Neuropsychological Assessment Lab Reports: Personality Correlates
NEUROPSYCHOLOGICAL ASSESSMENT LAB
PAUL JONES, ED.D.
UNIVERSITY OF NEVADA, LAS VEGAS

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STATE-TRAIT PERSONALITY CORRELATES:
Assessment Lab Report 1-2
May/1998

The test instruments in the series one studies included measures of both state and trait personality characteristics of the study participants. The Mood Scale 2, a state level personality measure, provides subscales for: activity, happiness, depression, anger, fatigue, and fear. It is included with the ANAM/TWB batteries., but there is little information in the literature about the relationship between the mood states and performance on the cognitive measures.

Schlegel, Gilliland, and Crabtree (1992) reported a design which included use of the Mood Scale 2 in association with the cognitive processing tasks. One-half of the subjects completed the Mood Scale 2 at the beginning and at the midpoint of a two-hour testing session. The other half completed the mood measures at the midpoint and at the end of the session. Differences in the mood state measures were not statistically significant, but the trends were in predicted directions. The happiness and activity scales declined through the test session; the fatigue and anger scales increased through the test session. Self-reports on the fear and depression scales remained essentially constant. They did not report correlations among the mood state and cognitive processing scales.

In a prior study with the ANAM battery, Jones (1994b) found statistically significant correlations between mood states of anger, fear, and happiness with accuracy and speed on several of the cognitive measures. That study also suggested some relationship between trait level measures of personality and cognitive performance.

This series one study is to provide continued exploration of the relationship between personality characteristics and performance on the cognitive measures. In addition to the state level personality measurement using the Mood Scale 2, this study included measurement of two trait level personality characteristics: 1) problem-solving approach and 2) problem-solving focus.

The personality traits identified here as approach and focus were selected for use in this study because prior research has found them related to learning style (Lawrence, 1984) and to preference/comfort with use of a computer (Jones, 1994a). Detailed explanation of the trait measurement is in the section on test instruments.

In essence, the approach trait identifies a data vs. ideas continuum for preferred problem solving modality. The focus trait identifies a things vs. people continuum expressing preference for emphasis on logical analysis vs. focus on interpersonal considerations when solving problems. Although labels differ in various personality assessment tools, these two traits are identified as distinct characteristics in the widely accepted five-factor model of personality structure.

Results: Personality State Characteristics

Tables 1 and 2 below provide correlation coefficients between the six mood scales and the level of accuracy and level of efficiency (correct responses/minute) scores for the ANAM/TWB cognitive scales. In this dataset, the only statistically significant correlation coefficients were between the fatigue scale and the accuracy and efficiency scores on the Matching to Sample test.

The study cited above (Jones, 1994b) found more significant relationships between mood states and cognitive measures than were apparent in this study. Both studies were done with with upper division teacher education students. Additional investigation is needed to resolve the divergent findings, but it may be relevant that inspection of Tables 1 and 2 indicates that cognitive performance correlation coefficients with several scales (activity, happiness, depression, and anger) approach statistical significance.

Table 3 provides descriptive data for each of the six mood state scales. The score reported is the obtained percentage of maximum points available on each scale. A higher score indicates more endorsement of adjectives associated with that scale. The data in Table 3, as expected, indicate a much higher endorsement of positive scales (happiness and activity) and a wide variation in individual scores.

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TABLE 1
MOOD STATE SCALES AND COGNITIVE ACCURACY CORRELATION COEFFICIENTS (n = 57)
react-1react-2mem-4mem-6mathspatialmatch
activity-.07-.15-.14.20.09.09 -.16
fatigue.15.20.17.19-.10-.01 .43*
happiness-.10-.18-.20.20.16.01 -.18
depression.10.09.12-.08-.26-.02 .13
anger.02.26.02.24-.13-.05 .24
fear.10.14.03.18-.15-.03 .13

* significant at .05 level

react-1: Procedural Reaction Time- Standard Version
react-2: Procedural Reaction Time- Degraded Stimuli
mem-4: Sternberg Memory Search- Four-Letter Set
mem-6: Sternberg Memory Search- Six-Letter Set
math: Mathematical Processing
spatial: Spatial Processing- Simultaneous
match: Matching To Sample
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TABLE 2
MOOD STATE SCALES AND COGNITIVE EFFICIENCY CORRELATION COEFFICIENTS (n = 57)
react-1react-2mem-4mem-6mathspatialmatch
activity.01.17.04.24.18.17 -.09
fatigue-.06-.09.00-.07.01-.09 .28*
happiness-.19-.08-.20.01-.06-.06 -.26
depression.10.02-.07-.17-.12.11 .01
anger.14.09.04.12.06.13 .17
fear.06-.02-.14-.11-.05-.09 .06

* significant at .05 level

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TABLE 3
MOOD STATE PERCENTAGE SCORES (n = 57)
means.d.highlow
activity67.920.91100.07.1
fatigue18.417.7366.70.0
happiness76.621.67100.016.7
depression12.118.883.30.0
anger15.820.491.70.0
fear12.117.7100.00.0

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Results: Personality Trait Characteristics

Tables 4 and 5 below provide correlation coefficients between the two trait level scales used in this study and the ANAM cognitive processing measures of accuracy and efficiency.

No significant relationships were evident between the approach (data-ideas) trait score and the cognitive measures. There was, however, significant relationship between the focus (things-people) trait and several of the cognitive processing efficiency scales.

Scores on the two trait scales were not significantly related (r = -.08). The mean score on the approach scale for this sample was -1.1 indicating a slight preference for the ideas approach. The mean score on the focus scale was -2.5 indicating a preference for the interpersonal, people focus.

Both Levinson and Reeves (1997) and Schlegel, Gilliland, and Crabtree (1992) noted a problem in use of the accuracy scales on the ANAM measures. The nature of the tasks typically results in a low ceiling and thus little differentiation among the accuracy scores. That feature was evident in this dataset with mean accuracy percentage scores ranging from 91 to 98 on the ANAM cognitive measures. Levinson and Reeves (1997) suggested that the cognitive efficiency score (correct responses/minute) which is more sensitive to individual differences may be more useful, particularly with nonclinical samples.

Cognitive efficiency was thus used as the dependent variable in further exploration of the relationship between the focus (things-people) personality trait and the ANAM cognitive measures. For this analysis, three levels of the focus trait were constructed. Scores greater than or equal to +5 were defined to represent strong preference for logical analysis, the "things" pole. Scores less than or equal to -5 were defined to represent strong preference for the "people" pole. Scores between these two points were identified as mixed. In this sample, this resulted in n=11 for the things preference, n=21 for the mixed group, and n=25 for the people preference. To simplify presentation of the results, these groups will be referred to as high, mid, and low preference for logical analysis.

In a one-way ANOVA, significant differences among the three trait level groups were evident in efficiency scores on five of the seven cognitive processing variables: Procedural Reaction Time with standard stimuli, F(2,54) = 3.90, p < .05, Sternberg Memory Search with four-letter set, F(2,54) = 7.12, p <.01 and with six-letter set, F(2,54) = 4.78, p < .05, Spatial Processing, F(2,54) = 7.96, p < .01, and Matching to Sample, F(2,54) = 8.15, p < .05. In each instance the low logical analysis preference group had significantly lower mean scores.

Differences among the groups in Procedural Reaction Time with degraded stimuli were not significant, F(2,54) = 1.73, p > .05. Differences in the logical analysis preference were also not significant in the efficiency score on the Math Processing scale, F(2,54) = .76, p > .05.

Studies with the Myers-Briggs Type Indicator (Jones, 1994a) have indicated that males are more likely than females to have higher scores on a things-people trait scale. To explore the possibility that the trait preference differences reported above could be an artifact of a gender difference, the data were reanalyzed with a two-way ANOVA for trait level and gender.

Results of the two-way ANOVA found only one instance of a significant gender effect: Matching to Sample, F(1,51) = 5.76, p < .05. Males had significantly higher efficiency scores. On this test, however, the interaction between gender and trait was not significant (F(2,51) = 1.66, p > .05). The only instance of a significant interaction was on the Procedural Reaction Test with standard stimuli, F(2,51) = 4.11, p < .05.

Inspection of the dataset indicates that the sharpest contrast in trait level performance is found when the subjects with low trait preference for logical analysis (n = 25) were compared with a group combining those with mid and high level preference. Results of this comparison are presented in Table 6 and indicate lower efficiency scores on all cognitive scales by the low logical analysis trait preference group.

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TABLE 4
PERSONALITY TRAIT SCALES AND COGNITIVE ACCURACY CORRELATION COEFFICIENTS (n = 57)
react-1react-2mem-4mem-6mathspatialmatch
approach (data-ideas)-.18-.09-.01-.17-.10.00 .09
focus (things-people).03-.33*.17.09.14.13 -.04

* significant at .05 level

react-1: Procedural Reaction Time- Standard Version
react-2: Procedural Reaction Time- Degraded Stimuli
mem-4: Sternberg Memory Search- Four-Letter Set
mem-6: Sternberg Memory Search- Six-Letter Set
math: Mathematical Processing
spatial: Spatial Processing- Simultaneous
match: Matching To Sample
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TABLE 5
PERSONALITY TRAIT SCALES AND COGNITIVE EFFICIENCY CORRELATION COEFFICIENTS (n = 57)
react-1react-2mem-4mem-6mathspatialmatch
approach (data-ideas).03-.03-.04-.09.06-.14 -.12
focus (things-people).27*.07.32*.28*.13.39* .18

* significant at .05 level

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TABLE 6
COGNITIVE EFFICIENCY SCORES WITH MID/HIGH AND LOW LOGICAL ANALYSIS TRAIT PREFERENCE
mid/hi (n=32)low (n = 25)
mean (s.d.)mean (s.d.)tp
react-1104.1 (12.57)95.1 (13.29)2.61.011
react-297.9 (23.36)89.3 (15.64)1.58.118
mem-486.9 (15.48) 68.7 (20.73)3.79.000
mem-686.2 (16.00)72.6 (17.33)3.07.003
math25.0 (6.60)22.8 (7.00)1.24.220
spatial30.9 (6.16)24.6 (5.92)3.88.000
match33.5 (10.98)25.6 (6.88)3.14.003

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Summary

A prior study of personality correlates and ANAM measures (Jones, 1994b) concluded that it would be advantageous to routinely include personality state and trait measures in clinical neuropsychology applications. The results of the present study are consistent with that conclusion.

In group interpretation there does not appear to be substantive relationship between the mood state measures and the cognitive performance measures. Only two of the obtained correlation coefficients were statistically significant, and even that finding must be interpreted with caution given the large number of comparisons. The Mood Scale 2 measurement, however, does provide potentially useful information about the probability that the scores of an individual represent maximum performance and are not unduly influenced by fatigue, anger, etc.

No relationship in this dataset was found between the cognitive measures and the trait identified here as approach (data-ideas). This trait, identified as "intellect" in some formulations of the five-factor model, has been moderately associated with measured intelligence (Costa & McRae, 1992), particularly with reference to divergent thinking skills. The ANAM/TWB tasks are not conceptualized as measures of divergent thinking, and the lack of relationship with the cognitive processing tasks here is thus as would be anticipated.

The strong relationship in these data between the cognitive measures and the trait identified here as a focus (things-people) warrants further study with additional samples and alternative measures of this trait. In study of second-order factors in the Sixteen Personality Factor Questionnaire, Cattell, Eber, and Tatsuoka (1970) identify an apparently comparable trait as "corteria" or tough poise. Persons with low scores on this trait are described as emphasizing "feeling" as opposed to "thinking" (a description also consistent with the Myers-Briggs definition of this trait). Corteria is an abbreviation of cortical alertness with high scores associated with quick reaction time, high alpha wave interruption in EEG, and other physical signs of high cortical activation level. They provide data supporting conclusion that this is a consistent personality trait, not a state of activation, and that different individuals characteristically operate at different levels of cortical arousal.

The characteristics of cortical alertness noted above would be consistent with the higher cognitive efficiency scores found in this study. Although additional confirmatory studies are needed, these data warrant suggestion that status on this trait is a consideration in overall assessment of neuropsychological function.

References

Cattell, R.B., Eber, H.W., & Tatsuoka, M.M. (1970). Handbook for the Sixteen Personality Factor Questionnaire. Champaign, IL: Institute for Personality and Ability Testing.

Costa, P.T., Jr., & McCrae, R.R. (1992). NEO PI-R: Professional manual. Odessa, FL: Psychological Assessment Resources.

Jones, W.P. (1994a). Computer use and cognitive style. Journal of Research on Computing in Education, 26, 514-522.

Jones, W.P. (1994b). Personality correlates of the ANAM battery. Archives of Clinical Neuropsychology, 9, 145-146.

Lawrence, G. (1984). A synthesis of learning style research involving the MBTI. Journal of Psychological Type, 8, 2-15.

Levinson, D.M., & Reeves, D.L. (1997). Monitoring recovery from traumatic brain injury using Automated Neuropsychological Assessment Metrics. Archives of Clinical Neuropsychology, 12, 155-166.

Schlegel, R.E., Gilliland, K., & Crabtree, M.S. (1992). Development of the UTC-PAB Normative Database: Technical Report. Springfield, VA: National Technical Information Service.

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