Analyzing and Reporting Results Essay Example | Topics and Well Written Essays - 500 words

Analyzing and Reporting Results - Essay Example The objective was to produce a forceful story which would bring in or introduce the latest product, THE 2014 CLA coupe. The objective was to not only get in the public talking but also to start getting the public to pay attention and get engaged (Daimler, 2013). The CLA will be having a class that would not be comparable with others. As the car is another version of the CLS, therefore this one would also appeal and attract to the target customer groups that have a unique approach or non-conformist approach. The advertising campaign therefore places the car as an extraordinary vehicle as mentioned by Mr. Anders-Sundt Jensen. Mr. Anders-Sundt Jensen is the Head of Brand Communications at Mercedes-Benz Cars (Daimler, 2013). This tool is used by the Mercedes Benz Company to evaluate the effectiveness of the advertising campaign in terms of how much target market respond to the advertisement campaign, and how they perceive the brand, do they perceive the brand according to the company’s perception. And the brand is presenting some uniqueness or not.   According to Ace Metrics, an self-governing analytics corporation which measures the effectiveness of advertising campaign, â€Å"Soul† scored the uppermost of any automotive commercial and joined for the 4th highest score on the whole for all advertisements on the Super Bowl,† stated the Mercedes-Benz press release. As mentioned, there are number of statistical analysis methods that can be used. However BevCo should use moving average as the statistical tools. In the beverage industry, the sales are seasonal and therefore exponential smoothing moving average should be used in order to reduce the impact of seasonal variation and other irregularities (Hyndman, Koehler, Ord, and Snyder, 2008). Daimler. (2013). Mercedes-Benz CLA-Class establishes new segment: Sleek as they come. Retrieved May 31, 2013 from

Questionaire Assignment Example | Topics and Well Written Essays - 500 words

Questionaire - Assignment Example The team is up against so many difficulties including lynch mobs of white people, arrest and near riot. Despite all those obstacles, the team finally manages to win a debate against a Harvard team that had a myriad of advantages. Cultural identities are constructed by human beings as a direct result of the experiences undergone by certain people or groups as well as the beliefs held by a specific group of persons. Through experiences and beliefs, people form attitudes that skew them towards associating with people from one culture or their own culture. Universally, people or groups of people have fears they harbor about certain cultures, hence making it difficult for them to associate. These fears lead to stereotypes which fuel cultural conflicts as people differ over worldviews and national cultures. http://www.slate.com/articles/health_and_science/science/2014/01/social_darwinism_and_class_essentialism_the_rich_think_they_are_superior.html the key theme here is social Darwinism, where the rich have the thought that they are superior to others. 3. Most of us have experienced privilege in some form (race, gender, age, looks, social class, status, etc.). What is privilege? Give examples describing how someone benefitted from privilege and how another has â€Å"lost out† because of someone else’s privilege. You may use personal or observed examples, but do not use hypothetical ones. According the Merriam Webster online dictionary, privilege is defined as a right or benefit that is given to some people and not to others. People may get privileges based on race, political inclinations as well as employment affiliations. In my own experience, I have seen a white man served first though he was at the rear end of the line while the others get to wait. In another instance, I witnessed a senior government official get away with over speeding without even a ticket due to his position. Finally,

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

Is Tiger That Great? :: essays research papers

There has been a vast amount of media exposure on Tiger Woods. The media made Tiger Woods out to be some sort of golf god that nobody could beat and he was totally reshaping the way the game was played. We did not believe that that Tiger Woods was really as good as the media said he was.   Ã‚  Ã‚  Ã‚  Ã‚  First off, we went to http://www.pgatour.com and http://www.tigerwoods.com to collect our data. We found the average scores of the top 100 PGA tour players. We found that Tiger Woods was actually ranked in second place, one spot behind the scoring average leader, David Duval. However, there are currently somewhere in the neighborhood of 400 players playing the PGA Tour. It’s not too bad to beat 398 of them! As a second to this incredible piece of information, came Woods’ biography and career statistics. Astounding considering his short professional career. All of these observations combined with our own calculations have changed our earlier mentioned view. After hours of research and study we have found Woods to be a great player. This change of heart led to our hypothesis: Even without the media hype Tiger Woods has proved himself, statistically, to be a great player.   Ã‚  Ã‚  Ã‚  Ã‚  This hypothesis is backed by a great deal of statistical information and factual, proven input through our own calculations. After finding our data online it was already clearly evident that Woods’ was a good player, but we wondered how much better he would seem after the math was done. We hope some of the following numbers impress you as much as they did us. The mean score of a PGA Tour Top 100 player is currently 70.63. Woods’ mean score is a dazzling 69.21. The total number of observations in our experiment was 104, because of certain PGA Tour ties within the averages. Our standard deviation, which is the measure of the spread of a distribution, was found to be .52 for the Top 100 scores. The inner quartile range (middle 50%) of the data is .81. This is the difference between quartile 3 and quartile 1, which are 71.06 and 70.25 respectively. The median, or middle of all 100 scores was 70.8, with a minimum score of 69.13 (#1 David Duval), and a maxim um of 71.24 (#100 Lee Rinker). To return to the basis of our project we found Woods’ standardized z-statistic, or how many standard deviations from the mean he lies, to be a –2.

Business Transaction Essay

1.1 – Account Receivable(AR) (AR and Management Policy: Theory and Evidence – Shehzad L. Mian & Clifford W. Smith, Jr) The basis of my subject â€Å"Bad debt expense estimation model† stems from account receivable. Account receivable is the term used by companies to describe money owed to them by clients or customers for goods and services provided. Bad debt expense is that portion of account receivables that will not be collected. Therefore, without any receivables a company will not have bad debts, thus no need to estimate any bad debt expense. Business to business transactions are mostly done with a promise to pay for goods and services provided at a later date. When a company sells its products or provides its services to other businesses or even individuals, it expects payment for the products or services. In most cases, these payments are not done immediately. The company then expects payment at some future date. This promise to pay becomes a receivable to the company providing the goods or services. Thus, the customer goes into a legal obligation to transfer cash to the company at some future date. Receivables form a large part of most company’s assets. Going through the balance sheet of every company, one would come across account receivables registered as an asset to the company. Financial and management accounting cannot over emphasize the importance of account receivable in every organization. Being an asset, account receivable management has gained momentum in recent years in organizations and financial institutions. Since receivables ultimately stem from extending credits to customers, the issue of who to extend credits to and by how much cannot be stressed enough. It might not always be the case, but companies want to grant credit to other companies that are financially sound in order to have a greater degree of certainty that payment will be received in the future. Thus, it becomes absolutely important to grade companies and even financial institutions with regards to their payment behaviors. Companies definitely do not want to write off a big part of their assets at the end of the year as bad debt expense. Generally, there are two main types of trade account receivable. -Current AR -Past due AR Current AR are debts that have not yet exceeded the amount of time allocated for the debt to be paid as agreed upon by the creditor and the debtor. In most cases, the length of time for the payment of a debt ranges from ten(10) to as long as ninety(90) days and even to a year in rear cases. This length of time could be longer for specific debts like notes receivable(loan related) issued by companies. Past due debts are those that have not been paid within the agreed payment term. These are the ones that mostly draw the attention of managers and credit professionals. This is because, the longer a debt is past due, the greater the chances of a debtor defaulting on the payment of the debt. Managing account receivable has always been a daunting task for managers and other finance professionals. Each organization has its unique operating characteristics and this also calls for different techniques and ways of managing AR. Nonetheless, the foundation behind AR is the policy and procedure for granting credit of the organization. Most organizations obviously want to increase their sales, but the policies they use to assess clients to whom they extend credit will ultimately determine the size of their receivables and to a greater extent, the size of the allowance for bad debt and bad debt expense. Thus, the credit policies an organization uses will determine the amount of receivables which they need to achieve at any given time. A credit policy is a key financial management guideline that should be prepared under the guidance of top finance managers and accountants. It should incorporate the company’s goal, the criteria and timetable of achieving these goals as they relate to credit functions and the type of accounts/clients that would be required to generate liquidity. Changes in business or economic environment sometimes require that credit policies be readjusted to cope with these changes. Some flexibility must be written into any credit policy to avoid adverse effects of over or too less rigidity. Different organizations adapt different credit policies. Basically, there are three credit policies and they include restrictive, moderate and liberal credit policies. 1 – Restrictive/conservative credit Policy This is a very conservative outlook on lending credit to potential customers. Companies that adopt this kind of credit policy mostly deal with only well established customers and customers that pay within terms of payment. The company is unwilling to take risks that are more than minor, preferring to do business with customers that are financially stable. Most companies adopting this policy are invariably in solid financial position themselves and would want to maintain this status quo. Most of them survive even long after more aggressive companies have failed. These companies do not have the need to make any estimate for bad debt expense or allowance since they will have almost no client defaulting on their debts. However, this policy of conservatism is not without its own inherent risks. It can stifle the growth and cash flow of the company to dangerous levels. The company becomes less competitive and potential customers become reluctant to do business with it. Receivables could reduce drastically since tough credit policies hinder the rapid replacement of old customers or customers that have gone out of business. 2 – Moderate credit(Middle-of-the-road) policy Companies adopting this policy generally extend credit to good customers as well as to average customers. It strives to find a healthy mix of customers that would both support company growth prospects as well as minimize risks of default. Most companies fall under this category with regard to their credit policies. These companies would tolerate late payments to an extent, they would mostly extend discounts to encourage risky customers to pay within agreed payment terms. They would also require bank guarantees to monitor cash flow and risks of default while attracting more customers. These companies do have a greater need to estimate bad debt expense and allowance since they do make risky sales that will result to nonpayment at the end of the period. Thus, by virtue of their moderate credit policy, they expect to write off some part of their receivables as bad debt. Applied Materials Europe B.V. is a good example of a company that adopts such a policy. 3 – Liberal credit policy This is the most dangerous of the three policies. Companies adopting this kind of policy are high risk takers in every area of their operation, mostly with the aim of propelling sales and company growth. They expand much too rapidly for the size and worth of the company, and this often indicates accepting customers that are not financially stable enough for the credit line they receive. The loss of receivables can be heavy and the danger to the company’s survival can be real. Liberal credit grantors are frequently incapable of handling any major loss due to customer defaulting their payments. In addition, undercapitalization and sporadic cash flows may afflict these companies with liberal credit policies. The companies may find themselves not being able to financially accommodate their rapid growth due to insufficient capital brought about by the loss of receivables and sporadic cash flows. These companies, more than others, have to have a robust model in place for estimating their bad debt expense and allowance since payment default probability from their clients will be high and it will happen frequently. It will not be surprising that companies like these will have a high percentage of their receivables written off as bad debt at the end of the period . 1.2 – Bad Debt Expense and Bad Debt Allowance(Allowance for Doubtful Accounts) Bad debt expense is that amount of money which a company is unable to collect from its debtors. This is regarded as an expense because it comes as a cost to the company. It is as a result of doing business with other companies that this cost/loss is incurred. This amount is periodically written off from the client’s account especially when the client goes bankrupt or when the company thinks that the cost of pursuing this client for payment will outweigh what is due by the client. At this stage, the amount owed by the client is credited in the client’s account to remove the balance due. Depending on the accounting system used by the company, the account that is debited is the â€Å"allowance for doubtful account†. Or, the write off could be done by debiting the bad debt expense account and crediting the allowance for bad debt(doubtful) account. Being an expense, bad debt expense is usually recorded on the income statement of the company since it affects revenue or sales. Bad Debt Allowance or Allowance for Doubtful Account(these account names mean one and the same thing and could be used interchangeably) is a balance sheet account. When a company is in doubt that a particular client will not pay, the company will record the amount owed to it by this client in this special account. This is a contra asset account that reduces the account receivable account. This account is adjusted periodically with current estimated amounts and it is from this account that write offs are made in conjunction to bad debt expense. The Financial Accounting Standard Board(FASB) Accounting Standard Codification(ASC) 310-10-35-7 through 310-10-35-9 requires companies to account for these losses from uncollectible receivables when it is probable that the asset(account receivable) will not be collected and when this amount can be reasonably estimated. The allowance balance is subtracted from account receivable to get the â€Å"net accounts receivable† as shown on the balance sheet of most companies. The amount in the â€Å"net accounts receivable† accounts is a more realistic figure of receivables since this takes into account the uncollectible.

Appropriate behaviour Essay

a) Explain why it is important to promote appropriate behaviour and respect for others (ref. 2.2) As learning take place in a social enviroment it will be fundamental to create a good atmosphere helping everyone attending to feel at ease in a space where learners feel safe and comfortable to express their opinion. Each student may have different background, culture, beliefs, experiences and needs, so it is very important to be open minded and to establish ground rules so to avoid any animosity, unrespectful behaviour and any kind of discrimination. Any inequality and discrimitation should be challenged to guarantee fairness, decency and respect beteween students. Creating a safe and relaxed enviroment, learners will be more motivated and focused, able to participate, voice their opinions, ask questions and be actively involved in determining how they will learn, allowing them to use their potential and achieve their goals. b) Ways to promote equality and value diversity (ref. 1.3) Equality is about the learner’s rights to have the same opportunity, access and partecipate in their chosen course/training regardless of age, ability or circumstances understanding that everyone are different but have the same rights. Promoting diversity means value and respect differences between students regardless of age, ability, circumstances. An open discussion at the first meeting within students and teacher allows an opportunity for everyone to contribute and know each other bringing up cultural or behavioural differences which can be expressed and may raise issues not previously considered by some. Each person can write out for themselves which types of appropriate behaviour they believe more important. This will give the chance to evaluate each individual’s preferences and gives an overall view on how to maintain a level of courtesy and respect establishing ground rules adequate for the group. Being a teacher means also to be a role model and my behaviour will reflect on my students. For this reason I will treat my students with respect and dignity, ensuring to be non-judgemental, to give same attention to each learner and that particular groups will not be offended (i.e. faith or religion). When planning lessons or activities I might use pictures in handouts and presentations representing different cultures, gender, age and ability in order to embrace all aspects of equality and diversity. c) The importance of identifying and meeting the needs of learners (ref. 1.4) Identify learners needs is one of the most important aspect of teaching as it will allows to differentate lesson’s plans. Learners needs can vary, they may have specific issues like lack of finance or challanges like English as a second language, knowing this a teacher will be able to provide learners support suggesting them points of referral to get help and advice (i.e. interpreters; Citizen Advice Bureau). Some may have special needs like dyslexia or diabetes, others may need advice before enrolment to know if the qualification will meet their career aspirations. In certain case it can be important to find out those informations in advance avoiding some learners to take a course which is unsuitable for them. For this reason an initial/diagnostic assessment will be carried out before the programme starts ensuring learners are making the right choise towards their expectations, results become a teacher’s tool who’s gainig an overall understanding of learners capability, aim, knowledge and specific requirements enabling the teacher to plan and facilitate individual learning and so meeting individual needs. Other aspects to be considered, for instance, are the classroom facilities and accessibility (i.e. layout of tables and chairs) to guarantee safety and meeting the needs of a learner who’s a wheel chair user. Feedback and informal formative assessments can take place throughout the course to ensure learner needs and learning styles are met and satisfy at all times. d) Ways to maintain a safe and supportive learning enviroment (ref. 2.1) As it is likely that learners will be a diverse group of students (from various backgrounds, with different levels of prior learning and expectations, as well as different learning needs), once ground rules have been established, taking an inclusive approach when teaching will help to ensure that your teaching meets everyone’s need enabling students to learn effectively. An  inclusive approach to learning and teaching that aims to meet every individual student’s learning requirements would benefit all students, as a result learners will feel they belong in the classroom. Using an inclusive approach means the teacher will not exclude anyone directly or indirectly, learners will be treated with fairness and trasparency, called by name and making eye contact whenever possible. All students will be involved in activities which may see them working in pair or groups, promoting socialization, tolerance, equality and valuing diversity. ‘’A suitable learning environment is crucial for effective learning to take place. This involves not only the venue and resources used, but also your attitude and the support you give to your students’’ ( Gravells A. 2012, pg 24). Delivering lessons with passion can help to motivate learners, however making sure the environment is clean and safe, at the right temperature, being considerate and open doors or windows is important as much. The lesson’s quality will tells learners when teachers are professional and serious about their job. If there is a break time it is good practice to inform learners right at the beginning of the lesson when this will take place. Knowing this can help your learners focus on their learning.