The College for Financial Planning Alumni Survey Report

The College for Financial Planning Alumni Survey Report Dr. Jacob Rodriguez, Director of Institutional Research and Organizational Effectiveness Dr. Natalie Wright, Assessment Manager Rebecca Henderson, Research Analyst November 2015

Contents Introduction... 3 Method... 4 Results... 5 Basic respondent information... 5 Influences on decision to obtain grad uate degree from the College... 6 Pre- and post-degree work information... 7 Preparation for job... 9 Income change... 13 Non-income outcomes... 17 References... 21 Office of Institutional Research & Effectiveness 2

Introduction In today s educational environment in which institutional accountability and educational return on investment are paramount, evaluating the outcomes of an educational program s graduates is important. Evaluating graduate outcomes is often a requirement in order for an institution to maintain its regional accreditation. For example, the Higher Learning Commission (HLC) requires institutions to evaluate the post-graduation outcomes of its graduates (HLC Criterion 4.A.6; HLC, 2015). Alumni research focuses on three key areas: alumni outcomes (satisfaction with employment, satisfaction with degree-granting institution, participation in civic and community activities), engagemen t and competencies (quality of student experiences while attending institution, application of competencies obtained in education to work and personal life), and alumni giving (alumni interest and ability to give back to the institution) (Cabrera, Weerts, & Zulick, 2005). Alumni surveys can be used to review program curricula and better align program learning objectives with the competencies that students need after graduating (Borden, 2005). In 2015, the College for Financial Planning began developing an alumni survey. Several purposes guided the development of this survey: 1. Compare pre- and post-graduation career outcomes, including income, job level, and industry 2. Investigate graduates reasons for choosing to complete a graduate degree at the College 3. Evaluate alumni satisfaction with their educational program 4. Evaluate coverage of program and graduate studies learning outcomes in curriculum and the extent to which students apply these knowledge sets and skills to their work 5. Determine how connected alumni feel to the College and identify the best ways to increase alumni s connection to the College 6. Obtain a demographic profile of College alumni When examining graduates job outcomes, it is important to recognize that the wider economic environment exerts a strong influence on individuals career trajectories. Students who graduate in a recession experience lower wages than those who do not graduate during a recession, and these earning losses do not fade until eight to ten years post-graduation (Oreopoulos, von Wachter, & Heisz, 2006). In the time period during which surveyed alumni graduated (1992-2015), two economic downturns occurred, including a recession from March to November of 2001 and the Great Recession from December 2007 to June 2009. As such, it was determined that a thorough and unbiased investigation of graduate incomes would need to control for macroeconomic variables. Office of Institutional Research & Effectiveness 3

Method Although standardized alumni surveys exist (for example, the Higher Education Data Sharing Consortium (HEDS) alumni survey, the American College Testing (ACT) alumni survey), these surveys are targeted toward an undergraduate population and do not provide the specific information required by the College. Thus, the Office of Institutional Research and Effectiveness developed an alumni survey based on the information needs of the College. Items covered eleven key content areas: 1. Year of graduation and graduate major 2. Reasons for choosing to earn a graduate degree at the College 3. Employment prior to completing graduate degree 4. Current employment 5. Ways in which employment changed as a result of obtaining graduate degree 6. How well graduate degree prepared graduate for current position 7. Development of major-specific knowledge and skills, and frequency with which these skills are used in current position 8. Development of broad skills identified in graduate studies goals 9. Perceptions of connectedness to the College 10. Preferences for maintaining connection to the College 11. Demographic information The survey questions were imported into SurveyMonkey. Graduate program alumni information was requested from the Office of Information Technology, who queried the College s student database (STARS) to generate a list of graduate program alumni names and email addresses. A letter requesting alumni participation was emailed to graduate program alumni, and the link to the alumni survey was included in this letter. The initial email requesting alumni survey participation was sent from the Office of Institutional Research and Effectiveness on September 21, 2015. The survey was closed on October 2, 2015. Economic conditions were accounted for by obtaining four measures of economic health: GDP growth, inflation, labor force participation, and unemployment. This information was gathered from the U.S. Inflation Calculator (inflation), the World Bank (GDP growth), and the U.S. Bureau of Lab or Statistics (unemployment and labor force participation). This data was gathered for all years for which respondents reported graduating (1990-2015). For each student, average values for these variables were calculated based on the range of years between the student s graduation and the present. Office of Institutional Research & Effectiveness 4

Results Basic respondent information Table 1 displays the number of alumni who were sent the invitation to participate in the survey. The response percentage is calculated based on the number of respondents out of the opened and unopened invitations. Table 1 Number of Respondents for 2015 Total invitations sent 1,146 Opened 369 Unopened 596 Bounced 144 Opted out 37 Number of respondents 126 Response percentage 10.99% Respondents were not required to answer demographic items, resulting in some missing data for demographic questions. Of those who provided demographic information, 77.8% were male, and 22.2% were female. Respondents ranged in age from 27 to 80 years old (M = 49.78, SD = 10.35). The majority of respondents were white (86.1%), while 6.1% were Hispanic, 1.7% were Asian, 0.9% were Black/ African American, 0.9% were American Indian/ Alaska Native, and 4.3% indicated other. Of those who indicated other, the majority wrote in that they preferred not to disclose their race. Nearly all (97.4%) of the respondents currently lived in the U.S. Thirty-seven states were represented. The highest percentage (8.7%) of respondents currently lived in New York, followed by California (6.3%), Illinois (5.6%) and Ohio (5.6%). Of those who did not, two lived in Germany and one lived in the Dominican Republic. Most of the respondents were married (82.8%), while 6.9% were divorced, 7.8% were single, 1.7% were living with a partner, and 0.9% were widowed. The number of individuals living in the respondent s household, including the respondent, ranged from 1 to 8, with a mean of 3.15 (SD = 1.62). Respondents graduated between 1990 and 2015. As such, the number of years since respondents had earned their graduate degree from the College ranged from 0 to 25, with a mean of 7.08 years (SD = 6.52). Most respondents (81.3%) were personal financial planning (PFP) majors, while 6.5% were financial analysis majors, 7.3% were finance majors, and 4.9% reported other for their major. The majority of respondents who chose other had earned degrees in the 1990s when only a master of science was offered. Office of Institutional Research & Effectiveness 5

Influences on decision to obtain graduate degree from the College Figure 1. Primary reason for earning graduate degree 6 5 49.2% 4 36.5% 3 2 1 4.8% 9.5% Potential earnings increase Personal development Career development Other N = 126 Figure 2. Primary reason for obtaining degree from the College 5 45.0% 4 35.0% 3 25.0% 2 15.0% 1 5.0% 17.5% 0.8% 23.0% 7.9% 43.7% 7.1% N = 126 Office of Institutional Research & Effectiveness 6

Pre- and post-degree work information Figure 3. Employment situation 9 8 81.7% 80.5% 7 6 5 4 3 2 1 2.4% 2.4% 15.9% 10.6% 2.4% 0.8% 3.3% Before degree After degree N = 126 pre-degree, N = 123 post-degree Office of Institutional Research & Effectiveness 7

Figure 4. Job level 45.0% 4 35.0% 3 25.0% 2 15.0% 1 5.0% 26.2% 23.6% 5.6% 2.4% 38.1% 26.0% 14.3% 24.4% 6.5% 4.0% 9.8% 7.9% 0.8% 6.5% 4.0% Before degree After degree N = 126 pre-degree, N = 123 post-degree Figure 5. Industry 6 5 49.6% 45.2% 4 3 2 1 12.7% 8.1% 9.5% 9.8% 3.2% 0.8% 1.6% 1.6% 8.7% 6.5% 3.2% 2.4% 16.7% 17.1% 3.3% Before degree After degree N = 126 pre-degree, N = 123 post-degree Office of Institutional Research & Effectiveness 8

Figure 6. Post-graduation work changes 4 35.0% 3 25.0% 24.6% 31.0% 34.9% 2 15.0% 13.5% 14.3% 16.7% 1 7.1% 5.0% Moved into new industry Took job with new org Moved into higher position Income increased Clients increased More Higher job interesting satisfaction work N = 123 Preparation for job Figure 7. Preparation for current job 5 45.0% 42.3% 43.9% 4 35.0% 3 25.0% 2 15.0% 1 6.5% 6.5% 5.0% 0.8% Poor preparation Fair preparation Average preparation Good preparation Excellent preparation N = 123 Office of Institutional Research & Effectiveness 9

Table 2. Personal Financial Planning program learning outcomes Learning outcome Developing, implementing, monitoring, and revising risk management strategies to address client objectives Developing, implementing, monitoring, and revisin g investment planning strategies to address client objectives Developing, implementing, monitoring, and revising income tax planning strategies to address client objectives Developing, implementing, monitoring, and revising pre- and post-retirement planning strategies to address client objectives Developing, implementing, monitoring, and revising estate planning strategies to address client objectives Abbreviation Risk management Investment Income tax Retirement planning Estate planning Figure 8. PFP knowledge and skill development 7 6 5 4 3 2 45.7% 39.4% 35.1% 34.0% 31.9% 59.6% 57.4% 54.3% 53.2% 43.6% 1 8.5% 6.4% 6.4% 4.3% 5.3% 4.3% 4.3% 2.1% 2.1% 2.1% Not at all A little Somewhat Very much Risk management Investment Income tax Retirement planning Estate planning N = 94 Office of Institutional Research & Effectiveness 10

Figure 9. PFP knowledge and skill use 6 5 46.8% 52.1% 53.2% 4 36.2% 39.4% 3 2 1 16.0% 14.9% 16.0% 12.8% 11.7% 8.5% 9.6% 9.6% 7.4% 8.5% 9.6% 8.5% 9.6% 9.6% 6.4% 26.6% 25.5% 22.3% 19.1% 2 Never Rarely Sometimes Often Very often Risk management Investment Income tax Retirement planning Estate planning N = 94 Table 3. Finance program learning outcomes Learning outcome Analyzing the effect of macroeconomic and microeconomic systems in the development, implementation, and monitoring of effective global financial strategies Identifying and analyzing appropriate financial models for use in security and firm valuation, risk and return measurement, and portfolio performance evaluation Applying financial statement analysis, investment tools, and asset valuation to investment management Applying effective capital budgeting techniques and risk management strategies in a corporate finance setting Making financial decisions that balance financial goals and acceptable ethical standards and social norms Abbreviation Economic systems Financial models Investment management Budgeting and risk management Financial decisions Office of Institutional Research & Effectiveness 11

Figure 10. Finance knowledge and skill development 7 6 64.7% 62.5% 58.8% 58.8% 58.8% 5 4 3 2 1 29.4% 23.5% 23.5% 23.5% 17.6% 17.6% 18.8% 18.8% 11.8% 11.8% Not at all A little Somewhat Very much Economic systems Financial models Investment management Budgeting and risk management Financial decisions N = 17 Figure 11. Finance knowledge and skill use 6 52.9% 5 4 35.3% 35.3% 3 23.5% 29.4% 29.4% 29.4% 23.5% 23.5% 29.4% 29.4% 23.5% 2 17.6% 17.6% 1 11.8% 11.8% 11.8% 5.9% 11.8% 5.9% 11.8% 11.8% 11.8% 5.9% Never Rarely Sometimes Often Very often Economic systems Financial models Investment management Budgeting and risk management Financial decisions N = 17 Office of Institutional Research & Effectiveness 12

Figure 12. Graduate Studies goal area development 7 6 5 4 3 45.4% 42.9% 37.8% 31.1% 61.3% 54.6% 50.4% 37.8% 2 1 1.7% 14.3% 5.9% 5.9% 2.5% 2.5% 0.8% 5.0% Not at all A little Somewhat Very much Critical thinking Problem solving Effective communications Lifelong learning N = 119 Figure 13. Recommend graduate programs 8 7 70.7% 6 5 4 3 25.2% 2 1 2.4% 1.6% Definitely not Probably not Yes with serious reservations Yes with some reservations Yes with no reservations N = 123 Income change Respondents were asked to report their income prior to receiving their graduate degree and after receiving their graduate degree. To avoid potential bias in income change due to unemployment, part-time employment, or retirement, only respondents who reported being employed full-time at both time points, did not select family/ caregiving or not applicable for job level at either time point, and did not select not applicable for industry at either time point are included in the following analyses. Income Office of Institutional Research & Effectiveness 13

values of less than $10,000 were recoded as missing data. Prior to earning a graduate degree, mean annual income was $138,866.54 (N = 100, SD = $185,568.78). Given the potential of outliers to affect the mean (note that the minimum pre-degree income reported was $20,000 and the maximum was $1,265,654), the m ed ian is likely a better representation of incom e pre-grad uation. The m ed ian pregraduate degree income reported was $85,000. Respondents reported a current mean annual income of $228,642.78 (N = 98, SD = $256,356.56). Again, given the potential of outliers to affect the mean (the minimum current income reported was $28,000 and the maximum was $1,600,000), the median is likely a better representation of current income. The median current income reported was $150,000. Pregraduation income was subtracted from current income for respondents who reported both values. The mean income change was $90,238.14 (N = 98, SD = $148,792.33). Given the large range of values (the minimum income change was -$50,000 and the maximum income change was $860,000), the median is likely a better indicator of income change. The median change was $42,000. There was a moderate positive correlation between income change and years since graduation (r(96) =.38, p <.001), indicating that less recent alumni generally reported more income change than more recent alumni. This is not surprising, as less recent alumni have spent more time in their position and/ or t heir industry and thus are more likely to have received raises, promotions, and increased numbers of clients. Mean comparisons A repeated measures t-test was conducted to determine whether current income differed significantly from pre-graduate degree income. Pre-graduate degree income (M = $139,831.48, SD = $188,167.93) was significantly lower than current income (M = $230,371.05, SD = $257,113.76), t(96) = - 5.96, p <.001, d = -0.40. Note that these values differ slightly from the values in the previous section due to the fact that some individuals reported pre-degree income but not current income and were thus excluded from the present analysis. A one-way ANOVA was conducted to examine whether income change (pre-graduate degree to current income) varied significantly across majors. There was no significant difference in income change between majors, F(3,94) = 0.40, p =.75, η 2 = 0.01. Note that the large disparities in group sizes (number of respondents for each major) may have impacted the resu lts of this analysis. To further investigate potential income change differences across majors, an independent samples t-test was conducted to compare the income change of personal financial planning majors to the income change of all other majors. There w as no significant difference in income change between personal financial planning majors and other majors, t(96) = -0.72, p =.47, d = 0.22. These results indicate that income change did not vary substantially across majors. A one-way ANOVA was conducted to determine if job level change was associated with differences in income change. Note that a repeated measures factorial ANOVA could not be conducted, as this would have resulted in several cells with only one respondent. Job level changes were coded for each respondent as went up one level, went up two or more levels, at the same level, self-employed to other level, changed to self-employed, and lost one level. There were no significant differences in income change across job level change groups, F(5,92) = 1.97, p =.09, η 2 = 0.10. As there were large differences in group sizes, however, these results must be interpreted cautiously. An independent samples t-test was conducted to examine differences in income change between men and women. Men s incom e change (M = $93,335.75, SD = $145,589.81) was significantly larger than women s income change (M = $33,038.18, SD = $39,700.16), t(90.30) = 3.13, p =.002, d = 0.57. These results indicate than men experienced a greater degree of income change than did w omen after earning a graduate degree. Regression analyses to predict income change Two hierarchical regression analyses were conducted to examine predictors of income change. The first analysis included only personal financial planning majors, and the second analysis included Office of Institutional Research & Effectiveness 14

all majors. Pre-graduate degree industry and current industry were recoded into two sets of two dummy variables: financial planning industry versus other financial industry, and financial planning industry versus nonfinancial industry. Current job level was recoded into five dummy variables: selfem ployed versus entry level, self-em ployed versus m id -level, self-em ployed versus senior level, selfemployed versus executive level, and self-employed versus chief executive level. To conserve space, regression coefficients are reported only for the final models. The income change of personal financial planning students was examined using a six-block hierarchical regression analysis. The first block included years since degree earned, and the second block added average unemployment and average GDP growth. Pre-degree industry was added in the third block, and current industry was added in the fourth block. Current job level was added in the fifth block, and graduate GPA, personal financial planning knowledge and skills development composite scores and knowledge and skills frequency of use composite scores were added in the final block. R 2 values can be found in Table 4. Only the addition of current job level caused a significant increase in R 2. Regression coefficients (Table 5) indicated that the only significant predictor of income change was the comparison of self-employed respondents to respondents at the chief executive level. These results suggest that income change was not predicted by economic or educational variables. Table 4. Personal financial planning regression model summary Model R 2 ΔR 2 F change df1 df2 p 1.05-3.91 1 75.05 2.08.03 1.09 2 73.34 3.08.00.15 2 71.86 4.08.00.03 2 69.97 5.30.22 3.97 5 64 <.01 6.33.03 1.05 3 61.38 N = 92 Office of Institutional Research & Effectiveness 15

Table 5. Personal financial planning regression model coefficients Variable b SE β p Years since degree earned 4,104.74 3,409.73.16.23 Average unemployment 14,856.78 23,756.58.08.53 Average GDP growth -27,501.32 43,986.67 -.08.53 Previous industry: Financial planning vs. other financial -36,201.61 45,856.05 -.12.43 Previous industry: Financial planning vs. nonfinancial -25,870.13 73,160.59 -.07.73 Current industry: Financial planning vs. other financial 27,153.13 53,040.95.08.61 Current industry: Financial planning vs. nonfinancial 32,651.75 80,586.72.09.69 Current employment: self-employed vs. entry level -23,337.09 115,355.37 -.03.84 Current employment: self-employed vs. mid-level -62,997.06 43,652.98 -.21.15 Current employment: self-employed vs. senior level -24,575.42 43,954.51 -.08.58 Current employment: self-employed vs. executive level 9,928.95 77,446.09.02.90 Current employment: self-employment vs. chief executive level 160,287.277 58,705.80.35.01 Graduate GPA -84,551.16 61,678.23 -.16.18 PFP Knowledge and skills growth composite -28,459.13 30,215.77 -.12.35 PFP Knowledge and skills frequency of use composite 21,338.03 19,037.54.17.27 The income change of all majors was also examined using a six-block hierarchical regression analysis. The first block included years since degree earned, and the second block added average unemployment and average GDP growth. Pre-degree industry was added in the third block, and current industry was added in the fourth block. Current job level was added in the fifth block, and graduate GPA and the graduate studies learning outcome growth composite were added in the final block. R 2 values can be found in Table 6. Only the addition of current job level caused a significant increase in R 2. As can be seen in Table 7, the only significant predictor of income change was again the dummy variable comparing self-employed respondents to respondents at the chief executive level. As with the personal financial planning graduates, income change was not predicted by economic or educational variables. Table 6. All majors regression model summary Model R 2 ΔR 2 F change df1 df2 p 1.09-9.19 1 93 <.01 2.10.01 0.62 2 91.54 3.11.01 0.26 2 89.77 4.11.00 0.00 2 87 1.0 5.29.18 4.02 5 82 <.01 6.29.00 0.16 2 80.85 N = 109 Office of Institutional Research & Effectiveness 16

Table 7. All majors regression model coefficients Variable b SE β p Years since degree earned 4,096.74 2,748.50.18.14 Average unemployment 5,968.17 18,895.69.03.75 Average GDP growth -2,646.31 37,065.56 -.01.94 Previous industry: Financial planning vs. other financial -32,325.22 40,277.96 -.12.43 Previous industry: Financial planning vs. nonfinancial -3,002.55 64,971.23 -.01.96 Current industry: Financial planning vs. other financial 22,153.45 43,784.67.08.61 Current industry: Financial planning vs. nonfinancial -22,362.13 61,962.61 -.06.72 Current employment: self-employed vs. entry level -59,350.10 102,151.47 -.06.56 Current employment: self-employed vs. mid-level -57,581.31 38,660.17 -.20.14 Current employment: self-employed vs. senior level -12,459.06 38,570.92 -.04.75 Current employment: self-employed vs. executive level 75,331.88 59,686.07.15.21 Current employment: self-employment vs. chief executive level 142,521.01 49,354.88.32.01 Graduate GPA -27,770.00 51,032.66 -.06.59 Graduate Studies outcomes growth composite -3,213.61 23,576.29 -.01.89 Non-income outcomes Non-parametric (Kendall s tau) correlational analyses were conducted to investigate predictors of two non-income related outcome variables: how well respondents perceived their graduate program had prepared them for their current job, and whether they wou ld recommend the graduate programs at the College to a colleague or friend. Results for personal financial planning majors can be found in Table 8. There is a strong association between alumni s judgments of their preparation for their jobs and how frequently they use the knowledge and skills they learned in their graduate program in their work. Whether the alumni would recommend the College s graduate programs to friends or colleagues was associated with the knowledge and skills that they learned, the freq uency of knowledge and skill use and their judgments regarding their growth in critical thinking and effective communications. Office of Institutional Research & Effectiveness 17

Table 8. Non-parametric correlations between outcomes and other variables for PFP majors Variable Preparation Recommend Graduate GPA.18*.16 Risk management knowledge.31**.27** Investment knowledge.22*.18 Income tax knowledge.23*.26* Retirement planning knowledge.31**.40** Estate planning knowledge.23*.27** Risk management use.37**.31** Investment use.45**.27** Income tax use.43**.28** Retirement planning use.42**.31** Estate planning use.39**.22* Critical thinking.40**.34** Problem solving.29**.20 Effective communications.28**.35** Lifelong learning skills.41**.30** N = 83. *p<.05, **p<.01 For finance and financial analysis majors (Table 9), respondents judgments of their preparation for their jobs was strongly associated with the knowledge and skills that they learned and their growth in critical thinking, problem -solving, and lifelong learning skills. Whether they would recommend the College s graduate programs to friends or colleagues was associated with knowledge and skill development and critical thinking growth. Office of Institutional Research & Effectiveness 18

Table 9. Non-parametric correlations between outcomes and other variables for Finance majors Variable Preparation Recommend Graduate GPA.08.02 Economic systems knowledge.32.51 Financial models knowledge.38.60* Investment management knowledge.45.30 Budgeting and risk management knowledge.45.30 Financial decisions knowledge.24.39 Economic systems use.29.02 Financial models use.31.02 Investment management use -.04 -.03 Budgeting and risk management use -.03 -.07 Financial decisions use.40.33 Critical thinking.50.52 Problem solving.53*.34 Effective communications.38.06 Lifelong learning skills.53*.34 N = 15. *p<.05, **p<.01 Multinomial logistic regression was used to examine predictors of respondents judgments of how well their graduate work enhanced their ability to perform their current job. To maximize sample size, only personal financial planning graduates were included in the analysis. Due to the small number of respondents who chose fair and average preparation, these scores were collapsed into a single category. Predictors included the personal financial planning knowledge and skills growth composite score, the personal financial planning knowledge and skill frequency of use composite score, the graduate studies outcomes growth composite score, and income change. The model accounted for approximately 41% of the variance in respondents ratings of preparation (McFadden R 2 = 0.407, N = 77). Neither the personal financial planning knowledge and skills growth composite (χ 2 (2) = 2.86, p =.24) nor income change (χ 2 (2) =4.88, p =.09) were significant predictors of preparation judgments. Both the personal financial planning knowledge and skill frequency of use composite (χ 2 (2) =32.55, p <.001) and the graduate studies outcomes growth composite (χ 2 (2) =19.44, p <.001) were significant predictors of preparation judgments. The personal financial planning knowledge and skill frequency of use composite differentiated between those who indicated fair or average preparation and those who indicated excellent preparation (b = -6.11, SE = 2.96, p =.04, exp(b) = 0.002), as did the graduate studies outcomes growth composite (b = -9.37, SE = 4.52, p =.04, exp(b) <.001). Note that the small number of respondents who chose the fair and average categories (8) likely accounted for the large standard errors in these estimates. The negative b values and the exp(b) values less than one indicate that those with high scores on the knowledge and skill frequency of use composite and the graduate studies outcomes growth composite were less likely to indicate fair or average preparation (as opposed to excellent). The personal financial planning knowledge and skill frequency of use composite differentiated between those who indicated good preparation and those who indicated excellent preparation (b = -0.99, SE = Office of Institutional Research & Effectiveness 19

0.36, p =.006, exp(b) = 0.37), as did the graduate studies outcomes growth composite (b = -2.17, SE = 0.75, p =.004, exp(b) = 0.11). Again, the negative b values and the exp(b) values less than one indicate that those with high scores on the knowledge and skill frequency of use composite and the graduate studies outcomes growth composite were less likely to indicate good preparation (as opposed to excellent). Binary logistic regression was used to examine predictors of respondents willingness to recommend the graduate programs at the College to friends and colleagues. To maximize sample size, only personal financial planning graduates were included in the analysis. Only two respondents chose probably not, so these individuals were removed from analysis. Yes, with serious reservations and yes, with some reservations were collapsed into a single category due to small numbers of responses in each category. The analysis thus compared those who chose either yes, with serious reservations or yes, with some reservations to those who chose yes, with no reservations. Predictors included the personal financial planning knowledge and skills growth composite score, the personal financial planning knowledge and skill frequency of use composite score, the graduate studies outcomes growth composite score, and income change. The model accounted for approximately 24% of the variance in respondents ratings of preparation (Cox and Snell R 2 = 0.24, N = 75) and predicted responses with 84% accuracy. Both the personal financial planning knowledge and skill frequency of use composite score (b = 0.94, SE = 0.31, p =.002, exp(b) = 2.56) and the graduate studies outcomes growth composite score (b = 1.95, SE = 0.71, p =.006, exp(b) = 7.00) were significant predictors of respondents degree of recommendation. The positive b values and the exp(b) values over 1 indicate that those with high scores on the frequency of use and graduate studies outcome growth composites were more likely to recommend without reservations the graduate programs at the College. Office of Institutional Research & Effectiveness 20

References Borden, V.M.H. (2005). Using alumni research to align program improvement with institutional accountability. New Directions for Institutional Research, 126, 61-72. doi: 10.1002/ ir.148 Cabrera, A.F., Weerts, D.J., & Zulick, B.J. (2005). Making an impact with alumni surveys. New Directions for Institutional Research, 126, 5-17. doi: 10.1002/ ir.144 Certified Financial Planner Board (2014). Making more room for women in the financial planning profession. Certified Financial Planner Board of Stand ard s. Retrieved from http:/ / w w w.cfp.net/ d ocs/ about-cfpboard/ cfp-board_win_web.pdf Higher Learning Commission. (2015). The criteria for accreditation and core components. Retrieved from https:/ / w w w.hlcom m ission.org/ Criteria-Eligibility-and-Cand id acy/ criteria-and-corecomponents.html Oreopoulos, P., von Wachter, T., & Heisz, A. (2006). The short- and long-term effects of graduating in a recession: Hysteresis and heterogeneity in the market for college graduates. National Bureau of Economic Research Working Paper. Retrieved from http:/ / www.nber.org/ papers/ w12159.pdf. Office of Institutional Research & Effectiveness 21