Work and non-work correlates of illness and behaviour in male and female Swedish white collar...

JOURNAL OF OCCUPATIONAL BEHAVIOUR, Vol. 8, 187-207 (1987)

Work and non-work correlates of illness and behaviour in male and female Swedish white collar workers

ROBERT KARASEK, BERTIL GARDELL and JAN LINDELL Department of Industrial and Systems Engineering, University of Southern California, University Park,

Los Angeles, California 90089-1452, U S . A .

SUMMARY

Four broad classes of dependent variables (psychological strain, physical illness symptoms, health-related behaviour and social participation) were associated with eleven categories of stressors and stress moderators from work and family life, using multiple logistic regression analysis for a random sample of 8700 full-time male and female members of T.C.O., a major Swedish white-collar labour federation (covering 25 per cent of the Swedish labour force). Our goal was to find broad patterns of associations by comparing relative magnitudes of effects for (a) stressors and stress moderators; (b) work and family activities, and (c) males and females.

Fifty per cent of the associations between environmental factors and dependent variables were significant in the predicted direction at the 5 per cent level. However, only 5 per cent of the associations are as strong, for example, as average smoking/ heart disease associations. Our primary conclusion is that job factors are the next strongest set of predictors of health and behaviour after age. Job factors are stronger than family factors for both men and women; proportionally increasing the explained variance by over 60 per cent versus roughly 20 per cent for family factors (over the 25 per cent of explanation due to demographic factors). The overall pattern of stressor/outcome associations is quite similar for men and women, although both job/ outcome and family burden/ outcome associations are stronger for women than for men. We failed to find a clear linkage between particular stressors and particular physical illnesses. Among the job factors, control and work load have the strongest associations; with the former predicting behaviour patterns and job satisfaction (along with social support), and the latter predicting mental strain symptoms. Family problems are associated with increased health risks (stronger for men) and family responsibilities and constraints affect health behaviour (stronger for women). Job satisfaction is the most successfully predicted outcome in the study, and is similarly affected for men and women.

INTRODUCTION

Psychological stress from the work or family environment has been convincingly identified as a contributor to a wide range of health and behaviour outcomes: psychological strain, physical illness, behaviour pattern change (Dohrenwend and Dohrenwend, 1974; Holmes and Rahe, 1967; House et al., 1979; Pearlin et al., 1981; Kasl, 1978; Karasek, 1976, 1979; Seligman, 1975). However, stress theory at present involves many undifferentiated causes and effects, with elucidation of more specific causal models coming slowly. Empirical and theoretical progress is being made in refining understanding of the multi-stage process by which stressors affect outcomes (Cronkite and Moos, 1984; Pearlin et al., 1981, House et al., 1979; Caplan, et al.,

0142-27741 871 030 187-21 $10.50 0 1987 by John Wiley & Sons, Ltd.

Received 11 January 1983 Final revision 14 July 1986

188 R. Karasek, B. Gardell and J. Lindell

1975), but the multi-stage approach makes it infeasible to simultaneously test a broad range of outcome measures or social contexts. There continue to be few findings illuminating the broad pattern of stress-related associations. For example, we still have no clear picture of the relative importance of (a) different social spheres (work or family stressors); or (b) different demographic contexts by age, sex, work or family roles (Haw, 1982); (c) different types of stressors; or (d) of the relative sensitivity of different types of health or behavioural outcomes.

Progress in finding broad patterns may have been impeded by the current concentration on more narrowly focused studies which seek to illuminate the complex multi-stage stress process on the basis of a limited number of stressors and outcomes variables (for example, Cronkite and Moos, 1984). An alternative approach, less powerful in its ability to describe multi-stage mechanisms, but with greater possibilities of revealing the gestalt of stress relationships, is a study of the comparative importance of a broad range of stessors. Such studies would concentrate on the patterns of associations. They would begin by defining important categories of stressors and important categories of outcomes and they would continue by taking substantial care in assessing the relative magnitude of associations between categories so as to delineate the pattern of effects. The result of such studies might be a platform on which a more differentiated stress theory could be built in the next stage of research.

In the existing literature such broad pattern analyses are rare because of the limitations in the range of variables studied or because of the limitations of the subpopulations. Often the populations examined are exclusively men or women, or they contain only work or non-work independent variables. Or the study may be done in a factory population which is rather ‘unique’ in production process or social composition of its work force. These subpopulations limit generalizability because they introduce the possibility that variations not present in that particular population remain important, unmeasured variables.

Comparing the pattern of associations between different studies (or different single variables) is made more complex by a little discussed problem for health and behaviour variables involving the most common analytic techniques used for these studies: multiple regression (ordinary least squares - O.L.S.). The magnitude of the O.L.S. regression beta coefficients are strongly dependent on the frequency of the variable for health variables with non-normal distributions (0,l variables or very skewed distributions; see p. 195). Thus, there is the great possibility of confusing differences in stress outcome frequency with differences in strength of association across variables or across studies.

To overcome these deficiencies, our study will examine a broad range of stressors and stress outcomes in a representative population. The study is based on a large, 8700 member population, broadly representative of Sweden’s white collar workforce and randomly selected. Males and females are both surveyed (each with equivalent, full- time work roles) to facilitate sex-role comparison of work and family life challenges for men and women. We study a broad range of stressors and stress moderators from both work and non-work spheres of activity. Our sample also allows a comprehensive examination of the scope of stress related effects because of its broad range of outcomes: psychological strain, physical illness, health-related behaviours and social participation outside of work. Our methodological strategy to overcome the O.L.S. regression problem utilizes multiple logistic regression to correct for differing

Correlates of Illness and Behaviour 189

frequencies among dependent variables (albeit, with some information loss). We will utilize a ‘linear’ model, based on simple hypotheses, which admittedly fail to capture interactions and non-linear relations; but the method should at least be suitable for deriving initial estimates of the patterns of stressor/stress outcome associations.

METHODS

Data

The data was collected in a large random survey (1:70) of approximately 12 000 members of Sweden’s large white collar labour union federation, T.C.O. (Tjanstemannens Centralorganisation), in March 1976. T.C.O. membership includes almost all (roughly 80 per cent) of Swedish white collar employees. Unionization is so common in the Swedish labour force that this union-based sample is still a very broadly representative group of employees. T.C.O. excludes university-trained professionals and high-level managers, as well as ‘blue collar’ workers. Thus, it is both a representative group and relatively homogeneous group with respect to social class. A response rate of 87 per cent was obtained for the researcher-distributed, and union- encouraged questionnaire (Wahlund and Nerell, 1976). Our analysis was restricted to full-time workers to minimize working hour related sex differences. The final full-time sample size was 5000 male and 3700 female respondents. Simple frequencies of job characteristics and health variables broken down by sex, age and union can be found in Wahlund and Nerell(l976).

Hypotheses

Our ‘pattern’ testing goal require clear differentiation between categories of independent and dependent variables to structure the analysis. Also, we must be able to predict at least the directionality of all our linear relationships. The assumptions made to separate groups of variables are listed below, followed by a set of hypotheses predicting directionality of effects.

Assumption 1: Individual characteristics may affect stress associations

It is generally presumed that characteristics of the individual (demographic or personality factors) could mediate environmental effects and thus should be controlled throughout the analysis. We differentiate somewhat arbitrarily ‘temporary’ phen- omena, (‘situational stressors’) from permanent, relatively stable and logically predetermined aspects of the individual’s demographic identity such as sex, age and marital status], the variables controlled for our multiple regressions (sex is handled by separate male and female analyses).

‘Our use of marital status as a demographic control variable introduces some ambiguity with respect to ‘social support’ stress moderator theory. However, we feel that marital status is a long term-1.e. structural-factor, and must be treated separately. Direct social support effects from the family or work situation are picked up by other variables (working spouse, children, co-worker/ supervisor support).


Assumption 2: Separate spheres of environmental activity can be defined and compared

We propose that work and family (or non-work) stressors can be analysed as separate spheres of causation (admittedly their linkage may be a very important additional phenomenon, but that is beyond our present study; see Piotrcakowski and Katz, 1982). Work and non-work variables are introduced into separate multiple regressions, reduc- ing the multicolinearity problems that occur when all are introduced simultaneously. Family income and commuting time are included with the ‘family’, because of their strong impacts on family life, in spite of links with work also.

Assuinption 3: Subcategories of illness and behaviour can he defined and compared

We presume that the associations between environmental stressors and physical illness may be more dependent on causal factors unmeasured in our analysis (physical agents, physical susceptibility) than the psychological strain measures; and thus these two groups of dependent variables are separated. Of course, behaviour patterns represent further distinct categories of outcomes (for a review of literature on these as dependent variables, see Karasek [ 19761). We separate leisure activities from health related behaviour since we consider the latter to be more closely related to physical health status and private decision-making, while the former may be more influenced by social milieu.

Hypothesis I : Appropriate class$ication of stressors, and moderating factors determines directionality ojeffects

An important criterion of effectiveness of our tests will be the ability to predict at least the directionality of stress effects. It is simple to define a stressor: a taxing burden for the individual which should be positively related to physical or psychological illness and negatively relate to active behaviour outside the job. However, we will further have to develop an apriori criterion to differentiate the effects of other types of stress factors: specifically, to differentiate ‘negative stressors’ from stress moderators, both of which would diminish adverse health outcomes. (‘Negative stressors’ are desirable, yet adjustment-demanding life events, such as marriage. See Dohrenwend (1975), for a discussion of positive and negative stressors). The ‘demand-control’ model (Karasek 1976, 1979) makes directionality predictions that allow differentiations of two types of factors: stressors (demands) and control factors. Low levels of control factors represent environmentally-determined constraints limiting personal strategies available to respond to stressors. Control factors (one subgroup of moderator variables) should: (a) relate negatively to psychological/ psychosomatic illness and (b) relate positively with active behaviour. Stressors should (c) relate positively with psychological/ psychosomatic illness and (d) relate positively* with active behaviour patterns.

’The most counter-intuitive prediction of the ‘demand-control’ hypotheses is (d) that active behaviour such as political or leisure activity might be positively related to psychological (but not physical) job stressors in spite of the ‘exhausting’ double load this implies. This prediction is consistent with hypothesis about active learning in situations of challenge and control, and is supported by empirical evidence (Karasek 1976, 1978, I98 1 ; Goiten and Seashore, 1980).


Scale construction: dependent and independent variables

Our variables are based exclusively on questionnaire self-reports of data on environmental stressors, and health and behavioural outcomes. While it would be more desirable to have expert observer ratings and medical record data as a supplement (such as House et al., 1979), such information is orders of magnitude more expensive to collect, and questionnaire data remains the only feasible research method for many purposes. The long tradition of research using self-report data underscores the limitations in accuracy of such data and the necessity of investigating the content and construct validity and scale reliability of each measure used, which we do below (see also Karasek, et al., 1985).

In general the content and construct validity is rather well supported by comparative analysis of our scales with scales in other studies. The internal reliability of the scales, measured by Cronbach’s alpha is generally good, but borderline for several scales (which are still included to insure coverage of a variable category).

A three-step procedure was followed to construct indicators from the T.C.O. questionnaire data (Karasek et al., 1985, Appendix A,B,C,D). First, separate factor analyses were performed on four groups of variables: work characteristics (stressors and control factors together and separately), psychological strain measures, physical illness measures, and social participation data. Then additive scales were formed from variables with high and unambiguous loadings. Scale reliabilities were calculated using Cronbach’s alpha statistics (Table 1). Finally, the dependent variable scales were dichotomized to permit calculation of standardized odds ratios (from multivariate logistic regressions).

Independent variables

In selecting questions for the job characteristics factor analyses, two criteria were followed wherever possible. Questions were selected which represented ‘objective’ assessment of working conditions. Thus, questions such as ‘is your job stressful’ were eliminated in favour of questions which recorded specific stressors (‘does your job have many deadlines’). Also avoided were questions which, in the researcher’s opinion, covered more than one theoretically relevant independent variable area (specifically ‘responsibility’ included aspects of both demand and control).

The job control (decision latitude) index is built of four very specific questions relating to ability to make decisions about specific task organization and one’s own use of skills on a job. The scale is similar in content to the ‘skill-freedom’ scale developed by Gardell (1971): the ‘decision latitude’scale of Karasek (1979); Kohn and Schooler’s (1 973) ‘occupational self-direction’ scale; and the important ‘autonomy’ and ‘variety’ components of Hackman and Lawler’s M.P.S. scale (1971).

The social support scale summarizes two sources of support from co-workers and supervisors. Similar scales are used in research of psychosomatic illness by LaRocco et al., (1980) and House et al. (1979). Since our co-worker and supervisor support measures were highly correlated, they were combined into a single scale.

The development opportunities scale represents a measure of one’s opportunities for expansive self-development on the job, as opposed to some other stress moderators

192 R. Karasek. B. Gardell and J. LindeIl

which focus on situational constraints. Unfortunately, this scale measures a rather self-reflexive interpretation of ‘enough’ opportunity instead of objective description of actual opportunities.

Two of the three ‘perceived work load’ questions also contain a clearly subjective appraisal of work load (the third question refers to more objective personnel levels). Thus, this scale may be substantially influenced by individual perceptual framework and even stress state (Lazarus, 1966). Nonetheless, other analyses based on similarly worded questions have shown that objectively measured and self-reported work load are highly correlated (Karasek, et al., 1981; Gardell, 1971; Ager, et al., 1975).

The role clarity index includes rather specific references to situations where unforseen difficulties may arise due to lack of information, deficiencies in organization, imprecise instructions. Empirical analysis shows that lack of clarity fits the pattern of stressor more clearly than that of a stress moderator (like control) (see p. 190)’.

Our conflict scale measures conflicting opinion between employees and supervisory personnel about how to do the job. Analysis shows this form of conflict to be the most common for groups of people at intermediate and low status levels in organizations (for high status personnel conflict with employees or co-workers can be of equal significance).

The data used for the family (non-work) stressor analysis include some single, specific and easily objectively answerable questions. These data include children at home (children under 7 years given double weight), average total daily travel time, income from job, working spouse. Also included is a ‘home problems’ scale based on a list of five stressors similar to the Holmes and Rahe scale (1967): recent financial problems, serious illness in family, divorce, births, other significant changes.

Dependent variables

The health dependent variables in most cases are formed from groups of three to five questions which were first presented as grouped sets of questions in the questionnaire, and then confirmed in their associations by factor analysis (question groups designed to facilitate such later scale development were written for the questionnaire by Dr Gunnar Nerell of T.C.O.). Questions that emerged with high, unambiguous loadings in factor analysis were then combined into additive scales. For the physical health questions the variable groups that emerge from factor analysis were usually very similar to question groupings present or the original questionnaire.

Frequencies of reported illnesses by age and sex in our data are generally comparable to levels reported in the Swedish national Level of Living Study 1968 and 1974 (LNU) in the cases of muscular skeletal aches, gastrointestinal problems,

’The clarity scale poses a problem for the stressors/control factor model. Role clarity as measured here, could conceivably be either a lack of control over work processes or a set of additional unexpected demands. Questions which appeared to relate more specifically to the person (‘I don’t know what is expected of me?, clustered as a separate ‘personality related’ factor in the factor analysis and were excluded.


respiratory problems, headache, smoking and job dissatisfaction. Some age dis- crepancies occur with dizziness. Pill consumption appears to tap only extreme cases in our data (for more detailed discussion see Karasek et al., 1985). An overall comparison of similar questionnaire responses in these Swedish national studies with validation data obtained from doctor-patient interviews has found that the main problem with such Swedish questionnaire data is a general tendency to underreport such problems, but there are few ‘false positive’ reports of non-detectable problems, (Johansson, 197 1). The heart disease scale is very similar to a scale successfully validated against cardiovascular mortality data in Sweden (Karasek et al., 1981, 1985) [a second, factor analytic-based scale is also reported 1.

The social participation scales may be less reliable than the other dependent variables since only eight dichotomous questions were available for analysis. Analysis of social participation activities was accomplished by computing tetrachloric correlation coefficients which were then used in the factor analysis. The importance of social participation as a set of outcomes with potentially different associations to environmental social stressors than the health measures (see p. 204 and Karasek, 1976) spoke for the inclusion of these scales.

The health-related behaviour scales are based on data from several sets of simple questions about smoking frequency and pill consumption and no factor analysis was needed to isolate component questions.

Scale statistics

In order to allow comparison of association strengths by the standardized odds ratio method (see Analysis section), we have dichotomized each dependent variable. Cutting points are selected to define the variables as ‘evidence of a significant health problem or of significant social participation’in each area. These are defined to occur on our scales when individuals report that they have at least one problem or take medication ‘often’ or ‘very often’ on at least one of three-to-five questions within a health problem area (such as ‘muscular skeletal aches’). For the health-related behaviour patterns the cut point are: ‘over 16 cigarettes/day’ and at least four sick periods from work during last 12 months. Table 1 records the percentages of men and women respectively who fulfill these criteria (for more detailed presentation of questionnaire structure and scale levels see Karasek et al., 1985, Appendix A,B,C,D). Women report higher levels of symptoms than men for exhaustion, depression, headaches, aches in the extremeties, respiratory problems and dizziness (see Wahlund and Nerell, 1976, for a more comprehensive discussion). Furthermore, women as a group have higher levels of some stressors (working spouse) and markedly lower levels of moderating variables (control, career development opportunities).

The correlation matrices (Karasek et al., 1985, Appendix Table 1) reveal moderate correlations between independent variables: work load, conflict, and role clarity are correlated; and development opportunities is correlated with control, role clarity and social support. Among women, there are also significant correlations between number of children and travel time, family problems and working spouse (especially marital status and working spouse).

194

Table 1. Variable statistics

R. Karasek, B. Gardell and J. Lindell

Reliability Men Women (Cronbach’scu) Mean S.D. Mean S.D.

1. Individual characteristics - controlled variables

1. Age 2. Marital status (% single) 3. Sex (separate analyses for men and women)

11. Independent variables

A. Job characteristics

I . Work load 2. Conflict 3. Role clarity (-) 4. Decision latitude (control) 5. Career development opportunities 6. Social support

B. Non-work situation

1. Income (SKR/mo) 2. Travel time (minutes) 3. Working spouse (% w/full time) 4. Children at home* 5. Home problem scale

I l l . Dependent variables (dichotomous) -- % affected

A. Psychological health

I . Depression 2. Exhaustion 3. Job dissatisfaction

B. Physical health

1 . Gastrointestinal problems 2. Heart disease 2 (factor analysis) 3. Heart disease 1 (theoretical definition) 4. Muscular skeletal aches 5. Respiratory illness 6. Headaches 7. Dizziness

C. Health-related behaviours

1. Pill consumption 2. Smoking1 3. Absenteeism$

D. Social participation

I . Political activity 2. Entertainment 3. Sports activity

One question One question

0.72 0.51 0.83 0.71 0.59 0.64

One question One question One question One question

n.a.

0.65 0.71 0.65

0.59 0.73 0.52 0.65 0.75

One question 0.56

n.a.t One question One question

n.a.7 0.89

One question

37.1 12.1 32.1 12.2 14.7 35.4 32.2 46.7

3.07 0.89 3.01 0.95 2.25 0.73 2.07 0.74 2.48 0.82 2.34 0.83 4.21 0.62 3.80 0.81 3.19 1.20 2.86 1.16 4.08 0.79 4.08 0.78

4890 1180 3640 830 35.0 41.0 42.0 39.0 26.7 44.2 62.6 48.4

0.86 0.81 0.64 0.79 0.191 0.41 0.228 0.44

15.3 36.0 22.8 42.0 39.6 48.9 45.1 49.8 20.1 40.1 17.5 38.0

17.4 37.9 19.7 39.8 13.9 34.6 13.7 34.4 5.1 22.0 4.6 20.9

19.0 39.2 25.3 43.5 9.4 29.2 13.3 34.4 8.8 28.3 20.0 40.0 5.1 22.0 12.8 32.5

3.0 17.1 3.9 19.4 15.2 35.9 12.2 32.7 7.1 25.7 17.0 37.6

10.6 30.8 7.0 25.5 43.4 49.6 51.3 50.0 59.6 49.1 43.1 49.5

* Children at home: No 0, Yes age 7 or over = I ; Yes age 0-6 = 2. t Cronbach’s 01 was not caiculated since only two questions were available. Correlation magnitudes are

1 Smoking over 16 cigarettes per day; more than four sick periods from work per year. necessarily low because very low frequency 0, I variables were involved.


Analysis

The dichotomous nature of our dependent variables (it seems easier to judge the existence of a health problem than to quantify its severity), and the low frequency nature of many illnesses creates a little discussed problem for comparative analyses based on O.L.S. regression analysis. For 0,I dependent variables of low frequency the magnitude of the regression beta coefficients for the independent variables, and the measure of overall association (r2) are strongly affected by the response frequency on the dependent variable (significance tests are very little affected, Morris and Rolph, 1978). This means that beta coefficients and r2 for ‘exhaustion’ (a high frequency problem) would be much higher than for ‘heart disease’ (a low frequency problem) even if the true strength of association was the same judged, for example, by plotting the covariation for the dependent variable by deciles of the independent variable. This makes comparison of regular regression coefficients inappropriate for our type analysis, since a requirement for ‘pattern’ analysis is comparison of relative strengths of association.

The commonly used solution for health variables with low frequency, non-normal distribution (but normally distributed independent variables) is to use multiple logistic regression analysis, which we will employ using the Morris and Rolph method (1978). Even the health-related behaviour and social participation dependent variables (which have more normal distributions) are dichotomized for comparison purposes with the penalty of some loss of information.

Logistic regression coefficients can be transformed into standardized odds ratios (S.O.R.) to facilitate comparisons between independent variables (Brand, et al., 1976).

x ) . For example, the association between serum cholesterol and heart disease has a S.O.R. of about 1.6: the frequency of heart disease increases 1.6 times for every standard deviation increase in cholesterol. Since the top and bottom deciles on any normally distributed variable are separated by 3.29 standard deviations, the heart disease ‘risk ratio’between high and low deciles on serum cholesterol is 1 .63.29 = 4.7 1 : 1-a large difference indeed. Decile-to-decile frequency differences can also be easily computed from the mean and the S.O.R. For example, given a mean heart disease incidence frequency of 3.4 per cent, the top and bottom deciles respectively have frequencies of 1.6 and 7.3 per cent (non-linear transformations must be used with percentages above 10 per cent or below 90 per cent).

Age and marital status are controlled in all regressions. Regressions will be performed separately for the work and the non-work variables to avoid multicolinearity problems associated with entering too many correlated variables similtaneously. The worst multicolinearity problem, a marital status/ working spouse correlation of r -0.88 for women, (but r -0.24 for men) almost guarantees bias when these variables are both present (thus we specially mark these table locations).

Tables 2 (men) and 3 (women) show our ‘patterns’ of association using the S.0.R.k The tables have been graphically subdivided to emphasize category boundaries. The independent variables are grouped into control variables (age, marital status), stressors and stress moderators from work, and non-work as discussed in the assumptions and hypotheses (p. 190). The dependent variables are divided into four groups: psychological strain, physical illness, health-related behaviour and social participation. In Tables 2 and 3 S.O.R. coefficients are printed where significance exceedsp 50.05 level. Table 4 summarizes the percentage of hypotheses confirmed at

s . 0 . ~ . (logitX X stddev.

Tab

le 2

. Sta

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ulti

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d m

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Phys

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symp

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Pr

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t.

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1.19

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2.13

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1.28

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889

1.20

8

1.22

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1.30

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194

1.15

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I .36 I

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1.78

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1.24

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208

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1.27

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352

1.23

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1.18

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182

1.23

7 1.

178

1.11

7 1.

250

1.14

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116

1.12

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166

3.9 I

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921

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844

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1.24

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154

1.26

1

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4 1.

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0.92

4

1.13

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106

2.08

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1.30

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245

1.21

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1.16

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1.11

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1.48

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918

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odds

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200 R. Karasek, B. GardeN and J. LindeN the 5 per cent level for each category of stressor and dependent variable (using a one-tail test, in all cases except age and marital ~ t a t u s ) ~ . Table 5 shows the relative increment of explained variance when work and non-work variables are added to demographic factors in 0. L. S. regressions. We first normalized the total variance explained (with all significant variables in the equation) to 100 per cent for each dependent variable. Since we are only looking at the proportional increases, not absolute level of r2, O.L.S. regression statistics can be utilized. However the absolute value of the r2’s cannot be compared to r2 in other research studies using continuous dependent variables, because they are necessarily much lower due to dichotomization.

FINDINGS

Individual characteristics

First, we examine the associations of individual characteristics age and marital status in Tables 2, 3 and 4. Not surprisingly, age emerges as the single most important variable in our analysis for both men and women, with 78 per cent of the associations significant (no directionality specified here-Table 4). Illness variables, such as heart disease, extremity aches and pill consumption increase with age, but job dissatisfaction, respiratory illness and absenteeism decline with age (these associations are observed in other U.S. and Swedish data). Dizziness increases for men and decreases for women. Sports participation decreases with advancing age but political participation increases (Swedish associations corroborated in Karasek, 1976).

Marital status is more significant correlate of health and behaviour variables for women (31 per cent of associations significant in Table 4) than for men (25 per cent significant). Single women are more likely to have psychosomatic illness and illness- related behaviour patterns and less likely to participate politically than non-single women. Health-related behaviour is the category of depenGent variables most strongly associated with marital status (83 per cent of associations are significant for both men and women). Our findings show more pill consumption, smoking (women) and absenteeism for singles. Social participation is strongly affected by marital status, (especially for women) and especially for ‘enjoyment’ leisure (confirmed in Karasek, 1976).

Work stressors and moderators

The primary conclusion from Tables 2 and 3 is that a full 57 per cent (103 of 180) of the directionally specified relationships between the job characteristics and the dependent variables are significant at the 0.05 level (see Table 4 for summary hypothesis confirmations) controlling for age, marital status (and implicitly social class). The associations are slightly stronger overall for full-time working women than for men: 60 per cent versus 55 per cent confirmed hypotheses. Tables 2 ,3 and 4 clearly reveal that

4Note that no hypothesis about the direction of association for the ‘enjoyment leisure’ category is advanced (see Karasek, 1976).


both work stressors and stress moderators, have strong associations with the health variables, 81 per cent of hypotheses confirmed for the psychological strain measures, 64 per cent confirmed for the physical health variables, and 36 per cent confirmed for the health-related behaviour variables. The social participation measures reveal only 29 per cent of hypotheses confirmed. Evidence, contrary to the hypotheses about directionality of effects, attains one-tailed significant levels in only 4 per cent of the tests (discussed below).

The strongest, most comprehensive associations occur for job control, especially for women. The associations with control are consistently negative for health problems and are generally positive for social participation (21 of 30 associations significant, men and women combined). Social support is almost equally negatively associated with illness (16 of 20 associations significant), but shows little association with social participation outside the job. Career development opportunities are strongly negative in associations with the psychological illness measures only.

Both work load and conflict are stressors positively and consistently associated with illness (19 and 22 significant associations respectively), and they display the predicted, but ‘counterintuitive’, positive associations for some social participation variables, especially for political activity and for women. An exception to our hypotheses occurs in the case of job dissatisfaction which decreases significantly, albeit weakly, as work load increases, (association corroborated in Karasek, 1979, and Gardell, 1979).

Family stressors and moderators

Overall, the associations with dependent variables are not as strong for the non-work measures as they are for the work measures: 40 per cent of hypotheses confirmed versus 57 per cent. As was true for the work variables, the non-work stressors have strong associations with the psychological strain variables (women: 53 per cent associations significant) in Table 4. The clearest family stressor associations occur for the home problem scale.

High income has strong associations (50 per cent of hypotheses confirmed) with reduced health problems and increased social participation. Thus, income has effects analogous to high control on the job [recall the control/income correlation is only moderately strong, r 0.333, 0.287 (men, women), as would be expected (Kohn and Schooler, 1973)]. These income findings suggest that our sample is not completely homogenous with respect to class, and that some class/ health associations remain. Travel time (low) also has an analogous pattern of associations to income and job control; travel time has very strong associations to psychological strain (83 per cent significant) and negative associations with social participation (75 per cent significant).

Sex differences

Our primary conclusion is that the overall patterns of associations for full-time working men and women are actually very similar for the majority of associations. The sex differences which are observable in the associations in Tables 2 and 3 most often involve ‘working spouse’ and ‘children at home’. [A significant note of caution

202

relating to Table 3 must be raised relating to the severe multicolinearity of working spouse and marital status for women--leading to several almost certainly spurious women’s ‘findings’ (noted by a special symbol and deleted from Table 4 tabulations)]. Children were hypothesized to be ‘stressors’, but surprisingly far more of the associations between number of children at home and strain/illness measures are significantly negative. This is true for both men and women, with men recording three significant negative associations while women have six negative associations. However, children at home are associated with increased exhaustion and respiratory illness for women, but not for men. Other sex differences occurring in our findings are stronger associations for women in several instances: between marital status and smoking; between job clarity and health-related behaviour; between having a working spouse and psychological strain; between travel time to work and absenteeism; and between job control and political participation.


Incremental variance explanation

Table 5 shows the normalized incremental additions to explained variance, when work and non-work independent variables are entered in blocks, (it must be recalled that the raw r2k are attenuated by dichotomization, p. 200). In the men’s analysis we find that when the work variables are added, the explained variance increases 61.9 per cent over the base of 2 1.8 per cent explained by individual characteristics (age and marital status). Adding the non-work variables to the base explained by age and marital status, adds only 21.3 per cent additional variance. For women these figures are 55.9 per cent added by work variables over the base of 25.9 per cent explained by age and marital status, and 23.6 per cent added by the non-work variables.

DISCUSSION

Overall, our findings show substantial support for the simple, but directionally specific hypotheses (stated on p. 190) linking work and family stressors and stress moderators with health and behaviour. Fifty per cent of the hypotheses relating stressors/ stress moderators to outcomes were confirmed (at the 5 per cent level) in the predicted direction, while only 5 per cent were significant contrary to hypotheses. Thus, the percentage of findings contrary to hypothesis is roughly what would be expected by chance, while the confirming hypothesis occur ten times as often as chance would provide. This implies that we can generate consistent predictions about the directionality of effects of work and non-work stressors on health and well-being.

However, only 18 of 352 associations could be considered ‘strong’on the basis of an external yardstick (and 11 of 64 age and marital status associations). For such a yardstick, we use the approximate strength of association between smoking and heart disease in other research studies: a standardized odds ratio of 1.32-representing a 2.5:l variation in heart disease frequency from the top to the bottom decile on smoking (for negative associations S.O.K. less than .76). Should we not conclude that the associations are actually quite weak overall, particularly given our large sample size? Since our variables are self-report questionnaire responses based on three or four level scales, with only moderate scale reliability; they are likely to be weak in predictive

Correlates of Illness and Behaviour 203 strength. With average scale alphas of 0.7 for both the dependent and independent variables, the joint variance is diminished to ((0.7) x (0.7)), one-half of the magnitude they could theoretically enjoy. An additional, potentially stronger, ‘underestimation’ error may be induced by the restricted information that results when we constrain our dependent variables to 0,l format to promote consistency in comparisons. Thus the appropriate conclusion is that these data do not really permit us to clearly judge in absolute terms, the ‘overall’ strength of the stressor/ outcome associations, although they do not constitute evidence of great strength for these associations.

Regardless of strength, important specific associations and interesting broad patterns do emerge. Our primary conclusion about patterns of relationships is that job factors are the strongest predictors of health and behaviour after age (age has 8 of 32 strong associations versus 14/ 192, 3/32 and 41 160 for job factors, marital status and family factors, respectively). In addition job factors in general appear more salient than family stressors in our full-time employed sample: adding job factors to explanations of health and behaviour increases the explained variance by roughly 60 per cent versus the 20 per cent increase that comes from adding family stressors (over a base of 20-25 per cent explained variance by age and marital status). Job characteristics contribute particularly strongly to the psychological strain measures (81 per cent of hypotheses confirmed versus 43 per cent for family factors), and also to physical illness (64 per cent of hypotheses confirmed versus 44 per cent for family factors). The social participation variables are most strongly affected by age and marital status, although job control and income have significant effects.

Of the independent variables control, conflict, and work load have the strongest and most consistent associations. The former correlates with behaviour patterns and job satisfaction (along with social support), and the latter two correlate with mental strain symptoms. Home problems are associated with increased health risks (stronger for men) and home circumstance affects absenteeism (stronger for women).

Among our dependent variables the most successfully predicted was job dissatisfaction (with 8 of 22 associations ‘strong’), predicted primarily by job factors and similarly predicted for men and women. Heart disease, pill consumption, absenteeism and political behaviour also had multiple ‘strong’ associations with demographic, job and family factors.

Our primary sex role finding is that almost exactly the same measures which associate work and family to psychological outcomes for men are also observed for women. The second major observation is that for full-time working women in Sweden, the associations are stronger with work than they are for family (60 per cent hypothesis versus 43 per cent confirmed). Thus, the most likely direction of causality is: ‘work-family’, and ‘work-well-being’, (which is also true for men). This finding is in contrast to common suggestions of major companies’ personnel departments, that it is ‘family problems’, and not job-related ones, that are the cause of employees’ psychological trouble5. Also the women’s associations for the most part are stronger than for men (work: 60 per cent versus 55 per cent hypotheses confirmed; non-work:

’One important exception is the strong significance of ‘family problems’ for our heart disease measure- stronger than for the work variables. A related finding is that having children at home is associated with increased job satisfaction for women.

204

43 per cent versus 37 per cent confirmed), and it must be remembered that the sample is based on full-time working women who probably still have substantial family responsibilities. Particularly noteworthy is the importance of control over job decisions for women (73 per cent of hypothesis confirmed) and social support for women (67 per cent confirmed). The similarity of associations between men and women plus the lower overall level of moderator variables such as control may explain the higher level of health problems reported by women in Table 1 .

In general the triad of measures relating to ‘family burdens;’ (marital status, children at home, and working spouse), are more strongly associated with psychological strain and illness for women than for men, but not always in the expected direction (i.e. number of children at home is generally negatively associated with illness for both men and women). Thus, the presence of children in the families of our white collar working men and women appears to be a health-promoting factor, with the significant exception that women find children ‘exhausting’. (An alternative explanation would be that ‘healthier’ individuals may decide to have more children, but the associations appear to be too specific for this to be the entire explanation.).

In light of the extensive research into the question of social class and psychological illness (Dunham, 1965; Dohrenwend and Dohrenwend, 1974; Dohrenwend, 1975; Seiler, 1973; Schwartz, et al., 1973), it is important to note that our consistent associations between environmental stressors and psychological well-being are found with broad class differences held relatively constant. Income differences admittedly do remain, so the sample is not totally homogenous, but our sample excluded the majority (75 per cent) of the Swedish population including those at both status extremes: management; college professionals; blue collar and marginal workers. We also similtaneously tested the associations for income (in the family variables regressions). The remaining income associations, while often significant, did not dominate our analysis.

The strongest non-demographic correlate of political participation in our study is control over job-related decisions, particularly for women. As predicted, several job stressors correlate positively with social participation. This somewhat counterintuitive prediction of our hypothesis is consistent with other findings regarding job decision participation and social participation in Swedish and U.S. data (Karasek, 1976, 1978; Goiten and Seashore, 1980; Benninghaus, 1981; Gardell and Svensson, 1981; Elden, 1981; Meissner, 1971; Pateman, 1970). These findings are also consistent with the explanation advanced in these sources that the combination of job ‘challenges’, along with opportunities for control, lead to increasingly active behaviour because learning processes are facilitated by self-structured decision-making.

Our primary goal in this paper was to illuminate the overall pattern of associations between several important groups of independent and dependent variables in the field of psycho-social environmental stress research. How well do our measures serve that purpose? Since dependent and independent variables are both self-reported, the overall correlations may be increased by ‘shared-methods bias’. However, we do not have reason to believe that such ‘shared-method’ influences would change the patterns of associations (for example, increasing them for men, and dropping them for women)6. Thus, the observed patterns probably do reflect underlying objective differences.

However, even the pattern validity would be underminded if variables were not homogeneous with respect to self-report bias. In this light it is important to note that


Correlates of Illness and Behaviour 205 the strongest associations are with the eminently subjective outcomes of psychological strain and dissatisfaction. The social participation associations may not really be relatively weaker than the associations for psychological strain since the former measures are probably quite imprecisely measured by our questionnaire. Reviewing independent variables, one might expect overestimated correlations for some of the subjectively appraised stressors and moderators (particularly ‘work load’, ‘family problems’, ‘career development opportunities’). However, the work environment measures ‘control’, ‘clarity’, ‘conflict’ are based on questions with fairly objective focus7. The control measure, along with income, are the most consistent stress moderators implying that self-report bias is not the dominant factor in prediction. Of the job factors only ‘work load’both shows strong associations, and is very subjective in formulation (low inter-occupation variance, Karasek, et al., 1982). Thus, our clear findings of a stronger correlation with work variables compared to family variables predicting psychological strain and illness would probably still stand (perhaps somewhat diminished). Also, the pattern of sex similarities and differences seems unlikely to be due to self-report bias, since it relates primarily to differences in strength effects between specific job or health variables. And, of course, the greater importance for women of some family variables is certainly consistent with the fact that their home responsibilities are probably larger.

The above discussion highlights the importance of methodological homogeneity in studies which would intend to illuminate broad patterns of associations. The problem is that differences in scale reliabilities may affect overall strength of association and thus the ‘pattern’ observed, particular using O.L.S. methodology (standardized odds ratios are less affected by scale structure). The requirement for ‘homogeneity of scale reliability’ has not been as explicit in studies of narrower focus, although they are not immune to the same problems when relative strengths of association must be assessed. Unfortunately this goal will not be easy to attain because of the diversity in quantifiability of factors potentially relevant to stress research (for example, income versus social support). We must balance this new methodological difficulty against the substantial rewards that broad ‘pattern’ research, if well done, could yield. The difficulty of extracting a ‘true’ understanding from multiple, narrowly focused studies, when each has non-comparable variables and strength of association measures, have long plagued social policy analysts. Most questions in the public policy realm address trade-offs between competing interests broadly defined, and so ‘pattern’ research could yield directly relevant findings, if only the methodological challenges can be overcome.

‘Most proposed ‘bias’ mechanisms are linear and uniform in effect. For example, an individual with high anxiety levels may exaggerate his estimations of the stressfulness of a situation and report high levels of psychological strain, but he is also likely to report higher levels of psychosomatic illness and stress-related behavioural change. Our multiplicity of causal factors and types of dependent variables allow differentiations of overall association shifts (due to bias) from specific association strengths (due, presumably to underlying structural factors). Also, while bias mechanisms may apply for environmental stressors, it is much less clear what effects ‘bias’ would have on stress moderators such as control. Thus, if self-report bias affects our findings it is more likely to increase overall association levels (particularly for stressors) than to account for particular patterns of associations.

’For job control, the literature suggests good agreement between self-reports and observer assessments: Karasek (1979), r = 0.64, r = 0.68; Hackman and Lawler (1971), r = 0.75; and Kohn and Schooler (1973), r 0.78.


ACKNOWLEDGEMENTS

We would like t o t h a n k t h e Swedish Tjans temannens Centralorganizat ion, t h e Swedish Arbetarskyddsfonden and Swedish Arbetslivscentrum for their suppor t and assistance for this project. We would also like t o t h a n k Ani ta Rissler for her assistance in o u r research conceptualization; Mar ianne Hubner , Laurie Beck, J o a n n e Factor and Shirley Tam of Columbia University, and J o h n H a d i of University Southern California for dedicated editorial a n d typing assistance, and Ulla St jarnborg for computing assistance.

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