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Journal of Behavioral Medicine, Vol. 29, No. 6, December 2006 ( C© 2006)DOI: 10.1007/s10865-006-9074-3

Positive and Negative Religious Coping in GermanBreast Cancer Patients

Christian Zwingmann,1,4 Markus Wirtz,2 Claudia Muller,1

Jurgen Korber,3 and Sebastian Murken1

Accepted for publication: July 27, 2006Published online: September 2, 2006

A growing interest has been focusing on the relationship between religious coping andpsychosocial adjustment among cancer patients. However, previous research mostly has notdifferentiated between positive and negative components of religious coping. The currentcross-sectional study investigated the role of both positive religious coping, i.e., a confidentand constructive turning to religion, and negative religious coping, i.e., religious struggle anddoubt, in a sample of 156 German breast cancer patients. Participants were assessed upon ad-mission to an inpatient rehabilitation program. In addition to religious coping, two basic non-religious coping styles (depressive coping and active problem-focused coping) and psychoso-cial adjustment (anxiety and depression) were measured. Major research questions concern-ing the mediating role of nonreligious coping and the relative predictive power of positive andnegative religious coping were primarily addressed using structural equation modeling. Re-sults indicated that the relationship between religious coping and psychosocial outcomes wascompletely mediated by nonreligious coping, whereby only depressive coping and not activeproblem-focused coping proved to be a mediating variable. Positive and negative religiouscoping were somewhat positively related to each other; their (indirect) predictive power onpsychosocial adjustment was identical though in an opposite direction. All in all, the resultscorrespond to previous Anglo-American research. There are, however, some discrepancieswhich may be due to the specific religious-cultural background in Germany.

KEY WORDS: positive and negative religious coping; breast cancer; Germany; structural equationmodeling.

INTRODUCTION

Within the last few years the connection be-tween religiousness and psychosocial adjustment hasbecome a subject of increasing interest within thefield of health care (Koenig et al., 2001; Planteand Sherman, 2001). Religious or—somewhat more

1Psychology of Religion Research Group, University of Trier, BadKreuznach, Germany.

2Department of Rehabilitation Psychology, University ofFreiburg, Freiburg, Germany.

3Rehabilitation Center Nahetal, Bad Kreuznach, Germany.4To whom correspondence should be addressed at Psychologyof Religion Research Group, University of Trier, Franziska-Puricelli-Str. 3, D-55543 Bad Kreuznach, Germany; e-mail:[email protected].

broadly defined—spiritual issues seem to play an im-portant role in the coping process for many patients,particularly those facing severe and potentially life-threatening illness such as cancer (Gall et al., 2005;MacLean et al., 2003). Across studies of participantswith diverse types of cancer the majority reported,often spontaneously, religious faith to be a majorsource of support in dealing with their illness (Fe-her and Maly, 1999; Flannelly et al., 2002). It wasassumed that religious involvement may help can-cer patients in order to relieve stress, retain a senseof control, maintain hope and self-esteem as well asa sense of meaning and purpose in life, and to fa-cilitate social support from a religious community(e.g., Coward, 1995; Levin, 1996; Moadel et al., 1999;Taylor, 1995).

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534 Zwingmann, Wirtz, Muller, Korber, and Murken

However, quantitative studies involving cancerpatients and investigating religiousness as a globaldispositional variable have yielded mixed results(Pargament et al., 2005; Stefanek et al., 2005). On theone hand, several results indicated religious involve-ment to be modestly but meaningfully associatedwith psychosocial adjustment, various dimensions ofhealth-related quality of life, and beneficial copingstrategies (e.g., Baider et al., 1999; Brady et al.,1999; Gall, 2000; Gioiella et al., 1998; Mehnert et al.,2003; Schnoll et al., 2000). On the other hand, nullfindings and even negative associations were alsoshown, at least for some of the target variables (e.g.,Cotton et al., 1999; Holland et al., 1999; Rifkin et al.,1999; Silberfarb et al., 1991; Tate and Forchheimer,2002). Thus, firm conclusions cannot yet be drawn(Stefanek et al., 2005).

Stronger and more clear-cut relationships mightbe found, when assessing religious coping strategiesin place of global dispositional religious variables:Whereas dispositional religiousness denotes religiousinvolvement in general, religious coping sets the fo-cus on the way in which patients specifically drawon religion in a situation of crisis (Pargament, 1997;Sherman and Simonton, 2001). Thus, an explorationof religious coping provides information about func-tional religious aspects which may be more or lessdistinct from dispositional religiousness. In samplesother than cancer patients several studies have al-ready examined specific types of religious coping (seePargament, 1997, for an overview). In these studiesadjustment to difficult circumstances appeared to bebetter predicted by religious coping than by generalreligious orientation (e.g., Pargament et al., 1990).Moreover, religious coping strategies showed differ-ential relationships to the outcomes of various stress-ful situations (see Pargament, 1997; Zwingmann andMurken, 2000, for an overview). More specifically,religious coping was helpful or harmful depending onthe particular type of religious coping strategy em-ployed. Thus, religious coping would appear to be anambivalent phenomenon which does not automati-cally entail beneficial outcomes.

Higher order factor analyses have revealed thatparticular religious coping methods can be classifiedinto two broad overarching patterns: positive andnegative religious coping (Pargament et al., 1998).In general, positive religious coping strategies, thosewhich reflect a confident and constructive turningto religion for support, tend to be beneficial forpeople undergoing stressful life events (Ano andVasconcelles, 2005; Koenig et al., 2001). In contrast,

negative religious coping strategies, those which re-flect an engaging in religious struggle and doubt, aregenerally more maladaptive (Ano and Vasconcelles,2005; Exline and Rose, 2005). The Brief RCOPE de-veloped by Pargament et al. (1998, 2000) can be con-sidered a well-established measure of positive andnegative religious coping (Sherman and Simonton,2001).

Returning to cancer patients, some studies haveindeed also examined the impact of religious cop-ing strategies (e.g., Alferi et al., 1999; Carver et al.,1993; Nairn and Merluzzi, 2003; Sherman et al., 2000).In psycho-oncological research these investigationshave helped shift the focus from patients’ disposi-tional religiousness to their specific responses to ill-ness. However, only few items or a single scale wereused and predominantly positive aspects of religiouscoping were measured (Thune-Boyle et al., 2006).Hence, only limited information concerning the vari-ous correlates of religious coping was provided, withreligious struggle and doubt in particular having re-ceived little attention. Only recently, Sherman et al.(2005) investigated positive and negative religiouscoping in a sample of multiple myeloma patients fac-ing bone marrow transplantation. Results indicatedthat after controlling for potential demographic andmedical confounders, only negative religious copingwas significantly related to various psychosocial out-come measures. For cancer care and research, thisfinding underscores the need to distinguish betweendifferent patterns of religious coping and points tothe importance of a potentially negative influence ofreligious struggle and doubt on the adaptation pro-cess.

Beyond the distinction of positive and negativereligious coping, there is some obscurity about thequestion as to whether religious coping contributesto psychosocial adjustment directly and uniquely orrather through other pathways, such as by stimulat-ing nonreligious coping strategies (Siegel et al., 2001).As Pargament et al. (1990) put it, religious copingmay be predictive of psychosocial adjustment butfunctionally equivalent to traditional nonreligiouscoping measures. If this were the case there wouldbe no general need to identify religious coping vari-ables as specific predictors of outcomes. However,in their cross-sectional study of members of Chris-tian churches who envisioned serious negative lifeevents, Pargament et al. (1990) showed that religiouscoping variables predicted outcomes beyond the ef-fects of nonreligious coping strategies. Similarly, a re-cent cross-sectional study conducted by Burker et al.

Positive and Negative Religious Coping 535

(2005) in a sample of lung transplant candidates con-cluded that religious and nonreligious coping werenot functionally redundant but independent compo-nents of psychosocial functioning. Moreover, Tix andFrazier (1998) found that after controlling for mea-sures of cognitive restructuring, internal control, andsocial support religious coping predicted life satisfac-tion 12 months after transplantation. These and otherfindings (see Pargament, 1997, for an overview) sug-gest that religious coping represents a distinctive re-source, one that cannot be “explained away” in termsof presumably more basic phenomena (Pargament,2002).

Research with cancer patients to date has sel-dom examined the question of unique contribu-tions of religious variables to psychosocial outcomes,and in doing so, has predominantly focused ondispositional religiousness or spirituality: A cross-sectional study by Gall (2000) found that religiousvariables, but not nonreligious resources predictedemotional well-being in long-term breast cancer sur-vivors. However, in a sample of patients with var-ious types of cancer, self-efficacy proved—at leastpartially—to be a mediating variable between a posi-tive religious coping style and adjustment (Nairn andMerluzzi, 2003). Yet another pattern was reportedby Holland et al. (1999). In participants with ma-lignant melanoma religious/spiritual predictors wereunrelated to psychosocial adjustment but substan-tially associated with an active-cognitive coping style.Whereas this correlational finding was replicated in arecent German study (Mehnert et al., 2003), a simi-lar investigation in an Israeli sample found religiouspredictors related to both psychosocial adjustmentand active-cognitive coping (Baider et al., 1999). Allin all, the picture is decidedly mixed. The poten-tial mediating role of nonreligious coping within thereligion-health relationship should be more directlyaddressed within cancer research.

A final point must be emphasized: So far,psycho-oncological research of religiousness orspirituality has been predominantly conducted inthe United States. Thus, findings are limited to thisspecific religious-cultural context and should beaugmented by contributions from other countries.For example, only little evidence is available fromGermany (Zwingmann, 2005b) although thereare considerable religious-cultural differences tothe United States: Whereas belief in God is verywidespread among Americans (>96%) and almostconstant since 1944 (Bishop, 1999), it is remarkablydeclining in Germany, presently totaling 63% in

the former West German states and merely 13%in the new East German states (Terwey, 2003).Concomitantly, there is an ongoing crisis of reli-gious institutions in Germany bringing about thephenomenon of “believing without belonging.” Thatis to say, religious beliefs are becoming increasinglypersonal, detached, and heterogeneous (Jagodzinskiand Dobbelaere, 1995). Against this background,religious coping may well be a less frequent or lessinfluential response to life-threatening illness inGermany (Frick et al., 2006; Zwingmann, 2005a).

Positive and negative religious coping patternshave not yet been differentially investigated inGerman cancer patients. However, in accordancewith the international literature, some Germanstudies also suggest that cancer patients view reli-giousness as important in dealing with their illness(Muthny et al., 1992). Furthermore, benevolentreligious and spiritual attitudes basically tend to befavorable in coping with cancer (Filipp et al., 1990;Ferring et al., 1994; Mehnert et al., 2003) and seem toconstitute specific psychosocial needs (Bussing et al.,2005; Frick et al., 2006).

The purpose in conducting the present investi-gation was to study the role of positive and negativereligious coping in a sample of German breast can-cer patients. According to our introductory remarks,three specific research questions were addressed:

1. Do patients with certain demographic andcancer-related characteristics prefer a partic-ular religious coping pattern?

2. Do positive and negative religious cop-ing contribute to psychosocial adjustmentuniquely or indirectly by affecting nonreli-gious coping strategies?

3. Are positive and negative religious copingpatterns equally predictive of psychosocialadjustment?

METHODS

Procedure and Patients

This was a cross-sectional study with 156German breast cancer patients participating. Patientscompleted questionnaires at admission to an inpa-tient rehabilitation program. Patients were eligiblefor participation in the study if they had been newlydiagnosed and were fluent in German. In excludingpatients with recurrent breast cancer, we attempted


to achieve a relatively homogeneous sample with re-gard to course of disease and treatment.

The study was reviewed and approved bythe Ethic Committee of the “LandesarztekammerRheinland-Pfalz,” a German federal medical as-sociation. Patients were recruited consecutively atan oncological inpatient rehabilitation center, the“Rehabilitationsklinik Nahetal,” Bad Kreuznach,Germany, between March and September 2003.They were asked to attend a group session duringtheir first week of stay. In this session, after a com-plete description of the study had been presented,written informed consent was obtained from thosewilling to take part. Of 247 patients approached,167 (68%) consented to participate and completedquestionnaires. Of those, 11 (7%) were excluded dueto limited data quality (missing values >10% in scaleitems). The final study sample therefore consisted of156 patients.

Participants’ mean age was 56 years (SD = 11,range = 31–83), 68% were married, and 74% livedtogether with a partner; for 54% the highest levelof education was equivalent to junior high school.The time since diagnosis ranged from 1 month to36 months with a mean post-diagnosis of 11 months(SD = 7). Diagnoses comprised Stage 0 (in situ car-cinoma, 5%), Stage I (31%), Stage II (47%), StageIII (7%), and Stage IV (3%) breast cancer; in 7%information regarding stage was unavailable. Nearlyall participants had received surgical treatment (65%breast conservation, 31% mastectomy, 1% both).In terms of religious affiliation, 49% of the womenwere Catholics, 35% Protestants, and 5% membersof other Christian churches; 10% had abandonedchurch membership, and 1% had never belonged toany denomination. These frequencies roughly corre-spond to the denominational distribution in the for-mer West German states: Catholics and Protestantseach comprise around 40% of the population, andother affiliation and no membership each constituteapproximately 10% (Terwey, 2003).

Measures

Religious Coping

Validated instruments for a refined assessmentof religious coping are not yet available in theGerman language. Therefore, quite analogous to theideas of the Brief RCOPE (Pargament et al., 1998,2000), we developed 16 items for use with cancer

patients that would fit the religious-cultural contextin Germany. In contrast to the Brief RCOPE, weavoided strong religious words like “devil” or “sin.”Participants rated the items on a 5-point scale from0 (“not at all”) to 4 (“very much”). An exploratoryfactor analysis (principal axis factoring, Varimaxrotation) clearly indicated the presence of twofactors (variance accounted for = 41.7 and 26.6%,respectively). The first scale, Positive ReligiousCoping (PRC), contained 8 items and measured thedegree to which religiousness offers cancer-specificsupport, meaning, and solace in response to cancer(Cronbach’s alpha = 0.97; e.g., “I’m finding solaceand hope in my religious faith”). The second scale,Negative Religious Coping (NRC), comprised 8items addressing bargaining and quarreling withGod, feelings of being punished by God, and reli-gious doubt as cancer-specific responses (Cronbach’salpha = 0.89; e.g., “I’m asking myself why Godscrutinized me”). PRC and NRC were somewhatrelated, but not significantly, r(156) = 0.12, p = 0.136.

Nonreligious Coping

We utilized two subscales from the short ver-sion of the Freiburg Questionnaire of Coping withIllness (FKV-LIS [FQCI]; Muthny, 1989). The FQCIis a well-validated coping measure widely usedin Germany. The items take the form of short state-ments of coping behaviors set out on a 5-point rat-ing scale from 0 (“not at all”) to 4 (“very much”).The two subscales employed are known to showthe strongest connections to psychosocial adjustmentand other outcome measures (Muthny, 1989) andconstitute the basic dimensions of the FQCI (Hardtet al., 2003): Depressive Coping (FQCI-Dep, 5 items,e.g., “Brooding”) and Active Problem-focused Cop-ing (FQCI-Act, 5 items, e.g., “Deciding to fightagainst the illness”). As reported in the manual,Cronbach’s alphas showed somewhat lower but ac-ceptable values (0.73 for FQCI-Dep and 0.65 forFQCI-Act). Also consistent with the manual, FQCI-Dep and FQCI-Act were unrelated, r(156) = 0.07,p = 0.418.

Psychosocial Adjustment

Psychosocial adjustment was assessed withthe German version of the Hospital Anxiety andDepression Scale (HADS-D; Herrmann et al., 1995),which contains two subscales (7 items each): Anxiety


and Depression. All items are scored on a 4-pointscale ranging from 0 to 3. The HADS-D is well val-idated and internationally comparable (Herrmann,1997). Cronbach’s alphas in the present study, 0.79for Anxiety (HADS-D-A) and 0.82 for Depression(HADS-D-D), are perfectly in line with prior find-ings. As expected, HADS-D-A and HADS-D-Dwere strongly correlated, r(156) = 0.66, p < 0.001.

Data Analysis

For the 156 patients included in the study, amaximum of 3 (1.9%) missing values in the itemsof the scales was observed. Prior to the main dataanalyses these missing values were imputed by theexpectation-maximization (EM) algorithm. The EMalgorithm estimates missing data using an iterativemaximum-likelihood procedure. It is one of therecommended methods for preventing biases causedby not completely random missing data processes(Allison, 2001; Schafer and Graham, 2002). Theimputation was performed with the software NORM(Graham et al., 2003).

After determining descriptive statistics for thescales, research question 1 was addressed applyingunivariate analyses (correlation, ANOVA) as wellas multiple regression analysis. To answer the ques-tion which patients tend to use which religious cop-ing pattern religious coping scales were related tovarious demographic and cancer-related predictors.SPSS 12.0 for Windows software (SPSS Inc., Chicago,IL) was used for these computations.

In order to estimate the multivariate depen-dencies addressed in research question 2 (mediat-ing role of nonreligious coping) and research ques-tion 3 (relative predictive power of positive andnegative religious coping), structural equation mod-eling (SEM) was employed (Bollen, 1989; Kline,2005). The maximum-likelihood estimation proce-dure (Hoogland and Boomsma, 1998; Kline, 2005)implemented in the software AMOS 5.0 (Arbuckleand Wothke, 1999) was used to develop and test allstructural models. The input for SEM is the empiricalcovariance matrix. The covariance matrix analyzedhere can be directly derived from the correlation ma-trix shown in the appendix.

SEM is a multivariate technique combining theproperties of factor analysis, regression analysis, andpath analysis, consequently enabling the definitionand estimation of complex model structures. SEMthus has the potential to account for multiple influ-ences, which may simultaneously affect various out-

come variables. Furthermore, if multiple valid in-dicators exist, theoretically derived constructs canbe modeled as so-called latent or structural vari-ables which possess desirable psychometric proper-ties: Since measurement error is explicitly separatedfrom true variance within the estimation process, la-tent variables, unlike observed manifest indicators,do not suffer from systematic restrictions in measure-ment quality, given that certain criteria are met (Hairet al., 2004).

In accordance with Kline (2005), a two-stepSEM procedure was applied. In the first step, a con-firmatory factor analysis (CFA) was conducted to de-termine whether the intended constructs were indeedmeasured. CFA assumes each manifest variable to bea distinct indicator of an underlying latent construct,whereby different constructs are permitted to corre-late with each other. The appropriateness of a spe-cific CFA model was assessed by measures of globaland local model fit.

Measures of global fit indicate whether the em-pirical associations among the manifest variables areappropriately reproduced by the model (Boomsma,2000; Kline, 2005). For a variety of these global fitmeasures certain criteria have to be met in order toaccept the model under study as plausible and parsi-monious. Measures of absolute fit like the goodnessof fit index (GFI) and the adjusted goodness of fit in-dex (AGFI) reflect the discrepancy between the em-pirical and model implied covariance matrices. Val-ues ≥ 0.90 indicate an acceptable fit. The root meansquare error of approximation (RMSEA) can be in-terpreted as the amount of information within theempirical covariance matrix that cannot be explainedby the proposed model. The model may be classi-fied as acceptable if only 8% or less of the informa-tion are not accounted for by the model (RMSEA ≤0.08). Furthermore, measures of incremental fit wereemployed (Tabachnik and Fidell, 1996): the normedfit index (NFI), the Tucker–Lewis index (TLI), andthe comparative fit index (CFI). The rationale of allthese three measures is that more complex, i.e., lessrestrictive models are penalized by a downward ad-justment, while more parsimonious, i.e., more restric-tive models are rewarded by an increase in the fitindex. According to Bentler (1990), the CFI explic-itly avoids the underestimation of fit often noted forthe NFI in relatively small samples. A rule of thumbfor incremental fit measures is that values ≥ 0.95 areindicative of good fit relative to the independencemodel, while values ≥ 0.90 may be interpreted as anacceptable fit.


Measures of local fit evaluate whether each con-struct can be reliably estimated from its indicators(Bagozzi and Baumgartner, 1994; Hair et al., 2004)and whether the constructs within the model aresufficiently distinguishable (Fornell and Larcker,1981). Because coefficient alpha wrongly assumesthat all indicators contribute to reliability equally(Bollen, 1989) we chose composite reliability, whichdraws on the unstandardized regression weights andmeasurement error components for each indicator(Baumgartner and Homburg, 1996; Fornell andLarcker, 1981).

In the second step, a path model, as implied byresearch questions 2 and 3, was specified and eval-uated using measures of global fit. The significanceof the relationships between the exogenous and en-dogenous latent variables, as well as the amountof variance explained in the endogenous variableswere examined. In order to then directly answer re-search questions 2 and 3, specific constraints wereimposed upon this model (or a reformulation of it)and tested through hierarchical model comparisons.Only if the constraints led to a significant decrease indata fit (�χ2 test; Homburg and Giering, 2001) thecorresponding model components were consideredimportant.

RESULTS

Descriptive Statistics

Descriptive statistics for all six scales are shownin Table I. Participants reported relatively high re-liance on positive religious coping, but made onlylimited use of negative religious coping strategies.In terms of nonreligious coping patients most fre-

Table I. Descriptive Statistics for Scales (N = 156)

ScaleTheoretical

range M SD Skewness

PRC 0–4 1.95 1.31 −0.07NRC 0–4 0.88 0.83 1.02∗∗∗FQCI-Dep 0–4 0.94 0.67 0.83∗∗∗FQCI-Act 0–4 2.81 0.72 −0.49∗HADS-D-A 0–3 1.05 0.52 0.27HADS-D-D 0–3 0.54 0.47 1.48∗∗∗

Note. PRC: Positive Religious Coping, NRC: Negative Reli-gious Coping, FQCI-Dep: Depressive Coping, FQCI-Act: Ac-tive Problem-focused Coping, HADS-D-A: Anxiety, HADS-D-D = Depression.∗p < 0.05.∗∗∗p < 0.001.

quently used active problem-focused coping. In con-trast, the mean score for depressive coping wascomparatively low. Patients’ anxiety and depressionscores fell below the midpoint of the theoreticalrange. However, comparison with a German sampleof healthy women revealed percentiles of approxi-mately 70 for both patients’ average anxiety and de-pression score (Herrmann et al., 1995). Participantsthus experienced fairly high levels of psychosocialdistress.

Research Question 1: Regression of ReligiousCoping on Demographic and Cancer-RelatedVariables

In order to determine which patients tend touse which religious coping style, positive and nega-tive religious coping were related to various demo-graphic variables (age, education, partner, religiousaffiliation) and cancer-related descriptors (time sincediagnosis, surgical procedure, tumor stage). Theseunivariate analyses (correlation, ANOVA) only in-dicated more negative religious coping for olderwomen [r(156) = 0.24, p = 0.003], least-educated pa-tients [F(2,143) = 5.63, p = 0.004, post hoc Scheffe],and partner-less women [t(154) = 2.28, p = 0.024]as well as both less positive religious coping[F(3,152) = 8.82, p < 0.001] and less negative reli-gious coping [F(3,152) = 5.03, p < 0.002] for religiousnones (post hoc Scheffe).

To account for multiple dependencies betweenthe seven demographic and cancer-related predic-tors, multiple regression analysis was conducted. Cat-egorical predictors were coded as follows: education:low (0; equivalent to junior high school and lower)versus high (1; equivalent to high school and higher);partner: living without (0) versus living with (1) apartner; Catholic affiliation: Catholic (1) versus other(0); Protestant affiliation: Protestant (1) versus other(0); surgical procedure: breast conservation (0) ver-sus mastectomy or both (1); tumor stage: less ad-vanced (0; Stages 0 to II) versus more advanced (1;Stages III and IV).

As can be seen in Table II, negative religiouscoping was significantly predicted by age and partner(beta = |0.19| to |0.21|, overall R2 = 0.21). That is,correlations to other predictors taken into account,older and partnerless women showed more negativereligious coping. No such tendencies were found forpositive religious coping. Education and religiousaffiliation variables failed to reach significance inmultiple regression analysis. As in the univariate


Table II. Multiple Regression Summary for Regressions ofPositive and Negative Religious Coping on Demographic and

Cancer-Related Variables (N = 124)

PRC NRC

Demographic variablesAge 0.13 0.21∗Education (0-1) 0.19 −0.14Partner (0-1) −0.06 −0.19∗Catholic (0-1) 0.25 0.11Protestant (0-1) 0.16 0.23

Cancer-related variablesTime since diagnosis −0.05 −0.15Surgical procedure (0-1) −0.01 0.05Tumor stage (0-1) 0.01 −0.13

R2 0.10 0.21F(8,115) 1.54 3.80∗∗∗

Note. See text for dichotomizing (0–1) categorical predictors.Standardized beta coefficients are shown. PRC: Positive Reli-gious Coping, NRC: Negative Religious Coping.∗p < 0.05.∗∗∗p < 0.001.

approach, cancer-related variables (i.e., time sincediagnosis, surgical procedure, and tumor stage) werenot significantly related to either negative or positivereligious coping.

Structural Equation Modeling

Confirmatory Factor Analysis

Initially, all 40 scale items were included as re-spective indicators of the underlying six latent con-structs (i.e., “Positive Religious Coping,” “NegativeReligious Coping,” “Depressive Coping,” “ActiveProblem-focused Coping,” “Anxiety,” and “Depres-sion”). However, according to global-fit measures,this “original CFA model” showed only poor agree-ment with the empirical data (see Table III, row 4):The χ2 value revealed significant differences betweenmodel-implied and empirical covariances. Further-more, the fit indices GFI and AGFI as well as theincremental fit measures NFI, TLI, and CFI fell sub-stantially below the critical threshold of 0.90. Hence,indicators with insufficient model compatibility weresequentially eliminated from the model until criteriafor a good model-fit were reached. Items were elim-inated if (1) indicator reliabilities were low (<0.3;Hair et al., 2004) or (2) modification indices sug-gested that residual correlations would entail a sub-stantial improvement of fit (Kline, 2005).

The resulting “modified CFA model” comprised20 items and exhibited a very good global data fit

(see Table III, row 5). Furthermore, indices of lo-cal fit proved that each latent construct was reliablymeasured by its indicators: For each manifest item,more than 30% of its information was predicted by itsunderlying construct (indicator reliability >0.3; seeTable IV, column 3) and all factor loadings were sig-nificant (see Table IV, column 4). The factor relia-bilities (see Table IV, column 5) as well as the av-erage proportions of indicator variance extracted bythe corresponding latent construct (see Table IV, col-umn 6) exceeded the critical values. Only for “Anx-iety” was a marginal violation observed. Finally, itwas tested whether each latent variable provided in-formation not covered by the most strongly corre-lated alternative construct. Significant �χ2 tests in-dicated discriminant validities (see Table IV, column7). That is to say, all latent variables could be con-sidered sufficiently distinguishable from each other(Fornell and Larcker, 1981).

Although the measurement models were mod-ified in order to achieve an appropriate model fit,the central properties of the structural model re-mained virtually identical after modification: In boththe “original” and the “modified CFA model” thesame structural path coefficients were significant anda similar amount of variance was explained in the en-dogenous constructs (“Anxiety”: 62% vs. 56%, “De-pression”: 71% vs. 71%). This indicates that modi-fications did not lead to changes in the meaning ofconstructs.

Research Question 2: Estimating Direct andIndirect Effects of Religious Copingon Psychosocial Adjustment

After having ensured a high measurementquality of the “modified CFA model,” structuralrelationships between latent variables were specified,as implied by research questions 2 and 3. This “fullpath model” (see Table III, row 7) yielded virtuallythe same measures of global fit as the “modified CFAmodel,” demonstrating that the model structure wasin accordance with the empirical data. Figure 1illustrates the structural model of the “full pathmodel,” indicating estimated path coefficients andthe percentage of explained variance for the en-dogenous structural variables. Measurement modelswere equivalent to those proved in the “modifiedCFA model” (see Table IV) and are not shown inFig. 1.


Table III. Measures of Global Fit for All Models Estimated and Hierarchical Model Tests (�χ2)

χ2 df p χ2/df GFI AGFI NFI TLI CFI RMSEA

Thresholds for acceptable fit > 0.05 <2 ≥0.90 ≥0.90 ≥0.90 ≥0.90 ≥0.90 ≤0.08Confirmatory factor analyses

Original CFA model 1220.1 725 < 0.001 1.68 0.74 0.70 0.72 0.85 0.86 0.07Modified CFA model 176.1 155 0.115 1.14 0.90 0.87 0.91 0.98 0.99 0.03

Research question 2Full path model 176.2 156 0.128 1.13 0.90 0.87 0.91 0.98 0.99 0.03Restriction 179.6 160 0.138 1.12 0.90 0.87 0.90 0.98 0.99 0.03�χ2 test 3.4 1 0.504

Research question 3Reformulated path model 90.2 84 0.303 1.07 0.93 0.90 0.94 1.00 1.00 0.02Restriction 1 90.2 85 0.329 1.06 0.93 0.90 0.94 1.00 1.00 0.02�χ2 test 0.0 1 0.980Restriction 2 90.5 85 0.321 1.07 0.93 0.90 0.94 1.00 1.00 0.02�χ2 test 0.3 1 0.586

Note. GFI: goodness of fit index, AGFI: adjusted goodness of fit index, NFI: normed fit index, TLI: Tucker-Lewis index, CFI:comparative fit index, RMSEA: root mean square error of approximation.

Information within the “full path model” canbe described as follows: The latent endogenous vari-ables “Positive” and “Negative Religious Coping”were positively correlated (ϕ = 0.18, C.R. = 2.10,p = 0.036). Both “Positive Religious Coping”(β = − 0.29, C.R. = − 2.95, p = 0.003) and “Negative

Religious Coping” (β = 0.29, C.R. = 2.99, p = 0.003)significantly affected “Depressive Coping,” but not“Active Problem-focused Coping.” Furthermore,“Depressive Coping” had a very strong impact on“Anxiety” (β= 0.67, C.R. = − 2.95, p < 0.001) and“Depression” (β= 0.79, C.R. = 5.83, p < 0.001). All

Table IV. Measures of Local Fit for the “Modified CFA Model”

Factor ItemIndicatorreliability

t-value of factorloading

Factorreliability

Averagevarianceextracted �χ2 (df = 1)

Thresholds for acceptable fit ≥0.3 ≥0.6 ≥0.5 p<0.05Positive prc1 0.74 –a 0.95 0.80 32.39∗∗∗Religious prc2 0.91 18.02∗∗∗Coping prc3 0.78 15.33∗∗∗

prc4 0.77 15.10∗∗∗prc5 0.83 16.36∗∗∗

Negative nrc1 0.89 –a 0.86 0.63 32.39∗∗∗Religious nrc2 0.76 15.01∗∗∗Coping nrc3 0.53 11.14∗∗∗

nrc4 0.31 7.56∗∗∗Depressive coping dc1 0.56 –a 0.77 0.54 10.99∗∗∗

dc2 0.38 6.57∗∗∗dc3 0.35 6.30∗∗∗

Active Problem-focused Coping ac1 0.48 –a 0.70 0.55 90.10∗∗∗ac2 0.60 0.66∗∗

Anxiety anx1 0.43 –a 0.73 0.47 35.02∗∗∗anx2 0.37 6.43∗∗∗anx3 0.70 7.86∗∗∗

Depression dep1 0.53 –a 0.74 0.50 10.99∗∗∗dep2 0.41 7.12∗∗∗dep3 0.49 7.77∗∗∗

Note. For thresholds of acceptable fit see Bagozzi and Baumgartner (1994), Hair et al. (2004).aUnstandardized values were set to equal 1 in order to ensure identifiability.∗∗p < 0.01.∗∗∗p < 0.001.


-.29

Pos. Religious Coping Anxiety

Depression

DepressiveCoping

.67.79.29

(-.14)

(.06)

(.21) (-.10) (-.07) (-.17)

.18 (-.01)

(-.09)

56%

71%

5%

14%

ActiveCoping

Neg. ReligiousCoping

Fig. 1. “Full path model”: estimated standardized path coef-ficients and percentage of explained variance for the endoge-nous structural constructs (non-significant paths appear dotted;total effects: “Positive Religious Coping”→“Anxiety” = − 0.27,“Positive Religious Coping”→“Depression” = − 0.35, “Nega-tive Religious Coping”→“Anxiety” = 0.31, “Negative ReligiousCoping”→“Anxiety” = 0.31; correlation of endogenous errorterms of “Anxiety” and “Depression”: ψ = 0.57, C.R. = 2.52,p = 0.012).

path coefficients from “Positive” and “NegativeReligious Coping” to the constructs representingpsychosocial adjustment yielded non-significantvalues β“PRC” →“Anxiety” = − 0.14, C.R. = − 1.55,p = 0.122; β“PRC”→“Depression” = − 0.01, C.R. =− 0.06, p = 0.955; β“NRC”→“Anxiety” = 0.09, C.R. =1.03, p = 0.305; β“NRC”→“Depression” = 0.06, C.R. =0.65, p = 0.514). In sum, 56% of the variance of“Anxiety” and 71% of the variance of “Depression”was explained by religious and nonreligious coping.Religious coping accounts for 14% of the varianceof “Depressive Coping,” but for only 5% of thevariance of “Active Problem-focused Coping.”

Referring to research question 2, this patternstrongly suggests that the predictive information ofreligious coping for psychosocial adjustment wascompletely mediated by nonreligious coping (espe-cially by depressive coping). In order to explicitly testthis, a hierarchical model comparison was conducted.To this end, all four direct paths from “Positive” and“Negative Religious Coping” to “Anxiety” and “De-pression” were simultaneously constrained to 0. Thisrestriction yielded a global model fit (see Table III,row 8, restriction) that was not significantly weakerthan the fit of the “full path model” (see Table III,row 9). Thus, all direct paths from religious copingto psychosocial adjustment could be eliminated fromthe structural model without any loss of systematicinformation.

Research Question 3: Comparing the PredictivePower of Positive and Negative Religious Coping

In order to address research question 3, the “fullpath model” had to be reformulated. The reformula-tion should allow a direct estimation of the total pre-dictive power of religious coping on psychosocial ad-justment. To this end, constructs representing nonre-ligious coping were eliminated. Explicitly, only directpaths from the exogenous constructs representing re-ligious coping to the endogenous constructs “Anx-iety” and “Depression” remained in the “reformu-lated path model.” Indirect effects mediated by non-religious coping were consequently implicitly mod-eled within the direct paths between exogenous andendogenous variables.

Again, the “reformulated path model” showedan excellent global model fit (see Table III, row 11).All path coefficients were significant and of mediumsize: β“PRC”→“Anxiety” = − 0.35, C.R. = − 3.61, p <

0.001; β“PRC”→“Depression” = − 0.28, C.R. = − 2.94,p = 0.003; β“NRC”→“Anxiety” = 0.28, C.R. = 3.11,p = 0.003; β“NRC”→“Depression” = 0.30, C.R. = 3.14,p = 0.002. The question whether “Positive” and“Negative Religious Coping” had identical predictivepower on “Anxiety,” was tested by constraining thecorresponding unstandardized regression weights insuch a way as to obtain equal values (Bollen, 1989),|b“PRC”→“Anxiety”| = b“NRC”→“Anxiety.” This restric-tion yielded a global model fit (see Table III, row12, restriction 1) that was not significantly weak-ened in comparison with the fit of the “reformu-lated path model.” As to “Depression,” the restric-tion |b“PRC”→“Depression”| = b“NRC”→“Depression” (seeTable III, row 14, restriction 2) also led to a non-significant decrease in model fit (see Table III, row15). Thus, we can conclude that both religious copingconstructs affected psychosocial adjustment to thesame extent.

DISCUSSION

The present cross-sectional study investigatedthe role of religious coping in a sample of 156German breast cancer patients. Previous psycho-oncological research concerning religious resourceshas predominantly dealt with general religiousnessor beneficial religious coping (Stefanek et al., 2005;Thune-Boyle et al., 2006). Only recently, Shermanet al. (2005) systematically explored both positiveand negative components of religious coping. The


current study takes a comparable approach. Infollowing Pargament et al. (1998), religious copingwas broken down into two broad patterns: posi-tive religious coping, i.e., the constructive searchfor support, meaning, and solace in religion, andnegative religious coping, i.e., religious struggle anddoubt. Descriptive statistics for scales indicated, inaccordance to the findings by Sherman et al. (2005),that participants predominantly relied on positivereligious coping and used negative religious copingonly to a limited degree (see Table I).

The specific purpose of the present studywas threefold: (1) The first research question waswhether patients with certain demographic andcancer-related characteristics tended to prefer a par-ticular religious coping pattern. The following tworesearch questions were addressed using structuralequation modeling, thereby promoting the use ofthis methodologically advantageous approach to re-ligious coping research: (2) It was examined whetherpositive and negative religious coping contributed tothe prediction of psychosocial adjustment indepen-dently of two basic nonreligious coping styles, i.e.,depressive coping and active problem-focused cop-ing. (3) Furthermore, the relative predictive power ofpositive and negative religious coping on psychoso-cial adjustment was explored.

According to the multiple regression analysiscarried out in research question 1 (see Table II),positive religious coping could not be predicted byvarious demographic and cancer-related variables. Incontrast, negative religious coping was found to bemore frequently reported by older women and pa-tients living without a partner; beta coefficients wereof low to moderate size (beta = |0.19| to |0.21|). Onepossible explanation is that older women may gener-ally been subjected to a more negative religious so-cialization. Being raised with the image of a vengefulGod, this may be especially actualized in particularlylonesome and traumatic times. Religious affiliationas well as cancer-related characteristics did not showup as significant predictors of religious coping.

As to research question 2, the SEM “full pathmodel” did not indicate significant direct paths be-tween religious coping and psychosocial adjustment.Figure 1 illustrates that nonreligious coping strate-gies (especially “Depressive Coping”) seemed toplay a prominent mediating role between religiouscoping and outcomes. In fact, when the direct pathsfrom religious coping to outcomes were dropped,there was no significant loss of fit in the structuralmodel (see Table III). Hence, it is plausible to as-

sume that religious coping was totally mediated bynonreligious coping strategies; positive and negativereligious coping contributed only indirectly to psy-chosocial adjustment. Thus, our SEM results in Ger-man breast cancer patients do not support the con-clusion by Burker et al. (2005), Pargament et al.(1990; Pargament, 2002), and Tix and Frazier (1998)that religious coping variables contribute to the pre-diction of outcomes beyond the effects of nonreli-gious coping strategies. However, it has to be notedthat this reasoning was based on investigations insamples other than cancer patients. Studies based oncancer patients have so far not revealed a clear pic-ture. Our result seems to be somewhat similar to thefinding of Nairn and Merluzzi (2003) that nonreli-gious self-efficacy was, at least partially, a mediatingvariable between religious coping and adjustment ina mixed sample of cancer patients. However, it maywell be possible that our mediation pattern is to someextent explained by the less important role of re-ligion in Germany, thus reflecting religious-culturalspecifics.

Some other aspects of the “full path model” de-picted in Fig. 1 are noteworthy: First, only “Depres-sive Coping” but not “Active Problem-focused Cop-ing” was a mediating variable. The paths betweenreligious coping constructs and “Active Problem-focused Coping” as well as those between “ActiveProblem-focused Coping” and outcome variablesfailed to reach significance. Our results thereforedo not replicate the consistent finding of the threeequivalent studies conducted by Holland et al. (1999)in the United States, Baider et al. (1999) in Israel,and Mehnert et al. (2003) in Germany, wherebyreligious/spiritual variables were strongly related tothis beneficial coping style. In part, this discrepancymay be due to the specific nonreligious copingmeasure utilized in the three foregoing studies. Thismeasure included four items relating to religious andspiritual themes and thus may account for the highcorrelation (Holland et al., 1999). Furthermore, how-ever, it also is possible that active problem-focusedcoping will play a more prominent mediating roleif beneficial outcomes (e.g., health-related qualityof life, psychological well-being, or stress-relatedgrowth) are studied as opposed to the negativeoutcomes (anxiety, depression) emphasized in thecurrent investigation. Further research would helpdelineate which mediating patterns of nonreligiouscoping are prominent in which outcome domains.

Second, the latent endogenous constructs“Positive” and “Negative Religious Coping”


were significantly positively related to each other(ϕ= 0.18). On the manifest level the original scalesPRC and NRC were also positively associated, albeitnot significantly (r = 0.12). The moderate positiverelationship between the two religious coping pat-terns denotes that patients may use positive andnegative religious coping simultaneously. Whilstsomeone may frequently engage in positive religiouscoping, this does not preclude religious struggleand doubt. This point was previously emphasizedby Fitchett et al. (2004, p. 191): “Positive religiouscoping is not the opposite of negative religiouscoping.”

Finally, whereas “Depressive Coping” wasnegatively related to “Positive Religious Coping”and positively to “Negative Religious Coping,” itcan be seen from Fig. 1 that β“PRC”→“Depressive Coping”

had the same magnitude as β“NRC”→“Depressive Coping”

(|β| = 0.29). This already suggests that “Positive”and “Negative Religious Coping” were equallypredictive of psychosocial adjustment. In orderto explicitly test this, thereby addressing researchquestion 3, the “full path model” was reformulatedand restricted in such a way that the direct pathsbetween both religious coping patterns on the onehand and “Anxiety” and “Depression,” respectively,on the other hand were of equal size. In fact, theseconstraints did not lead to a significant loss of fitin the structural model (see Table III) so that thepredictive power on psychosocial adjustment in thepresent study was virtually identical for “Positive”and “Negative Religious Coping.” Thus, our datado not confirm the recent finding by Sherman et al.(2005), whereby only negative but not positive reli-gious coping had predictive power on psychosocialoutcomes in multiple myeloma patients. The reasonsfor this discrepancy are not clear but may reflectdifferences in religious-cultural background, cancertype, stage of illness, or phase of treatment.

Altogether, the present findings in Germanbreast cancer patients suggest that religious copingaffects nonreligious coping strategies, especially de-pressive coping. In turn, depressive coping, althoughless frequently used in our sample (see Table I),turned out to be a strong predictor of anxiety anddepression (see Fig. 1). The contributions of positiveand negative religious coping to depressive copingwere of the same magnitude but opposite direction:Depressive coping was predicted negatively bypositive religious coping and positively by negativereligious coping. Depressive coping thus proved tobe in part religiously colored. In sum, religious cop-

ing accounted for 14% of the variance of depressivecoping in our sample.

Although religious coping appeared to be com-pletely mediated by depressive coping in the currentstudy, it should not be neglected in patients facingcancer. For practical reasons, appropriately ad-dressing positive and negative religious coping maysubstantially reduce patients’ use of depressive cop-ing. Specifically, interventions designed to encouragebenevolent religious reframing, facilitate one’s find-ing meaning, and take struggling with faith seriouslyseem promising (Pargament, 1997). In inquiringpatients, both positive and negative aspects of reli-gious coping should always be explored (Shermanet al., 2005). It should be noted that positive reli-gious coping does not preclude religious struggle anddoubt. Furthermore, according to our data, older andpartner-less women seem to be more prone to engagein negative religious coping. Clinical interviews couldhelp to understand patients’ religious coping patternsagainst the background of their religious preferencesand spiritual history (e.g., Anandarajah and Hight,2001; Frick et al., 2006; Puchalski and Romer,2000).

Limitations and Concluding Remarks

Notable features of this investigation includeevaluation of both positive and negative componentsof religious coping in a relatively homogeneous sam-ple of breast cancer patients. Moreover, this study isone of the few to examine religious coping amongGerman patients and one of the first in this realmto bring a structural equation modeling approach todata analysis. In focusing reliable latent constructs,SEM enables the description and estimation of com-plex relationships.

However, several limitations of the presentstudy also deserve to be mentioned. First, our ho-mogenous German sample limits the scope for gen-eralizing the findings to other populations of can-cer patients. The participants, newly diagnosed withbreast cancer, were exclusively women and had arelatively favorable prognosis. Second, psychologicaladjustment in this study was regarded as absence ofanxiety and depression as measured by the HADS-D. Surely, this does not reflect the complexity of psy-chological adjustment and also limits the ability tomake generalizations from the findings. Third, we didnot use a measure of response bias. Although Ger-man studies do not support a systematic response


bias in regard to religion (e.g., Zwingmann, 1991), thepotential role of such a tendency cannot be ruled out.Fourth, the data and the resulting structural equationmodel stem from a cross-sectional sample. There-fore, the path model does not demonstrate causal re-lationships. Moreover, it reflects only a snapshot intime. Patients may use different religious and non-religious coping methods and experience differentstates of psychosocial adjustment over the long-term(Pargament et al., 1998; Sherman et al., 2005). Onlylongitudinal research could help differentiate theseprobably more complex relationships (Stefanek et al.,2005). Fifth, in the first step of SEM several itemshad to be eliminated from the “original CFA model”in order to reach an excellent agreement with em-pirical data. Though this modification did not leadto changes in the meaning of the constructs, it waspartly exploratory in nature and needs cross valida-tion. Finally, the SEM approach applied here did notcontrol for potential demographic and cancer-relatedconfounders. However, after inspecting correlations,

it can be assumed that these variables have only lim-ited influences (max. |r| = 0.24).

Despite the study’s limitations, there appear tobe sufficient grounds for arguing that religious cop-ing may play a substantial role in the adjustment ofGerman breast cancer patients. Findings suggest thatreligious coping primarily affects depressive coping,thus indirectly influencing the outcomes of the adap-tation process. Our study also underscores the needfor investigators and clinicians to distinguish betweenpositive and negative forms of religious coping. De-spite the less important role of religion in Germany,the results of the present study by all means corre-spond to previous Anglo-American research. How-ever, some discrepancies may reflect differences inreligious-cultural background. Since US-Americanfindings are limited to their specific cultural context,they should be further augmented by contributionsfrom European as well as from other countries. Inthe end, we hope that our study will stimulate furtherGerman research in the area of religion and health.


AP

PE

ND

IX

Zer

o-O

rder

Cor

rela

tion

Mat

rix

(N=

156)

prc1

prc2

prc3

prc4

prc5

nrc1

nrc2

nrc3

nrc4

dc1

dc2

dc3

ac1

ac2

anx1

anx2

anx3

dep1

dep2

dep3

prc1

1.00

0.81

∗∗∗

0.78

∗∗∗

0.75

∗∗∗

0.79

∗∗∗

0.13

∗∗0.

03∗∗

∗0.

17∗

0.08

−0.

17∗

−0.

15−

0.17

∗0.

120.

19∗

−0.

26∗∗

∗−

0.13

∗∗∗

−0.

20∗

−0.

27∗∗

∗−

0.01

−0.

16∗

prc2

1.00

∗∗0.

84∗∗

∗0.

84∗∗

∗0.

88∗∗

∗0.

150.

070.

26∗∗

∗0.

09−

0.16

∗−

0.17

∗−

0.09

0.11

∗0.

14∗∗

∗ −0.

22∗∗

−0.

10∗∗

∗−

0.23

∗∗−

0.25

∗∗∗

0.01

∗∗∗ −

0.09

prc3

1.00

∗∗0.

79∗∗

∗0.

79∗∗

∗0.

19∗

0.10

0.28

∗∗∗

0.15

−0.

11−

0.12

−0.

100.

080.

15−

0.20

∗−

0.09

−0.

20∗

−0.

18∗∗

0.04

−0.

07pr

c41.

00∗∗

0.78

∗∗∗

0.20

∗0.

100.

26∗∗

∗0.

15−

0.16

∗−

0.09

−0.

080.

150.

14−

0.22

∗∗−

0.08

−0.

17∗

−0.

29∗∗

∗0.

00−

0.14

prc5

1.00

∗∗0.

16∗

0.09

0.24

∗∗0.

10−

0.17

∗−

0.19

∗−

0.15

0.13

0.13

−0.

29∗∗

∗−

0.15

−0.

24∗∗

−0.

26∗∗

∗−

0.02

−0.

16nr

c11.

00∗

0.82

∗∗∗

0.68

∗∗∗

0.52

∗∗∗

−0.

140.

22∗∗

0.16

−0.

03−

0.07

0.08

0.21

∗∗0.

17∗

0.08

0.20

∗0.

21∗∗

nrc2

1.00

0.63

∗∗∗

0.49

∗∗∗

−0.

140.

140.

13−

0.05

−0.

060.

110.

19∗

0.20

∗0.

130.

28∗∗

∗0.

22∗∗

nrc3

1.00

∗∗0.

42∗∗

∗0.

090.

130.

07−

0.07

0.00

0.05

0.09

0.12

0.06

0.18

∗0.

13nr

c41.

00∗∗

−0.

010.

060.

010.

040.

110.

080.

150.

110.

070.

150.

16dc

11.

00∗

0.41

∗∗∗

0.47

∗∗∗

0.01

0.00

0.36

∗∗∗

0.29

∗∗∗

0.44

∗∗∗

0.42

∗∗∗

0.43

∗∗∗

0.53

∗∗∗

dc2

1.00

∗∗0.

42∗∗

∗0.

00−

0.08

0.34

∗∗∗

0.40

∗∗∗

0.49

∗∗∗

0.36

∗∗∗

0.26

∗∗∗

0.33

∗∗∗

dc3

1.00

∗∗−

0.07

∗−

0.02

∗∗∗

0.23

∗∗0.

24∗∗

0.24

∗∗∗

0.31

∗∗∗

0.33

∗∗∗

0.31

∗∗∗

ac1

1.00

0.54

∗∗∗ −

0.03

−0.

06−

0.03

−0.

11−

0.03

−0.

16∗

ac2

1.00

−0.

12−

0.03

−0.

13−

0.18

∗−

0.07

−0.

09an

x11.

000.

34∗∗

∗0.

54∗∗

∗0.

45∗∗

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37∗∗

∗ −0.

47∗∗

∗an

x21.

000.

55∗∗

∗0.

34∗∗

∗0.

35∗∗

∗ −0.

31∗∗

∗an

x31.

00∗∗

0.54

∗∗∗

0.44

∗∗∗ −

0.43

∗∗∗

dep1

1.00

0.47

∗∗∗

0.52

∗∗∗

dep2

1.00

0.43

∗∗∗

dep3

1.00

∗∗M

3.03

3.02

2.77

2.84

2.78

2.06

1.95

1.96

1.71

1.60

2.58

1.53

3.65

2.95

1.00

1.36

1.14

0.83

0.46

0.51

SD1.

471.

391.

471.

371.

351.

171.

081.

201.

080.

871.

190.

731.

231.

460.

680.

920.

690.

780.

620.

72∗ p

<0.

05.

∗∗p

<0.

01.

∗∗∗ p

<0.

001.


ACKNOWLEDGMENTS

This research was supported by a grant fromthe Volkswagen Foundation (Germany) to SebastianMurken.

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