The American Academy of Clinical Neuropsychology, National Academy of Neuropsychology, and Society for Clinical Neuropsychology (APA Division 40) 2015 TCN Professional Practice and ‘Salary Survey’: Professional Practices, Beliefs, and Incomes of U.S. Neuropsychologists

Abstract

Objective: The current survey updated professional practice and income information pertaining to clinical neuropsychology. Methods: Doctoral-level members of the American Academy of Clinical Neuropsychology, Division 40 (Clinical Neuropsychology) of the American Psychological Association, and the National Academy of Neuropsychology and other neuropsychologists, as well as postdoctoral trainees in the Association of Postdoctoral Programs in Clinical Neuropsychology and at other training sites were invited to participate in a web-based survey in early 2015. The sample of 1777 respondents, of whom 1579 were doctoral-level practitioners and 198 were postdoctoral trainees, was larger than the prior 2010 income and practice survey. Results: The substantial proportional change in gender has continued, with women now a clear majority in the postdoctoral trainee sample as well as in the practitioner sample. Dissimilar from the median age trajectory of American Psychological Association members, the median age of clinical neuropsychologists remains essentially unchanged since 1989, indicating a substantial annual influx of young neuropsychologists. The question of whether the Houston Conference training model has become an important influence in the specialty can now be considered settled in the affirmative among postdoctoral trainees and practitioners. Testing assistant usage remains commonplace, and continues to be more common in institutions. The vast majority of clinical neuropsychologists work full-time and very few are unemployed and seeking employment. The numbers of neuropsychologists planning to retire in the coming 5–10 years do not suggest a “baby boomer” effect or an unexpected bolus of planned retirements in the next 10 years that would be large enough to be worrisome. Average length of time reported for evaluations appears to be increasing across time. The most common factors affecting evaluation length were identified, with the top three being: (1) goal of evaluation, (2) stamina/health of examinee, and (3) age of examinee. Pediatric specialists remain more likely than others to work part-time, more likely to work in institutions, report lower incomes than respondents with a lifespan professional identity, and are far more likely to be women. Incomes continue to vary considerably by years of clinical practice, work setting, amount of forensic practice, state, and region of country. Neurologists are the number one referral source in institutions and in private practice, as well as for pediatric, adult, and lifespan practitioners. Learning disability is no longer among the top five conditions seen by pediatric neuropsychologists; traumatic brain injury and seizure disorder are common reasons for clinical evaluations at all age ranges. There is a continued increase in forensic practice and a clear consensus on the use of validity testing. There is a substantial interest in subspecialization board certification, with the greatest interest evident among postdoctoral trainees. Income satisfaction, job satisfaction, and work–life balance satisfaction are higher for men. Job satisfaction varies across general work setting and across age range of practice. Work–life balance satisfaction is moderately correlated with income satisfaction and job satisfaction. Again in this five-year interval survey, a substantial majority of respondents reported increased incomes, despite experiencing substantial negative practice effects related to changes in the US health care system. Numerous breakdowns related to income and professional activities are provided. Conclusions: Professional practice survey information continues to provide valuable perspectives regarding consistency and change in the activities, beliefs, and incomes of US clinical neuropsychologists.

Keywords:

• Survey
• Salary
• Income
• Professional Practice
• Neuropsychology.

Introduction

Survey information pertaining to clinical practices, beliefs, and incomes can become outdated as a clinical specialty evolves in response to changes in knowledge and demands of the health care system. The present clinical neuropsychology practice survey continues the five-year cycle of information gathering from US clinical neuropsychologists and postdoctoral trainees by again soliciting information regarding salient characteristics of practice, training, research, and incomes. In addition, the 2015 survey solicited new information not included in prior surveys, including work–life balance, obstacles to professional and personal satisfaction, scholarly productivity, broader consideration of bases for income, interest in subspecialization, acceptance and use of response validity testing, and retirement planning. The most recent comparable practice survey had been conducted in 2010 (Sweet, Giuffre Meyer, Nelson, & Moberg, 2011), at which time discrepancies with other survey data collected by the American Psychological Association (APA) from a broad range of psychology generalists and specialists were noted to provide a strong justification for continued surveys within our specialty.

The term ‘salary survey’ in the survey title was originally used by Putnam (1989) and has been retained because of its recognition value to neuropsychology practitioners. To be clear, monies earned related to professional work are more accurately identified as ‘income’ when applied to the entire sample, in that ‘salary’ is only truly accurate for a subset of the sample who are employees of an organization or practice receiving a predetermined annual payment from an employer.

Method

Survey development began in the summer and fall of 2014. To facilitate comparisons, most items from the 2010 AACN/TCN Salary Survey (Sweet et al., 2011) were retained in their original format for the 2015 survey. Some items were deleted because they had become less relevant to the field or in the past had not been found to provide useful information regarding change in beliefs or in practices over time. Other items were modified to maximize information value or to prevent problematic data issues that had been identified during previous surveying. A small number of new items were added to address emerging practice issues. Pilot data were collected from board member volunteers of all three sponsoring organizations in January and early February 2015; in response to feedback, survey structure and content were adjusted in the final version of the survey (see Appendix).

Based on prior successful online survey experience with the 2005 and 2010 surveys, the commercial company PsychData (http://psychdata.com/) was again selected for the web-based survey. Web survey settings were set so that no identifying information was collected from respondents or their computers (e.g., IP address was not collected); therefore, survey respondents were completely anonymous. At the close of data gathering, participant data were downloaded from the PsychData website directly into an SPSS file, a process that precluded data entry errors.

On February 19 2015, the survey was opened online in concert with the sending of invitations via electronic means (e.g., listserv announcements, email blasts). Later in February, postcards were mailed to the integrated US mail address list compiled from separate lists provided by the American Academy of Clinical Neuropsychology (AACN), the International Neuropsychological Society (INS), and the National Academy of Neuropsychology (NAN). When combined, these separate lists provided 5342 unique names and addresses. Additional reminder postcard mailings were sent in March and April with the hope that if electronic communications had not been noticed, the physical postcard arriving via traditional mail delivery would be noticed. Readers should note that the Society for Clinical Neuropsychology (SCN; APA Division 40) members received electronic survey announcements directly from the organization’s central office, which precludes our ability to identify a total of unique individuals contacted via the four professional organizations.

All survey information received by the evening of May 15 was examined for usability. From February 19 through May 15, potential respondents accessed the survey and entered at least some data a total of 1816 times. Of these entries, 8 individuals were deleted as non-doctoral and non-postdoctoral; 4 were deleted as not practicing in the US; 15 were deleted as not practitioners or not psychologists, and through visual analysis of individual item responses, 12 were deleted as duplicates or for not providing sufficient data. The final sample included responses from 1777 individuals, of whom 1579 were doctoral-level practitioners and 198 were postdoctoral trainees.

Because the online survey was set to allow most items to be completed or skipped at the discretion of the respondent, sample sizes will vary across tables and sometimes within tables. Under the basic assumption that most meaningful survey results will be apparent to the reader, statistical analyses have been carried out sparingly. Because of the large sample size, where statistics have been utilized, statistical significances of p < .01 or p < .001 are reported. Two significance cut-offs were selected, in part, to provide a greater amount of information to readers and, in part, to maintain consistency with the 2005 and 2010 surveys (i.e., for ease of comparison of results). We view p < .01 as a minimal threshold for statistical significance in this instance, whereas p < .001 provides the added benefit of reducing false discovery. Related to statistical analyses, readers will note that, for some variables, the means do not fall halfway between the reported Min–Max values, indicating a non-normal distribution or the presence of outliers. A comparison of parametric and non-parametric analyses of these variables produced very similar results. Thus, we have retained parametric statistics, as was the case in past surveys. The exceptions were occasional differences found using the present data when comparing Pearson and Spearman correlations, both of which are presented in two specific Tables 12 and 16.

At various points within this article, data collected in prior surveys in the years 2005 and 2010 will be provided for comparison. To obviate the need for recurrent citations at these points, the reader should note that these survey years refer to survey information published as Sweet, Nelson, and Moberg (2006) and Sweet et al. (2011).

Results

Response rate

In past surveys, we provided estimates regarding how well the respondent sample approximated the total pool of clinical neuropsychologists who had been invited to participate. As was the case in 2010, for the present survey, we attempted to reach all members of the sponsoring organizations, as well as all members of the broader US community of neuropsychologists who may not belong to one of these organizations. Because many invitations were conveyed electronically and could therefore be easily forwarded to many additional individuals, there is no means of precisely specifying a true denominator (i.e., the number representing the total number of licensed practitioners and postdoctoral trainees, the true target audiences of the current survey, who qualify to complete the survey), which could then be used to compute a response rate. There is also no means of accounting with certainty for membership overlap between the many organizations and listservs, whose members received the invitation, to identify how many unique individuals received the invitation to participate. For all of these reasons, a computation of response rate is not possible. However, it is noteworthy that the sample of 1777 respondents in the present survey represents a 5.5% increase compared to the final sample size of 1685 in the preceding 2010 TCN/AACN Salary Survey (Sweet et al., 2011). It seems likely that the current sample size can be considered representative of US clinical neuropsychology practitioners and postdoctoral trainees.

Characteristics and incomes of postdoctoral trainees

Within this article, the term ‘postdoctoral trainees’ refers to individuals who have attained a doctoral degree and who are completing their final phase of training prior to beginning their own careers. Within our specialty, these individuals are sometimes referred to as residents and sometimes as fellows. Table 1 contains noteworthy information from postdoctoral trainees. With an average age of 32.4 years, female trainees are slightly more than three-fourths of the postdoctoral sample. Approximately two-thirds of trainees have attained a Ph.D. as their doctorate. Only two of the 198 respondents reported having a part-time position. Given the locations of most training programs within academic medical settings, most postdoctoral trainees are being trained in urban areas and 6.6% are being trained solely in a private practice setting. An exclusively pediatric identity is present in 26% of the postdoctoral trainees. Not surprising, perhaps related to maturation of the specialty (e.g., the fact that there is now a well-developed pathway to subspecialization in pediatric neuropsychology), only 13% of these trainees are developing a lifespan identity. A new question solicited information regarding forensic training, with slightly less than half indicating that it was part of postdoctoral training. Professional memberships are perhaps not as well developed in these individuals as they will be later in their careers (see Table 5 for comparison), with no one organization comprising more than 57% of the sample; 15.6% of the sample do not belong to any of these organizations.

Table 1. Characteristics of postdoctoral trainees

	Mean	SD	Min–Max
Age	32.4	5.1	27–61
	Frequency	%
Degree
Ph.D.	129	65.8
Psy.D.	61	31.1
Other	6	3.1
Gender
Women	151	76.6
Men	46	23.4
Ethnicity^a
African-American/Black	4	2.1
American-Indian or Alaskan Native	2	1.0
Asian or Pacific Islander	9	4.7
Hispanic/Latino(a)	10	5.2
Caucasian/White	158	82.7
Biracial/Multiethnic/Multiracial	5	2.6
Year of postdoctoral residency
1	93	49.5
2	87	46.3
>2	8	4.3
Licensure status
Licensed	85	42.9
Non-licensed	113	57.1
Work status
Full-time	183	96.8
Part-time	2	1.1
Combined (full-time + part-time)	4	2.1
Practice environment
Rural	9	4.8
Urban	164	87.2
Some time in both	15	8.0
General work setting
Institution only	126	87.5
Private practice only	9	6.3
Institution and private practice	9	6.3
Professional identity
Adult only	81	55.5
Pediatric only	38	26.0
Lifespan	19	13.0
Not a neuropsychologist (self designation)	8	5.5
Forensic training
Yes	82	45.6
No	98	54.4
Memberships
American Academy of Clinical Neuropsychology	87	45.3
International Neuropsychological Society	111	57.8
National Academy of Neuropsychology	64	33.3
Society for Clinical Neuropsychology (APA Division 40)	110	57.3
No membership in any of the four	30	15.6

Note: n = 198.

^a Three respondents chose not to disclose ethnic/racial heritage.

Table 2 compares key postdoctoral trainee characteristics using data from present and past surveys. Across a 10-year period, there is a steady increase in the percentage of Psy.D.s, now approaching one-third. After holding steady from 2005 to 2010, the 2015 data show an increase in women and slight increases in ethnic diversity and pediatric identity.

Table 2. Postdoctoral trainee characteristics across 10 years

	2005		2010		2015
	Frequency	%	Frequency	%	Frequency	%
Degree
Ph.D.	81	84.4	66	73.3	129	65.8
Psy.D.	14	14.6	24	26.7	61	31.1
Other	1	1.0	0	.0	6	3.1
Gender
Women	65	70.7	62	70.5	151	76.6
Men	27	29.3	26	29.5	46	23.4
Ethnicity
African-American/Black	1	1.0	0	.0	4	2.1
American-Indian or Alaskan Native	1	1.0	0	.0	2	1.0
Asian or Pacific Islander	6	6.3	6	6.8	9	4.7
Hispanic/Latino(a)	4	4.2	5	5.7	10	5.2
Caucasian/White	83	86.5	76	86.4	158	82.7
Biracial/Multiethnic/Multiracial^a	–	–	1	1.1	5	2.6
Professional identity
Adult neuropsychologist only	50	54.9	38	59.4	81	55.5
Pediatric neuropsychologist only	21	23.1	15	23.4	38	26.0
Lifespan neuropsychologist	17	18.7	9	14.1	19	13.0
Not a neuropsychologist	3	3.3	2	3.1	8	5.5

^a Biracial/Multiethnic/Multiracial was not an offered response in the 2005 survey.

Table 3 shows mean and median salaries of postdoctoral trainees in year one and in year two that are primarily in the low forty thousand dollar range. Income satisfaction and job satisfaction measured by 0–100 ratings are shown in Table 4 and are notably different. Not surprising, in keeping with training-level salaries, income satisfaction is substantially lower than job satisfaction. A new rating of work–life balance satisfaction is also well below the job satisfaction rating.

Table 3. Salaries of postdoctoral trainees by year of training

					Percentile
Year of training	n	Mean	Median	SD	25	75	95	99	Min–Max
1	51	40.7	41.5	6.3	36.5	45.0	50.4	54.5	26.0–54.5
2	44	42.7	42.0	5.7	40.0	45.0	53.0	57.0	30.0–57.0
>2	5	40.8	40.0	3.1	38.0	44.0	45.0	45.0	38.0–45.0

Notes: Includes postdoctoral trainees working full-time or more. Excludes outliers earning <$25,875 and >$67,250. Income values are in 1000s of dollars. With ns less than 10, reported income values may be less reliable or unreliable.

Table 4. Postdoctoral trainee satisfaction ratings

	n	Mean	Median	SD
Income satisfaction (0–100)	129	50.5	51.0	27.8
Job satisfaction (0–100)	129	77.3	86.0	22.0
Work–life balance satisfaction (0–100)	128	64.5	76.0	29.0

Characteristics of practitioner sample

As can be seen in Table 5, the vast majority of survey participants noted memberships in at least one of the major neuropsychology membership organizations, three of which were survey sponsors. Only 4.4% do not belong to any of the major organizations. Whereas most respondents hold multiple memberships, small percentages hold only a single membership. More than 80% of respondents attained a Ph.D. as their doctoral degree. Approximately three-fourths of the doctorates were awarded in clinical psychology, with the much smaller second largest group being doctorates in counseling psychology at 6.8%. The sample contains approximately 11.4% more women than men. Ethnic minorities represent 11.7% of the sample, with the largest subgroup of these being Hispanic/Latino(a). Work status was full-time or full-time plus a second part-time position for approximately 91% of the sample. Only three respondents were unemployed.

Table 5. Sample characteristics^a

	Frequency	%
Organizational membership (any)	1510	95.6
AACN	819	52.8
Exclusive (i.e., not INS, NAN, or SCN)	67	8.2
INS	1008	64.9
Exclusive (i.e., not AACN, NAN, or SCN)	87	8.6
NAN	878	56.6
Exclusive (i.e., not AACN, INS, or SCN)	103	11.7
SCN (APA Division 40)	933	60.1
Exclusive (i.e., not AACN, INS, or NAN)	69	7.4
No organizational membership	69	4.4
Degree^b
Ph.D.	1271	82.6
Psy.D.	261	17.0
Ed.D.	6	.4
Gender
Women	869	55.7
Men	691	44.3
Ethnicity
African-American/Black	18	1.2
American-Indian or Alaskan Native	1	.1
Asian or Pacific Islander	47	3.1
Hispanic/Latino	50	3.3
Caucasian/White	1347	88.3
Biracial/Multiethnic/Multiracial	35	2.3
Chose not to disclose	28	1.8
Field of doctoral study
Clinical psychology	1212	77.4
Counseling psychology	106	6.8
Neuropsychology^c	96	6.1
School psychology	45	2.9
Educational psychology	15	1.0
Neurosciences	8	.5
Cognitive psychology	5	.3
Biopsychology	11	.7
Experimental psychology	11	.7
Other	57	3.6
Work status
Full-time	1287	83.4
Part-time	125	8.1
Combined (full-time + part-time)	117	7.6
Retired	10	.6
Disabled, not working	1	.1
Unemployed	3	.2
Practice environment
Rural	121	7.9
Urban	1244	80.8
Some time in both	175	11.4
General work setting
Institution only	715	52.8
Private practice only	303	22.4
Institution and private practice	335	24.8
“Do you use a technician/psychometrician or other assistant to collect test data from your patients?”^d
Yes	848	54.9
Paid doctoral-level staff	53	6.4
Paid postdoctoral trainees	289	34.7
Paid technicians/psychometricians (not “trainees”)	652	78.4
Unpaid postdoctoral trainees	22	2.6
Unpaid predoctoral trainees	269	32.3
No	696	45.1
Professional identity
Adult neuropsychologist only	803	58.9
Pediatric neuropsychologist only	222	16.3
Lifespan neuropsychologist	307	22.5
Not a neuropsychologist (self designation) 32	2.3
Board certification
ABN	92 (of 1540)	6.0
ABPP (of any kind)	581 (of 1552)	37.4
ABPP-CN	536 (of 579)	92.6

Note: Useable n = 1579.

^a Includes all licensed and non-licensed respondents; excludes postdoctoral trainees.

^b Thirty-nine respondents indicated “other” as their highest degree attained. These data were excluded, and percents were recalculated for the remaining 3 degrees.

^c Participants were instructed to choose this option only if their neuropsychology program was separate from a clinical psychology program.

^d Checking multiple types of testing assistants was allowed; sums of these assistants will not equal the total number of respondents (n = 848).

A comparison of sample characteristics from 2005, 2010, and 2015 surveys is presented in Table 6. The average age has been quite stable across this time period, indicating that a substantial number of young neuropsychologists continue to join the specialty. A notable trend from 2005 to 2015 is evident in type of degree, with the percent of Ph.D. holders decreasing as a function of a percentage increase in Psy.D. holders. Gender has shifted substantially from women as a slight minority in 2005 to more than 11% more women than men in 2015. Diversity has increased meaningfully, but not in all categories. A shift is evident in the decrease in Caucasian/White from 92.9% in 2005 to 88.3% in 2015. Ethnicity types that have increased the most in this same 10-year period are Asian or Pacific Islander and Hispanic/Latino(a). Although not asked in 2005, in the last five years, Biracial/Multiethnic/Multiracial representation has increased somewhat since 2010. Work status (e.g., full-time vs. part-time) has been quite stable across time. Use of testing assistants (i.e., technicians, psychometricians, etc.) appeared to decline somewhat from 2005 to 2010 only to return to approximately the 2005 level in 2015. According to surveys preceding 2005, the percentage of private practitioners had already been increasing from well before 2005 and, as seen in Table 6 continued to increase from 2005 to 2010, but has now declined to a pre-2005 level from 2010 to 2015. Regarding professional identity, the percentage of lifespan neuropsychologists has declined steadily from 2005 to 2015, whereas both pure pediatric and pure adult identities have increased steadily. Figure 1 demonstrates that across all three neuropsychology identities, there has been an increase in the percentages working exclusively in institutions, which reverses the trend evident in prior surveys.

Table 6. Comparison of sample characteristics across time

Characteristics	2005			2010			2015
	Mean	SD	Min–Max	Mean	SD	Min–Max	Mean	SD	Min–Max
Age	47.2	9.5	29.0–82.0	47.4	10.5	28.0–85.0	46.2	11.6	28.0–82.0
	Frequency	%		Frequency	%		Frequency	%
Degree	(n = 972)			(n = 1582)			(n = 1577)
Ph.D.	865	89.0		1312	82.9		1271	80.6
Psy.D.	93	9.6		217	13.7		261	16.6
Ed.D.	9	.9		9	.6		6	.4
Other	5	.5		44	2.8		39	2.5
Gender	(n = 840)			(n = 1527)			(n = 1560)
Women	404	48.1		791	51.8		869	55.7
Men	436	51.9		736	48.2		691	44.3
Ethnicity	(n = 959)			(n = 1570)			(n = 1526)
African-American/Black	14	1.5		20	1.3		18	1.2
American-Indian or Alaskan Native	2	.2		5	.3		1	.1
Asian or Pacific Islander	11	1.1		28	1.8		47	3.1
Hispanic/Latino(a)	26	2.7		49	3.1		50	3.3
Caucasian/White	891	92.9		1411	90.1		1347	88.3
Biracial/Multiethnic/Multiracial^a	–	–		20	1.3		35	2.3
Chose not to disclose	15	1.6		33	2.1		28	1.8
Work status	(n = 959)			(n = 1570)			(n = 1543)
Full-time	808	84.3		1276	81.3		1287	83.4
Part-time	68	7.1		141	9.0		125	8.1
Combined (full-time + part-time)	71	7.4		135	8.6		117	7.6
Retired	9	.9		10	.6		10	.6
Disabled, not working	0	.0		0	.0		1	.1
Unemployed	3	.3		8	.5		3	.2
“Do you use a technician/psychometrician to collect test data from patients?”	(n = 931)			(n = 1566)			(n = 1544)
Yes	497	53.4		752	48.0		848	54.9
No	434	46.6		814	52.0		696	45.1
General work setting	(n = 873)			(n = 1354)			(n = 1353)
Institution only	402	46.0		581	42.9		715	52.8
Private practice only	225	25.8		396	29.2		303	22.4
Institution and private practice	246	28.2		377	27.8		335	24.8
Professional identity	(n = 898)			(n = 1361)			(n = 1364)
Adult neuropsychologist only	458	51.0		740	54.4		803	58.9
Pediatric neuropsychologist only	121	13.5		207	15.2		222	16.3
Lifespan	253	28.2		347	25.5		307	22.5
Not a neuropsychologist^b	66	7.3		67	4.9		32	2.3

Note: Includes all licensed and non-licensed clinicians; excludes postdoctoral trainees.

^a Biracial/Multiethnic/Multiracial was not an offered response in the 2005 survey.

^b A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Figure 1. Chronological comparison of practicing clinical neuropsychologists by professional identity and general work setting.

Chronological comparisons can also be made for gender. Table 7 provides a comparison of women and men with regard to work status, general work setting, and professional identity from 2005 through 2015. With regard to work status, there are no obvious gender-related trends; across surveys, substantially more women have worked on a part-time basis relative to men. Choice of general work setting shows the overall trend of women working increasingly in institutions only, with the percentage of men doing so from 2005 to 2010 actually declining, only to reverse and increase from 2010 to 2015. In all three eras, the percentage of women working only in institutions is substantially higher. Whereas percentages of women working in private practice increased from 2005 to 2010, this trend reversed completely from 2010 to 2015. For men, there was slightly lower percentage in private practice from 2005 to 2010, followed by a continued decrease in 2015. Regarding professional identity, increases in adult-only and pediatric-only practitioners occurred at the expense of lifespan practitioners. Men showed a different pattern with increases in adult-only practitioners in 2010 and 2015, and increasingly larger gender discrepancies in pediatric-only practitioners in the same time intervals. Male lifespan practitioners decreased proportionally in 2010 and 2015. Additional investigation of gender will be mentioned when relevant to select variables in the remainder of the article.

Table 7. Select gender comparisons across time

	2005				2010				2015
	Women		Men		Women		Men		Women		Men
Variable	Freq.	%	Freq.	%	Freq.	%	Freq.	%	Freq.	%	Freq.	%
Work status	(n = 375)		(n = 422)		(n = 771)		(n = 724)		(n = 843)		(n = 672)
Full-time	301	80.3	371	87.9	619	80.3	596	82.3	688	81.6	577	85.9
Part-time	42	11.2	10	2.4	104	13.5	32	4.4	92	10.9	32	4.8
Combined (full-time + part-time)	29	7.7	38	9.0	38	4.9	90	12.4	57	6.8	58	8.6
Retired	0	.0	3	.7	3	.4	6	.8	3	.4	4	.6
Disabled, not working	0	.0	0	.0	0	.0	0	.0	1	.1	0	.0
Unemployed	3	.8	0	.0	7	.9	0	.0	2	.2	1	.1
General work setting	(n = 340)		(n = 393)		(n = 650)		(n = 641)		(n = 733)		(n = 600)
Institution only	174	51.2	160	40.7	325	50.0	230	35.9	428	58.4	274	45.7
Private practice only	74	21.8	118	30.0	193	29.7	183	28.5	151	20.6	146	24.3
Institution and private practice	92	27.1	115	29.3	132	20.3	228	35.6	154	21.0	180	30.0
Professional identity	(n = 347)		(n = 405)		(n = 655)		(n = 642)		(n = 738)		(n = 606)
Adult only	175	50.4	209	51.6	333	50.8	373	58.1	407	55.1	381	62.9
Pediatric only	64	18.4	38	9.4	143	21.8	57	8.9	173	23.4	48	7.9
Lifespan	91	26.2	129	31.9	148	22.6	181	28.2	139	18.8	166	27.4
Not a neuropsychologist^a	17	4.9	29	7.2	31	4.7	31	4.8	19	2.6	11	1.8

Note: Includes all licensed clinicians; excludes postdoctoral trainees.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Returning to the contents of Table 5, a substantial majority of respondents work in urban practice environments exclusively, with only 7.9% working in rural areas exclusively and an additional 11.4% working in both urban and rural areas. Employment within an institution accounts for 52.8% of the sample, with 22.4% working exclusively in private practice, and 24.8% working in both settings. Testing assistants are used by 54.9% of the sample, with the vast majority of these being paid assistants. The professional identity of 54% of respondents is that of an adult neuropsychologist. Identity as a pediatric neuropsychologist is held by 16.3%, whereas 22.5% identify as a pediatric and adult (i.e., lifespan and hereafter referred to as such) neuropsychologist. Board certification through the American Board of Professional Psychology (ABPP) was reported by 37.4% of the sample, with more than 92.6% of these individuals being board certified by the American Board of Clinical Neuropsychology (ABCN). Six percent of the sample reported board certification through the American Board of Neuropsychology (ABN). The characteristics of the 198 postdoctoral trainees are not shown in Table 5, as these were presented earlier in this article.

Table 8 shows the primary practice locations of survey respondents among the 50 United States, District of Columbia, and Puerto Rico. Nine states account for more than half of the sample. The 22 lowest frequency entries (21 states and Puerto Rico) at the bottom of Table 8 account for only 10% of the sample. Figure 2 shows the same primary practice location data reconstituted into US regions.

Table 8. State of licensure and primary employment

State	Frequency	%	Cum. %
California	132	9.1	9.1
New York	110	7.6	16.6
Texas	102	7.0	23.6
Florida	86	5.9	29.5
Massachusetts	74	5.1	34.6
Pennsylvania	71	4.9	39.5
Illinois	68	4.7	44.2
Michigan	60	4.1	48.3
Minnesota	49	3.4	51.6
North Carolina	48	3.3	54.9
Maryland	46	3.2	58.1
Wisconsin	44	3.0	61.1
Ohio	43	3.0	64.1
Colorado	39	2.7	66.8
Washington	35	2.4	69.2
Tennessee	28	1.9	71.1
Virginia	27	1.9	72.9
Connecticut	26	1.8	74.7
Indiana	25	1.7	76.4
Missouri	25	1.7	78.2
Arizona	24	1.6	79.8
Georgia	24	1.6	81.5
New Jersey	22	1.5	83.0
Iowa	14	1.0	83.9
Maine	14	1.0	84.9
Oregon	14	1.0	85.9
District of Columbia	14	1.0	86.8
Nebraska	13	.9	87.7
Alabama	12	.8	88.5
Kentucky	12	.8	89.4
Rhode Island	12	.8	90.2
Utah	12	.8	91.0
Louisiana	11	.8	91.8
New Mexico	11	.8	92.5
South Carolina	11	.8	93.3
Oklahoma	10	.7	94.0
Alaska	9	.6	94.6
Arkansas	9	.6	95.2
Idaho	9	.6	95.8
Kansas	9	.6	96.4
New Hampshire	8	.5	97.0
North Dakota	7	.5	97.5
Delaware	6	.4	97.9
Hawaii	6	.4	98.3
Mississippi	6	.4	98.7
Nevada	5	.3	99.0
West Virginia	4	.3	99.3
Montana	3	.2	99.5
Vermont	3	.2	99.7
South Dakota	2	.1	99.9
Wyoming	1	.1	99.9
Puerto Rico	1	.1	100.0

Note: If licensed in more than one state, respondents were asked to provide state of primary employment; 1456 respondents provided this information.

Figure 2. Regions of residence (valid N = 1456): New England = Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Middle Atlantic = New York, New Jersey, Pennsylvania; East North Central = Illinois, Indiana, Michigan, Ohio, Wisconsin; West North Central = Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota; South Atlantic = Delaware, District of Columbia, Georgia, Florida, Maryland, North Carolina, Puerto Rico, South Carolina, Virginia, West Virginia; East South Central = Alabama, Kentucky, Mississippi, Tennessee; West South Central = Arkansas, Louisiana, Oklahoma, Texas; Mountain = Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah; Pacific = Alaska, California, Hawaii, Oregon, Washington.

As shown in Table 9, excluding postdoctoral trainees, the average age of licensed practitioners in the present sample is 46.7 (Min–Max = 28–75), with licensed practice having begun approximately 14 years ago on average (Min–Max = .5–45). Weekly professional activities in this licensed group are heavily practice based, with an average of 25.0 clinical neuropsychological practice hours and an additional average of 6.1 general clinical practice hours. It is noteworthy that average hours and median hours per week for non-clinical administration are higher than for all training, research, and volunteer activities. Moreover, whereas the modal response for involvement in teaching/training and for non-clinical administration was 5.0 h for each, the modal response for involvement in funded or unfunded research was zero.

Table 9. Age of licensed practitioners, years since licensed, and professional time per week devoted to clinical, research, and administrative duties, and volunteer activities

	n	Min–Max	Mean	Median	Mode	SD
Age of licensed practitioners	1339	28.0–75.0	46.7	44.0	35.0	11.0
Years since licensed	1356	.5–45.0	14.0	11.0	3.0	10.7
Hours per week^a devoted to:
Clinical neuropsychology practice activities	1171	0–60.0	25.0	25.0	30.0	13.1
General clinical practice activities	860	0–50.0	6.1	4.0	.0	8.1
Teaching/training	1017	0–50.0	5.0	4.0	5.0	5.8
Supervision of support personnel	885	0–30.0	2.0	1.0	.0	2.6
Research/writing (funded)	807	0–60.0	4.3	.0	.0	9.1
Research/writing (non-funded)	865	0–40.0	3.0	1.0	.0	4.6
Non-clinical administration	1095	0–50.0	5.9	5.0	5.0	5.7
Neuropsychologically related volunteer activities	889	0–18.0	1.9	1.0	.0	2.1

Note: Includes all licensed clinicians who are currently practicing full-time or more. Excludes postdoctoral trainees.

^a Entries of >60 h per week were excluded for general clinical practice activities, teaching/training, funded and non-funded research/writing, and non-clinical administration.

Income from professional activities

Throughout the remainder of the manuscript, whenever the term income is used, it refers to gross income earned only from psychology (and therefore also neuropsychology) activities. Income does not refer to billings or to revenue collected from services or to joint income with spouse or legal life partner. This is true for all respondents, including private practitioners, wherein numbers should not be assumed to represent net income after expenses.

Starting salaries

For postdoctoral trainees and their training directors, as well as for employers offering starting positions to new postdoctoral graduates, there is much discussion of entry-level income and what should be expected in the first years of practice. Table 10 shows starting and early career income for institution only respondents. Although there was a suggestion in the data that private practice incomes were higher even at the beginning of a career in neuropsychology, the sample sizes were quite low and thus those particular data are of unknown reliability and are not shown in Table 10. For those working only in institutions, there are small and steady increases in the first two years, followed by meaningful mean and median income increases in year three.

Table 10. Annual incomes in initial years of clinical practice by work setting

					Percentile
Work Setting^a	n	Mean	Median	SD	25	75	95	99	Min–Max
<1 year in practice
Institution only	43	85.6	85.0	14.6	76.0	96.0	111.9	121.0	50.0–121.0
1 year in practice
Institution only	26	88.8	87.6	17.4	83.1	94.3	138.0	153.0	50.0–153.0
2 years in practice
Institution only	45	91.0	86.8	19.3	81.3	91.5	130.0	184.0	62.8–184.0
3 years in practice
Institution only	52	101.0	95.0	20.1	88.3	105.8	146.8	175.0	71.2–175.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time. Excludes postdoctoral trainees and those earning <$50,000 annually.

^a The work settings private practice and institution and private practice were excluded as n < 10.

Years in clinical practice

Table 11 shows income increases in five-year practice increments. Due to skewness and/or outliers in these data, median incomes are a better indicator of income at each time interval, though mean values are also presented for historical comparisons. In the first 20 years of practice, median and mean incomes rose approximately 45.9% to 69.6%, respectively. These increases reflect an approximate median dollar increase of 14,000 every five years or a mean dollar increase of 22,000 over the same time period. Thereafter, in years 21–25, the median and mean increase is much smaller. Relative to the 21–25 year time point, those individuals with over 25 years of experience showed increases in income of 19.2% (median) to 24% (mean). Overall, it is not until the 6–10-year interval of practice that both mean and median incomes meaningfully surpass 100,000. Also, within each five-year interval, the range of reported incomes spans hundreds of 1000s of dollars, with dramatic expansion of the ranges after the tenth year of practice.

Table 11. Income at varying intervals of years in clinical practice

					Percentile
Years in practice	n	Mean	Median	SD	25	75	95	99	Min–Max
<1–5	306	98.8	93.2	25.8	84.0	107.0	140.7	189.7	50.0–315.0
6–10	234	120.8	105.0	38.6	98.0	131.3	201.3	267.1	50.0–275.0
11–15	148	143.6	125.0	62.3	107.0	160.0	240.5	463.8	50.0–525.0
16–20	112	167.6	136.0	84.0	110.0	200.0	351.8	446.8	60.0–450.0
21–25	130	168.2	142.5	96.3	114.8	180.0	350.0	732.9	70.0–860.0
>25	200	208.7	170.0	124.8	125.0	250.0	499.2	699.1	70.0–916.0
All years	1130	143.5	118.0	83.8	97.0	157.0	304.5	500.0	50.0–916.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

Of equal interest is whether incomes are increasing over time. Figure 3 shows a comparison of mean and median incomes in 2005, 2010, and 2015 across intervals of years of practice experience. As was the case when last reported with 2010 data, all practice intervals again reported higher incomes when compared to five years ago.

Figure 3. Five-year income comparisons at varying intervals of years in clinical practice.

Career satisfaction: Income, job, and work–life balance satisfaction

Using 0–100 rating scales, licensed practitioners working at least full-time expressed relatively high income satisfaction (M = 73.2; Median = 80.0; SD = 22.9) and high job satisfaction (M = 79.2; Median = 85.0; SD = 19.2). New to the present survey was a question addressing overall work-life balance, with results suggesting a somewhat lower level of satisfaction (M = 68.8; Median = 75.0; SD = 25.1) relative to income and job satisfaction, among the same group of respondents.

Correlates with income satisfaction, job satisfaction, and work–life balance satisfaction are shown in Table 12. Moderate size correlations are evident between income satisfaction and job satisfaction, as well as between job satisfaction and work–life balance, with a weaker, but still significant, correlation between income satisfaction and work–life balance. Interestingly, the correlation between psychology income and work–life balance is nearly zero and the correlation between psychology income and job satisfaction, though significant because of the large sample, is quite weak and accounts for very little variance. The more substantial correlation, not surprisingly, is between income and income satisfaction, though this is only at the threshold of weak to moderate size. Variables such as weekly hours of forensic work, weekly hours of clinical practice, years in practice, and number of peer-reviewed publications are, for all practical intents and purposes, not substantial correlates of income, job, or work–life satisfactions.

Table 12. Correlates of income satisfaction, job satisfaction, and work–life balance satisfaction

	Income satisfaction	Job satisfaction	Work–life satisfaction	Clinical practice hours	Years in practice	Forensic hours/week	Psych income	Peer-review publications
Income satisfaction	1.0	.45^**	.31^**	−.06	.14^**	.15^**	.29^**	.08
(n)	(1151)	(1135)	(1119)	(1108)	(1151)	(525)	(1107)	(1141)
Job satisfaction	.46^**	1.0	.46^**	−.11^**	.10^**	.06	.17^**	.06
(n)	(1135)	(1140)	(1110)	(1099)	(1140)	(519)	(1097)	(1130)
Work–life satisfaction	.27^**	.41^**	1.0	−.14^**	.00	−.04	−.02	.00
(n)	(1119)	(1110)	(1126)	(1084)	(1126)	(512)	(1083)	(1116)
Clinical practice hours^a	−.06	−.08^*	−.13^**	1.0	−.20^**	−.25^**	−.16^**	−.33^**
(n)	(1108)	(1099)	(1084)	(1194)	(1194)	(560)	(1085)	(1186)
Years in practice	.17^**	.14^**	−.01	−.21^**	1.0	.24^**	.47^**	.32^**
(n)	(1151)	(1140)	(1126)	(1194)	(1356)	(580)	(1130)	(1242)
Forensic work hours/week	.18^**	.09	−.01	−.21^**	.22^**	1.0	.36^**	−.04
(n)	(525)	(519)	(512)	(560)	(580)	(580)	(506)	(576)
Psychology income	.39^**	.24^**	−.02	−.19^**	.59^**	.32^**	1.0	.25^**
(n)	(1107)	(1097)	(1083)	(1085)	(1130)	(506)	(1130)	(1120)
Peer-reviewed publications	.04	.02	−.01	−.30^**	.22^**	−.03	.21^**	1.0
(n)	(1141)	(1130)	(1116)	(1186)	(1242)	(576)	(1120)	(1242)

Note: Pearson’s r correlations depicted above the diagonal in bold font; Spearman’s rho depicted below the diagonal.

^a Combines clinical neuropsychology and general clinical activities (hours/week). Entries of >60 h per week were excluded for clinical practice hours. Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral residents/fellows and those earning <$50,000 annually. No significant relationships were observed between any of the three satisfaction variables (job, income, work–life balance) and number of hours specifically devoted to: teaching/training; clinical supervision; research (funded and non-funded; administrative duties; and professional volunteerism hours/week).

^** p < .001

Related to the types of satisfactions of interest in the survey, Table 13 shows the mean satisfaction ratings associated with salient general variables: gender, general work setting, and professional identity. In absolute terms, all three satisfactions are higher for men, and in fact all three satisfactions are statistically significant for gender, with men significantly higher. The effect size for each of these gender comparisons was small in magnitude (d = .20). Private practice respondents reported substantially higher job satisfaction, and along with those working in institutions also had higher income satisfaction than respondents working in both settings. However, only within job satisfaction is there a significant effect of general work setting, with private practice significantly higher than both institution and combined institution/private practice. Respondents without a neuropsychologist professional identity produced what appear at face value to be the lowest income and job satisfaction ratings. Those with a lifespan practice reported the highest job satisfaction. Interestingly, the general comparison of all four identity groups produced statistically significant results for only job satisfaction, but none of the post hoc paired comparison t-tests were significant.

Table 13. Career satisfaction by gender, general work setting, and professional identity

	Satisfaction^a
	Income			Job			Work–life
	n	Mean	SD	n	Mean	SD	n	Mean	SD
Gender
Women	675	70.7^**	23.6	668	77.3^**	20.3	663	67.3^**	26.3
Men	573	75.3^**^c	21.6	568	81.3^**^c	18.0	559	72.3^**^c	23.3
General work setting
Institution only	654	73.4	21.6	650	77.8^**	18.7	648	70.1	24.7
Private practice only	291	74.2	23.5	287	84.0^**^d	19.5	282	70.8	25.6
Institution and private practice	315	70.6	24.7	310	77.6^**	20.3	304	67.1	25.4
Professional identity
Pediatric only	208	69.8	22.4	206	78.1^*	18.1	202	65.8	25.8
Adult only	736	74.2	22.5	730	78.6^*	20.1	724	71.2	24.3
Lifespan	293	72.1	23.3	288	82.0^*	17.4	283	68.0	25.6
Not a neuropsychologist^b	23	65.6	30.7	23	69.7^*	30.2	25	68.4	31.6

Notes: One-way ANOVA was significant between groups for job satisfaction, but post hoc analyses did not reveal any significant comparisons between group pairings. Effect sizes were small across all statistically significant variables.

^a On a 0–100 scale. Includes all respondents who are licensed and currently practicing, part-time or more; excludes postdoctoral trainees.

^b A self designation indicating identity as a psychologist, but not as a neuropsychologist.

^c Means for men are significantly greater than those for women across income, job, and work–life satisfactions.

^d Job satisfaction mean for private practice is significantly greater than institution only and combined institution and private practice, which are not different from each other.

^** p ≤ .001;

^* p ≤ .01.

Obstacles to career satisfaction

Also new to the current survey, 2015 respondents were asked to identify whether there were obstacles preventing greater income satisfaction or job satisfaction. Specifically, respondents were asked about two types of obstacles: personal/family and work environment. Relevant results related to gender, general work setting, and professional identity are presented in Table 14. It is apparent that more women than men find that both types of obstacles affect income satisfaction and job satisfaction. For both women and men, there is a much higher report of work environment obstacles as compared to personal/family obstacles. Given that there are apparent gender differences in all three types of satisfaction, as well as in reported obstacles to these satisfactions, Table 15 allows examination of the possibility that gender differences in psychology income, income satisfaction, and job satisfaction have been present at any of the three survey time points and/or that possible difference have changed over time. In each survey year, there is an obvious gender-related income discrepancy, as well as gender-related differences in income satisfaction and job satisfaction. In prior surveys, work–life balance satisfaction was not asked, but it is apparent that both income and job satisfaction questions have resulted in a relatively stable gender discrepancy in each of the last three surveys; this gender discrepancy is neither improving nor worsening over time.

Table 14. Personal/family and work/environment obstacles to income and job satisfaction

	Type of obstacle to type of satisfaction
	Personal/family				Work environment
	Income		Job		Income		Job
	n	% Yes	n	% Yes	n	% Yes	n	% Yes
Gender
Women	233	33.0	214	30.4	485	67.5	493	70.0
Men	136	23.1	114	19.4	338	57.2	362	62.4
General practice setting
Institution only	178	25.7	178	25.9	454	64.5	506	73.4
Private practice only	81	27.2	64	21.3	151	50.2	131	44.1
Institution and private practice	111	35.0	88	27.8	225	71.0	226	72.7
Professional identity
Pediatric only	69	32.9	70	33.2	146	68.2	152	72.4
Adult only	209	27.1	188	24.4	480	61.8	525	68.7
Lifespan	83	28.0	66	22.6	183	61.2	170	58.2
Not a neuropsychologist^a	9	29.0	6	19.4	21	65.6	16	51.6

Note: Excludes postdoctoral trainees.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Table 15. Gender comparison in income and job satisfactions across time, as well as actual incomes

	2005				2010				2015
	Women		Men		Women		Men		Women		Men
		Mean		Mean		Mean		Mean		Mean		Mean
	n	(SD)	n	(SD)	n	(SD)	n	(SD)	n	(SD)	n	(SD)
Psychology income	255	83.4	309	117.4	534	103.7	597	151.9	590	120.2	529	168.8
		(39.4)		(76.6)		(50.9)		(98.3)		(52.5)		(101.9)
Income satisfaction	330	65.9	387	70	638	69.7	631	74.1	695	70.2	589	75.2
		(25.0)		(24.7)		(23.9)		(23.0)		(24.1)		(21.8)
Job satisfaction	329	76.6	386	77.3	644	77.3	635	80	688	76.9	585	81
		(21.8)		(21.2)		(20.8)		(20.4)		(20.9)		(18.5)

Notes: Psychology income includes all licensed clinicians who are currently practicing full-time or more, and excludes postdoctoral trainees. Satisfaction variables include all licensed clinicians, and exclude postdoctoral trainees.

Also in Table 14, individuals who work in both institutions and private practice reported personal and family obstacles to income satisfaction and job satisfaction at higher frequencies, when compared to those working exclusively in institutions or exclusively in private practice. Interestingly, though this pattern was also true for identifying work environment obstacles to income satisfaction, a different pattern emerged for job satisfaction, with the combined work setting group reporting work environment obstacles to job satisfaction at a frequency very similar to those working exclusively in institutions. Private practice-only respondents reported work environment obstacles to job satisfaction at a much lower frequency.

Correlates of income

A number of variables may be correlated with psychology income. Table 16 shows additional correlates of psychology income, clinical practice hours, and years in practice, specifically as these three variables relate to specific work activities. Most of the significant correlations are only weak to moderate in strength. Not surprising, clinical practice hours are negatively correlated with professional activities, such as training, research and publishing, administrative, and volunteer activities. Within such activities, the variable of years in practice has a moderate correlation with number of peer-reviewed publications, which itself has a moderate negative correlation with clinical practice hours. As shown in prior surveys, number of forensic hours per week has a moderate correlation with psychology income. Not shown in Table 16, board certification attainment of any kind (i.e., ABN or any ABPP specialty) has a weak correlation of .26 (n = 1130). Also not depicted in Table 16, for the subset of the respondents who work only within institutions and whose incomes are based on relative value units (RVUs), the correlation of monthly RVUs to income is moderate at .39 (n = 103).

Table 16. Additional correlates of psychology income, clinical practice hours, and years in practice: Specific work activities

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) Psychology income	1.0	−.14^**	.47^**	.36^**	.25^**	.01	.08	.08	.05	.05	.12^**
(n)	(1130)	(764)	(1130)	(506)	(1120)	(921)	(803)	(734)	(784)	(992)	(802)
(2) Clinical practice hours^a	−.19^**	1.0	−.20^**	−.27^**	−.35^**	−.32^**	−.09	−.39^**	−.32^**	−.26^**	−.23^**
(n)	(764)	(836)	(836)	(408)	(830)	(766)	(712)	(680)	(699)	(791)	(709)
(3) Years in practice	.59^**	−22^**	1.0	.24^**	.32^**	.02	.13^**	.05	.09^*	.17^**	.18^**
(n)	(1130)	(836)	(1356)	(580)	(1242)	(1017)	(885)	(807)	(865)	(1095)	(889)
(4) Forensic work hours/week	.32^**	−.22^**	.22^**	1.0	−.04	−.08	−.09	−.08	.01	−.11	.03
(n)	(506)	(408)	(580)	(580)	(576)	(474)	(412)	(372)	(403)	(519)	(429)
(5) Peer-reviewed publications	.21^**	−.32^**	.22^**	−.03	1.0	.16^**	.07	.46^**	.36^**	.09^*	.22^**
(n)	(1120)	(830)	(1242)	(576)	(1242)	(1010)	(879)	(802)	(857)	(1088)	(883)
(6) Teaching/training, includes^b trainee supervision	−.01	−.37^**	.02	−.15^**	.24^**	1.0	.08	.18^**	.26^**	.12^**	.26^**
(n)	(921)	(766)	(1017)	(474)	(1010)	(1017)	(821)	(774)	(821)	(949)	(825)
(7) Supervision of clinical/supportive personnel^b	.14^**	−.13^**	.12^**	−.12	.17^**	.20^**	1.0	.11^*	.09^*	.15^**	.11^**
(n)	(803)	(712)	(885)	(412)	(879)	(821)	(885)	(733)	(758)	(857)	(764)
(8) Research/writing-funded^b	.19^**	−.39^**	.11^**	−.12	.52^**	.35^**	.24^**	1.0	.39^**	.05	.18^**
(n)	(734)	(680)	(807)	(372)	(802)	(774)	(733	(807)	(720)	(781)	(728)
(9) Research/writing-non-funded^b	.10^*	−.36^**	.07	−.02	.50^**	.43^**	.25^**	.46^**	1.0	.15^**	.21^**
(n)	(784)	(699)	(865)	(403)	(857)	(821)	(758)	(720)	(865)	(830)	(761)
(10) Administrative duties^b	.12^**	−.25^**	.15^**	−.05	.03	.13^**	.17^**	.08	.12^**	1.0	.16^**
(n)	(992)	(791)	(1095)	(519)	(1088)	(949)	(857)	(781)	(830)	(1095)	(853)
(11) Professional volunteerism^b	.21^**	−.30^**	.21^**	.04	.25^**	.34^**	.18^**	.28^**	.33^**	.22^**	1.0
(n)	(802)	(709)	(889)	(429)	(883)	(825)	(764)	(728)	(761)	(853)	(889)

Notes: Pearson’s r correlations depicted above the diagonal in bold font; Spearman’s rho depicted below the diagonal. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral residents/fellows and those earning <$50,000 annually. Entries >60 h per week were excluded for clinical practice hours, teaching/training, funded and non-funded research and writing, and administrative duties.

^a Combines clinical neuropsychology and general clinical activities (hours/week). Income values are in 1000s of dollars.

^b Activity represented in hours/week.

^* p < .01.

^** p < .001.

Specifically related to degree of categorical dissatisfaction or satisfaction with psychology income, respondents endorsed categories of income satisfaction as follows: completely dissatisfied (n = 21) = 1.8%; mostly dissatisfied (n = 118) = 10.2%, somewhat dissatisfied (n = 163) = 14.0%, somewhat satisfied (n = 206) = 17.7%, mostly satisfied (n = 522) = 44.9%, completely satisfied (n = 132) = 11.4%. Thus, 26% expressed a degree of income dissatisfaction compared to 74% who expressed a degree of satisfaction.

Table 17 shows categories of income satisfaction associated with actual psychology incomes. Of the 1117 licensed clinicians who were employed at least full-time and provided both income and categorical job satisfaction information, very few (n = 18; 1.65%) are “completely dissatisfied” with their incomes. In general, there is a similar pattern of the relationship of satisfaction to reported incomes, whether one focuses on mean or median incomes. Interestingly, those who chose this most extreme category of income dissatisfaction reported mean actual incomes that were higher than those with greater income satisfaction, as represented by their endorsements of “mostly dissatisfied,” “somewhat dissatisfied,” and “somewhat satisfied.” In fact, the mean actual income of the “completely dissatisfied” group was identical to the mean actual income reported by the “mostly satisfied” group. Moreover, the lowest reported income was above the lowest income in those same categories and the highest reported income was above the highest income in those same categories, with at least one individual in this group reporting an income of $500,000. This interesting observation for this small subgroup is at odds with the pattern evident between the remainder of the satisfaction categories and actual incomes, which shows a general increase in mean and median incomes as one’s satisfaction increases. Importantly, the largest group by far is the “mostly satisfied” group (44.8%). The two most satisfied groups (i.e., “mostly satisfied,” “completely satisfied”) together represent 56% of the respondents who were willing to provide this information.

Table 17. Gross psychology incomes and income satisfaction

	Estimated gross psychology income
	n	Mean	Median	SD	Min–Max
Completely dissatisfied	18	145.8	110.0	107.6	60.0–500.0
Mostly dissatisfied	109	111.3	96.0	53.5	50.0–335.0
Somewhat dissatisfied	158	117.3	104.1	52.9	50.0–450.0
Somewhat satisfied	200	127.0	112.5	47.1	55.0–336.0
Mostly satisfied	505	145.8	121.0	77.1	50.0–860.0
Completely satisfied	127	221.9	185.0	139.5	84.0–916.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

Region of United States and practice environment

Incomes by regions of the United States are shown in Table 18. Consideration of incomes by region should be influenced by the fact that the SDs are again quite large, in which case medians have greater meaning. Whereas mean incomes are highest in the Pacific and Middle Atlantic regions, median incomes are highest in the Pacific and New England regions. The region with the lowest mean income is Mountain, whereas the region with the lowest median income is West North Central.

Table 18. Income by region of the United States

Region	n	Mean	Median	SD
New England	101	143.4	130.0	63.7
Middle Atlantic	144	152.2	119.0	93.1
East North Central	185	143.6	112.0	96.6
West North Central	97	125.4	107.0	63.5
South Atlantic	199	137.3	111.0	79.6
East South Central	45	137.6	112.0	67.1
West South Central	99	140.8	116.0	72.6
Mountain	78	120.3	110.0	44.6
Pacific	127	167.4	127.0	112.1

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually. New England = Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Middle Atlantic = New York, New Jersey, Pennsylvania; East North Central = Illinois, Indiana, Michigan, Ohio, Wisconsin; West North Central = Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota; South Atlantic = Delaware, District of Columbia, Georgia, Florida, Maryland, North Carolina, Puerto Rico, South Carolina, Virginia, West Virginia; East South Central = Alabama, Kentucky, Mississippi, Tennessee; West South Central = Arkansas, Louisiana, Oklahoma, Texas; Mountain = Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah; Pacific = Alaska, California, Hawaii, Oregon, Washington.

Depicted in Table 19, across all states and regions, are incomes by practice environment (i.e., rural, urban, some time in both). Respondents working in both urban and rural environments report the highest mean and median incomes, followed by those working in an urban environment. The smaller group of neuropsychologists working in a rural environment has the lowest mean, but second largest median income.

Table 19. Income by practice environment

Practice environment	n	Mean	Median	SD
Urban	914	143.9	115.0	87.2
Rural	93	130.7	120.0	51.8
Some time in both	121	150.7	128.0	76.9

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000.

State of licensure and practice

With the understanding that regions and practice environments may have very different incomes, individual states and associated incomes are reported in Table 20. This table also shows income, job, and work–life balance satisfaction by state, as well as reported increases and decreases in income compared to five years earlier. The highest mean income is in California, whereas there is a tie in highest median income between California and Massachusetts. Some states, such as Illinois and Wisconsin, have a substantial discrepancy between mean and median, with the former much higher than the latter. Note that in each state the vast majority of individuals reporting income change compared to 5 years ago indicated that the change was an increase. Over 94% in Maryland reported an increase, which was the highest of any state, with no one in Maryland reporting a decrease. Only three states (Florida, Michigan, and Virginia) showed more than 10% of state respondents having experienced a decrease in income compared to five years ago. Few states have high ratings in all three satisfactions, with Tennessee being the only state that rated income, job, and work–life balance satisfaction in the 80s range on the 0–100 scale. The highest job satisfaction was reported in Colorado, which had the lowest mean and median incomes.

Table 20. Incomes, income changes, income satisfaction, job satisfaction, and work–life balance satisfaction by state of licensure and primary employment

					Respondents reporting 5-year income change^a				Satisfaction (0–100)
	Gross psychology income				Increase		Decrease		Income		Job		Work–life
State	n	Mean	Median	SD	n	%	n	%	n	Mean	n	Mean	n	Mean
Arizona	21	130.2	115.0	48.1	17	77.3	2	9.1	21	78.0	21	81.1	21	51.5
California	78	174.7	142.5	119.1	61	71.8	5	5.9	85	68.5	83	80.0	83	66.2
Colorado	25	111.6	101.0	35.5	24	88.9	2	7.4	27	74.8	26	86.4	26	76.9
Connecticut	20	148.9	127.9	89.4	15	83.3	1	5.6	17	80.5	18	85.8	18	72.0
Florida	68	146.0	120.5	82.4	51	76.1	10	14.9	67	73.6	66	80.2	66	69.7
Illinois	50	156.6	110.0	140.0	45	86.5	3	5.8	52	72.7	52	78.6	51	72.8
Maryland	35	155.8	117.8	110.1	33	94.3	0	.0	36	64.3	36	78.2	36	67.9
Massachusetts	54	151.9	142.5	59.5	34	63.0	5	9.3	51	71.8	51	78.1	50	64.2
Michigan	50	146.5	121.0	87.5	37	74.0	6	12.0	49	72.0	49	82.3	48	65.1
Minnesota	41	118.8	105.0	47.7	37	86.0	4	9.3	43	76.7	43	79.4	43	74.9
Missouri	20	133.9	102.9	98.0	17	85.0	1	5.0	19	64.3	20	74.7	19	69.1
New York	79	162.1	126.0	105.8	66	82.5	3	3.8	77	68.7	77	75.8	76	63.7
N. Carolina	34	112.1	104.5	38.7	27	77.1	3	8.6	35	74.4	34	79.2	35	76.1
Ohio	35	133.5	112.0	57.9	31	86.1	3	8.3	35	76.5	35	83.3	34	69.6
Pennsylvania	50	130.7	110.0	58.9	43	84.3	2	3.9	51	70.0	50	73.0	50	61.8
Tennessee	23	127.7	109.0	41.0	15	65.2	2	8.7	22	82.1	21	85.7	21	81.0
Texas	80	140.3	115.0	76.0	70	86.4	3	3.7	81	76.6	79	80.8	77	73.0
Virginia	20	124.2	104.3	52.3	16	72.7	3	13.6	21	64.9	21	77.0	20	61.6
Washington	28	117.7	114.0	26.6	21	75.0	2	7.1	26	78.7	25	83.2	26	76.9
Wisconsin	32	148.8	113.6	85.3	27	81.8	3	9.1	33	79.1	32	77.5	32	67.6

Notes: Income values are expressed in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees. States with fewer than 20 respondents providing income information are not reported.

^a Represents the frequency of respondents who reported having an increase or decrease in their income within the last 5 years. Percents of increase and decrease do not equal 100. The percent not shown that when added to the increase and decrease values would add to 100 is the percent reporting no change.

Professional identity

Data related to gender, work status, general and specific work setting, practice environment, and use of testing assistants are presented in Table 21, all organized by self-assigned professional identity as: pediatric, adult, lifespan, or not a neuropsychologist. Although the survey was intended for neuropsychologists, a small group of individuals indicated that they did not consider themselves to be neuropsychologists. These individuals were retained for comparison sake, as a separate group. In the two prior practice surveys, the label for this group was “no identity” or “none,” which was replaced in favor of the more accurate “not a neuropsychologist” in the present survey. Individuals with a pure pediatric identity are far more likely to be women, and are also more likely to be employed in an institution, specifically in a medical setting. They are much less likely to be in private practice or to be working in either a psychiatric or rehabilitation setting. Interestingly, those who work with both pediatric and adult patients, listed under lifespan, are more likely to be men, more likely to be working more than full-time, and are much more likely to be in private practice in a rural environment. Uniquely, those who do not identify as neuropsychologists are much less likely to use testing assistants compared to those with a neuropsychology identity.

Table 21. Professional neuropsychology identity by gender, work status, general and specific work settings, practice environment, and use of testing assistants

			Professional neuropsychology identity
	Total		Pediatric only		Adult only		Lifespan		Not a neuropsychologist^a
	Freq.	%	Freq.	%	Freq.	%	Freq.	%	Freq.	%
Gender	(n = 1351)		(n = 221)		(n = 792)		(n = 306)		(n = 32)
Women	742	54.9	173	78.3	409	51.6	139	45.4	21	65.6
Men	609	45.1	48	21.7	383	48.4	167	54.6	11	34.4
Work status	(n = 1356)		(n = 221)		(n = 798)		(n = 305)		(n = 32)
Full-time	1122	82.7	190	86.0	676	84.6	233	76.4	23	71.9
Part-time	112	8.3	21	9.5	53	6.6	34	11.1	4	12.5
Full + part-time	111	8.2	9	4.1	62	7.8	37	12.1	3	9.4
Retired	8	.6	0	.0	5	.6	1	.3	2	6.3
Disabled	1	.1	0	.0	1	.1	0	.0	0	.0
Unemployed	3	.2	1	.5	2	.3	0	.0	0	.0
General work setting	(n = 1353)		(n = 219)		(n = 796)		(n = 306)		(n = 32)
Institution only	715	52.8	154	70.3	460	57.8	86	28.1	15	46.9
Private practice	303	22.4	37	16.9	145	18.2	110	35.9	11	34.4
Institution and prvt. prac.	335	24.8	28	12.8	191	24.0	110	35.9	6	18.8
Specific work setting	(n = 1334)		(n = 219)		(n = 781)		(n = 303)		(n = 31)
Medical	654	49.0	143	65.3	404	51.7	104	34.3	3	9.7
Private/group practice	362	27.1	42	19.2	170	21.8	138	45.5	12	38.7
Psychiatric	88	6.6	8	3.7	59	7.6	17	5.6	4	12.9
Rehabilitation	151	11.3	14	6.4	106	13.6	27	8.9	4	12.9
College/university	34	2.5	1	.5	26	3.3	6	2.0	1	3.2
Other	45	3.4	11	5.0	16	2.0	11	3.6	7	22.6
Practice environment	(n = 1354)		(n = 221)		(n = 796)		(n = 305)		(n = 32)
Rural	114	8.4	14	6.3	58	7.3	35	11.5	7	21.9
Urban	1088	80.4	189	85.5	669	84.0	211	69.2	19	59.4
Some time in both	152	11.2	18	8.1	69	8.7	59	19.3	6	18.8
“Do you use a technician/psychometrician to collect test data from patients?”	(n = 1364)		(n = 222)		(n = 803)		(n = 307)		(n = 32)
Yes	752	55.1	124	55.9	459	57.2	160	52.1	9	28.1
No	612	44.9	98	44.1	344	42.8	147	47.9	23	71.9

Notes: Excludes postdoctoral trainees. Each n is consistent within variables.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Professional identity information is also presented in Table 22. Mean age increases across identity from pediatric to adult to lifespan to not identifying as a neuropsychologist. In fact, the mean age difference between the pediatric identity and the group that self identifies as not being a neuropsychologist is 12 years. The same pattern exists across these four identities for years in clinical practice, with a 10.5 year difference between the pediatric group and the group of non-neuropsychologists. The number of weekly hours engaged in forensic activities also follows this same identity pattern. For those with one of the three neuropsychology identities, outpatient evaluation time for the purpose of establishing diagnosis generally runs about 7 h, though large SDs indicate that evaluation times can be wide ranging. Evaluations aimed at treatment planning tend to be much shorter, especially for the adult neuropsychologists. Not surprising, educational evaluations are much longer for the pediatric and lifespan neuropsychologists. Also not surprising, forensic evaluation times are much longer for all the identities. Clinical and forensic fees are highest for pediatric neuropsychologists, with those not identifying as neuropsychologists having much lower fees of both types.

Table 22. Professional neuropsychology identity, basic demographics, and time spent in professional activity

	Professional neuropsychology identity
	Pediatric only			Adult only			Lifespan			Not a neuropsychologist^a
	n	Mean	SD	n	Mean	SD	n	Mean	SD	n	Mean	SD
Age	198	42.1	9.2	700	46.5	10.9	262	50.9	10.9	26	54.1	12.1
Years in clinical practice	199	10.1	9.2	710	13.8	10.5	265	18.2	10.9	26	20.6	12.2
Forensic neuropsychology hours/week	35	4.3	6.0	335	8.9	9.4	160	10.2	10.8	9	15.8	16.2
Hours for clin eval.	195	12.5	3.9	700	9.1	3.6	260	11.2	5.2	20	13.6	7.4
Avg. outpatient eval time (h) for diagnosis	169	7.0	5.7	628	6.7	5.4	240	7.5	5.9	16	3.7	3.1
Avg. outpatient eval time (h) for tx. planning	131	4.5	4.7	501	3.4	4.0	207	4.9	5.4	15	3.6	5.1
Avg. outpatient eval time (h) for baseline testing with probable later testing	139	7.1	4.2	513	5.7	4.0	197	6.0	4.9	10	3.5	2.9
Avg. educational eval time (h)	122	6.7	5.1	247	6.5	4.4	179	8.0	5.3	8	9.0	6.5
Avg. forensic eval time (h)	42	13.0	5.2	388	13.2	5.6	190	14.4	5.8	11	11.9	8.3
Hourly clinical fee	123	273.1	91.0	400	240.7	93.3	211	232.7	86.3	18	156.4	54.9
Hourly forensic fee	32	362.3	96.3	307	320.0	86.1	166	320.2	90.3	5	250.0	61.2

Notes: Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees. Forensic hourly fee and hourly clinical fee exclude erroneous numbers (e.g., zeros) and outliers (i.e., tails trimmed at 5th and 95th percentiles).

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist. With ns less than 10, reported income values may be less reliable or unreliable.

Finally, income information pertinent to professional identity is presented in Table 23. It is apparent that incomes vary substantially across professional identities, with those not identifying as neuropsychologists and pure pediatric respondents reporting the lowest mean and median incomes. Adult neuropsychologists report much higher mean and median incomes and, comparatively, the lifespan respondents report even greater mean and median incomes, with the latter group’s data showing a much higher SD. Figure 4 provides a graphic comparison of 2005, 2010, and 2015 mean incomes (top) and median incomes (bottom), broken down by professional identity. It is clear that for the three neuropsychology identity groups there has been a meaningful increase at each five-year interval for both mean and median incomes. Interestingly, those without a neuropsychology identity have shown almost no increase from 2010 to 2015.

Table 23. Annual incomes by professional neuropsychology identity

					Percentile
Neuropsychology identity	n	Mean	Median	SD	25	75	95	99	Min–Max
Pediatric only	186	119.7	100.0	63.5	85.8	125.6	241.3	485.9	50.0–525.0
Adult only	671	139.0	115.0	74.7	98.0	150.0	300.0	459.2	55.1–830.0
Lifespan	248	176.3	145.0	110.1	110.0	203.8	400.0	655.9	50.0–916.0
Not a neuropsychologist^a	25	114.2	110.0	40.1	92.5	0.128	225.6	0.240	50.0–240.0

Note: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Figure 4. Five-year income comparisons by professional neuropsychology identity. Note: Not a neuropsychologist refers to a self designation indicating identity as a psychologist, but not as a neuropsychologist.

General work settings

As noted previously, with regard to Table 6, there has been a general shift from working in private practice in the last five years, with increasing movement into institutional work. Table 24 presents general work setting income data. Respondents working in institutions reported the lowest mean and median incomes. Mean and median incomes were highest in private practice, the setting which also showed the highest SD in income data. Only at the 25th percentile is the private practice and combined institution/private practice settings comparable. For higher percentiles, there are substantially higher incomes favoring those in private practice, compared to the other two general work settings.

Table 24. Annual incomes by general work setting

					Percentile
General work setting	n	Mean	Median	SD	25	75	95	99	Min–Max
Institution only	619	113.4	104.0	37.4	92.0	125.0	184.0	266.0	50.0–387.0
Private practice only	227	194.0	156.0	121.5	110.0	240.0	450.0	672.0	50.0–916.0
Institution and private practice	284	168.6	141.0	92.1	112.3	197.5	350.0	516.5	50.0–860.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

Figure 5 compares 2005, 2010, and 2015 income data by work setting. There are clear increases across each five-year interval mean and median incomes for all three general work settings. The steepest increases appear to have occurred for the private practice group, whereas the interval increases appear considerably smaller in the institution only group.

Figure 5. Five-year income comparisons by neuropsychology practice setting.

For a final look at general work setting incomes, Table 25 shows incomes associated with years in licensed clinical practice by general work setting. Incomes of institutional respondents appear to lag behind those of private practice respondents and those working in both settings at every level of years in practice. The highest mean and median incomes for institutional respondents occurs in the greater than 25 year level for all three groups; for institution respondents the highest mean is 152.9, whereas the highest mean for private practitioners is 250.5, and for the combined setting respondents the highest mean is 229.4. Whereas both latter groups show steep mean income increases in years 6 through 20, those in institutional employment barely show an increase in this same time frame, without even considering the negative effects of inflation over that many years.

Table 25. Incomes by general work setting and years in clinical practice

	General work settings
	Institution			Private practice			Institution and private practice			All settings
	n	Mean	Median	n	Mean	Median	n	Mean	Median	n	Mean	Median
<1–5	238	96.0	92.0	27	110.2	106.0	41	107.4	108.0	306	98.8	93.2
6–10	143	110.6	103.0	33	122.9	110.0	58	144.7	130.0	234	120.8	105.0
11–15	67	119.2	115.0	24	185.2	155.5	57	154.8	140.0	148	143.6	125.0
16–20	48	118.6	110.5	29	216.3	220.0	35	194.4	160.0	112	167.6	136.0
21–25	48	132.9	123.5	38	190.9	160.0	44	186.9	150.0	130	168.2	142.5
>25	75	152.9	137.0	76	250.5	220.0	49	229.4	210.0	200	208.7	170.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

Specific work settings

Table 26 shows a detailed breakdown of specific employment positions for those working within institutions, including specific type of institution, department, academic rank, and position title. Primary university hospital/academic medical centers and academic affiliate hospital/medical centers continue to be, by far the largest institutional employment settings, accounting for 44.8% of institutional employment. The top five departments in which respondents are most frequently employed are: psychology (24.1%), neuropsychology (17.1%), psychiatry (15.6%), neurology (14.8%), and rehabilitation (10.0%). These percentages represent a change from 2010, with neuropsychology and neurology moving up in the rankings. For 19.3% of respondents, academic rank is not applicable, which is a dramatic decrease from over 40% in 2010. Perhaps reflecting the continued influx annually of a large number of entry-level neuropsychologists, instructors/lecturers (25.3%) outnumber assistant professors (20.5%) who outnumber associate professors (19.3%) who outnumber professors (12.6%). A staff position title of neuropsychologist or psychologist was applicable to 64.2%, with the next two most frequent titles being clinical program director at 10.8% and postdoctoral trainee at 11.9%.

Table 26. Breakdown of specific settings, departments, academic ranks, and position titles within institutions^a

	Frequency	%
Specific institutional setting
Primary university hospital/academic med center	292	28.5
Academic affiliate hospital/med center	167	16.3
Public general hospital (non-academic)	51	5.0
Private general hospital (non-academic)	61	6.0
Public specialty hospital (e.g., psych, rehab)	22	2.2
Private specialty hospital (e.g., psych, rehab)	64	6.3
Outpatient free-standing general clinic	17	1.7
Outpatient free-standing specialty clinic	43	4.2
VA hospital/med center (academic affiliated)	56	5.5
VA hospital/med center (non-academic affiliated)	81	7.9
Military hospital	24	2.3
Governmental/municipal hospital/clinic (e.g., state)	9	.9
College/university, no medical/doctoral program	20	2.0
College/university, with doctoral program	44	4.3
Doctoral psychology program (medical/educ setting)	2	.2
Research foundation (non-hospital)	4	.4
State prison or other correctional facility	7	.7
Other	52	5.1
Institutional department
Neuroscience	41	4.0
Neurosurgery	13	1.3
Neurology	151	14.8
Neuropsychology	175	17.1
Occupational medicine	1	.1
Orthopedic surgery	3	.3
Pediatrics	22	2.2
Psychiatry	159	15.6
Psychology	246	24.1
Primary care/family medicine/internal medicine	9	.9
Rehabilitation/physiatry/physical medicine	102	10.0
Not applicable	12	1.2
Other	87	8.5
Institutional academic rank
Lecturer/instructor	217	25.3
Assistant professor	176	20.5
Associate professor	108	19.3
Professor	57	12.6
Emeritus	122	14.2
Not applicable	166	19.3
Other	17	2.0
Institutional position title
Postdoctoral trainee	135	11.9
Staff neuropsychologist/psychologist	64.2	646
Clinical program director	109	10.8
Research program director	36	3.6
Clinical training director	40	4.0
Division head	63	6.3
Vice or associate or assistant chair	9	.9
Department chair	20	2.0
Other	81	8.1

^a Includes all respondents who work in institutional and combined institutional/private practices.

Tables 27–30 list incomes related to specific institutional settings, primary department of employment, academic rank, and position title, respectively. As might be expected and continuing from past surveys, it is apparent that each of these variables has a substantial impact on annual income, though it is also quite evident that the income range for each breakdown is very substantial. Outpatient free-standing general clinics have the highest mean and median incomes within specific institutional settings. However, these results are based on only 14 respondents, suggesting that this is not a common employment setting. Considering only those institutional settings with more than 10 respondents, public specialty hospitals have the lowest mean and median incomes. Among departments, the highest mean income was reported by the relatively small number of respondents working in neurosurgery departments, with lowest mean and median incomes reported by much larger numbers of employees in psychology and neuropsychology departments, as well as the small number in pediatrics departments. Not surprising, as they are associated with years in clinical practice, as rank increases so do the reported mean and median incomes. Similarly, some of the position titles commonly associated with increased professional responsibility show higher incomes, with clinical program directors, clinical training directors, and research program directors reporting higher mean incomes than staff line positions. However, there are noteworthy exceptions, such as vice, associate, and assistant chairs showing higher mean and median incomes than department chairs. Perhaps this reflects other salient institutional differences, such as medical settings vs. non-medical settings or the fact that there are fewer than 10 individuals reporting incomes for vice, associate, and assistant chair positions, making for less reliable data (n.b., the SD is extremely large). Also noteworthy, there are 47 individuals who carry institutional position titles other than the seven that were explicitly offered. Clearly, neuropsychologists working in institutions have a wide range of roles.

Table 27. Annual incomes by specific institutional setting^a

Specific institutional setting	n	Mean	Median	SD	Min–Max
Primary university hospital or AMC^b	246	129.0	112.0	56.5	50.0–425.0
Academic affiliated hospital or AMC^b (non-VA)	134	119.7	103.0	53.4	50.0–500.0
Public general hospital (non-academic)	39	114.2	105.0	31.3	62.5–200.0
Private general hospital (non-academic)	49	120.9	112.8	36.3	70.0–205.0
Public specialty hospital (e.g., psych, rehab)	19	109.9	109.0	24.3	75.0–184.0
Private specialty hospital	52	120.1	108.5	37.6	60.0–210.0
Outpatient free-standing clinic (general)	14	158.3	125.0	89.1	76.0–387.0
Outpatient free-standing clinic (specialty)	37	120.2	113.6	41.4	72.0–270.0
VA hospital/medical center (academic affiliate)	51	124.4	107.0	53.4	82.0–400.0
VA hospital/medical center (no academic aff)	73	109.6	102.0	30.1	55.1–250.0
Military hospital	20	128.3	124.0	33.5	66.0–200.0
Governmental/municipal hospital or clinic (e.g., state hospital)	5	104.4	106.0	18.1	80.0–130.0
University/college, with no doctoral program	11	115.5	120.0	43.5	64.0–220.0
University/college, with doctoral program	30	129.7	117.4	67.7	50.0–400.0
State prison	4	140.8	117.0	61.3	99.0–230.0
Other	31	128.9	120.0	41.8	71.2–240.0

^a Includes all licensed clinicians who work full-time or more in an institutional setting and licensed clinicians who work in a combined institutional/private practice setting and spend at least 80% of their time at the institutional setting. Excludes postdoctoral trainees and income outliers (<$50,000). Incomes reflect institutional incomes only and are in 1000s of dollars. With ns less than 10, reported income values may be less reliable or unreliable. Note that salaries for military service (outside of military hospital), doctoral psychology degree program in medical setting, and research foundation (non-hospital) are not reported as n = 1.

^b AMC refers to academic medical center.

Table 28. Annual incomes by institutional department^a

Institutional department	n	Mean	Median	SD	Min–Max
Neuroscience	30	140.3	125.0	49.4	85.0–310.0
Neurosurgery	10	161.1	122.5	99.8	95.0–425.0
Neurology	125	127.9	115.0	48.2	70.0–320.0
Neuropsychology	145	117.5	105.0	48.8	50.0–400.0
Orthopedic surgery	3	160.0	160.0	15.0	145.0–175.0
Pediatrics
Psychiatry	128	126.0	116.0	45.4	60.0–336.0
Psychology	188	115.8	104.3	41.7	50.0–400.0
Primary care/family practice/internal medicine	6	134.2	125.0	32.3	103.0–190.0
Rehabilitation/physiatry/physical medicine	86	122.8	106.0	59.7	60.0–500.0
Not applicable	7	109.2	102.0	33.1	76.0–175.0
Other	72	126.8	110.0	54.7	55.1–384.0

^a Includes all licensed clinicians who work full-time or more in an institutional setting and licensed clinicians who work in a combined institutional/private practice setting and spend at least 80% of their time at the institutional setting. Excludes postdoctoral trainees and income outliers (<$50,000). Incomes reflect institutional incomes only and are in 1000s of dollars. With ns less than 10, reported income values may be less reliable or unreliable.

Table 29. Annual incomes by institutional academic rank

Institutional academic rank	n	Mean	Median	SD	Min–Max
Not applicable	105	112.6	103.5	29.7	55.1–197.0
Lecturer/instructor	191	107.0	100.0	27.8	50.0–230.0
Assistant professor	140	124.0	115.0	40.0	50.0–318.0
Associate professor	91	166.6	150.0	60.9	70.0–384.0
Professor	34	216.2	200.0	99.4	78.7–500.0
Emeritus	104	113.2	103.1	40.3	64.0–387.0
Other	7	143.3	148.0	44.7	75.0–198.0

Notes: Includes all licensed clinicians who work full-time or more in an institutional setting and licensed clinicians who work in a combined institutional/private practice setting and spend at least 80% of their time at the institutional setting. Excludes postdoctoral trainees and income outliers (<$50,000). Incomes reflect institutional incomes only and are in 1000s of dollars. With ns less than 10, reported income values may be less reliable or unreliable.

Table 30. Annual incomes by institutional position title

Institutional position title	n	Mean	Median	SD	Min–Max
Staff neuropsychologist/psychologist	523	109.3	102.0	33.1	50.0–387.0
Clinical program director	89	134.5	126.0	43.6	65.0–400.0
Research program director	29	157.6	130.0	78.1	79.0–425.0
Clinical training director	36	125.1	118.0	32.2	81.0–207.0
Division head	58	117.6	160.0	65.9	70.0–355.0
Vice, associate, or assistant chair	8	215.4	175.0	119.9	135.0–500.0
Department chair	19	178.0	145.0	79.1	100.0–384.0
Other	47	124.9	106.0	57.9	50.0–400.0

Notes: Includes all licensed clinicians who work full-time or more in an institutional setting and licensed clinicians who work in a combined institutional/private practice setting and spend at least 80% of their time at the institutional setting. Excludes postdoctoral trainees and income outliers (<$50,000). Incomes reflect institutional incomes only and are in 1000s of dollars. With ns less than 10, reported income values may be less reliable or unreliable.

Bases for determining income

The bases for determining institutional income can potentially play a role in influencing incomes. Table 31 shows the frequency of individuals reporting five possible bases for determining income. A relatively small number of respondents are paid according to amount of money collected for services or for hours billed for services. A larger group is paid in relationship to RVUs for services provided. A fourth option of a combination of these bases was reported by an even larger group. However, the largest group by far reported a basis for income that is different from the options listed. We are not able to identify these different bases, of which there could be many. Interestingly, by far, the largest mean and median incomes were reported by the relatively small group that is paid on the basis of money collected for services rendered. Those paid for hours billed reported the lowest mean and median incomes. For those paid by RVUs generated, there has been much discussion regarding reasonable expectations within and across institutions. For this reason, mean, median, SD, select percentiles, and minimum–maximum incomes are reported for the 106 individuals who provided monthly RVUs in their practices.

Table 31. Annual incomes by basis for income

					Percentile
Basis for income	n	Mean	Median	SD	25	75	95	99	Min–Max
Amount of money collected for services provided	46	126.5	125.0	39.9	96.5	140.5	225.8	245.0	60.0–245.0
Relative value units (RVUs) for services provided	102	115.8	100.5	45.4	90.0	125.0	197.0	384.0	60.0–387.0
Hours billed for services provided	46	106.1	98.0	27.8	88.6	120.0	178.0	200.0	68.5–200.0
A basis for professional income other than the above	300	111.2	105.0	33.9	93.1	120.0	164.8	249.7	50.0–384.0
A combination of the above bases	125	114.7	104.0	39.8	90.7	127.3	193.2	304.5	50.0–325.0
	n	Mean	Median	SD	25	75	95	99	Range
Monthly RVUs, when relevant^a	106	203.1	200.0	83.6	147.5	250.0	366.5	462.3	40.0–467.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more; excludes postdoctoral trainees and those earning <$50,000 annually.

^a A subset from institutions only.

Past surveys have made it quite clear that a substantial number of individuals working in institutions also work outside the institution in private practice. Presumably, this extra work allows individuals to earn higher incomes. Table 32 is an attempt to demonstrate the possible added income value of engaging in extra private practice work by juxtaposing incomes from individuals who work solely in institutions with incomes of individuals who work in institutions at least 80% of their time and also work in private practice. Only institutional settings with more than 10 individuals reporting incomes are included, which provides seven points of comparison, all of which make it clear that the combination of institution and private practice work has a substantial impact on income.

Table 32. Incomes separated by institution and private practice

	General work setting
	Institution only (n = 665)				Institution and private practice (n = 197)
Specific institutional work setting	n	Mean	Median	SD	n	Mean	Median	SD
Primary university hospital or AMC^a	203	121.4	108.5	47.9	43	164.6	138.0	77.5
Academic affiliated hospital or AMC^a (non-VA)	100	104.8	98.4	27.4	34	163.4	142.5	81.1
Private specialty hospital	39	108.6	100.4	31.3	13	154.6	157.0	34.1
VA hospital/medical center (academic affiliate)	34	106.4	103.5	18.9	17	160.6	127.0	80.1
VA hospital/medical center (no academic aff)	58	104.0	100.0	22.5	15	131.0	112.0	44.3
University/college, with doctoral program	14	125.9	125.9	47.2	16	133.1	113.0	83.0
Other	17	120.5	116.0	40.5	14	139.3	142.5	42.4

Notes: Includes all licensed clinicians who work full-time or more in an institutional setting and licensed clinicians who work in a combined institutional/private practice setting and spend at least 80% of their time at the institutional setting. Excludes postdoctoral trainees and income outliers (<$50,000). Incomes reflect institutional incomes only and are in 1000s of dollars. Institutional settings with n < 10 were excluded.

^a AMC refers to academic medical center.

Similar to varying employment roles in institutional settings, private practice roles that potentially affect income can also vary. Excluding postdoctoral trainees, in the overall sample, approximately 66% of private practitioners are sole proprietors, with 8.9% working as partners, 10.2% as employees, and 12.1% as outside contractors. For the subset who reported private practice incomes and who work at least 80% of their work week in private practice, Table 33 shows various roles and associated incomes. Sole proprietors, who outnumber all others, report the highest mean and median incomes, except for the individuals who chose “other” as their role. Employees and outside contractors in a private practice owned by someone else report substantially lower mean and median incomes.

Table 33. Psychology incomes and private practice roles

Private practice role	n	%	Mean	Median	SD
Sole proprietor	160	62.8	208.3	170.0	139.5
Partner	36	14.1	185.5	152.5	104.5
Employee	37	14.5	137.0	120.0	55.5
Outside contractor	12	4.7	145.9	98.0	91.9
Other	10	3.9	254.7	225.8	60.9
Total	255	100.0

Notes: Includes licensed clinicians who work full-time or more in a private practice and licensed clinicians who work full-time or more in a combined institutional/private practice setting, with at least 80% of their time at the private practice. Incomes reflect private practice incomes only and are in 1000s of dollars. Excludes postdoctoral trainees and income outliers (<$50,000).

Clinical productivity expectations

Some institutional employers create and apply productivity expectations in a manner that can affect income. Data related to this topic are presented in Table 34. These data reveal an interesting pattern. Simply creating a quota or clinical productivity expectation is not associated with higher mean or median incomes. In fact, this approach produces substantially lower mean and median incomes when compared to clinical productivity approaches that can lead to increases (e.g., bonuses) or decreases in income. These differences are present despite the fact that hourly fees charged are higher in all three productivity approaches, not just the two associated with higher incomes. In fact, the higher difference between presence and absence of productivity standards being applied is highest in the least effective productivity approach (36 dollars per hour compared to approximately 13–15 dollars). Nevertheless, there is very little difference in income satisfaction, job satisfaction, and work–life satisfaction balance across the three groups.

Table 34. Clinical productivity expectations: Institutional income, hourly fee, income satisfaction, job satisfaction, and work–life satisfaction

	Productivity expectation
	Is your income based on a quota or clinical productivity expectation?		Does your clinical productivity allow you to increase income?		Could your clinical productivity result in a decrease in income?
Variable	Yes	No	Yes	No	Yes	No
Psychology income
n	484	356	169	310	110	365
Mean	111.8	111.7	121.9	106.4	125.0	108.2
Median	104.3	103.0	112.0	102.0	112.5	102.0
SD	33.3	37.7	42.1	25.9	45.7	27.6
Hourly fee
n	269	176	115	154	74	193
Mean	267.1	231.1	274.7	261.4	278.3	262.9
Median	250.0	205.0	252.0	250.0	250.0	250.0
SD	96.0	93.5	93.1	98.1	90.9	98.2
Income satisfaction
n	474	348	168	306	109	361
Mean	71.8	73.2	73.8	70.7	70.9	72.3
Median	80.0	80.0	80.0	80.0	80.0	80.0
SD	22.6	23.0	20.9	23.5	22.4	22.6
Job satisfaction
n	471	347	167	304	108	360
Mean	77.3	78.0	77.9	77.0	74.1	78.5
Median	84.0	83.0	85.0	80.0	80.0	85.0
SD	18.7	19.3	19.6	18.2	22.5	17.1
Work–life satisfaction
n	470	340	167	303	109	357
Mean	66.8	71.5	67.2	66.6	65.4	67.5
Median	75.0	80.0	75.0	75.0	70.0	75.0
SD	25.6	24.3	25.0	25.9	24.5	25.6

Notes: For all licensed clinicians who work full-time or more in institutions or institutions/private practice; excludes postdoctoral trainees and income outliers ($<50,000). Incomes are expressed in 1000s of dollars.

Effects of health care changes on clinical practice

Table 35 provides a rank order listing of possible effects of recent health care changes on clinical practice. Of the 11 possibilities offered for ranking, which included possible negative and positive effects, four stand out as having been more frequently identified. In order, these are decreased reimbursement (65.2%), being excluded from seeing certain patients or receiving referrals from certain sources (44.6%), having to perform shorter evaluations (42.4%), and having an increased caseload (38.2%). All other choices were endorsed by under 20% of respondents. Notably, very few respondents reported increased income, increased reimbursement, or being able to perform longer evaluations as a result of recent health care changes.

Table 35. Influence of recent US health care system changes on clinical practice

Variable	Frequency	%
Decreased reimbursement	567	65.2
Excluded from seeing certain patients/receiving referrals from certain sources	388	44.6
Shorter evaluations	369	42.4
Increased caseload	332	38.2
Difficulty maintaining income	161	18.5
Loss of income	147	16.9
Increased access to patients and referrals	117	13.4
Decreased caseload	68	7.8
Increased income	18	2.1
Increased reimbursement	14	1.6
Longer evaluations	8	.9

Note: n = 870.

Houston conference influence

Table 36 shows a breakdown of self-appraised compatibility of training with the Houston Conference training guidelines across years of licensed experience. With the percent of current postdoctoral trainees and the percent of individuals licensed within the last five years both well over 90%, there seems little question that the Houston Conference guidelines have had a very significant impact on training to become a specialist in clinical neuropsychology. As one might expect, the greater the number of years of licensed experience that a neuropsychologist reports, the less likelihood of reporting that prior training adhered to the Houston training guidelines, though many even prior to publication of the guidelines apparently had already been following a similar training model.

Table 36. Houston conference compatibility with training

“To the best of your knowledge, did your training in clinical neuropsychology follow the recommendations of the Houston Conference on Specialty Education and Training?”
By years of experience (n = 1713)	Frequency	%
Postdoctoral trainees
Yes	182	93.3
No	8	4.1
I don’t know	5	2.6
≤1–5 years post training
Yes	389	94.2
No	10	2.4
I don’t know	14	3.4
≤6–10
Yes	276	89.0
No	27	8.7
I don’t know	7	2.3
11–19
Yes	248	84.4
No	26	8.8
I don’t know	20	6.8
20–29
Yes	199	65.0
No	74	24.2
I don’t know	33	10.8
30–39
Yes	67	39.6
No	69	40.8
I don’t know	33	19.5
40±
Yes	8	30.8
No	14	53.8
I don’t know	4	15.4

Note: All licensed clinicians.

Philosophical approach toward test selection

Figure 6 shows a comparison of survey responses dating back to 1989 regarding philosophical approach toward test selection. The trend upward for the flexible battery has continued, with current endorsement at 82%. Similarly, the trend downward of the fixed/standardized battery approach has continued, now at only 3% endorsement. The flexible approach has declined to 14%.

Figure 6. Primary philosophical approach toward test selection. “Flexible Battery” = variable but routine groups of tests for different types of patients, such as head injury, alcoholism, elderly, etc. “Flexible” = based upon the needs of an individual case, not uniform across patients. “Fixed/Standardized Battery” = routine group of tests uniform across patients, such as Halstead-Reitan, Luria-Nebraska, Benton, or other standard battery.

Testing assistants

For the approximately 55% of the practitioners who rely on the services of testing assistants, Table 37 shows a number of associated significant and non-significant findings. Length of evaluation time is briefer and fees charged are higher among those who use assistants, with these variables showing a medium effect size. Interestingly, although income satisfaction is higher, actual incomes are not different. Also, work–life balance is not different based on use of testing assistants.

Table 37. Findings associated with use of a testing assistant

	“Do you use a technician/psychometrician or other assistant to collect test data from your patients?”
	Yes			No
	n	Mean	SD	n	Mean	SD	t	d
Avg. number of hours for a single evaluation^a	733	9.8	3.8	584	11.0	5.0	4.9^**	.3
Weekly clinical hours^b	701	26.5	15.0	522	25.4	16.3	1.3	.1
Hourly clinical fee^b	419	251.7	91.6	341	227.4	91.1	3.7^**	.3
Estimated gross psychology income^b	657	144.1	80.5	499	142.8	87.3	.3	.0
Discrepancy b/w time spent/billed^b	639	1.7	2.0	477	1.9	2.5	1.3	.1
Income satisfaction^a	723	74.1	21.9	580	70.2	25.1	3.0^*	.2
Job satisfaction^a	717	79.5	18.6	574	77.6	22.0	1.7	.1
Work–life balance^a	710	69.7	24.9	567	68.8	25.8	1.1	.0

^a Excludes postdoctoral trainees.

^b Includes all licensed clinicians who work full-time or more; excludes postdoctoral trainees and income outliers (<$50,000). Incomes are expressed in 1000s of dollars.

^* p < .01;

^** p < .001.

Table 38 shows the percentages of paid and unpaid assistants, as well as those who are provided benefits in addition to pay. It is notable that psychometricians are well represented among the numerous types of assistants, but are far from omnipresent. Slightly more than three-fourths of assistants receive benefits, but, surprisingly, nearly 20% of psychometricians and approximately 10% of postdoctoral trainees are not provided benefits.

Table 38. Types of testing assistant utilized, by pay and benefits

				Benefits provided?
	n^a	% of the sample	% who use specific type of assistant	n	Yes (%)	No (%)
Types of assistant
Paid predoctoral trainees	275	19.7	38.7	247	58.7	41.3
Paid doctoral-level staff	81	5.8	11.4	68	57.4	42.6
Paid postdoctoral trainees	228	16.3	32.1	209	90.4	9.6
Paid paraprofessionals (i.e., psychometricians)	556	39.8	78.3	501	80.6	19.4
Unpaid postdoctoral trainees	15	1.1	2.1
Unpaid trainees	200	14.3	28.2
Total^a	710	50.8		629	78.4	21.6

Note: Includes licensed clinicians who work full-time or more; excludes postdoctoral trainees.

^a Total n will not equal the sum of the frequencies provided in this column, as some respondents use more than one type of assistant.

Factors determining duration of evaluation

Table 39 shows the hours needed to complete evaluations, broken down by referral questions and referral context. Understandably, inpatient evaluations for determination of diagnosis and treatment planning are much briefer than outpatient evaluations of all types. Not surprisingly, the lengthiest evaluation time is for forensic evaluations, which is substantially longer than the next lengthiest category of education evaluations. In two of the outpatient categories, as well as the educational evaluation and the forensic evaluation, the maximum hours reportedly needed approached or reached the allowable limit of 25 h. In each category of evaluation type, there is a wide range of times, which likely are related to a variety of the factors addressed in Table 40, which identifies factors potentially affecting evaluation length. Respondents were asked to identify the “top five” factors affecting time needed to complete an evaluation. The five most frequently endorsed factors affecting evaluation time were in order of: (1) the goal of evaluation; (2) stamina/health of examinee; (3) age of examinee; (4) sensory, motor, cognitive limitation; and (5) context (clinical vs. forensic).

Table 39. Hours needed to complete evaluations related to referral questions and referral context

					Percentile
Referral question	n	Mean	Median	SD	25	75	95	99
Determination of diagnosis (inpatient)	565	3.3	2.0	3.2	1.0	4.0	9.0	20.0
Determination of diagnosis (outpatient)	1170	6.8	6.0	5.5	2.0	10.0	20.0	24.5
Treatment planning (inpatient)	445	2.5	1.0	3.1	.5	3.0	10.0	15.5
Treatment planning (outpatient)	950	3.9	2.0	4.5	1.0	6.0	12.0	20.0
Establishing baseline of function with probable subsequent testing (inpatient)	486	3.5	3.0	3.0	1.0	5.0	10.0	12.0
Establishing baseline of function with probable subsequent testing (outpatient)	934	6.4	6.0	4.6	3.0	9.0	15.0	25.0
Educational evaluation (excludes educational due process)	628	7.6	7.0	5.3	3.0	11.0	18.0	24.0
Forensic evaluation	765	13.5	13.0	5.8	10.0	18.0	24.5	24.5

Notes: Excludes postdoctoral trainees. The maximum allowable number of hours for any referral question was 25 h. The range for all referral questions and contexts was .5–25 h.

Table 40. “Choose Top Five” factors affecting the length of an evaluation

Factor	%
Goal of evaluation	80.9
Stamina/health of examinee	71.3
Age of examinee	64.8
Sensory, motor, cognitive limitation	54.2
Context (clinical vs. forensic)	45.6
Orientation/confusion	43.2
Rarity of condition	27.1
Comorbid condition	25.2
Re-evaluation	23.5
Reimbursement factors	16.9
Multiple report consumers	16.0
Employer limits	12.2
Scarcity of relevant norms	10.6

Notes: All licensed clinicians; excludes postdoctoral trainees. n = 1555.

Table 41 shows the percentages of item endorsements for all 14 factors, broken down by professional identity. Whereas age was identified as the number one factor affecting evaluation length for pediatric neuropsychologists, the goal of the evaluation was the number one factor affecting evaluation length for the other three groups. The second ranked factor for pediatric neuropsychologists was the goal of the evaluation, whereas for the other three groups the second most important factor was stamina/health of examinee. The majority of the top five factors are the same across all four groups, even though in slightly different order. The same is true for those rankings near the bottom. It is somewhat comforting to see that scarcity of relevant norms and limits placed on evaluation by employers appear near the very bottom of the table.

Table 41. Factors affecting the length of an evaluation by professional identity

Pediatric only		Adult only		Lifespan		Not a neuropsychologist^a
Factor	%	Factor	%	Factor	%	Factor	%
Age	92.3	Goal of evaluation	81.7	Goal of evaluation	76.8	Goal of evaluation	93.3
Goal of evaluation	82.0	Stamina/health of examinee	75.1	Stamina/health of examinee	70.3	Stamina/health of examinee	56.7
Sensory, motor, cognitive limitation	61.3	Age	56.4	Age	68.3	Sensory, motor, cognitive limitation	53.3
Stamina/health of examinee	60.8	Sensory, motor, cognitive limitation	55.4	Context (clinical vs. forensic)	52.6	Age	50.0
Comorbid condition	33.8	Orientation/confusion	53.7	Sensory, motor, cognitive limitation	45.8	Rarity of condition	40.0
Multiple report consumers	31.5	Context (clinical vs. forensic)	50.9	Orientation/confusion	40.5	Orientation/confusion	33.3
Re-evaluation	31.5	Rarity of condition	26.7	Comorbid condition	27.1	Reimbursement	30.0
Rarity of condition	29.7	Re-evaluation	22.9	Multiple report consumers	25.5	Comorbid condition	30.0
Reimbursement	19.8	Comorbid condition	21.9	Rarity of condition	25.2	Context (clinical vs. forensic)	26.7
Context (clinical vs. forensic)	19.4	Employer limits	13.3	Reimbursement	24.2	Employer limits	23.3
Orientation/confusion	10.4	Reimbursement	12.8	Re-evaluation	20.3	Re-evaluation	13.3
Employer limits	8.6	Scarcity of relevant norms	11.8	Scarcity of relevant norms	11.1	Multiple report consumers	10.0
Scarcity of relevant norms	6.3	Multiple report consumers	8.3	Employer limits	10.8	Scarcity of relevant norms	6.7

Notes: All licensed clinicians; excludes postdoctoral trainees. Percents represent individuals identifying factor as affecting length of evaluation.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Most common referral sources

Table 42 shows the top five rankings of referral sources by the key variables of general work setting and by professional identity. Across work settings and professional identities, neurology was the number one referral source, as it had been in 2010. Although not always appearing in the same exact order, rankings two through five tend to appear in all three general work settings, with the exception that law (attorney) is not present in the institution rankings, psychiatry and physiatry are not present in the private practice rankings, and pediatrics is not present in the combined institution/private practice rankings.

Table 42. Top five rankings of referral sources evaluated in neuropsychological assessment by general work setting and professional identity

	General work setting
Rank	Institution only	Private practice only	Institution and private practice
1	Neurology	Neurology	Neurology
2	Primary care medicine^a	Law (attorney)	Psychiatry
3	Physiatry	Primary care medicine^a	Primary care medicine^a
4	Psychiatry	Pediatrics	Physiatry
5	Pediatrics	Self-referral	Law (attorney)
	Professional identity
Rank	Pediatric only	Adult only	Lifespan
1	Neurology	Neurology	Neurology
2	Pediatrics	Primary care medicine^a	Primary care medicine^a
3	Other	Psychiatry	Pediatrics
4	Self-referral	Physiatry	Psychiatry
5	School system	Law (attorney)	Law (attorney)

Notes: Excludes postdoctoral trainees. Rankings are frequency based, as determined by the number of times a factor was ranked number one by respondents.

^a Primary care medicine includes family medicine and internal medicine.

Regarding professional identity, there are three unique rankings in the pediatric identity: “other,” self-referral, and school system ranked as third, fourth, and fifth, respectively. Limiting comparisons to adult and lifespan rankings, these share four of the same referral sources in the top five, with physiatry uniquely in the adult rankings and pediatrics uniquely in the lifespan rankings.

Most common diagnoses of examinees

As was the case with the 2005 and 2010 surveys, respondents in 2015 were asked to rank the top five diagnostic conditions for which examinees were referred. Table 43 shows the results from the present and two preceding surveys, broken down by professional identity. Whereas in 2005, ADHD did not make the top five for adult neuropsychologists, in 2010 ADHD made the top five for all three professional identities, which is also true in 2015. The top three for professionals serving adult patients have not changed across the five-year intervals, with elderly dementias, closed head injury/traumatic brain injury, and stroke or cerebrovascular accident, in that order. Although the category of “other medical/neurological conditions” did not make any of the three top five lists in 2005, in 2010 this category was fourth for the adult neuropsychologists and fifth for the lifespan neuropsychologists. In 2015, this category is only present in the top five as a tie for the pediatric identity and is not present in the other two identities shown in Table 43. Also interesting, whereas the diagnostic category of learning disabilities was ranked first in 2005 and second in 2010 for those with a pediatric identity, it is not present anywhere in the top five in 2015. Similarly, this category has dropped from the 2015 top five rankings in the lifespan identity group. The diagnostic category of ADHD continues to be present in all three top five rankings in 2015, as it was in 2010, which is the first time this had occurred.

Table 43. Top five rankings in 2005, 2010, and 2015 of diagnostic conditions serving as bases for neuropsychological evaluation, delineated by professional identity

Professional identity	Rank	Top 5 conditions (2005)	Top 5 conditions (2010)	Top 5 conditions (2015)
Pediatric only	1	Learning disabilities	ADHD	ADHD
	2	ADHD	Learning disabilities	Seizure disorder
	3	PDD	Seizure disorder	CHI/TBI
	4	CHI/TBI	CHI/TBI	Brain tumor
	5	Seizure disorder	PDD	PDD/Other med/neuro conditions^a
Adult only	1	Elderly dementias	Elderly dementias	Elderly dementias
	2	CHI/TBI	CHI/TBI	CHI/TBI
	3	Stroke or CVA	Stroke or CVA	Stroke
	4	Mood disorder	Other med/neuro conditions	Seizure disorder
	5	Seizure disorder	ADHD	ADHD
Lifespan	1	CHI/TBI	CHI/TBI	CHI/TBI
	2	ADHD	ADHD	ADHD
	3	Learning disabilities	Elderly dementias	Elderly dementias
	4	Elderly dementias	Learning disabilities	Seizure disorder
	5	Stroke or CVA	Other med/neuro conditions	Other med neuro

Note: Rankings are frequency based, as determined by the number of times a factor was ranked number one by respondents.

Attention deficit/hyperactivity disorder (ADHD). Pervasive developmental disorder (PDD). Closed head injury/traumatic brain injury (CHI/TBI). Cerebrovascular accident (CVA).

^a PDD and other medical/neurological conditions were a tie (rated as “1” at the same frequency). Excludes postdoctoral trainees.

It is apparent from the chronological survey data presented in Table 43 that closed head injury/traumatic brain injury (CHI/TBI) has been a disorder associated with frequent referral to clinical neuropsychologists. Based on new items included in the 2015 survey instrument, Table 44 shows percentages of TBI patients seen by practitioners at each injury severity level in general work settings, as well as by professional identity and involvement in forensic activities. It is readily evident that mild TBI (mTBI) cases are very common in all three general work settings, with those working in institutions seeing a slightly lower percentage of mTBI cases and a higher percentage of severe TBI compared to neuropsychologists in private practice. Professional identity appears to have less of an effect on severity level of patients, with the exception of pediatric neuropsychologists, who are more likely to work in institutions, appearing to evaluate a higher percentage of severely injured patients. Whether a neuropsychologist is involved in forensic evaluations does not appear to have a notable association with TBI severity.

Table 44. Traumatic brain injury (TBI): Severity level by general work setting, professional identity, and forensic involvement

	TBI Severity
	% Mild (including concussion)			% Moderate			% Severe			Not applicable
	n	Mean	SD	n	Mean	SD	n	Mean	SD	n
General work setting
Institution only	552	68.5	27.3	535	18.6	14.7	450	17.7	20.8	39
Private practice only	267	74.6	20.9	247	18.3	13.3	181	10.6	11.7	8
Institution and private practice	294	67.3	24.8	284	20.6	15.2	246	16.2	18.6	11
Professional identity^a
Pediatric only	151	66.7	29.8	137	19.1	15.8	114	19.9	21	27
Adult only	687	70.5	25.0	668	18.5	14.6	556	15.4	18.9	19
Lifespan	280	69.2	23.4	266	20.4	13.5	210	14.7	16.9	12
Forensic involvement
Yes	659	69.2	23.5	647	19.2	13.7	538	15.2	17.9	10
No	459	70.2	27.7	424	18.8	15.7	342	16.8	20.0	48

Note: Includes all licensed clinicians; excludes postdoctoral trainees.

^a No respondent who self-identified as not being a neuropsychologist provided a response to this item.

ABCN board certification

ABCN continues to be the largest board certification enterprise in the specialty of clinical neuropsychology (cf. Cox, 2010). The present survey sample included the largest subsample of ABCN board-certified neuropsychologists conducted to date (n = 536). Table 45 shows a number of salient variables, including incomes, years of clinical experience, peer-reviewed publications, professional meeting presentations, professional volunteer time, and professional satisfaction ratings of the ABCN respondents compared to non-ABCN respondents who provided responses to these same survey items. Of the eight variables examined, seven showed significant group differences, with effect sizes ranging from small to medium; work satisfaction was not significantly different between ABCN-certified and non-ABCN certified respondents. Given that the difference in income, peer-reviewed publications, and professional meeting presentations might be accounted for by the significantly greater years of clinical practice among the ABCN group, analyses of covariance were carried out for these three variables. Results for all three variables remained significant at p < .001, even after covarying years of clinical practice.

Table 45. Mean differences of income, income satisfaction, and job satisfaction for ABCN and non-ABCN respondents

	ABCN status
	ABCN certified			Not ABCN certified
Variable	n	Mean	SD	n	Mean	SD	t	d
Gross psychology income	433	161.6	89.3	812	125.3	77.3	5.6^***	.4
Years of clinical practice	535	17.1	10.4	988	12.9	11.0	7.2^***	.4
Peer-reviewed publications^a	517	24.1	39.5	898	11.5	22.9	6.2^***	.4
Professional meeting presentations^b	513	32.9	46.6	882	18.5	32.7	5.7^***	.4
Professional volunteer activities (hours)	352	2.1	2.3	537	1.7	1.9	3.3^***	.2
Income satisfaction (0–100)	483	75.7	21.0	820	70.4	24.5	4.0^***	.2
Job satisfaction (0–100)	476	80.6	18.7	815	77.6	21.0	2.6^**	.1
Work–life balance satisfaction (0–100)	474	70.3	24.2	803	68.7	25.9	1.1	.1

Notes: Excludes postdoctoral trainees. Income values are in 1000s of dollars for respondents who are licensed and currently practicing full-time or more.

ANCOVAs comparing gross psychology income, number of peer-reviewed publications, and number of professional meeting presentations between ABCN status using years of licensed clinical practice as a covariate continued to result in statistically significant differences in all three variables at p < .001.

^a Includes primary and co-authorship.

^b Refers to presentations of original research in poster or oral form at a professional meeting that required abstract submission.

^*** p ≤ .001;

^** p ≤ .01.

Characteristics of ABCN respondents

Table 46 shows some of the basic characteristics of the ABCN sample. Average age is 49 years, with an average of 17 years of experience post-licensure. The overall sample is comprised nearly equally of women and men. However, when years of experience are considered, gender proportions vary tremendously. Specifically, for the group with fewer than 15 years of experience, women represent 64.4%, whereas in the group with 15 or more years of experience, women represent 35.4%.

Table 46. Characteristics of ABCN respondents

	n	Mean (SD)	Min–Max
Age	525	48.8 (10.6)	31.0–83.0
Years of licensed clinical practice	535	17.1 (10.4)	2.0–50.0
	Frequency	%
Degree
Ph.D.	459	85.6
Psy.D.	64	11.9
Other	13	2.4
Gender
Total ABCN sample
Women	265	50.2
Men	263	49.8
ABCN with <15 years experience
Women	172	64.4
Men	95	35.6
ABCN with ≥15 years experience
Women	92	35.4
Men	168	64.6
ABN certification (in addition to ABCN)
Yes	18	3.4
No	516	96.6
Field of doctoral study
Clinical psychology	414	77.7
Neuropsychology	36	6.8
Counseling psychology	38	7.1
School psychology	14	2.6
Educational psychology	2	.4
Neurosciences	3	.6
Biopsychology	7	1.3
Experimental psychology	4	.8
Other	15	2.8
Practice environment
Rural	36	6.7
Urban	443	83.0
Some time in both rural and urban settings	55	10.3
Work status
Full-time	447	83.6
Part-time	35	6.5
Combined (full-time + part-time)	47	8.8
Retired	5	.9
General work setting
Institution only	246	49.3
Private practice only	92	18.4
Institution and private practice	161	32.3
Specific work setting
Medical	264	53.8
Solo private or group private practice	118	24.0
Psychiatric	27	5.5
Rehabilitation	58	11.8
College/university	14	2.9
Other	10	2.0
Professional identity
Pediatric only	78	15.6
Adult only	310	62.0
Lifespan	112	22.4
“Do you work with a technician/psychometrist or other assistant to collect test data from patients?”
Yes	346	64.6
No	190	35.4
Philosophical approach to test selection
Flexible (i.e., based upon needs of the individual)	54	10.9
Flexible battery (i.e., variable but routine grouping)	431	87.1
Fixed/standardized (e.g., Halstead-Reitan)	10	2.0
“Do you conduct forensic evaluations (e.g., civil, criminal, disability, educational, ‘due process’, worker’s compensation, other adjudicatory processes)?”
Yes	357	67.0
No	176	33.0

As was true of the overall sample, the vast majority of ABCN respondents earned a Ph.D. as their doctoral degree, with the doctoral specialty having been in clinical psychology, practice in an urban environment, and work full-time. With regard to general work setting, ABCN respondents are more likely to be employed for at least part of their work week in institutions, with only 18% working only in private practice. With regard to specific work setting, more than half of ABCN respondents are employed in medical settings, which is a much higher percentage than any other specific work setting. Professional identity is that of an adult neuropsychologist for 62%, with the remainder involved to some extent in pediatric care. ABCN respondents are more likely than the overall sample to rely on testing assistants, with 64.6% doing so. The proportion of ABCN respondents endorsing the flexible battery approach is 5% higher than the overall sample. A relatively high proportion of ABCN respondents are involved in forensic evaluations. Eighteen members in the sample, representing 3.4%, of the ABCN sample also hold ABN certification.

Incomes of ABCN respondents

As with the general sample, Table 47 shows that ABCN respondents who spend time working in urban and rural environments report higher incomes than those who work in only one of these environments. Practicing only in a rural environment is associated with lower income. Middle Atlantic and Pacific ABCN respondents report the highest incomes, whereas those in the West North Central and the West South Central regions report the lowest incomes. Those in private practice report much higher incomes than those working in institutions only or in both settings. The mean income of ABCN respondents in private practice is in excess of 200,000 dollars.

Table 47. Incomes for ABCN respondents by practice environment, region, and general work setting

	n	Mean (SD)	Min–Max
Practice environment
Rural	30	140.0 (58.0)	66.0–350.0
Urban	361	164.1 (94.0)	60.0–860.0
Some time in both rural and urban settings	41	157.7 (60.2)	60.0–310.0
Region
New England	36	160.7 (59.7)	84.0–320.0
Middle Atlantic	49	177.8 (108.7)	87.0–540.0
East North Central	89	159.6 (106.4)	60.0–860.0
West North Central	42	148.0 (83.4)	76.0–450.0
South Atlantic	77	158.1 (90.3)	60.0–600.0
East South Central	12	168.2 (59.4)	98.0–323.0
West South Central	37	148.4 (60.2)	94.0–350.0
Mountain	29	137.9 (43.9)	79.0–240.0
Pacific	45	182.1 (102.7)	86.0–500.0
General work setting
Institution only	218	127.7 (43.0)	60.0–384.0
Private practice only	76	204.2 (112.2)	60.0–600.0
Institution and private practice	131	191.5 (107.2)	79.0–860.0

Notes: Income values are in 1000s of dollars. Includes licensed clinicians who are currently practicing full-time or more. Excludes postdoctoral trainees and those earning <$50,000 annually. Regions of residence: New England = Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Middle Atlantic = New York, New Jersey, Pennsylvania; East North Central = Illinois, Indiana, Michigan, Ohio, Wisconsin; West North Central = Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota; South Atlantic = Delaware, District of Columbia, Georgia, Florida, Maryland, North Carolina, Puerto Rico, South Carolina, Virginia, West Virginia; East South Central = Alabama, Kentucky, Mississippi, Tennessee; West South Central = Arkansas, Louisiana, Oklahoma, Texas; Mountain = Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah; Pacific = Alaska, California, Hawaii, Oregon, Washington.

Subspecialization interest

Subsequent to the 2010 practice survey, a subspecialization in pediatric neuropsychology was established within ABCN (Baron, Wills, Rey-Casserly, Armstrong, & Westerveld, 2011). Related, new questions were asked in the current survey pertaining to interest in subspecialization, with key results shown in Table 48. Among the 190 postdoctoral trainees who responded to this survey item, 33.7% are interested in pediatric subspecialization and 51.6% would be interested in possible other subspecializations in the future. For those already board certified with ABCN, there are lower percentages interested in pediatric and additional subspecializations. Among those already holding the subspecialty certification in pediatric neuropsychology, 35.7% expressed interest in additional subspecializations. Finally, among those who are licensed and currently practicing who do not hold an ABCN credential, 23.3% are interested in pursuing pediatric subspecialization and 29% are interested in possible future ABCN subspecializations.

Table 48. Interest in pursuing ABCN subspecialization in pediatric neuropsychology and other possible future subspecializations

		% Yes
Postdoctoral trainees	(n = 190)
Interest in pursuing pediatric subspecialization	64	33.7
Interest in other possible additional subspecializations	98	51.6
ABCN (who do not presently hold pediatric subspecialization)	(n = 474)
Interest in pursuing pediatric subspecialization	89	18.8
Interest in other possible subspecializations in future	127	26.8
ABCN (who already hold pediatric subspecialization)	(n = 34)
Interest in other possible subspecializations in future	20	35.7
Non-ABCN (who do not presently hold ABCN credential)^a	(n = 947)
Interest in pursuing pediatric subspecialization	221	23.3
Interest in additional subspecializations	275	29.0

Notes: Because attainment of ABCN certification is required in order to attain subspecialization, interest in the parent board is assumed for individuals interested in the subspecialty board.

^a Includes all licensed clinicians who are currently practicing.

Forensic Practice

Characteristics of participants in forensic practice

Characteristics of those engaging in forensic practice vs. not engaging in forensic practice are presented in Table 49. Approximately 56% of female respondents do not participate in forensic practice, whereas approximately 44% of male respondents do not. Approximately 66% of those working exclusively in institutions do not participate compared to approximately one-fourth of respondents in private practice who do not participate. Lifespan practitioners are the most heavily engaged in forensic practice, at 72.3%, in contrast to 55.4% of those with a purely adult identity and 21.2% of those with a purely pediatric identity.

Table 49. Forensic practice characteristics: gender, general work setting, specific work setting, and professional identity

	Forensic practice (n = 805)		No forensic practice (n = 715)
	Frequency	%	Frequency	%
Gender
Women	364	43.8	468	56.3
Men	431	64.2	240	35.8
General work setting
Institution only	241	33.7	474	66.3
Private practice only	228	75.2	75	24.8
Institution and private practice	251	74.9	84	25.1
Specific work setting
Medical	264	40.4	390	59.6
Private or group practice	278	76.8	84	23.2
Psychiatric	45	51.1	43	48.9
Rehabilitation	86	57.0	65	43.0
College/university	23	67.6	11	32.4
Other	20	44.4	25	55.6
Professional identity
Pediatric only	47	21.2	175	78.8
Adult only	445	55.4	358	44.6
Lifespan	222	72.3	85	27.7
Not a neuropsychologist^a	12	37.5	20	62.5

Note: Excludes postdoctoral trainees.

^a A self-designation indicating identity as a psychologist, but not as a neuropsychologist.

Relationships of key variables to involvement in forensic practice are shown in Table 50. Those who are involved vs. not involved at all in forensic practice have a significantly higher number of years of clinical practice and a significantly higher number of peer-reviewed publications. Job satisfaction and income satisfaction are significantly higher in the group that is involved in forensic practice, whereas there is not a significant difference in work–life balance satisfaction. Finally, mean income is significantly higher in the group involved in forensic practice, albeit with an SD that is twice as large. Of the five significant differences between individuals involved in forensic practice and those not involved, only number of years of clinical practice and income produced large effect sizes.

Table 50. Forensic activity: extent, years in clinical practice, job satisfaction, income satisfaction, work–life balance satisfaction, and income

	n	Mean	SD	t	d
Years in clinical practice^a				15.1^**	.8
No forensic involvement	693	10.0	9.3
Some forensic involvement	802	18.0	11.0
Peer-reviewed publications^a				2.9^*	.2
No forensic involvement	632	13.5	25.0
Some forensic involvement	783	18.2	34.4
Job satisfaction^a				3.2^**	.2
No forensic involvement	606	76.7	20.4
Some forensic involvement	685	80.4	19.9
Income satisfaction^a				2.8^*	.2
No forensic involvement	608	70.5	24.3
Some forensic involvement	695	74.0	22.5
Work–life balance satisfaction
No forensic involvement	608	68.1	25.2	−1.14	.1
Some forensic involvement	518	69.8	25.0
Income^b				11.2^**	.7
No forensic involvement	546	115.9	48.7
Some forensic involvement	610	168.3	99.0

^a Excludes postdoctoral trainees.

^b Includes licensed clinicians who work full-time or more; excludes postdoctoral trainees and income outliers (<$50,000).

^* p ≤ .01.

^** p ≤ .001.

Beliefs and use of performance validity tests and symptom validity tests

New to the survey in 2015 were specific questions related to beliefs regarding whether there is sufficient empirical research/knowledge to support use of performance validity tests (PVTs) and symptom validity tests (SVTs). These results are shown in Table 51 for those still in postdoctoral training, as well as for practitioners who have no forensic practice and those who engage in forensic practice. What is readily apparent in Table 51 is that none of the postdoctoral trainees disagreed with the idea that there is sufficient empirical research/knowledge in support of using PVTs and only 1.1% disagreed with the idea that there is sufficient empirical research/knowledge in support of using SVTs. Among practitioners, there is also very strong support for PVT and SVT use, with approximately 98% in support even among clinicians who do not engage in forensic practice and approximately 99% in support among clinicians whose practices includes forensic activities. Interestingly, there is a much higher percentage strongly agreeing with use of such instruments among those actually engaging in forensic practice when compared to those who do not do so.

Table 51. Beliefs regarding the empirical foundation of response validity measures

	Postdoctoral trainees (n = 181)		Practitioners with no forensic practice (n = 700)		Practitioners with some forensic practice (n = 803)
Belief	Frequency	%	Frequency	%	Frequency	%
There is sufficient existing empirical research/knowledge to support use of performance validity measures to determine performance validity
Strongly agree	85	47.0	321	45.9	518	64.5
Agree	80	44.2	298	42.6	246	30.6
Neutral or uncertain	16	8.8	65	9.3	29	3.6
Disagree	0	.0	16	2.3	7	.9
Strongly disagree	0	.0	0	.0	3	.4
There is sufficient existing empirical research/knowledge to support use of symptom validity measures to determine symptom validity
Strongly agree	64	35.4	244	34.9	440	54.8
Agree	90	49.7	339	48.5	286	35.6
Neutral or uncertain	25	13.8	104	14.9	60	7.5
Disagree	2	1.1	12	1.7	15	1.9
Strongly disagree	0	.0	0	.0	2	.2

Table 52 compares forensic practitioners to non-forensic practitioners in terms of their use of stand-alone and embedded validity measures. Respondents not engaging in forensic practice nevertheless report using validity measures, with only 8.8% reporting that they do not use PVTs or SVTs within their clinical evaluations. Among those with forensic practices, only 4.7% are not using PVTs or SVTs within their clinical evaluations; these practitioners are more likely to use multiple measures. Within their forensic evaluations, approximately 99% use PVTs or SVTs, with 73.5% reporting use of multiple stand-alone PVTs, 80.1% reporting use of multiple embedded PVTs, and 69.2% reporting use of SVTs.

Table 52. Response validity practices in typical forensic and clinical settings

	Practitioners with no forensic practice (n = 700)		Practitioners with some forensic practice (n = 805)
Practice variable	Frequency	%	Frequency	%
In your clinical practice (excluding forensic cases), which best describes your typical practice:
One stand-alone PVT	307	42.9	324	40.2
Multiple stand-alone PVTs	160	22.4	309	38.4
One embedded PVT	119	16.6	108	13.4
Multiple embedded PVTs	408	57.1	552	68.6
I use SVTs	321	44.9	442	54.9
I do not use either PVTs or SVTs	63	8.8	38	4.7
In your forensic practice (excluding clinical cases), which best describes your typical practice:
One stand-alone PVT	n/a	n/a	162	20.5
Multiple stand-alone PVTs	n/a	n/a	581	73.5
One embedded PVT	n/a	n/a	61	7.7
Multiple embedded PVTs	n/a	n/a	633	80.1
I use SVTs	n/a	n/a	547	69.2
I do not use either PVTs or SVTs	n/a	n/a	9	1.1

Notes: Excludes postdoctoral trainees. n/a equals not applicable.

To examine possible changes in hours of involvement in forensic cases, data from the current and two prior surveys have been juxtaposed in Figure 7. It is apparent that with all three neuropsychology professional identities there have been increases in the numbers of hours invested in forensic activities. Figure 8 shows that even as incomes have risen in general every five years, neuropsychologist involved in at least some forensic activities fare better in terms of income in each time period.

Figure 7. Chronological comparison of mean weekly forensic hours by professional identity.

Figure 8. Chronological comparison of annual income among those with and without forensic involvement.

Figure 9 demonstrates that as the mean hours reported as necessary to complete outpatient diagnostic evaluations has not increased from 2005 to 2015, and even decreased from 2010 to 2015, the hours reported as necessary to complete forensic evaluations has increased steadily across each five-year interval. Median hours, which are not shown, depict nearly identical patterns.

Figure 9. Chronological comparison of forensic vs. clinical outpatient mean evaluation times.

Scholarly activity

Past surveys have demonstrated that a substantial number of clinical neuropsychologists have been involved in scholarly activities, such as peer-reviewed publishing and conference presentations, as well as teaching and training at the graduate, internship, and/or postgraduate levels. Results related to time invested in teaching/training were presented in Table 9 and discussed earlier. Table 53 shows peer-reviewed publications and research presentations at professional conferences by professional identity and general work setting. Pediatric neuropsychologists who do not engage in adult practice at all have substantially lower numbers of both publications and conference presentations, as compared to individuals who work only with adults and those who have a lifespan practice. However, those psychologists who do not consider themselves to be neuropsychologists have the lowest numbers of both types of scholarly activity. Related to general work setting, scholarly activity is highest for those whose practices combines institution and private practice settings and lowest for those solely in private practice.

Table 53. Number of peer reviewed publications and presentations by professional identity and general work setting^a

	Number of authored or co-authored peer reviewed publications					Number of authored or co-authored research presentations
	n	Mean	Median	SD	Range	n	Mean	Median	SD	Range
Professional identity
Pediatric only	219	10.0	5.0	18.8	0 to >200	217	18.3	11.0	23.9	0–160
Adult only	795	18.8	6.0	34.4	0 to >200	788	26.0	10.0	42.3	0 to >200
Lifespan	305	15.3	5.0	28.9	0 to >200	299	24.4	6.0	41.2	0 to >200
Not a neuropsychologist^b	32	7.7	2.0	14.3	0–50	28	13.4	3.0	26.0	0–100
General work setting
Institution only	708	17.0	6.0	31.5	0 to >200	699	26.8	13.0	40.7	0 to >200
Private practice only	299	7.6	4.0	14.0	0–135	293	9.3	4.0	16.6	0–104
Both institution and private practice	333	23.2	7.0	38.6	0 to >200	329	32.0	10.0	47.4	0 to >200

Note: Excludes postdoctoral trainees.

^a Survey response option range was from 0 to >200. Mean, median, and standard deviations were calculated with >200 translated to 201.

^b A self designation indicating identity as a psychologist, but not as a neuropsychologist.

Retirement planning

New to the 2015 practice survey were questions related to retirement. The specialty of clinical neuropsychology has existed long enough in the US with sufficient numbers of practitioners that one can ask whether, perhaps akin to the well-known “baby boomer” retirement cohort that has affected numerous occupations, neuropsychology’s practitioner workforce will begin to experience large-scale retirement in years to come. Such an observation of “baby boomer” psychologists retiring has been made within APA, according to APA’s Center for Workforce Studies “Demographics of the US Psychology Workforce: Findings from the American Community Survey” (downloaded on 15 November 2015 from http://www.apa.org/workforce/publications/13-dem-acs/report.pdf). Table 54 contains information relevant to retirement expectations and planning. Whereas slightly more than two-thirds of the 1330 respondents who answered this question expect to retire, the remainder do not expect to be able to retire, believe they will not want to retire, or do not know. Data within Table 54 indicate that relatively small numbers of individuals expect to retire in each of the coming nine years. Approximately 12.6% of respondents expect to retire within the next five years. The mean age of expected retirement is 66.8, with a median of 67.0, and a very wide range of 30 years (i.e., 50–80).

Table 54. Personal belief regarding retirement, expected number of years before retirement, and expected age of retirement

	Frequency	%
Personal belief regarding retirement
I expect to retire	906	68.1
I do not expect to be able to retire	136	10.2
I will not want to retire	137	10.3
I do not know	151	11.4
Expected number of years before retirement^a
1 year	20	2.3
2 years	14	1.6
3 years	20	2.3
4 years	31	3.6
5 years	24	2.8
6–9 years	87	10
10 years	19	2.2
>10 years	651	75.2
	n	Mean	Median	SD	Min–Max
Expected age of retirement	878	66.8	67.0	3.9	50.0–80.0

Note: Excludes postdoctoral trainees.

^a n = 866.

There are a number of factors that can influence an individual’s decision of whether to continue working or to retire. For the overall sample responding to the question of which variables are most important in planning for retirement, the following is the rank order of these factors: (1) financial status, (2) health, (3) quality of work life, (4) family considerations, (5) availability of Social Security or other government benefit. Rank orders of common factors by expected number of years before retirement are presented in Table 55. Clearly, the most prominent factor is financial well-being, with quality of life a common second place factor (or at least tie for second place) for those expecting to retire within the next five years, and health a common second place factor for those expecting to retire in more than five years. The continued presence or absence of social security benefits was not viewed as a top factor in retirement planning regardless of number of years away retirement was expected to be.

Table 55. Top 5 rankings of factors that will determine timing of retirement

	Expected number of years before retirement
Rank	1 year	2 years	3 years	4 years	5 years	6–9 years	10 years	>10 years
	(n = 20)	(n = 14)	(n = 20)	(n = 31)	(n = 24)	(n = 87)	(n = 19)	(n = 651)
1	Financial success	Quality of work life	Financial success	Financial success	Financial success	Financial success	Financial success	Financial success
2	Quality of work life	Financial success	Quality of work life^a	Quality of work life ^a	Quality of work life	Health	Health	Health
3	Health^a	Family matters	Health^a	Family matters^a	Health^a	Family matters	Family matters	Quality of work life
4	Family matters^a	Health	Family matters	Social Security	Family matters^a	Quality of work life	Quality of work life^a	Family matters
5	Social Security	Social Security	Social Security	Health	Social Security	Social Security	Social Security	Social Security

Note: Rankings are frequency based, as determined by the number of times a factor was ranked number one by respondents.

^a These factors were endorsed by the same number of participants (i.e., there was a tie) within each respective column.

Satisfaction with survey instrument

The final survey item asked respondents to rate the degree to which the survey had covered pertinent important information and identified variables relevant to their respective income and practice activities. The six categories offered to respondents consisted of three categories of satisfaction and three categories of dissatisfaction. A total of 1168 individuals responded to this item. The results in order from complete satisfaction to complete dissatisfaction were: completely satisfied (9%), mostly satisfied (63%), somewhat satisfied (21.1%), somewhat dissatisfied (5%), mostly dissatisfied (1.5%), completely dissatisfied (.3%). Combining all those with some degree of satisfaction and, separately, some degree of dissatisfaction, 93.1% were at least somewhat satisfied and 6.8% were at least somewhat dissatisfied.

Discussion

The present survey represents a five-year update of information regarding characteristics of clinical neuropsychologists and their practices, as well as provides information on a few new topics that were not addressed previously. Most of the information obtained in this type of straightforward solicitation of information from professionals does not require additional interpretation or discussion; the meaning of most of the data is inherently apparent in the numbers and graphics presented in the results section. Instead of attempting an exhaustive summary and explanation of these extensive results, we will provide a much more selective discussion and analysis.

Stability and change across time

Age

We have made the point previously (Sweet et al., 2011) that dating all the way back to survey data collected in 1989 (Sweet & Moberg, 1990), there has been a remarkable stability of age across surveys, now including the present survey data. The fact that the average age of clinical neuropsychologists remains relatively unchanged in more than 25 years, near the midrange of the 40s, reflects the steady annual influx of new specialists entering the field after completing training. This seems especially noteworthy as the years of licensed practice in the present survey had a range of 45 years. By comparison, data compiled by the Research Office of the American Psychological Association (APA) demonstrates an older and steadily advancing mean membership age for the combined membership categories of associate, member, and fellow: year 2000 = mean age 52.2; 2005 = 53.3; 2010 = 54.3; 2014 = 56.2 (American Psychological Association, Center for Workforce Studies 2000, 2005, 2010, 2014). Even more demonstrative of the aging of APA’s membership is the fact that in 2000 the percentage of those 60 and older was 20.1%, whereas in 2014 this percentage had more than doubled to 44.4%. (http://www.apa.org/workforce/publications/13-dem-acs/index.aspx downloaded November 15, 2015).

Gender

Whereas the average age of neuropsychologists across time has been remarkably stable, gender representation has been perhaps even more remarkable for changing over time. In the 1980s, an APA Committee on Employment and Human Resources (Howard et al., 1986) framed psychology’s impressive gender composition transition as the “feminization of psychology.” As noted by Ostertag and McNamara (1991), whereas in 1950 only 14.8% of new psychology doctorates were awarded to women, by 1984 women received half of the psychology doctorates. This trend continued to the point that for a number of years more than 70% of new doctorates have been earned by women (downloaded from http://www.apa.org/workforce/snapshots/2003/women-in-psych.aspx October 27, 2010). Nevertheless, our past surveys, some of which were conducted years after APA’s gender transformation, demonstrated that the specialty of clinical neuropsychology lagged far behind the clear trend of gender composition of psychology at large. Survey data collected in 1989, 1994, and 1999 demonstrated that regardless of other possible moderating factors, such as board certification status and general work setting, women appeared to comprise fewer than 30% of the specialty (Sweet, Moberg, & Suchy, 2000a, 200b). By 2005, female clinical neuropsychologists made up approximately 71% of that year’s postdoctoral trainees sample and the overall 2005 survey sample showed the proportions growing toward one another, but retaining a majority of men (Sweet et al., 2006). The 2010 survey again found a clear and substantial majority of women among postdoctoral trainees, but for those working independently in the specialty, there continued to be a slim majority of men. It was hypothesized at the time that the trend would continue and indeed it has. Currently, the clear majority of women that was shown in the past for the postdoctoral sample has continued and now has extended to the licensed practitioner sample. In fact, among pediatric neuropsychologists, the markedly decreasing presence of men that is evident across surveys makes one wonder whether in the future the presence of male pediatric neuropsychologists will be genuinely rare.

Diversity

Regarding ethnic/racial diversity, whereas there was a slight decrease in the category of the Caucasian/White majority from 2005 to 2010, the last five years has seen a more notable change in both the postdoctoral sample and the practitioner sample, though still requiring some effort to identify. The fact remains that the category of Caucasian/White continues to have the largest representation by far. Comparison of 2005 to 2015 data indicates a decline of 4.6% in the category Caucasian/White, which continues to predominate at 88.3%. Presumably, as evident in the postdoctoral sample, the change noted in the much larger practitioner sample in the direction of diversity is a reflection of early career specialists who have recently entered the specialty. In fact, using data from APA’s Center for Workforce Studies, Lin, Stamm, and Christidis (2015) found age distributions consistent with the hypothesis that the increases in diversity within its own membership reflect the presence of more diverse younger psychologists. It appears that the years of effort on the part of the American Psychological Association to increase membership and the discussions and efforts with the specialty of clinical neuropsychology (e.g., Mindt, Byrd, Saez, & Manly, 2010; Romero et al., 2009) will need to continue.

Educational background

Past surveys demonstrated a fair amount of stability across time with regard to the Ph.D. being the dominant degree choice. Although the vast majority of neuropsychology’s practitioners continue to have Ph.D. degrees in 2015, the last five years have evidenced a substantial decrease in Ph.D. holders and a similar degree of increase in Psy.D. holders. Moreover, postdoctoral trainees are more likely to be earning Psy.D. degrees in 2015 compared to 2005 and 2010 survey. Given the documented increase over time and resulting sheer volume of Psy.D. degree holders produced annually in the United States (i.e., a 300% increase from 1987 to 2007–2008, representing 55% of doctoral psychology graduates in 2007–2008; see http://www.apa.org/workforce/publications/08-hsp/psychology-degrees.aspx), this trend is likely to continue, even if it lags behind other clinical psychology practice areas. Nevertheless, the dominant doctoral degree area of study across surveys remains clinical psychology. Similarly, full-time work status continues to predominate and it is apparent that there continues to be a high rate of employment in the specialty.

General work setting

In the last five years, there appear to be changes taking place in the choice of general work setting. A major shift toward private practice as the work setting choice of the majority was identified 15 years ago (Sweet et al., 2000a), with this shift continuing to the point of the 2010 practice survey. However, since 2010, the trend appears to have reversed, with increasing numbers of neuropsychologists working in institutional positions only and decreasing numbers working in private practice only. From a national perspective, this change is unlikely to be specific to the specialty of neuropsychology, given that there is abundant data related to national trends among physicians showing a clear trend away from community-based private practice ownership and toward employed positions within large health care system-based practice groups affiliated with hospitals or employed directly by hospital systems. Current estimates are that approximately two-thirds of US physicians are now employed, rather than working in independent private practice, with 90% of newly hired physicians joining practice settings as an employee (The Physicians Foundation, 2014). This physician survey document also noted a marked change from 2004, when 55% of new hires were employees and the remainder went into independent private practice.

In prior surveys, it was clear that the mean incomes of individuals who worked in both institutions and private practice were higher than those working in only institutions. In the present survey, data analyses attempted to specify the amount of that higher income that was attributable to the addition of private practice to specific types of institutional work setting employment (see Table 32). There appears to be a very substantial higher income ranging from approximately 26–56%, for individuals who work in hospital or medical centers and who also work in private practice when compared to individuals who limit themselves to the former setting. Interestingly, for the smaller numbers of individuals whose institutional designation is either four-year university or college with a doctoral program or “other,” the incremental difference appears to be much smaller. In these latter two settings, the addition of private practice work appears to be more in the range of a 6–16% increase in income.

Testing assistants

There has been a relative stability regarding practitioner use of testing assistants. Past survey data on this point have tended to be in ranges near half of the respondents. The percentage has edged upward in the last five years, returning to approximately the 2005 level, perhaps because use of testing assistants is more common in institutional work settings and with ABCN board-certified individuals, two factors traditionally associated with higher testing assistant usage and noted to be on the increase since 2010.

Approach to test selection

With regard to philosophical approach to test selection, the graphic of responses since 1989, now including 2015 data, continues to show progressively higher endorsement of the “flexible battery” approach and continued decreased endorsement of the “fixed/standardized” approach. The data regarding the trajectories these two testing approach philosophies are now so overwhelming as to suggest that they are in a sense “etched in stone” in terms of their respective influence on current and future specialty practice, likely obviating the need to repeat this particular question in future surveys.

Involvement in activities that affect income

With nearly two-thirds of respondents reporting that recent US health care system changes have resulted in decreased reimbursement for clinical services and slightly more than one-third reporting having increased their caseloads, one might hypothesize that one explanation for incomes continuing to increase in 2015 could be an increase in the number of overall work hours. It appeared in 2010 that hours of clinical practice per week had not changed appreciably from 2005, with total weekly clinical hours in the low 30s in 2010. Interestingly, the 2015 data appear similar, with the combined means of neuropsychological and general clinical practice hours at approximately 31. Thus, for the overall sample of practitioners, simply investing more time into billable hours does not appear to account for continued income increases. Instead, factors such as the documented increase in higher reimbursement activities (e.g., forensic services) and perhaps other clinician-based choices that have the overall effect of improving overall payor mix (e.g., discontinuing participation in low-reimbursement insurance panels and Medicare or restricting the number of patients with such insurance coverage) might explain the ability of the majority of clinicians to report income increases again in this latest five-year interval. In fact, 2014 data from a national physician compensation survey (available at http://www.medscape.com/features/slideshow/compensation/2015/public/overview) increase the probability that this hypothesis regarding continuing increases in neuropsychologists’ income over time is correct. These data indicate that 22% of physicians have dropped insurers that pay poorly and only 64% of self-employed and 79% of employed physicians report willingness to continue to accept Medicare and Medicaid patients in their practices. It seems likely that such clinician-based decisions are influential across US health care disciplines and specialties, including within clinical neuropsychology.

Income increases across time

For the third consecutive survey interval, when asked to compare current income to that of five years earlier, the vast majority of respondents reported an increased income compared to the preceding five years. Moreover, the graphic comparing actual reported incomes in 2005, 2010, and 2015 shows substantial increases at every level of years of practice experience. Though incomes again vary by state and by region, which can reflect a number of variables, such as differences in supply/demand of practitioners, practice costs, and cost of living, once again the associated income satisfaction ratings can be seen to diverge from actual earnings. Variables such as gender and professional identity appear to have appreciable impact on income and income satisfaction, as well as in some instances an appreciable impact on job satisfaction, and work–life balance satisfaction. Gender also appears to have considerable impact on identification of personal/family and work environment obstacles to these satisfactions, with women reporting the presence of obstacles at considerably higher frequencies. Despite the proportional increase in women in the specialty noted earlier, there continue to be substantial income differences between men and women, which have been found in many professional groups that are surveyed, including health service psychologists in general (see http://www.apa.org/monitor/2014/12/datapoint.aspx). Income-related variables, such as work setting and likelihood of working part-time, likely are part of the reason for these differences. Survey data from male and female physicians, who also report income differences, indicate that income-related factors vary by gender, with female physicians less likely to be in private practice and less likely to be employed full-time (available at http://www.medscape.com/features/slideshow/compensation/2015public/overview). It is unlikely then that the continued gender differences in income are unique to neuropsychology’s practitioners.

Forensic practice

As noted above, there appears to be an increase across time in the weekly time invested by neuropsychologists in forensic practice, which may be one of the factors producing income increases across time. Other data from the present survey pertaining to forensic practice are also notable. Given the increased involvement of licensed practitioners, it stands to reason that postdoctoral training would include exposure to this area of practice. In fact, it appears that slightly under one half of postdoctoral trainees are receiving forensic practice training. Moreover, it seems absolutely clear that the viewpoints of postdoctoral trainees regarding validity testing, a practice topic that has been well established as very important in forensic evaluations, is completely in synch with the viewpoints of experienced licensed practitioners. Both performance validity testing and symptom validity testing were endorsed by the vast majority of individuals in both groups. Among individuals who engage in forensic practice, there is a higher utilization of validity tests in clinical (i.e., non-forensic) evaluations as well, suggesting what many have observed anecdotally over recent decades, namely that forensic practice has influenced routine clinical practice. Importantly, as questions about validity testing were new to the 2015 survey, the results of these specific questions are quite consistent with a recent survey that focused exclusively on forensic practice, the data for which were collected in the same era as our 2015 practice data (Martin, Schroeder, & Odland, 2015). This forensic survey was in fact following up on a prior 2007 survey by independent authors and in so doing makes the point strongly that forensic practice beliefs have crystallized and become consensual over time on a number of salient practice topics. Together with the present broader survey data, both strongly suggest that forensic practice is increasingly prominent and important to US neuropsychology practitioners.

Houston conference

First included in the 2010 survey was an item aimed at determining the extent to which the 1997 Houston Conference training guidelines (Hannay et al., 1998) had affected the specialty continued to produce impressive results in 2015. Among postdoctoral trainees, approximately 94% report training consistent with the Houston training guidelines, and among neuropsychologists with 5 or fewer years of licensed practice, the approximate percentage remains a very healthy 93%. With increasing and overwhelming percentages of early career clinicians reporting having been trained in accord with the Houston model (e.g., in 2010, the figure had been 88% of postdoctoral trainees), and even large percentages of senior clinicians indicating training consistent with the model, the question appears well settled and will not need to be included in future surveys. The Houston Conference training guidelines have essentially saturated the specialty, leaving no question that it has spawned the currently influential training model.

Professional identity

Professional identity remains an interesting variable that is associated meaningfully with income, gender, work status, and hours required to complete an evaluation. In 2015, there is again strong evidence that an exclusively pediatric identity is associated with a lower income, a greater proportion of women practitioners, institutional work setting, and lengthier evaluations. If the trajectory of the increasing percentage of women pediatric neuropsychologists continues, there may come a day when male pediatric neuropsychologists are extremely rare. Among those still completing their postdoctoral training, a substantial proportion is interested in subspecializing and becoming board certified in pediatric neuropsychology. Interestingly, there also appears to be substantial interest among respondents for additional subspecialty board certifications.

Within the two most recent practice surveys based on 2005 and 2010 data, the label for the group currently self-identified as “not a neuropsychologist” were identified as “no identity” or “none.” We believe the current label provides a more accurate description of the wording of the self-determined identity survey query. These individuals belong to professional neuropsychology associations because they provide a substantial amount of neuropsychological services and, yet, likely because of breadth of practice and/or perhaps depth of prior training, do not present themselves professionally as neuropsychologists. Because these individuals are visibly present in our associations and at our professional conferences and are not trivial in number, and because they have participated in our surveys time after time, we believe it is important to provide their data for comparison sake. Readers will note that there are a number of tables from which this group has been excluded. The group is retained in tables that we believe are informative, basically providing a distinction from those who are not neuropsychologists. For example, in Table 6, it is evident that this group is declining proportionately across time, which makes sense as the specialty matures. In Table 21, it is apparent that this group is much less likely to work in a medical setting and, relative to survey respondents who identify as Pediatric only and Adult only, is more likely to work in private practice. Table 22 shows that this group is older, has been practicing longer, engages in a greater proportion of forensic practice, and charges less for both clinical services and forensic services. Also, as demonstrated in Figure 4, this group has had a smaller increase in income from 2010 to 2015, and in Table 23 it is apparent that on an average and percentile basis this group tends to make less money.

Understanding various types of career satisfaction: context matters

The relationship between money and the general construct of “happiness” is relatively complex. A simple recurrent finding from relevant research is that there is not a linear relationship between income and happiness. However, as the research on this topic has accumulated, some specific relationships have been found. For example, wealth can act as a buffer against the stress associated with inevitable or at least common negative events (Cummins, 2000). Moreover, some researchers have opined that as a society gains wealth, well-being is less influenced by income and more influenced by relationships and work enjoyment (Diener & Seligman, 2004). But again, relationships of income to income satisfaction and job satisfaction are not powerful in isolation. For example, using meta-analytic techniques, Judge, Piccolo, Pdsakoff, Shaw, and Rich, (2010) found that based on 115 correlations from 92 independent samples, the overall correlation between income and income satisfaction was only .23 and there was an even lower correlation of .15 between income and job satisfaction. Using a creative approach to the complex topic, Kahneman and Deaton (2010) parsed these relationships such that a threshold effect of income was evident. Based on 450,000 US residents who completed the Gallup-Healthways Well-Being Index, these researchers found that emotional well-being has a positive relationship to income up to 75,000 dollars annually, but not beyond that point. These findings appear to support the previously mentioned opinion of Cummins (2000), basically that a certain amount of income can serve as a buffer against negative events. Presumably, once basic needs and substantial buffering against negative events takes place, greater amounts of income do not increase well-being.

In clinical neuropsychology, the mean and median starting salaries are higher than the dollar figure identified by Kahneman and Deaton as influencing emotional well-being. Thus, it is possible that basic entry-level employment as a neuropsychologist brings with it a base foundation of well-being or satisfaction. Perhaps this base foundation of satisfaction helps to explain that the correlations between actual income and income satisfaction in 2005 (.37), in 2010 (.33), and in 2015 (.30) for clinical neuropsychologists are all appreciably higher than the meta-analytic study of Judge et al. (2010), which reported a correlation of only .23. After all, the average income of the numerous studies aggregated by Judge et al. was in fact substantially lower (64,119) than was found in our surveys of neuropsychologists. However, the correlations between actual income and job satisfaction in 2005 (.19), in 2010 (.18), and in 2015 (.17) are quite similar to the .15 correlation reported by Judge et al., which continues to suggest that actual income plays a minor role in determining job satisfaction, including for clinical neuropsychologists. In this sense, our specialty’s practitioners are no different than other American workers.

Fairly sophisticated studies of career satisfaction among psychologists have been conducted, some of which offer interesting observations that may shed light on factors that are important in determining neuropsychologists’ career satisfaction. An excellent example is provided by Rupert, Miller, Tuminello Hartman, and Bryant (2012), who used optimal discriminant analysis to identify factors that might predict career satisfaction among practicing psychologists. These researchers identified several predictive algorithms that separate highly satisfied from moderately satisfied psychologists. Variables that appeared to have the best predictive value for the largest number of practicing psychologists were the percentage of “direct pay clients” (i.e., reduced reliance on third-party reimbursement for services) and the use of strategies that increased work–life balance and led to increased sense of control at work. Variables such as gender, age, marital status, weekly work hours, weekly therapy hours, and weekly assessment hours were not predictive. Some similarities between psychologist and physician career satisfaction have also been found, though as can be imagined in these two very different disciplines there are also differences. Physician career satisfaction has also been found to be unrelated to gender; instead, more negative satisfaction ratings were associated with being a practice owner, having greater reliance on managed care, and having an uncontrollable lifestyle (Leigh, Tancredi, & Kravitz, 2009). Within the present survey, we asked about three separate types of professional satisfaction, rather than overall career satisfaction, which may or may not be represented in a summary judgment inclusive of all three satisfactions that we solicited. Nevertheless, the findings of these two studies, with both pointing toward concepts of financial security and a sense of control over work–life balance, may help us understand why our neuropsychology data-set, as evident in Table 12, appears to show work–life balance ratings that are relatively independent of ratings of income. What is less clear is why in Table 13 gender appears to be the only variable that has a consistent relationship to all types of career satisfaction, a finding that might not be expected based on the Rupert et al. (2012) or the Leigh et al. (2009) career satisfaction studies. It might be advisable in future surveying of neuropsychologists to ask for an overall rating of career satisfaction, separately from income, job, and work–life balance satisfactions, in order to better investigate the presumably complex relationships between types of satisfaction and important professional variables relevant to clinical neuropsychologists.

How do clinical neuropsychologists compare with regard to income satisfaction to psychologists in general? APA reported data in 2009 that was collected from employed doctoral-level psychologists showing that 66.7% reported some degree of satisfaction with their income. By comparison, in 2005, 67% of respondents expressed some degree of income satisfaction, increasing slightly to 70% in 2010 and increasing again to 74% in 2015.

Comparisons of income satisfaction can also be made to physicians as a group and by specialty (available at http://www.medscape.com/features/slideshow/compensation/2015public/overview). The Medscape survey of physicians asked whether respondents felt “fairly compensated.” Among primary care physicians, 53% reported not being fairly compensated, whereas 50% of specialists expressed this sentiment. Across physician specialties, the perception of being fairly compensated varied widely, from a low of 40% for ophthalmology to a high of 61% for dermatology. For the two medical specialties that provide high percentages of employment opportunities for neuropsychologists within their respective departments and who also rank highly as referral sources, 45% of neurologists felt fairly compensated and 56% of psychiatrists felt fairly compensated. Again, as was the case when compared to psychologists in general with APA’s data, when compared to physicians there are higher rates of income satisfaction among neuropsychologists.

Beyond income satisfaction, the Medscape survey also inquired as to overall career satisfaction and likelihood of choosing a medical career and the specific specialty again based on personal professional experience. As was the case with income satisfaction, overall career satisfaction varied greatly across specialties from a low overall career satisfaction of 47% among internal medicine specialists to a high of 63% among dermatologists. The reader will recall that in the prior two surveys so few individuals were intent on leaving the profession that this type of career satisfaction was dropped from the current survey. Remaining queries are slightly different than in the Medscape survey of physicians, but could suggest a higher degree of overall career satisfaction. For example, job satisfaction as expressed by neuropsychologists on a 0–100 scale was a median of 85.

Perhaps most interesting of all in terms of available comparisons to the specialty of clinical neuropsychology, a recent study by Mayo Clinic researchers has reported a notable decline in satisfaction with work–life balance and a notable increase in perceptions of burnout from 2011 to 2014 (Shanafelt et al., 2015). The degree of these perceived negative changes varies by medical specialty, but is present in all, including those most closely tied to neuropsychology, such as neurology and psychiatry. Notably, these declines among physicians occurred within a broader context of US workers that showed relatively stable ratings. Within clinical neuropsychology, relevant ratings by survey respondents have not suggested a downward trend in either income or job satisfaction between 2005 and 2015. With only 2015 ratings of work–life balance and no prior data points for comparison, it will be interesting to see if future survey results identify trends similar to our physician colleagues.

Final comments

In the current era of US health care changes and related worries of clinicians and consumers, it is reasonable for clinical neuropsychologists to wonder about the overall health of their specialty. Though not without reason to worry, given the constant drumbeat from the US Government regarding the need to drastically alter payment systems, including amounts paid to clinicians, and expressly to lower the proportion of the US budget committed to Medicare and other annual health care costs, there are a number of broad observations that provide assurance at this point of the US health care change process. Clearly, the mean age stability of clinical neuropsychology’s specialists is very different from that of the overall APA membership, and at present raises no concerns regarding the overall attractiveness of the specialty to trainees planning a career path. Whereas there may eventually be serious consequences for APA’s graying membership, the professional practice organizations related to clinical neuropsychology are maintaining membership or even growing. The best example of the difference in trajectories can be seen in the decreasing membership of APA at large, whereas SCN, the specific APA Division providing a home for clinical neuropsychologists, has maintained a stable membership size over the years and in so doing in very recent years has become APA’s largest Division. Clearly, these two trajectories are independent. Moreover, annual incomes from psychology-related activities have continued to go up for the vast majority of those reporting incomes when compared to prior surveys. Also, a sign of relative specialty health, the income increases can be seen across the range of practice years, from early career (perhaps best demonstrated by “starting salaries” in institutions) to very senior practitioners with 20 or more years of practice experience. Unrelated to current survey data, but apparent to any specialist who observes position announcements on neuropsychology-specific email listservs, the number of position announcements posted on a monthly basis is truly impressive. Related to this observation, as in past surveys, present data show very few individuals who are unemployed and searching for a position. Equally important, combining the concepts of income satisfaction, job satisfaction, and work–life satisfaction into a unitary concept of career satisfaction, the level of satisfaction among neuropsychologists is high. In fact, comparison of income satisfaction ratings from 2005 to 2015 indicates an increase of approximately five points for men and women. Comparisons in this same 10-year interval for job satisfaction show no change for women and an increase of approximately four points for men. Though specific types of career satisfaction vary by such variables as gender and professional identity, once again in 2015 there appear to be relatively few individuals who are seriously dissatisfied. Somewhat humorous is the fact that the very small group of individuals reporting complete dissatisfaction with their incomes actually has a higher mean income than many of their much more satisfied peers, suggesting that it is not the income that produces this reported dissatisfaction. Because very few individuals in past surveys were so dissatisfied that they were entertaining leaving the field altogether, and in fact this percentage declined previously from 2005 to 2010, this question was dropped from the 2015 survey. Overall, whereas neuropsychology’s specialists may have worries, no doubt shared by other types of psychologists and health care specialists in other disciplines, importantly, there are numerous substantive signs of good health within the specialty of clinical neuropsychology.

Survey data pertinent to professional practices, beliefs, and characteristics of clinical neuropsychologists have been collected since the 1980s. Such data collection appears to remain a valuable source of information concerning trends within the specialty. Based on the 2010 practice survey data, we noted the very large discrepancy between income data from neuropsychologists and more general income data collected by APA in 2009 (available online at http://www.apa.org/workforce/publications/09-salaries/index.aspx). A similar discrepancy was also noted between data collected by the Society for Industrial Organizational Psychology based on 2009 data reported by Khanna and Medsker (2010), with this specialty group data much more aligned with neuropsychology incomes than the general APA data. We viewed such examples as providing additional justification for specialties to gather their own professional practice and income data. There is no more recent APA data available for comparison, but the most recent data collected from industrial/organizational psychologists in 2012 (Khanna, Medsker, & Ginter, 2013) again seem more comparable to neuropsychologists in 2015 than to prior APA data in showing that the majority of psychologists in both specialties experienced income increases compared to the prior surveys that had been conducted in each specialty. The most recent median incomes in both specialties are also comparable (i.e., 113,200 for I/O in 2012; 118,000 for neuropsychologists in 2015). As was concluded five years ago, we continue to believe that a five-year interval survey within a practice specialty can provide useful information for specialists within the given specialty, in our case for specialists in clinical neuropsychology.

Author Note

Portions of the 2015 survey data were presented as a scientific poster and in the form of a conference bag handout to attendees of the annual AACN meeting in San Francisco in June 2015.

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgments

The survey team is grateful and extends thanks for the cooperation and assistance of the sponsoring organizations, as well as the Association of Postdoctoral Programs in Clinical Neuropsychology, International Neuropsychological Society, numerous regional and state neuropsychology organizations, Be Ready for Accreditation in Clinical Neuropsychology leaders, AACN’s Pediatric Neuropsychology Special Interest Group, and the NPSYCH listserv, who all assisted in distributing the invitation to participate in the survey. Finally, thanks to all the respondents who took time to participate.

Additional information

Funding

This work was supported equally by the American Academy of Clinical Neuropsychology, National Academy of Neuropsychology, and Society for Clinical Neuropsychology (APA Division 40).

Appendix