The Effect of God-Locus of Control on Cognitive Resource Theory

Running head: THE EFFECT OF GOD-LOCUS OF CONTROL ON COGNITIVE 1

Russell L. Huizing, Ph.D.

Toccoa Falls College

rhuizing@tfc.edu

Submission Date: January 29, 2011

Abstract

Fiedler and Garcia’s (1987) Cognitive Resource Theory (CRT) predicts job stress moderates the

relationship between an employee’s job experience and job performance. When job stress is

greater, job experience (rather than intelligence) will be a better predictor of job performance.

Other research has shown that Locus of Control (LOC) and spirituality may alter the perception

of stress. God-Locus of Control (God-LOC), which combines aspects of both LOC and

spirituality, could moderate the perception of job stress. Analysis of data collected on the

relevant variables was inconclusive on both anticipated CRT results and whether altered

perception of stress changed anticipated CRT outcomes.

The Effect of God-Locus of Control on Cognitive Resource Theory

All other things being equal, when faced with two candidates for a high stress position,

does an employer hire the individual who scored higher on an intelligence test or the individual

with more work experience? Cognitive Resource Theory (CRT) (Fiedler & Garcia, 1987) would

recommend the individual with more work experience due to its prediction of a greater

correlation between job experience and job performance when job stress is high. CRT examines

the conditions under which intelligence and relevant job experience relate to job performance

when moderated by job stress (Fiedler & Garcia; Hooijberg & Choi, 1999; Yukl, 2006). For

instance, when job stress is low, CRT predicts that intelligence has a higher correlation with job

performance than relevant job experience. However, when job stress is high, CRT predicts that

relevant job experience has a higher correlation with job performance than intelligence. Research

has supported the diverse application of this theory (Avery, Tonidandel, Griffith, & Quinones,

2003; Barnes, Potter, & Fiedler, 1983; Blades, 1976; Borden, 1980; Chajut & Algom, 2003;

Fiedler & Garcia; Fiedler, Potter, Zais, & Knowlton, 1979; Frost, 1981; Judge, Colbert, & Ilies,

2004; Potter & Fiedler, 1981; Tucker, et al., 2009; Wiener, Arnot, Winter, & Redmond, 2006).

However, if the presence of different levels of stress can predict the relationships of

intelligence and relevant job experience with job performance, then it would seem that variables

that alter the perception of stress would alter those predictions. For instance, several meta-

analytic research articles have found consistently negative correlations between spiritual belief

and the perception of stress (Weaver, Flannelly, Case, & Costa, 2004; Weaver, Flannelly,

Garbarino, Figley, & Flannelly, 2003; Weaver, Pargament, Flannelly, & Oppenheimer, 2006;

Weaver, Strock, Flannelly, Flannelly, & Krause, 2005). The results of these meta-analytic

articles suggest that a stronger belief in God’s control over circumstances (a God-Locus of

Control), including stress, could lead to a buffering of perceived stress. This study will test a

theoretical model that explains the relationship of God-Locus of Control to the variables

predicted by CRT.

Theoretical Background

There are two primary theories that interact in the proposed research – Cognitive

Resource Theory and Locus of Control Theory. Prior to presenting how they may interact, a

background of relevant research in each theory is appropriate.

Cognitive Resource Theory

Fiedler and Garcia (1987) showed that both intelligence and experience-based knowledge

were significantly impacted by the amount of job stress that a leader was experiencing (Chemers,

2002; Fiedler, Murphy, & Gibson, 1992; Yukl, 2006). Fiedler (1993) defined intelligence as “the

end-product of individual development in the cognitive-psychological domain” (p. 3). This

definition excluded motor, emotional, and social intelligences. Fiedler defined relevant job

experience as current job tenure. Types of job stress included unattainable goals from a

supervisor, frequent emergencies, time constraints in the work environment, or conflicts with

subordinates (see also Avery et al., 2003; Yukl). Job performance was defined as the degree to

which an individual exceeds or fails to meet job requirements (Fiedler).

CRT began to take shape with the research by Potter and Fiedler (1981). The positive

correlation of intelligence and job performance under low job stress was not statistically

significant in this study (r=.16, N=60). However, the research supported the negative correlation

of intelligence and job performance under high job stress (r=-.27, N=51, p<.05). Furthermore,

the prediction of no correlation between job experience and job performance under low job stress

(r=.03, N=48, p<.01) and the correlation between job experience and high job stress (r=.44,

N=48, p<.01) were both supported. With further research, Fiedler (1993) found that leaders

under high job stress were more likely to rely on job experience for job performance whereas

leaders under low job stress tended to rely more on their intellectual ability for job performance.

Additional research showed that when leaders in high job stress situations had relevant

experience-based knowledge, they exceeded job requirements (Chemers, 2002).

Threat-Rigidity Thesis (TRT) may help in explaining why CRT occurs. TRT predicts that

when an individual is confronted with an imminent expectation of loss that a restriction of

information processing occurs that results in more rigid conduct tending to favor well-learned

behavior (Staw, Sandelands, & Dutton, 1981). TRT research has shown that a relationship exists

between an existence of a threat (potential harm, loss, or challenge) and the increase of perceived

stress (Daly, 2009; Gladstein & Reilly, 1985; Griffith, 2004; Harrington, Lemak, & Kendall,

2002). In these stressful situations, individuals revert to an automaticity response that minimizes

the intellectual focus and instead lapses into past problem-solving patterns learned over time

(Staw, et al.; see also Fiedler, 1993; Yukl, 2006). Given the similarities between TRT and CRT

predictions, TRT may be one explanation for the results from CRT. Fiedler also suggested that

this phenomenon happened because intellectual ability interfered with experience and experience

interfered with intellectual ability such that a leader cannot act reflexively based on learned

experience while at the same time thinking creatively and analytically (see also Hooijberg &

Choi, 1999).

Locus of Control Theory

Rotter developed the concept of Locus of Control (LOC) in 1966. This concept predicts

the degree to which an individual believes that any possible behavior on their part determines

outcomes (Furnham & Steele, 1993; Ivancevich, Konopaske, & Matteson, 2008). The theory

predicts that a person with an internal locus of control (I-LOC) perceives outcomes are

dependent on personal behavior or characteristics (Yukl, 2006). On the other hand, individuals

with external locus of control (E-LOC) perceive outcomes are dependent on forces outside the

individual (Yukl). Rotter developed a 29 item self-reported Internal-External Control scale to

measure LOC (Furnham & Steele).

I-LOC has been linked with many favorable psychosocial benefits (among many others,

motivation, responsibility, learning – for more detail see Ivancevich, et al., 2008; Spector, 1982;

Welton, Adkins, Ingle, & Dixon, 1996; Yukl, 2006). E-LOC, on the other hand, has been

associated with much less desirable characteristics (for instance, conformity to authority, lower

emotional stability, counterproductive behavior – for more, see Ivancevich, et al.; Key, 2002;

Trevino & Youngblood, 1990). Despite these differences, I-LOC does not necessarily lead to

better job performance. In tasks requiring initiative, employees with higher I-LOC are likely to

perform better (Ivancevich, et al.). However, in tasks requiring high structure and routine,

employees with higher levels of E-LOC are likely to perform better since the structure and

routine act as an external source of control over the individual (Ivancevich, et al.).

Levenson’s Multidimensional Locus of Control scale refined Rotter’s LOC scale by

expanding the external forces to include powerful others and chance (Jackson & Coursey, 1988;

Welton, et al., 1996). It was anticipated that those who find their master motive in religion,

believing in an all-controlling higher being, would tend to measure strong in external

dimensions. However, research done over several studies has shown a wide variety of LOC

scores (Jackson & Coursey). An answer for these varied results may come in the way that Rotter

and Levenson developed the external items. Welton et al. argued that Levenson’s powerful other

items do not distinguish between powerful other people and powerful other beings, such as God.

Thus, when religious individuals took LOC surveys, there may have been uncertainty in how to

answer questions regarding powerful others. Welton et al. added God control items (G-LOC) to

the Multidimensional Locus of Control scale. This resulted in a small negative correlation

between I-LOC and God-LOC items (r=-.17, p<.01) and no significant correlation for powerful

others or chance items suggesting an additional external dimension. Coefficient alpha values

ranged from .82-.89 demonstrating a high internal consistency for God-LOC items. Welton et al.

also provided evidence suggesting concurrent criterion-related validity by comparing higher

God-LOC scores with higher religiosity scores from other studies. Results ranged from .65-.78.

Welton, et al.’s research suggested that one can measure an individual’s perception that the

actions of a higher being determine outcomes.

Several studies have shown that LOC also influences the perception of stress on an

individual (Anderson, Hellriegel, & Slocum, 1977; Bernardi, 1997; Lester, Leitner, & Posner,

1985; Syrotuik & D’Arcy, 1982). In each of these studies, LOC altered the perception of stress.

I-LOCs tended to perceive lower stress while E-LOCs tended to perceive higher stress. The I-

LOC’s perception of lower stress seemed to be connected to two abilities inherent in the I-LOC:

(a) the ability to manipulate the environment to obtain necessary information to overcome the

threat (Syrotuik & D’Arcy), and (b) a stronger view of self-esteem equiped the I-LOC with a

sense of greater access to personal resources to overcome the threat (Anderson, et al.). Although

God-LOC is a form of E-LOC, Furnham and Steele (1993), Jackson and Coursey (1988), and

Welton, et al. (1996) all noted that their research suggested individuals who were high in God-

LOC exhibited positve psychosocial benefits similar to I-LOC. These positive psychosocial

benefits may occur because the individual believes in both the all-powerful and all-loving nature

of the higher being, thus relieving the fatalism normally associated with E-LOC’s negative

psychosocial characteristics (Welton, et al.). If this explanation of God-LOC’s positive

psychosocial benefits is accurate, it is possible that God-LOC alters the perceptions of stress in a

way similar to I-LOC.

Synthesizing these theoretical backgrounds, one would expect that when a threat in the

form of harm, loss, or challenge is presented in a work environment, the individual perceives a

higher degree of stress. The effects of this stress causes an automaticity response such that as

stress increases the individual relies more upon past job experience rather than intelligence to

accomplish job expectations. Thus, in high job stress situations, job experience will be a better

predictor of job performance than intelligence. However, research has shown that spirituality

(Calicchia & Graham, 2006; Collins W. L., 2005; Delgado, 2007; Leblanc, Driscoll, & Pearlin,

2004; Maddi, Brow, Khoshaba, & Vaitkus, 2006; Mascaro & Rosen, 2006) and I-LOC can alter

the perception of stress. Since God-LOC mirrors many of the psychosocial benefits of I-LOC

and has aspects of spirituality, it is expected that God-LOC could also alter the perception of

stress.

Theoretical Model

Given this theoretical backdrop, it is possible to propose the relationship of G-LOC with

CRT as shown in Figure 1.

Figure 1. Proposed model indicating the relationship of

God Locus of Control to Cognitive Resource Theory.

Stress

Prior Experience

Cognitive Ability

Performance

God Locus of

Control

According to CRT, under high job stress situations, job experience (rather than

intelligence) will be a better predictor of job performance (Fiedler & Garcia, 1987). However,

Fiedler and Garcia predicted that in low stress situations, intelligence (rather than past job

experience) will be a better predictor of job performance. In order to establish the effect that

God-LOC has on CRT, it is necessary to establish that CRT is actually occurring in the data.

This results in the following two hypotheses:

H1: Job experience is related to job performance such that when job stress is high job

experience will be a better predictor of job performance than intelligence.

H2: Intelligence is related to job performance such that when job stress is low

intelligence will be a better predictor of job performance than job experience.

However, as already noted, God-LOC may alter the perception of job stress. In cases

where God-LOC buffers the perception of job stress, one would anticipate that intelligence

(rather than job experience) would be a better predictor of job performance. In individuals where

God-LOC is lower, one would not anticipate that the perception of job stress would be buffered

and therefore results would be as predicted by CRT (that job experience would be a better

predictor of job performance). Additionally, in situations where perceived job stress is low, it is

not anticipated that either high or low God-LOC would significantly alter the situation any

differently than what is predicted by CRT (that intelligence would be a better predictor of job

performance). To understand the moderating effects that both God-LOC and job stress have on

the relationship of the independent variables (job experience and intelligence) to the dependent

variable (job performance), both job stress and God-LOC can be categorized as high (above the

median) and low (equal to and below the median). Figure 2 provides a visual representation of

these categorizations and anticipated outcomes.

These relationships between God-LOC and CRT result in the following hypotheses:

H3: When God-LOC is low and job stress is high, job experience will be a better

predictor of job performance

H4: God-LOC alters the perception of stress such that when God-LOC is high and job

stress is high, intelligence will be a better predictor of job performance

H5: When job stress is low, the level of God-LOC will not change the relationship of

intelligence being a better predictor of job performance.

Method

High G-LOC

Low G-LOC

High Stress

Low Stress

G-LOC alters perception of stress

Intelligence

Predicts Performance

Hypothesis 4

Stress unaffected by G-LOC

Past Experience

Hypothesis 3

G-LOC alters perception of stress

Intelligence

Hypothesis 5

Stress unaffected by G-LOC

Intelligence

Hypothesis 5

Figure 2: Representation of G-LOC affect on Stress and Past Experience/Intelligence relationship to Performance.

When identifying the sample and proposing data collection, an exploratory study

approach was used (Kerlinger & Lee, 2000). Although more rigorous and systematic testing of

the hypotheses will be necessary, this research lays the groundwork for future research.

Sample

The sample was obtained using a cross-sectional convenience sampling. Given the broad

phenomena of job stress there is no reason to believe that this particular convenience sample

would respond differently than a random sample of the greater population. Since self-report data

collection was used, it was important to collect the least biased data possible. Spector (1994)

suggested that self-reports can be useful in studies where job conditions have changed and thus

can be assessed more objectively by the individual. Thus, to minimize self-bias, the researcher

asked participants to recollect their perceptions of job experiences from two years earlier. All

participants in the sample were 18 or older and had a full time position for a portion of 2008.

Initial participants were contacted through the social networking site Facebook. This network

was comprised of more than 400 million individuals 13 years of age or older with a valid e-mail

address (Facebook, 2010). The researcher used his own Facebook networks for initial contacts.

Snowball sampling was encouraged. This method of contact was used for cost effectiveness. The

limitations present in contacting through online resources have been shown to be similar to the

limitations of mail based surveys (Coomber, 1997; Wright, 2005).

The sample (N=122) ranged in age from 20 to 67. Twenty-three percent were between 20

and 30, 27% were between 31 and 38, 23% were between 39 and 46, and the remaining 27%

were between 48 and 67. Non-supervisory roles represented 55.7% of the sample. Thirty-two

percent of the sample had worked 4 years or less in their position as of 2008, 51.6% between 4.5

and 8 years, 75.4% between 9 and 15 years, and the remaining between 16 and 48 years. High

school graduates represented 16.4% of the sample, 13.9% had obtained an Associate’s degree,

32% had obtained a Bachelor’s degree, 29.5% had obtained a Master’s degree, and 8.2% had

obtained a PhD.

Measures

A factor analysis to confirm discriminant validity resulted in six factors representing JSS

(2 factors), God-LOC, JP, Age/Experience, and Educational Level/Intelligence with no cross-

loading or unloaded items. The six factors explained 68.75% of total variance.

Intelligence (IN) –The Wonderlic Personnel Test-Q (Wonderlic, 2004) was used to

measure intelligence. This test, designed specifically for unproctored, online completion, used as

many items as the participant could complete in an 8 minute period to measure general

intelligence. Past research has shown internal reliability (α=.81) and concurrent validity with the

Wonderlic Personnel Test (r=.77-.96). Internal reliability for the current study was not provided

by Wonderlic.

Job Experience (JE) - Following Fiedler and Garcia (1987) and much of the CRT

research, the author measured JE by the number of years in the same career that the participant

was working in 2008.

Job Stress (JS) - To measure job stress, the researcher used the 13 items from the Job

Stress Scale (Fields, 2002). The Job Stress Scale measures two job stress dimensions (anxiety

and time stress). Internal reliability for the survey was between .71-.82 (Fields). Jamal & Baba

(1992) showed convergent validity with role ambiguity (r=.30) and role overload (r=.57). The

internal reliability for the two JS factors in the current study were .86 (anxiety items) and .91

(time items).

Job Performance (JP) – Job performance was measured through four self-report items.

These items had been developed from recommendations in several performance self-report

studies (α=.61-.86 for studies providing internal consistency) (Brown, Mowen, Donavan, &

Licata, 2002; DeCarlo, Argarwal, & Vyas, 2007; Fine & Nevo, 2008; Kuvaas, 2006). Internal

reliability in the current study was .87.

God-Locus of Control (GLOC) – The measurement of God-LOC followed the work of

Welton et al. (1996). The six items recommended by Welton et al. to measure God-LOC showed

internal consistency (α=.82-.89) and concurrent criterion-related validity (.65-.78) (Welton, et

al.). Internal reliability in the current study was high (α=.88).

Control variables (CV) – Control variables for job performance included education level

(EL), working in a non-supervisory or supervisory role (SR), and age, all based on 2008.

Appendix A provides an example of the items in this instrument.

Procedures

Due to the confidential nature of the Wonderlic portion of the instrument, the data

collection contained two steps. An initial email contained a full disclosure of the research being

done. Those who expressed interest in participating in the survey were sent instructions on how

to complete the surveys on both the Wonderlic and SurveyMonkey sites, including a unique PIN

that allowed the researcher to associate data from the two different measures. For those who did

not complete the surveys within a six day period, a reminder email was sent. A final reminder

was sent after a 12 day period for those who did not respond. For those who completed the

surveys, an email was sent requesting them to pass the survey information on to others who

might be interested in participating. The online survey was both cost-effective and allowed for

distribution of the survey to a wide demographical population. Many of the same limitations

associated with email contact existed for an online survey collection method (Coomber, 1997;

Wright, 2005). However, in this case, the general population sampling should not have been

significantly impacted by drawing from a population that represented only those connected to the

internet.

Analysis

Data were reviewed to make sure that complete information had been obtained. Data

from the Wonderlic survey were consolidated with data from the SurveyMonkey survey based

upon matching PIN numbers used on both surveys. Survey data that did not have a matching PIN

number on either of the two surveys were discarded. Any survey data indicating that the

participant either did not work a full-time position in 2008 or was not 18 or older in 2008 were

also discarded. The final sample size was N=122. Mean, median and standard deviation for each

of the variables are provided in Table 1. Bi-variate correlations for all variables are provided in

Table 2.

Table 1

Descriptive Statistics for All Variables

Variable N M Mdn SD Skewnessa Kurtosis

Job Experience 122 11.43 8.00 10.17 1.30 1.53

Age 122 39.84 38.50 11.39 .41 -.63

Educational Level 122 2.99 3.00 1.20 -.28 -.82

Supervisory/Non- 122 1.44 1.00 .50 .23 -1.98

Job Performance 122 4.10 4.00 .62 -.44 .08

Job Stress 122 3.52 3.38 1.32 .31 -.49

God-LOC 122 5.96 6.17 1.13 -2.26 6.78

a SE=.22

b SE=.44

Table 2

Bi-variate correlations for All Variablesa

Variable 1 2 3 4 5 6 7

1. Job Experience

2. Age .63**

3. Educational Lvl -.09 .08

4. Intelligence -.17 -.17 .18*

5. Supervsry/Non- .20* .23* .10 .10

6. Job Performance .03 .02 .03 .07 .20*

7. Job Stress -.18 -.15 -.01 -.06 .10 .20*

8. God-LOC -.24** -.17 .09 .06 -.08 .06 -.04

a N=122

* p<.05 **p<.01

Linear regression analysis was used to test the hypotheses. It was anticipated that the

interaction effects of the moderating variables would have a small effect size, especially when

researching a moderating variable (GLOC) of a moderating variable (JS). One way of

overcoming this was allowing the significance level to be decreased to p<.10 (McClelland &

Judd, 1993). Also, nominal scales, such as the one used for supervisory role, can be coded for

regression analysis in a linear fashion (Hair, Black, Babin, Anderson, & Tatham, 2006). Non-

supervisory were coded as ‘1’ and supervisory as ‘2.’ However, the regression analysis resulted

in a high number of significant relationships for supervisory role even in models that were not

significant predictors of job performance. Thus, supervisory role was removed from the

regression analysis.

To test H1, job performance was regressed on the control variables, job stress, job

experience, and the moderated variable (JE*JS). This regression was insignificant. Detailed

results are provided in Table 3. The anticipated outcome of a significant, positive β value for job

experience was not supported by the data and thus H1 must be rejected.

Table 3

Multiple Linear Regression Results for H1a

Variable Model 1 β Model 2 β Model 3 β

Age .02 .02 .02

Educational Level .03 .04 .03

Job Stress .21* -.24

Job Experience .06 .10

JE*JS .32

.00 .05 .06

ΔR2 .04 .01

F .08 1.38 1.40

F for change 2.67** 1.47

a N=121; Dependent Variable: Job Performance

* p<.10 ** p<.05

To test H2, job performance was regressed on the control variables, job stress,

intelligence, and the moderated variable (IN*JS). The regression representing the moderated

variable was not significant (p>.10). Detailed results are provided in Table 4. The anticipated

outcome of a significant, negative β value for Intelligence was not supported by the data and thus

H2 must be rejected.

Table 4

Multiple Linear Regression Results for H2a

Age .02 .07 .07

Job Stress .21* .67

Intelligence .09 .30

IN*JS -.50

.00 .05 .06

ΔR2 .05 .01

F .08 1.56 1.42

F for change 3.03** .87

* p<.10 ** p<.05

experience, and the moderated variable (JE*JS) for all data where God-LOC was less than the

median. The regression representing the moderating variable was not significant. Detailed results

are provided in Table 5. To further test this hypothesis, JP was regressed on the control variables,

job stress, intelligence, and the moderated variable (IN*JS) for all data where God-LOC was less

than the median. The regression representing the moderating variable was significant

(F(5,58)=3.36, p<.01) accounting for 23% of the variance. None of the variables demonstrated

significant effects on job performance (p>.10) except job stress (β=1.39, p<.10) and intelligence

(β=.83, p<.05). The regression of independent variables was significant (F(4,59)=3.60, p<.05)

accounting for 20% of variance in job performance. This was also a significant change from the

regression on control variables (ΔR2=.19, F(2,59)=6.92, p<.01). The only variables

demonstrating significant effects on job performance were job stress (β=.38, p<.01) and

intelligence (β=.31, p<.05). Detailed results are provided in Table 6. Hypothesis 3 anticipated the

job experience β would be significant and positive and that the intelligence β would be

significant and negative. This was not the case in either regression analysis, therefore, H3 must

be rejected.

Table 5

Multiple Linear Regression Results for H3 with JE Predicting JP When G-LOC is Lowa

Age .03 .03 .04

Job Stress .34*** .18

Job Experience .03 -.36

JE*JS .42

.01 .12 .13

ΔR2 .11 .01

F .23 1.98 1.77

F for change 3.71** .94

* p.10 ** p<.05 ***p<.01

Table 6

Multiple Linear Regression Results for H3 with IN Predicting JP When G-LOC is Lowa

Age .03 .14 .16

Educational Level .08 -.03 -.05

Job Stress .38*** 1.39*

Intelligence .31** .83*

IN*JS -1.08

.01 .20 .23

ΔR2 .19 .03

F .23 3.60** 3.36***

F for change 6.92*** 2.14

a N=63Dependent Variable: Job Performance

* p<.10 ** p<.05 ***p<.01

experience, and the moderated variable (JE*JS) for all data where God-LOC was equal to or

greater than the median. The regression representing the moderating variable was insignificant

(p>.10). Detailed results are provided in Table 7. To further test this hypothesis, job performance

was regressed on the control variables, job stress, intelligence, and the moderated variable

(IN*JS) for all data where God-LOC was equal to or greater than the median. The regression

representing the moderating variable was insignificant (p>.10). Detailed results are provided in

Table 8. Hypothesis 4 anticipated that the intelligence β would be significant and a better

predictor of job performance than the JE β. None of the regression proved to be significant,

therefore, H4 must be rejected.

Table 7

Multiple Linear Regression Results for H4 with JE Predicting JP When G-LOC is Higha

Age -.00 -.02 -.02

Educational Level -.03 -.02 -.02

Job Stress .07 .04

Job Experience .07 -.03

JE*JS .09

.00 .01 .01

ΔR2 .01 .00

F .03 .10 .09

F for change .17 .08

Table 8

Multiple Linear Regression Results for H4 with JE Predicting JP When G-LOC is Higha

Age -.00 -.01 -.01

Educational Level -.03 -.03 -.02

Job Stress .06 .40

Job Experience -.12 .03

IN*JS -.40

.00 .02 .02

ΔR2 .02 .01

F .03 .24 .24

F for change .45 .24

To test H5, job performance was regressed on the control variables, job experience, God-

LOC and the moderated variable (JE*GLOC) for all data where job stress was less than the

median. The regression representing the moderating variable was insignificant (p>.10). Detailed

results are provided in Table 9. To further test this hypothesis, job performance was regressed on

the control variables, intelligence, G-LOC and the moderated variable (IN*GLOC) for all data

where job stress was less than the median. The regression representing the moderating variable

was not significant (p<.11). Detailed results are provided in Table 10. Hypothesis 5 anticipated

that the intelligence β would be significant and a better predictor of job performance than the job

experience β. Recognizing that a significance level has to be delineated at some point, had the

second analysis significance been slightly stronger, the results of this regression would have

supported the hypothesis. However, given the significance level of the results, H5 must be

rejected.

Table 9

Multiple Linear Regression Results for H5 with JE Predicting JP When JS is Lowa

Age .06 .17 .17

Job Experience -.09 .11

G-LOC .26** .32

JE*GLOC -.20

.01 .08 .08

ΔR2 .07 .00

F .29 1.18 .95

F for change 2.07 .11

* p<.10 ** p<.05

Table 10

Multiple Linear Regression Results for H3 with IN Predicting JP When JS is Lowa

Age .06 .12 .08

Intelligence .08 1.25**

G-LOC .27* 1.35**

IN*GLOC*JS -1.68**

.01 .08 .15

ΔR2 .07 .07

F .29 1.21 1.91

F for change 2.11 4.42**

* p<.10 ** p<.05 ***p<.01

Discussion

Several benefits surfaced from this research. First, the validity and reliability of the job

stress, job performance, and God-LOC measurements were further supported. The result of all

three measures (JS, GLOC, JP) factoring out without cross-factors or unloaded items

strengthened past validation results. As anticipated, the factor analysis showed two factors within

the job stress measurement, which was consistent with prior research (Fields, 2002).

Additionally, the internal reliability of each factor (α=.86 and .91) was even stronger than past

research for this measurement (α=.71-.82) (Fields, 2002). The internal reliability of job

performance (α=.87) was also consistent with past research (α=.61-.86). Finally, the internal

reliability of God-LOC (α=.88) fell within the range of past God-LOC research results (α=.81-

.89). All these results suggest that the validity and reliability measures of this research were

consistent with past research results.

Several of the bi-variate correlations are as might be anticipated. For instance, age being

related to job experience (r=.63, p<.01), intelligence being related to educational level (r=.18,

p<.05), supervisory role being related to job experience (r=.20, p<.05), and age (r=.23, p<.05)

and job performance (r=.20, p<.05) being related to supervisory role. Two correlations that stand

out as somewhat less than expected are the relationship of job stress to job performance (r=.20,

p<.05) and God-LOC related to job experience (r=-.24, p<.01). Particularly with this last

correlation, it is intriguing that with less job experience a person has a higher perception of

God’s control and with more job experience a person has a lower perception of God’s control.

None of the current literature on God-LOC addresses its possible relationship to job experience

and this result presents a potential for future research. Additionally, much of the earlier CRT

research was done with bi-variate correlations so it was anticipated that relationships between JE,

IN, and JP would surface. There were no significant correlations with any of these variables.

Though the regression analysis is a more robust statistical measurement, this early result

suggested the anticipated outcomes of CRT were not present in the data.

It was unanticipated that none of the moderation tests had significant results. Given that

past research has supported the moderating effect that JS has on the relationship between IN/JE

and JP, it is somewhat unusual that the current research found no significant moderating effect.

Further testing of the CRT model was analyzed with H1 and H2. These hypotheses together

define the moderating relationship of job stress on the relationship of intelligence/job experience

and job performance. Yet another test of CRT came in the form of H3 which, although adding

God-LOC to categorize the results into high and low, did not anticipate the added variable to

change the CRT model at all (i.e. low God-LOC should have no effect on the CRT model).

However, once again, the models did not prove significant.

Unfortunately, H4 was an important test of the CRT model that did not produce any

signficant results. If CRT is to accurately predict performance than it must be able to do so in the

context of variables that alter the perception of stress. No specific research has supported that

God-LOC is capable of altering stress – that was hypothesized based on the theoretical basis of

past research. However, this is a test environment that CRT must be placed in for future research.

Results will assist in fully testing CRT’s reliability at predicting job performance in the context

of other variables that moderate, change, or buffer variables within the CRT model. Further

research for God-LOC should use criterion-related validity tests that identify its relationship to

other religiosity tests and spirituality tests to determine whether it is similar to these other

constructs.

Finally, H5 was also a hypothesis designed to test exactly what is predicted by the CRT

model with a variable that should not confound the anticipated CRT outcomes. Unfortunately,

the significance (p=.108) was just outside of significance level.Had the significance level been

slightly stronger, all the results would have supported the hypothesis (the greater than 1 values

for β do not necessarily devalue their legitimacy – see Deegan, 1978).

Of course, none of these results help to further CRT theory or to add any support for it. It

is possible that the use of self-report Likert scales for both DV and most IVs caused the data to

be unreliable. Additionally, it is possible that the skewness and kurtosis results suggest non-

normal data which would cause irregularities in the regression analysis. Greater controls on

normal distribution for future research should help to gather more reliabile data. However,

another factor that may be unanticipated is the relationship that shows up between G-LOC and

job experience (r=-.24, p<.01). If the data underlying this relationship is reliable, this would

suggest that as a person works longer at a job, their belief in God as an agent of control over the

factors of their job decreases. If this is the case, then it would significantly change the original

theoretical model since job experience would then be a moderating variable on G-LOC which

may or may not (the evidence in this research is inconclusive) alter the perception of job stress.

This presents a far more complex environment of variables than originally proposed in this

research. Two avenues of future research emerge from this study. First, further research should

be pursued to understand the relationship between job experience and G-LOC. Additionally,

further research should continue to test CRT’s ability to predict job performance in the context of

variables that moderate, change, or buffer the effects of job stress.

Limitations

There are several limitations to the study. One of the primary limitations of this research

is in its sampling procedure. Although the researcher drew the sample from a population that

meets the characteristics desired for the research, there is limited means of knowing whether

those contacted through email and willing to take an online survey are truly representative of the

population (Coomber, 1997; Wright, 2005). Just as significant a limitation is the likelihood of

mono-method bias and percept-percept inflation due to the use of a single questionnaire to gather

data about both the independent and dependent variables (Kuvaas, 2006). Though multiple

methods of performance rating (e.g. supervisor rating, objective organizational performance

measures, etc.) would overcome the mono-method bias and the percept-percept inflation, these

measurements are not without their own potential problems including halo, leniency, intentional

manipulation, race, gender, and age biases (Facteau & Craig, 2001). Though these limitations

existed, the study should have sufficiently presented information of an exploratory nature.

Appendix A

Below is a listing of the items that were included in the data collection instrument.

Abbreviations in parentheses represent the variable being measured by the item. For copyright

purposes, the 30 Wonderlic items are not detailed. Items 2-14 were a 7-point Likert-scale

(Strongly Disagree, Disagree, Slightly Disagree, Uncertain, Slightly Agree, Agree, Strongly

Agree). Items 15-18 were a 7-point Likert scale (extremely low, low, average, above average,

extremely high). Items 19-24 were a 7-point Likert-scale (Strongly Disagree, Disagree, Slightly

Disagree, Uncertain, Slightly Agree, Agree, Strongly Agree). Other items were measured as

noted.

1. Wonderlic items - 30 items (IN)

Based on your full-time employment in 2008…

1. My work made it hard to spend enough time with my family. (JSS)

2. I spent so much time at work, I could not see the forest for the trees. (JSS)

3. My work left little time for other activities. (JSS)

4. I frequently got the feeling I was married to the company. (JSS)

5. I had too much work and too little time to do it in. (JSS)

6. I sometimes dreaded the telephone ringing at home because the call might be job-

related. (JSS)

7. I felt like I never had a day off. (JSS)

8. Too many people at my level in the company got burned out by job demands. (JSS)

9. I felt fidgety or nervous as a result of my job. (JSS)

10. My job got to me more than it should. (JSS)

11. There were many times that my job drove me right up the wall. (JSS)

12. When I thought about my job I got a tight feeling in my chest. (JSS)

13. I felt guilty taking time off from my job. (JSS)

Based on your full-time employment in 2008…

1. How would you rate the overall quantity of your work? (JP)

2. How would you rate the overall quality of your work? (JP)

3. How would you rate your job performance? (JP)

4. How would you rate the overall effort that you put into your work? (JP)

1. As of 2008, how many years had you worked in the same career that you were

working in that year? (numerical response) (JE)

1. What is your age? (numerical response) (CV)

2. Was your 2008 position a supervisory position? (No or Yes) (CV)

3. What is the highest level of education completed? (five options: high school,

Associate’s Degree, Bachelor’s Degree, Master’s Degree, PhD) (CV)

I believe that…

1. What happens in my life is determined by God’s purpose. (G-LOC)

2. My life is primarily controlled by God. (G-LOC)

3. Whether or not I get into a car accident depends on God’s plans. (G-LOC)

4. In order to have my plans work, I make sure they fit in with the commands of God.

(G-LOC)

5. When things do not go my way, I ought to pray. (G-LOC)

6. When good things happen to me, it is because of God’s blessing. (G-LOC)

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About the Author

Russell L. Huizing is currently pursuing his PhD. in Organizational Leadership at Regent

University. He has organizational experience in a family business, a Global Fortune 100

corporation, and currently in the non-profit sector at Pleasantview Alliance Church.

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