train operators final draft

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


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.


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.


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


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


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


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.


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


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.


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.


Tables & Figures

Table 1 – Descriptive Statistics

N Range Minimum Maximum Mean Std. Deviation

ID_Number 170 169 1 170 85.50 49.219

Age 170 34 27 61 42.45 7.648

Polychronicity 170 2.67 1.00 3.67 2.3237 .58900

Cognitive_Ability 170 26.00 13.00 39.00 29.0000 5.57997

Seniority 170 33 3 36 11.45 5.819

Neuroticism 170 16 3 19 14.16 3.345

Extraversion 170 12 0 12 4.78 2.861

Agreeableness 170 11 5 16 13.14 2.212

Conscientiousness 170 11 2 13 9.12 2.408

Intellect 170 10 0 10 6.14 2.705

Gender 170 1.00 1.00 2.00 1.1529 .36099

Absence 170 5.33 -1.87 3.47 .0217 .95634

Lateness 170 4.36 -.46 3.90 -.0229 .95513

Performance_Rating 170 3.19 -2.00 1.19 -.0123 .75475

Valid N (listwise) 170


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


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


Cognitive_Ability Equal variances

assumed

.936 .335 -.763 168 .447 -.90812 1.19049 -3.25837 1.44213

Equal variances not

assumed

-.816 36.840 .420 -.90812 1.11306 -3.16373 1.34749

Absence Equal variances

assumed

.978 .324 -4.386 168 .000 -.84914 .19361 -1.23135 -.46692

Equal variances not

assumed

-5.010 39.519 .000 -.84914 .16950 -1.19184 -.50643

Lateness Equal variances

assumed

1.700 .194 -.857 168 .393 -.17459 .20369 -.57671 .22752

Equal variances not

assumed

-.825 33.580 .415 -.17459 .21172 -.60506 .25587

Performance_Rati

ng

Equal variances

assumed

3.403 .067 2.814 168 .005 .44355 .15763 .13235 .75475

Equal variances not

assumed

3.223 39.649 .003 .44355 .13761 .16534 .72175

Table 14 – Independent Samples Test: Absence & Lateness

Mean Difference

Std. Error

Difference t df Sig. (2-tailed)

Equal variances assumed .045 .104 .430 338.000 .667

Equal variances not

assumed

.045 .104 .430 337.999 .667

Table 15 - Independent Samples Test: Cognitive Ability & Performance

Mean Difference

Std. Error

Difference t df Sig. (2-tailed)

Equal variances assumed 29.012 .432 67.180 338.000 .000

Equal variances not

assumed

29.012 .432 67.180 175.182 .000

train operators final draft

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