Ohio Psychological Association Database Report

Transcription

1 1 Roger Blashfield PhD May 31 st 2011 Ohio Psychological Association Database Report I retired from Auburn University in the summer of 2010 and moved to Hilliard, Ohio. In order to keep some contact with my professional past, I joined a group for retired psychologists (PROs) at the Ohio Psychological Association (OPA). When the PRO group met, one discussion issue was the apparent growth of OPA members who are of retirement age. Our group wondered whether this increase in the number of retirement age members was sizeable and, if so, what actions could our group take to help meet the needs of OPA members who were moving into retirement. We felt that a helpful action would be to look at the OPA membership database and analyze information about the older members of this organization. I volunteered to take on this task because I had experience analyzing other databases. Like many administrative databases, the OPA database has a number of uses and probably has had somewhat different goals over time. The current database was established in 1998 with the intent of providing quick and easy access for the staff in dealing with OPA members, OPA-MCE registrants, OPA-MCE providers and workshop attendees. All entries prior to that year were back-entered in For the last 13 years, the database has been kept by the staff of the OPA and has gradually built in size. The main uses for the database are (1) providing information to the membership committee (and other committees) of OPA for decisions about how to recruit, maintain and meet the needs of the membership and (2) having a mailing list resource that can be used by OPA staff to keep the membership aware of continuing education programs that are available as well as to provide notification of other issues of general interest to the membership. In April 1998 when the data base was established, there were 4847 individuals whose records were back entered into the database. After mid-1998, there have been an additional 7564 individuals for whom new entries were created. The current (current = January 2011) database consists of 7470 entries. The highest CIVIC ID is in the database. Since unique identifiers are associated with entry, starting with CIVIC ID =1, and are bumped up with each new entry, this means that there had been entries in the database, of which 4941 have been dropped for various reasons (e.g., death, individual has requested to be dropped from the database, individual has moved to another state, etc.). For the 7470 entries, the distribution of membership categories were A Member 1176 B Old category (academics) 58 C Early career member (5-7 yrs) 117 D Early career member (5 yrs or less) 459 E Old category (special) 57 F Affiliate member 91 G Old category (Masters) 49 H Old category (out of state) 75 L Life-time member 111

2 2 M Emeritus 191 X Student who only receives s 701 Y Old category (affiliates) 17 Z Dues paying students 419 Non-member 3561 Sustaining member 30 President s Club member 9 Blank (no entry in membership status) 348 In order to focus the subsequent analyses only on the core membership of the Ohio Psychological Association, I created a second version of the database in which all students (membership categories X and Z), all individuals listed as Non-members, and everyone for whom their membership status was blank were eliminated. [Note: 120 Non-members were added back to the database, because these individuals were past members who dropped out of OPA and their status had been switched to Non-members at that time. These 120 individuals are listed as Rcode under membership status. Thus, Non-member, as a categorical term, refers to anyone in the database who has NEVER been a member of the organization, e.g., an Ohio political figure who receives OPA mailings but is not a psychologist.] In addition, there were a few duplicate entries as well as some entries in which there appeared to be errors when the data were originally entered. These latter entries were deleted as well. Overall, the effect of these deletions was major. The end result of this action was that there were 2430 individuals left in the database (i.e., about 1/3 rd of the entries in the current (Jan 2011) database and about 1/5 th of all individuals who have been assigned a CIVIC ID at some point in the history of the OPA). [Comment: The deletion of students from these analyses does not mean that students are unimportant to the OPA. Encouraging students to belong and to be active in OPA is a healthy aspect of the organization. Given the focus of the subsequent analyses, however, students represent a qualitatively different membership grouping; hence, I decided to not merge them with the remainder of the membership when performing these statistical analyses. Future analyses can be conducted on the student data alone or with the student data recombined with the general membership information.] In the final database used for the analyses reported below, the distribution of membership categories was as follows using the 2430 entries: A Member 1132 B Old category (academics) 52 C Early career member (5-7 yrs) 114 D Early career member (5 yrs or less) 446 E Old category (special) 49 F Affiliate member 82 G Old category (Masters) 42 H Old category (out of state) 73 L Life-time member 102

3 3 M Emeritus 171 President s Club member 8 Rcode 120 Sustaining member 30 Y Old category (affiliates) 10 For reasons that will become more obvious later in the report, I decided to code the OPA membership by cohorts. Each cohort represents the decade (e.g., 2000 to 2009) in which that individual joined OPA. In making cohort assignments, I used both the listed OPA Join Date in the database as well as the CIVIC ID (membership number). Since the CIVIC ID could give an indication of the date that the individual was added to the database, I generally relied on that entry to assign cohort. However, there were 260 individuals for which the CIVIC ID and the OPA JOIN DATE suggested different cohorts. Many of these individuals had originally been added to the database in order to receive CEU mailings. Later, these psychologists decided to join the organization. Hence, I used the OPA JOIN DATE to determine the cohort for those individuals. COHORTS 1969 and earlier to to to to to Jan A. Older Psychologists in OPA Age information was available for 1862 (76%) of the OPA database. For these nearly 2000 individuals, the median age was 59 years old (i.e., half of the OPA are older than 59, the other half are younger). The IQR (inter-quartile range) was 48 to 66. The IQR statistic tells us the age range that covers the middle 50% of all OPA psychologists for whom age data were reported. Thus, one-quarter of OPA psychologists were 47 and younger while another quarter were 67 years old or older. The complete range of ages for OPA members in January 2011 was 29 (the youngest member) to 96 (clearly the oldest member). According to current Social Security guidelines, full retirement age currently is set at 66 years old. This means that anyone born in 1945 or earlier can retire with full Social Security benefits. Currently 26.6% of the membership of OPA are at retirement age or older (i.e., are age 66 or above). In terms of gender, the membership of OPA contains more women (56.4%) than men. [Note: OPA does not request information about gender on its membership form. The gender of the members was estimated using the first name of the members. When the gender association of a name was uncertain (e.g., the first name of Leslie ), gender was left unassigned. Using this approach, 94% of the membership could be assigned to a gender category.

4 4 Not surprisingly, age and gender were related demographic statistics. The mean age of the men was 61 years old (SD = 11.7) and the mean age of the women was 56 (SD = 13.5). These means were statistically different (t = 9.815, p <.001). Ethnicity was determined on the OPA membership form by a voluntary designation given by the OPA member when that individual joined the organization. Just about 2/3rds of the members did list ethnicity (62%). Of the individuals who did list ethnicity, whites (92.1%) were the clear majority. The members listing other ethnicities primarily were African-American, Appalachian, or Hispanic. When the ethnicity of the retirement-age members was compared to the ethnicity for the entire membership, a slightly greater percentage was white. Of the 526 members who were age 66 or older, 384 listed ethnicity. Within this group, 93.5% were white. WORK SETTING Work Setting All members Retirement Age Academic 14.60% 9.00% Business 2.60% 2.60% Hospital 9.70% 2.60% Military 0.60% None Private Practice 54.10% 61.40% Public Sector 13.90% 8.50% Retired 4.60% 15.90% N Note: N = the number of individuals for which data are available As shown in the table above, there do appear to be interesting differences in the work settings of OPA members who have reached retirement age. The relatively few OPA members in the military have all left that setting. Most individuals doing hospital work and many in both public sector and academic careers have left those areas. Private practice has increased. Interestingly, however, of the OPA psychologists who have reached retirement age only one in six has actually retired. Differences also appeared in the distribution of type of advanced degree between individuals of retirement age vs. the general membership.

5 5 DEGREE Degree All members Retirement Age Bachelor's 0.80% None Master's 5.60% 14.80% EdD 3.50% 6.90% PhD 74.30% 72.20% PsyD 16.40% 6.10% N Master s degrees and EdD s were more common among members of retirement age. OPA also requests that members voluntarily note their annual earnings from their work as psychologists as ranges. The majority of the membership does not respond to this request. However, from those who did respond, it was possible to estimate their average annual earnings. For the 985 individuals in the general membership who did list income (40.5%), the median annual earnings were $70,000 (nearest $10k) with an IQR of $30,000 to $90,000 and a range of $10,000 to $180,000 (top option available). Again, there was a fairly distinct difference between the retirement age individuals and the general membership on the income variable. For the 222 retirement age individuals who listed earnings (44.8%), the median annual earnings were $30,000 with an IQR of $10,000 to $70,000 and the range was $10,000 to $180,000. Finally, the current address of OPA members was used to try to determine where they lived. LOCATION Location All members Retirement Age Cincinnati 14.10% 15.20% Cleveland 15.60% 17.40% Columbus 17.00% 14.50% Akron/Canton 7.20% 7.50% Dayton 5.40% 5.30% Toledo 3.90% 2.90% Youngstown 0.80% 1.30% Smaller/Rural 23.40% 22.20% Out of State 12.60% 13.70% N

6 6 Not surprisingly, nearly half of all OPA members live in or near one of the three major cities in Ohio. In this regard, retirement age members do not appear to differ in any major way from the general membership. Interestingly, slightly less than one-quarter of the OPA membership live in the smaller towns or in the relatively rural areas of Ohio. My Comments: I was surprised when I first computed these statistics. Even though I had been told that many OPA members were at retirement age, I had not anticipated the degree to which this claim was correct. One in every four OPA members are of retirement age or older! However, most of these individuals are not retired. Instead, many of them appear to be continuing some level of work, although probably at a reduced work load (since the annual earnings of these individuals were decidedly lower than the reported earnings by the general membership). In general, the age of the entire OPA membership was distinctly older than I had expected. A median age of 59 was striking. Assuming that most psychologists obtain their doctoral degrees while in the late 20s, this means that about half of the OPA membership has been working as psychologists for 30 years or more. An important caveat to these results is methodological. An old saying is There are lies, damned lies and statistics. Statistics are used to provide summaries of data in order to make sense of the data. However, interpreting statistical information is often complicated. For example, a major limitation with the OPA database is prevalence of missing data (i.e., variables for which OPA members did not complete an answer). The missing data issue is quite obvious, for example, with the income information. Less than half of the membership listed income. How the missing data on this variable influences the overall estimate of income is unknown. For example, it could be that individuals with low incomes do not list this information out of embarrassment. If this was so, then the income estimates from the reported data are too high. However, it is also possible that individuals with relatively high incomes do not list this information because of the fear that they will be singled out by OPA to either pay higher dues or to donate money to various OPA projects. Finally, my choice of descriptive statistics to use when reporting these data was deliberate. Variables such as age and income generally are skewed (i.e., plots of their distributions do not look like normal curves). The use of the median as the estimate of central tendency and the IQR (inter-quartile range) as a measure of variability is more descriptive with skewed data rather than using means and standard deviations. If you as a reader do not understand how to interpret these descriptive statistics, please ask. For most variables, I will attempt to consistently report a median, the IQR, the range (highest vs. lowest values), and the N (number of data points on which these statistics were estimated). B. Cohorts based upon the date that an individual joined OPA As mentioned earlier, the current OPA membership was subdivided into cohorts based upon the year in which the member joined OPA. Cohort analysis can be a useful way to look differences in a dataset as a function of time. Since a surprisingly large percentage of current OPA members are of retirement age, I thought that might be useful to see if there were any differences between the six cohorts.

7 7 Analyses of Demographic Information organized by Cohorts License Cohort N Age Median Age IQR Age Range Age N Gender % Male 1969 and before to to % 1970 to to to % 1980 to to to % 1990 to to to % 2000 to to to % 2010 to to to % Now the results are becoming interesting. Consider the table above. What I did was take all current members of OPA and divide those individuals into cohorts based upon the decade in which these members joined OPA. I ended up with six cohorts, four of which represent a decade. The first (everyone who joined in 1969 or earlier) and the last (those joining 2010 and 2011) do not represent actual decades of time. The second column on the table N age refers to the number of OPA members in each cohort for which age data existed remember that, on average, about 30% of OPA members do not list their age. Key to reading the table: N age = number of OPA members within a cohort for which age data existed Median age = Median age (50% are older, 50% are younger) IQR age = Inter-quartile range for age (range including the middle 50% of the age data) Range age = Youngest to oldest individuals within a cohort N Gender = number for which gender could be estimated by first names % Male = Percentage of males within the cohort Since the middle four cohorts represent decades, the expectation was that the median age of the OPA members per cohort would decrease by 10 years per cohort. This expectation was true for the first three cohorts. But the cohort was an anomaly -- the median age for the 1990s cohort was only 3 years different than the median age for the cohort. Notice also the 14 year jump in median ages between the cohort and the cohort. The other interesting phenomena shown in the first table is feminization of psychology. The older the cohort, the smaller the percentage of females. By the time we reach 2000 to 2009 cohort, nearly 2 of every 3 OPA members (67%) who joined in the most recent decade are women. This trend does not appear to be changing. According to a 2011 survey of doctoral students in psychology who are seeking clinical internships, 79% of all current trainees are women (Keilin, 2011).

8 8 Distribution of Degrees per Cohort Cohort N EdD PhD PsyD Masters Bachelors Before % 77.10% 2.90% 17.10% None 1970 to % 74.40% 1.50% 21.90% None 1980 to % 79.50% 12.70% 3.20% None 1990 to % 75.30% 14.80% 4.40% None 2000 to % 69.10% 25.40% 3.10% 0.30% 2010 to % 75.90% 20.70% None None The above table shows the distribution of degrees across the cohorts. The percentage of OPA members per cohort who had earned a PhD stayed almost constant at 3 of 4 individuals. The relative percentage of OPA members with PsyD degrees, not surprisingly, has increased steadily over time. Many of the members from the 1960s and 1970s had terminal Master s degrees, but this trend has shown a marked decline since that time (Note: this trend can be attributed to the change in the licensing law in Ohio in 1972 to require a doctoral level degree for psychologists. Many Master s level individuals were grandfathered in at the time of the change in the law). The next table shows changes in the earnings and the average dues paid by members from the different cohorts. Earning and Dues paid by OPA Cohort members Cohort N Earning Mean Earn SD Earn N Dues Mean Dues SD Dues Before to to to to to As noted earlier, the amount of missing data for these variables is very large. Drawing any major conclusions about earnings or dues is difficult. Even more striking is how similar the mean estimates are for the earnings data from cohorts from 1980 to The variation in these mean values are quite small ($68,000 plus or minus $3000), especially relative to the size of the standard deviations for the data. Despite the small differences, the 1990s cohort is somewhat different than the other cohorts. The mean earnings for the 1980s, 2000s and 2010s cohorts are almost exactly $ The 1990s cohort earned slightly less. Nonetheless, the dues paid by the 1990s cohort were higher than the dues paid by any other cohort.

9 9 Because the age changes across cohorts did follow the expected pattern (i.e., the median age would decrease about 10 years per cohort), I wondered whether there was any difference among the cohorts in terms of how many individuals who originally joined OPA still were members of the organization (i.e., retention rates). To compute this statistic, the number per cohort who represented active members was the numerator and the divisor was the number per cohort who had joined OPA at any point in time. For the first three cohorts (i.e., the 1980s and earlier), these estimates of retention rates are unreliable because the data for these cohorts were back entered in the late 1990s and it is unlikely that any entries were made for individuals who were not active members of OPA at that point in time. Retention Rates per Cohort 1960s 88.5% 1970s 66.1% 1980s 65.1% 1990s 38.0% 2000s 57.6% 2010s 90.1% In the retention rates tables, the 1990s cohort is strikingly different than the other groups. In the cohort, approximately 3 of every 5 individuals who joined during that time have dropped out of OPA. For all other cohorts, the majority of individuals per cohort still remain in OPA. Comments: Two themes emerged from the cohort data. The first was the retention rate data. In this regard, the most reliable and meaningful data are from the 1990s and 2000s cohorts. For these two cohorts, 53% of the individuals who have joined OPA have subsequently left the organization. I have no idea how this compares to statistics for other professional organizations, but this dropout rate seems high to me. Understanding the factors associated with the dropout/retention issue does seem like an important future consideration. The other theme that appears is how different the 1990s cohort is from other subgroups of the OPA membership. During this time period, there was apparently an increase of non-traditional students who had after being in other fields returned to school, earned their doctorates and began their professional careers relatively late in their life. As a result, these individuals were generally older. C. Cohorts based on Year of Licensure Because determining OPA membership cohort status was not always certain, I decided to try a somewhat redundant analysis by organizing the data in terms of year that the psychologist was licensed. The same decades were used when performing these analyses. The year of licensure was available for 2298 of the 2430 individuals in the database (94.6%).

10 10 Licensure Year Cohorts License Cohort N Age Median Age IQR Age Range Age N Gender % Female Retention% 1970 to to to % 62.50% 1980 to to to % 55.20% 1990 to to to % 45.70% 2000 to to to % 64.50% 2010 to to to % 95.20% There are two striking features in this table. First, the distribution of current/past OPA members in the database by year of licensure is surprisingly nearly equally distributed (see N Age and N Gender above) with about 500 to 600 licensed psychologists per decade joining OPA. Second, the 1990s cohort, as noted earlier, remains the unusual group. These psychologists generally were older and they were more likely to drop out of the organization. AGE OF LICENSURE License Cohort N Median IQR Range Most OPA members reported being licensed during their 30s. Only about one-quarter of the members were younger than that when obtaining their license and another quarter were older than 42 at the time of licensure. The distinctive cohort, of course, is the group who were licensed in the 1990s. These individuals were generally older individuals, probably reflecting a trend of second career individuals entering the field as well as women who had delayed completing their degrees in order raise a family. D. Retention Analysis -- Current vs. Past Members The final focus of the analysis was to examine what might be associated with drop-out/retention issues for the OPA membership. To do this, I split the data set into two parts: current OPA members (as of January 2011) and past members. There were 1336 current members (55.0%) and 1094 past members (45.0%).

11 11 Membership Category Member Category Description Current Past A Member B Old category (academics) 52 C Early career member (5 7 yrs) D Early career member (5 yrs or less) E Old category (special) 49 F Affiliate member G Old category (Masters) 42 H Old category (out of state) 73 L Life time member 102 M Emeritus President's 7 1 R Code 120 Sustain 23 Total number The table above shows the membership categories for both current and past members. The old membership categories (B, E, G and H) only appear in the listings for past members. Lifetime and sustaining members only occur in the current member column. The most interesting row is Category F (affiliate members). Many of these have left OPA. At the current time, there are only 17 individuals with this status in the active membership. Note: When interpreting this table, an important caveat is that the meaning of some membership categories has changed over time. For example, Category C currently refers to early career members (5-7 years of licensure), but at other times it meant early career (2-5 years) and member licensed prior to 1979 with no doctorate degree. The next table returns the basic demographic statistics. Given the apparent graying of the OPA membership, I was particularly interested in seeing if there was an age difference between current and past members. Demographic Statistics for Current vs. Past Members Demographic Variable Current Past Age N Median IQR 49 to to 65 Range 30 to to 93 Gender N % Female 54.60% 58.60% Ethnicity N % Non white 7.90% 7.70%

12 12 Although there is a slight tendency for past members to be younger and female, these differences are so small that I do not think that the retention issues are associated with any demographic variable. The reader should also note that the representation of non-white individuals is almost exactly the same in current and past members. Thus, OPA does not seem to be losing its minority members at faster rate than white members. Because the cohort analysis had been useful, I also compared the current and past members in their year of licensure. Consistent with what has been discussed earlier, it is the 1990s cohort that is the anomaly in this analysis. Year of Licensure Cohorts License Cohort Current Past 1970s 25.40% 20.30% 1980s 25.30% 27.30% 1990s 21.60% 34.10% 2000s 24.70% 18.10% 2010s 3.00% 0.20% At this point, there are five remaining variables that I decided to analyze in which I compared current vs. past members: (1) the age at which the individual joined OPA, (2) the highest degree obtained by the OPA member, (3) the work setting for the OPA member, (4) the amount of dues paid by the OPA member, and (5) the geographic setting in which the OPA member resided. Interestingly, there were no differences between current and past members on the first two of these variables (i.e., current and past members joined OPA at roughly the same age and had nearly the same distributions of final degrees). For the remaining three variables, there were some interesting differences. Dues Paid by OPA Members Dues per Year Current Past $0 7.60% $ % 8.70% $ % 4.70% $ % 31.80% $ % 7.20% $ % 46.60% $ % 0.80% $ % 0.10% A priori, one might expect that the dues paid by OPA members who left the organization would be higher than those who remained in the organization. Instead, the reverse occurred. The likely reason is that the dues paid by members gradually are raised as the members stay with the

13 13 organization for a longer period of time. These data suggest that when members graduate to a higher dues status, they are relatively likely to consider the cost-benefits of organization membership and to leave OPA at that time point if they were ambivalent about the usefulness of the organization to them. Work Setting Work Setting Current Past Academic 13.70% 16.90% Business 1.90% 4.50% Hospital 9.40% 10.50% Military 0.40% 1.10% Private Practice 55.80% 49.60% Public Sector 14.00% 13.90% Retired 4.90% 3.80% Members who primarily were in private practice were the individuals who were most likely to stay with the organization. In contrast, members in academia and, especially, in the business community, were the most likely to leave. Geographic Location of OPA Member Geographic Setting Current Past Cincinnati 15.90% 11.90% Cleveland 17.60% 13.30% Columbus 18.60% 15.00% Akron/Canton 7.90% 6.40% Dayton 6.00% 4.70% Toledo 3.60% 4.20% Youngstown 0.90% 0.70% Rural 22.30% 24.70% Out of State 7.30% 19.10% The most obvious difference in this table between current vs. past members is with out-of-state individuals. My guess is that many of these individuals have left Ohio, but think that they may return, hence they maintain their membership. But at a later time, when they decide that returning is very unlikely, they drop their membership with OPA. Because out-of-state members to OPA are quite different than members from Ohio, I re-analyzed this table with the non-ohio members deleted. Interestingly, another difference then appeared. For individuals from smaller towns and rural areas, a disproportionate number have left OPA. Among current members who live in Ohio, 24.1% are from rural or small towns. The proportion in rural/small towns jumps to 32.9% (i.e., nearly 1 in every 3 individuals who have left OPA are from small cities/towns) for past members when the non-ohio members were deleted.

14 14 E. Concluding Remarks The preceding analysis has been rather simple in its scope. I focused on basic descriptive statistics and tried to use the fundamentals of exploratory data analysis to attempt to explore this database. When I was a graduate student, I was taught the acronym KISS as rule to follow when doing statistical analyses (Keep It Simple, Stupid). I have tried to adhere to that rule in this overview of the OPA database. Three themes emerged from the analysis: (1) the large percentage of OPA members who are at retirement age or older, (2) the issue of retention, and (3) the unusual characteristics of the 1990 to 1999 cohort of members. I shall comment briefly on all three. (1) Retirement Age Members -- The fact that 1 in every 4 OPA members are of retirement age surprised me. Also interesting to note was that most of these psychologists at retirement age were not retired. My analyses did not explain why this apparently large graying of the OPA membership has occurred. From my point of view, the needed future analyses are to look at membership patterns in other state psychological associations as well as with the national APA to see if the same phenomenon is occurring. For instance, West, Kohout, Pion, et al. (2000) reported a national study of mental health practitioners in the United States. Using 1999 data on American psychologists, they estimated that 12.8% of all psychologists were age 65 or older while 48.7% were women. Even though these data are one decade older, the age/gender demographic differences between this national estimate and this data on Ohio psychologists is notable. I also wonder if there are age differences between all licensed psychologists in Ohio vs. individuals who are members of OPA. The membership of OPA is relatively heavily weighted towards psychologists who are in private practice. This type of work may allow more individuals to continue to be active even past retirement age when compared to individuals who are employed by hospitals, public institutions or some other large employer. (2) Retention -- The fact that almost half of the individuals who joined OPA have left the organization was another surprise. Interestingly, the subsequent analyses did suggest some possibilities for why the retention issue was occurring. From these analyses, the data suggested that individuals who were out-of-state members, who lived in small towns or in rural Ohio, or whose primary work setting was the business community were relatively likely to drop out of the organization. In addition, many of these drop-outs occurred at the time point when these members were having the amount of their dues increase because of the length of time that they had been members. (3) 1990 to 1999 cohort -- The use of cohort analysis was a simplistic way to look for changes over time in the OPA membership. The consistent, but surprising feature in the cohort analysis was how the 1990s cohort of OPA members differed from the other groups. This 1990s cohort attracted attention regardless of whether the cohort was defined by the years that the individual joined OPA or if the cohort was defined in terms of the year that the individual was first licensed as a psychologist. Members of the 1990s cohort were more likely to drop out of OPA, they tended to be older than expected, they

15 15 were the first cohort in which most individuals were women, and they reported somewhat lower earnings than other groups, yet they paid the highest dues of any cohort group. This analysis of the OPA database also leads to some recommendations about future decisions about the collection of data on OPA members and how the database can be managed. Currently, information on the gender of OPA members is not collected. For any type of demographic analysis of the membership, this information is important. Relying on the first name of the individual to infer gender is not a useful longterm decision. Deleting entries from the database, including those individuals who are deceased, should be avoided. A valuable part of this analysis was to look at cohorts in order to track changes in the membership over time. Deleting entries makes this analysis more difficult. Coding changes will be needed, however, so that the families of deceased members, for example, are not sent mailings from OPA. Changing the meaning of codes in the database is problematic (e.g., the changes in the meaning of Membership Category C) for long-term tracking. Given the multiple administrative uses of the database, however, the database must be kept flexible so that it can be easily used by the staff of OPA. Decisions about changes in the structure of the database should be thoughtfully made in order to reflect both current and future uses of this data resource. Currently, there is no good way in the database to track individuals who joined OPA, left, then rejoined. Nor is there any way to follow changes in dues paid over time. Both of these are likely to be important to future analyses of membership retention. One final comment. I have worked as a teacher, as a clinician, as a researcher and as an administrator at different time points in my career. In both my research and administrative work, I have spent considerable time analyzing databases that I did not create. I have learned that the first task when working with a new database is to spend the necessary time to clean up the database because data entry errors occur. This step was one of the important initial actions with the OPA database. However, generally I found this database to be in good shape, and the frequency of data entry errors was relatively small. More importantly, Denise Brenner was very helpful, knowledgeable, useful resource to me when I was working with the database. She answered my questions quickly. She clearly understands this database and has spent considerable effort to maintain this data set so that it would be a useful resource to OPA. I greatly appreciated both her expertise and her helpfulness as I worked on this project. None of these analyses would have been possible without her assistance. References Keilin, G. (2011). Match news: Applicant survey results. ( sent to all APPIC members). West, J., Kohout, J, Pion, GM, et al. (2000). In Autry, J.H. and Arons, G.S. (Editors) (2000). Mental Health, United States, Washington DC: U.S. Government Publication, pp