Effect of Social Networks on Teaching Methods

Effect of Social Networks on Teaching Methods ABSTRACT Background. Research on social networks in schools is increasing rapidly. Network studies outside education have indicated that the structure of social networks is partly affected by demographic characteristics of network members. Yet, knowledge on how teacher social networks are shaped by teacher and school demographics is scarce. Purpose. The goal of this study was to examine the extent to which teachers work related social networks are affected by teacher and school demographic characteristics. Method. Survey data were collected among 316 educators from 13 elementary schools in a large educational system in the Netherlands. Using social network analysis, in particular multilevel p2 modeling, we analyzed the effect of teacher and school demographics on individual teachers probability of having relationships in a work discussion network. Conclusions. Findings indicate that differences in having relationships were associated with differences in gender, grade level, working hours, formal position, and experience. We also found that educators tend to prefer relationships with educators with the same gender and from the same grade level. Moreover, years of shared experience as a school team appeared to affect the likelihood of teacher relationships around work related discussion. INTRODUCTION Relationships among educators are more and more regarded as an important element to schools functioning, and a potential source of school improvement. Educational practitioners and scholars around the world are targeting teacher interaction as a way to facilitate knowledge exchange and shared teacher practice through a variety of collaborative initiatives, such as communities of practice, professional learning communities, and social networks (Daly Finnigan, 2009; Hord, 1997; Lieberman McLaughlin, 1992; Wenger, 1998). The growing literature base around these concepts suggests that relationships matter for fostering a climate of trust and a safe and open environment to implement reform and engage in innovative teacher practices (Bryk Schneider, 2002; Louis, Marks, Kruse, 1996; Coburn Russell, 2008; Penuel, Fishman, Yamaguchi, Galagher, 2007). Social network literature asserts that relationships matter because the configuration of social relationships offers opportunities and constraints for collective action (Burt, 1983, Coleman, 1990; Granovetter, 1973; Lochner, Kawachi, Kennedy, 1999). For instance, the extent to which an organizational network supports the rate and ease with which knowledge and information flows through the organization may provide it with an advantage over its competitors (Nahapiet Ghoshal, 1998; Tsai, 2001). While social network studies have mainly concentrated on the consequences of social networks for individuals and groups, less attention has been paid to how social networks are conditioned upon individual characteristics and behavior (Borgatti Foster, 2003). A developing set of studies in organizational literature is focusing on how attributes of individuals such as personality traits affect their social network (e.g., Burt, Janotta Mahoney, 1998; Mehra, Kilduff, Brass, 2001; Madhavan, Caner , Prescott, Koka, 2008), how individuals select others to engage in relationships (Kossinets Watts, 2006; McPherson, Smith-Lovin, Cook, 2001), and how organizations enter into alliances with other organizations (Gulati Gargiulo, 1999). These studies offer valuable insights in potential individual and organizational attributes that may affect the pattern of social relationships in school teams. Attributes that are especially worth investigating for their potential to shape the social structure of school teams are demographic characteristics (cf. Ely, 1995; Tsui, Egan, OReilly, 1992). Demographic characteristics are more or less constant elements that typify teachers, their relationships, and schools based on socio-economic factors such as age, gender, teaching experience, and school team composition. Several network studies have suggested that networks are at least in part shaped by demographic characteristics of individuals, their dyadic relationships, and the network (Brass, 1984; Heyl, 1996; Ibarra 1992, 1995; Lazega Van Duijn, 1997; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). For instance, several studies reported that relationships among individuals with the same gender are more likely than relationships among individuals with opposite gender (a so-called homophily effect) (Baerveldt, Van Duijn, Vermeij, Van Hemert, 2004; McPherson, Smith-Lovin Co ok, 2001). These studies, however, seldom purposely aim to examine the impact of demographic characteristics on social networks and consequently only include few demographic variables of network members. Insights in the extent to which social relationships are formed in the light of multiple individual and organizational demographic characteristics are limited, and even more so in the context of education. We argue that such groundwork knowledge is crucial for all those who aim to optimize social networks in support of school improvement and, ultimately, student achievement. This chapter aims to examine the extent to which social networks in school teams are shaped by individual, dyadic, and school level demographic variables, such as teachers gender and age, school team composition and team experience, and students socio-economic status. We conducted a study among 316 educators in 13 Dutch elementary schools. Results of this study were expected to increase insights in the constant social forces that may partly define teachers relationships in their school teams, and discover potential tendencies around, for example, homophily and structural balance. Based on a literature review of social network studies that include demographic variables in a wide range of settings, we pose several hypotheses on the extent to which demographical variables at the individual, dyadic, and school level may affect teachers social networks. THEORETICAL FRAMEWORK Individual level demographics that may shape teachers social networks Social network literature has suggested various individual demographic characteristics to affect their pattern of relationships, and as such social networks as a whole (Heyl, 1996; Lazega Van Duijn, 1997; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). Following these suggestions, we will first review how individual level demographic characteristics may affect teachers social networks. We focus on the individual demographics gender, formal position, working hours, experience at school, age, and grade level for their potential influence on teachers patterns of social relationships and school teams social network structure. Gender. The likelihood of having relationships in a network may be associated with gender (Metz Tharenou, 2001; Moore, 1990; Stoloff et al., 1999; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). Previous research has indicated that gender affects network formation (Burt et al., 1998; Hughes, 1946; Ibarra, 1993, 1995, Moore, 1990; Pugliesi, 1998; Van Emmerik, 2006) and that, in general, women tend to have more relationships than men (Mehra, Kilduff, Brass, 1998). These differences are already found in childhood (Frydenberg Lewis, 1993) and continue to exist through life (Parker de Vries, 1993; Van der Pompe De Heus, 1993). In various settings and cultures, both men and women were found to use men as network routes to achieve their goals and acquire information from more distant domains (Aldrich et al., 1989; Bernard et al., 1988). Following these findings, we hypothesize that male teachers will have a higher likelihood of receiving more relationships than female tea chers, and women will send more relationships than men (Hypothesis 1a). Formal position. Previous research in organizations (Lazega Van Duijn, 1997; Moore, 1990) and education (Coburn, 2005; Coburn Russell, 2008; Daly Finnigan, 2009; Heyl, 1996) suggests that the formal position of individuals may be related to their relational activity and popularity. For instance, Lazega Van Duijn (1997) found that lawyers were more often sought out for advice when they held a higher hierarchical position. Research has indicated that the network position of an organizational leader is important in terms of access and leveraging social resources through social relationships as well as brokering between teachers that are themselves unconnected (Balkundi Harrison, 2006; Balkundi Kilduff, 2005). In line with these studies, we expect that principals will be more sought out for work related discussions than teachers. We also expect that principals will report to be involved in more relationships than teachers, since they depend on these relationships to gather informat ion and convey knowledge, plans, and expertise to support student learning and monitor the functioning of teachers and the school. Moreover, principals are reported to occupy a strategic position in the flow of information between the district office and teachers and relay important policy and organizational information from the district office to the teachers (Coburn, 2005; Coburn Russell, 2008). Therefore, we hypothesize that principals have a higher likelihood of sending and receiving relationships (Hypothesis 1b). Working hours. In addition, the number of working hours that an educator spends at the school may also affect his/her opportunity to initiate and maintain social relationships. Recent research suggests that the relationship between network embeddedness and job performance is related to working hours (Van Emmerik Sanders, 2004). In line with this finding, it is hypothesized that educators who work full time will have a higher probability of sending and receiving relationships than educators with part time working hours (Hypothesis 1c). Experience at the school. Another demographic characteristic that may affect an individuals pattern of relationships is seniority, or experience at the school. The previously mentioned law study (Lazega Van Duijn, 1997) indicated that senior lawyers had a higher probability of being sought out for advice than junior lawyers. Besides having more work experience, a perceived network advantage of senior lawyers may be that they have built more strong, durable, and reliable relationships over time, and therefore have access to resources that are unattainable for more junior lawyers. Accordingly, we hypothesize that educators who have more experience in their school team have a higher likelihood of sending and receiving work discussion relationships than educators who have less experience in the school team (Hypothesis 1d). Age. Network research in other contexts found age differences in relation to the amount of relationships that individuals maintain (Cairns, Leung, Buchanan, Cairns, 1995; Gottlieb Green, 1984). In general, these studies suggest that the amount of relationships that people maintain tend to decrease with age. However, with increased age, experience at the school also increases together with the amount of relationships based on seniority (Lazega Van Duijn, 1997). In concordance with the latter, we hypothesize that age will positively affect the probability of work related ties, meaning that older teachers are more likely to send and receive work related relationships than younger teachers (Hypothesis 1e) Grade Level. Within schools, formal clustering around grade level may affect the pattern of relationships among educators. The grade level may to a certain extent affect the amount of interaction among educators since grade level teams may have additional grade level meetings and professional development initiatives are often targeted at the grade level (Daly et al., in press; McLaughlin Talbert, 1993; Newmann, Kings, Youngs, 2000; Newmann Wehlage, 1995; Wood, 2007; Stoll Louis, 2007). Dutch elementary schools are relatively small compared to U.S. elementary schools, and are often divided into a grade level team for the lower grades (K 2) and a grade level team for the upper grades (3 6). The amount of relationships that teachers have, may partly be defined by the requirements of and opportunities provided by their grade level team. We may expect that teachers that teach upper grade levels send and receive more relationships than teachers that teach lower grade levels because o f the increasingly diverse and demanding curriculum in the upper grades combined with intensified student testing and preparation for education after elementary school. These conditions may require more work related discussion of upper grade level teachers than of lower grade level teachers. As such, we expect that teachers that teach upper grade levels have a higher likelihood of sending and receiving relationships than teachers that teach lower grade levels (Hypothesis 1f). Dyadic level demographics that may shape teachers social networks Dyadic level demographics are demographics that typify the relationship between two individuals. Dyadic level effects give insights in network homophily. Network homophily is arguably the most well-known social network concept that often explicitly focuses on demographic characteristics of network members. The concept of homophily, also known by the adage birds of a feather flock together, addresses similarity between two individuals in a dyadic (paired) relationship. Homophily literature builds on the notion that individuals are more likely to develop and maintain social relationships with others that are similar to them on specific attributes, such as gender, organizational unit, or educational level (Marsden, 1988; McPherson Smith-Lovin, 1987; McPherson, Smith-Lovin, Cook, 2001). Similarly, individuals who differ from each other on a specific attribute are less likely to initiate relationships, and when they do, heterophilous relationships also tend to dissolve at a faster pace than homophilous relationships (McPherson et al., 2001). Homophily effects result from processes of social selection and social influence. Social selection refers to the idea that individuals tend to choose to interact with individuals that are similar to them in characteristics such as behavior and attitudes. At the same time, individuals that interact with each other influence each others behavior and attitudes, which may increase their similarity (McPherson et al., 2001). This is a process of social influence. In addition, individuals who share a relationship also tend to share similar experiences through their relationship (Feld, 1981). Homophily is related to the concept of structural balance. In the footsteps of cognitive balance theory, structural balance theory poses that individuals will undertake action to avoid or decrease an unbalanced network (Heider, 1958). Over time, people tend to seek balance in their network by initiating new strong relationships with friends of friends and terminate relationships with friends of enemies or enemies of friends (Wasserman Faust, 1997). As a result from this tendency towards structural balance, relatively homogenous and strong cliques may be formed that give the network some stability over time (Kossinets Watts, 2006). Structural balance and network homophily may have also have a negative influence on individuals social networks as the resulting network homogeneity and pattern of redundant relationships may limit their access to valuable information and expertise (Little, 1990; Burt, 1997, 2000). In this study we focus on two types of similarity that may define teachers relationships, namely gender similarity and grade level similarity. Gender similarity. A dyadic attribute that may affect teachers patterns of social relationships is the gender similarity between two teachers. Several studies have shown that work and voluntary organizations are often highly gender segregated (Bielby Baron, 1986, McGuire, 2000; McPherson Smith-Lovin, 1986, 1987; Popielarz, 1999; Van Emmerik, 2006). This gender homophily effect already starts at a young age (Hartup, 1993; Cairns Cairns, 1994; Furman Burmester, 1992). In the context of education, Heyl (1996) suggested an effect of gender homophily on interactional patterns among teachers, indicating that for men and women relationships with the opposite gender are less frequent or intense than relationships among men or relationship among women. In line with this suggestion, we hypothesize a homophily effect for gender, meaning that educators will prefer same-gender relationships over relationships with teachers of the opposite gender (Hypothesis 2a). Grade level similarity. Another dyadic attribute that may shape the pattern of teachers relationships is the grade level. In the Netherlands, schools are relatively small compared to the Unitesd States, with often only one full time or two part time teachers per grade level. Commonly, Dutch school teams are formally divided into two grade level levels representing the lower (onderbouw, often K-2 or K-3) and upper grades (bovenbouw, often grades 3-6 or 4-6), which are often located in close physical proximity. Recent research suggests that teachers who are located closely to each another are more likely to interact with each other than with teachers that are less physically proximate (Coburn Russell, 2008). Moreover, most schools have separate breaks for the lower and upper grades, and some schools hold additional formal meetings for the lower/upper grades to discuss issues related to these grades. Since shared experiences are argued to result in greater support among individuals (Fe ld, 1981; Suitor Pillemer, 2000; Suitor, Pillemer, Keeton, 1995), these organizational features will increase the opportunity for teachers from the same grade level to interact relative to teachers from a different grade level. Therefore, we hypothesize a homophily effect for grade level, meaning that teachers will more likely maintain relationships with teachers from their own grade level than with teachers that teach the other grade level (e.g., lower or upper level) (Hypothesis 2b). School level demographics that may shape teachers social networks Although teachers can often choose with whom they interact, the social structure of their schools network is partly outside their span of control (Burt, 1983; Brass Burkhardt, 1993; Gulati, 1995). Just as individual relationships may constrain or support a teachers access to and use of resources (Degenne Forse, 1999), the social structure surrounding the teacher may influence the extent to which teachers may shape their network so as to expect the greatest return on investment (Burt, 1992; Flap De Graaf, 1989; Ibarra, 1992, 1993, 1995; Lin Dumin, 1986; Little, 1990). Because of the embeddedness and interdependency of individuals in their social network, relationships and attributes at a higher level will affect lower-level relationships (Burt, 2000). As such, demographic characteristics at the school level may affect teachers patterns of relationships. We pose that the following school level demographic characteristics affect teachers pattern of social relationships: gender ratio , average age, school team experience, school size, school team size, and socio-economic status of the schools students. Gender ratio and average age. Above and beyond the influence of individual demographics on the tendency to form relationships, there may be aggregates of these individual demographics at the level of the school team that may affect teachers tendency to form and maintain relationships. Research in a law firm demonstrated that above the influence of individual level seniority, a lawyers position in the firms network was in part dependent on the ratio of juniors to seniors in the team (Lazega Van Duijn, 1997). For school teams, a compositional characteristic that may affect patterns of relationships is gender ratio, or the ratio of the number of female to male teachers. In a school team with a high ratio of female teachers (which is not unusual in Dutch elementary education) male teachers have fewer options for homophily friendships with same-sex peers than women. Therefore, male teachers in such a team may have a lower tendency to maintain relationships in general and a higher propens ity towards relationships with women than men in school teams with relatively more male teachers. Research confirms that the gender composition of a team may significantly affect gender homophily, with the minority gender often having much more heterophilous networks than the majority (McPherson, Smith-Lovin, Cook, 2001). Therefore, we expect that the gender ratio of the school team will shape teachers social networks. In line with previous empirical work suggesting that women tend to have more relationships than men (Mehra, Kilduff, Brass, 1998), we expect that teachers in school teams with a high female ratio will have a higher likelihood of sending and receiving ties than individuals in teams with relatively more male teachers (Hypothesis 3a). Along the same lines, if we expect that age will increase the likelihood of sending and receiving relationships, then increased average age of a school team may also enhance the probability of relationships. Therefore, we hypothesize that average age is positively related to the probability of ties (Hypothesis 3b). Team experience, school size, and team size. Prior research has indicated that individuals are more likely to reach out to others with whom they had previous relationships (Coburn Russell, 2008). Given the time and shared experiences that are necessary for building relationships, we may assume that the number of years that a school team has been functioning in its current configuration, without members leaving or joining the team, may affect teachers lilelihood of maintaining relationships. Therefore we include school team experience as a school level demographic that may positively affect teachers patterns of relationships (Hypothesis 3c). Other school demographics that may affect teachers inclinations to form relationships are school size (number of students) and team size (number of educators). Previous literature has suggested that the size of organizations and networks is directly related to the pattern of social relationships in organizations (Tsai, 2001). In general, the amou nt of individual relationships and the density of social networks decrease when network size increases. As such, we may expect a lower probability of relationships in schools that serve more students (Hypothesis 3d) and schools with larger school teams (Hypothesis 3e). Students socio-economic status. Social networks can be shaped by both endogenous and exogenous forces (Gulati, Nohria, Zaheer, 2000). An exogenous force to the school team that has been demonstrated to affect schools functioning is the socio-economic status (SES) of its students (Sirin, 2005; White, 1982). We argue that the socio-economic status of the children attending the school may influence the probability that teachers will form relationships. For instance, teachers perceptions of the urgency for communication and innovation may be dependent on the community surrounding the school. Typically, schools that serve more high-needs communities are associated with greater urgency in developing new approaches (Sunderman, Kim Orfield, 2005), which may relate to an increased probability of relationships among educators. Therefore, we hypothesize that teachers in low SES schools will have a higher probability of having relationships than teachers in high SES schools (Hypothesis 3f). METHOD Context The study took place at 13 elementary schools in south of The Netherlands. The schools were part of single district that provided IT, financial, and administrative support to 53 schools in the south of The Netherlands. At the time of the study, the district had just initiated a program for teacher development that involved a benchmark survey for the monitoring of school improvement. We selected a subsample of all the district schools based on a team size of 20 or more team members, since trial runs of the p2 estimation models encountered difficulties converging with smaller network sizes and more schools. The original sample consisted of 53 schools that, with the exception of school team and number of students, did not differ considerably from the 13 sample schools with regard to the described demographics. The context of Dutch elementary schools was beneficial to the study in three ways. First, the school teams were relatively small, which facilitated the collection of whole network data. Second, school teams are social networks with clear boundaries, meaning the distinction of who is part of the team is unambiguous for both researchers and respondents. Third, in contrast to many organizations, school organizations are characterized by relatively flat organizational structures, in which educators perform similar tasks and job diversification is relatively small. Often, educators have had similar training backgrounds, and are receiving school wide professional development as a team. Therefore, despite natural differences in individual characteristics, teachers in Dutch elementary school teams are arguably more comparable among each other than organizational employees in many other organizations, making demographic characteristics possibly less related to differences in tasks or task-rel ated status differences. Sample The sample schools served a student population ranging from 287 to 545 students in the age of 4 to 13. We collected social network data from 13 principals and 303 teachers, reflecting a response rate of 94.5 %. Of the sample, 69.9 % was female and 54.8 % worked full time (32 hours or more). Educators age ranged from 21 to 62 years (M = 46.5, sd = 9.9 years). Additional demographic information is depicted in Table 1 and 2. Instruments Social networks. We assessed the influence of demographic variables on a network that was aimed at capturing work related communication among educators. The network of discussing work related matters was selected because it is assumed to be an important network for the exchange of work related information, knowledge, and expertise that may affect individual and group performance (Sparrowe, Liden, Wayne, Kraimer, 2001). Moreover, according to the previous analysis into network multiplexity (see Chapter 1), this network appeared to be an instrumental network with relatively small overlap with expressive networks. We asked respondents the following question: Whom do you turn to in order to discuss your work? A school-specific appendix was attached to the questionnaire comprising the names of the school team members, accompanied by a letter combination for each school team member (e.g., Ms. Yolanda Brown = AB). The question could be answered by indicating a letter combination for each colleague who the respondent considered part of his/her work discussion network. The number of colleagues a respondent could indicate as part of his/her network was unlimited. Individual, dyadic, and school level attributes. We collected demographic variables to assess how individual, dyadic, and school level attributes shape the pattern of social relationships among educators. At the individual level, we examined the following individual attributes: gender, formal position (teacher/principal), working hours (part time/full time), number of years experience at school, age, and whether a teacher was teaching in lower grade or upper grade. At the dyadic level, we included similarity of gender and similarity of grade level (lower/upper grade). At the school level, we investigated school size, team size, gender ratio, average age, years of team experience in current formation, and students socio-economic status (SES). Data analysis Testing the hypotheses Since our dependent variable consisted of social network data that are by nature interdependent (relationships among individuals), the assumption of data independence that underlies conventional regression models is violated. Therefore, we employed multilevel p2 models to investigate the effect of individual, dyadic, and school level demographics on having work-related relationships (Van Duijn et al., 2004; Baerveldt et al., 2004; Zijlstra, 2008). The p2 model is similar to a logistic regression model, but is developed to handle dichotomous dyadic outcomes. In contrast to a univariate logistic regression model, the p2 model controls for the interdependency that resides in social network data. The model focuses on the individual as the unit of analysis. The p2 model regards sender and receiver effects as latent (i.e., unobserved) random variables that can be explained by sender and receiver characteristics (Veenstra, et al., 2007). In the multilevel p2 analyses, the dependent variable is the aggregate of all the nominations a team member sent to or received from others. A positive effect thus indicates that the independent demographic variable has a positive effect on the probability of a relationship. We used the p2 program within the StOCNET software suite to run the p2 models (Lazega Van Duijn, 1997; Van Duijn, Snijders, Zijlstra, 2004). This software has been recently modified to fit multilevel data (Zijlstra, 2008; Zijlstra, Van Duijn, Snijders, 2006). We make use of this recent development by calculating multilevel p2 models for our data. The social network data in this study have a three-level structure. Network data were collected from 13 schools (Level 3) with 316 educators (Level 2) and 11.241 dyadic relationships (Level 1). To examine the influence of individual, dyadic, and school level demographics on the likelihood of having work related relationships we constructed two multilevel models. In the first multilevel model, the effects of individual and dyadic level demographics on the possibility of having relationships were examined. In the second multilevel model, school level demographic variables were added to the model in order to explain the additional effect of school level demographics on the possibility of having relationships, above and beyond the effects of individual and dyadic level demographics. For the multilevel p2 models, we used a subsample of the 13 schools with a team size of 20 educators or more. We selected this subsample of 13 schools from a larger sample of 53 schools to reduce computing ti me and to examine schools that were more comparable in network size. Still, each model estimation took about six hours of computing time. How to interpret p2 estimates In general, effects in p2 models can be interpreted in the following manner. Results on the variables of interest include both sender effects and receiver effects, meaning effects that signify the probability of sending or receiving a relationship nomination. A positively significant parameter estimate can be interpreted as the demographic variable having a positive effect on the probability of a relationship (Veenstra et al., 2007). For instance, a positive sender effect of formal position with dummy coding (teacher/principal) means that the position with the upper dummy code (principal) will have a higher probability of sending relationships than the position with the lower dummy code (teacher). To assess homophily effects, dyadic matrices were constructed based on the absolute difference between two respondents. For example, the dyadic relationship between male and female educators would be coded as a relationship between educators with a different gender because the absolute difference between male (dummy variable = 0) and female (dummy code = 1) is 1. Smaller numbers thus represent greater interpersonal similarity in gender. The same procedure was carried out for grade level differences. To facilitate the interpretation of the models, we labeled the dyadic parameters different gender and different grade level. A negative parameter estimate for different gender would thus indicate that a Effect of Social Networks on Teaching Methods Effect of Social Networks on Teaching Methods ABSTRACT Background. Research on social networks in schools is increasing rapidly. Network studies outside education have indicated that the structure of social networks is partly affected by demographic characteristics of network members. Yet, knowledge on how teacher social networks are shaped by teacher and school demographics is scarce. Purpose. The goal of this study was to examine the extent to which teachers work related social networks are affected by teacher and school demographic characteristics. Method. Survey data were collected among 316 educators from 13 elementary schools in a large educational system in the Netherlands. Using social network analysis, in particular multilevel p2 modeling, we analyzed the effect of teacher and school demographics on individual teachers probability of having relationships in a work discussion network. Conclusions. Findings indicate that differences in having relationships were associated with differences in gender, grade level, working hours, formal position, and experience. We also found that educators tend to prefer relationships with educators with the same gender and from the same grade level. Moreover, years of shared experience as a school team appeared to affect the likelihood of teacher relationships around work related discussion. INTRODUCTION Relationships among educators are more and more regarded as an important element to schools functioning, and a potential source of school improvement. Educational practitioners and scholars around the world are targeting teacher interaction as a way to facilitate knowledge exchange and shared teacher practice through a variety of collaborative initiatives, such as communities of practice, professional learning communities, and social networks (Daly Finnigan, 2009; Hord, 1997; Lieberman McLaughlin, 1992; Wenger, 1998). The growing literature base around these concepts suggests that relationships matter for fostering a climate of trust and a safe and open environment to implement reform and engage in innovative teacher practices (Bryk Schneider, 2002; Louis, Marks, Kruse, 1996; Coburn Russell, 2008; Penuel, Fishman, Yamaguchi, Galagher, 2007). Social network literature asserts that relationships matter because the configuration of social relationships offers opportunities and constraints for collective action (Burt, 1983, Coleman, 1990; Granovetter, 1973; Lochner, Kawachi, Kennedy, 1999). For instance, the extent to which an organizational network supports the rate and ease with which knowledge and information flows through the organization may provide it with an advantage over its competitors (Nahapiet Ghoshal, 1998; Tsai, 2001). While social network studies have mainly concentrated on the consequences of social networks for individuals and groups, less attention has been paid to how social networks are conditioned upon individual characteristics and behavior (Borgatti Foster, 2003). A developing set of studies in organizational literature is focusing on how attributes of individuals such as personality traits affect their social network (e.g., Burt, Janotta Mahoney, 1998; Mehra, Kilduff, Brass, 2001; Madhavan, Caner , Prescott, Koka, 2008), how individuals select others to engage in relationships (Kossinets Watts, 2006; McPherson, Smith-Lovin, Cook, 2001), and how organizations enter into alliances with other organizations (Gulati Gargiulo, 1999). These studies offer valuable insights in potential individual and organizational attributes that may affect the pattern of social relationships in school teams. Attributes that are especially worth investigating for their potential to shape the social structure of school teams are demographic characteristics (cf. Ely, 1995; Tsui, Egan, OReilly, 1992). Demographic characteristics are more or less constant elements that typify teachers, their relationships, and schools based on socio-economic factors such as age, gender, teaching experience, and school team composition. Several network studies have suggested that networks are at least in part shaped by demographic characteristics of individuals, their dyadic relationships, and the network (Brass, 1984; Heyl, 1996; Ibarra 1992, 1995; Lazega Van Duijn, 1997; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). For instance, several studies reported that relationships among individuals with the same gender are more likely than relationships among individuals with opposite gender (a so-called homophily effect) (Baerveldt, Van Duijn, Vermeij, Van Hemert, 2004; McPherson, Smith-Lovin Co ok, 2001). These studies, however, seldom purposely aim to examine the impact of demographic characteristics on social networks and consequently only include few demographic variables of network members. Insights in the extent to which social relationships are formed in the light of multiple individual and organizational demographic characteristics are limited, and even more so in the context of education. We argue that such groundwork knowledge is crucial for all those who aim to optimize social networks in support of school improvement and, ultimately, student achievement. This chapter aims to examine the extent to which social networks in school teams are shaped by individual, dyadic, and school level demographic variables, such as teachers gender and age, school team composition and team experience, and students socio-economic status. We conducted a study among 316 educators in 13 Dutch elementary schools. Results of this study were expected to increase insights in the constant social forces that may partly define teachers relationships in their school teams, and discover potential tendencies around, for example, homophily and structural balance. Based on a literature review of social network studies that include demographic variables in a wide range of settings, we pose several hypotheses on the extent to which demographical variables at the individual, dyadic, and school level may affect teachers social networks. THEORETICAL FRAMEWORK Individual level demographics that may shape teachers social networks Social network literature has suggested various individual demographic characteristics to affect their pattern of relationships, and as such social networks as a whole (Heyl, 1996; Lazega Van Duijn, 1997; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). Following these suggestions, we will first review how individual level demographic characteristics may affect teachers social networks. We focus on the individual demographics gender, formal position, working hours, experience at school, age, and grade level for their potential influence on teachers patterns of social relationships and school teams social network structure. Gender. The likelihood of having relationships in a network may be associated with gender (Metz Tharenou, 2001; Moore, 1990; Stoloff et al., 1999; Veenstra et al., 2007; Zijlstra, Veenstra, Van Duijn, 2008). Previous research has indicated that gender affects network formation (Burt et al., 1998; Hughes, 1946; Ibarra, 1993, 1995, Moore, 1990; Pugliesi, 1998; Van Emmerik, 2006) and that, in general, women tend to have more relationships than men (Mehra, Kilduff, Brass, 1998). These differences are already found in childhood (Frydenberg Lewis, 1993) and continue to exist through life (Parker de Vries, 1993; Van der Pompe De Heus, 1993). In various settings and cultures, both men and women were found to use men as network routes to achieve their goals and acquire information from more distant domains (Aldrich et al., 1989; Bernard et al., 1988). Following these findings, we hypothesize that male teachers will have a higher likelihood of receiving more relationships than female tea chers, and women will send more relationships than men (Hypothesis 1a). Formal position. Previous research in organizations (Lazega Van Duijn, 1997; Moore, 1990) and education (Coburn, 2005; Coburn Russell, 2008; Daly Finnigan, 2009; Heyl, 1996) suggests that the formal position of individuals may be related to their relational activity and popularity. For instance, Lazega Van Duijn (1997) found that lawyers were more often sought out for advice when they held a higher hierarchical position. Research has indicated that the network position of an organizational leader is important in terms of access and leveraging social resources through social relationships as well as brokering between teachers that are themselves unconnected (Balkundi Harrison, 2006; Balkundi Kilduff, 2005). In line with these studies, we expect that principals will be more sought out for work related discussions than teachers. We also expect that principals will report to be involved in more relationships than teachers, since they depend on these relationships to gather informat ion and convey knowledge, plans, and expertise to support student learning and monitor the functioning of teachers and the school. Moreover, principals are reported to occupy a strategic position in the flow of information between the district office and teachers and relay important policy and organizational information from the district office to the teachers (Coburn, 2005; Coburn Russell, 2008). Therefore, we hypothesize that principals have a higher likelihood of sending and receiving relationships (Hypothesis 1b). Working hours. In addition, the number of working hours that an educator spends at the school may also affect his/her opportunity to initiate and maintain social relationships. Recent research suggests that the relationship between network embeddedness and job performance is related to working hours (Van Emmerik Sanders, 2004). In line with this finding, it is hypothesized that educators who work full time will have a higher probability of sending and receiving relationships than educators with part time working hours (Hypothesis 1c). Experience at the school. Another demographic characteristic that may affect an individuals pattern of relationships is seniority, or experience at the school. The previously mentioned law study (Lazega Van Duijn, 1997) indicated that senior lawyers had a higher probability of being sought out for advice than junior lawyers. Besides having more work experience, a perceived network advantage of senior lawyers may be that they have built more strong, durable, and reliable relationships over time, and therefore have access to resources that are unattainable for more junior lawyers. Accordingly, we hypothesize that educators who have more experience in their school team have a higher likelihood of sending and receiving work discussion relationships than educators who have less experience in the school team (Hypothesis 1d). Age. Network research in other contexts found age differences in relation to the amount of relationships that individuals maintain (Cairns, Leung, Buchanan, Cairns, 1995; Gottlieb Green, 1984). In general, these studies suggest that the amount of relationships that people maintain tend to decrease with age. However, with increased age, experience at the school also increases together with the amount of relationships based on seniority (Lazega Van Duijn, 1997). In concordance with the latter, we hypothesize that age will positively affect the probability of work related ties, meaning that older teachers are more likely to send and receive work related relationships than younger teachers (Hypothesis 1e) Grade Level. Within schools, formal clustering around grade level may affect the pattern of relationships among educators. The grade level may to a certain extent affect the amount of interaction among educators since grade level teams may have additional grade level meetings and professional development initiatives are often targeted at the grade level (Daly et al., in press; McLaughlin Talbert, 1993; Newmann, Kings, Youngs, 2000; Newmann Wehlage, 1995; Wood, 2007; Stoll Louis, 2007). Dutch elementary schools are relatively small compared to U.S. elementary schools, and are often divided into a grade level team for the lower grades (K 2) and a grade level team for the upper grades (3 6). The amount of relationships that teachers have, may partly be defined by the requirements of and opportunities provided by their grade level team. We may expect that teachers that teach upper grade levels send and receive more relationships than teachers that teach lower grade levels because o f the increasingly diverse and demanding curriculum in the upper grades combined with intensified student testing and preparation for education after elementary school. These conditions may require more work related discussion of upper grade level teachers than of lower grade level teachers. As such, we expect that teachers that teach upper grade levels have a higher likelihood of sending and receiving relationships than teachers that teach lower grade levels (Hypothesis 1f). Dyadic level demographics that may shape teachers social networks Dyadic level demographics are demographics that typify the relationship between two individuals. Dyadic level effects give insights in network homophily. Network homophily is arguably the most well-known social network concept that often explicitly focuses on demographic characteristics of network members. The concept of homophily, also known by the adage birds of a feather flock together, addresses similarity between two individuals in a dyadic (paired) relationship. Homophily literature builds on the notion that individuals are more likely to develop and maintain social relationships with others that are similar to them on specific attributes, such as gender, organizational unit, or educational level (Marsden, 1988; McPherson Smith-Lovin, 1987; McPherson, Smith-Lovin, Cook, 2001). Similarly, individuals who differ from each other on a specific attribute are less likely to initiate relationships, and when they do, heterophilous relationships also tend to dissolve at a faster pace than homophilous relationships (McPherson et al., 2001). Homophily effects result from processes of social selection and social influence. Social selection refers to the idea that individuals tend to choose to interact with individuals that are similar to them in characteristics such as behavior and attitudes. At the same time, individuals that interact with each other influence each others behavior and attitudes, which may increase their similarity (McPherson et al., 2001). This is a process of social influence. In addition, individuals who share a relationship also tend to share similar experiences through their relationship (Feld, 1981). Homophily is related to the concept of structural balance. In the footsteps of cognitive balance theory, structural balance theory poses that individuals will undertake action to avoid or decrease an unbalanced network (Heider, 1958). Over time, people tend to seek balance in their network by initiating new strong relationships with friends of friends and terminate relationships with friends of enemies or enemies of friends (Wasserman Faust, 1997). As a result from this tendency towards structural balance, relatively homogenous and strong cliques may be formed that give the network some stability over time (Kossinets Watts, 2006). Structural balance and network homophily may have also have a negative influence on individuals social networks as the resulting network homogeneity and pattern of redundant relationships may limit their access to valuable information and expertise (Little, 1990; Burt, 1997, 2000). In this study we focus on two types of similarity that may define teachers relationships, namely gender similarity and grade level similarity. Gender similarity. A dyadic attribute that may affect teachers patterns of social relationships is the gender similarity between two teachers. Several studies have shown that work and voluntary organizations are often highly gender segregated (Bielby Baron, 1986, McGuire, 2000; McPherson Smith-Lovin, 1986, 1987; Popielarz, 1999; Van Emmerik, 2006). This gender homophily effect already starts at a young age (Hartup, 1993; Cairns Cairns, 1994; Furman Burmester, 1992). In the context of education, Heyl (1996) suggested an effect of gender homophily on interactional patterns among teachers, indicating that for men and women relationships with the opposite gender are less frequent or intense than relationships among men or relationship among women. In line with this suggestion, we hypothesize a homophily effect for gender, meaning that educators will prefer same-gender relationships over relationships with teachers of the opposite gender (Hypothesis 2a). Grade level similarity. Another dyadic attribute that may shape the pattern of teachers relationships is the grade level. In the Netherlands, schools are relatively small compared to the Unitesd States, with often only one full time or two part time teachers per grade level. Commonly, Dutch school teams are formally divided into two grade level levels representing the lower (onderbouw, often K-2 or K-3) and upper grades (bovenbouw, often grades 3-6 or 4-6), which are often located in close physical proximity. Recent research suggests that teachers who are located closely to each another are more likely to interact with each other than with teachers that are less physically proximate (Coburn Russell, 2008). Moreover, most schools have separate breaks for the lower and upper grades, and some schools hold additional formal meetings for the lower/upper grades to discuss issues related to these grades. Since shared experiences are argued to result in greater support among individuals (Fe ld, 1981; Suitor Pillemer, 2000; Suitor, Pillemer, Keeton, 1995), these organizational features will increase the opportunity for teachers from the same grade level to interact relative to teachers from a different grade level. Therefore, we hypothesize a homophily effect for grade level, meaning that teachers will more likely maintain relationships with teachers from their own grade level than with teachers that teach the other grade level (e.g., lower or upper level) (Hypothesis 2b). School level demographics that may shape teachers social networks Although teachers can often choose with whom they interact, the social structure of their schools network is partly outside their span of control (Burt, 1983; Brass Burkhardt, 1993; Gulati, 1995). Just as individual relationships may constrain or support a teachers access to and use of resources (Degenne Forse, 1999), the social structure surrounding the teacher may influence the extent to which teachers may shape their network so as to expect the greatest return on investment (Burt, 1992; Flap De Graaf, 1989; Ibarra, 1992, 1993, 1995; Lin Dumin, 1986; Little, 1990). Because of the embeddedness and interdependency of individuals in their social network, relationships and attributes at a higher level will affect lower-level relationships (Burt, 2000). As such, demographic characteristics at the school level may affect teachers patterns of relationships. We pose that the following school level demographic characteristics affect teachers pattern of social relationships: gender ratio , average age, school team experience, school size, school team size, and socio-economic status of the schools students. Gender ratio and average age. Above and beyond the influence of individual demographics on the tendency to form relationships, there may be aggregates of these individual demographics at the level of the school team that may affect teachers tendency to form and maintain relationships. Research in a law firm demonstrated that above the influence of individual level seniority, a lawyers position in the firms network was in part dependent on the ratio of juniors to seniors in the team (Lazega Van Duijn, 1997). For school teams, a compositional characteristic that may affect patterns of relationships is gender ratio, or the ratio of the number of female to male teachers. In a school team with a high ratio of female teachers (which is not unusual in Dutch elementary education) male teachers have fewer options for homophily friendships with same-sex peers than women. Therefore, male teachers in such a team may have a lower tendency to maintain relationships in general and a higher propens ity towards relationships with women than men in school teams with relatively more male teachers. Research confirms that the gender composition of a team may significantly affect gender homophily, with the minority gender often having much more heterophilous networks than the majority (McPherson, Smith-Lovin, Cook, 2001). Therefore, we expect that the gender ratio of the school team will shape teachers social networks. In line with previous empirical work suggesting that women tend to have more relationships than men (Mehra, Kilduff, Brass, 1998), we expect that teachers in school teams with a high female ratio will have a higher likelihood of sending and receiving ties than individuals in teams with relatively more male teachers (Hypothesis 3a). Along the same lines, if we expect that age will increase the likelihood of sending and receiving relationships, then increased average age of a school team may also enhance the probability of relationships. Therefore, we hypothesize that average age is positively related to the probability of ties (Hypothesis 3b). Team experience, school size, and team size. Prior research has indicated that individuals are more likely to reach out to others with whom they had previous relationships (Coburn Russell, 2008). Given the time and shared experiences that are necessary for building relationships, we may assume that the number of years that a school team has been functioning in its current configuration, without members leaving or joining the team, may affect teachers lilelihood of maintaining relationships. Therefore we include school team experience as a school level demographic that may positively affect teachers patterns of relationships (Hypothesis 3c). Other school demographics that may affect teachers inclinations to form relationships are school size (number of students) and team size (number of educators). Previous literature has suggested that the size of organizations and networks is directly related to the pattern of social relationships in organizations (Tsai, 2001). In general, the amou nt of individual relationships and the density of social networks decrease when network size increases. As such, we may expect a lower probability of relationships in schools that serve more students (Hypothesis 3d) and schools with larger school teams (Hypothesis 3e). Students socio-economic status. Social networks can be shaped by both endogenous and exogenous forces (Gulati, Nohria, Zaheer, 2000). An exogenous force to the school team that has been demonstrated to affect schools functioning is the socio-economic status (SES) of its students (Sirin, 2005; White, 1982). We argue that the socio-economic status of the children attending the school may influence the probability that teachers will form relationships. For instance, teachers perceptions of the urgency for communication and innovation may be dependent on the community surrounding the school. Typically, schools that serve more high-needs communities are associated with greater urgency in developing new approaches (Sunderman, Kim Orfield, 2005), which may relate to an increased probability of relationships among educators. Therefore, we hypothesize that teachers in low SES schools will have a higher probability of having relationships than teachers in high SES schools (Hypothesis 3f). METHOD Context The study took place at 13 elementary schools in south of The Netherlands. The schools were part of single district that provided IT, financial, and administrative support to 53 schools in the south of The Netherlands. At the time of the study, the district had just initiated a program for teacher development that involved a benchmark survey for the monitoring of school improvement. We selected a subsample of all the district schools based on a team size of 20 or more team members, since trial runs of the p2 estimation models encountered difficulties converging with smaller network sizes and more schools. The original sample consisted of 53 schools that, with the exception of school team and number of students, did not differ considerably from the 13 sample schools with regard to the described demographics. The context of Dutch elementary schools was beneficial to the study in three ways. First, the school teams were relatively small, which facilitated the collection of whole network data. Second, school teams are social networks with clear boundaries, meaning the distinction of who is part of the team is unambiguous for both researchers and respondents. Third, in contrast to many organizations, school organizations are characterized by relatively flat organizational structures, in which educators perform similar tasks and job diversification is relatively small. Often, educators have had similar training backgrounds, and are receiving school wide professional development as a team. Therefore, despite natural differences in individual characteristics, teachers in Dutch elementary school teams are arguably more comparable among each other than organizational employees in many other organizations, making demographic characteristics possibly less related to differences in tasks or task-rel ated status differences. Sample The sample schools served a student population ranging from 287 to 545 students in the age of 4 to 13. We collected social network data from 13 principals and 303 teachers, reflecting a response rate of 94.5 %. Of the sample, 69.9 % was female and 54.8 % worked full time (32 hours or more). Educators age ranged from 21 to 62 years (M = 46.5, sd = 9.9 years). Additional demographic information is depicted in Table 1 and 2. Instruments Social networks. We assessed the influence of demographic variables on a network that was aimed at capturing work related communication among educators. The network of discussing work related matters was selected because it is assumed to be an important network for the exchange of work related information, knowledge, and expertise that may affect individual and group performance (Sparrowe, Liden, Wayne, Kraimer, 2001). Moreover, according to the previous analysis into network multiplexity (see Chapter 1), this network appeared to be an instrumental network with relatively small overlap with expressive networks. We asked respondents the following question: Whom do you turn to in order to discuss your work? A school-specific appendix was attached to the questionnaire comprising the names of the school team members, accompanied by a letter combination for each school team member (e.g., Ms. Yolanda Brown = AB). The question could be answered by indicating a letter combination for each colleague who the respondent considered part of his/her work discussion network. The number of colleagues a respondent could indicate as part of his/her network was unlimited. Individual, dyadic, and school level attributes. We collected demographic variables to assess how individual, dyadic, and school level attributes shape the pattern of social relationships among educators. At the individual level, we examined the following individual attributes: gender, formal position (teacher/principal), working hours (part time/full time), number of years experience at school, age, and whether a teacher was teaching in lower grade or upper grade. At the dyadic level, we included similarity of gender and similarity of grade level (lower/upper grade). At the school level, we investigated school size, team size, gender ratio, average age, years of team experience in current formation, and students socio-economic status (SES). Data analysis Testing the hypotheses Since our dependent variable consisted of social network data that are by nature interdependent (relationships among individuals), the assumption of data independence that underlies conventional regression models is violated. Therefore, we employed multilevel p2 models to investigate the effect of individual, dyadic, and school level demographics on having work-related relationships (Van Duijn et al., 2004; Baerveldt et al., 2004; Zijlstra, 2008). The p2 model is similar to a logistic regression model, but is developed to handle dichotomous dyadic outcomes. In contrast to a univariate logistic regression model, the p2 model controls for the interdependency that resides in social network data. The model focuses on the individual as the unit of analysis. The p2 model regards sender and receiver effects as latent (i.e., unobserved) random variables that can be explained by sender and receiver characteristics (Veenstra, et al., 2007). In the multilevel p2 analyses, the dependent variable is the aggregate of all the nominations a team member sent to or received from others. A positive effect thus indicates that the independent demographic variable has a positive effect on the probability of a relationship. We used the p2 program within the StOCNET software suite to run the p2 models (Lazega Van Duijn, 1997; Van Duijn, Snijders, Zijlstra, 2004). This software has been recently modified to fit multilevel data (Zijlstra, 2008; Zijlstra, Van Duijn, Snijders, 2006). We make use of this recent development by calculating multilevel p2 models for our data. The social network data in this study have a three-level structure. Network data were collected from 13 schools (Level 3) with 316 educators (Level 2) and 11.241 dyadic relationships (Level 1). To examine the influence of individual, dyadic, and school level demographics on the likelihood of having work related relationships we constructed two multilevel models. In the first multilevel model, the effects of individual and dyadic level demographics on the possibility of having relationships were examined. In the second multilevel model, school level demographic variables were added to the model in order to explain the additional effect of school level demographics on the possibility of having relationships, above and beyond the effects of individual and dyadic level demographics. For the multilevel p2 models, we used a subsample of the 13 schools with a team size of 20 educators or more. We selected this subsample of 13 schools from a larger sample of 53 schools to reduce computing ti me and to examine schools that were more comparable in network size. Still, each model estimation took about six hours of computing time. How to interpret p2 estimates In general, effects in p2 models can be interpreted in the following manner. Results on the variables of interest include both sender effects and receiver effects, meaning effects that signify the probability of sending or receiving a relationship nomination. A positively significant parameter estimate can be interpreted as the demographic variable having a positive effect on the probability of a relationship (Veenstra et al., 2007). For instance, a positive sender effect of formal position with dummy coding (teacher/principal) means that the position with the upper dummy code (principal) will have a higher probability of sending relationships than the position with the lower dummy code (teacher). To assess homophily effects, dyadic matrices were constructed based on the absolute difference between two respondents. For example, the dyadic relationship between male and female educators would be coded as a relationship between educators with a different gender because the absolute difference between male (dummy variable = 0) and female (dummy code = 1) is 1. Smaller numbers thus represent greater interpersonal similarity in gender. The same procedure was carried out for grade level differences. To facilitate the interpretation of the models, we labeled the dyadic parameters different gender and different grade level. A negative parameter estimate for different gender would thus indicate that a

Cross cultural values and conflicts Essay

The modern society is made up of different cultures which are constantly interacting with each other. This interaction helps in the enrichment of the society. However, it is also the cause of intercultural tensions that have been witnessed in many countries such as the United States. Intercultural conflicts may take different forms. This might lead to problems in different communities and fuel high levels of hatred and confrontation. It is a known fact that cultures differ from one community to another. There has been increased violence that can be attributed to marginalization and impoverishment of some cultures as compared to others. The other factors that contribute to this are ignorance coupled with prejudice. The result of this is disagreements between different communities, resentment and possibility of uncontrollable violence erupting. These conflicts arise due to opposition of certain cultures and reluctance to accept the diverse cultures of the world. Sources of conflicts Intercultural conflicts can, therefore, be said to be due to three causes. These are: political causes, social causes and economic causes. Political sources may be due to territorial differences that might lead to conflicts between different groups of people or nations. The fight for certain resources in particular regions fall under economic causes of these conflicts. Dispute may sometimes arise regarding the ownership, accessibility to or control of certain resources. These resources might include jobs, contracts, credits or education. Allocation of these resources should be done in a fair manner so that everyone gets an equal share regardless of their cultural backgrounds. The tough economic times and conditions may exacerbate intercultural hostility especially when these are seen to be the key causes of unemployment and degradation of peoples’ welfare (LeBaron and Pillay, p 42). Economic policies that favor certain groups of people or nations and ignoring the disadvantaged ones may hasten these conflicts. Such disadvantaged groups include immigrants and workers who might be looked down upon (LeBaron and Pillay, p 42). Social causes of conflicts might be due to differences in religion and languages. These are cultural issues which should not be taken lightly. For example, a communication barrier as far as language is concerned. This can be seen in institutions regarding the particular language used in the teaching process and examinations. Secondly, language used in the military during command and other government departments for communication. These might cause disparities between people of different ethnic and cultural backgrounds. It has been established that religion is a major cause of conflict between groups of people (LeBaron and Pillay, p 43). The main cause of social conflicts in the U. S. is immigration and the income levels between the two races (whites and blacks). This also includes the natives and immigrants in the United States as well as the poor and rich people (Morin, para 2). Other factors that may worsen this situation include; potential threats posed by certain groups to the interest of the group considered as the majority. For example, in the United States, most whites see the non white immigrants as criminals who pose a great danger to their families, jobs and institutions. This leads to exclusion of such migrants in certain sectors such as taking up high grade jobs and government positions. It has heightened the levels of discrimination in the United States (Ting-Toomey and Oetzel, p 23). Conclusion There is need for a global approach in finding a lasting solution to this problem. These efforts should be directed towards ending cultural, racial and ethnic conflicts worldwide. It will help in ending xenophobia, racism and racial segregation in the U. S. In doing so, the tension that is usually witnessed between different groups of people will be greatly reduced hence leading to a harmonious society. Despite the fact that conflicts will always arise at times, these should be solved amicably and democratically without bias or favoring any side or group. Works Cited LeBaron, Michelle and Venashri Pillay. Conflict across Cultures. Boston: Intercultural Press, 2006. Morin, Rich. â€Å"What Divides America? † September 24, 2009. August 10th, 2010 Ting-Toomey, Stella and John Oetzel G. Managing Intercultural Conflict Effectively. California: Sage Publications, 2001

Introduction of Gps

GLOBAL POSITIONING SYSTEM (GPS) GLOBAL POSITIONING SYSTEM (GPS) 3802 O/C AMTR DASSANAYAKE MTS INTAKE 28 3802 O/C AMTR DASSANAYAKE MTS INTAKE 28 HISTORY OF GPS SEGMENTS OF GPS APPLICATIONS OF GPS GEOSTATICS ASSIGNMENT 01 HISTORY OF GPS SEGMENTS OF GPS APPLICATIONS OF GPS GEOSTATICS ASSIGNMENT 01 ASSIGNMENT I Prepare a detail report regarding GPS including following features†¦Ã¢â‚¬ ¦ 1. Historical development. 2. Segment of GPS. 3. Wide variety of applications of GPS. INTRODUCTION * GPS is a satellite-based navigation system originally developed for military purposes and is maintained and controlled by the United States Department of Defense. GPS permits land, sea, and airborne users to determine their three-dimensional position, velocity, and time. * It can be used by anyone with a receiver anywhere on the planet, at any time of day or night, in any type of weather. * There are two GPS systems: NAVSTAR – United States system, and GLONASS – the Russian version. * The NAVSTAR system is often referred to as  the  GPS (at least in the U. S. ) since it was generally available first. * Many GPS receivers can use data from both NAVSTAR and GLONASS; this report focuses on the NAVSTAR system. 1. Historical development GPS is primarily a navigational system, so a background on navigation will give insight as to how extraordinary the Global Positioning System is. * People first navigated only by means of  landmarks  Ã¢â‚¬â€œ mountains, trees, or leaving trails of stones. * This would only work within a local area and the environment was subject to change due to environmental factors such as natural disasters. * For traveling across the ocean a process called  dead reckoning, which used a magnetic compass and required the calculation of how fast the ship was going, was applied. The measurement tools were crude and inaccurate. It was also a very complicated process. * When traveling over the ocean, people began using the stars as guidelines. * Th e stars appear different from different locations on Earth so analyzing the stars gave sailors the basic direction to follow. * Celestial navigation  was our primary means of navigation for hundreds of years. It was a time-consuming and complicated task of measuring the angles between stars – a process of triangulation. * The degree of precision was limited. The sextant was developed during this time but since it only measured latitude, a timepiece was also invented so that the longitude could also be calculated. * This type of navigation only worked at night and in clear weather which was a great disadvantage. * It was not until the 20th century that  ground-based radio navigation systems  were introduced. Some are still in use today. * GPS is a satellite radio navigation system, but the first systems were ground-based. * They work in the same way as does GPS: users (receivers) calculate how far away they are from a transmitting tower whose location is known. When seve ral towers are used, the location can be pinpointed. * This method of navigation was a great improvement, yet it had its own difficulties. An example of such a system is LORAN. * Each tower had a range of about 500 miles and had accuracy good to about 250 meters. * LORAN was not a global system and could not be used over the ocean. Because ground based systems send signals over the surface of the earth, only two-dimensional location can be determined. * The altitude cannot be calculated so this system could not be applied to aviation. The accuracy of such systems could be affected by geography as well. The frequency of the signal affected accuracy; a higher frequency would allow for greater accuracy, but the user would need to remain within the line of sight. * The first global navigation system was called OMEGA. It was a ground-based system but has been terminated as of 1997. * Timeline of GPS Development * Late 1960s, concept development. * Early 1970s, program funding and establi shment of a Joint Program Office within the Department of Defense. * December 1973, proposal for GPS approved by the Defense System Acquisition and Review Council (DSARC). * Mid-1970s, ground testing of the GPS concept. February 22, 1978, launch of the first GPS satellite. * 1989, Magellan Corporation introduces the first hand-held GPS receiver. * 1991, detection and fix of a major a glitch that slowed progress. * January 1991, military use of GPS in Operation Desert Storm in Iraq. * December 1993, declaration of Initial Operational Capability (IOC) by the U. S. Secretary of Defense. * May 2, 2000, SA is turned off by presidential directive; inexpensive civilian GPS receivers increase their horizontal accuracy from â€Å"no worse than† 100 meters to 15-25 meters. * Oct 1, 2005 First Modernized GPS Satellite with improved accuracy. . SEGMENTS OF GPS GPS uses radio transmissions. The satellites transmit timing information and satellite location information. The system can be se parated into three parts: i. Space segment ii. Control segment iii. User segment Connection of three segments, i. Space Segment * The space segment consists of the satellites themselves. * According to the  United States Naval Observatory, there are currently 27 operational GPS satellites about 11,000 miles up in space. * This constellation (see Figure 2 below) provides between five and eight GPS satellites visible from any point on the earth.The Space Segment * It takes each satellite about twelve hours to orbit the earth. There are six orbital planes with at least four satellites in each plane. * The orbits are tilted to the  equator  of the earth by 55 ° so that there is coverage of the  Polar Regions. * The satellites continuously orient themselves to ensure that their  solar panels  stay pointed towards the sun, and their  antennas  point toward the earth. * Also each satellite carries 4  atomic clocks. ii. Control Segment * The control segment is a group of ground stations that monitor and operate the GPS satellites. There are monitoring stations spaced around the globe and one Master Control Station located in Colorado Springs, Colorado (see Figure 3 below). * Each station sends information to the Control Station which then updates and corrects the navigational message of the satellites. * There are actually five major monitoring systems, the figure below does not include the Hawaiian station. * The stations constantly monitor the orbits of the satellites and use very precise radar to check  altitude, position and speed. * Transmitted to the satellites are  ephemeris  constants and clock adjustments. The satellites in turn, use these updates in the signals that they send to  GPS receivers. The Control Segment iii. User Segment * This part consists of user receivers which are hand-held or, can be placed in a vehicle. * All GPS receivers have an  almanac  programmed into their computer, which tells them where each satellite is at any given moment. * The GPS receivers detect, decode and process the signals received from the satellites. * The receiver is usually used in conjunction with computer software to output the information to the user in the form of a map. As the user does not have to communicate with the satellite there can be unlimited users at one time. * The user requires a GPS receiver in order to receive the transmissions from the satellites. * The GPS receiver calculates the location based on signals from the satellites. * The user does not transmit anything to the satellites and therefore the satellites don't know the user is there. * The only data the satellites receive is from the Master Control Station in Colorado. * The users consist of both the military and civilians. 3. Applications of GPS Today, GPS has a wide variety of applications and GPS is finding its way into cars, boats, planes, construction equipment, movie making gear, farm machinery and even laptop computers. * The most o bvious application for GPS is satellite navigation in vehicles, aircraft and ships. * It allows anyone with a GPS receiver to pinpoint their speed and position on land, air or sea, with incredible accuracy. * Drivers can use in-vehicle portable navigation devices to follow a route, find detours around traffic problems and with additional software receive traffic alerts and warnings on safety camera locations. GPS is used for tracking devices; people can pinpoint any object on the earth. For example, GPS vehicle tracking systems or GPS fleet tracking systems can point out where their stolen vehicle is or where their ship sails at present. * Main uses of GPS technology are as follows: a) Location †The first and foremost palpable application of GPS system is the simple determination of a position? or location; Navigation † b) The primary design of GPS tracking system was to provide navigation information or ships and planes; c) Tracking â€Å"With the accurate data provide d by the system, monitoring mobile objects or people is not difficult task anymore; d) Mapping â€Å"GPS can help in creating maps and models of everything in the planet. Mapping the earth had never been an easier task; e) Timing† GPS satellites carry highly accurate atomic clocks, and GPS tracking devices here on the ground when synchronized with those in the satellites are themselves atomic accuracy clocks providing accurate time. * There are many applications for military in GPS, * The military utilizes GPS in land, marine, and airborne navigation. In addition, GPS satellites are equipped with sensors to monitor and detect the donations of nuclear weapons. * Navigation is the main function of GPS with uses in all branches of the military. * Some examples are; photo reconnaissance, low-level navigation, target acquisition, command and control, en route navigation, and missile guidance. * Although GPS was designed for military use, civilian use of the navigation technology h as dramatically increased with the advent of affordable, portable GPS receivers and the ability to increase the accuracy of civilian GPS readings. A major use of GPS is for surveying and mapping, including land, marine, and air borne surveying, local and global deformation monitoring, and geodetic control. * Applications in transportation and communication and include automotive navigation aids, with an automated display of the vehicle position on an electronic map. This is particularly useful for emergency vehicles and search and rescue missions. * Monitoring the location and movement of vehicles such as taxis, trucks, and boxcars can also be achieved using GPS. Recreational activities have also become a large market for low-cost, portable receivers. Boating, backpacking, biking, and horseback riding are a few of the activities whose participants use fairly inexpensive, relatively low accuracy GPS receivers. * GPS is also available for other uses: hikers and ramblers can use GPS re ceivers to ensure they are following their chosen route and to mark rendezvous points along the way. * While gamers can take part in geocaching, a kind of treasure hunt for the digital age, which uses precise GPS signals to help the players track down a hidden stash. The emergency services, for instance, can use GPS not only to find their way to an incident quicker than ever before but also to pinpoint the location of accidents and allow follow-up staff to find the scene quickly. * This is particularly useful for search and rescue teams at sea and in extreme weather conditions on land where time can be a matter of life or death. * Scientists and engineers also have applications for GPS receivers, in scientific experiments, and in monitoring geological activity such as earth tremors, earthquakes and volcanic rumblings. They can use strategically positioned GPS devices to assist them in tracking climate change and other phenomena. Fundamentally, GPS can now be used to produce very acc urate maps. * GPS is a term that most commonly conjures up images of vehicle navigation systems, space-age satellite technology, and interactive maps for outdoors-types and sportsmen as well as below usages, * Know where children are using services from companies like uLocate Communications. * Keep track of elderly members of your family, so that they don’t wander off alone. * Plan a road trip around interesting points of interests, landmarks, campsites, diners, etc. Get emergency road side assistance at a touch of a button from the vehicle, so you can get help exactly where and when people need it. * Keep a visual journal and bookmark collection of your favorite hot spots, sceneries, and points of interests that may not be listed in any travel guide. * Find lost pets easily using collars with built-in GPS. * Feel safer with cellular phone emergency calls, so emergency person can pinpoint your location once you make an emergency call. * Track your luggage, laptops, and anythi ng of importance while traveling. Track and find family, friends in a crowded concert, graduation, or any social gathering. * When going on a vacation, feel free to separate from group for a while to venture on your own based on your own interests and find them later on with your GPS enabled device- even in an unfamiliar place. * Creative and educational uses of GPS; * Stay physically active and fit by playing Ray Gun! A locational based cell phone game based on GPS technology. * Become more cultured, make global friends, and learn about the world playing Geocache, a global GPS based treasure hunt. GPS boosts productivity across a wide swath of the economy, to include farming, construction, mining, surveying, package delivery, and logistical supply chain management. * Major communications networks, banking systems, financial markets, and power grids depend heavily on GPS for precise time synchronization. Some wireless services cannot operate without it. * GPS saves lives by preventi ng transportation accidents, aiding search and rescue efforts, and speeding the delivery of emergency services and disaster relief. GPS is vital to the Next Generation Air Transportation System (NextGen) that will enhance flight safety while increasing airspace capacity. * GPS also advances scientific aims such as weather forecasting, earthquake monitoring, and environmental protection. * GPS use to determine a position from measurements of distances is known as triangulation  (not  triangulation, which involves the measurement of angles). * GPS  receivers  receive satellite signals; they do not transmit or bounce signals off the satellites. GPS Systems are a passive, receive-only system, GPS Systems can support an unlimited number of users, both military and ivilian. * GPS system provides a 24 hour per day global coverage. GPS systems are an all-weather system which is not affected by rain, snow, fog, or sand storms. * GPS use to measure distances to four or more satellites simultaneously and knowing the exact locations of the satellites (included in the signals transmitted by the satellites), the receiver can determine its latitude, longitude, and height while at the same time synchronizing its clock with the GPS time standard which also makes the receiver a precise time piece.

A Parents Guide to Notre Dame

Paris may have its legendary cathedral, but when it comes to famous Catholic universities, there is only one Notre Dame - and its in South Bend, Indiana. Heres the scoop: everything a parent should know about the Golden Dome and the Fighting Irish. The College: This venerable university with its glowing Golden Dome and breathtaking gothic architecture dates back to 1842. Its founder, a 28-year-old French priest, named it after Our Lady of the Lake, Notre Dame du Lac. The school is known for its top-notch academics - it regularly appears on the U.S. News World Reports top 25 - as well as its famous athletic programs and a 1,250-acre campus that belongs on any most beautiful list. Its stunning.Your child does not have to be Catholic to go here, but you should know that mass is held daily, the campus has 47 chapels and its prayer grotto is modeled after the one at Lourdes. Spirituality is important here and community service part of the schools ethos. Notre Dames 12,000 students - a figure that includes 8,400 undergrads - attend classes on the semester system. But the single most important thing parents should know is that Notre Dame students are so very happy here, both academically and personally, that 95% of the freshmen retur n sophomore year. And 95% of those students end up graduating from Notre Dame. Only Harvard and Princeton boast better stats.The Tab: Of course, all that glory - and all those small class sizes - comes at a hefty price. Tuition at the University of Notre Dame was $41,417 in 2011-12. Some 80% of the universitys students live in the colleges 29 single-sex dormitories. Add room and board - $11,388 - to the tab for a grand total of $52,805 per year. There is no Greek system here, but students remain in the same residence hall for all four years, which creates a tight-knit sense of community.The College Town: Technically, Notre Dame is in its own small town of Notre Dame, just outside South Bend. But from a parent perspective, thats mere envelope addressing. South Bend is the college town, and its a very nice one indeed with all the benefits of cosmopolitan life and small-town charm. In addition to visiting your college kid, you can also hike the winding riverbank trails, go white water rafting on the East Race Waterway or head for the recreational possibilities of Lake Michigan.Notre Dame is a two-hour drive (90 miles or so) from Chicago, so youll likely fly into OHare - although South Bend has its own small airport too. Just be aware that Notre Dame is on Eastern time, Chicago on Central. Chicago makes a great hub for any college tour. There are scores of terrific universities - Purdue, Northwestern, Loyola and more - all within a few hours drive. But if your interest lies in Notre Dame and Notre Dame alone, stay in South Bend, where there are plenty of hotels, including the much-beloved, 60-year-old Morris Inn, which is located right on campus. The Morris closed its doors in late 2012 for major renovations; it is expected to re-open in August 2013. (Tip: some hotels will give college visitors discounts, so be sure to call and ask - its not usually advertised online. Check with the Morris directly, when it reopens, to see if they plan to resume their parents club offers.)If youre visiting in the winter months, pack for snow. Its not as cold here as in Minnesota, but South Bend gets 81 inches of snow per year and January temperatures drop down to the 20s and 30s Fahrenheit. One last thing: When its time for junior to fly home for the holidays, theres a bus service that runs between campus and OHare for about $35 one-way.More Important Details: This is a highly competitive school, but it produces some pretty incredible results. That high retention rate comes from the universitys first year of studies program, which teaches college study skills, helps students explore interests and offers support and guidance. Got a a possible pre-med? The Notre Dame acceptance rate into med school runs around 80% - the national average is closer to 40%. Community service is part of the culture here. Some 80% of the colleges students volunteer; more than 10% go on to join the Peace Corps.Got a musician with a penchant for sports? Notre Dames famous marching ba nd dates back to 1843. Got a bel canto soprano, a bassoonist or jazz pianist? Notre Dame has an opera program, as well as jazz and classical performance and music education majors, and its performing arts center boasts five stages. But music majors here don’t declare until sophomore year, its possible to double major in music and another field, and auditions are for ensemble placement and scholarship consideration, not university admission. (Translation: Its a very fine program, but if your kid is considering Juilliard or Curtis, he probably wouldnt apply here. And if thats where you are in the decision-making process, this article on College Admissions for Music Majors may help.)Notre Dame is famous for its Division I athletics, and especially its Fighting Irish football team, which has notched 11 national championships and seven Heisman Trophy winners. More than 60 former players are in the College Football Hall of Fame. But Notre Dame also fields 25 other mens and womens v arsity teams, as well as more than 80 intramural and club sports. Broom ball, anyone?Incoming Frosh Stats: Notre Dame is considered one of the nations 20 most selective universities, with a 29% acceptance rate. The average incoming freshman is in the top 4% of his high school class, with a SAT score of 1,390-1,490 out of 1,600 or an ACT of 32-34.The Law School: Notre Dames law school dates back to 1869 and its programs include the standard 3-year Juris doctor degree, as well as programs in international human rights and an LL.M. (Masters of Law) program in international law from Notre Dames London Law Centre. Admissions are extremely competitive, with more than 3,000 applicants vying for 183 places per year. The average accepted law student had a 3.64 college GPA and a 166 out of 180 on the LSAT.More? Visit the University of Notre Dames campus website for details on admissions for undergraduate study, law school, and grad school. This link will take you straight to information on sc heduling a campus visit. If youre headed to the campus itself, visitor parking is at the corner of Eddy and Holy Cross Drive in Notre Dame, Indiana.