The Demand-Control Model: Specific Demands ... - Study at UniSA

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276 de Jonge, Dollard, Dormann, Le Blanc, and Houtmanlinear terms as well as an interaction term (Bollen, 1989; Kenny & Judd, 1984).This was done by performing two analytic steps (Aiken & West, 1991; Jaccard,Turrisi, & Wan, 1990): (1) the job characteristics were mean-centered in orderto prevent multicollinearity, and (2) the interaction terms of the job characteristicswere computed from these centered variables.Next, two structural models were fitted to the data in two steps. First, amodel M 1 without interaction effects was specified, followed by a model M 2with interaction effects. Accordingly, the assumptions of the DCM were testedagainst the null hypothesis (H 0 ) that there are no interactive relationships. Differentnested models can be compared by a chi-square differences test (Bentler &Bonett, 1980; Jöreskog & Sörbom, 1993). Second, LISREL can also be used toanalyse several samples simultaneously to determine the extent to which a structuralequation model fits for several different groups, as well as to examinewhether model differences exist between these groups in the analysis. So, usingthis technique, it is possible to test whether the proposed pattern of relationshipsis identical in all five subgroups (Schumacker & Lomax, 1996).Subsequently, several commonly used fit indices have been used to assessthe overall model fit (Jöreskog, 1993; Schumacker & Lomax, 1996): the chisquarestatistic (χ 2 ), the LISREL adjusted goodness-of-fit index (AGFI), the rootmean square error of approximation (RMSEA), and finally the comparative fitindex (CFI).RESULTSPrior to the LISREL analyses, the means, standard deviations, and coefficientalphas were computed for both the five separate subgroups and the totalgroup (see Table 2). Table 2 shows that all multiple-item measurement instrumentshad a coefficient alpha equal to or higher than .75. This also demonstratesthat the instruments applied were equally valid (reliable) across the differentsubgroups. Furthermore, we computed coefficients of variation (standard deviationdivided by mean; see Ganster & Fusilier, 1989) for each of the job characteristicsand displayed the results in Table 3. In general, it is apparent that thereis as much variability in the five subgroups as there is in the total group.Covariance matrices were used in order to analyse the structural equationmodels (available from the first author). We first analysed the covariance matrixobtained for the whole sample (n = 2,485). Model M 1 was considered withoutinteraction effects and model M 2 was considered with interaction effects. Thecorresponding chi-square differences test showed that the difference betweenthe two chi-squares was not significant (∆χ 2 (12) = 15.51, n.s.), which means thatH 0 is not rejected. In other words, an interactive model (M 2 ) has no better statisticalfit (in terms of chi-square) than an additive model (M 1 ) within the total
Demand-Control ModelTable 2. Means (M), Standard Deviations (SD), and Coefficient Alphas (α) of the Study VariablesHealth care Transport Bank/Insurance Retail trade Warehouse TotalMeasures M SD α M SD α M SD α M SD α M SD α M SD αJob control 3.09 .65 .86 3.09 .93 .89 3.50 .68 .86 3.17 .81 .88 3.28 .81 .87 3.27 .80 .87Workload 3.09 .63 .86 2.91 .73 .87 2.99 .65 .87 2.61 .70 .85 2.94 .68 .86 2.89 .69 .87Phys. demands 2.24 .93 .91 2.20 .90 .90 1.64 .59 .84 2.74 .85 .86 2.91 .82 .85 2.35 .94 .89Emot. demands 2.85 .79 .75 2.26 .77 .82 2.02 .81 .86 2.44 .74 .80 1.76 .71 .80 2.14 .83 .84Job satisfaction 3.85 .96 — 4.11 .90 — 3.83 .96 — 3.95 .93 — 3.82 1.00 — 3.90 .96 —Job challenge 3.86 .93 — 3.88 1.07 — 3.72 1.01 — 3.42 1.13 — 3.33 1.15 — 3.60 1.10 —Emot. exhaustion 2.34 .77 .86 2.18 .83 .87 2.24 .76 .86 2.11 .73 .81 2.23 .86 .86 2.22 .80 .85Health complaints 1.43 .38 .77 1.41 .39 .80 1.41 .39 .79 1.44 .39 .77 1.45 .40 .80 1.43 .39 .79277
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