A more specific test. The demands-control model of job strain: Toby D. Wall", Paul R. Jackson, Sean Mullarkey and Sharon K. Parker

Transcription

1 Journal of Occupaiional and Orgmizationd Psychology (1996), 69, The British Psychological Society Printed in Great Britain 153 The demands-control model of job strain: A more specific test Toby D. Wall", Paul R. Jackson, Sean Mullarkey and Sharon K. Parker lnstiiuie of Work Psychology, University of Sheffield, ShejjfiId SI 0 2TN. UK Karasek (1979) proposed that job demands and decision latitude interact to cause psychological strain. Main effects of these job variables on strain have been often found, but the predicted interaction between them has been less consistently demonstrated. We argue that this lack of support results from inadequate specification and operationalization of the independent variables. In particular, most empirical tests of the interaction have been based on a general measure of decision latitude which encompasses a wide range of job properties including control, task variety and learning opportunities. Yet, theoretically, it is control which is the crucial factor. In this study we used more focused measures of demands and control, with a sample of manufacturing employees, and found clear evidence of the predicted interaction effect. Parallel analyses using a measure of decision latitude rather than of job control did not show an equivalent effect. It is recommended that greater attention be paid to congruence between theory and measurement. In a widely cited paper, Karasek (1979) proposed and tested a new model of job stress. His core hypothesis was that high job demands were not harmful in themselves, but when accompanied by low decision latitude would result in psychological strain. The impact on strain was predicted to be evident in terms of both immediate affective reactions (e.g. job satisfaction and depression) and, given long-term exposure, stress-related illnesses (e.g. cardiovascular disease). Karasek's hypothesis continues to command interest. This has been sustained for two main reasons. First, evidence has accumulated showing both generally positive relationships between job demands and indices of strain and ill-health, and even stronger and more consistent negative relationships for decision latitude (e.g. Fletcher &Jones, 1993; Karasek, 1979; Landsbergis, 1988; Parkes, Mendham & Rabenau, 1994). Thus the general statement that work characterized by high demands and low decision latitude is detrimental to employee well-being is well supported. Second, the basic interactive proposition, in addition to being intuitively appealing, is consistent with a wide range of more general theoretical and basic research on the importance of control in mitigating the effects of stressors (e.g. Arntz & Schmidt, 1989; Frese, 1989; Miller, 1979; Mineka & Kelly, 1989; Seligman, 197 5). The crucial issue, however, is not simply whether jobs with high demands and low *Requests for reprints.

2 154 Toby D. Wall et al. decision latitude are stressful, compared with those with only one or neither of these characteristics, but whether there is an interaction of the kind proposed. As Karasek & Theorell (1990) make explicit, the contention is that the elevation of risk with a demanding job appears only when these demands occur in interaction with low control on the job (p. 9). That suggestion is of considerable practical importance, since, if correct, the implication is that demands can increase with little or no threat to psychological strain as long as decision latitude is also enhanced. It is with regard to this more specific issue that the evidence to date has been less than convincing. In his original study Karasek (1979) did not conduct an adequate test for the proposed interaction effect (Edwards & Cooper, 1990; Ganster & Fusilier, 1989). Subsequent studies of the relationship of demands and decision latitude with stress-related illness (e.g. cardiovascular disease), health-related behaviour (e.g. consumption of pills, smoking) and physiological factors (e.g. epinephrine levels) generally have been of a design which does not allow proper examination of such an effect (Fletcher & Jones, 1993; Ganster & Fusilier, 1989). Investigations focusing on affective reactions, however, have been mostly of a type which has enabled investigators to address the question more directly and stringently. In a study of health care employees, Landsbergis (1988) found no interaction between demands and decision latitude in predicting job satisfaction, depression, reported physical strain or sleep disturbance. Similarly, Marr (1990a) obtained no support for the prediction in an investigation of job-related anxiety, job-related depression and job satisfaction, within a heterogeneous sample of more than 1600 employees. Other failures to confirm the predicted interaction effect include those reported by Carayon (1993), Fletcher & Jones (1993), Hurrel & McLaney (1989), Kauppinen- Toropainen, Kandolin & Mutanen (1983), Payne & Fletcher (1983) and Spector (1987). Ganster & Fusilier s (1989) conclusion that the model s empirical validity has yet to be established (p. 254) appears well justified. There are, however, three recent exceptions to this trend of negative findings. One is a study by Dwyer & Ganster (1991) who found that interaction between perceived workload and control predicted job satisfaction and absence, and also that the interaction between an objective index of demand (derived from job analysis) and perceived control predicted sickness absence and tardiness. In both cases the form of the interaction was as expected. The second case is an investigation by Fox, Dwyer & Ganster (1993) who report interactions between perceived quantitative workload and control as predictors of job satisfaction, systolic blood pressure, diastolic blood pressure and cortisol levels. The third is a study by Parkes et al. (1994), who found that the interaction between demands and discretion predicted job satisfaction; and that the three-way interaction between demands, discretion and social support predicted somatic symptoms. One possible conclusion to draw from these divergent results is that the majority of non-supportive investigations represent the reality and the minority of supportive studies are anomalous. An alternative possibility, however, is that the supporting studies have succeeded in revealing confirmatory evidence because they differ in some important respect from the others. Comparison between the non-supportive and supportive studies suggests two factors which may account for these divergent results, both of which relate to more general conceptual and methodological problems inherent in this area of inquiry. That such problems exist was raised by Kasl (1989) who concluded that fundamental questions of conceptualization, measurement and evidence remain unanswered and it is

3 The demands-control model of job strain 155 imperative that we avoid premature closing regarding any aspect of the overall problem (pp ). The first of these problems concerns the measurement of job demands. Following Karasek s (1979) lead, job demands typically have been conceptualized and operationalized at a general level using self-report measures which incorporate affective judgements. Respondents have been asked to indicate, for example, the extent to which they have excessive work to do or have to work hard. These measures are not purely descriptive, but include a component reflecting how stressful the job is perceived to be. Karasek & Theorell (1990) recognize this may present problems when they note that broad questions about work load ( not enough time ) are more likely than specific questions to be affected by self-report biasing factors (p. 344). The particular difficulty this creates is that, with an affective element incorporated in the independent variable as well as being the core of the dependent variable, a spurious main effect is built into the observed relationship. Such common measurement variance augments the main effect of job demands on psychological strain, which in turn restricts the opportunity to demonstrate any underlying interaction between demands and decision latitude. As Evans (1985) has demonstrated on the basis of Monte Carlo studies, correlated error serves to attenuate the effects of interactions by as much as 50 per cent. It is perhaps significant that in the studies which support Karasek s hypothesis this measurement problem is minimized or avoided. Two of the four interaction effects (with tardiness and sickness rates) found by Dwyer & Ganster (1991) are based on the measurement of job demands through job analysis, which removes the possibility of affective bias. Moreover, their measures focused on specific, cognitive demands, such as the degree of vigilance and extent of close tolerance work. The other two interactions they found (with job satisfaction and absence) involved a self-report measure of workload (Caplan, Cobb, French, Harrison & Pinneau, 1975), which arguably is more descriptive and less affective than the measures of job demands drawn from Karasek s (1979) original study. The supportive findings reported by Fox et al. (1993) are also based on Caplan s measure of workload, whereas in the case of Parkes et al. (1994) the researchers controlled for affectivity in their statistical analysis. The second problem is more fundamental, and concerns the conceptualization and measurement of decision latitude. A feature of the theoretical development surrounding the demands-decision latitude hypothesis is the way that the latter construct increasingly has been equated with job control. This change is reflected in the recent relabelling and elaboration of the hypothesis as the demands-control model (Karasek & Theorell, 1990). There are good reasons for this development. As mentioned earlier, it links Karasek s hypothesis much more closely to the theoretical thrust of stress research more generally, where, in animal and human studies, control has been identified as a factor which mitigates the effects of a wide range of extrinsic stressors (Katz & Kahn, 1978; Steptoe & Appels, 1989). It also fits in with assumed mechanisms through which the potentially detrimental effects of increased demands are avoided. The basic argument is that increased control reduces the effects of stressors by allowing individuals to face demands when they are best able to do so and in ways they find most acceptable. In other words, control provides the opportunity for individuals to adjust to demands according to their needs and circumstances (Karasek & Theorell, 1990; Sutton & Kahn, 1986). The important point with regard to the available empirical evidence on the

4 156 Toby D. Wail et al. demands-control model, however, is that this theoretical realignment, or clarification, has not been carried through into measurement practice. Most investigators have continued to use scales based on Karasek s (1979) original measure of job decision latitude, which represents a much broader construct than that of job control. Decision latitude includes not only items concerned with job control, but also ones covering the extent to which the job involves the opportunity to learn new things, to develop skills, to exercise a high level of skill, to show creativity, to use one s abilities and to experience variety (Karasek, 1979; Karasek & Theorell, 1990). This is equivalent to overall measures of job scope or job complexity (e.g. Sims, Szilagyi & Keller, 1976; Stone, 1975, 1976; Warr, Cook & Wall, 1979), but is not self-evidently an index of control per se. As Kasl (1989) observes, few possibilities to learn does not easily fit into the demands-latitude formulation. Undoubtedly, many jobs with little opportunity to learn are also jobs with low discretion or control. But the dimensions are far from the same thing (p. 177). Similarly, skill should not be equated with job control. It is evident that some jobs regarded as highly skilled offer less opportunity for people to exercise control than less skilled jobs. A skilled operator of computer controlled equipment, for example, is as likely to have to respond when circumstances dictate, and to have as restricted a choice of response, as an unskilled employee doing more mundane support tasks. Indeed, in a recent study, it was found that, whereas skilled jobs were distinguishable from less skilled ones in having higher levels of production responsibility, monitoring demand and problem-solving demand, they showed no difference with regard to job control (Wall, Jackson & Mullarkey, 1995). There is thus a strong possibility that the common failure to obtain empirical support for the demands-control model results in part from inadequate operationalization of control. Certainly there is a poor match between the theoretical construct and the measure used in most studies. Here again it is instructive to note that this is not the case in the supportive investigations reported by Dwyer & Ganster (1991) and by Fox et ai. (1993), where the relevant measure was clearly focused on job control. In the other supporting study, reported by Parkes et af. (1994, the nature of the relevant measure is less explicit, but it is labelled discretion and described as being comprised of seven items concerned with opportunities for control and decision-making in the work setting (p. 96). The present study addresses these two problems. With regard to job demands, the problem of common measurement variance is minimized by the use of clearly descriptive measures focused on the cognitive requirements of the job. In relation to decision latitude we compare tests of the predicted interaction effects on job satisfaction, depression and anxiety using alternative measures. In one set of analyses a focused measure of job control is used; in the other a measure of decision latitude. If our argument is correct, and Karasek s hypothesis is also valid, the findings will show the predicted interaction effects in the first case but not in the second. Sample Method The sample for this study consists of 1451 employees from four manufacturing companies. More specifically it comprises: 847 employees of a leading British clothing manufacturer; 153 employees from a British company making electro-mechanical components; 300 employees from a British plant of an American-owned chemical process company; and 15 1 employees from an American-owned British micro-electronics factory. The large majority of employees were engaged on direct production work (e.g. sewing, manual assembly,

5 The demands-control model of job strain 157 machine operation and process control), with the remainder in support and supervisory roles such as office work, technical support, supervision and management. Thus the sample covers a wide range of jobs in very different industry sectors, providing the degree of variation in job demands and job control desirable for testing the hypothesis. Sixty-one per cent of the sample were women, the average age was 33.0 years (range years) and the mean length of service was 7.4 years. Procedure The data collection procedure was essentially the same in all four companies. The measures of present interest were administered as part of a larger questionnaire completed during normal working hours by groups of 5-25 employees. Individuals were assured of strict confidentiality. The response rates in the different companies varied from 70 per cent to 90 per cent, with a mean for the total sample of 83 per cent. Measures The questionnaire was in three main sections, the first covering biographical and occupational factors, the second job characteristics and the third psychological strain. Biographical and occupational infwmation. To enable statistical control of biographical factors likely of themselves to affect psychological strain, the following information was collected for each respondent: age (in years); length of service (in years); gender (1 = male; 2 = female); and job title. Respondents were identified as belonging to one of three major occupational groups, namely, direct production workers (80 per cent), technical support staff (e.g. maintenance and chemical engineers, quality control inspectors, 11 per cent), and supervisory staff (9 per cent). For the purposes of analysis occupational group membership was coded using dummy variables. Job characteristics. Consistent with the rationale for the study, three job properties were measured. A measure of job detirands was derived by combining the items from the monitoring demand (four items) and problemsolving demand (three items) scales developed by Jackson, Wall, Martin & Davids (1993). This consists of solely descriptive items, such as To what extent does your work need your undivided attention? and To what extent do you have to solve problems which have no obvious correct answer?. The five-point response scale runs from not at all to a greac deal ; an3 a total score is obtained by averaging item scores, with higher values representing greater demands. The internal reliability of the demands measure (Cronbach s alpha) for the present sample was.71. A measure of job control was obtained from the same source, by combining the items from Jackson etal. s (1993) timing control (four items) and method control (six items) scales. All these items, such as To what extent do you decide the order in which you do things? and To what extent can you decide how to go about getting your job done?, are focused clearly on control icself and do not encompass elements such as skill use and task variety which are characteristic ofdecision latitude measures. The response scale and scoring is the same as for the demands measure described above, with higher scores representing greater control. The internal reliability of the measure for the present sample was.86. Finally, decision latitude was measured by the Perceived Intrinsic Job Characteristics Scale (PIJCS; Warr, Cook ek Wall, 1979). This is a 10-item instrument with well-established psychometric properties which is equivalent in content to the alternative measures ofdecision latitude used in previous studies. Thus while it includes items on control (e.g. the freedom to choose your own method of working ), it also covers the broader elements encompassed by previous operationalizations of decision latitude, such as your opportunity to use your abilities and the amount of variety in your job. The measure has a five-point response scale; and total scores are obtained by averaging the item scores, with higher values representing greater decision latitude. The internal reliability of the measure for the present sample was.86. Job strain. Three measures of job strain were included to parallel those used in previous studies. Warr et d s (1979) seven-item intrinsic job satisfaction scale was used as the measure of job satisfaction. This has a sevenpoint response scale running from extremely dissatisfied to extremely satisfied. Total scores are the average of the item scores, with higher values representing greater satisfaction. This measure had an internal reliability for the present sample of.86. Depression and anxiety were tapped using the relevant six-item

6 158 Toby D. Wall et al. instruments from the set of measures of well-being and mental health developed by Warr (19906; see also Sevastos, Smith & Cordery, 1992). A five-point response scale was used and scored so that higher values (average item score) represent greater depression or anxiety. Their internal reliabilities for the present sample were.78 and.80 respectively. Statistical analysis The data were analysed using multivariate moderated regression analysis, based on the general linear model as implemented in the MANOVA procedure in SPSS (Norusis, 1988; Tabachnick & Fidell, 1989). This approach is an extension of the standard procedure for testing interactions among continuous variables (e.g. Cohen & Cohen, 1983), where, as in the present study, there is a set of conceptually and empirically interrelated dependent variables. As for standard moderated regression, interaction effects in multivariate moderated regression are tested by including in the analysis both main effects and the appropriate cross-product term (carrying the interaction). The test for the interaction effect is based on the variance explained by the cross-product term over and above that accounted for by the main effects. Any background factors to be held constant can also be included. However, multivariate moderated regression analysis has the advantage of allowing an initial test of these effects on the dependent variables considered jointly, in the present case providing a rest of the effects on job strain as a whole. This is supported by univariate tests for each dependent variable considered separately (ignoring its relationships with other dependent variables), which is directly equivalent to separate moderated regression analyses for each dependent variable. Where the multivariate test showed an interaction effect, parameters from the univariate moderated regression analyses were used to plot the form of the fitted model. In line with recommendations for dealing with problems of multicollinearity that arise from the use of cross-product terms (Aiken & West, 1991; Jaccard, Turrisi & Wan, 1990), variables were standardized prior to calculating their cross-product terms and conducting the analyses. Results Table 1 presents the descriptive statistics and intercorrelations for the study variables. Three features of these zero-order correlations are of interest and relevant to the main analytic strategy. The first concerns the relationships between the job characteristics and job strain measures. The demands measure shows virtually no association with either job Table 1. Descriptive statistics and correlations among study variables Mean 1. Age 2. Gender 3. Lengthof service 4. Demands 5. Control 6. Decision latitude 7. Job satisfaction 8. Depression 9. Anxiety O Note. N = depending on missing values; r ,p <.001.

7 The demands-control model of job strain 159 satisfaction or depression, and is only weakly correlated with anxiety (r =.12,p <.001). Control, however, has moderately strong and statistically significant relationships with all three outcome measures, being positively associated with job satisfaction (r =.37, p <.001) and negatively related to both depression and anxiety (r = -.26,p <.001 and r = -.25, p <.001, respectively). Decision latitude shows the same pattern as control, but is more strongly correlated with the outcome measures (for job satisfaction r =.67, p <,001; for depression r = -.46,p <.001; and for anxiety r = -.38,p <.001). Thus, consistent with the previous literature, control and decision latitude are clearly associated with job strain. The second feature is that the biographical variables of age, gender and length of service are statistically significantly correlated with both the job characteristics and the job strain measures. This raises the possibility that one or more of these background variables may act as third factors in accounting for the relationships of job characteristics (and their interactions) with outcomes. Finally, as would be expected, it is evident that the three outcome measures are strongly intercorrelated. This underlines the importance of testing for their joint effects as well as for their separate ones. These considerations underlie the choice of using multivariate moderated regression to analyse the data, the findings from which follow. Tests of interaction effts The hypothesis of this study is that demands and control will interact to predict job strain; but that there will be no equivalent interaction effects for demands and decision latitude. More specifically, the form of the interaction is expected to show that demands are positively associated with job strain under conditions of low control but not under conditions of high control. The primary tests of this proposition were conducted using multivariate moderated regression analysis. There were four steps in the regression procedure. The first involved controlling for background factors. For the reasons mentioned above, it was important to control for age, gender (dummy coded) and length of service. However, two further factors were also taken into account. The first was occupational group (dummy coded), since it has been proposed this may be a confounding factor (e.g. Kasl, 1989; Payne & Fletcher, 1983; Spector, 1987). The argument is that different occupations are likely to be associated with different levels of strain and, though there may be corresponding differences in the combination of demands and control, it is other occupational factors such as working conditions, pa.y, status or security, that affect strain. Similar arguments can be mounted with regard to organizational membership, which is likely to be associated with the combination of demands and control afforded by jobs and, for other reasons, to psychological strain. Thus organizational membership (dummy coded) was the final variable included in the set of background factors included in the regression equation. (Note that for reasons of clarity and parsimony the zero-order correlations for the five dummy variables required to represent occupational group and organizational membership are not presented in Table 1.) The remaining three steps, following standard analytical procedure, consisted of the demand variable (main effect), the control or decision latitude variable (main effect), and finally the relevant cross-product term (interaction effect). The initial focus is on that part of the hypothesis concerning demands and control. The relevant findings are shown in the first four rows in the body of Table 2. As expected, the

8 Table 2. F ratios from multivariate moderated regression analysis: main and interaction effects of demands and control, and demands and decision latitude, on job satisfaction, depression and anxiety, controlling for background factors" Multivariate test Univariate test Job satisfaction Depression Anxiety Independent variables Effect tested F F F F Demands and control, Background factors with background Main effect-demand factors held Main effect-control constant Interaction 15.31** 21.08** 15.34** 24.68** 16.75** * 44.46** ** 59.40** 35.51** 38.74** 5.44** ** 13.04** 7.81* 2 s I4 Demands and decision Background factors 11.62** 10.76** 8.35** 14.20** z latitude, with background Main effect-demand 38.45** 64.01** 46.91** 83.64** factors held Main effect-decision latitude ** ** ** ** constant Interaction *p <.01; **p <.001. Note. All variables were standardized prior to analysis and the calculation of the interaction (cross product) term. a The background facton were: age; gender (dummy coded); length of service; occupational group (production, suppor~ and supervisory stai34l dummy coded); and organization (dummy coded).

9 The demands-control model of job strain 161 multivariate test reveals statistically significant main effects for the set of background variables (F = 15.31,p <.001), as well as for demands (F = 16.75,p <.001) and control (F = 24.08,p <.001) on the outcomes considered. More to the point, as predicted, there is also a statistically significant interaction effect (F = 5.44, p <.001). The univariate tests add more detail to this finding, showing that there is an interaction effect for each of the three dependent variables. This is strongest in the case of depression (F = 13.04, p <.001), next strongest for job satisfaction (F = 10.76, p < :001) and weakest, but still clearly evident, for anxiety (F = 7.81,p <.01). The second part of the hypothesis was that equivalent interaction effects would not be found where demands and decision latitude were used instead of demands and control as predictors. The relevant results are shown in the last four rows in the body of Table 2. It is clear that this prediction is also supported. As before there are main effects for the set of background factors, demands and decision latitude, both when the dependent variables are considered jointly and when they are considered separately. The effects for decision latitude are much stronger than those for control. This is to be expected, since decision latitude covers a much broader range ofvariables. It may also be the case that there is greater common method variance between the decision latitude and outcome measures. The key point, however, is that in no instance is there a statistically significant interaction effect. Thus consistent with the hypothesis, where demands and control are used to predict strain, an interaction effect is found; but, where demands and decision latitude are used as predictors, no interaction effect is evident. The foms of the interactions Having established the presence of interaction effects between demands and control on the indices of strain, the final issue in testing our hypothesis is whether these are of the form predicted. This question was addressed by conducting standard moderated regression analyses for each outcome separately, and plotting the form of the interaction following the procedures recommended by Jaccard et al. (1990). As for the multivariate moderated regression analysis, all predictor variables were standardized, but we retained the unstandardized scores for the dependent variables to give a clearer indication of the impact on scale scores. For all three dependent variables, the form of the interaction between demands and control was as predicted, and we illustrate these for the two strongest effects. The findings relating to job satisfaction are presented in Fig. 1. These show that demands are negatively associated with job satisfaction where employees report low and very low levels of control, but there is little or no effect of demands on job satisfaction at moderate and high levels of control (and even a slight positive effect at very high levels of control). For depression (Fig. 2) there is an equivalent pattern. Where employees perceive low levels of control, greater demands are associated with higher levels of depression, whereas this effect is much less marked (and even reversed) where control is higher. Discussion In this paper we have addressed a methodological issue with the potential to explain conflicting findings in the existing literature relating to the demands-control hypothesis.

10 162 Tohy D. Wall et al. 5.0 Very high control (+2) J 0 b S a t i S f a C t I 0 n High control (+1) fi Moderate control (0) Very low control (-2) I +2 Demands Figure 1. Interaction between demands and control in the prediction of job satisfaction (scale on x axis for demands, and separate regression lines for control, correspond to -2, - 1, 0, 1 and 2 standard deviations from the mean). An analysis of previous research suggested that where investigators use a measure of demand with an evaluative component, and a general measure of decision latitude, they fail to obtain empirical support for the predicted effect of the interaction between these job variables on psychological strain; but where a more clearly descriptive measure of demands is used in conjunction with a more focused measure of control, such support is forthcoming. Such general analysis is necessarily speculative. Thus in the present investigation we tested the underlying hypothesis by using a manifestly descriptive measure of demands, and comparing the effect on job strain of its interaction with job control with the effect of its interaction with decision latitude. The findings were consistent with the hypothesis. There was a statistically significant effect for the interaction between demands and control for the measures of job strain considered jointly; but no equivalent effect for the interaction between demands and decision latitude. Moreover, the findings showed that the confirmatory interaction effects were evident for job satisfaction, depression and anxiety separately. These findings were obtained having controlled for background factors. It should be noted that the omission of background factors from the analysis results in exactly the same pattern of effects which are slightly stronger.

11 The demands-control model of job strain Very low control (-2) 3.4 D e P r e s S 1 0 n Low control (-1) Moderate control (0) High control (+1) 2.4 Very high control (+2) Demands Figure 2. Interaction between demands and control in the prediction of depression (scale on x axis for demands, and separate regression lines for control, correspond to -2, - 1,0, 1 and 2 standard deviations from the mean). It is relevant to comment on the size of the observed interaction effects. As is typical for field investigations (Champoux & Peters, 1987; Chaplin, 1991), the amount of variance explained by the interaction effects in the present study is small, amounting to approximately 1 per cent of the variance. This, however, does not negate the theoretical relevance of the findings. Nor should it necessarily be interpreted as suggesting that the interaction effects have little substantive significance. This is so for three main reasons. First, as O Grady (1982) makes explicit, empirical studies are necessarily limited in the amount of variance they can explain because they use only a small number of operationalizations of the relevant constructs. In the present case we examined cognitive or mental demands, but there are clearly other demands such as those posed by the amount and physical difficulty of the work. Similarly we considered job control, and did not encompass other possible aspects such as control in the wider organizational environment. More comprehensive coverage of demands and control would be likely to reveal larger effects. Second the size of effect is attenuated by measurement error. This is the case for main effects, but is greatly exacerbated when variables are multiplied together to form a cross-product term as required to test interactions in regression analysis (Aiken & West, 1991; Busemeyer &Jones, 1983). Third, the form of the predicted interaction is important. Where the prediction is of an ordinal interaction, as in the present case, this constrains the amount of variance for which it can account (McClelland & Judd, 1993).

12 164 Toby D. Wallet al. This last point warrants further consideration. The ordinal interaction predicted in the present study signifies that at higher levels of control no effect of demands on strain is expected, but that at lower levels of control demands will be associated with strain. In other words, for a substantial proportion of the sample, the expectation is that no variance is explained; whereas for the other part of the sample variance will be explained. The important question is not simply how much variance is explained overall by this interaction, but how much is explained under the relevant conditions. Even given the constraints posed by partial operationalization of the constructs and measurement error (points 1 and 2 above), the variance explained in the relevant subgroup can be quite large even where the overall effect is small. In the present study, for example, we found that demands accounted for 11 per cent of the variance in depression for employees reporting low levels of control (1.5 or more SDs below the mean), but less than 1 per cent where control was higher. Thus for those with low control, demand was a relatively strong predictor of depression, but for the others it was unimportant. That is a relevant finding if one has the choice of creating or avoiding relatively low levels of control. Moreover, it illustrates why judging the importance of a finding solely in terms of the amount of variance explained is not necessarily appropriate (O Grady, 1982). An aspect of this study which warrants comment is the use of perceptual measures of job properties. While this approach is consistent with previous research, it does carry the risk that observed relationships between job and psychological variables are affected by self-report bias or common method variance. This is not a major threat in the present case, however, since the impact of such a bias would be to inflate the main effects at a cost to the detection of interaction effects. It should also be recognized that the present study is cross-sectional, and this approach needs to be complemented by future work based on longitudinal designs. A final point is in order. A single study is of course insufficient to substantiate such a general proposition as that tested here. The results of the present investigation could simply be part of a larger pattern of occasional findings in support of the demandscontrol hypothesis set among more frequent failures to find such support. Nevertheless, the underlying rationale, and its compatibility with the wider theoretical basis for the demands-control model, encourage the belief that this is not the case. Certainly, it seems desirable that the present line of inquiry should be pursued in future studies. In the empirical literature to date there is evidence of a lack of correspondence between theoretical constructs and measures which may have undermined the accumulation of adequate evidence. The use of measures of demands which accentuate common method variance with outcomes, and more importantly the use of measures of decision latitude which are not clearly focused on the key construct of control, is likely to have limited the chances of uncovering any true interactive effects. To reiterate Kasl s (1989) conclusion, fundamental questions of conceptualization and measurement remain unanswered and it is imperative that we avoid premature closing regarding any aspect of the overall problem (pp ). Acknowledgements We gratefully acknowledge the cooperation received from the several organizations and many participants in the study who, for reasons of confidentiality, must remain anonymous.

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