Applied Neuropsychology: Adult

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Normative Data for the Neurobehavioral Symptom Inventory John E. Meyers, James English, Ronald M. Miller & Amy Junghyun Lee To cite this article: John E. Meyers, James English, Ronald M. Miller & Amy Junghyun Lee (2015) Normative Data for the Neurobehavioral Symptom Inventory, Applied Neuropsychology: Adult, 22:6, 427-434, DOI: 10.1080/23279095.2014.968919 To link to this article:

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Date: 05 November 2015, At: 14:23

APPLIED NEUROPSYCHOLOGY: ADULT, 22: 427–434, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 2327-9095 print/2327-9109 online DOI: 10.1080/23279095.2014.968919

Normative Data for the Neurobehavioral Symptom Inventory John E. Meyers

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Meyers Neuropsychological Services, Mililani, Hawaii

James English Private Practice, Helena, Montana

Ronald M. Miller and Amy Junghyun Lee Department of Psychology, Brigham Young University-Hawaii, Laie, Hawaii

The demographically diverse populations served by large health care systems (Veterans Affairs, Department of Defense, Medicare, Medicaid) are routinely screened with the Neurobehavioral Symptom Inventory (NSI). The extent to which a patient’s report of symptoms either initially and/or across time is affected by demographic variables— gender, ethnicity, age, or education—has not been investigated despite widespread use of the NSI. In practice, the effectiveness of this tool might be improved with demographically based norms. A large data set of normal community-dwelling individuals was collected using the NSI. Emphasis was made to collect data from individuals of diverse ethnic backgrounds. It was hypothesized that ethnic/cultural backgrounds would have an impact on NSI scores. The results provide normative data for the NSI applicable to a wide variety of individuals of various ages and ethnic backgrounds. An analysis of variance indicated there was no significant difference in NSI responses based on ethnic/ cultural background; however, age and gender were found to contribute significantly to the variance associated with symptom endorsement. The NSI appears to be a reliable measure of self-report postconcussive symptoms. Age is a variable associated with differential symptom endorsement on the NSI. Follow-up studies are needed to provide a measure of the sensitivity and specificity of this measure.

Key words:

adulthood, cognition, ethnic-minority studies, neuropsychology

In its present form, the Neurobehavioral Symptom Inventory (NSI) is an adaptation of 22 items from a structured interview used by Levin and colleagues to assess postconcussive symptoms (PCS) named the Post-Mild Traumatic Brain Injury (mTBI) Symptom Checklist (Levin et al., 1987). The NSI uses a 5-point scale (0 ¼ none, 1 ¼ mild, 2 ¼ moderate, 3 ¼ severe, 4 ¼ very severe) for patient selfreporting of PCS. In a study of 50 consecutively referred patients with a diagnosis of traumatic brain injury (TBI),

Address correspondence to John E. Meyers, Psy.D., Meyers Neuropsychological Services, 94-553 Alapoai St., #162, Mililani, HI 96789. E-mail: [email protected]

Cicerone and Kalmar (1995) utilized this modified mTBI Symptom Checklist as a 22-item scale and identified four factors comprising PCS. Heterogeneity was discovered among patients who sustained mTBI in their study due to “a high incidence” of older women and the potential influence of litigation. This 22-item scale later became the NSI, a measure of PCS in rehabilitation settings, and has since become widely utilized across varied clinical settings as it has been found to be a reliable and valid instrument for self-reported symptoms (King et al., 2012). The prevalence of a symptom within a given population is the “base rate” of that symptom. Among the reasons that clinicians should understand a symptom’s base rate is to

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increase the accuracy of clinical decision making. That is, the diagnostic utility of any symptom is relative to the base rate of that symptom in the population of interest (McCaffrey, Palav, O’Bryant, & Labarge, 2003). By way of example, the symptom of nausea in association with a migraine headache varies across populations considerably. The incidence of nausea with a headache among 27- to 28-year-olds in Switzerland is 93% (Merikangas, Angst, & Ilser, 1990). A survey by the International Headache Society of a panel of 15,000 participants chosen to be representative of the population of the United States revealed that of those participants reporting migraines, the incidence of nausea with headache was 73.7% among women and 66.2% among men for physician-diagnosed cases and 60.0% and 49.3% for women and men, respectively, for non-physician-diagnosed cases (Lipton, Stewart, Celentano, & Reed, 1992). A Denmark community study of 24- to 64year-olds showed an incidence of 6% (Rasmussen, Jensen, Schroll, & Olesen, 1992). From these studies, it is clear that there may indeed be cultural biases in symptom reporting. It is surprising to find that the NSI has not been specifically examined for ethnic or cultural influences in symptom reporting as no published studies of ethnic/cultural influences were found in a literature review. The NSI is used in many large health care systems as a routine screening measure, and it is often utilized as a functional outcome measure in rehabilitation settings (Wilde et al., 2010). The VA has mandated specialized evaluation of veterans with a history of mTBI with an emphasis on use of the NSI as a measure of PCS (Benge, Pastorek, & Thorton, 2009). Due to the large number of individuals in these health care systems, large ethnic and demographically diverse groups are served. Given the widespread use of the NSI as a screening tool, a surprising finding is that the NSI has no ethnic or demographically based norms. Widespread use of the NSI underscores the importance of investigations pertaining to its underlying factor structure and comorbid conditions on symptom reporting. This is of particular interest as TBI is estimated to affect 11% to 19% of veterans from both recent U.S. military operations (Operation Enduring Freedom, Operation Iraqi Freedom). It is estimated that 300,000 service members returning from these operations possibly have sustained an mTBI (Tanielian & Jaycox, 2008). The NSI is often used as a screening instrument for combat veterans. Their injuries are not always typically comparable to TBI in civilian populations because veterans’ injuries are frequently complicated by posttraumatic stress disorder (PTSD; Benge et al., 2009). The NSI items have been found to have a three-factor model (cognitive, affective, and somatic/sensory) of PCS in two large nonclinical military samples (N ¼ 2,420 and N ¼ 4,244) and one sample of individuals with recent mTBI (N ¼ 617; Caplan et al., 2010). The same authors concluded that as a measure of PCS, the three-factor model derived

from the NSI fits the data more parsimoniously than data from previous research, such as those suggesting a fourfactor model (Cicerone & Kalmar, 1995) or a six-factor solution (Benge et al., 2009). A validation study of the three- and four-factor models was undertaken by Meterko et al. (2012), who found that a four-factor solution was preferable to the three-factor solution. They used very large samples of n ¼ 6,001 for the derivation sample and n ¼ 5,987 for the validation sample. Participants were from a VA sample. Given the wide variety of individuals with whom the NSI is being used, we wanted to gather normative data and specifically check if ethnic background has an effect on the NSI scores. We chose to also calculate the norms data for the three- and four-factor scales identified by Caplan et al. (2010) and Meterko et al. (2012). Due to the previously identified effects of ethnicity/culture on symptom reporting, we hypothesized that normative data will need to be stratified by ethnic background.

METHOD Participants and Materials Data were collected from a total of 361 participants from multiple sites across the United States. (See Appendix A for a list of contributors to the data set.) Individuals were selected based on the following criteria: (a) They were normal community-dwelling individuals, (b) they had no active cognitive or behavioral health issues, and (c) they reported no history of TBI. Meeting these criteria was based on the individual’s declaration of no TBI, health, or behavioral issues. In a brief interview, the participants were asked if they had ever been treated for a mental disorder or if they were taking any psychotropic medication. They were asked if they had any history at any time of a head injury or central nervous system injury or disease. They were also asked if they had any active health issues that might affect their answers to the NSI. If they reported any of these conditions, they were not included in the data sample. For example, if an individual had high blood pressure or other health issues, but the condition was stable or well controlled and the individual met Conditions a through c, then the individual was included in the data collection. Data were obtained from each educational region of the United States as identified by Barona, Reynolds, and Chastain (1984). Participants were collected to match the 2012 ethnic makeup of the United States (U.S. Census Bureau, 2015). Individuals were recruited based on word of mouth and fliers placed on college bulletin boards, public bulletin boards, and church bulletin boards. Recruitment was also made via social media. Not all data collection sites used all of these methods, but all used one or more of these methods. To obtain a diverse ethnic/cultural data collection, data


collection was also targeted to certain populations (i.e., Native American, Pacific Islander). This targeting was specifically due to the hypothesis that ethnicity/culture would have an effect on NSI scores. No participants were paid for their participation. Individuals were asked to complete the NSI based on symptoms present during the previous 2 weeks including that day (see Table 4 for a list of questions on the NSI). No participants were dropped from the study participant pool.

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Statistical Procedures and Results Initially, the total score from each item that made up the NSI was calculated. Then the scores for each of the factor scales were calculated using both Caplan et al.’s (2010) three-factor structure for the NSI subscales and Meterko et al.’s (2012) four-factor structure. The individual items included in each factors’ scale are presented in Appendix B. The individual scores were first tested for internal reliability, which resulted in a Cronbach’s alpha of .86, indicating high reliability (StataCorp, 2013). Initially, the total score of the NSI was coded by adding together the scores for each individual item on the NSI. Then the demographic descriptions of the samples were calculated. The total sample of this study was 361 (192 women and 169 men), and the age range of the sample was 18 years to 81 years old (M ¼ 38.76 years, SD ¼ 16.62). Among these sample participants, 298 were right-handed and 63 were left-handed. The ethnic makeup can be seen in Table 1. In the present study, the range of education level was 10 to 22 years (M ¼ 14.60, SD ¼ 2.65). There was a significant, though small, correlation between age and education, as measured by whether participants graduated high school or not (r ¼ .22, p < .001). These findings were not unexpected as younger individuals have not yet had the opportunity to complete their education. The data were then separated into age-related groupings of 18 to 30 years, 31 to 59 years, and 60 years and older. Then correlations with education were again calculated for all three age groupings with the finding that the correlation was, again, small (r ¼ .20, p < .05), with an explanatory r2 of only .04. Given Cohen’s TABLE 1 Count and % for Ethnicity (N ¼ 361)

Ethnicity African American Caucasian Asian Native American Hispanic Pacific Islander



National Sample Characteristics* %

48 224 19 4 63 3

13.30 62.05 5.26 1.11 17.45 0.83

13.1 63.0 5.1 1.2 16.9 0.2

*U.S. Census Bureau (2015).


(1988) guidelines that this is considered a small effect, it was decided that it was not necessary to stratify the data by education if the data were stratified by age. To examine the relationship between ethnicity and NSI score performance, a series of analyses of variance (ANOVAs) was used. The results indicated that ethnicity was not significantly related to NSI total score performance at a 95% confidence interval, F(5, 355) ¼ 0.35, p ¼ .88, gp2 ¼ .005, nor was it significantly related for the three-factor (Caplan et al., 2010) subscale solution: Affective subscale, F(5, 355) ¼ 0.30, p ¼ .91, g2p ¼ .004; Cognitive subscale, F(5, 355) ¼ 0.51, p ¼ .77, g2p ¼ .007; and Somatosensory subscale, F(5, 355) ¼ 0.69, p ¼ .63, g2p ¼ .01. There were also no significant differences using the four-factor (Meterko et al., 2012) NSI subscale solution: Affective, F(5, 355) ¼ 0.30, p ¼ .91, g2p ¼ .004; Cognitive, F(5, 355) ¼ 0.30, p ¼ .91, g2p ¼ .004; Somatosensory, F(5, 355) ¼ 0.79, p ¼ .56, g2p ¼ .01; and Vestibular, F(5, 355) ¼ 0.51, p ¼ .77, g2p ¼ .007. Next, a t test was used to analyze two different relationships: the relationship between handedness and the total score of the NSI, and the relationship between gender and the total score of the NSI. The t test showed that there was not a significant relationship between handedness and the total score of the NSI, t(359) ¼ 0.19, p ¼ .85. Gender differences were analyzed by a between-subjects t test, t(359) ¼ 4.22, p < .001, with the NSI total indicating that women (M ¼ 10.49, SD ¼ 7.72) report more symptoms than men (M ¼ 7.19, SD ¼ 7.05). Thus, the variable gender was needed for normative stratification. An ANOVA was also used to examine the relationship between the age-stratified groups and the NSI total score. The result was significant, F(2, 358) ¼ 3.39, p ¼ .04, g2p ¼ .02. A Tukey Honestly Significant Difference (HSD) post-hoc test showed the 18- to 30-year-old age group (n ¼ 149) was significantly different from the 31- to 59-year-old (n ¼ 164) age group, but the group aged 60 years and older (n ¼ 48) was not different from the 31- to 59-year-old age group; thus, it was concluded that the normative data set could be divided into two groups based on age: 18 to 30 years old and 31 years and older. Another ANOVA was then performed to examine the two categories of age (18 to 30 years old and 31 years and older) and the two categories of education (less than and greater than high school education [12 years]) in conjunction with gender, in terms of the NSI total score. The results showed that the overall interaction was nonsignificant, F(1, 353) ¼ 0.99, p ¼ .32, and only the main effect for gender, F(1, 353) ¼ 26.01, p < .0001, g2p ¼ .07, and the interaction between gender and the age categories, F(1, 353) ¼ 11.12, p < .0001, g2p ¼ 0.03, were significant. As a result, we determined that we would categorize the norms based on age and gender. We present the normative data as means and standard deviations for each age group (18 to 30 years old and

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31 years and older) by gender in Table 2. Each of the age groups by gender were calculated along with the means and standard deviations for the three-factor scales of the NSI as recommended by Caplan et al. (2010). It can be observed in Table 2 that the younger individuals tended to report more difficulties as identified by the NSI total score. The normative data were also analyzed in percentiles in Table 3. According to this table, the cognitive, affective, and somatosensory factors, including total score, mostly showed that the female 18- to 30-year-old age group had a slightly higher percentile than the female group aged 31 years or older; however, in the male groups, the results from the group aged 31 years and older are either the same as or slightly higher than those of the 18- to 30year-old age group. Overall, the female 18- to 30-year-old age group indicated a higher percentile than the other three groups. Next, frequency counts of the individual scores on the NSI were calculated with the results shown in Table 4. Most normal individuals scored 0 to 2, with scores of 3 to 4 being less common in normal individuals. Less than 6% of the sample showed scores of 3 or 4 on each of the 22 items of the NSI. See Table 4 for the frequency count for each item on the NSI. A subset of the participants agreed to repeat the NSI after 2 weeks, which was acquired in a 13- to 15-day window. Participants were gathered from the Eastern (n ¼ 18), Midwest (n ¼ 7), and Western (n ¼ 12) United States. If a participant agreed to repeat the NSI in 2 weeks and returned to do so, the 2-week data test–retest data were collated. This group consisted of 37 individuals with a mean age of 38.73 years (SD ¼ 14.77) and a mean 14.38 (SD ¼ 2.56) years of TABLE 2 Normative Data Means and SD (in parentheses) for the Total Score and Factor Scales of the NSI for the Three-Factor (Caplan et al., 2010) and Four-Factor (Meterko et al., 2012) Solutions Male

Variable Age Total NSI score

Age 18–30 Years (n ¼ 66)


Age 31 Years and Older (n ¼ 103)

Age 18–30 Years (n ¼ 83)

Age 31 Years and Older (n ¼ 109)

23.82 (3.70) 50.00 (13.28) 22.43 (3.92) 49.61 (12.40) 5.88 (6.81)

7.94 (7.10)

12.95 (6.66)

8.61 (7.98)

Affective 2.27 (3.10) Cognitive 1.61 (2.14) Somatosensory 2.00 (2.67)

Three Factors 3.16 (3.26) 2.24 (2.19) 2.54 (2.81)

5.23 (3.44) 3.99 (2.57) 3.73 (2.83)

3.29 (3.28) 2.49 (2.62) 2.83 (3.28)

Affective Cognitive Somatosensory Vestibular

Four Factors 3.15 (3.25) 1.71 (1.96) 1.90 (1.97) 0.62 (1.06)

5.22 3.00 2.83 1.18

3.29 1.77 2.41 0.66

2.27 1.01 1.46 0.74

(3.10) (1.68) (1.85) (1.31)

NSI ¼ Neurobehavioral Symptom Inventory.

(3.44) (2.40) (2.21) (1.36)

(3.27) (2.37) (2.49) (1.14)

education; 22 were male and 15 were female; 32 were righthanded and 5 were left-handed. The ethnic makeup of the sample was 7 African American, 22 Caucasian, 6 Asian, TABLE 3 Percentiles for Each Age Group by Gender for Both the Three- and Four-Factor Solutions Male

Percentile Total 10th 25th 50th 75th 90th Affective 10th 25th 50th 75th 90th Cognitive 10th 25th 50th 75th 90th Somatosensory 10th 25th 50th 75th 90th Affective 10th 25th 50th 75th 90th Cognitive 10th 25th 50th 75th 90th Somatosensory 10th 25th 50th 75th 90th Vestibular 10th 25th 50th 75th 90th

Age 18–30 Years (n ¼ 66) 0 1 4 9 18


Age 31 Years and Older (n ¼ 103)

Age 18–30 Years (n ¼ 83)

1 2 6 12 18 Three-Factor

Age 31 Years and Older (n ¼ 109)

All Groups (n ¼ 361)

5 7 13 18 22

1 3 6 13 20

1 3 7 14 20

0 1 1 4 6

0 1 2 5 8

1 3 5 8 9

0 1 2 5 9

0 1 3 6 9

0 1 1 2 5

0 1 2 3 5

1 3 3 5 8

0 1 2 4 6

0 1 2 4 6

0 1 4 6 7

0 1 2 4 7

0 1 2 5 7

0 1 1 4 7

0 1 2 4 6 Four-Factor

0 0 1 4 6

0 1 2 5 9

1 3 5 8 10

0 1 2 5 9

0 1 3 6 9

0 0 0 4 6

0 0 1 3 5

0 1 3 5 6

0 0 1 3 5

0 0 1 3 5

0 0 1 2 4

0 0 2 3 5

0 1 3 4 5

0 1 2 4 6

0 0 2 3 5

0 0 0 1 2

0 0 0 1 2

0 0 1 2 3

0 0 0 1 2

0 0 0 1 2


NEUROBEHAVIORAL SYMPTOM INVENTORY TABLE 4 Frequency Count % of Individual Scores for the Normative Sample (N ¼ 361)

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Question 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Feeling dizzy Loss of balance Poor coordination, clumsy Headaches Nausea Vision problems, blurring, trouble seeing Sensitivity to light Hearing difficulty Sensitivity to noise Numbness or tingling in parts of my body Change in taste and/or smell Loss of appetite or increased appetite Poor concentration, cannot pay attention, easily distracted Forgetfulness, cannot remember things Difficulty making decisions Slowed thinking, difficulty getting organized, cannot finish things Fatigue, loss of energy, getting tired easily Difficulty falling or staying asleep Feeling anxious or tense Feeling depressed or sad Irritability, easily annoyed Poor frustration tolerance, feeling easily overwhelmed by things

and 2 Hispanic. The mean total score at the first testing was 5.76 (SD ¼ 6.74) and the mean total score on the second testing was 5.68 (SD ¼ 6.46; r ¼ .86). The test–retest data for the total score and subscale scores are presented in Table 5 and were transformed into T scores based on the normed means and standard deviations from the entire normative sample. Next, a sample of 89 participants was also gathered; these participants were specifically selected for their cultural background. These individuals self-identified as having close cultural ties to their native culture. Table 6 shows the demographic makeup of these groups. The Asian group was composed of individuals who were of Japanese

None (0)

Mild (1)

Moderate (2)

Severe (3)

Very Severe (4)

75.6 78.4 79.2 51.5 83.1 77.8 76.5 82.8 80.9 73.4 92.8 76.7 59.0 53.5 70.6 71.7 50.7 57.1 56.2 69.5 55.4 62.9

21.1 18.6 16.3 31.0 14.4 15.5 18.3 11.1 13.6 19.1 6.4 18.6 28.3 34.3 22.4 21.6 33.0 21.6 27.1 22.2 33.2 25.5

3.3 2.8 3.6 13.9 2.2 5.0 4.4 5.5 5.0 6.6 0.8 4.4 10.5 9.7 6.4 5.8 13.6 16.1 14.7 6.1 10.2 9.1

0 0.3 0.8 3.3 0 1.4 0.6 0.3 0.3 0.6 0 0 1.9 2.5 0.6 0.6 1.9 5.3 1.4 2.2 1.1 2.2

0 0 0 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0 0.3 0.3 0 0 0.3 0.8 0 0.6 0 0 0.3

culture. The Hawaiian group included individuals who were culturally Hawaiian. The Samoan group was composed of individuals who were culturally Samoan. The Samoan, Japanese, and Hawaiian groups were collected in Hawaii, on the island of Oahu. The Native American Group was collected primarily from Arizona and Southern California. All were collected using the same standards and methods as those used with the normative sample. A series of one-way ANOVAs comparing the cultural groups showed there was no significant difference between NSI scores for the total NSI, F(3, 85) ¼ 1.04, p ¼ .38, g2p ¼ .04, nor for the three-factor (Caplan et al., 2010) subscale solution: Affective, F(3, 85) ¼ 1.49, p ¼ .22, g2p ¼ .05,

TABLE 5 Test–Retest Data (2-Week Interval) Mean of T Scores and Correlations First Testing

Second Testing

n ¼ 37













< .001

Affective Cognitive Somatosensory

44.43 45.56 44.86

8.09 10.41 7.33

8.18 11.17 6.23

.90 .81 .74

< .001 < .001 < .001

Affective Cognitive Somatosensory Vestibular

47.22 49.31 47.14 48.06

8.41 10.24 7.76 7.22

8.30 12.82 5.89 7.23

.87 .83 .61 .69

< .001 < .001 < .001 < .001

Three-Factor 44.85 46.39 43.79 Four-Factor 47.21 51.37 45.34 47.42


MEYERS ET AL. TABLE 6 Demographic Description of Cultural Groups

Ethnic/Cultural Group

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Asian (n ¼ 23) Native American (n ¼ 14) Pacific Islander (n ¼ 27) Samoan (n ¼ 25)

Age (SD) 28.09 33.21 35.22 35.68

(13.16) (13.94) (11.13) (13.18)

Education (SD) 13.78 13.00 13.96 14.80

Cognitive, F(3, 85) ¼ 1.28, p ¼ .29, g2p ¼ .04; and Somatosensory, F(3, 85) ¼ 0.22, p ¼ .89, g2p ¼ .008. There were also no significant differences using the four-factor (Meterko et al., 2012) NSI subscale solution: Affective, F(3, 85) ¼ 1.49, p ¼ .22, g2p ¼ .05; Cognitive, F(3, 85) ¼ 0.96, p ¼ .42, g2p ¼ .03; Somatosensory, F(3, 85) ¼ 0.59, p ¼ .62, g2p ¼ .02, and Vestibular, F(3, 85) ¼ 0.98, p ¼ .41, g2p ¼ .03. A series of t tests comparing performance for the normative group and the cultural groups was performed and showed no difference in performance on the total NSI, t(448) ¼ 1.07, p ¼ .29, nor for the three-factor (Caplan et al., 2010) subscale solution: Affective, t(448) ¼ 0.54, p ¼ .59; Cognitive, t(448) ¼ 0.71, p ¼ .48; and Somatosensory, t(448) ¼ 1.50, p ¼ .14. There were also no differences using the four-factor (Meterko et al., 2012) NSI subscale solution: Affective, t(448) ¼ 0.54, p ¼ .59; Cognitive, t(448) ¼ 0.33, p ¼ .74; Somatosensory, t(448) ¼ 1.67, p ¼ .10, and Vestibular, t(448) ¼ 1.45, p ¼ .15.

DISCUSSION A large data set of normal community-dwelling individuals was collected using the NSI. An emphasis was made to recruit individuals of diverse ethnic backgrounds, as it has been hypothesized that ethnic/cultural background would have an impact on NSI scores. The results of data analysis showed that this hypothesis of an ethnic bias was not supported. An ANOVA indicated there was no significant difference in NSI performance based on ethnic/cultural background. Next, age and education were evaluated to identify if the normative data would need to be stratified by these two demographic variables. The results indicate that once age was accounted for by two broad age groups, education was not a significant factor for normative stratification. Gender did have a significant relationship with NSI scores, with women reporting more symptoms than men, so the normative data were stratified by age and gender. Furthermore, the test–retest reliability was calculated for both the total score and subscales, and all showed adequate test– retest reliability. The means and standard deviations for the test–retest data are presented in Table 5. The frequency count indicates that all symptoms listed on the NSI are reported by some percentage of the normal population. Thus, the PCS as listed on the NSI are “normal”

(2.13) (2.44) (2.15) (2.33)

Handed Right ¼ 20, Right ¼ 13, Right ¼ 23, Right ¼ 25,

Left ¼ 3 Left ¼ 1 Left ¼ 4 Left ¼ 0

Gender Female ¼ 16, Male ¼ 7 Female ¼ 9, Male ¼ 5 Female ¼ 15, Male ¼ 12 Female ¼ 10, Male ¼ 15

occurring symptoms in a normal population. As an example, 45% of the normative sample reported difficulty with concentration, and 51% reported forgetfulness as a problem. The data presented in Table 4 indicate the frequency of reported symptoms. It is observed in the data that about 95% of the normal individuals reported scores of 0 to 2; thus, it is likely that scores of 3 or 4 are unusually high scores and likely are of more clinical significance. The results of this study provide normative data that appear applicable to a wide variety of individuals of various ages and ethnic backgrounds. We were surprised to find that culture/ethnicity was not a significant variable. We had hypothesized that culture/ethnicity would have a significant effect. In looking at the data, ethnicity/culture was not a significant variable in the normative sample, even when we used strongly polarized groups. Thus, we conclude that the level of cognitive complaints as measured by the NSI and its factor scales seems to be more of a biological constant rather than a cultural variable. These results would suggest that the NSI is usable with other cultural groups and populations outside of the United States. The norms presented in this article would appear to be adequate for use with various cultural and ethnic groups. The NSI appears to be a reliable measure of self-report cognitive complaints. The NSI is not a diagnostic instrument but is a description of self-report symptoms and severity of symptoms. Diagnostic sensitivity and specificity were outside the scope of the current article; however, this would be the next logical step in validating the use of the NSI in PCS reporting. Having a normative comparison can then lead to identifying expected score levels for different injuries such as mild-to-severe TBI, other diseases/injuries, or behavioral health diagnoses. Future research could look at the level of reported symptoms and the severity of depression, anxiety, PTSD, or other behavioral health conditions. Having a normative comparison may also provide a comparison for “over-reporting of symptoms.” Thus, the presence of normative data may lead to other areas of study to improve the use of the NSI in different clinical samples. The clinical application of the current normative data allows the clinician to not only examine the overall level of self-report cognitive distress presented by the patient, but also to look at the specific areas of reported distress by examining the factor scales (Affective, Cognitive, Somatosensory). As an example, if a patient is reporting symptoms that show more distress in the Affective scale,


then therapies could be directed toward addressing that affective distress. The current data provide good normative comparisons for the NSI and the factor scales for both younger and older individuals.

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REFERENCES Barona, A., Reynolds, C. R., & Chastain, R. (1984). A demographically based index of premorbid intelligence for the WAIS-R. Journal of Consulting and Clinical Psychology, 52, 885–887. Benge, J. F., Pastorek, N. J., & Thorton, M. G. (2009). Postconcussive symptoms in OEF-OIF veterans: Factor structure and impact of posttraumatic stress. Rehabilitation Psychology, 54, 270–278. Caplan, L. J., Ivins, B., Poole, J. H., Vanderploeg, R. D., Jaffee, M. S., & Schwab, K. (2010). The structure of postconcussive symptoms in 3 US military samples. Journal of Head Trauma Rehabilitation, 25, 447–458. Cicerone, K. D., … Kalmar, K. (1995). Persistent postconcussion syndrome: The structure of subjective complaints after mild traumatic brain injury. Journal of Head Trauma Rehabilitation, 10, 1–17. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. King, P. R., Donnelle, K. T., Donnelly, J. P., Dunnam, W. G., Warner, G., Kittleson, C. J., … Meier, S. T. (2012). Psychometric study of the Neurobehavioral Symptom Inventory. Journal of Rehabilitation Research & Development, 49, 879–888. Levin, H. S., Mattis, S., Ruff, R. M., Eisenberg, H. M., Marshall, L. F., Tabaddor, K. … Frankowski, R. F. (1987). Neurobehavioral outcome following minor head injury: A three center study. Journal of Neurosurgery, 66, 234–243. Lipton, R. B., Stewart, W. F., Celentano, D. D., & Reed, M. L. (1992). Undiagnosed migraine headaches: A comparison of symptom-based and reported physician diagnosis. Archives of Internal Medicine, 152, 1273–1278. McCaffrey, R. J., Palav, A. A., O’Bryant, S. E., & Labarge, A. S. (Eds.). (2003). Practitioner’s guide to symptom base rates in clinical neuropsychology. New York, NY: Kluwer Academic/Plenum. Merikangas, K. A., Angst, J., & Ilser, H. (1990). Migraine and psychopathology. Archives of General Psychiatry, 47, 849–853. Meterko, M., Baker, E., Stolzmann, K. L., Hendricks, A. M., Cicerone, K. D., & Lew, H. L. (2012). Psychometric assessment of the Neurobehavioral Symptom Inventory-22: The structure of persistent postconcussive symptoms following deployment-related mild traumatic brain injury among veterans. Journal of Head Trauma Rehabilitation, 27, 55–62. Rasmussen, B. K., Jensen, R., Schroll, M., & Olesen, J. (1992). Interrelations between migraine and tension-type headache in the general population. Archives of Neurology, 49, 914–918. StataCorp. (2013). Stata 13 base reference manual. College Station, TX: Stata Press. Tanielian, T., & Jaycox, L. H. (2008). Invisible wounds of war: Psychological and cognitive injuries, their consequences, and services to assist recovery. Santa Monica, CA: RAND. U.S. Census Bureau. (2015, February). State & county quickfacts. Retrieved from Wilde, E. A., Whiteneck, G. G., Bogner, J., Bushnik, T., Cifu, D. X., Dickmen, S., … von Steinbuechel, N. (2010). Recommendations for the use of common outcome measures in traumatic brain injury research. Archives of Physical Medicine and Rehabilitation, 91, 1650–1660.

APPENDIX A List of Contributors: Ben O’Brian, M.D. Kapolei, HI Cynthia E. Avila, MA Argosy University, Hawaii Honolulu, HI James English, Psy.D., ABN, ABPP-Cl VA Healthcare System Helena, MT Frederick Kadushin, Ph.D., ABN Massachusetts Neurobehavioral Institute Longmeadow, MA Jason Miller, BA Brigham Young University, Hawaii Laie, HI John Knippa, Ph.D., ABN Coast Psychiatric Association Long Beach, CA John Meyers, Psy.D., ABN, ABPdN Meyers Neuropsychological Services Mililani, HI Margaret M. Zellinger, Ph.D., ABPP-CN Merrymeeting Neuropsychology Brunswick, ME Michelle C. Winston, Psy.D. Private Practice Boulder and Niwot, CO Peter R. Kaplan, Ph.D. Neuropsychological Associates Sarasota, FL Scott Sindelar, Ph.D. Private Practice Scottsdale, AZ Hallory A. Sindelar Pitzer College Claremont, CA Stephanie Moore, Psy.D., ABN University of California, Irvine Irvine, CA Thomas E. Staats, Ph.D. Louisiana State School of Medicine Shreveport, LA Tim Kockler, Ph.D. St. George, UT




APPENDIX B Based on the three-factor solution recommended by Caplan et al., 2010: Affective Q17 þ Q18 þ Q19 þ Q20 þ Q21 þ Q22 Cognitive Q4 þ Q13 þ Q14 þ Q15 þ Q16

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Somatosensory Q1 þ Q2 þ Q3 þ Q5 þ Q6 þ Q7 þ Q8 þ Q9 þ Q10 þ Q11 þ Q12

Based on the four-factor solution recommended by Meterko et al. (2012): Affective Q22 þ Q21 þ Q19 þ Q20 þ Q18 þ Q17 Somatosensory Q7 þ Q6 þ Q4 þ Q9 þ Q5 þ Q10 þ Q11 Cognitive Q16 þ Q13 þ Q14 þ Q15 Vestibular Q2 þ Q1 þ Q3

Normative Data for the Neurobehavioral Symptom Inventory.

The demographically diverse populations served by large health care systems (Veterans Affairs, Department of Defense, Medicare, Medicaid) are routinel...
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