How Good is Your Network? The Effect of Network Quality on

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How Good is Your Network? The Effect of Network Quality on Organizational Outcomes

K. Jurée Capers

June 3, 2011

Prepared for the Public Management Research Association Conference 2011, Syracuse, NY

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A hallmark question of management literature is “does management matter,” and how

much? Various scholars have produced research, through case studies and quantitatively, to support

the claim that management does matter for organizational functioning, outcomes and performance

(Boyne 2003; Meier and O‟Toole 2001, 2003; Rainey 2009). Although the literature has suggested

other factors that matter for outcomes, management has been the single, most consistent factor in

performance outcomes (Lynn et al. 2001; Boyne 2003). Specific managerial actions such as blocking

external disruptions, initiating interaction, or networking have also been found to matter for

organizational outcomes (Meier and O‟Toole 2007). Although multiple managerial actions may be

beneficial to organizational outcomes, scholars have consistently found that networking with the

external environment and actors matters for organizational outcomes (Hicklin et al, 2008; Meier and

O‟Toole 2001, 2003; O‟Toole and Meier 2004). Given these findings on the importance of

networking to outcomes, this research considers if all networking yields the same benefits for

outcomes. More specifically, the research examines the relationship between network quality and

organization performance. Does the quality of a network matter for organization performance

outcomes? Do lower quality networks provide the same performance outcome benefits as higher

quality networks? The paper is organized to first provide some general knowledge of management‟s

contribution to performance outcomes; then I will introduce and test the theory and hypotheses of

network quality in relation to performance outcomes, and finally I will discuss the findings and

implications for these findings.

Managers and Performance

Management literature offers some insight into factors that affect performance outcomes for

organizations. Specifically, the literature discusses how managers may shape organization

performance, from their behavior to personal traits. Research has considered manager

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characteristics such as gender distinctions as a factor that influences management styles and leads to

varying performance outcomes. Scholars have found that men and women do in fact manage

differently, leading to different influences on performance outcomes (Meier, O‟Toole and Goerdel

2006). Gender scholars investigate the mechanism that makes male and female management

different, suggesting that the use of female emotional labor allows women to influence an

organization and its performance outcomes differently (Hsieh and Guy 2009; Meier, Mastracci, and

Wilson 2006; Stivers 2002). Such research has found a relationship between emotional labor and

performance outcomes, indicating that a manager‟s personal characteristics can influence the

organization‟s success (Hsieh and Guy 2009; Meier, Mastracci and Wilson 2006).

Personal motivation characteristics, skill, and talent have also been studied as ways to

examine a manager‟s personal abilities and their effect on organizational performance (Meier and

O‟Toole 2002; O‟Toole and Meier 2004; Avellaneda 2008). Essentially these questions seek to figure

out what managers bring to the table that may help organizational outcomes. Scholars have found

that managerial quality, as assessed by any additional compensation beyond a manager‟s base pay,

has positive effects for organizational performance outcomes (Meier and O‟Toole 2002). Their level

of education and job experience, described as managerial qualifications, also show a positive

influence on organizational outcomes (Avellaneda 2008). Both studies show that managers may add

some personal and unique elements to the organization to benefit outcomes.

There is also support in the management literature for managerial actions or behaviors that

influence performance outcomes. Literature on managerial succession investigates the effect of

managerial behavior on organizational performance outcomes. Scholars such as Boyne and Dahya

(2002) and Hill (2005) contend that a new manager‟s motives, means, and opportunity to change

organizational behaviors, tasks, and norms affect the resulting performance of that organization. In

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other words, managerial succession‟s effect on organizational performance outcomes is a function of

both pursuit of managerial strategy and constraints (both internal and external to the organization)

on managerial action. Hill‟s (2005) research indicates that managerial succession has no short term

effect on performance outcomes; however, it does provide a long term benefit to an organization.

He also finds that the manager‟s motives and opportunities to affect the organization play a large

role in the amount of change managerial succession has on outcomes.

The literature on leadership considers the characteristics, talent and skill and strategies and

actions that managers add to an organization to influence outcomes. These studies look for an

individual‟s ability to lead an organization and the specific actions and policies one implements that

can affect organizational performance outcomes (Lieberson and O‟Connor 1972; Weiner and

Mahoney 1981; Thomas 1988). Weiner and Mahoney (1981) consider the specific environmental,

organizational, and leadership factors that shape organizational performance outcomes, finding that

leadership attributes significantly to organizational outcomes in the form of profit. Similarly,

Thomas (1981) finds that leadership accounts for nearly 51 percent of the profit variance in retail

profits, suggesting that leadership has a positive effect on organizational performance outcomes.

Differences in management behavior and actions due to management style have also been

explored as a factor that can affect organizational performance outcomes. Goerdel (2005) explores

the distinctions between a proactive and reactive management style, and assesses their independent

effects on performance, finding that proactive management is indeed better for performance

outcomes. Proactive managers contributed positively to greater performance outcomes

(Goerde2005). Additionally, some scholars have considered the likely effect that charismatic and

transformational leadership style has on organizational performance (Finkelstein and Hambrick

1996). Lastly, management research on networking also highlights managerial actions that affect

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performance outcomes. The next section focuses on the specific benefits that networking brings to

an organization.

Networking in Management

As previously mentioned, networking has been shown to be a valuable managerial action for

positive performance outcomes. Networks are defined as structures of interdependence with

multiple organizations without a larger hierarchical arrangement that makes one organization or unit

a formal subordinate of other organizations (O‟Toole 1997). Operating in such arrangements is

considered networking. Networking requires dyadic negotiations in which a manager may alter a

network to make it more favorable to self, or he/she may act within the network to arrange

cooperation in the network (O‟Toole 1997; Meier and O‟Toole 2001). Networking and managing

networks is important and helpful in dealing with policies or organizational demands that are

ambitious, complex, and require many resources (O‟Toole 1997; Provan and Milward 1995).

Network activity allows organizations to reduce transaction costs and gain resources and power

(Provan and Milward 1995). Furthermore, they also promote group learning and strategy building

(Agranoff 2007). Research has consistently shown that networking matters and has a positive effect

on performance outcomes (Meier and O‟Toole 2001; Meier and O‟Toole 2003; O‟Toole and Meier

2004; Agranoff and McGuire 2003). In a study examining how managers‟ efforts in their networks

affect program performance, Meier and O‟Toole (2001) find that networking matters for

performance. School districts with greater network management generated relatively higher outputs

as shown by greater increases in student test score performance (Meier and O‟Toole 2001).

Extending their work on the relevance of networks for organization performance, Meier and

O‟Toole (2005) also consider how the number of nodes in a network affects organization outcomes,

finding that adding more nodes to a network does not necessarily add more to the network. In other

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words, the number of members in the network is less important when the network has the most

frequently occurring and most important network actors (Meier & O‟Toole 2005). Nevertheless,

their research shows that networks and the act of networking are important for performance

outcomes, but it does not assess distinctions in network benefits and it assumes that all networking

is beneficial.

Provan and Milward (1995; 2001) challenge the basic assumption of networking‟s benefit

through their assessment of network effectiveness. They argue that past research does not provide

enough support to demonstrate that networks are an effective means to address complex policy

problems (Provan and Milward 2001). Instead, networking scholars must evaluate networks on the

grounds of community effectiveness, network level effectiveness, and organizational level

effectiveness to really consider networking beneficial and related to the observed outcomes (Provan

and Milward 2001).

Although Provan and Milward address a central question of the current research, they focus

mainly on devising a way to evaluate networks. They seek to identify effective and ineffective

networking, but do not consider the mechanism in which in which some networking becomes

effective while other networking does not. They also do not move to probe the consequences of this

ineffective network activity. Similar to their research, the current study asks, do all networks and

networking activity lead to positive benefits for organizations? Is it the case that all networking is

considered “good” networking for shaping organization outcomes? Is all networking equally

beneficial for outcomes? However, I seek to answer these questions by assessing the quality of

networking and its effect on performance outcomes. I argue that the quality of a network actor

affects the networking behavior of a manager, essentially shaping the performance outcomes for an

organization. First, a clear understanding of quality is necessary.

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Understanding Quality

Attempts to explain and define the concept of quality have often led to inconsistent results,

yet scholars agree that quality is important for outcomes (Reeves and Bednar 1994). Various

definitions have been used to explain quality, demonstrating its multiple, yet muddled dimensions. In

one of its simplest forms, Hardie and Walsh (1994) define it as a difference between two states—

such as good and bad or hot and cold. Service quality literature defines quality as “conforming to

requirements and satisfying customers,” (Jain and Gupta 2004). The current research considers the

difficulty in defining quality and defines it as any attempt to improve outcomes.

Quality in Networking

Considering network quality is a unique addition to the management and organizational

performance literature because previous scholars have mainly discussed quality in terms of managers

and their leadership abilities (Finkelstein and Hambrick 1996; Meier and O‟Toole 2002).

Additionally, previous research on evaluating networks focuses primarily on its ability to effectively

provide services (Provan and Milward 1995; 2001). By bringing the concept of quality to networking,

the current study moves beyond simply showing that networks matter, can have an effect on

performance outcomes, and evaluating their ability to do this. Instead, the research is an attempt to

explain and demonstrate the causal mechanism that motivates managers to work in networks, the

quality of his/her network. This research shows that quality matters, not only for managers and what

they can contribute independently to an organization‟s performance, but also for what one‟s

network contributes to performance, via managers‟ interaction with certain network actors.

Just as networking has shown to be beneficial to organization outcomes and performance, I

contend that the quality of the network may explain why this networking is beneficial to

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performance outcomes and is likely to also have a direct effect on performance outcomes. A formal

definition of network quality has not been located in the literature; however, marketing literature

provides possible indicators of quality that may be used to assess network quality. For example,

indicators of trust, commitment or satisfaction may be used to assess quality from a client

perspective (Roberts, Varki, and Brodie 2003). Clients also assess service providers‟ or products‟

dependability, durability, and reputation to determine quality. Considering these indicators and the

basic understanding of quality, network quality may be defined as an assessment of one‟s system of

network actors on the indicators of innovativeness, dependability, trustworthiness, commitment,

reputation, and overall satisfaction. Trust is particularly important to network quality because

networks are built on trust (O‟Toole 1997). Individual network actors that posses a large amount of

these traits may be consider of high quality. High quality network actors are ranked high because

they are more likely to provide managers with more innovative techniques, skills, knowledge,

support, and resources that are beneficial to meeting a manager‟s goal and contributing to an

organization‟s success. Conversely, low quality network actors may lack the skill, resources, or ability

to provide these benefits; therefore, they are perceived as lower quality and less influential to the

manager and organization‟s success.

Rational choice literature suggests that actors make decisions based on a maximization

process; actors will choose options that provide the greatest benefit to self (Tversky and Kahneman

1986). This understanding of rational choice is rooted in one of its most obvious normative

principles, dominance. The dominance principle is central to rational choice normative arguments,

and it argues that if one option is better than another and is at least good as any other option, an

actor will choose the dominant option (Tversky and Kahneman 1986). Public managers are also

rational actors looking to maximize the benefits of any action. As rational actors, managers are

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more inclined to work in a system with high quality actors, given the previously mentioned benefits

and perceived traits. Therefore, the quality of network actors and the overall network system

motivates a rational manager to network. Being of higher quality provides managers with an

incentive to network and interact with certain actors. The benefits of gaining resources in a scarce

resource environment, learning innovative techniques and skills to address a complex clientele, and

possibly shifting some tasks and responsibilities to another actor all work as incentives for managers

to network . However, these incentives are the greatest in higher quality networks. Lower quality

provides a disincentive for managers to network. They are less likely to provide the resources,

knowledge, and expertise necessary to motivate managers to network. As such, managers of lower

quality networks may choose to reduce the time spent in networking and allocate the time to

alternative means for achieving organizational success or reaching his/her goals.

Measuring Network Quality

This understanding of quality is relevant and measurable with a manager‟s self report; however, I

argue that an action based understanding of network quality may also indicate quality while picking

up some of the previously stated indicators of quality. Considering the frequency of network

interaction may indicate a network‟s quality. Using the assumption that managers are rational actors,

they may interact more frequently with higher quality network actors given the maximizing benefit

of such interactions. Managers working more frequently with these network actors develop a higher

quality network system and receive benefits that are likely to shape organizational performance

outcomes. Here network quality as indicated through frequency of interaction acts as a mechanism

that perpetuates more networking, improves organizational performance, and can lead to the

additional subjective indicators of quality previously mentioned.

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On the other hand, less frequent interaction with network actors may indicate low network

quality. Considering rational choice literature again, a manager is less likely to operate or interact

with network actors that add little to no maximizing benefit to his/her organization. The optimal

choice for a utility maximizing manager is to refrain or interact less with network actors that do not

provide benefits. Less frequent interaction may also indicate that the quality is less adequate or

potentially harmful to the manager or the organization. As such, managers with fewer interactions

receiver fewer of the network system benefits assumed to positively affect organizational

performance outcomes, and are more likely to view their network as lacking the subjective quality

indicators discussed above. In this case, network quality acts as mechanism to perpetuate fewer

interactions.

An alternative reason a manager may interact less with certain network actors may be to

prevent “over networking.” Over networking may take away from the organization‟s main goals or

negatively affect performance. Hicklin, O‟Toole and Meier (2008) find support for this claim. They

find that networking can have a “diminishing returns” effect, where there are positive gains up to an

optimal point, and the gains gradually decline from after meeting the optimal point. Specifically the

authors find that networking has a positive effect student test scores until the value of networking

reaches 4.25; a negative effect on test scores follows this point (Hicklin, O‟Toole and Meier 2008).

Nevertheless, the current research approaches any networking, especially higher quality networking,

as a positive asset to organization performance, consistent with most networking research.

An exception to the rational choice logic on interacting with low quality networks can occur if

the network relationship is mandatory and unavoidable. Managers that must network with particular

actors due to their salience or centrality to the organization may interact more frequently with lower

quality actors, despite their limited benefit to the organization. For example, school

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superintendents, managers of a school district, may find themselves obligated to a network

relationship with their school board and parent organizations. However, networking with business

leaders or other superintendents may be less salient and subject to the manager‟s discretion.

Assessing the quality of the discretionary actors may provide the best insight on the effect of

network quality.

The types of actors managers may interact with to assess network quality can be grouped

into two categories: necessary actors and optional actors. Necessary network actors are those that

are included in the network because they are important to an organization‟s daily functioning and

success. Some programs are explicitly mandated to operate in complex networked organizations

with certain networkers; necessary network actors are found in these types of structures. For

example, in school systems, school boards and state education agencies are necessary network

actors. School boards set the districts educational policies and standards, and state education

agencies manage accountability and affect budgeting. School managers, superintendents do not have

the leeway to choose not to include these actors in the network.

Optional network actors are found in voluntary, negotiated, self-organized arrangements.

Optional network actors are those who are disposable to the network. They enhance the network and

organization, but are not necessarily needed for functioning and success. In other words, the

organization could still run smoothly without optional network actors. They bring additional

perspectives, opinions, and contributions to the network and hence to the organization. Managers

may select optional actors, and when and how to use them in the network. The distribution of their

contributions to the organization will vary as some optional network actors will be used more often

and in more important capacities than others. Yet, optional network actors are still going to

contribute to the network, leading to some type of effect on an organization‟s outcomes. Although

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managers may choose to use them for various reasons and the frequency of use may also vary for a

plethora of reasons, I argue that the amount of time spent and frequency of interaction with

optional network actors may speak to their quality.

Network Quality and Performance

Based on the previously mentioned benefits of networking in higher quality network and

assumptions of rational choice literature, high quality networks (those with more high quality actors)

may be more important to organization performance when compared to lower quality networks and

possibly necessary networks.

Therefore, I hypothesize that when managers operate in a high quality network, as indicated through

frequency in interaction with the network actors, there will be positive performance outcome gains. In other words,

network quality will be positive and significantly related to performance outcomes.

Because the test of network quality assumes variation in reported frequency of interaction

among managers and is most interested in performance outcomes when quality is high or low, I

hypothesize that network quality will have a greater effect on high performing organizations compared to average

performing organizations. In other words, network quality will affect the performance outcomes of all

types of organizations; however, it will have a larger impact on organizations that are performing at

the highest levels compared to those that perform at an average or below average level.

Measures

Using 2005 Management Survey data and 2004-2005 performance data from Texas

Education dataset, the current research seeks to assess network quality and its effect on organization

performance. The Management survey had a 61 percent response rate from Texas public school

districts, providing information on superintendents‟ actions, practices and perspectives on a range of

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education topics. The main independent variables are taken from the Management Survey for 2004-

2005. The student performance data and data for control variables are taken from the Texas

Education dataset, provided through the Texas Education Agency for all Texas school districts.

Performance measures

Two measures of student performance are used to assess the effect of network quality on

outcomes. The percentage of students that pass the state-required standardized test, Texas

Assessment of Knowledge and Skills (TAKS) during the 2004-2005 school term is used as the main

dependent variable to represent student program performance. Although controversial and limiting,

standardized test scores are used as the main dependent variable because they indicate students‟

ability to master basic academic skills at the tested grade level, are highly salient to the organization

and public, and are a sensible measure of management activity toward organizational performance.

Every TAKS test is aligned to the Texas Essential Knowledge and Skills, the Texas academic

curricula guidelines. The test is required for students in grades three through eleven (Texas

Education Agency 2010). The test for students in grade eleven is a high stakes test required to

receive a regular diploma in Texas. The total pass rate measure used in the current study includes the

percentage of students in each school district that passes the assessment‟s reading, writing, and math

sectors. Based on the theory previously discussed, I expect the total pass rate to have a positive

relationship with higher quality networks.

SAT and ACT scores are used as a second dependent variable to examine the effect of

quality managerial networks on student performance. Both tests are national assessments for

indicating students‟ college readiness. Here, the percentage of students taking the ACT and/or SAT,

average ACT and SAT score and the percentage of students scoring above 1110 on the SAT (or the

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equivalent score on the ACT)1 are used as indicators of college readiness and managers‟ ability to

shape outcomes through quality networking. The measure is used because college readiness is also a

highly salient issue for the organization, policymakers, and the community. Using SAT and ACT

scores as a dependent variable may also be helpful for a few reasons. First, the tests move beyond

the baseline of mastering “basic academic skills;” therefore I am able to examine network quality for

a higher level performance measure. Secondly, SAT and ACT scores provide an alternative ground

to test the theory. If it is the case that network quality affects student performance, it may also be

the case that it has a significant effect on student performance that is less directly related to internal

school factors. Using both types of tests allows me to compare quality‟s effect on two separate tests

with different purposes.

Independent Variables

The frequency of interactions with optional actors is the main independent variable. Based

on my theory, optional actors are used as the independent variable because network relations and

activity with these actors are disposable. If a manager sees the network relationship as less beneficial

he/she can choose to weaken or cut ties with the optional actors; whereas relationships with

necessary actors are permanent. Other superintendents, local business leaders, teacher groups and

parent groups/associations are the optional actors selected for the current empirical analysis because

they are optional network actors that typically have an interest in school district activity,

performance, and outcomes. Additionally, each optional network actor serves a unique

supplemental role to the district via contact with the superintendent. Superintendents were asked to

rank the frequency on interaction with the four actors on a six-point scale ranging from no

interaction to daily interaction.

1 The ACT is scaled from one to 36; however the score is converted to match SAT score rankings (ACT User

Handbook 2008).

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Interaction with the four optional network actors was factor analyzed to create network

quality scale. The factor analysis method was used to provide preliminary information on which

actors were most important to the optional networking set. Optional network actors with higher

factor scores were consider more important to the set, and potentially more influential to the overall

network quality. Additionally, it is assumed that these network actors, with the unequally distributed

weights, provide a better understanding of the individual quality of the network actor. Lastly, this

measure is best used because it could allow the researcher to pinpoint which actors “high quality

networking” superintendents rely on most to influence performance. Again, the theory assumes that

if the superintendent is interacting with the network actor more frequently, the network actor is of

better quality, hence adding to the overall quality of the network and potentially having a positive

effect on organizational performance. The optional network quality actors positively loaded on the

first factor, producing an Eigenvalue of 2.59 (see Table 1). The clustering of the values was fairly

tight, with most of the factor loadings factoring at .83 to .86. Only one factor score, interactions

with teacher associations, fell outside of the common range at .63. Together, these factor scores

were used as measures of network quality.

These optional network actors are also examined individually as independent variables to see

the effect of each interaction on student performance. Since optional network actors produce a

suitable factor loading, it may be assumed that they also matter individually for student performance.

Considering them individually allows me to examine their individual influence on student

performance, as well as compare the strength of their relationship in comparison to other network

factors considered.

The frequency of interactions with necessary actors was also used as an independent

variable. As a comparison, I also generated a factor analysis for necessary network actors—school

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boards, the state education agency, federal education officials, and state legislators.2 The necessary

network actors also positively loaded on the first factor with a 2.56 Eigen value (see Table 1). Similar

to the optional network actors‟ factor scores, they were also clustered fairly tight, ranging from .81

to .88, with the interaction with federal education officials score falling outside of the range at .61.

The strong correlation (.91) between the optional network and necessary network seems suggest that

the groupings of “optional” and “necessary” is not necessary because the two factors are likely

measuring the same concept. Perhaps having a high quality network overall, regardless of the group,

is more important. Yet grouping all the actors together into one factor does not allow one to

distinguish between the quality that is mandatory and the quality that is additional and really

providing a benefit to organizations. Therefore, this paper focuses mainly on optional network

actors to examine network quality based on the previously stated theory.

Controls

A set of controls potentially related to performance are also included based on education

literature. Controls are grouped as resources for performance or constraints to performance. As

assumed by grouping names, resources are expected to have a positive effect on performance, while

constraints are expected to be negatively related to performance outcomes. The literature suggests

that having more resources can have a positive effect on student performance (Hedges and

Greenwald 1996). Included resource measures are average teacher salary, average years of teacher

experience, average class size, percentage of teachers with less than five years experience, and

percentage of funds from state government. I expect teacher salaries and teacher experience to have

a positive relationship with performance. Conversely, I predict negative relationships between

2 State legislators may be considered both optional and necessary network actors depending on the context. They

may be necessary because they loosely set the education policy agenda for the state. However, they may also be optional network actors because they do not have many direct or individual dealings with the school districts. For this comparison they were used as necessary network actors.

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performance and class size, and percentage of teachers with less than five years of experience and

performance. The percent of funds from the state government is included as a control variable

because of the state‟s large contribution to district resources.

On the other hand, the literature also notes race and poverty as highly correlated with

education problems. Minority and poor students have been reported as more difficult to educate and

hence, less likely to have positive (high) performance outcomes as a function of managerial actions.

Additionally, the challenges that accompany educating these students is likely to affect the districts

overall performance outcome (Thernstrom and Thernstrom 2003). Based on this knowledge, the

same effect can be expected for network quality. Therefore, this study also includes measures for the

percentage of Latino, black, and poor (students receiving free or reduced lunch) students as

constraint control variables (Jencks and Phillips 1998; Thernstorm and Thernstorm 2003). A

negative relationship is expected for all constraint variables. Descriptive statistics for all variables are

included in Table Two.

Methodology

To assess the question of network quality‟s effect on performance outcomes, I use an

ordinary least squares regression (OLS) followed by a substantively weighted analytical technique

(SWAT). The OLS regression tests for a linear relationship between the variables. The stated theory

also leads me to hypothesize that atypical cases are distinct in using network quality to produce

favorable outcomes compared to average cases. Consequently, a basic SWAT method, substantively

weighted least squares (SWLS) is employed to test the relationship, to distinguish the high cases

from the average cases. Meier and Keiser introduced the SWAT to combine sensitivity analysis with

traditional multiple regression (Meier and Bohte 2000; Meier & Keiser 1996). The method works off

the principle that the average cases explained best by linear regression models take focus away from

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the above or below average cases that may also reveal interesting and important findings, depending

on the context. “SWAT is a context dependent tool,” (Meier and Gill 2000). Essentially the method

is employed by separating the high and low performing cases through the application of weights to

the high performing cases. Weighing the highest performing cases shows that some variables are

potentially more important for effective performance outcomes beyond what a linear regression is

able to show (Meier and Gill 2000). Cases with a studentized residual larger than .7 are designated as

“high performing” and weighted equal to 1. The weight is reduced on the low cases to distinguish

the use of inputs and the resulting outcome Cases with a studentized residual smaller than .7 are

weighted equal to .9. A series of successive regressions are run until the weight on the cases with

smaller studentized residual is equal to .1 (Meier, Wrinkle, and Polinard 1999). Final regression

results demonstrate the difference in the dependent variable‟s effect for high performing

organizations compared to average and low performing organizations. Results for both linear and

weighted tests are discussed in the next section.

Results

Network Quality and TAKS

Model One of Table Four uses the optional network quality factor analysis to measure the

effect of network quality on student performance outcomes. Using a linear regression model,

results show that optional network quality, as measured by frequency in interaction, is positively and

significantly (p<.10) related to student performance. Although the significance is small, a one unit

change in optional network quality still yields a nearly one and a half point increase in student

performance when all other factors are held constant. This finding suggests that the quality of one‟s

network matters, and networking with optional network actors does provide some benefit to student

performance.

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The first model of Table Four also shows results necessary network quality actor‟s effect on

performance as a control group. Findings show a negative and significant relationship between

necessary network quality and student performance at the .05 level of significance. A one unit

increase in network quality as measured by frequency in interaction yields a 1.414 decrease in

students‟ test scores on TAKS, all other factors held constant. This finding seems to suggest that

networking with necessary actors may not yield desired benefits to student performance.

Most included control variables were significant and signed correctly, corresponding with

previous research. The percentage of black, Latino, and low income students were all statistically

significant and signed in the expected direction. All three constraint variables demonstrated a

negative relationship with performance, suggesting that as the percentage of black, Latino, and low

income students increases in a district, overall pass rates decrease. The percentage of teachers with

less than five years of experience was also statistically significant and correctly signed. There was a

negative relationship between performance and the percentage of teachers with less than five years

experience. Lastly, teacher experience was also statistically significant, but in the opposite direction

from expected. There was a negative relationship between teacher experience and performance,

suggesting that teacher experience does not necessarily help improve student performance on

standardized tests. This finding conflicts with previous literature, but could be an outcome of the

other controls included in the model.

To check for outliers and cases of interests for to test the second hypothesis, I ran a

regression diagnostic test—the Cook‟s D test, to check for the amount of leverage extreme outlying

cases had on the model. The test measures the distance from the top point (i.e. the outliers used in

the model) to lower points (i.e. the model without the outliers); it picks up the extreme distances in

the model. My theory suggests that cases with greater outlying leverage may signify high network

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quality or very low network quality. It is important to test the leverage of these cases because they

may distort the accuracy of a regression line, and may also reveal the necessity of an alternative

method to the linear regression line. Unique to this study, cases with a Cook‟s D value greater than

0.0039 were considered “leverage holding cases.” The Cook‟s D value was calculated by creating a

ratio of four to the number of observations (Di > 4 / n).3 Running this diagnostic, I found that the

model did in fact include cases with more leverage on the model, suggesting that the simple ordinary

least squares regression is likely biased and cannot pick up on the cases that matter most to my

theory. The result also suggest that the relationship between student performance and network

quality may not be linear, but in fact, a non linear relationship. However, the test was not able to

indicate the direction of leverage. In other words, it does not distinguish the high quality networks

from the low quality networks, but does show that cases of high and low network quality that are

not being addressed in the linear model. The diagnostic also shows that the linear model may be

underspecified.

I also examined the means for all explanatory variables for both average cases and high

quality cases. Means are examined to determine if the average cases of network quality and the high

quality cases are the similar in inputs. In other words, the two types of cases should look about the

same, with only their quality varying. If the inputs are not similar, the differences in outcomes could

be due to differences in resources or any other extraneous factor. Essentially, examining the means

allows the research to check for any other extraneous factors that may make the cases distinct, which

could influence outcomes (Meier and Gill 2000). Means estimation results (See Table 3) do not

suggest any significant difference between the high and low cases.

Network Quality for High versus Average Cases

3 For a more detail understanding of the Cook’s D method of identifying outliers, see Cook (1979) and Cook (2000).

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To test the second hypothesis that managers with high performance outcomes differ in their

network quality and interactions from those of average performance outcomes, I used a SWAT

method that modifies the weighted least squares (WLS) regression called SWLS. Prior to running the

SWLS regression, I separated the high performing cases from the average cases based on their

studentized residuals. As a rule of thumb, Meier and Gill (2000) suggest grouping cases with a

Studentized residual above 0 .70 as “high performing.” This allows the researcher to capture about

20 percent of the cases and determine what high performing case do differently that leads to

favorable outcomes (Meier and Gill 2000; Fernandez 2007). Two hundred thirty-one cases were

pulled as “high performing” using this rule of thumb, and the remaining cases were coded as

“average performance.”

A series of weighted regressions, in which the average performing cases were weighted on a

decreasing scale of 0.9 to 0.1 were run to assess the difference in effect of network quality on low

and high performing cases. A comparison of the SWLS final regression results to the original OLS

regression is presented in Table Four. There are a number of distinctions between the original OLS

regression and the SWLS regression for high performing cases; however, most of the control

variables remained the same in direction and significance. Model Two of Table Four shows that

network quality has positive and significant effect on pass rate for high performing districts. A

comparison of the two models of Table Four show that optional network quality‟s effect is larger for

high performing districts; the statistical significance increases to p<.05 when one observes the effect

optional network quality has on high performing districts compared to average performing districts.

The same relationship is found for necessary network quality‟s effect on outcomes, but signed in the

opposite direction. Again, an increase in necessary network quality as indicated through frequency of

interaction with the actors is related to a decrease in overall pass rate for high performing school

21

districts. The observed negative effect is stronger for high performing school districts with statistical

significance moving from p<.05 to p<.01. These findings support the second hypothesis that

network quality will have a greater effect on high performing districts. They also provide additional

support for the first hypothesis of network quality‟s significant effect on organizational performance

outcomes.

Most of the control variables remain the same in direction and statistical significance with

the exception of teacher experience and state aid. The constant findings across average performing

and high performing districts indicate that the effect of constraints and resources to the overall pass

rate is nearly the same for both types of districts. Comparison of the two cases show that teacher

experience has a greater negative effect on the overall pass rate in high performing school districts

compared to average performing school districts, while an increase in state aid has a positive effect

on performance. The negative teacher experience finding could due to a lack of training for

experienced teachers in the new techniques and standards, or the amount of experienced teachers

represented in higher performing school districts. The state aid finding suggests that higher

performing school districts use their funds differently from average performing districts, possibly on

more academic related resources.

Network Quality and College Readiness

Table Five provides the regression results for the four college readiness variables examined.

Table Five uses the average SAT scores, average ACT scores, percentage of students taking each

test, and the percentage of students scoring above 1110 on the respective tests as dependent

variables to test the effect on network quality on student performance outcomes. Results show that

optional network quality is positive, but not statistically significant for all college readiness variables

except the percentage of students taking the SAT or ACT. Optional network quality does not have

22

an effect on student performance. For the percentage of students taking the SAT or ACT, the

coefficient is negative and statistically insignificant; nevertheless, network quality is still shown is not

related to college readiness outcomes. Like the optional network findings, necessary network quality

is also not statistically significant for performance outcomes. As seen in the previous linear model

that examined network quality and TAKS pass rates, necessary network quality is also negative in the

current model that considers factors that measure college readiness, excluding the percentage of

students taking SAT or ACT. The relationship is positive, but not statistically significant.

Network Quality for High versus Average Cases

A second analysis of the difference between high performing districts and average

performing districts was conducted. The same SWLS method was used for the college readiness

variables; tables seven and again provide the results. Comparing the SWLS final regression results

for the average SAT score to the results from the original OLS regression shows the differences in

network quality for the most successful school districts. Table Seven shows optional and necessary

network quality do not have an effect on the SAT or ACT rate for high performing school districts.

Furthermore, results also indicate that these high performing school districts are not different in the

effect that network quality has on their college readiness indicators and in their network quality. The

similarities seem to indicate that managers in high performance organizations are not doing anything

different from those in typical performance organizations. The network quality does not differ for

managers when SAT or ACT scores are the outcomes being affected. The likelihood of having the

same outcomes is the same. The same insignificant relationship is observed for necessary network

quality; however the variable is signed differently from the optional network quality variable.

Although the findings for the effect of network quality on average Act for both average and high

performing school districts is insignificant, the two cases differ in direction. High performing cases

23

have a negative but insignificant relationship with optional network quality, while average cases hold

a positive but insignificant relationship. The opposite relationship is observed for the effect of

necessary network quality on the average ACT score for average and high performing school

districts.

In the first two models that examine the relationship between network quality and average

SAT score, all of the control variables remain same with the exception of percentage of black and

Latino students and teach salary. Both variables move to statistical significance in the high

performing model, suggesting that the perceived constraints in typical measures of performance

outcomes are not constraints for high performing school districts. In fact, there is a positive and

significant relationship observed, indicating an increase in these students is related to an increased

in the average SAT score in high performing school districts. This finding could be due to

population effects. Teacher salary also gains significance in the high performing case; an increase in

teacher salary is harmful to the average SAT score. Class size and percentage of teachers with less

than five years of experience change in direction but remain insignificant.

The control variables in the final two models considering the relationship between network

quality and the average ACT score for average and high performing cases provide some interesting

findings. Similar to the SAT models, teacher salary has a significant effect on the average ACT score

in both average and high performing school districts, but there is a positive relationship. However,

the effect is smaller for high performing school districts than average performing districts. This

suggest high performing school districts are able to get better ACT scores with lower salaried

teachers and likely less experienced teachers. Although insignificant, the negatively signed finding for

teacher experience in the high performing cases model seems to provide some support for this

24

assumption. Both class size and state aid gain statistical significance in the high performing cases,

although both are unexpectedly negatively related to the average ACT score.

Table Eight includes the models OLS and SWLS models for two additional college readiness

variables—the percentage of students taking the ACT/SAT and the percentage of students scoring

above 1100. Results remain the same as Table Seven‟s college readiness variables. Network quality

has no effect on percentage of students taking the ACT/SAT or the percentage scoring above 1110.

Additionally, there is no significant difference between its effect for high performing cases in

average performing cases. Similar statistical significance and directional changes are observed for

control variables in Table Eight as well.

Discussion and Implications

The current study is a first attempt to move beyond the classic management question, “does

management matter.” Instead, this study acknowledges the past findings that management and a

particular management technique, in this case networking, matter for performance outcomes, and

considers what makes these things matter. If networking matters, what particular characteristics

make it matter for performance outcomes? Here, I test the characteristic of quality for networks to

determine if it matters for performance outcomes. Perhaps quality is the mechanism that makes

networking matter for managers. Results provide mixed support for this assumption and the stated

hypotheses.

Model One of Table Four provides some support for the first hypothesis. Although the

statistical significance is small, this finding is important because it extends the literature‟s

understanding of networks beyond any networking simply mattering for outcomes. Not only does

the simple act of networking matter, but the quality of one‟s network also matters for performance

25

outcomes. Quality‟s relevance to performance outcomes provides some indication that quality may

be the causal mechanism driving previous results on networking‟s significant to organizational

performance.

On the other hand, the negative and significant finding for necessary network actors pose

an interesting problem for the theory of network quality and does not support the first hypothesis.

Although the theory argues that examining optional network actors is best for observing network

quality because the interaction is not required, the negative findings for necessary actors challenges

the basic understanding of network quality. Network quality is defined by actions or series of actions

that improves the network functions or enhances performance. Managers evaluate networks based

on the benefits they provide in meeting this challenge; therefore, any increase in this quality should

result in an increase in performance outcomes. Model One indicates that an increase in network

quality for necessary actors is related to a decrease in student test scores. This relationship suggests

that necessary network actor‟s quality does not add to organizational outcomes, instead it hinders it.

This finding could be suggesting that managers interact with necessary actors more frequently on

other issues besides student performance. Current research is limited in an understanding of the

specific issues in which managers meet particular actors to discuss. Perhaps the other issues take the

focus away from student performance when managers interact with necessary network actors.

Therefore, it would be important to network with optional actors where necessary actors cannot

focus all of their attention or fail to shape outcomes positively. The increase in interaction with

necessary network actors may also be related to a decrease in test scores. Often managers are

required to work more frequently necessary actors when the organization is not performing well.

Therefore, current findings could be capturing this increase in interaction related to a drop in

26

outcomes. Examining a lagged effect on test scores would possibly allow for a test of this

assumption.

When the “high performing” districts are separated out of the original regression and

weighted heavier, there is support for both the first and second hypothesis. Model Two of Table

Four shows both optional and necessary network quality as statistically significant. Networking with

higher quality network actors provides a greater benefit to high performing school districts. This

finding suggests that managers of high performing school districts network more frequently in high

quality network systems of optional actors than average performing districts and receive a larger

benefit from this network activity. The frequency of interaction indicates that their networks are of

even higher quality than those of average performing school districts. Managers operating in high

performing organizations are distinct from managers operating in average performing organization

in their frequency of interaction, the quality of their network, and the benefits that gain from the

network system. Therefore, managers who alter their networks to include a greater amount of high

quality optional network actors are likely to receive the greatest benefits and better performance

outcomes. These optional actors prove to provide an additional benefit to managers and the

organizational overall.

The negative relationship between necessary actors‟ network quality and performance

outcomes is observed for high performing school districts also. As previously discussed for the

average cases, this relationship could indicate more interaction with necessary actors for non-

performance related issues. It may also be due to a significant gap in achievement or a relative

decline from previous performance. Nevertheless, comparison of necessary actors‟ network quality‟s

effect in both models, OLS and SWLS show that necessary network quality has a greater effect in

27

high performing school districts. Managers seeking to improve overall performance outcomes may

seek to network less frequently with necessary network actors regardless of the quality.

Table Five provides OLS regression results for four measures of college readiness. This

second measure of performance outcomes was used to add more validity to the theory by testing it

with an additional dependent variable. They were also included because college readiness is also an

important issue to future success but is less salient to school districts. OLS results on Table Five did

not support the first hypothesis or theory. Optional network quality was positive but insignificant in

all four models. This finding seems to suggest that network quality is not relevant for college

readiness. This finding could be due to the perceived salience of these tests. Although the SAT and

ACT are important for future success, preparation for these test are more personal and less

emphasis is placed on it in the state curriculum. The SAT and ACT may not be as salient to

managers (in comparison to TAKS); therefore, networking to improve individual scores on these

tests is less likely. The SAT and ACT are also self selected because people choose to take these test,

whereas TAKS is mandatory. Therefore, superintendents (managers) may find it more important for

them to use their networking energy and resources to gain benefits on TAKS, the more salient test

of the group.

Tables Seven and Eight provides hypothesis test results for the second hypothesis, with the

studentized residuals of the college readiness dependent variables. Results of the quality variables,

the main variables of interest do not provide any support for hypothesis one or two. Both the

original and high performance models show that optional network quality does not have an effect

on any of the college readiness variables. The quality of the manager‟s network has no direct effect

on students‟ scores or the likelihood that they will take the college readiness assessments.

Additionally, high performance organizations are not distinct from typical organizations when

28

college readiness is the performance outcome is considered. This finding could possibly be due to

the fact that superintendents are more removed from college readiness. Taking and scoring well on

these tests is not required for graduation or promotion like TAKS. The outcomes of students‟

college readiness performance do not necessarily affect the manager or entire district, so managers

network less in regards to improving it. Future research may consider when networking quality

would matter for these indicators. It may also probe which actors are most beneficial to shaping

college readiness indicators. Furthermore, the noted findings may also be suggesting that the pure

nature of networking is enough to affect college readiness. The quality, as measured by frequency of

interaction, matters less, but some form of networking may yield gains in student college readiness

performance.

Conclusion

Overall, there is mixed support for my hypotheses. The research shows that network quality,

when observing the quality of optional network actors, is beneficial to organizational performance

outcomes in some situations and less beneficial in others. Network quality‟s relevance to outcomes

depends on the task. It was positively related to the overall pass rate of the Texas state assessment;

however, it was not related to any of the college readiness indicators. The mix findings could be due

to measurement limitations. The measure for network quality is possibly the largest problem in the

current research. Data limitations led me to use frequency in interaction with network actors as a

measure of network quality; however, this measure is probably not the best for quality. Frequency of

interaction may be a function of accessibility or necessity, and not due to the quality of the

relationship between managers and network actors. My theory assumes that managers are rational

actors and assessing network quality with interactions, but it may be the case that managers are not

rational actors and are interacting frequently or less frequently with actors for other reasons such as

29

contract requirements or personal relationships with other actors. Even if the quality is subpar,

managers may still be tied to optional network actors through other means such as the two

previously suggested. Future research may consider constructing a better, more valid measure of

network quality.

Roberts, Varki, and Brodie (2003) construct a measure for relationship quality that may be

useful in providing a better measure of network quality in the current research. Relationship quality

is defined as the “degree of appropriateness of a relationship to fulfill the needs of the customer

associated with the relationship.” Likewise, network quality is also associated with meeting the needs

of the client (managers), but to a high degree. Their scale of relationship quality is used to measure

the quality of relationship that service firms have with their clients. The measure considers trust,

satisfaction, commitment, and affective conflict as main indicators of relationship quality (Roberts,

Varki, and Brodie 2003). There seem to be some similarities between the concept of relationship

quality and network quality that leads me to believe that the measures are the same concept in

different venues with different terminology. Therefore, future research on network quality may also

consider using these indicators to measure network quality.

A good portion of their measure is based on the service quality scale measure

(SERVQUAL), which may also be helpful for constructing a future measure of network quality

(Roberts, Varki, and Brodie 2003; Parasuraman, Berry and Zeithaml 1991). SERVQUAL is a broadly

designed instrument to measure customer perceptions of service quality and is based on perceived

quality, the customer‟s judgment of the organization‟s overall excellence or superiority

(Parasuraman, Zeithaml, and Berry 1988). Unlike the measure used for the current research,

SERVQUAL considers the importance of perception in assessing quality. Adding this concept

should improve the current measured used. The researchers use 10 dimensions of service quality to

30

create the SERVQUAL scale, tangibles; reliability; responsiveness; assurance; empathy.4 Although

all of these dimensions do not fit into the current study‟s understanding of network quality, the

reliability, assurance, and responsiveness dimensions do fit into concept of network quality because

they are dimensions that would help indicate efforts to improve the network functions or enhance

performance. Future research may consider incorporating all or portions of these measures into a

new measure of network quality. Alternatively, it may also use these measures as a framework for a

new, stronger measure of network quality distinct from their research and unique for management

and networking.

Lastly, mixed results could be due to incorrect groupings. Perhaps local business leaders are

not optional network actors to superintendents. Instead, they view local business leaders as

necessary actors, which may have an effect on the results. Alternatively, the groups may not be

necessary. Future research may challenge the groupings of “necessary” and “optional” altogether.

Superintendents may use every actor as a necessary actor; therefore they may be no observable effect

when the actors are separated into two distinct groups. Maybe they must all be considered together

to see a network quality effect on performance outcomes. Probing any of these suggestions would

continue to take the networking and management literature beyond the central question, “does

management matter?”

4 Assurance and empathy contain items from seven of the original dimensions, communication; credibility,

security; competence; courtesy; understanding/knowing customers; and access because these items were not distinct after the scale purification.

31

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35

Appendix

Table 1: Measurement of Network Quality Using Factor Analysis

Optional Network Indicators

Factor Loadings Necessary Network Indicators

Factor Loadings

Local Business Leaders

.857

School boards

.882

Parent Groups .833 Texas Education Agency .873

Teacher Associations .631 Federal Education Officials

.609

Other Superintendents

Eigenvalue

.857

2.59

State Legislators

Eigenvalue

.801

2.56

Table 2. Descriptive Statistics

Variable Observations Mean Standard Deviation

Min Max

Interaction w/Other Superintendents 1106 1.88 1.61 0 5

Interaction w/ Local Business Leaders 1106 1.76 1.58 0 5

Interaction w/Parent Groups 1106 1.30 .927 0 5

Interaction w/Teacher Associations 1106 .700 .927 0 5

Percentage of Black Students 1037 8.09 12.00 0 86.1

Percentage of Hispanic Students 1037 31.38 26.83 0 100

Percentage of Low Income Students 1037 52.41 18.97 0 100

All Students Pass Rate 1036 63.03 11.98 21 98

Percentage of Students Taking SAT/ACT 940 20.31 12.64 0 100

Average ACT 870 19.74 1.75 14 25.2

Average SAT 708 967.78 81.11 612 1228

Percentage of Students above 1100 on SAT/ACT

927 20.31 12.64 0 83.3

Teacher Salary ($) 1037 37,537.8 3055.36 29209 49,652.13

36

Percentage of Teachers with Less Than 5 yrs Experience

1037 30.91 11.42 0 93.05

Average Teacher Experience (in years) 1037 12.26 2.34 4.00 30.47

Percentage of State Aid ($) 1037 4668.841 1255.441 2631 15,569

Student-Teacher Ratio 1037 12.61 2.46 4.00 30.47

Table 3: Comparison of Means: Average versus High Performing Districts

Average Cases High Cases

Explanatory Variable Mean Standard Error Mean Standard Error

Teacher Salary

Class Size

% Teachers <5 yrs Experience

Teacher Experience

% Low Income

Students

% Latino Students

% Black Students

% State Aid

37596.19

12.75

30.94

12.24

51.97

31.45

8.22

4606.067

106.52

.0865

.3932

.0804

.6738

.9625

.4299

42.342

37236.97

12.12

30.95

12.31

53.96

31.12

7.709

4848.43

201.50

.1510

.8006

.1662

1.213

1.660

.7437

84.57

37

Table 4: Total Network Quality Comparisons: Effect on Average vs. High Performing Districts (TAKS)

Independent Variable Coefficient (T-Score)

Original OLS

Coefficient

(T-Score)

High Stud. Resid SWLS

Optional Network Quality

Model 1

1.346* (1.91)

Model 2

1.404** (2.19)

Necessary Network Quality -1.427** (-2.01)

-1.695*** (-2.52)

Percentage of Black Students

-.2017*** (-7.41)

-.2534*** (-9.38)

Percentage of Latino Students

-.0615*** (-3.60)

-.0949*** (-5.64)

Percentage of Low Income Students

-.3052*** (-13.20)

-.2261*** (-10.34)

Teacher Salary .0005*** (4.11)

.0005*** (3.96)

Class Size .1023 (0.53)

.0626 (0.34)

Percentage of Teachers with less than 5 years experience

-.2621*** (-5.81)

-.6659*** (-5.84)

Average Teacher Experience

-.6210** (-2.86)

-.6659*** (-3.43)

Percentage of State Aid .0005 (1.54)

.0010*** (3.21)

Constant 75.81*** (5.897)

79.60*** (14.32)

R2 .5153 .4611 F 108.96*** 87.69**

Standard Error 8.379 8.070 N 1036

2311

*** p<.01 **<p .05 *p<.10 two tailed test

1 Sum of Weight is 3.1240e+02.

38

Table 5: Total Network Quality: Linear Regression Results for College Readiness AVERAGE SAT AVERAGE ACT % TAKING SAT/ACT %ABOVE 1110


Coefficient (T-Score)




Model 1

9.440 (1.34)

Model 2

.1512 (1.21)

Model 3

-1.238 (-0.91)

Model 4

.9600 (1.00)

Necessary Network Quality -8.721 (-1.26)

-.0549 (-0.44)

1.159 (0.86)

-.8804 (-0.92)


.0024 (0.01)

-.0107** (-2.20)

.1789*** (3.50)

.0387 (1.03)


.2000 (1.10)

-.0036 (-1.12)

.2073*** (6.15)

.0152 (0.63)


-2.448*** (-10.11)

-.0526*** (-12.05)

-.4048*** (-8.63)

-.3605*** (-10.91)

Teacher Salary -.0004 (-0.32)

.0004** (2.04)

.0002 (0.94)

.0006*** (3.40)

Class Size 1.591 (0.70)

-.0356 (0.87)

-1.210*** (-3.31)

-.4278 (-1.39)

Percentage of Teachers with less than 5 years

experience

-.1275 (-0.23)

-.0020 (-1.08)

-.2815** (-3.09)

.0398 (0.58)


5.411** (2.10)

.0189 (0.43)

.0461 (0.10)

.3674 (1.14)

Percentage of State Aid -.0035 (-0.73)

-0.008 (-1.02)

.0012* (1.45)

-.0012** (-2.13)

Constant 1030.645*** (15.90)

21.783*** (19.25)

85.345*** (7.38)

21.03** 2.38

R2 .3369 .4266 .16 .3050 F 35.41*** 63.91*** 18.34*** 40.21***

Standard Error 66.52 1.3312 14.971 10.595 N 708

870 940

927

*** p<.01 **<p .05 *p<.10

two tailed test

39

Table 6: Mean Comparisons: Average vs. High Performing SAT Scores

Average Cases High Cases

Explanatory Variable Mean Standard Error Mean Standard Error

Teacher Salary Class Size % Teachers <5 yrs Experience Teacher Experience % Low Income Students % Latino Students % Black Students % State Aid

38323.34 13.57 31.10 12.21 49.27 31.47 9.196 4277.67

123.23 .0812 .4230 .0812 .8376 .1.171 .5494 32.94

38096.62 13.12 30.21 12.49 53.26 34.67 8.817 4371.45

227.87 .1556 .7905 .1558 1.375 2.066 .8461 69.54

40

Table 7: Total Network Quality Comparisons: Effect on Average vs. High Performing Districts (SAT and ACT)

5 Sum of weight is 2.0670e+02.

6 Sum of weight is 2.6070e+02)


SAT Original OLS


SAT High Stud. Resid SWLS


ACT Original OLS


ACT High Stud. Resid SWLS


Model 1

9.440 (1.34)

Model 2

. 9.099 ( 1.29)

Model 3

.1512 (1.21)

Model 4

-.0351 (-0.29)

Necessary Network Quality

-8.721 (-1.26)

-5.493 (-0.82)

-.0549 (-0.44)

.1338 (1.14)


.0024 (0.01)

.4362* (1.47)

-.0107** (-2.20)

-.0069 (-1.37)


.2000 (1.10)

.5334*** (2.71)

-.0036 (-1.12)

-.0008 (-0.26)


-2.448*** (-10.11)

-2.484*** ( -8.92)

-.0526*** (-12.05)

-.0540*** (-12.01)

Teacher Salary -.0004 (-0.32)

-.0027** (-2.10)

.0004** (2.04)

.00004* (1.90)

Class Size 1.591 (0.70)

-2.655 (-1.08)

-.0356 (0.87)

-.1169*** (-3.18)


experience

-.1275 (-0.23)

.6048 (1.17)

-.0020 (-1.08)

-.0010 (-1.19)


5.411** (2.10)

9.20*** (3.60)

.0189 (0.43)

-.0016 ( -0.04)

Percentage of State Aid -.0035 (-0.73)

-.0011 (-0.22)

-0.008 (-1.02)

-.0001* ( -1.85)

Constant 1030.645*** (15.90)

1142.0*** ( 16.99 )

21.783*** (19.25)

24.59*** (23.74)

R2 .3369 .2361 .4266 .4047 F 35.41*** 21.54*** 63.91*** 58.40***

Standard Error 66.52 67.84 1.3312 1.2766 N 708 1495 870 1936

41

Table 8: Total Network Quality Comparisons: Effect on Average vs. High Performing Districts-

Percentage Scoring above 1110 and Percentage Taking the SAT/ACT




Percentage taking ACT/SAT OLS


Percentage taking ACT/SAT SWLS


Percentage scoring 1110+ OLS


Percentage scoring 1110+SWLS

Optional Network

Quality

Model 1 -1.238 (-0.91)

Model 2 -.4157 (-0.34)

Model 3 .9600 ( 1.00)

Model 4 -.2691 (-0.27)

Necessary Network

Quality 1.159 (0.86)

.3154 (0.26)

-.8804 (-0.92)

.4410 (0.45)


.1789*** (3.50)

.0792* (1.78)

.0387 ( 1.03)

.0493 (1.04)


.2073*** (6.15)

.1424*** (4.65)

.0152 ( 0.63)

.0270 (0.98)


-.4048*** (-8.63)

-.2815*** (6.95)

-.3605*** ( -10.91)

-.4510*** (-11.80)

Teacher Salary .0002 (0.94)

-.00008 (-0.04)

.0006*** ( 3.40)

.0005** (2.39)

Class Size -1.210*** (-3.31)

-1.521*** (-3.15)

-.4278 ( -1.39)

-1.603*** (-4.72)


experience

-.2815** (-3.09)

-.2533*** (-3.15)

.0398 ( 0.58)

.2343*** (3.20)


.0461 (0.10)

-.2388 (-0.63)

.3674 ( 1.14)

.9033*** 2.60

Percentage of State Aid

.0012* (1.45)

.0013* ( 1.84)

-.0012** ( -2.13)

-.0014** (-2.07)

Constant 85.345*** (7.38)

108.1*** ( 10.22)

21.03*** ( 2.48)

42.56*** (4.63)

R2 .16 .1627 .3050 .3071 F 18.34*** 18.05*** 40.21*** 40.60***

Standard Error 14.971 13.85 10.595 11.457 N 940

1497 927 1868

How Good is Your Network? The Effect of Network Quality on

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