TOWARD A FOUR-DIMENSIONAL MODEL OF BURNOUTA MULTIGROUP FACTOR-ANALYTIC STUDY

INCLUDING DEPERSONALIZATION AND CYNICISM

MARISA SALANOVA, SUSANA LLORENS,MÓNICA GARCÍA-RENEDO, RAÜL BURRIEL, AND EDGAR BRESé

Universitat Jaume 1, Castellón

WILMAR B. SCHAUFELIUtrecht University

This article investigated whether cynicism and depersonalization are two differentdimensions ofbumout or whether they may be collapsed into one construct of mental dis-tance. Using confinnatory factor analyses in two samples ofteachers (n = 483) and blue-

collarworkers (n =474), a superiorfit was foundforthe four-factormodel thatcontainedcynicism, depersonalization, exhaustion, and professional efficacy as dimensions ofburnout. In particular, cynicism and depersonalization emerged as unique bumout

dimensions. Moreover, it appeared from multigroup analyses that this four-dimensionalstructure ofbumout is partially invariant across both samples. Cynicism and depersonal-ization seemed to playa different Tole in both samples, particularly as far as their relation-ship with professional efficacy is concerned. It is recornrnended that future research on

burnout should include the cynicism and depersonalization constructs.

cynicism; depersonalization; bumout; mental distancKevwords;

Originally, bumout was defined as a syndrome of emotional exhaus-tion, depersonalization, and reduced personal accomplishment that occursexclusively among professionals who deal directly with recipients such as

This research was supported by grants from fue Ministerio de Ciencia y Tecnología(SEC2000-1031), Jaume I University (Castellón, Spain), and Bancaixa Foundation (11232.0111). Correspondence concerning this article should be addressed to Marisa Salanova, Ph.D.,Universitat Jaume 1, Department of Psychology, Campus de Riu Sec, s/n. 12071 Castel1ón,

Spain; e-mail: salanova@psi.uji.es.

Educational and Psychologicai Measurement, Vol. 65 No. 5, October 2005 901-913

DOr: 10.1177/0013164405275662rg 2005 Sage Publications

EDUCAll0NAL ANO PSYCHOLOGICAL MEASUREMENT902

students, pupils, clients, patients, or delinquents (Maslach, 1982, 1993).Emotional exhaustion refers to fue depletion or draining of emotional re-sources caused by interpersonal demands. Depersonalization refers to animpersonal and dehumanized perception of recipients, characterized by acallous, negative, and detached attitude. Finally, lack of personal accom-plishment is fue tendency to evaluate one's work with recipients negatively.These three components represent fue energetic (e.g., feeling used up), attitu-dinal (e.g., being excessively detached), and self-evaluative (e.g., doubtingone's competence) nature of burnout, respectively (Maslach, Schaufeli, &

Leiter,2001).Emotional exhaustion is regarded as fue basic individual stress compo-

nent of the syndrome that comes close to an orthodox job strain variable(Maslach, 1993), whereas personal accomplishment is akin to fue concept ofefficacy beliefs (Bandura, 1999). This leaves depersonalization as fue mostinnovative component of burnout. However, from fue outset, fue validity ofdepersonalization has been questioned, for instance, by Garden (1987), whoargued that this dimension of burnout gauges several distinct attitudes,including distancing, hostility, rejection, and unconcem. This might also befue reason why fue intemal consistency of fue depersonalization scale of fueMaslach Burnout Inventory (MBI; Maslach, Jackson, & Leiter, 1996)-themost popular instrument to assess burnout-is often found to be relativelylow when compared to both other scales that measure emotional exhaustionand personal accomplishment (Lee & Ashforth, 1996).

Although initially bumout was restricted to fue helping professions, it waslater broadened and defined as a crisis in one' s relationship with work in gen-eral and not necessarily as a crisis in one's relationship with people at work(Maslach et al., 2001). The three original bumout dimensions were rede-fined, and an altemative version of the MBI-the MBI-General Survey(MBI-GS)-was developed that can also be used outside human servicesoccupations (Schaufeli, Leiter, Maslach, & Jackson, 1996). That is, exhaus-tion as operationalized in fue MBI-GS refers to severe fatigue irrespective ofits cause, cynicism reflects an indifferent or distant attitude toward one'swork instead of other people, and lack of professional efficacy encompassesboth social and nonsocial aspects of occupational accomplishment.

However, in contrast to exhaustion and personal accomplishment, fuebroadening of fue bumout concept changed fue meaning of depersonaliza-tion in a rather fundamental way. The reason is that, by definition, deperson-alization involves other people so that its meaning cannot be broadenedbeyond fue social relationships in which it occurs. This problem was solvedin fue case of fue MBI-GS by considering depersonalization as a special caseof "mental distancing." That is, where depersonalized human services pro-fessionals exhibit a psychological distance toward their recipients, cynicalnon-human services employees show a similar psychological distanceregarding their work in a more general sense. In other words, fue target of fue

SALANOVA ET AL. 903

mental distancing differs. In fue case of bullan services employees, fue tar-gets are their recipients, whereas for employees who work with things or withinformation, fue target is the job itself. This agrees with Dean, Brandes, andDharwadkar (1988), who argued that organizational cynicism has tour dif-ferent targets: fue work organization at large, organizational change, fuework environment, and fue persons at fue job (i.e., other employees andrecipients). These last two types of cynicism correspond with MBI deperson-alization and MBI cynicism, respectively. Recently, in a study among cus-taller services representatives, Abraham (2000) discriminated empiricallyamong fuese tour types of organizational cynicism and showed that each wasrelated in a slightly different way to several outcomes such as job satisfaction,organizational commitment, and organizational citizenship behavior. In con-clusion, it seems that depersonalization and cynicism, as measured with fueMBI, are distinct constructs, yet they can be considered manifestations offue broader concept of organizational cynicism. To date, fueTe has been noempirical test of fue distinctiveness of MBI depersonalization and MBIcynicism in relation to both other burnout dimensions.

The purpose of this article, therefore, is to examine whether MBI deper-sonalization and MBI cynicism can be considered two different componentsof burnout or whether they merge together into one component of mental dis-tancing. In other words, can two different targets of mental distancing be dis-tinguished (i.e., people and work) within fue broader concept ofburnout? Wesought to answer this question by studying two different samples consistingof bullan services professionals (secondary school teachers) and non-bullan services employees (blue-collar workers). Similar results across bothsamples would support fue robustness of OUT findings. We used the MBI-GSto assess burnout (Schaufeli et al., 1996) as well as fue depersonalizationsubscale of fue original MBI (Maslach et al., 1996). U sing confirmatory fac-tor analysis, fue three-factor structure of fue original MBI (e.g., Byrne, 1993;Gold, Bachelor, & Michael, 1989; Gorter, Albrecht, Hoogstraten, &Eijkman, 1999; Lee & Ashforth, 1990; Schaufeli & Van Dierendonck, 2000)as well as fue MBI-GS (e.g., Bakker, Demerouti, & Schaufeli, 2002; Leiter &Schaufeli, 1996; Schutte, Toppinen, Kalimo, & Schaufeli, 2000; Taris,Schreurs, & Schaufeli, 1999) has been convincingly demonstrated.

Method

Participants and Procedure

The total sample consisted of 957 Spanish employees (499 woman,52.1 %, and 453 men, 47.3%). About half of fue sample (n = 483,49%) weresecondary school teachers (Sample 1), and the other half (n = 474, 51 %) were

hllll'--~n113T wnTkeT~ frnm the tile industrv (Samole 2). The mean ae:e of fue

904 EDUCATlONAL AND PSYCHOLOGICAL MEASUREMENT

.

l

total sample was 36 years, 8 months (SD = 9.0), and ages ranged from 18 tc62 years. The teachers worked in 34 schools, most of which were public(83%), whereas fue tile workers were employed in three private companiesIn Sample 1 (43.6% men, 56.4% women), ages ranged from 23 to 60 years(M = 40.2, SD = 8 years, 2 months). In Sample 2 (51.7% men, 48.3%woman), ages ranged from 18 to 62 years (M = 33.2, SD = 8 years, 4 months).Statistically, teachers were signiticantly older than blue-collar workers,t(928) = -12.73, p < .001, with a large effect (d = .84; Cohen, 1988).

Participants were asked to till out fue MBI-GS as part of an occupationalhealth and safety audit. Human resources officers and school managers dis-tributed fue questionnaires in fue tile companies and fue secondary schools,respectively. A covering letter explained fue purpose ofthe study, explainedthat fue participation was voluntary, and guaranteed contidentiality. Respon-dents were asked to return the completed questionnaires in a sealed envelope,either to fue person who had distributed them or directly to fue research team.

~.

~

~Instrumenti

Exhaustion (EX), cynicism (CY), and professional efficacy (PE) wereassessed with fue Spanish version (Salanova & Schaufeli, 2000) ofthe MBI-GS (Schaufeli et al., 1996). Depersonalization (DP) was measured with fuecorresponding scale afilie original MBI-HSS (Maslach et al., 1996). In thecase of fue blue-colIar workers, recipients was replaced by coworkers in fueitems tapping depersonalization. Exhaustion was measured with 5 items(e.g., "1 feel emotionalIy drained by my work"), cynicism was measured with4 items (e.g., "1 have become more cynical about whether my work contrib-utes anything"), depersonalization was measured by 5 items (e.g., "1 dealwith people with whom I work like objects"), and professional efficacy wasmeasured with 6 items (e.g., "In my opinion, I am good at my job"). Highscores on EX, DP, and CY and low scores on PE are indicative ofburnout. AlIitems were scored on a 7-point frequency scale, ranging from O (never) to 6(everyday). As shownin Table 1, exceptforDPin theteacher's sample, inter-nal consistencies (Cronbach's a) of scores on rol scales satisfied fue criterionof .70 (NunnalIy & Bernstein, 1994), and in at least in the teachers sample,most also satisfied fue more stringent criterion of .80 (Henson, 2001). As wasnoted in fue introduction, for DP, slightly lower a values have been foundmore often.

Data Analyses

Confirmatory factor analyses (CFA), as implemented by AMOS(Arbuckle, 1997), was used to test fue fit of various models to fue data ofbothsamples. First, the fit of fue tour-factor burnout model (EX, CY, DP, FE) was

905SALANOVA ET AL

Table 1Means, Standard Deviations, Internal Consistencies (Cronbach's a), and Inten:orrelations

Blue-Collar Workers(n = 474)

~

. lntercorrelation

EX CY DP PEM SD IX

1.00.54**.37**

-.34**

1.081.20.83.80

.88

.84

.68

.81

.86

.83

.71

.78

EXCyDPPE

2.031.711.054.27

~,,

t~,+

Note. EX = exhaustion; CY = cynicism; DP = depersonalization; PE = professional efficacy. Teachers are be

low !he diagonal, and blue-collar workers are above !he diagonal.

*p < .05. **p < .001.

.

t

compared in each sample with that of fue three-factor model (EX, CY + DP,FE) and with fue one-factor model that assumes that all items weight on onesingle underlying dimension (i.e., burnout). Next, using fue so-caIled multiple-group method, fue factoriaI invariance of fue best -fitting model was examinedacross both samples simultaneously (Byrne, 2001).

The goodness of fit of fue models was evaluated using absolute and rela-tive indices. The four absolute goodness-of-fit indices calculated were (cf.Joreskog & Sorbom, 1986) (a) fue X2 goodness-of-fit statistic, (b) fue good-ness-of-fit index (GFI), (c) fue adjusted goodness-of-fit index (AGFI), and(d) fue root mean square error of approximation (RMSEA). Following fuerecommendations ofMarsh, BaIla, and Hau (1996), we also computed threerelative indices: (a) Tucker-Lewis index (TLI), (b) comparative fit index(CFI), and (c) fue incremental fit index (IFI). Because fue distributions of fueGFI and fue AGFI are unknown, no statistical test or critical vaIue is available(Joreskog & Sorbom, 1986). Values smaller than .06 for fue RMSEA areindicative of an acceptable fit (Hu & Bentler, 1999), whereas a cutoff valueclase to .90 for fue IFI is suggested for a good fit (Hoy le, 1995). For fueremaining fit indices (TLI, CFI), as a rule of thumb, values greater than .95are considered as indicating an adequate model fit (Hu & Bentler, 1999).

i.

~..

1Results

Descriptive Analyses

Table 1 shows fue means, standard deviations, internal consistencies,and intercorrelations of fue tour burnout dimensions in both samples. Asexpected, EX, DP, and CY are significantly and positively related, whereasPE is negatively related with fue other burnout dimensions. However, the

.

.55**1.00.45**

-.45**

.33**

.48**1.00

-.29**

-.10*

-.19**

-.07

1.00

2.03 1.191.24 1.22.90 .96

4.41 .93

:DUCAnONAL AND PSYCHOLOGICAL MEASUREMENT906

negative correlation between PE and DP was not statistically significant infue blue-collar sample.

Four analysis of variance tests were carried out to assess differences inbumout levels among both samples (Huberty & Morris, 1989). Resultsshowed that compared to blue-collar workers, teachers felt more cynical,F(I, 956) = 34.26,p < .001, and depersonalized, F(I, 956) = 7 .06,p < .01, and

they experienced less professional efficacy, F(I, 956) = 6.93, p < .01. How-ever, a statistically nonsignificant difference was obtained for exhaustionbetween fue samples, F( 1, 956) = .004, n.s. In terms of effect sizes, fue differ-ences between both samples were "medium" for cynicism (d = .38) and"small" for depersonalization and personal efficacy (both ds = .16; cf. Cohen,

1988).

Model Testing

Next, three altemative models were tested for each sample separatelyusing CFA: a Que-factor model (MI) that assumes one latent factor, a three-factor model (M2) that assumes three latent and correlated factors (EX, CY +DP, FE), and a four-factormodel (M3) that assumes four latent and correlatedfactors (EX, CY, DP, FE).

MI and M2 fit very poorly to fue data of fue teachers (see Table 2), withDone of fue fit indices meeting its criterion. The fit of M3 was somewhatbetter and superior to that of M2 and M 1 as indicated by fue statisticalIy sig-nificant values of LlX2 (see Table 2). The so-called modification indices indi-cated that fue fit of M3 could be further improved by allowing three pairs oferrors (exl-ex2, cy3-cy4, and pe4-pe5) to correlate (see fue Discussion sec-tion for a rationale). Indeed, a subsequent test of fue revised model (M4) thatincluded fuese three correlated error terms revealed a statistically significantimprovementoverM3, LlX2(3) = 206.13,p < .001, withmostfitindices meet-

ing or approaching their critical values.As is shown in Table 3, a similar pattem of results was obtained in fue

blue-collar sample. Again, neither MI flor M2 fit well to fue data, and fue fitofM3 was superior to that ofMI and M2. Like fue teacher sample, fue fit ofM3 could be further improved (M4) by including fue same correlated errors(exl-ex2, cy3-cy4, and pe4-pe5; see fue Discussion section for a rationale.

Next, fue best-fitting model (M4) was simultaneously fit to both samplesusing multigroup analyses to test fue invariance of fue factor loadings, corre-lated errors, and correlations between factors across both samples. Asexpected, M4 provided reasonable fit to fue data across both samples, withmost fit indices meeting their corresponding critical values or at leastapproaching them (see Table 4). However, the fit deteriorated somewhatwhen all factor loadings and correlations (between factors and betweenerrors) were constrained to be equal in both samples (M4j. This means that

... ~t

~"'"11

-=-

~-8'3~..~.,...,¿

.~'<..So'"~c-

So

N

~

Z'?

~d

11 11

~~;;-"

" "]]":: s1.:

~

~

'¡j.=

..

..'"'~

S~

oEO

11

~~

!1: ..u-.. .g.g

Sc

...-

O~

~

.~

~~

'"

j ,

~~

.1!; 11

FN11 ~

~~§ S

.=

~..

~

S .~

'¡< ...

O

"..

Cc.Og. 11

'""-O~

..O .

t: -" 8"

.~

v

" *'"

'#:*

á*-"

..s

~

~

á

~511

c00-( .~¡I¡

~

"'.::-

~];;

,E.g

5c

~

~

~~

~

o ~

~i

c s

8,600 c'g

~~

~

" ~

';g'E11 ~

!1: g

o ~

~~~'6

-oMc

~.~

"

oE

-5 .

~-

.g-0-,

-(

~

" s

" "

..c

-o o

~

c ~

o "::

~ool.:,+

;,11 3

~

!1:coE

~e'g

~

~

.~c

g ~

~

- ~

~~11

-=.t:

~~~8".::~...~~.C

;-~"(...~~~~

'" ~

;z'?~

8

11 11¡¡:...-~;<

.;:.g.go: O.=

e

!Q ..

" S

i.>

"

.'" ..

..'"~

~

~.p,

o 11

~'"

~¡¡:

..

u"ü.. "Ó

~ ~

o: ...- o

"' ~

.- ~

~

'"j

,~

~

]-5" 11

~'"11 ~

~~;; e

o ..

.'" o

.. ~

,5 ~

x '

~§g. 11

o~g..;,,8"

.~

V

Q

*""

g'*

§*.."

..

§ '§

§!:E11

~.(

.-~

"'

~~

~~;<

.~.g

~o:

"'.-

¡¡

oE

".,!.

~o

"'

cb"ü

¡:¡ "8

o: e

"8 "

OD

,s"Ó

o:

~

3"'

~Q

~

'g'B11 ¡:¡

¡¡: ~

~

~..~~

'6"Ó

No:

~.-

"

oE,s

.

.,!.-"ü

9<"8

~

" e

g .g

.."Ó

o:

oo

.- ~

o,~

~O

D-

,11

] ~

¡¡:o:.p,

~~

-g~

~

.~

~.s

~

907

QO

R

.,..\0\0""""N

-

-11 ]

~~

o> 5

¡(!!,s.g

] o~

oS c

--

o.. "

lC..'8

" '"

e,.

c0=

o

-~

u

o

Q,:]]e

11 ~

o "=

u-o11

~

...

~ ~

11

U

'"ii 'O

"~

¡(~~

.g e

o,

c 0=

]o~

'"

C'"

" O~

o~

""

~-",

~

~

§,,~

u'"

u~bQ

F

¡s oS

11 e-g

- -

o~

o- -

-~

oo,

"" -

c ~

~og =

""

2,se

." o~

o~

.. ~

.."'-8

o~

"Q

,~'8

g. 11 e

'"-'0"-~

~ s

o o

ut:-.g"8

~!!

o =

lO

v..2 o

50 *"'" 11

~"'*

~."

2* '0""

" "'"

~

ee

- o,

Q,

- 2]

"2

u c

,.¡:

lC

o.. 0=

11 02

.. e

bQ

'" "

<

o~

C

~~

'"

o o~

~""u~

-~..,

~]se

o, '"

u P

.. o:;

.g .t=

.gSC

bQC

'"

" C

o~

!! "

0=

~,,~

'3..!.

~

2, '"

o",!!,

- "'-

'" ~

C

"

~

¡s og

'8

~e.!S

e¡s

" !!

"

bQ,s~

u~

C

8 2~

H ,s

O¡

=

~

o~

,.'6'

'¡¡ !

oS..

.c "

-11

~ '8 s

~ge~

9 ~

~

~

""",,-=o

U

o..

o~

.. ...

.g;;"Z"

C

~

=

'"

o~

" o

'O"

~,s~

~9~

8-g

'" o-.t

C'"

.. ~

o~

1" .g

.;: ~

oS

.g ~

- o

obQ

lC

e u

11 3

- ~

~

C

S

o"

" ~

o~

o e",,-

~

!! ~

~

~

oS ..§ 8

909SALANOVA ET AL.

although fue underlying factor structure is apparently similar in both sam-pIes, fue size of fue factor coefficients and interfactor correlations mar differ

somewhat.To assess invariance in greater detail, three additional models were tested

to fue data: (a) a model that as sumes only the correlations between factors tobe invariant (M4co)' (b) a model that assumes only fue factor loadings to beinvariant (M4ca)' and (c) a model that assumes only the correlations betweenfue error terms to be invariant (M4er). As can be seen from Table 4, fue fit ofall three models was slightly inferior compared to that of M4. This suggeststhat neither fue correlations between factors, nor fue factor coefficients, norfue correlations between error terms are completely invariant across both

samples.In the final step, an iterative process was used as recommended by Byme

(2001) to assess fue invariance of each estimate separately. That is, fueinvariance of each factor loading, correlated error term, and correlationbetween factors was assessed individually by comparing fue fit of fue modelin which a particular estimate was constrained to be equal across both sam-pIes with that of fue previous model in which this was not the case. When thefit did not deteriorate, this constrained element was included in fue nextmodel in which another constrained estimate was added, and so oo. The finalmodel (M4fi) showed that fue correlation between EX and DP, and betweenCY and DP, as well as fue correlations between the two errors (ex1-ex2, cy3-cy4) are invariant across both samples. In addition, fue factor coefficients, ofallEX items, 3 of 4 CY items (cy1, cy2, cy3), as well as 2 of5 DP items (dep1,dep4) and 3 of 6 PE items (pe1, pe2, pe6), tumed out to be invariant acrossboth samples as well. Thus, it appeared that EX has fue highest proportion ofinvariant items (100%), followed by CY (75%), PE (50%), and DP (40%),

respectively.

Discussion

The aim of ibis artic1e was to investigate whether cynicism and deperson-alization mar be considered two different dimensions of burnout or whetherthey can be col1apsed into one construct of mental distance. We studied twosamp1es: teachers who work with other peop1e (i.e., students) and b1ue-collarworkers from a ti1e factory who work with objects and who process datausing advanced computerized machinery. According to fue traditional view,bumout occurs on1y in human services professionals such as teachers, butrecent1y, it has been acknow1edged that burnout also might occur in otheroccupational groups such as b1ue-collar workers (Mas1ach et al., 2001). Infue former case, it is assumed that depersonalization is an essential dimen-sion of burnout, whereas in fue latter case, depersonalization is substituted bycynicism. Conceptually speaking, both cynicism and depersonalization aremanifestations of mental distancing. Por depersonalization, this distancing is

910 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT

directed toward the people with whom one is working (i.e., students in teach-ing and colleagues in blue-collar work), whereas with cynicism, fue dis-tancing is directed toward fue broader context of fue job itself.

Our results show that instead of one mental distance construct, burnoutcan be characterized in both samples by separate depersonalization and cyni-cism dimensions, which along with exhaustion and reduced professionalefficacy, constitute the burnout syndrome. That is, in both samples, fue four-factor model with separate depersonalization and cynicism dimensions fitbetter to the data than fue three-factor model with depersonalization and cyn-icism collapsed into one factor. However, to increase the fit ofthe four-factormodel, we had to allow three pairs of error terms to correlate. Although thismight increase the risk of chance capitalization (Curdeck & Brown, 1993),this procedure is thought to be justified because similar correlated error termswere observed previously in other samples: exl-ex2 among South Afrlcanpolice officers (Storm & Rothmann, 2003); cy3-cy4 among students fromPortugal, Spain, and the Netherlands (Schaufeli, Martínez, Marques-Pinto,Salanova, & Bakker, 2002) and blue- and white-collar workers from Sweden,Finland, and fue Netherlands (Schutte et al., 2000); and pe4-pe5 amongSpanish information and communication technology users (Salanova,Schaufeli, Llorens, Peiró, & Grau, 2000) and white-collar workers (Schutteet al., 2000). Hence, it seems that instead of being sample specific, fue corre-lated errors refIect cornmon variance between items that is independent fromcountry and occupation. For instance, the items "1 feel emotionally drainedfrom my work" (exl) and "1 feel used up at fue end of fue workday" (ex2)share some unique variance, probably because both refer to extreme tired-ness after work, whereas fue other EX items refer to tiredness in fue momingor less often to intensive fatigue.

Although fue four-factor model (including fuese three correlated errors)fit well to fue data in both samples, it was not entirely invariant across bothsamples. However, an interesting pattem emerged from an iterative proce-dure that was followed to assess invariance in greater detail. It appeared thatall EX items, three of four CY items, and fue correlations between fuese twoscales-and fue included error terms-were invariant across both samples.This means that the core of bumout-namely, EX and CY (Maslach et al.,2001)-was invariant across both samples. Different factor coefficients andcorrelations were obtained for DP and FE, though. This can be explained bythe fact that depersonalization has a quite different meaning in both samples.For teachers, relationships with students are critical for their job performanceand hence for their feelings of professional efficacy, whereas for blue-collarworkers, colleagues do not play such an essential role in this respecto This isexemplified by fue negative statistically significant correlation between DPand PE in fue teacher sample as compared to fue statistically nonsignificant

SALANOVA ET AL. 911

corresponding correlation in fue blue-collar sample(see Table 1). In otherwords, teachers who depersonalize their students feel inefficacious becausestudents are, after all, fue "essence" of their work (i.e., teaching), whereasblue-collar workers who depersonalize their coworkers do not feel ineffica-cious necessarily. For teachers, good relationships with students are essentialfor being successful, but blue-collar workers can do their work well evenwhen relationships with colleagues are poor.

In conclusion, our study suggests that cynicism and depersonalizationeach contribute in a distinct way to fue bumout syndrome. However, wefocused only on fue relationships among bumout dimensions. Futureresearch should establish whether cynicism and depersonalization are differ-ently related to particular job characteristics (i.e., job demands andresources) and outcome variables (i.e., workers performance, absenteeism).Hence, we recornmend including cynicism in addition to fue three traditionalMBI bumout dimensions when studying human services and to includedepersonalization in addition to fue three MBI-GS dimensions when study-ing non-human services occupations.

References

Abraham, R. (2000). Organizational cynicism: Bases and consequences. Genetic, Social andGeneral Psychology Monographs, 126, 269-292.

Arbuckle, J. L. (1997). Amos users' guide version 4.0. Chicago: Smallwaters Corporation.Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2002). Validation of the Maslach Burnout

Inventory-General Survey: An Internet study across occupations. Anxiely, Stress and Cop-ing, 15, 245-260.

Bandura, A. (1999). Social cognitive theoryofpersonality. InL. Perwin & O. John (Eds.),Hand-book ofpersonalily (2nd ed., pp. 154-196). New York: Guildford.

Byrne, B. M. (1993). The Maslach Inventory: Testing for factorial validity and invariance acrosselementary, intennediate, and seconctary teachers. Journal of Occupational and Organiza-tional Psychology, 66, 197-212.

Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applicationsand programming. Mahwah, NJ: Lawrence Erlbaum.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ:Lawrence Erlbaum.

Cudeck, R., &Browne,M. W. (1993). Alternativewaysofassessingmodelfit. InK. A. Bollen&J. Scott Long (Eds.), Testing structural equation models (pp. 1-9). Newbury Park, CA: Sage.

Dean, J. W., Brandes, P., & Dharwadkar, R. (1988). Organizational cynicism. Academy of Man-

agement Review, 23, 314-352.Garden, A. M. (1987). Depersonalization: A valid dimension ofburnout? Human Relations, 140,

545-560.Gold, Y., Bachelor, P., & Michael, W. B. (1989). The dimensionally of a modified fonn of the

Maslach Burnout Inventory for university students in a teacher-training programo Educa-tional and Psychological Measurement, 49, 549-561.

Gorter, R., Albrecht, W., Hoogstraten, J., & Eijkman, M. (1999). Pactorial validity of theMaslach burnout inventory-Dutch version (MBI-NL) among dentists. Joumal ofOrganiza-tional Behaviol; 20, 209-217.

912 EDUCAnONAL AND PSYCHOLOGICAL MEASUREMENT

Henson, R. K. (2001). Understanding internal consistency re1iabi1ity estirnates: A conceptualprimer on coefficient alpha. Measurement and Evaluation in Counseling and Development,

34,177-189.Hoy1e, R. H. (1995). The structura1 equation mode1ing approach: Basic concepts and fundamen-

tal issues. In R. H. Hoy1e (Ed.), Structural equation modeling: Concepts, issues and applica-tions (pp. 1-15). Thousand Oaks, CA: Sage.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for lit indexes in covariance structure analysis:Conventiona1 criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

Huberty, C. J., & Morris, J. D. (1989). Mu1tivariare analysis versus mu1tip1e univariate analyses.Psychological Bulletin, 105, 302-308.

Joreskog, K. G., & Sorbom, D. (1986). USRELuser guide version VI (4th ed.). Mooresvi11e, IL:Scientific Software International.

Lee, R. T., & Ashforth, B. E. (1990). On fue meaning ofMas1ach's three dimensions ofburnout.Joumal of Applied Psychology, 75, 743-747.

Lee, R. T., & Ashforth, B. E. (1996). A meta-ana1ytic examinationofthe corre1ates ofthe threedimensions ofjob burnout. Joumal of Applied Psychology, 81,123-133.

Leiter, M. P., & Schaufe1i, W. B. (1996). Consistency of fue burnout construct across occupa-tions. Anxiety, Stress and Coping, 9, 229-243.

Marsh, H. W., Bal1a,J. R., & Hau, K. T. (1996). Anevaluationofincrementalfitindices: Ac1ari-fication ofmathernatical and empirical properties. In G. A. Marcou1ides & R. E. Schumacker(Eds.), Advanced structural equation modeling, issues and techniques (pp. 315-353). Mah-wah, NJ: Lawrence Er1baum.

Mas1ach, C. (1982). Understanding burnout: Definitional issues in analyzing a comp1ex phe-nomenon. In W. S. Paine (Ed.),Job stress and bumout(pp. 29-40). Bever1y Hil1s, CA: Sage.

Maslach, C. (1993). Bumout: A mu1tidimensional perspective. In W. B. Schaufe1i, C. Maslach,& T. Marek (Eds.), Professional bumout: Recent developments in theory and research(pp. 19-32). Washington, DC: Tay1or & Francis.

Mas1ach, C., Jackson, S. E., & Leiter, M. P. (1996). Maslach Bumout 1nventory: Manual (3rded.). Palo Alto, CA: Consulting Psychologists Press.

Maslach, C., Schaufe1i, W. B., & Leiter, M. P. (2001).Job burnout.AnnuaIReviewofPsychology,

52,397-422.Nunnally, J. C., & Bernstein, l. H. (1994). Psychometric theory (3rd ed). New York: McGraw-

Hil1.Salanova, M., & Schaufe1i, W. B. (2000). Exposure to technology and its re1ationship to bumout.

Behovior and 1nfonnation Technology, 19, 385-392.Salanova, M., Schaufe1i, W. B., L1orens, S., Peiró, J. M., & Grau, R. (2000). Desde el "burnout"

al "engagement": una nueva perspectiva? [From burnout to engagement: A new perspec-tive?] Revista de Psicología del Trabajo y las Organizaciones, 16, 117-134.

Schaufe1i, W. B" Leiter, M. P" Maslach, C" & Jackson, S. E. (1996). Mas1ach Burnout Inven-tory-General Survey. In C. Mas1ach, S. E. Jackson, & M. P. Leiter (Eds.), The MaslachBum-out 1nventory: Test manual (3rd ed.). Palo Alto, CA: Consulting Psycho1ogists Press.

Schaufe1i, W. B., Martínez, l. M., Marques-Pinto, A., Salanova, M., & Bakker, A. (2002). Burn-out and engagement in university students: Across-national study. Joumal ofCross-CulturalPsychology, 33, 464-481.

Schaufeli, W. B., & Van Dierendonck, D. (2000). UBOS: Utrechtse Bumout Schaal-TestHandleiding [UBOS-TestManua1: Dutch version ofthe Maslach Bumout Inventory]. Lisse,fue Nether1ands: Swets and Zeitlinger.

Schutte, N" Toppinen, S" Kalimo, R., & Schaufeli, W. B. (2000). The factorial validity oftheMas1ach Bumout Inventory-General Survey across occupational groups and nations. Jour-nal ofOccupational and OIRanizational Psychology, 73,53-66.

913SALANOVA ET Al

Storm, K., & Rothmann, l. (2003). A psychometric analysis of fue Utrecht Work EngagementScale in fue South African police service. South African Journal of Industrial Psychology,

29,62-70.Taris, T. W., Schreurs, P. J. G., & Schaufeli, W. B. (1999). Construct validity of fue Maslach

Bumout Inventory-General Survey: A two-sample examination of its factor structure and

correlates. Work and Stress, I3, 223-237.