations. While also being a chain-referral method, RDS employs a specific recruitment technique to provide researchers with a sample that is claimed independent from initial set of subjects, that means the initial sample does not have to be random anymore. Moreover, RDS reduces other biases that usually affect chain-referral methods and allows us to bear the bias resulting from different sizes of peers’ personal networks.

Recruitment process is “two-step” in meaning that we use both primary and secondary incentives. Primary incentive consists of a reward for participation in the research (or threat of punishment for refusal), whereas secondary one serves for so-called group-mediated social control. For example, as a form of secondary incentive, participants could also be rewarded for the peers they invite into the research. For this purposes, recruitment technique is modified comparing to snowball-sampling: instead of gathering contact lists from recruits, researcher asks them to invite new recruits themselves for additional reward. This approach has lower compliance costs and provides a possibility to recruit persons that are not interested in material incentives through the symbolical influence of their peers, which accelerates the recruitment process significantly.

Another important feature of Respondent-Driven Sampling is that respondents are not asked to identify their peers to the researcher, but to invite them into research on their own. This difference from traditional chain-referral methods is very important as it allows increasing number of recruits in cases of frequent masking, which is a typical case for hidden populations.

Finally, there exists a major problem arising from the fact that all the links in the recruitment tree are chosen with the same probability (which means probability to select any given node is proportional to its degree), so the resulting sample will be biased towards nodes that have larger sets of contacts. In order to compensate for this bias researchers will also have to collect some additional data about network properties and use them to compute average group degree estimators that can be used to correct both categorical and continuous variables estimated by RDS. Recent studies confirmed both analytically and computationally that RDS estimators are asymptotically unbiased and the resulting sample is independent of the initial seeds.

During the last fifteen years, RDS has been used in numerous large sociological studies all over the world. Without a doubt, this method is the best sampling solution for the research in field of social work.

Kuleshov Aleksandr

Belarusian State University

(Belarus, Minsk)