Effect of Big-Five Personality Traits on Job Performance: A quantitative research plan

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 [email protected]

A00083286

AMDS PhD, Finance Specialization

KAM Assessor and email address: Dr. Javier Fadul, [email protected]

Faculty Mentor and email address: Dr. Javier Fadul, [email protected]

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:

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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:

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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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understanding the relationship between job stress, performance, and absenteeism.

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Behling, O . (1998). Employee satisfaction: Will intelligence and conscientiousness do the job?

T he Academy of Mana g ement

xecutive . 12:77-86.

Benfield, J. A., & Szlemko, W. J. (2006). Internet-based data collection: Promises and realities.

Jour nal of Resea r ch P r actice , 2(2), Article D1. Retrieved 27 April, 2010 from,

http://jrp.icaap.org/index.php/jrp/article/view/30/51

Chandler, C. Ray. (1995). Practical considerations in the use of simultaneous inference for

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Conard, M. A. (2006). Aptitude is not enough: How personality and behavior predict academic

performance. Jour

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Resear ch in Pe

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onality

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Psychological Assessment Resources.

Digman, J.M. (1990). Personality structure: Emergency of the five-factor model. Annual Review

of Psych ol og y. 41:417-440.

Eggert, J., Levendosky, A., & Klump, K. (2007). Relationships among attachment atyles,

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atin g

Dis or der s. 40(2): 149-156.


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Fass, D., Benson, R., & Leggett, D. (2008). Accessing prevalence and awareness of violent

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Effect of Big-Five Personality Traits on Job Performance: A quantitative research plan

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