text:
Boca Raton artist Dorene Ginzler's oil, watercolor and mixed media collage paintings have been selected by the Art in Public Places program of the City of Boca Raton for exhibit and sale at the Spanish River Public Library (SRL), through February 4, 2018.
The project is supported by the Friends of the Library Gallery and curated by Victoria Sheridan and are shown in the SRL Gallery on the second floor elevator lobby during library hours. There are 13 works for sale.
Ginzler's work "is all about color," as the show is named. For Ginzler, one of the over-riding themes in her work is the spectacular influence both nature and the sea have profoundly impacted her use of color shape composition and light.
"Mixing endless color possibilities with spontaneity in painting takes on a spirituality and beautiful life of their own," says Ginzler. "The colors and shapes speak for themselves."
She is represented by the European gallery AVA Galleria. Her most recent group show was at Carrousel du Louvre, Paris, France. The Ginzler Studio Gallery is located in Boca Raton. Visit the website
For Immediate Release
Dec. 26, 2017
Contact: Andrea Gurvitz
KGMedia 631 278-1966


text:
As*sev`er*a"tion (#), n. [L. asseveratio.]
The act of asseverating, or that which is asseverated; positive affirmation or assertion; solemn declaration.
Another abuse of the tongue I might add, -- vehement asseverations upon slight and trivial occasions.
© Webster 1913.
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NSSE Multi-Year Data Analysis Guide
- Lynne Foster
- 3 years ago
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1 NSSE Multi-Year Data Analysis Guide About This Guide Questions from NSSE users about the best approach to using results from multiple administrations are increasingly common. More than three quarters of NSSE participating institutions have administered the survey more than once. Some look for changes in the way their current students are engaged, some track possible trends, and others evaluate specific campus initiatives. Regardless of the reason, a variety of questions quickly emerges, such as: Can existing reports be used to evaluate changes from year to year, or should multi-year data sets be merged for separate analyses? I know the NSSE survey has changed over time, so how can I quickly identify comparable survey items and benchmarks? What is an appropriate method for determining if there has been a meaningful change between years? The answers to these questions may be complicated because especially in the early years NSSE survey items as well as the construction of institutional reports have undergone several improvements and adjustments. Thus, the purpose of this document is to provide researchers with helpful resources, information, and suggestions for suitable approaches to NSSE multi-year analysis and to strengthen the validity of any final conclusions drawn. Inside 1 About This Guide 2 Task 1: Identify and Focus on Specific Questions 2 Task 2: Select and Employ Appropriate Methods of Analysis 5 Task 3: Attend to Data Quality for Each Year in the Analysis 5 Task 4: Take into Account Changes in NSSE Items and Reports Across Years 6 Task 5: Merge Multiple Years of Data 7 Case Study: A Multi-Year Analysis at NSSEville State University 2. Select and employ appropriate methods of analysis. Determine your analytic approach and the statistical tests that would best identify changes from yearto-year, and the criteria that might be used for evaluating the magnitude of the change. 3. Attend to data quality for each year in the analysis. You should consider, for example, the response rate, total count, sampling error, representativeness, and circumstances surrounding the administration for each year included in your analysis. 4. Take into account changes in NSSE items and reports across years. Determine which NSSE items changed over the years and how your reports might differ in terms of weighting, construction of benchmarks, etc. 5. Merge multiple years of data. We provide a link to an Excel workbook to assist with identifying comparable NSSE variables and a SPSS syntax file and script to merge data sets. The five tasks, explained in more detail in the following pages, are presented in this order as our recommendation, although it is probably not necessary to accomplish them sequentially. For example, the first thing you might do is merge your data before diving into the analysis. We recommend the following tasks for any multi-year analyses of NSSE data: 1. Identify and focus on specific questions. Are you looking for general shifts in engagement, or something more specific to assess a particular campus initiative? Ball State University MULTI-YEAR DATA ANALYSIS GUIDE 1
2 Task 1: Identify and Focus on Specific Questions Your research questions should be specific, answerable, and relevant to campus priorities. Tying them to current campus issues, initiatives to be evaluated, or policy decisions increases the study s chances of being well-received and actually used. Below are three possible research questions for the fictional NSSEville State University that represent typical approaches to multi-year analysis. 1. How stable was the level of academic challenge at NSSEville State University from 2007 to 2008? In the absence of a major campus initiative, one would expect NSSE results to be relatively stable from one year to the next. 1 Working from the assumption that little has changed on campus, institutions may conduct multi-year analyses to confirm stability, an indicator of their data s reliability. When results are consistent between years, a campus can be more confident in the accuracy of population estimates found in the NSSE reports. 2. Given the implementation of initiative X in , how much did the level of active and collaborative learning at NSSEville State University increase from 2006 to 2008? With budgets tightening and calls for increased accountability from both internal and external stakeholders, many schools use NSSE to gauge whether new initiatives are associated with higher levels of student engagement. With data collected prior to and after the implementation of an initiative or program on campus, a multi- year analysis can mirror a pre-test/post-test research design by comparing engagement levels before and after implementation. 3. Given several campus initiatives aimed at increasing contact between faculty and students over the past decade, what trends are apparent in student-faculty interaction at NSSEville State University from 2001 to 2008? Institutions with more than two years of data may be interested to see if results indicate any trends (e.g., trending up or staying consistently high). Keep in mind that the number of data points needed for a trend analysis is subjective and, in part, relies on institutional context and the particular questions being investigated. Some researchers may be tempted to examine NSSE data for any type of results or findings without determining specific questions up front. Though such exploratory studies are possible, we generally advise against this approach. If you do proceed with this type of exploration, we suggest that you set limits to focus the effort, such as organizing the analysis by content area (e.g., student-faculty interaction or high-impact educational experiences). Task 2: Select and Employ Appropriate Methods of Analysis Approaches to Multi-Year Analysis Figure 1 illustrates comparisons that can be made with multiple years of NSSE data. The paths labeled A or B require multiple years of data. The paths labeled C require a single year of data but involve comparing students from different class years. We generally recommend cohort comparisons (A), but under the correct conditions longitudinal comparisons (B) may be appropriate. We discourage comparisons between first-year students and seniors from the same year (C). 2 In addition, with normative comparisons appearing in NSSE reports, some analysts may attempt to track and analyze changes or stability in these comparisons. We also discourage this type of approach because peer group composition often changes from year-to-year and peer group population estimates have not been consistently derived. 1 Test-retest analyses using and NSSE benchmarks indicate a high level of reliability. Correlations ranged between.78 and The first year and the senior year are different educational contexts, with different engagement patterns. A student s engagement should not necessarily be expected to increase over the years, but rather can be high or low depending on the circumstances of the student s activities. An equally important consideration when comparing differences between first-year and senior students is whether they are, in fact, consistent populations. That is, at some institutions as many as a quarter to a third of all first-year students will not persist to the second year, and even fewer make it to the senior year. The senior cohort includes only those students who made it plus, potentially, a significant proportion of transfer students and other part-time students who may have been studying for six or more years before making it to senior status. For these reasons, making comparisons between first-year students and seniors is challenging. 2 MULTI-YEAR DATA ANALYSIS GUIDE
3 Figure 1: Multi-Year Approaches NSSE 2005 NSSE 2008 First-Year C Senior Cohort comparisons (recommended) This approach compares your estimate of first-year student engagement in one year with your estimate of first-year student engagement in another year (likewise for seniors). In Figure 1, this relationship is represented by the A arrows. For example, you can examine how much firstyear student engagement has changed over three years (e.g., 2005, 2006 and 2007). You may want to compare at least two years of data with a base year before concluding a systematic change has occurred. Longitudinal (first-year to senior) analyses A A This approach tracks the engagement of a panel of firstyear respondents as they become seniors three years later, represented in Figure 1 by the B arrow. The objective is to examine how the senior-year experience compares with the first-year experience and, to the extent possible, estimate changes over time in the nature and frequency of student engagement with the institution s resources for learning. Schools may be particularly interested in assessing whether or not a program initiated after the first year of data collection has had any observable effects in the students who are now seniors. The panel data have some advantages. Individual characteristics such as sex, race, family background, ability, and pre-college experiences are held constant. Thus, interpretation of results allows you to focus on environmental factors that may influence the nature and frequency of student engagement in various areas. However, several caveats apply with the longitudinal approach. First, like most panel studies, attrition from the original cohort is a concern. For various reasons, only a fraction of first-year respondents will become senior respondents for many institutions. Second, transfer students are excluded from the analysis, although they may constitute a large portion of seniors. Additionally, a notable proportion of first-year students do not persist to the senior year at some schools or take differing amounts of time to do so. As noted with the single year cross-sectional model (C), B First-Year C Senior the longitudinal panel design faces the same problem with comparing NSSE results between two different populations and educational contexts. Methods for Identifying Significant and Meaningful Change Once you have decided upon your research questions and your approach, you need to choose analytic methods that will help identify both statistically significant and meaningful changes from year-to-year. You may find many statistically significant differences (especially if your sample is large), but it is important to determine if the differences are large enough to warrant serious attention. A number of approaches help in this respect. Although not a step-by-step guide, the following presents several useful and statistically straightforward ideas for looking at change, stability, or trends. Keep in mind that these methods are not mutually exclusive and can be used together to test your year-to-year changes in results. Refer to Table 1 for a summary of each method s characteristics. Statistical Analyses t-test. To determine if a statistically significant difference exists on a single measure between two years, employ t-tests. This test can tell you the likelihood that any differences between two years of data occurred by chance. Unfortunately, it does not speak to the magnitude of the difference. Use this test as an initial step to determine what items or benchmarks should be reviewed for change. Although best used with interval-level data, as with NSSE benchmark scores, many use this robust test (and other similar tests) with ordinal-level data (e.g., means of individual survey items that use a four category ordered response set as seen on NSSE). ANOVA. Analysis of Variance provides a very good approach for reviewing a large number of survey items or benchmarks for which three or more years of results exist. A finding of no significant difference between years can be used to conclude that the data are relatively stable. Alternatively, if this test identifies significant differences between years, run post hoc tests such as Tukey or Scheffé to help identify where the differences exist. Regression. Multivariate statistical models can also address questions regarding significant changes between years, either using individual survey items or benchmarks. By creating a set of dummy variables for the administration year in your regression and using the base year as the reference group, you can determine if there is any statistically significant difference between the base year and MULTI-YEAR DATA ANALYSIS GUIDE 3
4 Table 1 Considerations for Multi-Year Analytical Methods t-test ANOVA Regression Effect Size Percentage Change How many years of data required? 2 3 or more 2 or more 2 2 Determines statistical significant difference? Yes Yes Yes No No Determines magnitude of difference? No* No* No* Yes Yes Are statistical controls possible? No Yes Yes No No * Determining the magnitude of the difference is possible but requires additional manipulations to variables of interest, inclusion of ad hoc software commands, or additional calculations. subsequent results. If no statistically significant coefficients emerge, you can conclude results have remained stable relative to the base year. In addition, assess trends by analyzing the size of year coefficients in sequential order; coefficients may consistently increase from year to year, decrease, or stay stable. Furthermore, using regression analysis with student characteristics as controls provides more sophisticated information for evaluating the source of any changes from year to year. With controls in place, you will have more evidence that changes may be attributed to such things as campus initiatives and not changing student demographics. Descriptive Analyses Effect Size. After finding statistically significant differences between years with various benchmarks, you will need to assess the meaningfulness of any apparent changes. Effect size statistics are often used to assess the magnitude of any change. Cohen s d, the effect size calculation most often used at NSSE, is based on the mean difference between two years divided by the pooled standard deviation. An alternative way to estimate effect size would be to run regression models with standardized dependent variables and include administration year as an independent variable (as described above in the section on regression analysis). Regardless of your approach, changes between years can be interpreted in terms of standard deviation units. Using effect size statistics requires the analyst to establish criteria for determining whether a meaningful change has occurred. For additional information about interpreting effect sizes, see Contextualizing NSSE Effect Sizes: Empirical Analysis and Interpretation of Benchmark Comparisons, at nsse.iub.edu/pdf/ effect_size_guide.pdf. Percentage Change Using Dichotomized Variables. Having multiple response options and a large number of survey items presents significant challenges for analysis and reporting. One way to handle this is to dichotomize survey variables by combining, for instance, very often and often into one category ( frequently ) and sometimes and never into another ( rarely ). If pertinent to your particular research question, you might also create a variable that specifically identifies those who say they never did something. SPSS syntax is available on our Web site to assist you with either approach. After you recreate variables, develop a table showing the percentage of respondents by year reporting they frequently or never do something and establish criteria for meaningful change. For example, you can use a minimum change of four percentage points between two years before declaring an upward or downward change. Because this approach does not test for statistical significance and uses a more subjective evaluation, you may want to take into account sampling error statistics to establishyour criteria. The greater the sampling error, the more conservative you will want to be with establishing criteria for change. Regardless of the method, as with any statistical analysis, there will be uncertainty with your findings. Given this uncertainty, we recommend triangulating all available data sources before implementing any policy change based on a NSSE multi-year analysis. If possible, NSSE should be one of several tools employed to assess change on campus. 4 MULTI-YEAR DATA ANALYSIS GUIDE
5 Identifying Trends If you plan to identify trends, you will have to decide what type of trend is most interesting to your audience. Typically, upward and downward trends interest audiences most, however consistently high or low results may warrant identification as well. As with other methods, establishing specific criteria for high and low will be important and depends in large part on campus context. Another issue that emerges soon after you begin reviewing trend lines is whether or not you require successive year changes to label something as trending upward or downward. Create general rules for what constitutes a trend, taking into account such things as data plateaus. Establishing a clear picture from the start about what a trend line looks like will help ensure consistency throughout your analysis. Task 3: Attend to Data Quality for Each Year in the Analysis Evaluating Your Data Some NSSE administrations yield more precise population estimates than others. Before embarking on your analysis, review the quality of your data for both first-year and senior respondents in each year. NSSE provides various pieces of information to help with this evaluation. The Respondent Characteristics report shows response rates and sampling errors, both of which are based on sample size. If one or more of your administrations showed very low response rates, you may want to consider excluding it from your analysis. Sampling error is an estimate of how much your respondents could differ on survey measures from the entire population of students at your institution. Smaller sampling errors (e.g., +/- 3% or 5%) are preferred. Data with larger sampling errors (such as +/-10%) need not be dismissed off hand, but any results using them should probably be interpreted more conservatively. Each administration s Respondent Characteristics report also details the student characteristics of your sample. Examine the data to see if there are major differences between your sample and the population. If you see significant differences, conduct additional investigations into whether NSSE results vary by these same characteristics. Even though NSSE provided weights based on gender and enrollment status in your data sets starting in 2005 (and can supply weights from earlier years upon request), there may be grounds to develop your own weights based on other student characteristics that appear to have a greater impact. Taking this additional step will help you determine whether or not changes in your data can be linked to such things as changing student demographics or campus initiatives. If you are interested in comparing particular subgroups over time, be aware that a small number of respondents in a given subgroup can prevent you from making reliable conclusions. Choosing a Base Year Determining if your data have changed between years or if trends are present requires that you identify a stable base year, typically the first year used in your study. For this reason, double check the data quality of your base year. However, we do not recommend using the 2000 version of NSSE as a base year due to the number of changes made since that administration. Lastly, you may find it beneficial to combine administrations to form a more reliable baseline. Task 4: Take into Account Changes in NSSE Items and Reports Across Years Should you analyze reports or data sets? Some have asked if it would be appropriate to simply track results that appear in your standard NSSE reports, in place of the potentially arduous task of working with your raw data. Currently, given changes over time in how NSSE reports have been developed, the cleanest approach would be to merge the data sets and generate comparable statistics across multiple administrations. As shown in Table 2, changes to how weights have been applied and the types of respondents used to develop institutional estimates are two primary sources of variation. To produce comparable statistics across several years, we recommend using all randomly sampled respondents 3 and adjusting for non-response with weight1 (or stuwt2 in 2003 and prior years). Finally, a major benefit of working with the data includes the flexibility to conduct tests to identify statistically significant differences between years. Working with Benchmark Scores In 2004, NSSE began calculating benchmark scores at the student level before aggregating them to the institution level. Thus, to compare benchmark results across years, be advised that benchmarks appearing on NSSE reports during the years 2000 through 2003 are not comparable with those produced since To select on randomly sampled respondents, use the variables smpl01 (2004 and earlier) and smpl05 (starting in 2005). Values of 1 and 2 should be used with smpl01 and 1, 2, and 3 with smpl05. MULTI-YEAR DATA ANALYSIS GUIDE 5
6 Change to Enriching Items Response Set in 2004 The response set for several items used in the creation of the Enriching Educational Experiences benchmark, such as participation in a learning community or completion of a study abroad program, also changed significantly in the research with faculty item), and SCE (Supportive Campus Environment). As previously mentioned, starting in 2005, benchmark scores and weights were included in your data file; institutions can obtain prior year scores and weights by contacting NSSE. Since 2008 each participating institution receives a Multi- Year Benchmark Report within their NSSE Institutional Report to facilitate within-institution benchmark comparisons across years. The multi-year report presents comparable benchmark scores and relevant statistical information for each year the institution participated in NSSE. Using statistics on the multi-year report, institutions can compute simple t-tests and effect sizes across years without using raw data files from different years. To learn more, see the NSSE Institutional Report Web site at: nsse.iub.edu/links/institutional_reporting. University of Tampa The result is that Enriching Educational Experiences scores through 2003 cannot be compared with 2004 and more recent data. For similar reasons, the only way to compare the early years of Student-Faculty Interaction with scores since 2004 is to create a new variable (named SFC in data sets) by removing one of the survey items (research with faculty). Task 5: Merge Multiple Years of Data Preparing a NSSE multi-year data set for analysis involves identifying variables that have not changed over the years and then merging the cases from all years into a single file. We have developed a couple of tools to assist you with this process. As shown in Table 3, the following benchmarks can be analyzed regardless of administration: ACa (Level of Academic Challenge), ACL (Active & Collaborative Learning), SFC (Student Faculty Interaction excluding Table 2 NSSE Report Development Across Years* Mean & Frequency Reports Benchmark Reports Year Respondent Type Weight Used Respondent Type Weight Used 2003 and earlier All randomly selected plus targeted None Base random stuwt2 All randomly selected plus targeted None All randomly selected ** Weight1 All randomly selected plus targeted, if requested None All randomly selected Weight All randomly selected Weight3 All randomly selected Weight Present All randomly selected Weight1 All randomly selected Weight1 * This information reflects how NSSE developed population estimates for institutions, not peer groups. ** Starting in 2004, NSSE used a new calculation for institution-level benchmark statistics. 6 MULTI-YEAR DATA ANALYSIS GUIDE
7 Tips on Developing Comparable NSSE Statistics Tip #1: Use All Randomly Selected Cases For 2004 and earlier, use values of 1 and 2 for smpl01 For 2005 to the present, use values of 1, 2, and 3 for smpl05 Tip #2: Use Weights stuwt2 for 2001 to 2003 administrations weight1 for 2004 to current administration Tip #3: Exclude Ineligibles For 2005 to the present, use values of 1 for inelig to ensure only eligible respondents are included NSSE Year-to-Year Survey Variable Tracking Sheet To merge your data sets, you need to identify the individual survey items that have not changed substantially across years. Using Excel, NSSE created a variable tracking spreadsheet that documents the changes to item wording and/or response sets (in addition to other information) for all variables from all survey years starting with the 2000 administration. Also within the Excel workbook is a codebook for understanding the various fields included in the variable tracking spreadsheet. In order to query the file, become familiar with the filter command within Excel. By using filters, you can select only those variables that have not changed substantially from one year to the next. Alternatively, if you already have identified particular variables you want to analyze, reference the file to confirm that they are comparable across years. NSSE began including benchmark scores and weights in data sets in Contact NSSE if you need benchmark scores from earlier years. SPSS syntax and script to merge data sets Once you identify a list of variables to use in your analysis, go to the SPSS command syntax file to merge your data sets in an automated fashion. The syntax file not only provides step-by-step instructions on how to run the script to merge your data, but it also helps with finalizing some important details of your multi-year data set. The NSSE Year-to-Year Survey Variable Tracking Sheet and SPSS files are located on the NSSE Web site at nsse.iub.edu/html/analysis_resources.cfm. Case Study: A Multi-Year Analysis at NSSEville State University This case study from the fictional NSSEville State University is based on our observations of how institutions typically analyze their multi-year data and illustrates some of the recommendations in this guide. So, consider the following situation: In 2003, NSSEville State University found that their students interacted with faculty significantly less often than students attending NSSEville s peer institutions. Given these results, the provost initiated first-year learning communities with seminars taught by senior faculty, a faculty fellows program in the residence halls, and a new student-faculty research program for upper-division students. Following NSSEville s 2007 NSSE administration, the provost asked the director of Institutional Research to analyze the student-faculty interaction benchmark to test whether the campus initiatives had a substantial effect. Table 3 NSSE Benchmark Variables by Year to present Level of Academic Challenge ACa ACa ACa ACa Active & Collaborative Learning ACL ACL ACL ACL (NSSE) brings more focus to teaching and Supportive Campus Environment SCE learning initiatives and demonstrates institutional interest Student-Faculty in and commitment Interactionto improving student S01 engagement. It provides information and data that Student-Faculty Interaction helped us to identify areas for improvement and SCE S01 SCE S01 SCE SFI establish (without baselines Research for with future Faculty measurement. item) SFC SFC SFC SFC Enriching Pam Montgomery, Educational Director Experiences of Educational E01 E02 E02 EEE Planning, Vancouver Island University MULTI-YEAR DATA ANALYSIS GUIDE 7
8 The following summarizes the approaches taken to address this problem, corresponding to the five tasks described above. 1. Identify and focus on specific questions The analyst focused the research questions to ask, Did the average student-faculty interaction score significantly and substantially increase between 2003 and 2007 for both first-year and senior students? 2. Select and employ appropriate methods of analysis It was determined that a cohort comparison approach, rather than a longitudinal analysis, would be appropriate to answer the research question. After merging the two years of data (see 5. below), the analyst ran t-tests for statistically significant differences between 2003 and 2007 student-faculty interaction scores for both firstyear and senior results. Upon discovering a statistically significant increase in scores, the analyst calculated Cohen s d effect sizes to assess whether or not the magnitude of the change was meaningful. Reference material on NSSE s Web site helped the analyst establish criteria for a meaningful change. To be sure that differences in student demographics could not account for the shift in scores, the analyst then developed regression models that included a dummy variable for year and controls for various student characteristics. The dependent variable, student-faculty interaction, and all continuous independent variables were standardized so that the regression coefficients could be interpreted as effect sizes. The analyst employed weights provided by NSSE to adjust for the disproportionate representations of gender and part-time status in the data set. The analyst also gathered alternative sources of information (campus focus groups, course evaluations, etc.) for evaluating the research question at hand so the NSSE findings could be confirmed. 3. Attend to data quality for each year in the analysis Before running the analysis, the analyst reviewed Respondent Characteristics reports from 2003 and 2007 to evaluate data quality. Upon reviewing response rates, the number of respondents, and sampling error statistics, the analyst felt comfortable that population estimates using NSSE data were sufficiently precise. 4. Take into account changes in NSSE items and reports across years Realizing that 2003 and 2007 student-faculty interaction scores (SFI) cannot be directly compared, the analyst correctly used the variable SFC (see Table 3) to analyze the changes between 2003 and The analyst identified that the weight and sample type variables were named differently in 2003 and 2007 ( stuwt2 / weight1 and smpl01 / smpl05, respectively). In order to make the two years as comparable as possible, the analyst included only randomly selected cases in the analysis (i.e., respondents with smpl01 values of 1 and 2 in 2003 and smpl05 values of 1, 2, and 3 in 2007). 5. Merge multiple years of data Unable to find stuwt2 or SFC in their 2003 data file, the analyst contacted NSSE for the data. Using the NSSE Year-to-Year Survey Variable Tracking Sheet, the analyst identified comparable variables between the two years. Anticipating other questions, however, the analyst created a data set not only with SFC but with all potential variables of interest. In order to seamlessly merge the data sets, the analyst found the SPSS script on the NSSE Web site to automatically merge the two data sets. We want to hear from our users. Please send us descriptions of how NSSE multi-year results are being used on your campus. We also invite suggestions for improving the NSSE Multi-Year Data Analysis Guide. Please direct correspondence to the project staff at 8 MULTI-YEAR DATA ANALYSIS GUIDE
9 Additional Resources on Selected Topics General Statistics StatSoft. The Statistics Homepage. Regression Analysis Cohen, J. (2003). Applied multiple regression/correlation analysis for the behavioral sciences, (3rd ed.), Mahwah, NJ: Lawrence Erlbaum Associates. Effect Size Vacha-Haase, T. & Thompson, B. (2004). How to estimate and interpret various effect sizes. Journal of Counseling Psychology, 51(4), Advanced Topics Singer, J. D. & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press. Baumert, J., Little, T. & Schnabel, K. (Eds.). (2000). Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples. Mahwah, NJ: Lawrence Erlbaum Associates. Indiana University Center for Postsecondary Research 1900 East Tenth Street, Suite 419 Bloomington, IN Phone: Fax: Web: nsse.iub.edu MULTI-YEAR DATA ANALYSIS GUIDE 9
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It is easy to feel like marketing is in a no-win situation. Caught between trying to delight customers with amazing experiences and trying to dazzle business with amazing results, CMOs and marketers around the world must appeal to and appease two very distinct sets of demands to succeed in today's business world.
Stuck in this perceived Catch-22, many of us default to addressing one constituency at the expense of the other. In the process, we fail to live up to our full potential on either front at a time when the stakes are high and there's no more middle ground—brands are either disruptors or the disrupted, growing or shrinking.
Takin' It to the Streets
Meeting and exceeding the demands of these two distinct groups is the single biggest challenge that we all face, and from the conversations I have every week, I know there are no easy answers.
On one hand there is Main Street, where customers demand and expect a personalized, hypercustomized and dazzling digital experience, plus a one-to-one marketing experience at massive scale. This requires long-term thinking around the customer relationship, a right-brain focus on customer relationships and behaviors, and serious creative genius to captivate customers and keep them engaged over the long haul.
On the other hand, or better still, on the other block, there is Wall Street, aka the board of directors, investors and stockholders, each demanding and expecting revenue growth, profit growth and predictable performance, quarter over quarter. This requires the CMO to exhibit near-term thinking around financial performance that can force "batch-and-blast" marketing style tactics, a left-brain focus on analytics and number-crunching, and serious measurement acumen to prove marketing's value to the board.
Wearing Two Hats
We all know it can be a challenge to wear two hats, and I will not argue that point. In the context of marketing, and how we can meet and exceed the increasing demands from both Main Street and Wall Street, there are technologies that can do so, some in extraordinary fashion.
The reality is that the technology and everything else the CMO needs to be successful is available, but is not being applied correctly due to several factors, including false promises, hype, internal roadblocks, and technology clutter (as there is no shortage of options for the CMO to choose from today).
As a means to cut through the clutter and provide some clarity, we recently partnered with the CMO Club on a new ebook, "The CMO Solution Guide to Leveraging New Technology and Marketing Platforms."
Featuring insights from CMOs of some of the biggest brands in the world, the guide includes five solutions we identified to help marketers make their way through what I refer to as the "Wild West of technology vendors" that make big promises but all too often deliver a fragmented and obscured path to success.
What Lies Ahead
The incredible complexity of balancing the expectations of Wall Street and Main Street is only going to increase as the expectations of the boardroom and consumer continue to skyrocket.
To successfully tackle this complexity and move beyond the false promises and hype that are just making matters worse, I ask all CMOs to consider the following:
- What if you could balance the needs of both Wall Street and Main Street while moving into this new world order at your own pace?
- What if you didn't have to navigate technology complexity, worry about the technologies your competitors are using, or even understand marketing technologies in order to take advantage of them?
- What if you could quickly and easily share real business results by spending less time shoveling haystacks of data and more time finding needles of insight?
- And what if we could clear the air of all this marketing hype so that you can focus on achieving your full business and creative potential, and simply do real marketing for real people?
Before you answer, consider one last thing: It no longer has to be a case of "what if."
With the right technology, it is possible to meet both near-term demands to satisfy Wall Street's revenue expectations and Main Street's long-term aspirations to deliver amazing customer experiences, integrating cross-channel, content and social marketing with data management.
Wrapping It Up
Not long ago I penned a piece for Forbes in which I wrote: "The reality of how customers interact with brands is sinking in, and many marketers are starting to realize that the customer journey is anything but a straight path. Rather, it's a never-ending, dynamic, constantly evolving relationship, one that requires management of real interactions with real people."
Read that again and let it sink in for a minute.
All this talk about a customer journey or path to purchase culminating in someone buying something from your brand is a myth. There is no end. There can't be an end. For if there is an end, you will eventually lose that customer forever, right?
Beyond the hype and false promises, the reality is that as marketers, there is no quick fix to meet the expectations of both Wall Street and Main Street. And we all know that. What I can share, though, is some of the questions that the world's best marketers are asking themselves as they make the transition. I encourage you to ask yourself these same questions as the answers will go a long way in helping you tackle the incredibly complex balancing act we all face:
- Do your current technologies and marketing platforms embrace the unstructured nature of customer data by connecting behaviors that take place across a variety of online and offline channels to establish one unique account or individual?
- Do your current technologies and marketing platforms enable and empower you and your team to adapt marketing activities to real-time customer behaviors and adjust the customer experience in real time?
- Do your current technologies and marketing platforms allow you and your team to align content to key inflection points throughout the entire customer lifecycle?
- And last, but certainly not least, do your current technologies and marketing platforms offer you the ability to leverage new technology and innovation as it happens?
About the Author
Kevin Akeroyd is general manager and senior VP for the Oracle Marketing Cloud. He is responsible for leading all customer-facing aspects at Oracle Marketing Cloud, including sales, account management, marketing and customer support and services.
Prior to Oracle, Mr. Akeroyd was senior VP-field operations at Badgeville. Before Badgeville, Mr. Akeroyd was general manager of the Data.com business unit for Salesforce.com, chief operating officer of Jigsaw prior to its Salesforce.com acquisition and has held a variety of CXO-level positions at companies including Gallery Player, the Loan Page, Acxiom, Angara, Noosh and RR Donnelly & Sons PreMedia. Follow on Twitter @akeroyd.
About the Sponsor
Modern marketers choose Oracle Marketing Cloud to build customer-obsessed cultures, create and manage ideal customers and power revenue performance. They transform marketing by truly knowing the customer, engaging with cross-channel marketing and achieving data-driven accountability. Integrated information from cross-channel, content and social marketing with data management and activation along with hundreds of app and data partners enables them to target, engage, convert, analyze and use award-winning marketing technology and expertise to deliver personalized customer experiences at every interaction.


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Travel expenses: travel for necessary attendance: more than one permanent workplace at the same time: example
An employee is a make-up artist employed by a large chain of chemist shops. He works 5 days each week but spends each day in a different shop in a different town. He works in the same shop on the same day each week. No deduction is due for any of his travelling expenses.
He attends each shop regularly for the performance of the duties of his employment. His attendance is not to perform a task of limited duration and nor is it for a temporary purpose. Therefore each shop is a permanent workplace, see EIM32065.


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The Forest Flyers
"You have no idea what a forest fire really looks like until you see one in person," declares Zhang Yinghai, command pilot and vice director of the aviation detachment of the forest fire services division of China's armed police forces. On May 17, 2017, he and his teammates battled with a huge fire in the Old Barag Banner near the city of Hulunbuir in Inner Mongolia Autonomous Region.
The fire had already burned 8,400 hectares of forest when Zhang's brigade arrived. Both the aviation detachment and the ground officers, a force of 12 aircraft and more than 9,000 people, joined the firefighting efforts. In less than four days, the fire was extinguished. However, the cost of the victory was tremendous hardship, sacrifices, and danger.
"Danger is always part of our work," says Zhang. "Recently, a fireman was rappelling from a helicopter when the rope got stuck, leaving him hanging from the helicopter. But our pilot was calm enough to carefully find a place to set him on the ground."
China's First Aerial Firefighting Force
Globally, aerial forest firefighting is nothing new. In the wake of World War II, the United States refitted many of its seaplanes from the war to carry and drop fire retardant. Canada, home to vast expanses of forests, developed a special amphibian aircraft to fight forest fires in the 1960s. It could fill up with water and travel to the fire without missing a beat.
Compared to those Western countries, China's development of an aerial forest firefighting force started late. In July 2009, the aviation detachment was officially formed in Daqing, Heilongjiang Province in northeastern China, becoming the first forest aviation force in the country. After years of rapid development, the detachment now has 18 helicopters, three command pilots and six first-grade pilots. They provide transportation, forest protection, firefighting, patrol and search and rescue services.
Just mentioning the phrase "aviation detachment" brings aerial firefighting to many people's minds. Along with their direct fire control and prevention efforts, another major duty of the detachment is to take firefighters to areas near the fire. Air transportation of firefighters has gradually become common practice in China. "We save tons of time traveling by air, which enables us to get fires under control before they get too big," comments Zhang. "The people involved in the firefighting save tremendous energy, so we don't need as many."
However, since weather in the mountains can be severe and change fast, turbulence is frequent and air-land operations are always filled with tension. Years ago, when then-vice director Lin Hongri was returning from a mission to transport firefighters for a land operation, he found the runway shrouded in clouds so thick that landing seemed impossible. He had already been in the helicopter for more than three hours and fuel was running out. Lin eventually found a small gap in the clouds to the southeast of the airport. With help from the weather radar, the experienced pilot quickly determined the precise location of the clearing. Lin flew the chopper at an altitude of just 200 to 300 meters around the mountainous areas southeast of the airport for more than 20 kilometers before landing safely.
Training in the Field
For the comparatively young aviation detachment, firefighting experience is gained both through drills and real operations.
In August 2011, the forest fire services organized a large-scale fire extinguishing field drill just as the detachment had begun formal training. When the detachment was asked about participation in the joint drill, some objected. "We had just begun training. We didn't want to lose face by screwing up during the drill."
However, Wang Xingkun, then air squadron commander of the detachment, was thinking differently. "We should not only join the drill, but even lead it," he declared. "If we are afraid of losing face in peacetime, we may lose our lives to the fires. A forest fire will never wait for us to be fully prepared."
"In recent years, firefighting experience in the United States, Russia and Canada has evidenced that dropping water from helicopters is one of the most effective methods for fire suppression," commented Wang. The detachment decided to participate in the maneuver and used helicopters to drop water from special buckets attached with cables. Although such water drops are frequently performed in many developed countries, technical difficulties had prevented China from using them in the past.
A helicopter piloted by Wang lifted off with a massive bucket for the first time. After 30 minutes of flight, the plane's body began to shake suddenly. "What bad luck! I ran into severe turbulence," recalls Wang. As his speed dropped, the helicopter experienced even worse shaking. In such conditions, emergency safety protocols recommend Wang release the bucket to save the helicopter. However, the veteran pilot was not keen on destroying a piece of equipment worth more than US$70,000 so fast. Gripping the controls tightly, Wang slowly reduced the amplitude of oscillation and pulled the helicopter up with all its power. Ten minutes later, the helicopter had regained stability and raced out of the turbulence. This experience provided Wang with first-hand information that he later passed on to other pilots trained to perform a helicopter water drop.
The team never stops learning. During drills, the detachment realized that if the flame front exceeds 10 meters in height, water dropped from a helicopter, if insufficient in amount, will break up into particles and oxygenate and feed the fire. After discussions, the detachment decided to do four drops right in a row. They performed experiments to determine the ideal density of following helicopters as well as air speed and the most effective maneuvers to drop the water. All these problems were solved one by one in three months. On the day when the field maneuver was carried out, Wang's aircraft lifted off first, followed by three other helicopters, each over an interval of 30 meters. Forest fire experts spoke highly of the formation after seeing its firefighting operation, and believed that it filled the gaps in China's forest aerial firefighting.
Their newly-developed techniques were soon put into practice. On September 18, 2015, a fire broke out in the forests of the Greater Hinggan Mountains in northeastern China. Due to many sudden changes in wind direction, the fire spread erratically and the situation remained serious for a long time. The detachment dispatched eight orange helicopters in two formations. The four choppers in the first formation performed water drops with buckets. The second formation, consisting of four helicopters with belly tanks, flew over the fire at a low altitude and dropped water from the air. The fire was contained quickly.
By now, the detachment has performed several hundred flights and accumulated data on more than 3,000 fires. They have also studied more than 30 difficult scenarios involving potential extreme conditions and developed more than 10 new aerial firefighting tactics.
"Fire can decimate old-growth forests in minutes," insists Zhang. "And forest fires pose a great threat to personal and property safety, not to mention the environmental protection and water and soil preservation efforts. We carry out dangerous missions throughout the year, but we are happiest when we see our lush forests thriving safely."


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"...imagine what if we had never seen flowers, and they were sent to us as a reward for our goodness. Imagine how carefully we'd watch the growth of the stem and every unfolding of each leaf in wonder. And then imagine our astonishment when a bud appeared, and began to unfold in all its delicate, colorful beauty. Well, we have been seeing flowers for years-but our children haven't."
-Charlotte Mason, Home Education, volume 1, page 53
Jan 30: Our seeds have sprouted. If you look closely you might be able to see the seed cases still attached to the newly sprouted leaves.
Feb. 9: Wow, haven't our sunflower plants grown!
Feb. 25: First repotting.
March 25: They are starting to reach past the middle of the window and look like they plan to keep going! Quentin's plant looks like it will bloom first.
March 27: We had to replant them again.
Quentin's sunflower was the first to bloom with a tiny bloom that is more like a yellow daisy!
May 15: We finally planted those sunflowers in the front yard. Sam had the camera, so the pictures are mostly of me this time, except for the one self-portrait Sam took while hold the camera at arm's length in front of him. James had a lot of fun discovering new creatures that I churned up with my trowel. Quinney liked clipping the weeds away with clippers, and Sam enjoyed watching it all through the camera lens.
Here are the sunflowers on June 21. They had such an early start that they were ready to harvest the first day of summer. We will keep the seeds to plant and see the cycle again.


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"Did you ever expect to see this in your lifetime?" asked my Colorado Springs Business Journal colleague Cameron Moix, 25. "I thought I'd be your age [in other words, 74] before I saw it."
No, I didn't expect to see it. When news came last week of the Supreme Court's decision to allow same-sex marriages in all 50 states, my eyes were full of tears as I thought of the long, difficult road that LGBT people nationwide have traveled during my lifetime, and I rejoiced. I thought particularly of the dark days after Colorado voters approved Amendment 2 to the state constitution in 1992.
I remember the principled courage of Richard Skorman, Carolyn Cathey, David Sellon, Christy Le Lait, Frank Whitworth, Kathryn Eastburn, Amy Divine, Nori Rost and so many others, who fought for what the amendment attempted to deny: equal protection under the law.
Amendment 2 was conceived in Colorado Springs by an ad hoc coalition of social conservatives. Inspired in part by the relocation of Focus on the Family to Colorado Springs, a group calling itself "Colorado for Family Values" was created locally. Headed by auto dealer Will Perkins, CFV took aim at the "radical homosexual agenda." Aided by national groups, Perkins and his allies sought to amend the Colorado constitution by adding these words:
Neither the State of Colorado, through any of its branches or departments, nor any of its agencies, political subdivisions, municipalities or school districts, shall enact, adopt or enforce any statute, regulation, ordinance or policy whereby homosexual, lesbian or bisexual orientation, conduct, practices or relationships shall constitute or otherwise be the basis of or entitle any person or class of persons to have or claim any minority status, quota preferences, protected status or claim of discrimination.
At the time, I thought CFV was just another crazy fringe group, one of the sad leftovers of the antigay crusades of the 1970s and '80s. Surely no sensible person could believe this crap, though CFV was able to collect enough signatures to get it on the ballot. Supporters such as Focus founder/CEO James Dobson seemed like utter wackos.
In March 1991, Dobson wrote: "There are people in our society who find sexual satisfaction from engaging in intercourse with animals. ... Would anyone suggest that these groups deserve special protection?"
The wackos prevailed, 53-47 percent. But Amendment 2's passage didn't drive the local gay community underground — to the contrary, it became empowered and assertive. CFV and its allies imagined that they had won a great victory, but they were wrong.
In retrospect, November 1992 was high tide for the religious right in Colorado. Democrats, moderate Republicans and independent voters joined together to work for equality, and new voices emerged in Colorado Springs. Citizens Project, formed in 1992 by Amy Divine and Doug Triggs, was one — and this newspaper was another.
"There wouldn't be an Independent without Amendment 2," said Indy publisher John Weiss. "Kat Tudor and Raphi Sassower were so mad at the Gazette for supporting it that they brought me out here to start a competing paper."
The would-be partners soon parted ways, but Weiss eventually partnered with Kathryn Eastburn and the Colorado Springs Independent was born in 1993.
Game on! The progressive community celebrated diversity, fought for equal rights and had fun. Le Lait figured out how to handle anti-gay demonstrators at the annual Pride Parade, using a clever fundraising appeal.
Every year, the same group of lunatics would gather at the corner of Platte and Tejon, bearing signs advising gays and their supporters to repent or burn in Hell. Le Lait asked Pride's core supporters to ante up 5, 10, 15 or 20 bucks per demonstrator. As the event got underway, she approached the group's leader and asked him to round up more demonstrators.
"We get hundreds of dollars for every one of you!" she said, explaining her scheme. "So c'mon, get busy."
Four years after its passage, a 6-3 majority on the U.S. Supreme Court struck down Amendment 2.
"Its sheer breadth is so discontinuous with the reasons offered for it that the amendment seems inexplicable by anything but animus toward the class that it affects..." Justice Anthony Kennedy wrote. "It identifies persons by a single trait and then denies them protection across the board. The resulting disqualification of a class of persons from the right to seek specific protection from the law is unprecedented in our jurisprudence."
Nineteen years later, Kennedy's words last week were just as powerful.
"No union is more profound than marriage, for it embodies the highest ideals of love, fidelity, devotion, sacrifice, and family," he wrote in Obergefell v. Hodges. "In forming a marital union, two people become something greater than once they were."
In dissent, Chief Justice John Roberts said, "...five lawyers have enacted their own version of marriage."
Blame it on Chief Justice John Marshall's 1803 opinion in Marbury v. Madison (5 U.S. 137 1803), which established the basis of judicial review.
"Those, then, who controvert the principle that the Constitution is to be considered in court as a paramount law," Marshall wrote, "are reduced to the necessity of maintaining that courts must close their eyes on the Constitution, and see only the law."
And why do we have a federal Constitution?
"...to establish Justice, insure domestic Tranquility, provide for the common defence, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity."
Blessings indeed.


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2-3 chicken breasts, sliced thinly
1 can of full fat coconut milk
2 Tbsp coconut oil (one for the chicken and one for the veggies to stir fry them)
2 Tbsp curry powder
2 tsp onion powder + a few shakes (meat cooking)
1 tsp garlic powder or minced garlic + a few shakes (veggie cooking)
1/2 tsp chili powder + a few shakes (meat cooking)
1/2 tsp ginger powder or fresh ginger
1/4 tsp chili flakes
sea salt
Veggies of your choice, these seem to go well: (I tend to use 3-4)
Chopped sweet potato / yam / purple potato
1/4-1/2 chopped butternut squash
2 zucchini, chopped
1/2 yellow onion or 1/2 purple onion
2-4 carrots peeled & chopped
red or yellow pepper, thinly sliced and seeds removed
**A few other add-in options that top it off:
diced pineapple
can of bamboo
If you like it more saucy/soup like, then I'd suggest adding a little chicken stock to make it more liquid-soup like.
Take half the coconut oil, sauté the veggies for about 8-10 minutes, add a few shakes of sea salt and a few shakes of garlic powder on medium-low and set aside. Take the other half of the fat and sauté the chicken for about 8-10 minutes on medium low. Throw in a few shakes of onion powder, chili powder and sea salt.
Add vegetables to the chicken. Add the coconut milk and turn temp down just a little. Add in the seasonings: curry, onion powder, garlic powder, chili powder, ginger, chili flakes. Let it simmer for 15-20 minutes. Test the taste for your liking…add in a few more pepper flakes for some heat or a little more salt or curry if it is lacking some pizzazz.


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For Cynthia Blinkinsop, there was a need to regain her smile that had been missing.
Naval Hospital Bremerton's (NHB) Health Promotion and Wellness Department's ShipShape Program provided just the resource to make Blinkinsop's happy expression return.
"I didn't laugh as much as I used to. My granddaughter makes me smile and I want to be able to keep up with her. The ShipShape Program has given me a creative and supporting environment to improve my overall health to be able to do that," exclaimed Blinkinsop.
The ShipShape Program is the Navy's official weight management program that assists active duty, reservists, beneficiaries, and Department of Defense civil service workers with making healthy behavior changes in order to lose weight. The program consists of eight sessions with two online sessions and six in-person group sessions that can be taken in any order. The program focuses on three important components for weight loss: mindset, nutrition, and physical activity. The goal is to equip participants with the skills and resources necessary to jumpstart a healthier lifestyle.
"We're fueling the body and mind of those enrolled by providing a lot of information on everything from weight control and strengthening muscles to reducing stress and improving sleep. ShipShape is designed to enhance a person's health and wellbeing and increase their chance of having a longer life," said Patricia Skinner, NHB Health Promotion and Wellness Department Health Educator.
There were over 100 beneficiaries, retirees and dependents enrolled last year, along with 25 active duty members.
"We are seeing more and more beneficiaries, retirees and dependents getting more involved with our classes due to more providers throughout the hospital taking part in referring those patients to us," said Hospital Corpsman 1st Class (Fleet Marine Force) Matthew Oberg, Health Promotion Leading Petty Officer and Active Duty Shipshape Coordinator.
"I noticed the program listed on the Health Promotion calendar of events and after caring for my husband I decided it was now time to care for me. The group provides accountability. I like meeting with like-minded individuals and brainstorming on ways to improve on things I think I know and on things I should know," Blinkinsop said.
According to Oberg, there are multiple overlapping benefits associated with the program for those who enroll.
"It's about an ongoing lifestyle that includes long-term changes in daily eating and exercise habits to help each person reach and maintain a healthy weight for life. This can benefit anyone both personally and professionally by increasing readiness, enhancing peak performance, and building resilience. Facilitators work to help design customized action plans that address nutrition, physical activity, and psychological readiness, and apply weight loss concepts to daily living. We ensure everyone has the right mindset, nutrition, and physical activity to meet their goals," explained Oberg, noting that the program's effectiveness stems from being informative as well as interactive throughout the entire eight-week course.
"I've already mentioned that we focus on mindset, nutrition, and physical activity. Some individuals may know how to portion their meals, but may not know how to conduct physical activity to maintain or lose their weight. This is an excellent program to show members how to do that," added Oberg.
The mindset portion of the class consists of asking program participants questions such as, 'what motivates you to adopt a healthier lifestyle?' 'Have you, or could you, make working out or increasing your physical activity a priority in your life?' 'What have you done, or should you continue to do, in order to modify your environments to support healthy behaviors?'
"Based on the member's answers to the questions, we'll guide them in the right direction to help them stay on track and meet their goals," said Oberg.
The nutrition portion of the program has each person tracking their food intake, and researching nutritional information from available sites such as the United States Department of Agriculture Nutrient Database. In the Psychology of Weight Management portion of the program, the goal for enrollees is to find a healthy alternative to one of their 'craved' foods.
The physical activity portion consists of establishing a workout plan, evaluating the plan for balance to ensure it includes elements of cardio, strength training, and flexibility. Oberg advocates that a workout should include 200 to 250 minutes of exercise a week distributed over four to six days.
"After the member has been working out for at least a couple of weeks we evaluate their program to see if it's working for them. We help them identify a quick and easy physical activity that they can do vice eating when they know they aren't really hungry, such as going for a walk or doing some air squats. Every little bit helps," Oberg stressed.
Program enrollees will even learn smart tips on shopping for groceries.
"We'll take a field trip to some place like one of the commissaries and explain just how a grocery store is laid out and what sections and aisles to avoid and the best way to shop. What we're doing is not concentrating on the scale as much as being able to feel if someone's clothes fit looser. ShipShape is a change in lifestyle by replacing some old habits with the new. It's not about being on a diet," Skinner said.
Program Date/Time:
Mondays: 1200-1:30 p.m.
Tuesday: 7:30 a.m. to 9 a.m.
Wednesday: 9 a.m. to 10:30 a.m.
Thursday (except first Thursday of month): 1 p.m. to 2:30 p.m.
Friday: 7 a.m. to 8 a.m.
For more information on this class and others, please call 360-475-4541. For appointments, call Puget Sound Military Appointment Center at 1-800-404-4506.
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The Pursuit of Great Jobs and Great Lives
The unique online learning environment at Western Governors University encompasses a competency-based model that has provided extraordinary value to students. Learn why nearly three-fourths of WGU graduates overall strongly agree that their education was worth the cost.
The factors that determine a graduate's success in higher education today encompass more than just getting a good job and having a good salary.
The path to connecting graduates to good jobs should also include a meaningful, holistic view of life after college. With the Gallup-Purdue Index, a study that examines long-term success of graduates as they pursue a good life and better job, Gallup has turned a microscopic lens on how graduates' experiences shaped their lives.
The WGU report highlights findings from a study of outcomes among WGU alumni who graduated between 2000 and 2016, with a focus on two of the university's critical subpopulations: underrepresented minorities and alumni who are military service members or veterans. Take a closer look to reveal findings that can provide a renewed foundation for policymakers and higher education institutions to create action plans to empower these students, and all students, to live their best life after college.
Nearly three-fourths of WGU grads strongly agree their education was worth the cost.
Gallup data show:73%
of alumni are extremely likely to recommend the university to family, friends and colleagues, compared with41%
of graduates nationally.
of WGU graduates strongly agree that their education was worth the cost. The odds of being extremely likely to recommend the university are over7x
higher for that group than for those who do not strongly agree.
WGU alumni believe that their education prepared them for life after college, and they strongly agree that their education was worth the cost.
The Gallup Study of Western Governors University Alumni: The Pursuit of Great Jobs and Great Lives report provides insights on how WGU graduates compare to college graduates nationally, including those who are minorities and military service members and veterans. The report, which uses insights from the Gallup-Purdue Index, provides leaders, policymakers and higher education institutions with important information about the growing population of nontraditional students.
Download this report to learn:
- whether graduates believe their education was worth the cost
- the extent to which graduates believe their school was the perfect place for them
- whether students feel their university prepared them well for life outside of college
- the extent to which graduates feel that WGU is passionate about the long-term success of its students
- the extent to which WGU military service members and veterans feel supported by the university
- the percentage of graduates who are extremely likely to recommend their alma mater
Download the Full Report
To receive an email with a link to download this report, please complete the form. Someone from Gallup may also contact you via email about your interest in this topic.


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