Video disparador

4.6.1 Boundary Specification

A key issue of importance in the design of network studies concerns the specification of boundaries on a set of units (respondents or nodes) to be included in a network. Network analysts (Laumann et al., 1989; Marsden, 1990) caution that care must be taken in specifying rules of inclusion pertaining to both the selection of actors or nodes for a network and the choice of types of relationships to be studied. This is a concern for both ego network and whole network studies. In both approaches, the analysis relies on the interrelationship of nodes, hence omission of a relevant element or arbitrary delineation of boundaries can lead to misleading results (Barnes, 1979).

Laumann et al (1989), in a review of boundary specification strategies for whole networks, distinguishes between the realist and the nominalist approach. The realist approach is based on the subjective perceptions of the participants and assumes that there is a ‘true’ network of relationships out there which researchers should uncover. The nominalist approach offers a more realistic option of using a-priori conceptual frameworks dependent on the research question. Three procedural tactics are commonly employed by researchers to specific network boundaries (Marsden, 1990). Those based on the attributes of participants consider membership criteria set by formal organizations such as schools (Coleman, 1988), or occupancy of specific social positions deemed relevant by the researcher such as professional communities (Wellman, 1983). The second tactic relates to using social relations to delimit boundaries as in snowball sampling procedures (Erickson, 1979). The last approach involves using participation in certain events as the basis for membership of a network, such as publication in journals (Brieger, 1976).

For ego centric network data, the challenge is to determine the people who should be regarded as part of a given individual’s network. Usually, data is collected on direct contacts such as one’s friends or one’s family. In principle one could also collect data on those who are linked to the participant by one intermediary such as friend of a friend. However, pragmatic pressures tend to restrict the attention of the researchers to direct contacts only (Marsden, 1990). Boundaries for ego networks are typically set via one or more name generators that elicit names of relevant people the participant shares a certain relationship with. These and associated issues will be discussed in more detail in chapter 5, section 5.6.3.2.

A related issue is the type of tie to be examined. In practice, network analysts tend to focus on one or more of four types of ties as described in section 4.2.2.

4.6.2 Respondent Burden

One of the challenges of conducting a network study is related to managing respondent burden. In whole network studies, data about interrelationships between network members is either collected from the network members through survey, observation or secondary data. In ego network studies, structural data is collected from the respondents by asking them about the ties between their network members. McCarty and colleagues (2007) argue that whole network data collection is high on researcher burden, and low on respondent burden because the task of collecting data on interrelationships of members is distributed across the participants who the researcher must observe or interview individually. In contrast, ego network data collection is low on researcher burden and high on respondent burden because the respondents are required to provide the researcher with information related to the attributes of each network member and the presence or absence of a relationship between every pair. This is a key difference.

Researchers address these issues by employing various methods to reduce respondent burden such as asking for fewer alters or collecting detailed relational information on only a few alters chosen from a larger list (McCarty et al., 2007). Each strategy has its own advantages and disadvantages as will be discussed in more detail in chapter 5, section 5.6.3 and researchers are often guided by their study aims in making the choice.

More recently a growing interest in Web-based computer-assisted self-interview (CASI) applications has demonstrated that such methods can present substantial opportunities for personal network data collection (Vehovar et al., 2008). Recent studies (Lackaff, 2010; Lackaff, 2012) employ the web-based Propitious Aggregation of Social Networks (PASN,

http://pro.pitio.us), a survey instrument which reduces this burden by leveraging network data already available in context of social network websites, and by providing an intuitive click- and-drag interface for survey responses. An experiment conducted on 85 participants using this tool reports producing networks which were significantly larger and more diverse than those produced using standard survey methods, yet required significantly lower time investments from the participants. However, other studies (Matzat and Snijders, 2010) using web based methods report that while such methods reduce costs and interviewer biases relative to face to face data collection methods, they produce lower quality data as a consequence of the respondents answering inattentively, almost mechanically to the questions.

4.6.3 Causality

Determining the exact nature of causal relationships between networks and their effect on an individual’s behaviour or perceptions is a challenging endeavor. Fowler and others (2011), in a recent publication argue that four assumptions are critical in making causal inferences on

network data. First, it is necessary to assume that the network members elicited in a study are an appropriate proxy for all the peer influences an individual will receive. Second, identification of peer effects is only possible after assessing network selection based on the principle of similarity or homophily. Third, it is necessary to assume that the respondent will appropriately recall, and truthfully describe, the attributes and behaviours of network members in relation to the area of interest. Fourth, it is necessary to elicit the respondent’s contextual influences. A common criticism often raised in relation to observational studies is that unobserved factors can influence the relationship between the respondent and the network members.

Though well designed longitudinal studies can be a suitable way of addressing these concerns, Doreian (2001) notes that “there needs to be a very tight coupling of theory, mechanisms, and credible empirical information before we can delineate the actual operation of causes in the empirical world and before we can tell causal stories” (p111)