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An Initial Investigation of the Factor Analytic Structure:Impact of Event Scale-Revised with a VolunteerFirefighter SampleShannon L. Wagner a & Callie Waters aa University of Northern British ColumbiaAccepted author version posted online: 07 Jun 2013.

To cite this article: Journal of Loss and Trauma (2013): An Initial Investigation of the Factor Analytic Structure: Impact ofEvent Scale-Revised with a Volunteer Firefighter Sample, Journal of Loss and Trauma: International Perspectives on Stress &Coping, DOI: 10.1080/15325024.2013.810443

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An Initial Investigation of the Factor Analytic Structure: Impact of Event Scale-

Revised with a Volunteer Firefighter Sample

Shannon L. Wagner1, Callie Waters1

1University of Northern British Columbia

Received 2-7-13, accepted 5-21-13

Abstract

Purpose: To provide an initial evaluation of the factor structure of the IES-R when used

with a volunteer firefighter and a similar community participant sample.

Methodology: A volunteer firefighter sample (n = 65) and a sample of similar community

respondents (n = 103) completed a questionnaire study, including responses to the IES-R.

The IES-R data from both groups were entered into a three factor principal components

analysis with direct oblimin rotation.

Findings: We found further support for the validity of the IES-R when used with a

community sample. However, our data suggested that when using the IES-R with a

community sample, the choice between a two and a three factor model may depend on

the composition of the participants. For volunteer firefighters, the factor analytic structure

of the IES-R appeared to be similar to that of the community sample, with more scatter in

terms of item loadings.

Originality/Value: To our knowledge, there is no previous research considering the use of

the IES-R with a strictly volunteer firefighting sample. In addition, despite adequate

research on the factor analytic structure of the original IES, little research has considered

the factor analytic structure of the more recent IES-R, even with community samples.

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KEYWORDS: psychological assessment; Impact of Events Scale; IES; IES-R; PTSD;

factor analysis; volunteer; firefighters; traumatic stress

Characteristics Of The IES And The IES-R

The Impact of Events Scale (IES) was developed to assess subjective distress currently

being experienced in response to a life event (Horowitz et al, 1979). It represents a

widely applicable measure that has been used extensively to assess for symptoms of

PTSS. The original IES scale contained 15 items describing the symptoms of intrusion

and avoidance; participants were asked to indicate how many times each symptom

occurred over the past week (Horowitz et al, 1979). Each item was then ranked using a

five-point Likert scale resulting in an overall score between 0 and 75, with the avoidance

subscale giving a score between 0 and 40, and the intrusion subscale giving a score

between 0 and 35. A score of 26 is generally used as a cutoff score to indicate possible

presence of Post-traumatic Stress Disorder (PTSD; Chemtob et al., 1997).

Test-retest reliability of 0.87 for the total score on this original scale has been

demonstrated, with specific scores of 0.89 for the intrusion subscale and 0.79 for the

avoidance subscale (Horowitz et al., 1979). Other scores provide additional strength for

the psychometric value of the scale. Specifically, Cronbach’s alpha of 0.85 for the total

scale, and 0.80 and 0.79 for the intrusion and avoidance subscales respectively, have also

been reported (Eid et al., 1999). Several studies have found the scale to be sensitive

towards gender differences, as well as to change-over-time (Horowitz et al., 1979;

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Schwarzwald et al., 1987; Zilberg et al., 1982.). Finally, from the perspective of the

current authors, a significant strength of the IES is found in the fact that the respondent is

not required to identify a specific incident for which the reported symptoms are linked.

When working with an emergency service population, identification of only a single

traumatic event may be a difficult request.

A revised version of the IES was completed in 1997 (Weiss & Marmar, 1997) and the

revised version included a total of 22 items in place of the original 15 items. Eight of the

new items were intended to measure intrusions, 8 intended for symptoms of avoidance,

and 6 intended for symptoms of hyper-arousal. For the revised version, Creamer et al.

(2003) reported good internal consistency (Cronbach’s alpha = .79-.94) and Weiss and

Marmar (1997) reported strong 6-month test re-test reliability (.89-.94). The primary

impetus for the new version was to provide more alignment with the three domains of

PTSD as described in the Diagnostic and Statistical Manual of Mental Disorders IV Text

Revision (DSM-IV; APA 2000). However, despite the cognitive appeal of a measure

intended to evaluate all three domains, the factor structure of the IES-R has continued to

be questioned in the literature. Although a three factor solution has indeed been supported

by some researchers (Beck et al., 2008; Brunet et al., 2003; Mystakidou et al., 2006;

Wagner, 2011) other researchers have found that a two factor solution, measuring an

intrusion/hyperarousal factor and an avoidance factor, is a better fit (Asukai et al., 2002;

Creamer et al., 2003).

Previous Research Using The IES Or IES-R With Firefighters

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Previous research most applicable to our current hypotheses includes Wagner (2011).

Wagner collected responses from a sample of paid-professional firefighters, as well as a

sample of community participants and found that, for community participants, the IES-R

three factor solution appeared to be appropriate. In contrast, for the paid-professional

firefighters, a two factor solution appeared to be the most appropriate. Specifically, for

the firefighters, Wagner described a primary factor called post-traumatic

symptomatology, and a secondary factor called sleep; it should be noted however, that

the sleep factor was based on only two items. In response to these findings, Wagner

provided further support for the validity of the three factors of the IES-R when used with

a community sample. She also indicated that, until additional research evaluates the use

of the three subscales with emergency response workers, she suggests using an overall

score when using this assessment tool with emergency service workers.

In addition to the work of Wagner (2011), other research has employed the IES and/or

IES-R with emergency service populations, although not specifically for the purpose of

evaluating the robustness of its factor structure when used with these populations. For

example, Regehr and Hill (2000) used the IES and found that for paid-professional

firefighters, a single session of critical incident debriefing resulted in a higher score on

the intrusion subscale, although participants reported lower levels of overall stress.

Additional studies completed by Regehr and colleagues are described in the literature

with each of the articles based on the same sample of career (n = 99) and volunteer

firefighters (n=65). Specifically, Regehr et al. found that alienation from others, feelings

of insecurity, and lack of personal control were the best predictors of depression and

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post-traumatic symptoms (2000). In other work, this research group concluded that the

mental impact of negative events can be reduced by supportive relationships, and the

intensity of symptoms is directly linked with the ability to maintain such relationships

(Regehr et al., 2001).

Dean et al. (2003) considered a sample composed of 75 career and 67 auxiliary

firefighters and employed the IES-R as a measure of traumatic stress in this population.

These authors found that career firefighters reported higher levels of psychological

distress than did auxiliary firefighters, and suggested that this might be a factor of years

of service. Dean et al.’s findings are partly supported by research by Wagner and

colleagues (2010; 2012) in that, the combined results of the 2010 and 2012 studies

suggest volunteer firefighters were at lower risk for self-reported post-traumatic

symptomatology than paid-professional firefighters; however, both groups were elevated

as compared to a community sample. In comparison to Dean et al, Wagner and

colleagues found no relationship between years of service and PTSS. Complicating the

picture further, Murphy et al. (1999) completed a study with 188 urban firefighters and,

in support of Dean et al.’s findings, suggested that post-traumatic symptomatology as

well as additional stress symptoms increased as years of service increased.

Also similar to Wagner et al. (2010), Paton (1994) found that for a sample of paid-

professional firefighters (n = 16) as compared to a sample of non-emergency service

participants (n = 21), the paid-professional firefighters self-reported levels of stress with

greater intensity of symptoms and were more likely to perceive event demands and

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characteristics of stressors. Based on this data, Paton suggests that the training and

experience of paid-professional firefighters does not prepare them sufficiently for the

major disaster work to which they respond (Paton, 1994).

Other research, completed by McFarlane, found that intensity of traumatic recurring

memories as measured by the IES was just as indicative of arousal and mood

disturbances as actual exposure to trauma for a sample of volunteer firefighters (n = 290;

McFarlane, 1992). Bryant and Harvey worked with a sample of volunteer firefighters (n

= 751) between the ages of 18 and 87 years old and demonstrated that post-traumatic

stress symptoms were best predicted by proximity to death, event and response-related

aspects of trauma, perceived severity of trauma, fear of the traumatic event itself, and

levels of stress post-event (1995).

Finally, Al-Naser and colleagues reported Post-traumatic Stress Disorder (PTSD) at a rate

of 18.5 percent in a sample of 108 Kuwaiti firefighters (Al-Naser & Everly, 1999). Also,

using this tool, this research group reported that positively valenced cognitive

interpretations were associated with healthy and adaptive responses, as compared to

negatively valenced interpretations which led to less positive outcomes.

Previous Work On The Factor Structure Of The IES

Few research studies have been completed with respect to the factor structure of the IES

and/or the IES-R when used with emergency service samples. Previous research in this

area has primarily focused on victims or non-clinical samples. Smith and Paton (1997)

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looked at the psychometric properties according to occupational grouping, including first

responders, and found that structural differences were apparent according to both

occupational grouping and context. Their results also suggest a gap in the literature

regarding the factor structure of the IES/IES-R when using an emergency service sample

and that this occupational group may differ from other samples with respect to the

measured structural properties of the IES/IES-R scales.

Alternate studies have been completed and make available valuable data on the structural

properties of the IES tools, despite the lack of emergency-responder specific data. Two

papers provide comprehensive reviews of the current literature regarding the structural

properties of the IES and both agree that it is a satisfactory tool for measuring post-

traumatic stress and is useful for screening for appropriate treatment; however, the

studies similarly agree that the IES does not give a precise measure of PTSD as outlined

by the DSM-IV-TR (Joseph, 2000; Sundin and Horowitz, 2002). These reviews further

agree that the majority of research regarding the structural properties of the IES supports

the two-factor structure, avoidance and intrusions. A two-factor structure has been

supported by further factor analysis (Hodgkinson & Joseph, 1995); however, there is

often a third, weaker factor present which may suggest the distinction between emotional

avoidance or denial from active behavioral avoidance (Orsillo, 2002). On the other hand,

a separate paper suggested that the scale may reflect a single “general factor”, explained

in part as a reflection of negative affinity (Larsson, 2000). In addition to the general

factor, underlying analysis exposed three subscales, avoidance, intrusions and sleep

disturbances (Larsson, 2000). It has also been suggested that there may be some practical

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difficulties in interpreting the results given by post-trauma screening tests such as the

IES, especially with respect to determining whether identified symptoms are in direct

response to the event of interest or to other, more general conditions (Brewin, 2005).

Four recent studies have specifically looked at the factor structure of the IES-R, and each

of these available studies has been completed with nonemergency service samples. Two

international studies from 2010 considered the factor structure of the IES-R under

conditions of major disaster, specifically war (Morina et al., 2010) and earthquake

(Wang, Zhang, Shi, Zhou, Huang, & Liu, 2010). Morina et al suggested that for their

sample of war survivors, a five factor model including intrusion, avoidance,

hyperarousal, numbing, and sleep disturbance was the most appropriate fit for their data.

Similarly, Wang et al found that a four factor solution including intrusion, avoidance-

numbing, hyperarousal, and sleep disturbance, was the best fit for their data. A study

completed in 2009 with samples of Israeli emergency room patients and US

undergraduate students also supported a four factor solution including intrusion,

avoidance-numbing, hyperarousal, and sleep (King, et al., 2009). A single North

American study (Beck, et al., 2007) looking at victims of a serious motor vehicle accident

found support for the intended three factor solution including intrusions, avoidance, and

hyperarousal.

The previous review indicates considerable gaps in regards to the structural properties of

the IES and the IES-R. Specifically, gaps exist with respect to the factor structure of the

scale when used with emergency service workers, and the majority of available factor

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analytic studies have investigated the two-factor IES as compared to the IES-R, with its

revised three subscale structure (Beck et al., 2008). Due to the fact that prior research has

suggested differences in structural properties with respect to occupational grouping and

context (Smith & Paton, 1997), it is necessary to invest in further research intended to

provide clarity in regards to the suitability of the IES/IES-R scales for an emergency

service population. Therefore, the present project was intended to reduce this gap and

provide an initial evaluation of the structural properties of the IES-R and the described

three-factor structure when using this scale with a volunteer firefighting sample.

Methods

Firefighters from four volunteer fire departments within the surrounding area of an urban

centre in Northern British Columbia (n = 65) completed a demographic questionnaire and

the Impact of Events Scale – Revised (IES-R; Weiss & Marmar, 1997) as part of a larger

questionnaire study. Although this sample size could be considered small for this type of

analysis (see Limitations), Mundfrom et al. (2005) suggest that for a three factor structure

with 21 or more variables, a sample size of 75-100 can be considered appropriate.

Further, Costello and Osborne (2005) state that “strict rules regarding sample size for

exploratory factor analysis have mostly disappeared” and that “a surprisingly high

proportion (almost one-sixth) of reported factor analyses are based on subject to item

ratios of only 2:1 or less” and 40.4% with a ratio of less than or equal to 5:1 (p. 4).

Tabachnick and Fidell (2001) suggest that for situations where more than one factor loads

at .32, the researchers should expect that crossloading is occurring and that the item is

not a clean measure of a single underlying construct. For the two factor solutions in the

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present data, crossloading, according to the given criteria, occurred infrequently (3 items

for the firefighter sample; 5 items for the community sample). Similarly, Costello and

Osborne (2005) suggest that communalities below .4 should not be interpreted.

Communalities of below .4 were also infrequent in our data (4 items for the firefighter

sample; 0 items for the community sample) with the majority of our identified

communalities consistent with those typically found in social science data (i.e., 0.4-0.7;

Costello & Osborne, 2005). Despite the suggestions provided, we recognized that our

firefighter sample was below a typically expected minimum sample size; however, we

saw our sample size as adequate to discuss initial results for use of this scale with this

population. Our initial review was further justified give that this question has not, to our

knowledge, been addressed elsewhere in the literature; therefore, our exploratory data in

this area can provide a useful guide for initial interpretations and as well as to inform

future research asking similar questions about the validity of three factors when using the

IES-R with emergency service populations.

Prior to the collection of data, all participants were informed of the procedures and

potential ethical issues. Administration of data collection occurred face to face with the

primary researcher or a trained research assistant, during a regularly scheduled fire

department practice session. All members who were in attendance at each respective fire

department practice session agreed to participate. Questionnaire packages were collected

directly after completion.

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A sample of community workers, similar to the volunteer firefighting sample in terms of

age, gender, marital status, number of children etc. were recruited from the same

community region (n = 103; see Table 1 for full demographic information). Potential

participants were contacted through researcher networks and other convenience sampling

methods (e.g., word of mouth). Post-recruitment procedures paralleled those completed

by the volunteer firefighting sample. Participation was voluntary for both groups;

however, a five-dollar donation was made to the British Columbia Burn Fund or an

alternate charity named by the participant, on behalf of each person who took part. With

respect to statistical analysis, responses from the respective groups were analyzed using a

Principal Components Analysis with Direct Oblimin (delta = 0) and an initial process of

3 factors selected (avoidance, intrusions, hyperarousal; Costello & Osborne, 2005;

Fabrigar et al, 1998; McCallum et al, 1999).

RESULTS

Comparison Sample

For the comparison sample, our initial analyses included the planned approach of

a predetermined three factor solution. Contrary to our expectations based on published

descriptions of the IES-R, our three factor analysis revealed all items as loading on only

two factors (see Table 2). Specifically, the first factor included all items intended as

measuring either intrusions or hyperarousal, and the second factor included all items

intended to measure avoidance. The single exception to this pattern of item loadings was

evidenced by Item 12, "I was aware that I still had feelings about it, but didn't deal with

them". Item 12 loaded on the first factor, intrusions and hyperarousal, rather than on the

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avoidance factor as would be expected. Given the results of the original three factor

solution, we reanalyzed the data using a set two factor solution. As anticipated, the

subsequent two factor solution suggested a “clean” two factor model with the exception

of Item 12; this item continued to load on the first factor, rather than on the second factor

as would be expected according to published questionnaire subscale descriptions (see

Table 3). However, the factor loadings for this item were similar on both factors (.513 on

intrusions/hyperarousal; .419 on avoidance). For the comparison sample in our study, the

scree plot also suggested a two factor solution as a good fit for the data; however, with

this group, the scree plot did not clearly reveal the two factor solution as the only possible

solution, and suggested that a three factor solution may also fit.

Volunteer Firefighter Sample

Similar to our approach with the comparison sample, our initial analysis with the

firefighting sample involved the use of a predetermined three factor solution. Using the

three factor approach, no discernible pattern was evident regarding the placement of

respective items according to their published subscale placement (see Table 4). Given the

lack of fit with the three factor solution, we completed a scree plot to see the most

appropriate model as indicated by this approach. According to the scree plot, a two factor

solution was clearly the best fit for the data with this group; consequently, a subsequent

two factor analysis was completed. The two factor solution provided some evidence of

factors similar to those seen in the comparison sample, with the first factor including

many items of intrusion and hyperarousal, and the second factor including many items of

avoidance (see Table 5). However, the items loading on the particular factors were not

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considered "clean" as six items loaded on the factor opposite to that which would have

been anticipated given published descriptions of the items. Specifically, two items

described as belonging on the avoidance subscale loaded onto the intrusions and

hyperarousal factor. These items included Item 7, "I felt as if it hadn't happened or wasn't

real", and item 8, "I stayed away from reminders about it". In addition, four items

expected to load on the intrusions factor, instead loaded on the avoidance factor. In

particular, these items included Item 1, "Any reminder brought back feelings about it",

Item 3, "Other things kept making me think about it", Item 5, "I thought about it when I

didn't mean to", and Item 9, "Pictures about it popped into my mind". In general, the

volunteer firefighter responses were somewhat consistent with the two factor solution,

similar to the comparison sample; however, our data suggested less confidence in a clean

two factor solution for the volunteer firefighter group as compared to the community

participant responses.

DISCUSSION

Consistent with previous results looking at the factor structure of the IES-R, the

present study provides additional support for the validity of the recommended subscales.

However, also in concert with other literature in the field (e.g., Asukai et al., 2002; Beck

et al, 2008), our results suggest that appropriateness of a two factor versus a three factor

solution may be specific to the sample considered. That is, other research (e.g., Wagner et

al., 2011) has supported a three factor model as appropriate for use with a community

sample, a model consistent with the published recommendations for IES-R. This model

includes the original two factors, avoidance and intrusions, as well as the inclusion of the

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subsequent third factor, hyperarousal. In contrast, the current research suggests a two

factor model, collapsing the hyperarousal and intrusion subscales, may be the best fitting

model for particular community samples. It should be noted that the participant sample

used in the current study differed from that analyzed by Wagner (2011). Specifically, our

current sample had an overall lower average age and included female participants.

Consequently, it is suggested that the applicability of the three factor model when using

the IES-R may be partially dependent on the gender and age distribution of the

population under study. Additional research will be required in order to determine

whether this suggestion can be supported through further study on alternate sample

populations using the IES-R. Although some questions remain regarding the appropriate

number of subscales according to the population under consideration (i.e., two versus

three), the currently available body of literature appears to support the validity of the

published IES-R subscales when used with community samples.

In addition to providing additional evaluation of the factor analytic structure for the IES-

R when used with a community sample, the present study was also intended to evaluate

the factor analytic structure of this scale when used with the volunteer firefighting

sample. Previous research (Wagner, 2011) considered the factor analytic structure of the

IES-R when used with a paid-professional firefighting sample. This author found no

support for a three factor model when using the IES-R with paid-professional firefighters,

and instead reported a two factor solution including a primary factor she termed

“posttraumatic symptomatology”, and a secondary factor she termed “sleep”. As a result,

Wagner concluded that, until additional evidence suggests otherwise, use of an overall

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score should be considered a responsible approach when employing the IES-R with a

paid-professional firefighting sample. In contrast to the findings reported with a paid-

professional sample, the present data suggest that for volunteer firefighters, the pattern of

results appears to be more similar to the community sample. That is, although for the

volunteer firefighters six items loaded on the factor opposite to that which was expected,

the overall pattern of results was similar to that found with the community sample.

Specifically, for the volunteer firefighters the best fitting solution revealed a primary

factor reflecting hyperarousal and intrusions, and a secondary factor primarily reflecting

avoidance. This finding suggests that, when using the IES-R with a volunteer firefighting

sample, there is some evidence for use of a hyperarousal/intrusions subscale and an

avoidance subscale. However, given the lack of clarity regarding six of the items, in

combination with the apparent dependency of the IES-R factor structure on participant

composition, researchers using the IES-R with volunteer firefighters would be prudent to

ensure validity of the subscales prior to using subscale data in subsequent analyses.

LIMITATIONS

Several limitations must be considered when evaluating the present outcomes.

First, all data collected was based on self-report questionnaire; consequently, responses

potentially reflect a social desirability bias. Second, our data were collected from

volunteer fire fighters working in the surrounding region of a small, northern, urban

center. Therefore, our results may not be generalizable to other volunteer firefighters

working in other types of environments. Third, our participants completed the

questionnaire packages on a volunteer basis, potentially leading to a selection/volunteer

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bias in the composition of our sample. Fourth, our singular measure of traumatic stress

was the IES-R and, as a result, it is unclear whether the traumatic stress reported by the

participant was related to emergency service work, or something unrelated to the fire

services. Finally, our results were based on an initial sample that was smaller than that

often suggested as ideal for this type of analysis. Consequently, the present data should

be taken only as a guide to future research addressing the factor structure of the IES

scales with emergency service participants; in particular, we recommend future studies

with larger, more comprehensive samples.

CONCLUSIONS

Use of the IES-R with emergency service samples may require caution, especially

if use of the three-factor subscales is desired. Future research with large, representative

samples will be required to provide more clarity about the appropriateness of the

suggested subscales with specific groups of emergency care providers.

#ACKNOWLEDGEMENTS

Special thanks is extended to all of the firefighters and community members who agreed

to participate in this study as well as to the Melanie Perrin and the fire chiefs who

provided on-going support to the research program.

REFERENCES

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Table 1. Self-reported demographic information of volunteer firefighters and community

participants

Group Variable Mean St. Dev. Count Percent

Volunteer

Firefighters

Age 39.05 12.66

Years in Occupation 14.22 11.55

Male 54 83.1

Female 11 16.9

High school 26 40.0

Greater than high

school

39 60.0

Married 55 84.6

Single 7 10.8

Other marital status 3 04.6

No children 20 30.8

One child 4 6.2

Two or more children 41 62.9

Caucasian ethnicity 62 95.4

Other 2 03.1

Very good health 21 32.3

Above average health 22 33.8

Average health 22 33.8

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Community Sample

Age 42.04 10.41

Years in Occupation 15.90 11.30

Male 86 83.5

Female 17 16.5

High school 40 38.8

Greater than high

school

63 62.2

Married 81 78.6

Single 12 11.7

Other marital status 10 09.7

No children 23 22.3

One child 12 11.7

Two or more children 67 65.0

Caucasian ethnicity 97 94.2

First Nations ethnicity 3 02.9

Asian ethnicity 1 01.0

Other ethnicity 2 01.9

Very good health 36 35.0

Above average health 27 26.2

Average health 28 27.2

Below average health 6 5.8

Poor health 6 5.8

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Table 2. Three Factor Pattern Matrix for Community Sample

Factor

1 2 3

Item 1 .579 .292 -.358

Item 2 .857 .073 .236

Item 3 .705 .256 -.157

Item 4 .771 .214 .054

Item 5 -.140 .282 -.130

Item 6 .687 .261 -.293

Item 7 -.024 .620 -.168

Item 8 .161 .683 .429

Item 9 .660 .168 -.182

Item 10 .684 .150 .323

Item 11 -.083 .863 .057

Item 12 .528 .331 -.290

Item 13 .321 .396 -.487

Item 14 .845 -.156 -.226

Item 15 .956 -.137 .002

Item 16 .781 -.013 -.412

Item 17 .260 .692 .124

Item 18 .793 .090 -.025

Item 19 .981 -.233 .243

Item 20 .825 -.119 .074

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Item 21 .585 .285 .020

Item 22 .296 .496 -.053

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Table 3. Two Factor Pattern Matrix for Community Sample

Factor

1 2

Item 1 .585 .401

Item 2 .853 -.010

Item 3 .707 .300

Item 4 .769 .819

Item 5 -.148 .865

Item 6 .692 .348

Item 7 -.028 .670

Item 8 .142 .540

Item 9 .663 .221

Item 10 .676 .040

Item 11 -.096 .840

Item 12 .531 .419

Item 13 .328 .547

Item 14 .854 -.089

Item 15 .959 -.144

Item 16 .792 .114

Item 17 .249 .645

Item 18 .794 .092

Item 19 .917 -.317

Item 20 .826 -.148

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Item 21 .582 .272

Item 22 .292 .507

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Table 4. Three Factor Pattern Matrix for Firefighting Sample

Factor

1 2 3

Item 1 .130 .853 -.052

Item 2 .618 .157 .156

Item 3 .193 .709 .186

Item 4 .629 .328 -024

Item 5 -.004 .171 .494

Item 6 .118 .310 .619

Item 7 .301 -.124 .452

Item 8 .195 -.293 .803

Item 9 .489 .381 .117

Item 10 .800 -.091 .213

Item 11 -.115 .533 .501

Item 12 .704 .237 .022

Item 13 .329 .527 .171

Item 14 .956 -.026 -.141

Item 15 .674 .153 .145

Item 16 .685 .286 .106

Item 17 -.076 .096 .838

Item 18 .713 .039 .261

Item 19 .882 -.120 -.044

Item 20 .514 .127 .233

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Item 21 .800 -.118 .004

Item 22 .118 .035 .706

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Table 5. Two Factor Pattern Matrix for Firefighting Sample

Factor

1 2

Item 1 -.133 .862

Item 2 .638 .228

Item 3 .053 .848

Item 4 .538 .303

Item 5 .113 .471

Item 6 .240 .685

Item 7 .496 .121

Item 8 .556 .154

Item 9 .427 .448

Item 10 .914 -.008

Item 11 -.102 .861

Item 12 .656 .230

Item 13 .240 .641

Item 14 .934 -.155

Item 15 .692 .215

Item 16 .651 .332

Item 17 .179 .598

Item 18 .803 .161

Item 19 .919 -.153

Item 20 .566 .247

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Item 21 .851 -.159

Item 22 .349 .447

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