1. ANALYZING PERSONALITY CHARACTERISTICS OF TRAIN OPERATORS 1
Analyzing Personality Traits and Other Characteristics of Train Operators
Alex Larson
San Diego State University
PSY 497 – Dr. Conte
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Abstract
“Big Five” personality traits and other employee characteristics have been used as predictors of
performance and other work outcomes in previous studies. This study analyzes significant
differences and relevant relationships between different qualities and characteristics of train
operators. These variables include age, polychronicity, cognitive ability, seniority, “Big Five”
traits, gender, absence, lateness, and performance ratings. Statistical analysis in Statistical
Package for the Social Science (SPSS) was conducted to analyze these variables.
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Method
Participants
The participants in this study were 170 train conductors. The sample included 144 males and 26
females. The age range was 27 to 61 years old with a mean age of 42.45 years and standard
deviation of 7.65 years.
Variables
Data were collected from multiple sources. Demographic characteristics included age, seniority,
and gender. Polchronicity and “Big Five” personality traits were self-reported. Cognitive ability
was assessed using a 40 question test developed for this data collection project. Dimensions
tapped included memory, problem sensing, inductive reasoning, deductive reasoning,
information ordering, and verbal ability; it overlaps with the Stanford-Binet and contains
measures of reasoning, problem solving, and memory. Absence and lateness were objective
performance measures from employee personnel files and performance ratings were subjective
performance measures rated by the supervisor; these variables were standardized.
Procedure & Data Analysis
Data were analyzed using Statistical Package for the Social Science (SPSS). Descriptive
statistics, frequencies, correlations, regressions, multiple regressions, and independent-samples t-
tests were used to analyze the reported numbers and look for significant differences in data.
Results
Descriptive Statistics
Descriptive statistics of all variables including N, range, minimum, maximum, mean, and
standard deviation of train conductors are shown in Table 1.
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Frequencies & Histograms
Figures 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 are histograms representing the frequencies of polychronicity,
cognitive ability, “Big Five” personality traits, absence, lateness, and performance rating. The
normal curve is also shown on each histogram as a comparison. Figures 1, 4, 7, 8, and 10
representing polychronicity, extraversion, intellect, absence, and performance have a normal
distribution; Figures 2, 3, 5, 6 representing cognitive ability, neuroticism, agreeableness, and
conscientiousness are slightly skewed to the left; Figure 9 representing lateness is skewed to the
right. All histograms are unimodal except Figure 10 which is slightly bimodal. The modes for
polychronicity and lateness stand out compared to the other frequency values in Figures 1 and 9.
Correlations & Regressions
A Pearson correlation was run in order to analyze relationships between age, cognitive ability,
seniority, absence, and performance rating. Relevant associations include the relationship
between age and cognitive ability (R= -0.47), cognitive ability and seniority (R= -0.16), age and
performance (R= 0.10), cognitive ability and performance (R= -0.16), seniority and performance
(R= 0.06), absence and performance (R= -0.52), and lateness and performance (R= -0.16). The
tests revealed cognitive ability and seniority, age and performance, cognitive ability and
performance, seniority and performance, and lateness and performance all to have small
associations; age and cognitive ability has a medium-large association; absence and performance
has a large association. The correlations are shown in Table 2. Linear regression analyses were
also conducted. Relevant coefficients of determination include the relationship of age to
cognitive ability (R2= 0.22), seniority to cognitive ability (R2= 0.03), age to performance (R2=
0.01), cognitive ability to performance (R2= 0.22), seniority to performance (R2< 0.01), absence
to performance (R2= 0.27), and lateness to performance (R2= 0.03). The tests revealed cognitive
5. ANALYZING PERSONALITY CHARACTERISTICS OF TRAIN OPERATORS 5
ability and seniority, age and performance, seniority and performance, and lateness and
performance to have small coefficients of determination; age and cognitive ability as well as
cognitive ability and performance have a medium-large coefficient of determination; and
absence and performance has a large coefficient of determination. Results are shown in Tables 3,
4, 5, 6, 7, 8, and 9.
Multiple Regressions
Multiple regressions were also run to analyze performance ratings and cognitive ability. “Big
Five” personality predictors were run as independent variables to test performance ratings and
cognitive ability as dependent variables. Absences and lateness were also run as independent
variables to test performance as a dependent variable. Table 10 shows “Big Five” personality
predictors to performance (R= 0.32, R2= 0.10), Table 11 shows “Big Five” personality predictors
to cognitive ability (R= 0.30, R2= 0.09), and Table 12 shows absence and lateness to
performance (R= 0.54, R2= 0.29). “Big Five” personality predictors to performance and
cognitive ability had medium associations and medium coefficients of determination while
absence and lateness to performance had a large association and a large coefficient of
determination.
Independent-Samples T-Tests
Independent-samples t-tests were run with 95% confidence intervals for difference to test for a
significant difference in cognitive ability, absence, lateness, and performance rating with gender
as the grouping variable. Results revealed two-tailed significant differences for absence (p<
0.001) and performance (p= 0.005), while cognitive ability (p= 0.447) and lateness (p= 0.393)
were insignificant. Results are shown in Table 13. Independent-samples t-tests from summary
data were also run with 95% confidence intervals for difference to test for a significant
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differences between absence and lateness, as well as cognitive ability and performance rating.
Table 14 shows an insignificant difference between absence and lateness [t(338)= 0.430, p=
0.667] while Table 15 shows a significant difference between cognitive ability and performance
[t(338)= 67.180, p< .001].
Discussion
Analysis of the train operator data has revealed relevant relationships and significant
differences which help lead to conclusions and guide future studies. The shape of the distribution
for frequencies tells a lot about the variables too. Cognitive ability, neuroticism, agreeableness,
and conscientiousness are all skewed left. The mean score of cognitive ability is 74%, right
around what most consider an average test score. The skew also shows that train conductors tend
to rate themselves higher on these three of the “Big Five” traits than extraversion and intellect.
Future studies could focus on how employees of different occupations rate themselves on “Big
Five” personality traits and if the samples show significant differences. Different jobs could
relate to higher self-sense of different traits. Absence has a normal shape, however, lateness is
skewed right meaning that these employees are late more often than they are absent. All
histograms are unimodal except for performance which is bimodal. This reveals that in this
study, supervisors tend to rate the train conductors as either a mildly positive or negative, not in
the middle or to the extremes.
Correlations show associations in the relationship between two variables while
regressions show the effect of the independent variable on the dependent variable. Age and
cognitive ability had a negative, medium-large association and a medium-large effect size.
Besides the relationship between age and seniority which is obvious, this is the strongest in the
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study. This data shows that cognitive ability decreases with age. Rushton and Ankney also
studied the correlational relationship of age, cognitive ability, and other factors, and discovered
that “brain size and cognitive ability show a curvilinear relation with age, increasing to young
adulthood and then decreasing” (1996). Train operator data did not reveal this as it did not
have measures from earlier years prior to the position, but results did confirm the decrease in
cognitive ability as age increased. This rise and then fall trend was also found by Minbashian,
Earl, and Bright, who researched trajectories and rates of deceleration in performance based on
“Big Five” predictors (2013). Many different studies have looked at the relationships of these
variables and well as their predictive value. There was a negative, small association and small
effect size for seniority and cognitive ability, cognitive ability and performance, and lateness and
performance. These show that cognitive ability does not necessarily depend on how long an
employee has worked as a train conductor and test scores don’t necessarily lead to better
performance. The small associations and effect sizes reveal train conducting is more of a skill-
based job rather than knowledge based. Also, just because an employee is late doesn’t mean they
can’t still perform well. This goes against previous claims stating employees who are late also
underperform (Talacchi, 1960). Absence and performance on the other hand has a negative, large
association and large effect size, so managers’ ratings of employees are based more off absences
than lateness. Age and performance along with seniority and performance have small
associations and small effect sizes. These relationships show that age and time spent at a
company doesn’t necessarily translate to one’s performance. These relationships of employee
characteristics and performance are specifically for this sample of train conductors, but further
research could focus on characteristics of others for other jobs or for overall work performance.
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Some jobs may be more hands on and task-focused than knowledge-based or vice versa and
employee characteristics could have more of an impact in certain jobs than others.
Absence and performance as well as lateness and performance both showed a negative
association but showed different effect sizes when looked at individually. Although they differ,
multiple regression analysis with both as predictors of performance shows a large coefficient of
determination so both have an impact together on performance. Koslowsky, Sagie, Krausz, and
Singer had researched correlations between lateness, absence, performance, and other variables.
They found the lateness-absence correlation to be higher than the lateness-turnover correlation,
and lateness could be used by management as an early predictor of turnover (1997). Multiple
regression with “Big Five” personality predictors as the independent variables and performance
rating or cognitive ability as the dependent variable showed a medium effect size. The five traits,
“openness to experience,” “conscientiousness,” “extraversion,” “agreeableness,” and
“neuroticism,” have been used as the key personality predictors for job performance (McCrae &
John, 1992). While these predictors can help predict performance, situational factors and other
characteristics will also have an impact on performance. “Big Five” personality traits as
predictors of performance have been a big focus in workplace research studies.
Independent-samples t-tests show differences between sample means. When gender was
used as a grouping variable, cognitive ability and lateness had insignificant differences between
males and females while absence and performance had significant differences. Gender doesn’t
seem to matter for train operators’ cognitive ability or lateness, but it does for absence and
performance. Operating a train could be considered a more masculine job and a male may be
able to perform better, but the data could be biased or misleading due to the large difference in
sample size of men and women. Independent-samples t-tests from summary data tested for
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significant differences between absence and lateness as well as cognitive ability and
performance. Absences and lateness did not show significant differences while cognitive ability
and performance did. Absence and lateness are not too different, something previously shown
from how they both impact performance. The significant difference between cognitive ability
and performance also relates to the small association and small coefficient of determination and
how cognitive ability doesn’t significantly impact performance of train operators.
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References
Koslowsky, M., Sagie, A., Krausz, M., & Singer, A. D. (1997). Correlates of employee lateness:
Some theoretical considerations. Journal of Applied Psychology, 82(1), 79-88.
McCrae, R.R., & John, O.P. (1992). An introduction to the five-factor model and its
applications. Journal of Personality, 60(2), 175-215.
Minbashian, A., Earl, J., & Bright, J.E. (2013). Openness to experience as a predictor of job
performance trajectories. Applied Psychology: An International Review, 62(1), 1-12.
Rushton, J. P., & Ankney, C. D. (1996). Brain size and cognitive ability: Correlations with age,
sex, social class, and race. Psychonomic Bulletin & Review, 3(1), 21-36.
Talacchi, S. (1960). Organizational size, individual attitudes, and behavior: An empirical
study. Administrative Science Quarterly, 44, 216–223.
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Table 3 – Age & Cognitive Ability
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .468a
.219 .214 4.94684
a. Predictors:(Constant),Age
Table 4 – Seniority & Cognitive Ability
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .159a
.025 .019 5.762
a. Predictors:(Constant),Cognitive_Ability
Table 5 – Age & Performance
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .104a
.011 .005 .75289
a. Predictors:(Constant),Age
Table 6 – Cognitive Ability & Performance
R R Square
Adjusted R
Square
Adjusted R
Square
Std. Error of the
Estimate
.161a
.026 .020 -.002 .75546
a. Predictors:(Constant),Cognitive_Ability
Table 8 – Absence & Performance
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .522a .273 .268 .64558
a. Predictors:(Constant),Absence
Table 9 – Lateness & Performance
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
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1 .164a
.027 .021 .74671
a. Predictors:(Constant),Lateness
Table 10 – “Big Five” to Performance
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .316a
.100 .073 .72681
a. Predictors: (Constant),Intellect, Conscientiousness,Agreeableness,
Extraversion, Neuroticism
Table 11 – “Big Five” to Cognitive Ability
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .302a
.091 .063 5.40079
a. Predictors:(Constant),Intellect, Conscientiousness,Agreeableness,
Extraversion, Neuroticism
Table 12 – Absence & Lateness to Performance
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .535a
.286 .278 .64154
a. Predictors:(Constant),Lateness,Absence
Table 13 – Independent Samples Tests: Gender
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
