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8/8/2019 Effect of Big-Five Personality Traits on Job Performance: A quantitative research plan
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RSCH 8200 Quantitative Reasoning and Analysis
Professor: Dr. Thomas Spencer III
Final Project: Quantitative Research Plan
Week 11
Robin L. M. Cheung Robin.Cheung@WaldenU.Edu
A00083286
AMDS PhD, Finance Specialization
KAM Assessor and email address: Dr. Javier Fadul, Javier.fadul@waldenu.edu
Faculty Mentor and email address: Dr. Javier Fadul, Javier.fadul@waldenu.edu
Walden University
21 September 2010
Comment [RLMC1]:Reviewed by: Thomas Spencer ...
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Introduction
O rganizations are human systems. As such, each member of the system brings to bear the
full range of psychological and emotional behavior while also applying themselves to their work
which is highly prescriptive and ostensibly rational. Because of this, human systems experience
high levels of inefficiency as emotions, personality, and unconscious impulses interfere with the
objectivity and logical processes at work. To what extent, then, do individuals' idiosyncrasies
interfere with the expectations of their performance? Previous studies have independently
demonstrated a correlation between neuroticism and absenteeism (Judge, Martocchio, &
Thoresen, 1997) and a negative correlation between conscientiousness and absenteeism (Behling,
1998). In todays high-performance business world where investors demand increasingly
rigorous accountability and value, hiring managers need to move away from the traditional
practice of relying on gut instinct toward structured defensible recruitment and selection
practices. Just as an MBA once provided a competitive advantage but is increasingly becoming
requisite to transcending middle management, firms who fail to take advantage of new research
in the behavioural sciences to maximize performance will be at a disadvantage in a world whereeven the largest banks and manufacturers can fail.
Dependent variables proposed for this study include measures of conscientiousness,
which measure an individuals achievement orientation, thoroughness, and diligence (Behling,
1998). Judge, Martocchio, & Thoresen (1997) found that emotional instability, anxiety, and
depression are positively correlated to absenteeism; thus, the other independent variable will
assess neuroticism. Number of absences, both excused and unexcused, without regard for
absence length will comprise the independent variable; this better approximates the number of
absence incidences, which should better approximate the number of underlying causes of
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absence; counting days absent could unduly give greater weight to absences that require longer
convalescence periods and would be a better proxy for measuring severity of condition than
number of causative conditions.
The Big Fiv e Mod el as Pr e dictor of Job P e rformanc e
Although early research showed that personality tests were among the least valid types
of selection tests, with an overall mean sample-size weighted correlation of 0.21 for predicting
job performance (Hurtz & Donovan, 2000, p. 869), more recent meta-analyses have shown that
certain traits specified by the Big Five personality traits are good predictors of job performance
(Hurtz & Donovan, 2000). Comprising O penness, Conscientiousness, Extroversion,
Agreeableness, and Neuroticism, the Big Five personality traits were distilled through data
reduction through four levels of abstraction (Digman, 1990). I therefore propose to use a
persons Big Five measures as measures of an individuals idiosyncrasies as related to job
performance. Although there are multiple factors attributed to employee performance, this study
will focus on Absenteeism, which Judge, Martocchio, & Thoresen (1997) correlates with job
performance, as a proxy for job performance.O
ne weakness with Judge, Martocchio, &Thoresen (1997), however, was its small sample size of 89 respondents out of a pool of 320 non-
academic university employees. Arsenault & Dolan (1983) drew from a pool of 1,200 hospital
employees and measured instances of absences, regardless of length, not only because of its
popularity in the literature and how well the data fit the normal distribution, but in a
psychometric sense, this would more closely approximate the number of situations causing an
absence without putting undue weight on specific causes, which may result in differential
absence lengths. Arsenault & Dolan (1983), however, conducted their study prior to the
development of the Big Five Personality Traits model (Digman, 1990) and thus used contrived
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measures of personality without rigorous data reduction through factor analysis. While Judge,
Martocchio, & Thoresen (1997) attempted to control for absence type by segregating voluntary
and involuntary absences, Arsenault & Dolan (1983) included all absence types. This has the
effect of mitigating some human confounding factors that Judge, Martocchio, & Thoresen (1997)
had, in fact, attempted to control for: psychological conditions can manifest themselves as
physical ailments. Additionally, different organizational cultures and specific interpersonal
dynamics may attribute an inexcusable absence to an excusable ailment; vice versa, an excusable
absence may be recorded as an inexcusable one.
Purpos e Stat e m e nt
The purpose of this study is therefore to confirm that there is a positive relationship
between neuroticism and absenteeism, and a negative correlation between conscientiousness and
absenteeism within the same population.
Studying P e rsona l ity and Job P e rformanc e th roug h Cross-S e ctiona l De sign
The optimal research design to examine to what extent individuals' idiosyncrasies
interfere with the expectations of their performance is a cross-sectional design. Using a factorialdesign would allow capturing the effects of interactions amongst the personality traits (Frankfort-
Nachmias & Nachmias, 2008); for example, what if a high degree of conscientiousness alone is
inversely correlated with absenteeism, and neuroticism alone is positively correlated with
absenteeism, but when an individual both has high levels of conscientiousness and neuroticism in
combination, absenteeism is highest? It is possible that a factorial design considering all of the
Big Five traits together increase construct validity over evaluating the traits in isolation. While
the Big Five traits were distilled from a larger set of traits through data reduction through factor
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analysis that maximizes orthogonality of the variables, it is possible that there are interactions
amongst the variables that make their combinations different to their simple linear combination.
This confirmatory quantitative research study will employ a survey research design.
Random sampling of employees will be used to test both hypotheses to maximize validity.
Sample size will be calculated using G*Power 3. Conscientiousness and neuroticism will be
assessed using a standardized Big Five personality trait survey; absenteeism will be taken as the
number of absence incidences reported as missed by the employer. Multiple linear regression
will be used to elucidate relationshipsif anybetween neuroticism, conscientiousness, and
absenteeism and indicate their magnitude and directionality.
Inappropriat e De signs
A traditional experimental design is not feasible for this study because it involves
measuring constructs that are not possible to change or that are unethical to change. For
example, it would be unethical to measure subjects absences then change their personalities or
health conditions and measure absences again. It is also impossible to control for all other
factors that could influence absenteeism, such as family emergencies or transportation issues. Itis also not possible to use a quasi-experimental design because it is also not possible to segregate
a matched group of employees whose personalities will be changed to compare absenteeism
before and after having their personalities changed. O ther related designs described in Frankfort-
Nachmias & Nachmias (2008) that would not be appropriate for this study include experimental
designs (Solomon Four Group design, Post-test only design) since there is no ability to modulate
personality traits of the subjects, as well as quasi-experimental designs, such as planned variation
or time-series designs, because there is no external response to a stimulus being examined.
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Rather, personality traits are innate qualities about an individual that an individual either has or
does not have.
H ypot he se s
Where B c and B N represent regression coefficients for the predictor variables of conscientiousness and
neuroticism, respectively,
H I0: Conscientiousness is not related to workplace absenteeism (R 2 = 0; B c = 0)
H IA. Conscientiousness is correlated with workplace absenteeism (R 2 > 0; B c 0)
H II0: Neuroticism is not related to workplace absenteeism (R 2 = 0; B N = 0)
H IIA. Neuroticism is correlated with workplace absenteeism (R 2 > 0; B N 0)
Since the hypotheses are two-tailed tests, the significance will be taken as /2 = 0.025.
L e vel s of M e asur e m e nt Important to Study
The proposed study will assess two personality traitsneuroticism and
conscientiousnessusing an off-the-shelf personality inventory, such as NE O PI-R (Judge,
Martocchio, & Thoresen, 1997) which assesses neuroticism based on six dimensions: anxiety,
hostility, depression, self-consciousness, impulsiveness, and vulnerability to stress. Neuroticism
is measured on an interval-level scale, with measurements expected to approximate a normal
distribution (Judge, Martocchio, & Thoresen, 1997). Because the measurement is based on
subjective responses to survey questions and normed to average scores, i t carries attributes of
being norm-referenced. Similarly, the NE O PI-R assesses conscientiousness on six dimensions
competence, order, dutifulness, achievement striving, self-discipline, and deliberation (Costa &
McCrae, 1985). Because the conscientiousness questions are also answered on a five-point
Likert scale, they are similar in level of measurement to the neuroticism measures.
Judge, Martocchio, & Thoresen (1997) measured number of hours missed during two-
week pay periods. While this level of data were available for their study, this level of data are
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not always available for all employers or for many salaried employees; indeed, Judge,
Martocchio, & Thoresen (1997) studied only staff from one university and excluded faculty due
to the lack of information regarding absences. Even for employers who have designed record-
keeping systems to track absences by hour, these data may not be available if the employees are
not required to sign in and out using an automated machine. Arsenault & Dolan (1983)
measured absences by frequency per year without regard for the length of absence of each
instance because of its popularity in the literature as well as how the data fit the normal
distribution better using that definition; in a psychometric sense, this would more closely
approximate the number of situations causing an absence without putting undue weight on
specific causes, which may result in differential absence lengths. This measurement is therefore
a criterion-referenced measure.
Cont e nt Va l idity
Content validity refers to whether an instrument is comprehensive in uncovering all
relevant factors contributing to a phenomenon. It comprises face validity, which pertains to an
investigators subjective judgement that an instrument is appropriate for measuring the phenomenon being studied and sampling validity, which pertains to whether the items on the
instrument in question are adequately representative of the infinite set of possible relevant
questions (Frankfort-Nachmias & Nachmias, 2008). Content validity is maximized by using an
off-the-shelf personality inventory, such as the NE O PI-R, which has established content validity
(Costa & McCrae, 1985).
Empirica l Va l idity
Empirical validity refers both to the predictive ability of the instrument to assess the
underlying phenomenon as well as how well it predicts future behaviorin this case,
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improve validity. That is, it is possible that absenteeism increases for a certain range of
neuroticism or conscientiousness, reaches a maximal relationship, then fallsa regression could
reveal coefficient confidence intervals that include 0, implying no relationship; further, it would
have a low Pearson correlation coefficient overall.
R el iabi l ity
The traditional implementation of the NE O -PI instrument assesses the personality traits
according to a five-level Likert scale (Costa & McCrae, 1985). Eggert, Levendosk y, & Klump
(2007) reported a high internal consistency and reliability of the NE O PI-R with reported alphas
ranging from 0.69 to 0.90, which corroborated previous studies. Steca, Alessandri, V ecchio, &
Caprara (2007) demonstrated significant discriminative power in the NE O PI-R was
demonstrated using cluster analysis, a partitioning methodology.
Str e ngt h s and Limitations of S ele ct e d Instrum e nt
The NE O PI-R (Costa & McCrae, 1985) has been selected for this study. In that it
assesses the Big Five personality traits, it has known validity and reliability. Previous studies
had assessed personality traits as a single aggregated construct and were not able to examine thecomponent factors of personality in isolation; consequently, they found no significant
relationship between personality and absenteeism (Arsenault & Dolan, 1983). Since Judge,
Martocchio, & Thoresen (1997) was able to assess the five major personality traits separately,
they were able to resolve a significant relationships between neuroticism, extraversion,
conscientiousness, and absences; further, that the relationship between neuroticism and
absenteeism was inverse whereas the relationship between conscientiousness and absenteeism
was positive would explain why Arsenault & Dolan (1983), who studied personality only as an
aggregated measure, may not have noticed any net effect of personality on absenteeism.
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O ne significant weakness of the proposed design is that different methods and practices
for reporting absences exist between organizations. These differences may exist due to cultural
reasons, such as how nurturing the organizational culture is toward taking sick days either when
ill either physically or emotionally; conversely, practical limitations, such as the practice of time-
stamping working hours being more prevalent for jobs that are paid according to employment
hours versus salaried positions, which often do not track employment hours rigorously.
Arsenault & Dolan (1983) indicated that obtaining data at the hour-resolution for professional
and executive positions posed a challenge but seemed to make no accommodation for this
weakness and used the data with that caveat. This poses a threat to validity in that the number of
absences for executive and professional subjects would be severely understated. Judge,
Martocchio, & Thoresen (1997), however, addressed this weakness by excluding faculty and
workers who were expected not to be tracked as fastidiously as the blue-collar positions. This
poses a threat to validity oppositely to that of Arsenault & Dolan (1983) in that the results are
less generalizable and may not be valid at all for the salaried positions.
R andom Samp
ling Most Appropriat
e
In order to study the relationship between personality traits and absenteeism, the
population may comprise all working humans anywhere in the world. It may be limited to a
specific country, culture, industry, or job description. O nes, Mount, Barrick, & Hunter (1994)
evaluated two major meta-analyses of personality traits and job performance and found that job
type was a moderating variable on the relationship between personality traits and performance;
for example, they claimed that extraversion was a strong predictor of job performance in such
occupations as sales and management, but not statistically significant to other job types. They
further confirmed this theory by noting other studies that considered aggregate jobs and did not
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separate job types did not show any consistent relationship (Tett, Jackson, Rothstein, 1991). It is
therefore important that the sample either be restricted to one job type or stratified across job
types. Probability sampling, which allows researchers to specify the probability that each
sampling unit will be included (Frankfort-Nachmias & Nachmias, 2008) is therefore required to
ensure that the sample is representative of the population of interest.
R andom Samp l ing Strat e gy
Walden University maintains a participant pool which acts as a clearing house for
behavioral science students to recruit participants for their research. Due to the availability of
respondents from the Participant Pool, therefore, the members of the Walden Participant Pool
who are gainfully employed will comprise the target sampling frame. Within the Walden
University Participant Pool sampling frame, a random sample will be selected who self-report as
employed. Using a random sampling strategy will maximize the ability to generalize inferences
about the sample to the population of Walden students in the participant pool.
Units of Ana l ysis
The participant pool will comprise members of the Walden community who have electedto participate in the Walden Participant Pool. This would ensure respondents comprise a greater
variety of educational, demographic, psychographic, and professional backgrounds than a single
firms employees. This necessitates some care in ensuring that the data are used in context and
that it is not used, for example, to predict the behaviours of an arbitrary individual (ecological
fallacy) . Walden Universitys target market has been working professionals; while
undergraduate programmes are increasing, Waldens original target market primarily comprised
professionals who already had an undergraduate degree. It is, therefore, important not to draw
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inferences from these results about the workforce, in general, which includes a much more
diverse set of educational backgrounds in order to mitigate individualistic fallacy.
Samp l ing M e th odo l ogy: Ps e udorandom vs.random consid e rations
Prior to computer-based sampling and surveying software, researchers often relied on
random number tables to select sampling units or methods that arbitrarily select every K th
element in a set (Frankfort-Nachmias & Nachmias, 2008). Since the advent of computer-aided
sampling and surveying software, such as CATI, however, pseudorandom number generating
algorithms are commonly used to select sampling units. The problem, however, with all
algorithm-based pseudorandom number generators is that without a truly random external seed,
pseudorandom numbers are inevitably predictable and cannot be truly random (Ranasinghe, Lim,
Devadas, Abbot, & Cole, 2005); for most purposes, other than the most high-security
cryptographic applications, however, properly-seeded pseudorandom algorithms suffice to
generate samples that appear to have enough randomly-distributed elements in the short run that
they can be used to generalize about a population. Frankfort-Nachmias & Nachmias (2008)
describe one method that can be employed with a random number table wherein numbers areselected from the table such that they are within range of the sampling frame reference numbers
and the first x records that fall within the range are used. A computer could easily implement the
same technique using a pseudorandom number table generated on-the-fly.
Str e ngt h s and W e akn e sse s of Samp l ing Strat e gy
Fass, Benson, & Leggett (2008) point out that modern schools require students to use
computers as a matter of routine; this results in a high outreach efficiency. Indeed, they were
easily able to notify 10,568 students through their school-issued email addresses. They also
pointed out that completing an Internet-base survey on their own time without scheduling
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appointments with researchers makes responding possible and convenient to a larger number of
students (Fass, Benson, & Leggett, 2008); that said, without the verbal commitment to attend an
in-person interview or survey, it is also easier to defer or neglect to complete the survey. Even
with a high degree of certainty that a large number of students were notified of their survey,
however, only 254 students responded. Turner, Rogers, Lindberg, Pleck, & Sonestein (1998)
further found that administering a computer-based instrument increased the validity and
reliability of the survey especially in topics of a sensitive or personal nature.
The Walden Participant Pool itself suffers from some degree of self-selection bias
because it is not a requirement of all students to participate. Walden students are somewhat less
representative of the general population also because they were identified and targeted as
Walden Universitys target market segment; as a university, Walden targets individuals who had
the motivation and conscientiousness to qualify for tertiary education. Beginning as a graduate
school primarily, the student population may continue to be biased towards graduate students,
who represent a further type of individual who has not only achieved an undergraduate degree,
but excelled and maintained motivation and conscientiousness to qualify for graduate studies.Walden University also markets itself as a non-traditional program that students can complete
while continuing to work. This further specifies that Walden students are conscientious and
motivated enough to manage both a working life as well as extra academic obligati ons. While
the ultimate goal may be to elucidate a relationship between conscientiousness, neuroticism, and
absenteeism across all employee types, therefore, studying the Walden population can serve as a
starting point the results of which can guide further study into different populations that may
have distinct interactions between conscientiousness and neuroticism with respect to
absenteeism, or more representative general ones. Benfield & Szlemko (2006) noted that due to
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the lack of specific time and in-person appointment, many internet surveys had response rates
dismal enough to make the time-honored mail-in surveys seem more attractive (p.8). O n of
their studies, which directly emailed 25,000 students an invitation including mention of a
nominal prize only achieved a 2% response rate from the freshman group until physical paper
reminders were placed into their mailboxes, which raised their response rate to 5% (Benfield &
Szlemko, 2006). While Walden students may be more conscientious than undergraduate
freshmen, it is advisable to be cognizant not only of the possible contribution of this nonresponse
error (Frankfort-Nachmias & Nachmias, 2008) but also since one of the personality traits the
survey aims to assess is conscientiousness, prospective respondents who would have scored on
the lower end of the conscientiousness scale may be underrepresented simply because they were
not conscientious enough to complete the survey.
Another strategy Benfield & Szlemko (2006) adopted to improve response rates on
Internet-based surveys was a snowball sampling technique, in which they sent initial e-mail
invitations to friends, family, and colleagues, containing links to the survey, as well as
instructions to forward the email, similar to a chain-letter. While this yielded a three-foldresponse, they acknowledged that it reduced validity of community-specific analyses
significantly (Benfield & Szlemko, 2006).
Appropriat e Samp le Siz e
Frankfort-Nachmias & Nachmias (2008) state that the following calculation based on the
ratios of variance to mean standard error can be used if cost and other practical limitations do
not enter into the decision:
2
2
SE s
n
n
sSE
!@
!3
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Previous literature sizes ranged from 89 respondents out of a pool of 320 (Judge, Martocchio, &
Thoresen, 1997) to meta-analyses over 12,000 respondents over 90 studies (Tett, Jackson, &
Rothstein, 1991) for an average sample size of 150. In order to calculate our desired sample size,
we will use the following factors: = 0.05, indicating a 5% probability of Type I error and (1- )
= 0.8 ( represents probability of Type II error) are generally accepted parameters (Mendenhall,
Reinmuth, & Beaver, 1989); effect size will be estimated as effect size = 0.2, based on
previously-published correlations from a multiple regression (Judge, Martocchio, & Thoresen,
1997). Because I intend to use a multiple regression studying two predictor variables, the sample
size required would be 52 individuals:
) WHVWV /LQHDU PXOWLSOH UHJUHVVLRQ )L[HG PRGHO 5s GHYLDWLRQ IURP ]HUR$QDO\VLV $ SULRUL &RPSXWH UHTXLUHG VDPSOH VL]H,QSXW (IIHFW VL]H Is
HUU SURE3RZHU HUU SURE1XPEHU RI SUHGLFWRUV
2XWSXW 1RQFHQWUDOLW\ SDUDPHWHU&ULWLFDO )1XPHUDWRU GI'HQRPLQDWRU GI7RWDO VDPSOH VL]H$FWXDO SRZHU
Table 1. G*Power 3 O utput for multiple regression sample size calculation
Post- h oc Pow e r Ana l ysis of Ars e nau l t & Do lan (1983)
Although Arsenault and Dolan limited their population to health-care workers in Qubec
public hospitals, stratified according to job type and whether it was an Anglophone or
Francophone hospital to account for cultural differences, their sampling frame was much larger,
comprising employees from eight hospitals; consequently, post-hoc power analysis using
G*Power 3 on their AN OV A test revealed a very high power:
) WHVWV $129$ )L[HG HIIHFWV RPQLEXV RQH ZD\$QDO\VLV 3RVW KRF &RPSXWH DFKLHYHG SRZHU,QSXW (IIHFW VL]H I
HUU SURE7RWDO VDPSOH VL]H1XPEHU RI JURXSV
2XWSXW 1RQFHQWUDOLW\ SDUDPHWHU
8/8/2019 Effect of Big-Five Personality Traits on Job Performance: A quantitative research plan
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Table 2. Post-hoc Power Analysis of AN OV A of hospital workers in Arsenault & Dolan (1983)
While their sampling strategy afforded Arsenault & Dolan (1983) high statistical power,
its significance was mitigated an underlying fault in their content validity; for example, their
study formed four intuitive personality types: hot dog (high motivation, external locus of control)
to cool cat (low motivation, internal locus of control). Later, through formal data reduction using
factor analysis, Digman (1990) established the Big Five personality traitswhile Arseneault &
Dolan (1983) could claim tremendous power through their use of large sample and defensible
sampling strategy, their use of contrived personality types ultimately made their results less
compelling. Indeed, before techniques such as Wards cluster analysis or k-means partitioning
(Lilien & Rangaswamy, 1998), marketing researchers often intuitively established market
segments based on conveniently-understood segmentation bases; only with the advent of
analytics and the ability to define statistical clusters that may transcend linguistic labels could
true market segments be defined.
R e comm e nd e d Statistica l Te st: Mu l tip le Lin e ar R e gr e ssion
In order to determine whether or not there is any relationship between neuroticism and
conscientiousness and absenteeism, a multiple linear regression will be run with neuroticism and
conscientiousness as independent variables and absences as a dependent variable. Regression
was selected because the NE O PI personality test reports scores and standard deviations for
neuroticism and conscientiousness on a ratio-level scale from 0 to 100. Absences is a ratio-level
measurement. The use of linear regression assumes that the dependent variables are normally -
distributed for each level and that the population variances for each level are the same:
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homoscedasticity; further, the cases represent a random sample from the population and that the
scores are independent between cases (Green & Salkind, 2008). Multiple linear regression is
advantageous over factorial AN OV A in that it allows for step-wise hierarchical evaluation of
dependent variables and can help identify the factors that contribute most significantly to a
model.
Factorial AN OV A is another parametric test that assumes normally-distributed attributes
and that can consider multiple factors concurrently to identify a putative relationship (ref). The
independent variables, however, each have 100 levels and would practically require a 100 x 100
ANOV A. Segregating high-low neuroticism/conscientiousness bins and analyzing a 2x2 or 3x3
(low-medium-high) could also establish relationships between the independent and dependent
variables. The post-hoc test can then determine directionality, if there is any significant
relationship. Indeed, an AN OV A can also identify non-monotonic relationships, such as the
cases where low and high neuroticism are associated with increased absenteeism, but medium-
level neuroticism is not; a regression would manifest this case as a low R 2 along with a wide
confidence interval relative to range, for the coefficients. It does not, however, establishmagnitude of the relationship, which the regression coefficients can do whilst also establishing
whether or not there is a relationship.
While a 2 test can also be used to compare multiple factors using interval or nominal
level data, it is a non-parametric test. While non-parametric tests can be easier to compute, they
tend to have less power than parametric ones, less likely to reject a false null hypothesis when it
is true (Chandler, 1995). Pair-wise t-testing of mean absences for each level of neuroticism and
conscientiousness would amplify the type I error with each test; while the Bonferroni correction
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makes allowance for this increase, it does not increase the power of the tests and it compensates
at the cost of increasing Type II error (Chandler, 1995).
Et h ica l Consid e rations
Because absenteeism could be used as grounds for reprisal or even dismissal and could
impact respondents livelihoods, the data are sensitive in nature and must be treated as valuable,
confidential data. Since the individuals are each contributing at least 30-45 minutes to complete
surveys and data about them are being used, they should be entitled to learning the findings of
the study, presented in terms that are meaningful to them. The data collection and handling
would be designed in accordance with the National Institutes of Health Protecting Research
Participants certification, which is required as a standard to obtain NIH funding (Protecting
Human Research Participants, 2008). These guidelines maintain the overarching themes of
Respect for Persons, Beneficence, and Justice (Protecting Human Research Participants, 2008)
and conform to the US Department of Health and Human Services codes (US Department of
Health and Human Services, 2010).
Because the study attempts to characterize putative relationships between innate personality traits and absenteeism, care must be taken to emphasize the applicability and
generalizability of findings. That is, managers involved in recruitment and selection must
recognize that the findings from this study may or may not be applicable to their applicant pool,
and the factors examined in this study are not exhaustive. While the research can provide
direction for future research and help understand human behaviors, it would be unduly
discriminatory to base selection decisions on personality traits based on this study as they are by
no means definitive nor do they imply a deterministic relationship.
Futur e Imp l ications for C h ang e
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The findings of this study could have far-reaching consequences. Since business-owners
and investors continue to demand higher and higher performance from their human capital, this
study could provide insights into factors that affect job performance. Caution must be taken,
however, to avoid discriminating job applicants based on these innate personality traits. Instead,
the findings could find methods of identifying individuals with absenteeism issues and
developing interventions that could reduce absenteeism or change the individuals environment
to match their personality types better and improve performance in other ways.
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R e f e r e nc e s
Arsenault, A., & Dolan, S. (1983). The role of personality, occupation, and organization in
understanding the relationship between job stress, performance, and absenteeism.
Jour nal of Occ upati onal Psych ol og y . 56:227-240.
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