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Perceptions of Data-Driven Decision Making on Student Success: A Study of Culture, Collaboration, and Advocacy Among Community College Leaders.

Background

The U.S. community colleges remain as a valuable component of the complex postsecondary system across the country. With nearly half of undergraduate students attending community colleges nationwide, successful student outcomes are critical for economic and social factors at the local, state, and national level (American Association of Community Colleges, 2012). The challenge of closing persistent economic and social inequalities are vital to the continued success of community colleges in an increasingly complex period. Notably, public community colleges are under increasing political pressure to improve student outcomes. Policy reforms include calls for increasing institutional transparency, stronger accountability measures, and more efficient use of public resources (Baldwin et al., 2012). Community college leaders are being pressured to create an evidence-based culture and set benchmarks for institutional growth and sustainability.

In response to calls for increased accountability measures, data-driven decision making (DDDM), or the use of data to inform educational practices, has gained considerable attention among educational leaders and researchers (Mandinach & Glimmer, 2013). In fields such as business and medicine, multiple, rich data sources have been analyzed to identify patterns, predict outcomes, and yield more informed decisions (Hersh, 2002; Ngai, Xiu, & Chau, 2009). While educators are attracted to the successful application of data in these other fields, "the use of analytics within the education sector is still in its infancy" (MacNeill, Campbell, &. Hawksey, 2014, p.3). Reinforcing this observation, a recent EDUCAUSE survey found that a majority of higher education institutions are collecting data, but not using the gathered information for predictive or actionable decisions (Bichsel, 2012).

In 2013, the American Association of Community Colleges (AACC) revised the description of competency areas for community college leadership (AACC, 2013). The AACC expected community college leaders to move institutions to improve student success through shifting the leadership focus to accountability. Understanding DDDM and its effective implementation can assist community college leaders in meeting this expectation. As such, it is imperative to examine current perspectives among community college leaders on institutional DDDM practice for student success.

Purpose and Research Questions

The purpose of this study is to examine community college leaders' perspectives on institutional DDDM practice. With an eye toward replication across regional and national contexts, our study aims to test the tenability of a DDDM survey among community college leaders in the state of Iowa. Increasing attention to accountability measures for student success, the shifting political attitude toward public community colleges, and the new AACC standards for leadership combine to create unique issues for community college leaders. In this study, we are especially interested in better understanding the following: (a) how to quantitatively measure AACC leadership competencies and community college leaders' perception of institutional DDDM practice and (b) how to reveal and examine predictors of community college leaders' perceptions of institutional DDDM practice for student success. The following research questions guided this study:

Based on available survey measures, how does one quantitatively measure community college leadership competency areas, community college leaders' perception of institutional DDDM practice, and institutional DDDM practice for student success?

To what extent do the demographic background of leaders, the community college leadership competency areas (i.e., collaboration and advocacy), and perceptions of institutional DDDM culture predict leaders' perceptions of DDDM practice as it relates to student success?

Review of Literature

Data-Driven Decision Making

Data-Driven Decision Making (DDDM) focuses on the use of data, statistical analysis, and explanatory and predictive models to gain insights and inform policies around complex issues (Bischel, 2012). DDDM moves leaders away from conclusions based on undefined intuition and more toward decisions based on a systematic collection, analysis, examination, and interpretation of data to inform practice and policy in educational settings (Mandinach, 2012). The current scholarship provides various goals for data-driven decision making. For example, Rudy and Conrad (2004) state that the goal of DDDM in education is to "collect, analyze and interpret meaningful data to make institutional improvement in the areas of curriculum, instruction, institutional efficiency and student learning outcomes" (p. 2). Hamilton and Armstrong (2013) suggest that DDDM offers a systematical way to collect and analyze data. Particularly in the K-12 education context, DDDM would guide teachers and schools to promote student success.

In fields of business and medicine, multiple rich data sources have been analyzed to identify patterns, predict outcomes, and yield more informed decisions (Hersh, 2002; Ngai, Xiu, & Chau, 2009). McElheran and Brynjolfsson (2016) found that the use of DDDM model among U.S. businesses tripled between 2005 and 2010. Four factors influenced the use of DDDM--a significant investment in information technology, large share of college-educated workers, size of the organization, and awareness of the DDDM model. While educators are attracted to the successful application of data, DDDM practices in education are still rising through its beginning stages (MacNeill, Campbell, & Hawksey, 2014). From recruitment through degree attainment, educational institutions have collected volumes of data, particularly student information. However, the transition from data collection for departmental dissemination to a systematic data connection for institutional analysis remains stagnant.

As postsecondary education leaders start emphasizing DDDM practices to improve student success, adopting and transforming DDDM practice from business context to higher education context remains a significant challenge due to institutional and leadership limitations. Bichsel (2012) found that a considerable number of higher education administrators, faculty, and staff fear or mistrust institutional data, measurement, analysis, reporting, and change. Higher education administrators might consider DDDM as a harbinger of a more business-like approach to higher education (Bichsel, 2012). Further, Starobin and Upah (2014) concluded that DDDM in postsecondary education is limited by the lack of coordination between and across institutions. Institutions tend to place limited resources in the development of a centralized data analysis system. Data collection and analysis tend to be either decentralized or fall under the responsibility of a small, under-resourced institutional research office (Starobin & Upah, 2014).

DDDM and Community College Leadership

DDDM has become an essential requirement for current and future leaders in community colleges. For example, the 21st-Century Implementation Team, charged with making recommendations for a new framework for leadership, drew the following four major conclusions: (a) successful leaders move institutions to achieve high and improving student success rates; (b) community colleges need dramatic steps, as well as a greater sense of urgency and alignment, in order to change student success results; (c) expectations and priorities for leadership must shift to accountability for improving student success; and (d) deliberate preparation is needed in order to produce leaders with the right competencies, particularly competencies in risk-taking and change management (AACC, 2013). Understanding DDDM and its effective implementation can assist community college leaders in meeting the expectations outlined in this new framework.

Specifically, DDDM can serve as a key lever for serving students from underrepresented backgrounds. In particular, students with underrepresented racial/ethnic backgrounds comprise 42% of enrollment at community colleges across the country (National Center for Educational Statistics, 2012). With a diverse background, community college students are often underprepared for college-level course work as evidenced by their reading, writing, and math skills (Price & Tovar, 2014; Snyder & Dillow, 2011; Bailey, Jeong, 6k Cho, 2010). Thus, meeting the credential attainment challenge is especially critical for community colleges, whose students traditionally experience a variety of barriers to degree attainment (Cohen 6k Brawer, 2008; Kim, Sax, Lee, 6k Hagedorn, 2010; Price & Tovar, 2014). It is critical to provide the academic, social, and administrative support to help students be successful.

DDDM practice is one of the emerging best practices among other educational contexts such as K-12 context (Mandanich, 2012). Stimulated by the bipartisan No Child Left Behind legislation (NCLB) in 2001, K-12 researchers and educators actively studied DDDM and its implementation. According to the research, DDDM practice greatly influences student success in classroom level, school level, and district level (Mandanich, 2012; Mandinach 6k Honey, 2008; Luo, 2008). In community college context, however, there is a literature gap of DDDM literacy among leaders. Similar to other higher education institutions, data is collected and available but is not effectively utilized (Bichsel, 2012). For the purpose of improving student success, it is critical for community college leaders to identify factors predicting student success and allocate time and institutional resources into the administrative and academic areas that uniquely shape the identified factors. Community college leaders are expected to make evidence-based decisions regarding a variety of academic and administrative tasks, including critical practices aimed at improving student success (AACC, 2012).

Theoretical Framework

We utilize two theoretical frameworks to inform this study. First, we adopted the DDDM framework developed by K-12 researchers (Mandinach, Honey, & Light, 2006; Mandinich, 2012). This DDDM framework suggests six cognitive actions drive the data-to-knowledge continuum. Specifically, at the data level, the action is focused on collection and organization; at the information level, there is action around analysis and summary; and at the knowledge level, synthesis and prioritization are the primary actions (Mandinach, Honey, 6k Light, 2006). As stakeholders, institutional leaders identify the context (e.g., classroom, departments, schools, districts) and lead the actions along the continuum. The final outcome for this six-step process is an implantation decision. Through this study, we examine the data-to-knowledge continuum and emphasize the role of leadership competencies in this process. In particular, we examine how specific leadership competency areas and DDDM literacy may influence how community college leaders perceive DDDM practice as it relates to student success.

The second theoretical framework is the model of institutional action (Tinto & Pusser, 2006). This model evolved from higher education studies on student attrition and persistence (Tinto & Pusser, 2006) and has been redeveloped through various research on underrepresented and low-income students attending two- and four-year institutions. According to Tinto and Pusser (2006), the institutional action model filled the gap of transferring the knowledge from studies on underrepresented and low-income college students into practical knowledge in institutional policy making and practice.

This multilayered model posits the effect of institutional actions on student success by administrative leadership as well as faculty members and staff. It argues that the effect of administrative leadership might be largely indirect. However, such actions influence the behaviors of faculty and staff, whose actions directly impinge upon student lives either through direct contacts with students or indirectly through programs that affect students (Tinto & Pusser, 2006). The goal of this study is to contribute to the general improvement of community college students' success through effective leadership, specifically the leadership and institutional practices informed by DDDM. The institutional action model justified the linkage between institutional input and student success in community college.

Methodology

Population and Sample

The population for this study were community college leaders employed across the 15 community colleges districts in Iowa. While the community colleges have different organizational structures, for the purposes of this study, we included all leaders who had management responsibilities at the department level or above in addition to senior leaders such as president/vice president, chief executive officers (CEOs), chief academic officers (CAOs), deans and department chairs. Faculty members with administrative roles were also included. The final list of potential participants included 468 Iowa community college leaders and administrators.

We utilized an online survey to collect data. A total of 220 Iowa community college leaders and administrators responded to the survey. These 220 responses make up our sample for data analysis.

Among the 220 participants, the largest category was White female between 46 and 55 years old. Specifically, the participants were predominantly White (90%, n=200). Nearly 60% (59.5%, n=131) of the participants were female. Additionally, more than 60% of the survey participants were older than 45 years old. The highest age group was 46-55 years old (31.4%, n=69), followed by those who were between 55 and 65 years old (30%, n=66). The majority of the survey participants (78.1%) held a master's degree or above.

Instrument

This study used the Data-Driven Decision Making (DDDM) Survey as the instrument. The survey instrument was developed for this study. We ensured the instrument validity through critically reviewing existing surveys that were focused on related topics, including The community college presidency: Demographics and leadership preparation factors survey (Duree, 2007), Learning Analytics Readiness Instrument (LARI) survey (Arnold, Lonn, & Pistilli, 2014), and EDUCAUSE analytics survey (Yanosky, Brooks, Thayer, & Morgan, 2015). The survey also reviewed AACC community college leadership competency (AACC, 2013) elements to develop valid leadership competency measures for community college leaders. In sum, the DDDM survey measures perceptions of institutional DDDM, community college leadership competencies, and demographics/background information of survey participants.

To further strengthen instrument validity and ensure its reliability, a pilot study was conducted during the fall 2015 semester. A total of 131 leaders and administrators from three selected community colleges in the state of Iowa participated in the pilot study via the online survey software Qualtrics. Statistical analyses such as exploratory factor analysis and reliability test (i.e., Cronbach's alpha) were conducted. Based on the statistical results and pilot participants' feedback, we shortened the survey by removing less important and repetitive items. The final survey instrument has 43 questions and 150 items.

Data Collection

We conducted the data collection during the summer of 2016. The names and email addresses of potential participants were collected from Iowa Associate of Community College Trustees and participating community colleges. We disseminated the DDDM survey through online survey software Qualtrics. The survey was open for three months. Weekly email reminders were sent out to those who did not respond to the initial invitation. Among all potential participants, 220 responses were included in the final data set resulting in a response rate of 47%. Our study obtained necessary institution IRB approvals. To fulfill the IRB requirements, we allowed potential participants self-select to participate, not participate, or drop out. For those who voluntarily participated, we removed all personal identifiers and assigned random IDs to individual responses before any data analysis.

Data Analysis

Both descriptive and inferential statistical methods were adopted to analyze the data. First, we calculated frequencies and percentages of participants' demographic characteristic such as age, gender, race, education background, and years in current position.

Next, exploratory factor analysis (EFA) was adopted to answer the first research question. EFA revealed the structure of constructs that measure levels of leadership competency areas, DDDM culture, and perceptions of DDDM practice as it relates to student success. Principle component analysis and varimax rotation were adopted to extract and interpret the constructs. The internal reliability of each construct was ensured by Cronbach's alpha. Constructs with a Cronbach's alpha equal to .70 or higher were considered acceptably reliable (Urdan, 2010).

Finally, multiple regression analysis was conducted to explore if community college leadership competencies and DDDM culture significantly influence perceptions of DDDM practice pertaining to student success (the second research question). We formed and tested a standard regression model, where the dependent variable is the participants' perceptions about institutional DDDM practice for student success. This dependent variable is a reliable construct that measures how participants rate the DDDM practice related to student success in their respective institutions. The independent variables included four other EFA constructs which reported participants' leadership competencies and perceptions of DDDM culture in their institutions. We also included several demographic variables as control variables. These variables included age, gender, race (White or non-White), education background (highest degree), and years in current position. Our hypothesis is that the four EFA constructs (i.e., DDDM culture in general, DDDM culture in student success, collaboration, and community college advocacy) will demonstrate statistically significant influences on the dependent variable. F ratio and R square statistics were used to evaluate the model fit. Statistically significant predictors were highlighted and interpreted. All statistical analyses were conducted in IBM SPSS 23.0.

Limitations

There are three limitations in this study that are worth mentioning. First, this analysis relied upon self-reported data. For future studies, it is desirable to obtain objective measures of how leaders utilize DDDM in student-related issues. Second, this study collected data only within the state of Iowa. In other words, the research findings largely speak to the community college administrators who are mostly White and working in the Midwest. Researchers and practitioners should be cautious when applying the findings anywhere outside of Iowa.

Analysis

Descriptive Analysis

Descriptive analysis results revealed demographic characteristics of survey participants. In terms of race, gender, and age, the largest category was White females between 46 and 55 years old. Specifically, the participants were predominantly White (90%, n=200). Only 3.6% (or n=8) of the sample was Black or African American. There were no participants reported as Latino/a or Hispanic. Nearly 60% (59.5%, n=131) of the participants were female.

More than 60% of the survey participants were older than 45 years old. In particular, the highest age group was 46-55 years old (31.4%, n=69), followed by those who were between 55 and 65 years old (30%, n=66). Relative to the age results, nearly half (48.7%) of the responses have been in their current role for more than 10 years. On the other hand, it is noticeable that 44.5% (n=98) of the sample have spent only 1 to 5 years in the current position.

Additionally, the majority of the survey participants (78.1%) held a master's degree or above. In particular, more than half of the participants had a master's degree (55%, n=121). More than 20% had a doctoral degree (16.4% Ph.D. and 4.1% Ed.D.).

Exploratory Factor Analysis

There are five constructs that emerged from the EFA analysis. The first four constructs revealed the underlying measurement structure of DDDM culture and leadership competencies. The DDDM culture was measured by two constructs: "DDDM culture in general" and "DDDM culture in student success." The other two constructs were named as "collaboration" and "community college advocacy" which fit the corresponding definition of AACC leadership competencies of these two areas (AACC, 2013).

All four constructs had high factor loadings and strong reliability. For example, factor loadings of the two DDDM culture constructs ranged from .810 to .527. Each construct had four survey items. Their Cronbach's alpha were higher than .70. Similarly, the two AACC competency constructs also had very strong reliability scores (>.90) and high factor loadings (ranged from .840 to .650). Construct "community college advocacy" had six survey items and construct "collaboration" had eight survey items.

The fifth EFA construct is "perceptions of DDDM on student success," which served as the dependent variable in the forthcoming regression analysis. This construct also had a high Cronbach's alpha score at .868. This construct contained six survey items; and the factor loadings ranged from .806 to .703. Table 2 reported the detailed results of EFA.

Multiple Linear Regression

A multiple linear regression analysis was conducted to examine predictors of DDDM practice for student success. In the model, we included four EFA constructs (i.e., collaboration, community college advocacy, DDDM culture in general, and DDDM culture in student success) as well as five demographic (i.e., age, gender, race, highest degree, number of years in current position) variables as independent variables. The fifth EFA construct, "perception of DDDM on student success" was the dependent variable.

The model was statistically significant (F=10.851***, df=9). It explained 37.8% of the variances on the dependent variable (adjusted [R.sup.2]: .378). Three out of the four EFA constructs were significant predictors of the dependent variable. Specifically, "DDDM culture in general" ([beta]=.293**, p<.05) and "DDDM culture in student success" ([beta]=.284**, p<.05) were significant predictors with positive coefficients. That means, with a better DDDM culture building, community college leaders reported higher in institutional DDDM practice of student success. However, the leadership competency construct "collaboration" only had a marginal significance ([beta]=.144*, p<.1). Also, the other leadership competency construct "community college advocacy" was not a significant predictor in the model.

Among the five demographic variables, education level indicated significant but negative influences ([beta]=-.201**, p<.05). That means, with a higher educational credential, community college leaders tended to rate lower in institutional DDDM practice of student success.

Discussion and Conclusions

Findings of this study revealed statistically supported relationships between variables that are worth discussion and further analysis. First, the regression analysis revealed two significant predictors of community college leaders' perception of DDDM practice for student success: the two DDDM culture constructs (i.e., DDDM culture in general and DDDM culture in student success). Our hypothesis of significant influences of the DDDM culture was confirmed. Specifically, Iowa community college leaders would rate higher on institutional DDDM practice for student success if the DDDM culture (both in general and in student success context) were better developed. A strong DDDM culture can be a strong driving force of initiating successful DDDM practice in a higher education institution. Specifically, a general DDDM culture may include a general belief among administrators in terms of using data to inform decision-making. In comparison, DDDM culture in student success may have an emphasis on using student data to make decisions regarding teaching/learning, advising, and student engagement. Critically, it is not recommended that administrators stress more effort on one side over the other (i.e., focus on culture building in student success context over the general context). As we found in regression analysis, the two DDDM culture constructs (i.e., DDDM culture in general and DDDM culture in student success) had the same amount of statistical influences on DDDM practice in student success.

Second, the two AACC leadership competencies (community college advocacy and collaboration) did not show recognizable statistically significance (p<.05) in this study. Or, our hypothesis of significance for these two constructs were disapproved. This might be influenced by the relatively small sample size (n=220). Based on the theoretical framework, we strongly believe that leadership competency is critical to institutional DDDM practice. Previous quantitative studies in K-12 areas demonstrated statistically significant relationships between leadership constructs and DDDM practice (Luo, 2008). Thus, it would be beneficial if future research could involve a larger sample to re-examine this relationship in community college context.

In addition, among all demographic variables, educational level showed a significant negative influence on the dependent variable. In particular, community college leaders who have higher education credentials would rate lower on the DDDM practice for student success in their institutions. This finding may infer that community college leaders who held a higher education credential have a more realistic perception/estimation of the DDDM practice in their institutions. Since these community college leaders are more capable of comprehensively evaluating the current status of institutional DDDM practice, they might be more likely to study and initiate strategies to further improve DDDM practice in their institutions. Thus, this negative influence can be viewed as a potential tool to identify those administrators who are more interested in learning DDDM-related knowledge, skills, and effective strategies to further improve DDDM practice in their institution.

Lastly, descriptive findings revealed Iowa community college leaders' demographic characteristics. Some of these characteristics aligned well with the national ttend. For example, more than half of the participants are of older working adult age and have more than 10 years of experience in the current position. This finding is congruent with the conclusions from the AACC that 75% of current community college leaders will retire by 2022 (AACC, 2012). This also infers that a transition to the new community college leadership might occur soon in Iowa community college districts. On the other hand, demographic findings reveled a surprising result regarding the high percentage of female leaders' involvement. National statistics suggest men are still the majority in higher education leadership (American Council on Education, 2017). We believe the high female participation rate in this study may be because (a) we included mid-level administrators such as deans, department chairs, and faculty members with administrative roles into this study, and (b) women might be more willing to participate in the survey. However, it is noticeable that race and gender characteristics mirror the current student enrollment at Iowa community colleges. The majority of the student body in Iowa community colleges is White women (Iowa Department of Education, 2016).

Recommendations

Recommendations for Policy and Practice

Findings of this study formulated implications for encouraging DDDM leadership and practice for student success among Iowa community colleges. The state of Iowa recently launched its Future Ready Iowa Alliance with the goal of helping 70% of Iowans receive a postsecondary degree or credentialed training beyond high school by 2025 (Iowa Workforce Department (IWD), 2016). Data-driven decision making is critical in ensuring Iowa has the capacity and culture to track performance, identify problems, and eventually reach the goal.

Based on the regression findings, DDDM culture building is essential for better institutional DDDM practice for student success. Thus, community colleges need to work on building a consistent and sustainable DDDM culture within and across campuses. Effective strategies can be formed based on currently available platforms and networks. In Iowa, the state-wide data-sharing platform Voluntary Framework Accountability (VFA) is a good example of existing statewide platforms. Through data sharing in the same platforms, DDDM culture can be developed and nurtured across all institutional users.

Further, it is important to involve and educate every community college leader and educator, regardless of their position, with the vision of using data to inform decision making that connects to student success. It is critical that community colleges start to use student data beyond the purpose of reporting. For example, institutional researchers in community colleges may consider using both statistical methods to discover an institution-specific student success model. In this model, community college administrators may learn what factors are most important in fostering student academic success in their institutions. To make this practice possible, institutional researchers have to have the accessibility to student data in multiple divisions (e.g., registrar office, financial office, etc.) and necessary skills in data analysis. Community college administrators and other data users need knowledge on accurately interpreting the statistical findings. With an institution-wide DDDM culture, administrators, leaders, and institutional researchers can work as a team to develop effective strategies that focus on their unique student population.

Additionally, this study highlighted the importance and urgency of diversifying leadership among Iowa community colleges. With over 270,000 baby boomers in the current workforce between the ages 55 to 64, the need to improve student success becomes even more critical (IWD, 2016). Specifically, Latino/a residents who are between the ages of 25 and 64 currently represent 18% of degree attainment in the state of Iowa (Lumina Foundation, 2016). Along with this demographic change, however, only 14% of the participants in this study identified as non-White and none of them is Latino/a. This finding is even more alarming because Latinos are projected to be the largest minority in Iowa by 2025 (IWD, 2016). As a major drive behind educating the future workforce, Iowa community colleges need to recruit and retain more leaders of color with the intention to reflect the student demographics.

Recommendations for Future Research

Findings of this study may also inspire future studies on DDDM practice and leadership in community colleges. For example, future studies may focus on measures that describe how community college leaders emphasize DDDM practice related to fostering success for underrepresented minority students, low-income students and first-generation students. Further, it might be interesting to examine how DDDM culture may be different among senior-level leaders (presidents, CEOs, CAOs, deans, department chairs, etc.), academic advisors, counselors, and faculty members. Since creating and sustaining a DDDM culture is crucial for satisfied DDDM practice, we should pay attention to how DDDM culture can be established and maintained from different perspectives.

Further, it would be beneficial to extend this study to a nationally representative dataset. This study has its limitations on the geographic scope (i.e., Iowa only) and a small sample (n=220). Nevertheless, the survey measures and regression model still can serve as a solid base for similar studies using large-scale nationally representative data. Future studies with a larger sample size may include additional potential predictors and discover more interesting findings. With a nationally representative data, future studies can also generate implications that directly benefit community college leaders and practitioners across the country.

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Marvin L Dejear Jr. is Executive Director of the Evelyn K. Davis Center for Working Families.

Yu "April" Chen is an Assistant Professor in the School of Education, Louisiana State University.

Lorenzo DuBois Baber is an Associate Professor in the School of Education at lowa State University.

Ran Li is a Postdoctoral Research Associate in the School of Education at Iowa State University.
Table 1. Descriptive Analysis Results (n=220)

Variables                                      n     %

Number of years in your current position
  1 yr-5 yrs                                  98  44.5
  6 yrs-10 yrs                                59  26.8
  11 yrs-15 yrs                               29  13.2
  16 yrs-20 yrs                               15   6.8
  21 yrs-25 yrs                                2    .9
  26 yrs-30 yrs                                9   4.1
  Missing (no response)                        8   6.8
Gender
  Male                                        83  37.7
  Female                                     131  59.5
  Missing (no response)                        6   2.7
Age
  24-35                                       21   9.5
  36-45                                       59  26.8
  46-55                                       69  31.4
  56-65                                       66    30
  65 or greater                                5   2.3
  Missing (no response)                        0
Race/ethnicity
  Non-resident alien                           1    .5
  Asian                                        1    .5
  Black or African American                    8   3.6
  Native Hawaiian or Other Pacific Islander    1    .5
  White                                      200  90.9
  Two or more                                  3   1.4
  Missing (no response)                        6   2.7
Highest degree earned
  AA/AAS                                      II     5
  Bachelor's                                  29  13.2
  Master's                                   121  55
  Ed. Specialist                               3   1.4
  Ph.D.                                       36  16.4
  Ed.D.                                        9   4.1
  j.D.                                         3   1.4
  Other                                        1    .5
  Missing (no response)                        7   3.2

Table 2. Exploratory Factor Analysis Results (n=220)

                                                             Factor
Variables                                                    Loading

Collaboration (a =.936)
Demonstrate cultural competence in a global society          .840
Facilitate shared problem solving and decision-making        .812
Develop, enhance, and sustain teamwork and cooperation       .803
Manage conflict and change                                   .791
Embrace and employ the diversity                             .768
Establish networks and partnerships                          .727
Involve students, faculty, staff, and community members      .714
Work effectively and diplomatically                          .650
Community College Advocacy ([alpha] =.950)
Commitment to the mission of community colleges and student  .838
success
Advocate the community college mission                       .824
Represent the community college in a variety of settings     .816
Promote equity, open access, teaching, learning, and         .812
innovation
Advance lifelong learning and support a learning-centered    .789
environment
Value and promise diversity, inclusion, equity, and          .614
academic excellence
DDDM Culture in General ([alpha] = .783 )
Administrators generally accept the use of data              .810
for decision making
Ready to put resources behind the research                   .685
necessary to implement DDDM
My institution has a culture that accepts                    .598
the use of data to make decisions
My institution has had conversations regarding               .568
the sustainability of DDDM
DDDM Culture in Student Success ([alpha] = .727)
Faculty largely accept the use of DDDM for improving         .675
teaching and learning
Share the definition of "student success" with               .646
faculty, staff, and students
Clear vision of where it can make changes                    .527
for student success
Perceptions of DDDM on Student Success ([alpha] = .868)
Student Degree Planning                                      .806
Student Progress (retention, graduation, etc.)               .784
Student Learning (real-time or on-demand assessment          .758
and feedback)
Enrollment management, admissions, and recruiting            .735
Student Learning (learning outcomes, course completion)      .723
Cost to complete degree                                      .703

Table 3. Regression Analysis Results (n=220)

Variable                               B       [beta]        t

Constant                              .397                  .496
Number of years in current position   .004     .031         .425
Age                                   .002     .030         .412
Gender                                .126     .081        1.220
White                                -.185    -.062        -.932
Highest Degree                       -.197    -.201 (**)  -2.962
Collaboration                         .152     .144 (*)    1.687
Community College Advocacy            .079     .084         .975
DDDM Culture in General               .363  293 (**)       3.411
DDDM Culture in Student Success       .325     .284 (**)   3.160

Variable                              P

Constant                             .425
Number of years in current position  .672
Age                                  .681
Gender                               .225
White                                .353
Highest Degree                       .004
Collaboration                        .094
Community College Advocacy           .331
DDDM Culture in General              .001
DDDM Culture in Student Success      .002

*p<. 1, (**) p<.05, (***) p<.001
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Author:Dejear, Marvin L., Jr.; Chen, Yu; Baber, Lorenzo DuBois; Li, Ran
Publication:Community College Enterprise
Article Type:Report
Geographic Code:1USA
Date:Mar 22, 2018
Words:6184
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