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Chapter 11 - Body Image of Pre-adolescents
- from Part 2 - Dimensions of Health and Wellbeing
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- By Galina Daraganova, Acting Executive Manger, Longitudinal Studies, Australian Institutue of Family Studies and Honorary Melbourne University Fellow specialising in survey methodology, social statistics, and statistical models for networkbased social processes.
- Edited by Susanne Garvis, Göteborgs Universitet, Sweden, Donna Pendergast, Griffith University, Queensland
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- Book:
- Health and Wellbeing in Childhood
- Published online:
- 21 June 2018
- Print publication:
- 01 September 2017, pp 177-194
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Summary
Introduction
There is a growing amount of evidence that body image dissatisfaction is an issue of increasing concern and is associated with a number of damaging consequences for health and wellbeing (Cook, MacPherson & Langille, 2007; Croll, 2005; Liechty, 2010;). This issue can affect anyone regardless of age, gender, ethnicity, body size and shape (UK Parliamentary Report, 2012). Historically, the onset of body image concerns was attributed to adolescence as this is the period when puberty begins and the most dramatic body changes are experienced (Cash, 2002b). However, a ‘thin’ ideal is already present among children in primary school years, and children as young as seven years old report dissatisfaction with their bodies (Dohnt & Tiggemann, 2006; Levine & Piran, 2004; Liechty, 2010).
This chapter uses a nationally representative sample of Australian children to demonstrate the importance of understanding the desired and perceived body images of eight-to 11-year-olds and the discrepancy between these images, as well as to highlight the extent to which physical health and socio-emotional wellbeing of children is already associated with body image dissatisfaction at age 10– 11 years.
Rationale: Impact of Body Image on Health
Body image can be understood as the way children think and feel about their body. Negative self-evaluation of body shape may affect children's feelings and thoughts, and lead them to modify their behaviour and develop physical and psychological problems (Cash, 2002b). Studies have shown that children who are dissatisfied with their body size are more likely to follow unhealthy diets (Cash, 2002a; Stice et al., 1998), use anabolic steroids (mainly among boys) (Cohane & Pope, 2001), and have excessive levels of physical activity (Neumark-Sztainer et al., 2006). Dieting and excessive exercise in turn can lead to other health problems, such as fatigue and gastrointestinal problems, as well as joint or bone injuries (Neumark-Sztainer et al., 2006). Body dissatisfaction has also been found to be associated with a variety of risky behaviours, including early sexual activity, self-harm, and suicide planning (Cook, MacPherson & Langille, 2007). Dissatisfaction with one's own body not only affects physical health and behaviours but also may cause psychological distress. Children who report concerns with their body size are likely to report lower levels of global self-worth and poorer self-esteem (Tiggemann, 2005).
8 - Social Selection, Dyadic Covariates, and Geospatial Effects
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
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- 19 November 2012, pp 91-101
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Summary
Individual, Dyadic, and Other Attributes
In this chapter, we introduce models that include effects for actor attributes and dyadic covariates. Actor attributes are individual-level measures on the nodes of the network. Social selection models examine whether attribute-related processes affect network ties (e.g., homophily processes whereby network ties tend to occur between individuals with similar actor attributes) (McPherson, Smith-Lovin, & Cook, 2001). A dyadic covariate, in contrast, is a measure on each dyad, that is, on a pair of actors, and may similarly affect the presence of a tie. For instance, in a study of a trust network within an organization, the formal organizational hierarchy might partly shape the formation of trust ties. In that case, inclusion of the hierarchy as a dyadic covariate permits inferences about whether trust ties tend to align with hierarchical relationships (e.g., Tom is the boss of Fred). A binary dyadic covariate can be used to represent whether people share the same attribute or membership – that is, work at the same place, live in the same household, or attend the same church. Continuous dyadic covariates are also possible. Although spatial embedding of networks, to an extent, can be captured by dyadic continuous covariates, geospatial effects are a distinctive feature, so we provide a separate section in this chapter.
The preceding chapters outline the general ERGM methodology but concentrate exclusively on models for endogenous tie-based effects. The presence or absence of individual ties is affected by a surrounding neighborhood of other ties, with that neighborhood determined by the prevailing dependence assumption. These endogenous effects represent processes of network self-organization.
18 - Autologistic Actor Attribute Model Analysis of Unemployment: Dual Importance of Who You Know and Where You Live
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
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- 19 November 2012, pp 237-247
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Summary
Unemployment: Location and Connections
Persistent regional unemployment disparities have been characterized as a major cause of regional decay and impose significant costs on communities (Bill, 2005; Mitchell & Bill, 2004). Macroeconomic explanations for the persistence of unemployment often revolve around economic factors, including spatial changes in the skill requirements of jobs, migration of jobs to the suburbs, persistent demand constraints, wage differentials, low labor mobility and related structural impediments, and variations in the distribution of industries across space (see reviews, for example, in Ihlanfeldt and Sjoquist (1998) and Ramakrishnan and Cerisola (2004)). Outside traditional macroeconomic explanations of unemployment at the local area level (e.g., suburb), explanations draw on theories of residential segregation (Cheshire, Monastiriotis, & Sheppard, 2003; Hunter, 1996), which suggest that similar educational background and socioeconomic status along with housing market factors play a substantial role in determining how people are distributed across geographic space. Over time, these differences may become more pronounced as people sort further along lines of race and income (Bill, 2005). Cheshire et al. argued that where people live does not drive inequality but rather determines geographic location of inequality:
Where people live and the incidence of segregation and ultimately of exclusion, mainly reflects the increasing inequality of incomes. So if either the incidence of unemployment rises and/or if the distribution of earning becomes more unequal then social segregation intensifies…the poor are not poor, isolated and excluded for the reason which makes them poor. They are not poor because of where they live; rather they live where they do because they are poor. (2003, 83–84)
6 - Exponential Random Graph Model Fundamentals
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
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- 19 November 2012, pp 49-76
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7 - Dependence Graphs and Sufficient Statistics
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
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- 19 November 2012, pp 77-90
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Summary
Chapter Outline
This chapter is written for those interested in a more detailed understanding of how assumptions regarding various forms of dependence can be formalized. What is treated here is not essential for applying exponential random graph models (ERGMs) and may be skipped at a first reading. The general idea, laid down by Frank and Strauss (1986), is nevertheless crucial to the formulation of statistical models treated in this book.
Important key points in this chapter are as follows:
Subgraph counts are not arbitrarily chosen in ERGMs but correspond to specific dependency structures.
The subgraph counts in ERGMs are intricately nested and interdependent, so care has to be taken in interpreting parameters in isolation.
An ERGM is akin to a log-linear model where the subgraph counts are represented by interactions of tie-variables.
ERGMs try to reduce the complexity of observed networks into systematic underlying principles and stochastic components.
A homogeneous ERGM assigns equal probability to graphs that are structurally identical.
In this chapter, we focus on models for undirected graphs. Dependence graphs for directed models are a natural extension of what we describe here, but we only discuss them briefly.
9 - Autologistic Actor Attribute Models
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
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- 19 November 2012, pp 102-114
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Summary
Social Influence Models
So far, we focused on how a particular network structure may be a product of endogenous network processes (clustering, transitivity, popularity, etc.) and exogenous nodal and dyadic factors (gender, membership, geography, etc.). This chapter presents a class of cross-sectional network models that, rather than modeling network structure, allows us to understand how individual behavior may be constrained by position in a social network and by behavior of other actors in the network. For this purpose, we take network ties to be exogenous and model behaviors of the actors. We use the term “behavior” to refer to whatever nodal attribute we are interested in modeling, but this is understood to also cover, for example, attitudes and beliefs. The behavior is assumed to represent states and, at least in principle, may be liable to change, and possibly to change several times. However, the network ties are treated as exogenous and not changed by the attributes. In this chapter, we deal with binary attribute variables as measures of behavior, and if the variable is 1, we say that the actor displays the behavior, or that the behavior is present for that actor.
Social networks are often important to understand because social processes – such as diffusion of information, exercise of influence, and spread of disease – may be potentiated by network ties. There are relatively few models available for assessing the nature of this association between individual outcomes and network structure. An early instance of a general network approach to model social influence processes originates in network autocorrelation models (Doreian, 1982, 1989, 1990; Doreian, Teuter, & Wang, 1984; Erbring & Young, 1979; Leenders, 2002), based on work in spatial statistics (Anselin, 1982, 1984; Cliff & Ord, 1973, 1981; Ord, 1975). In this approach, network ties are taken to reflect dependencies among individual variables. An explicitly dynamic but deterministic theory of network-mediated social influence was developed by Friedkin (1998), who termed it the structural theory of social influence. This theory has its roots in the work of social psychologists and mathematicians, including DeGroot (1974), Erbring and Young (1979), French (1956), Friedkin and Johnsen (1997), Harary (1959), and others. Friedkin described it as “a mathematical formalization of the process of interpersonal influence that occurs in groups, affects persons’ attitudes and opinions on issues, and produces interpersonal agreement, including group consensus, from an initial state of disagreement” (2003, 89).