james and james

Journal of Applied Psychology 1989, Vol. 74, No. 5,739-751 Copyright 1989 by the American Psychological Association, In...

0 downloads 262 Views 1MB Size
Journal of Applied Psychology 1989, Vol. 74, No. 5,739-751

Copyright 1989 by the American Psychological Association, Inc 0021-9010/89/$00.75

Integrating Work Environment Perceptions: Explorations into the Measurement of Meaning Lois A. James and Lawrence R. James Georgia Institute of Technology Many of the perceptual variables used in industrial/organizational psychology assess the meaning that work environment attributes have for individuals (e.g., the ambiguity of role prescriptions). This study represents an initial attempt to test the hypothesis that a unifying theme exists for integrating diverse measures of meaning. The unifying theme is based on a hierarchical cognitive model wherein each assessment of meaning reflects a general appraisal of the degree to which the overall work environment is personally beneficial versus personally detrimental to the organizational wellbeing of the individual. Results of confirmatory factor analyses on multiple samples supported a hierarchical cognitive model with a single, general factor underlying measures of meaning. These results are used to explain the substantive impact of work environment perceptions on individual outcomes.

Industrial/organizational (I/O) psychologists use many vari-

as descriptive cognition and descriptive meaning (Mandler,

ables to assess perceptions of work environments. Examples include perceptual indicators of job attributes (e.g., job challenge,

(Stotland & Canon, 1972), and denotative meaning (Osgood,

1982), cold cognition (Zajonc, 1980), descriptive schemas

job autonomy), characteristics of leaders and leadership processes (e.g., leader consideration and support, leader work facili-

Suci, & Tannenbaum, 1957). Examples of variables from the I/O literature that reflect primarily descriptive meaning include technological complexity, span of control, centralization of de-

tation), workgroup characteristics and processes (workgroup cooperation, workgroup esprit), and interfaces between individuals and subsystems or organizations (e.g., role ambiguity, fair-

cision making, functional specialization, physical space characteristics (temperature, lighting, sound), formal communication

ness and equity of reward system). The following two principles have guided many applied psychologists' efforts to measure

networks, and formal rules, regulations, and reward structures. Information processing may proceed beyond describing what is "out there" to a valuation of environmental attributes

work environment perceptions: (a) Individuals respond to environments in terms of how they perceive them and (b) the most important component of perception is the meaning or mean-

(Mandler, 1982). Valuation refers to cognitive appraisals of environmental attributes in terms of schemas derived from values

ings imputed to the environment by the individual (Ekehammer, 1974; Endler & Magnusson, 1976; Lewin, 1938, 1951;

such as equity, freedom from threat, and opportunity for gain (Mandler, 1982). In comparison with descriptive meaning, valuation is more internally oriented and requires additional infor-

Mischel, 1968).

mation processing to judge how much of a value is represented

The Concept of Meaning

in or by (perceived) environmental attributes (e.g., how much

Our intention is to explore the concept of meaning in the context of work environments. We begin with basics by noting

ent in a set of job tasks, how much friendliness is represented in

equity is represented by a pay raise, how much challenge is presinteractions with coworkers; cf. Jones & Gerard, 1967; Mandler,

that the attribution of meaning, or a meaning analysis, refers to

1982; Stotland & Canon, 1972). The subjective, value-based

the use of stored mental representations or schemas (i.e., beliefs that are products of learning and experience) to interpret (i.e.,

meanings furnished by the valuation process are variously referred to as evaluative meaning (Mandler, 1982), affective (con-

to make sense of) stimuli; in this case, work environment attributes (e.g., events, objects, processes, structures; cf. Jones &

notative) meaning (Osgood, May, & Miron, 1975; Osgood et al., 1957), emotionally relevant cognitions or simply emotional

Gerard, 1967; Mandler, 1982; Stotland & Canon, 1972). The objective of a meaning analysis is often to describe what is "out

cognitions (Reisenzein, 1983; Schacter & Singer, 1962), and cognitive appraisals (Lazarus & Folkman, 1984). Each of these

there" in the environment (Mandler, 1982, p. 8). This form of cognition focuses on perceptions of the presence or absence of

designations points to the central role played by valuation in causal models of emotion and well-being.

features and structures of environmental attributes. The emphasis on description is reflected in the use of designations such

To review this role briefly, Lazarus and his colleagues (Lazarus, 1982, 1984; Lazarus & Folkman, 1984) viewed emotional responses to environmental attributes as functions of cognitive appraisals of the significance of these attributes for one's wellbeing. Valuation appears to be the key to such cognitive apprais-

Correspondence concerning this article should be addressed to Lois A. James, School of Psychology, Georgia Institute of Technology, Atlanta, Georgia 30332.

als inasmuch as (a) values serve as standards for assessing welfare (Locke, 1976), where welfare is defined in terms of a sense




of well-being (Mirriam-Webster, 1975); and (b) valuation provides appraisals of the degree to which these standards are rep-

were developed, using interviews, observations, and literature reviews, to obtain empirical indicators of what we now refer to

resented in environmental attributes. The cognitive appraisals

as valuations or emotional cognitions (see James & Sells, 1981,

thus described are viewed as "emotional cognitions" (Reisenzein, 1983; Schacter & Singer, 1962) because they reflect the

and Jones & James, 1979, for reviews of the development of the

subjective meanings that, in combination with perceived physi-

between the four latent values described previously and the em-

ological arousal, help to label the emotion and to determine the direction and intensity of the experienced emotion. Finally, and

pirically derived factors underlying the PC variables in Table 1. Such a congruence is evident. Four PC factors that have demon-

most importantly, a single latent component is believed to be shared by all emotionally relevant cognitions. It is this single

strated factorial invariance over diverse work environments are (a) Role Stress and Lack of Harmony, (b) Job Challenge and

latent component, or g-factor, that most directly furnishes the

Autonomy, (c) Leadership Facilitation and Support, and (d)

emotional cognitions with the facility to assess significance for

Workgroup Cooperation, Friendliness, and Warmth (see Table

well-being. As proposed by Lazarus (1982, 1984), this g-factor

2 and James & Sells, 1981).

comprises a higher order schema for appraising the degree to which the environment is personally beneficial versus person-

The four PC factors above were derived by exploratory factor analyses, which included orthogonal rotations. As shown later in Table 2, the PC variables tended to cluster within a priori

ally detrimental (damaging or painful) to the self and therefore to one's well-being (Lazarus, 1982, 1984). It is noteworthy that perceptions may vary between those conveying primarily descriptive meaning and those conveying primarily valuations (Mandler, 1982). We are interested in the latter because we wish to understand the psychological processes linking cognitions of work environments to affect and, ultimately, to behavior. In pursuit of this objective, we shall now apply the theoretical perspective to measures reflecting valuations of work environment attributes, with special attention given to the hypothesis that a general factor underlies emotional cognitions.

A Hierarchical Model of Emotional Cognitions in Work Environments Examples of emotional cognitions are easily found in I/O psychology. These are constructs that provide cognitive appraisals of work environment attributes in terms of schemas engendered by work-relevant values. Based on Locke (1976), a workrelevant value is denned as that which one desires or seeks to attain because it is that which one regards as conducive to one's sense of organizational well-being. Sense of organizational wellbeing is defined as the degree to which positive affect exceeds negative affect in regard to one's overall work experiences (this is a focused definition based on overall well-being; see Bradburn, 1969; Diener, 1984). Values serve as standards for assessing organizational well-being and may be expressed in quite spe-

PC measures). It follows that we would expect a congruence

domains denned by an environmental referent (e.g., all leadership PC variables loaded on a single factor). These results suggest the somewhat obvious notion that specific environmental attributes (e.g., workgroup members) are related most directly to specific values (e.g., friendliness and warmth). At a more psychological level of explanation, the results also suggest that individuals tend to separate emotional cognitions pertaining to jobs, leaders, workgroups, and individual/organizational interfaces into separate internal compartments. However, compartmentalization need not imply the lack of relations among the factors indicated by orthogonal rotation. The preceding discussion furnishes two related bases for proposing that the PC factors are related. First, each PC factor, and thus each PC variable, has in common the judgmental process of a cognitive appraisal of an environmental attribute or attributes in terms of that attribute's significance to the well-being of the individual. To be specific, each PC variable furnishes information pertaining to the efficacy of the perceived environment to provide for the achievement of a desired standard or standards and thereby to promote or to detract from the welfare of the individual in the work environment. By efficacywe mean facility to produce an intended effect (Mirriam-Webster, 1975). For example, perceptions that a job is challenging are believed to be efficacious to personal welfare because increasing levels of perceived job challenge have been shown to produce higher levels of job satisfaction (James & Jones, 1980; James & Tetrick,

cific terms, such as one desires a $ 10,000 raise. However, a po-

1986). Second, judgments of significance to well-being—that is, efficacy to promote or detract from personal welfare—are

tentially large number of specific expressions are believed to be manifestations of a much smaller number of latent or psycho-

hypothesized to be based substantively on a single, higher order schema or general factor, which is the degree to which the indi-

logical values (Locke, 1976). A nonexhaustive but important set of latent values appears to include desires for (a) clarity, har-

vidual believes that membership in this work environment is personally beneficial versus personally detrimental to his or her

mony, and justice; (b) challenge, independence, and responsibility; (c) work facilitation, support, and recognition; and (d)

organizational well-being. This hypothesized general factor not only implies that the four PC factors are correlated but also

warm and friendly social relations (Locke, 1976, p. 1329). Psychological climate (PC) furnishes perhaps the most

provides a basis for integrating the PC factors and explaining, at least in part, the substantive impact of perceived work envi-

readily identifiable set of variables in I/O psychology for appraising work environments in terms of schemas based on these

ronments on individuals. Our objective is to test the hypothesis that a general factor

latent values (see PC variables in Table 1). This is not a chance occurrence; the PC variables were designed to assess work environments as they are "cognitively represented in terms of their

underlies PC perceptions. We propose that each of the four PC factors reflects a cognitive appraisal of the degree to which the overall work environment is believed to be personally beneficial

psychological meaning and significance to the individual" (James, 1982, p. 219). Indeed, items and scales for PC variables

versus personally detrimental to the organizational well-being of the individual. This prediction presupposes a hierarchical,



workgroup Cooperation, Warmth and Friendliness

Figure 1. A hierarchical model of meaning.

integrative model of PC perceptions wherein "personal benefit versus personal detriment to organizational well-being" serves as a single, higher order, general factor. Such a model is graphically displayed in Figure 1, where the general factor is designated PCg (i.e., general factor of psychological climate). The causal arrows extending from PQ to each of the PC factors denote that PC8 is a latent, psychological common denominator for the factors. The substantive interpretation of these causal arrows is that perceptions reflecting valuations of individual/ organizational interfaces (Role Stress and Lack of Harmony), job characteristics (Job Challenge and Autonomy), leaders (Leadership Facilitation and Support), and workgroups (Workgroup Cooperation and Friendliness) have in common the latent, cognitive appraisal of the degree to which the overall work environment is personally beneficial versus personally detrimental to the organizational well-being of the individual.

Empirical Tests of the Model Confirmatory factor analysis was used to test the goodness of fit of the model in Figure 1 on data from four different work environments. The key assumptions subjected to empirical tests were as follows: 1. The 4 first-order PC factors are correlated. 2. The correlations among the 4 first-order PC factors may

be explained by a single general factor, which we have designated PCg. 3. PCB explains reasonable amounts of variance in the initial, manifest variables used to measure PC (i.e., the PC variables in Table 1). Issues pertaining to covariation between PCg and affect, and potential contamination due to systematic bias in the method of measurement, are also addressed.

Method Sample Sampling was designed to include individuals and work environments representative of varying levels of technology and of civilian versus military settings. The samples were drawn from the data sets used by James and Jones (1980) and James, Hater, and Jones (1981). The four samples of nonsupervisory personnel were as follows: (a) aircraft maintenance personnel from two Navy Air Training Commands located in the southern portion of the United States (N = 422). Jobs varied across a fairly wide range of technologies, from the very routine (e.g., fueling aircraft) to the very complex (e.g., repairing sophisticated equipment). Average age was 22.2 years, average tenure was 3.1 years, and average education was 12 years (12 years of education represents completion of high school), (b) Systems analysts and programmers from an informationsystems department of a large, western, private health care program. Average age was 36.5 years, average tenure was 1.5 years, and average



education was 15 years (N = 128). (c) Front-line firefighters from a large metropolitan fire department. Average age was 37.5 years, average tenure was 7 years, and average education was 12.6 years (N = 288). (d) Production-line personnel from four plants of a paper-product manufacturing organization. Average age was 34.1 years, average tenure was 3.5 years, and average education was 11.2 years (Ar = 208).


Table 1 Classification of the Item Composites by the Four Psychological Climate (PC) Domains and Their Respective Coefficient

Alphas for Each Sample Coefficient alphas PC variables





Role stress and lack of harmony All data included in this study were collected by means of questionnaires administered to the nonsupervisory personnel on a confidential basis. All items were measured on 5-point Likert scales or Likert-type scales. The data sets involved measures of the perceived work environment and variables used for additional construct validation analyses. A brief description of each data set follows. Psychological climate. The latest version of the psychological climate (PC) inventory developed by James, Jones, and colleagues was used to measure perceived work environment variables (cf. James & Sells, 1981). Seventeen PC variables and accompanying items were common to the four studies. One additional PC variable, esprit de corps, was included for Navy personnel. Each of these variables represents a composite of 3-11 items designed to measure a perceptual construct. Coefficient alpha for each PC variable in each of the four samples is reported in Table 1. Scores on the variables are means taken over items. In general, the coefficient alphas are acceptable for research purposes. It is more important, as we shall see, that the modest values in some cases do not appear to influence overall results of the analyses. Variables used in additional construct validation analyses. As explained in the next section of this article, the variables added to the analysis were overall job satisfaction and personality indicators of achievement motivation, rigidity, and self-esteem. The overall job satisfaction variable was based on the Minnesota Satisfaction Questionnaire (Weiss, Dawis, England, & Lofquist, 1967), with minor revisions to insure that (a) the items were applicable for all samples, (b) all a priori factors of PC were represented in the item domains (i.e., satisfaction with aspects of jobs, individual/organizational interfaces, groups, and leaders). The same 20 items were used in all studies. An assessment of general satisfaction was used as a manifest indicator of overall organizational well-being. This variable was based on a composite of the 20 items, for which coefficient alpha was .90 for Navy personnel, .91 for systems analysts, .91 for production-line personnel, and .89 for firefighters. In regard to the personality variables, the item-composites used to

measure achievement motivation (13 items, alpha = .77 on the total sample) and rigidity (12 items, alpha = .74 on the total sample) were the same as those described in James, Gent, Hater, and Coray (1979). The achievement motivation item-composite was designed to assess orientation toward success and included items that measured need for achievement, aspiration level, persistence, and preference for achievement-related activities. The rigidity item-composite was based on a need for certainty and included items such as "I don't like things to be uncertain or unpredictable" and "I like to have everything organized before I start a task." The measure for self-esteem was a general manifestation of self-esteem and general self-confidence in the work setting (11 items, alpha = .68 on the total sample) and included items such as "I take a positive attitude toward myself and "My ability gives me an advantage in my current job" (cf. James & Jones, 1980).

Analytic Procedure The goodness of fit of the proposed hierarchical model in Figure 1 was tested by the confirmatory factor-analytic program for higher order latent variables contained in LISREL vi (Joreskog & Sorbom, 1986). The 17 PC variables (18 PC variables for Navy personnel) served as manifest

Role ambiguity Role conflict Role overload Subunit conflict Lack of organizational identification Lack of management concern and awareness

.70 .81 .80 .60 .79

.83 .63 .36 .82 .87

.68 .66 .64 .66 .81

.76 .71 .52 .63 .71





.82 .73 .66

.67 .60 .62

.72 .69 .65

.87 .81 .61 .75 .63

.85 .89 .73 .73 .61

Job challenge and autonomy Challenge and variety Autonomy Job importance

.83 .71 .73

Leadership facilitation and support Leader trust and support Leader goal facilitation Leader interaction facilitation Psychological influence Hierarchial influence

.85 .86 .80 .82 .84

.85 .88 .77 .76 .70

Workgroup cooperation, friendliness, and warmth Workgroup cooperation Workgroup friendliness and warmth Reputation for effectiveness Esprit de corps

.75 .73 .59 .61

.76 .82 .64

.74 .67 .41

.74 .77 .61

Note. NP = Navy personnel, SA = systems analysts, PL = production line, FF = firefighters.

indicators, the four PC factors from Table 1 were treated as first-order latent variables or factors, and PCg was the predicted higher order latent variable or factor. In the first-order analysis—that is, the test of relations between the 4 first-order factors (FOFs) and the 17 (or 18) manifest PC variables—each manifest variable was allowed to be an indicator of only one FOF (i.e., a free parameter to be estimated, although one such parameter, randomly selected, had to assume a fixed value of 1.0 for each FOF; Joreskog & Sorbom, 1986). Relations between each such indicator and the other three factors were fixed at zero. On the basis of prior exploratory factor-analytic work by Jones and James (1979), we expected that the 17 (or 18) manifest PC variables would factor into the domains indicated in Figure 1. Two differences between this study and the prior research by Jones and James were that (a) the PC questionnaire was revised and shortened by collapsing the role and subsystem/organizational domains to obtain the individual/ organizational interface domain (a product being a single factor to represent the interface) and (b) the first-order factors were left free to be correlated. The second-order analysis ascertained whether a single higher order factor could account for the covariation among the four FOFs. With four FOFs, this model is overidentified and thus the model is disconfirmable by tests of fit (cf. James, Mulaik, & Brett, 1982). All coefficients in the vector relating PCg to the FOFs were free to be estimated, and the variance of PCg was fixed at unity. It is noteworthy that, in each of the four samples, the determinant of the observed variance-covariance matrix for the 17 (or 18) PC manifest

MEASUREMENT OF MEANING variables was very small relative to the magnitude of the diagonal elements (e.g., .000006 for the Navy sample). These results indicated a high degree of collinearity. In such conditions, Joreskog and Sorbom (1986) recommended the use of unweighted least squares (ULS) to estimate the values of the free parameters. The statistical distribution for ULS is unknown. One ramification is that chi-square (x2) cannot be used to test the goodness of fit of the model. Even though LISREL does provide other indices to assess the overall fit of the model to observed data, we did attempt to obtain a maximum likelihood (ML) solution and included a chi-square test to assess the fit of the estimated models. The models were therefore tested by using both ULS and ML. Comparisons are provided between the ULS and ML (a) parameter estimates; (b) correlations among the first-order factors; (c) the goodness-of-fit index (GFI), a measure of the relative amount of variance and covariance jointly accounted for by the model; (d) the root-mean-square residual (RMSR), the average residual variance and covariance, respectively; and (e) the squared multiple correlation (J?2), a measure of how well the indicators jointly accounted for the latent variables. Standardized data were used in these analyses to deal with the scale dependency associated with ULS estimation (cf. Long, 1983). The observed covariance matrix was then used to determine a chisquare for the first-order factor model (x2) and the second-order factor model (x!), based on the ML solution. The x? was used as a stand-alone index (cf. Marsh, Balla, & McDonald, 1988) to assess the fit of the firstorder model. The fit of the hierarchical model was assessed with (a) a difference chi-square test (\l - x? with dfc- df, degrees of freedom), a measure of the adequacy of PC, to explain the covariation among the first-order factors; and (b) the Tucker-Lewis (1973) index (TLI) [(XJ/ 4/i — Xi/dK)/(xtJdfn - 1.0), where n represents the null model], a measure of the covariance accounted for by the hierarchical model relative to the total covariance among the PC composites.



1989). Closer inspection of the normalized residual matrix

suggested that this was a viable explanation for the large number of inflated residuals. This was because the majority of the large residuals were associated with PC variables that loaded on a common FOF. Second, it was possible that some of the PC composites loaded on more than one factor. We tested both of these assertions with a ML solution as follows. Covariation among composites unaccounted for by a common FOF was assessed by allowing several of the unique variances for the PC composites (i.e., the off-diagonal elements of the theta epsilon [TE] matrix) to covary. Specifically, the offdiagonal TE within each factor with the highest modification index was set free to be estimated. We then allowed a PC composite to load on more than one factor if it was theoretically reasonable and if the modification index indicated that the freed parameter would result in a substantial gain in the fit of the model. Overall, these analyses were conducted following the guidelines recommended by MacCallum (1986) for follow-up analyses. The ML standardized solutions provided by the first-order CFAs for all four samples are presented in Table 3. These results are highly similar to the ULS solution. Differences between the solutions included the correlated TEs and the additional factor loadings obtained by composites loading on more than one FOF. There were 3-5 correlated TEs over samples, all but one of which were within a common factor. The one exception occurred in the production sample, in which it was indicated that identification with an organization was conditional on the reputation of the individual's workgroup. This relationship could not be accounted for by the four FOFs, even when the PC com-

Results Results of the confirmatory factory analyses (CFAs) are reported in an order that corresponds to the empirically testable assumptions presented earlier. Additional construct validation efforts are then addressed.

Tests for PCg The ULS standardized solution provided by the first-order CFA on all four samples (i.e., Navy personnel, systems analysts, production line, and firefighters) are presented in Table 2. The

posites for identification and group reputation were allowed to cross-load on the factors. In regard to PC composites loading on more than one factor, this occurred rarely and primarily for the psychological influence composite. In general, however, results reported in Tables 2 and 3 are highly consistent. The chi-squares provided by the ML analysis for the four samples were as follows: Navy, x2(123, AT = 422) = 446.51; systems analysts, x2(109, jV = 128) = 196.28; production line, X 2 (107,JV= 208) = 248.23; and firefighters, x2(109,JV = 288) = 323.35. The chi-squares appear to be acceptable given the sam-

data in Table 2 are consistent with prior research (cf. James &

ple sizes and the possible violation of multivariate normality. Although lack of multivariate normality does not affect param-

Sells, 1981) regarding relations between manifest (PC) variables

eter estimates, it might inflate the chi-square (Bentler & Chou,

and the four FOFs (see Figure 1). The goodness-of-fit indices

1987). We thus checked the joint normality assumption for all

based on the ULS solution for all four samples generally indi-

four samples. Specifically, squared generalized distances were

cated that the fit of the first-order model to the data was good (e.g., the GFI was greater than .95 for all samples). Nonetheless,

Wichern, 1988, p. 154). Because the squared generalized dis-

inspection of the normalized residual matrix revealed a large number of normalized residuals with absolute values greater

tances behave like chi-square random variables with sample sizes greater than 30, a chi-square test of significance could be

than two. This finding is indicative of a possible specification error in the first-order model (cf. Hayduk, 1987; Joreskog &

used to test the multivariate normality assumption. All four chi-

Sorbom, 1986). A host of potential misspecifications exist, some of which are

data sets from which the four samples were drawn were not

computed for each observation in each sample (cf. Johnson &

square tests were significant (p < .001), which indicates that the distributed according to multivariate normal distributions. Additional support for the model was provided by the TLI,

indigenous to the hierarchical mode) and are not substantively damaging to the theoretical fit of the model. For example, when using item-composites as manifest indicators, it is plausible that

which was greater than .90 for all samples. More importantly, for each of the four samples, there were only five normalized

the items from different composites within the same FOF covary beyond the covariation accounted for by that FOF (Hert-

which were associated with role overload. These results suggest

residuals slightly greater than 2 (the range was 2.1 to 2.7), all of



Table 2 Standardized Estimates of Relations of Manifest Psychological Climate (PC) Variables on 4 First-Order Factors, Usingan Unweighted Least Squares Solution Navy personnel PC variables

Systems analysts


Production line


1 Role stress and lack of harmony

1. 2. 3. 4. 5. 6.

Role ambiguity Role conflict Role overload Subunit conflict Lack of organization identification Lack of management concern and awareness

.86" .76 .48 .48 .69

.87* .65 .38 .62 .90

.76" .65 .53 .67 .82

.80" .67 .39 .62 .68





Job challenge and autonomy 7. Challenge and variety 8. Autonomy 9. Job importance













Leadership facilitation and support 10. Leader trust and support 1 1 . Leader goal facilitation 1 2. Leader interaction facilitation 13. Psychological influence 14. Hierarchical influence





.88 .84 .83 .87

.85 .75 .72 .75

.75 .63 .83 .78

.85 .83 .79 .78

Workgroup cooperation, friendliness, and warmth 15. 16. 17. 18.

Workgroup cooperation Workgroup friendliness and warmth Reputation for effectiveness Esprit de corps

.71" .69 .67 .85

.76" .77 .90




.91" .85 .76

" Fixed parameter (not equal to 1.0 because results are presented in terms of a standardized solution such that the Psi matrix has 1 .Os in diagonal).

the need for additional psychometric work on this PC composite. Of particular interest are the results shown in Table 4, which demonstrate high correlations among the four FOFs for all four samples. The similarity of the correlations among the FOFs for ULS (below the diagonal) and ML solutions (above the diagonal) are noteworthy.

nificant in three of the samples. (Even though the difference chisquare was significant in the production sample, the difference was small.) The TLI was greater than .90 in all samples. The ULS and ML goodness-of-fit indices were also generally comparable. The RMSR values were uniformly low, and the multiple correlations were high across all samples. The GFI values were all .97 or .98 for the ULS solution but dropped to the middle to

correlations among the first-order factors—were essentially the

upper .80s for the ML solution. This indication of a possible discrepancy in the ULS and ML solutions may reflect the effects

In sum, the key parameter estimates—factor loadings and same for the ULS solution and the ML solution. The ML solu-

of skewed distributions or collinearity, or both, on the ML esti-

tion was more complex in that parameters were added to the model to address correlated errors and factorial complexity. Al-

mates. However, these effects do not appear to have been sub-

though this added complexity enhanced the statistical fit of the ML model, it had no meaningful effect on the estimates of key

still reasonably high and not seriously inconsistent with the GFI values based on ULS estimates.

parameters. We thus proceeded to use the simple structure ULS

Estimated loadings for the FOFs on PC, are presented in Table 6. The generally high magnitude of these estimates suggests

first-order factor model as well as the more complex ML firstorder factor model to test for a single, higher order factor. Specifically, a second-order factor structure was created in which each first-order factor was accounted for by a common component due to PC, and a residual first-order factor. Results of analyses designed to test the goodness of fit of a single, higher-order factor model are reported in Tables 5 through 7. Tests of overall fit, presented in Table 5, generally

stantial given that the GFI values based on the ML solution are

that PCg explained substantial portions of variance in each of the FOFs. The estimated loadings between the ULS and ML solutions were again similar. The mean loading for absolute values for both ULS and ML was .82, which denotes that an average of 67% of the variance in the FOFs was attributable to PCg.

indicate that the single, higher-order factor model was con-

These results, combined with those of the goodness-of-fit tests, furnish strong support for the prediction that the FOFs share a common element of meaning.

firmed in all samples. The difference chi-square was nonsig-

It should be mentioned that the patterns of loadings varied



Table3 Standardized Estimates of Relations of Manifest Psychological Climate (PC) Variables on 4 First-Order Factors, Using a Maximum Likelihood Solution Systems analysts

Navy personnel

Production line


PC variables Role stress and lack of harmony 1. 2. 3. 4. 5. 6.

Role ambiguity Role conflict Role overload Subunit conflict Lack of organization identification Lack of management concern and

.79* .76 .48 .51 .62

.87' .63 .35 .70 .85

.75" .67 .57 .68 .79




.87" .72 .42 .48 .59 -.33


Job challenge and autonomy 7. Challenge and variety 8. Autonomy 9. Job importance

.69 .82' .61

.76 .78' .60

.51 .74' .45

.70 .66' .40

Leadership facilitation and support 10. 11. 12. 13. 14.

Leader trust and support Leader goal facilitation Leader interaction facilitation Psychological influence Hierarchical influence

15. 16. 17. 18.

Workgroup cooperation Workgroup friendliness and warmth Reputation for effectiveness Esprit de corps


.92' .87 .87 .55 .88


.81* .88 .77 .28 .76


.87* .79 .65 .55 .78

.90' .85 .83 .81 .76

Workgroup cooperation, friendliness, and warmth

.68" .65 .87

.63" .60 .69 .84

.69.63 .71

.93' .87 .71

Theta epsilon TEI6 TE 5 TEH TE 3 TE 7

15 = .38 6 = .25 12 = .08 2 = .25 9 = .25

TE16 15 = .42 TE1013 = .17 TE 7 9 = .13

TE15 TE 17 TE 7 TE 3

16 = 5= 9= 2=

.35 .17 .37 .17

TE 6 1 = .18 TE13 11 = -.11 T E H 12 = .10

• Fixed parameter.

somewhat among samples. Nevertheless, the general pattern of high loadings suggests a general factor in all samples.

Indirect Effects


The indirect effects of PC, on the 17 (or 18) manifest variables are presented in Table 7. The indirect effects are a product of the direct effects of PC, on the FOFs and the direct effects of the FOFs on the manifest variables. For example, the total effect of PCg on perceived role ambiguity equals the direct effect of PCg on the first-order factor Role Stress and Lack of Harmony times the direct effect of Role Stress and Lack of Harmony on the PC composite ambiguity (i.e., for ULS, the solution [•>•] i = -.77] [X,i = .86] = indirect effects of-.662). The range was from .29 to .84 over samples, and the means of the indirect effects were essentially identical over samples. These results indicate that reasonable amounts of variance in the initial measures of meaning were explained by PCg (an aver-

age of 37% of the variance was explained) and thus that PQ was an important cause of the initial perceptions of meaning. Consequently, the third and final assumption tested by CFA was supported.

Additional Construct Validation Issues The results reported previously furnish empirical support for the hierarchical model of PCg and, within the context of the CFAs, one may say that the model was confirmed. However, confirmation of a model suggests that the model furnishes a useful basis for explaining how and why events occurred. It does not connote that the confirmed model is unique or has been proven to be true (James et al., 1982), which is to say that alternative models may explain the data equally well. An alternative model of particular concern here is based on systematic or nonrandom measurement errors. Such a model predicts that the higher order PCg factor is actually a measure of common re-


LOIS A. JAMES AND LAWRENCE R. JAMES sponse biases such as acquiescence or social desirability. In current jargon, one might propose that this general factor is a product of a pervasive method factor and thus lacks the substantive meaning that we have attributed to it (i.e., an appraisal of personal benefit vs. personal detriment). To test the hypothesis that the general factor represents a perS I

I S3 8 '

vasive method factor, we estimated the correlations between the general factor and overall job satisfaction (OJS) and three personality variables (achievement motivation, self-esteem, and ri-


r r


oo ,'


«rj '


8 '

i I

gidity) known to be predictors of PC perceptions or moderators, or both, of relations between PC perceptions and individual outcomes (James etal., 1979, 1981; James & Jones, 1980). The rationale underlying this test is that if the general factor is interpreted as PC8, then PCg should be positively, and probably highly, correlated with OJS. This prediction follows from the

£ 8

theory that an appraisal of the degree to which one is personally benefited versus personally hindered or harmed is the basis on which an overall evaluation is made of one's organizational well-being (as measured by OJS). However, one might argue that a strong, positive correlation between the general factor and OJS is a mere extension of the pervasive method factor that engendered the general factor in




the first place. If this is a valid inference, then it would also follow that the pervasive method factor would influence responses to other measures in the questionnaire used to assess meaning. In particular, this method factor should also influence responses to items measuring achievement motivation, self-esteem, and rigidity. These personality variables (a) were measured in precisely the same manner and by the same method as the PC variables and OJS, (b) had psychometric characteristics (e.g., reliabilities) that were similar to the PC variables and OJS, and (c) could be assumed to be subject to the same types of response biases as the PC variables and OJS. It follows that a

m o oo r- t- >o


i &

pervasive method factor that produces spuriously high correlations between the general factor and OJS should also promote spuriously high correlations between this same general factor and the personality variables. The squared correlations provided by ULS between the general factor and OJS, and between the general factor and each of the personality variables, are reported in Table 8. A differential pattern of relations is clear; the general factor correlated much more highly with OJS than with the personality variables. Even though these results are, again, only indicative of construct validity, they question an alternative model that poses a pervasive method factor as an alternative explanation of PCg.

Discussion This study was based on the theory that factors underlying emotionally relevant cognitions (valuations) of work environments reflect appraisals of the degree to which the overall work environment is believed to be personally beneficial versus personally detrimental to the organizational well-being of the individual. Personal benefit versus personal detriment to organizational well-being thus served as a proposed general factor, designated PCg, in a hierarchical model of value-based meanings for work environments. Results of confirmatory factor analyses on multiple and diverse samples supported most empirical predictions suggested by this model. Specifically, (a) first-order factors



Table 5 Goodness-of-Fit Indices for a Hierarchical Model of Meaning Measures





Chi-square for hierarchical model Chi-square difference* Tucker-Lewis index

447.30 .79 .91 125 422

200.19 3.91 .92 111 128

256.40 8.17* .92 109 206

327.89 4.54 .91 111 288

Comparison of relevant goodness-of-nt indices ULS

GFI RMSR R2,first-orderfactors JJ!, second-order factors

.98 .06 .99 .89


.89 .06 .99 .90







.98 .07 .98 .90

.85 .06 .99 .90

.97 .07 .98 .93

.86 .06 .97 .96

.97 .06 .99 .92

.88 .06 .99 .93

Note. NP = Navy personnel, SA = systems analysts, PL = production line, FF = firefighters. ULS = unweighted least squares; ML = maximum likelihood; GFI = goodness-of-fit index; RMSR - root-meansquare residual. * Chi-square difference is between first-order model and higher order model with 2 df.

(FOFs) underlying manifest perceptual variables were correlated, (b) a general factor (PC,) explained much of the covaria-

an endogenous variable, it is likely that Person X Situation interaction models will be required to explain PC, (cf. Endler &

tion among the FOFs, and (c) the general factor explained reasonable portions of variance in the manifest perceptual vari-

Magnusson, 1976; James et al., 1 979, 1981; James, Hater, Gent, & Bruni, 1978). Discussions of value development are also

ables. Additionally, a model that attempted to attribute results to a pervasive method factor was disconflrmed, and PC, was highly correlated with an indicator of overall organizational

likely to be productive (cf. Locke, 1976), as is recent cognitive research on top-down versus bottom-up processing models (cf. Diener, 1984). As a causal variable, models may be developed

well-being. Such empirical support for PCg denotes only that this is an area potentially worthy of future research. It does not suggest

that relate PC, to affect (see later discussion), behavioral dispositions, and behaviors themselves. Finally, measurement issues require further attention. The rudimentary construct valida-

that the PC, model is true, unique, or proven. The tenuous nature of this model is evident when one considers the large number of assumptions on which the model was based but which were not tested in the reported analyses. Future research is

tion effort reported here suggests only that a pervasive method factor could not explain the data. More intense efforts that use multiple measures of each construct, combined with more sophisticated analyses (cf. Schmitt & Stults, 1986; Widaman,

needed to assess if indeed values, and perhaps other components of belief systems (e.g., self-concepts, self-regulatory systents), engender the cognitive constructs used to impute mean-

1985), are required before one may feel confident that models based on nonrandom measurement errors are clearly disconfirmable.

ing to work environments. Research is also needed that ad-

In sum, our effort represents a first step in what may prove

dresses other causes as well as effects of PC,. When viewed as

to be a fruitful area of research. It has many limitations, but it

Table 6 Standardized Estimates of Relations of the 4 First-Order Latent Variables on a Single, Higher Order Latent Variable for ULS and ML Solutions PC, for each sample NP




First-order factors









Role Stress and Lack of Harmony Job Challenge and Autonomy Leadership Facilitation and Support Workgroup Cooperation, Friendliness, and Warmth

-.77 .80 .80

-.84 .79 .77

-.81 .71 .90

-.83 .69 .88

-.95 .67 .87

-.97 .70 .82

-.80 .92 .93

-.78 .90 .94









Note. ULS = unweighted least squares; ML = maximum likelihood. NP = Navy, SA = systems analysts, PL = production line, FF = firefighters.


LOIS A. JAMES AND LAWRENCE R. JAMES Table 7 Indirect Effects

ofPCs on Manifest Indicators for VLS and ML Solutions

NP PC variables










Role stress and lack of harmony Role ambiguity Role conflict Role overload Subunit conflict Lack of organization identification Lack of management concern and awareness

-.66 -.59 -.37 -.37 -.53



-.70 -.52 -.31 -.50 -.73




-.64 -.40 -.43

-.72 -.52 -.29 -.58 -.71





-.62 -.50 -.64

-.65 -.56 -.66 -.77

-.54 -.31 -.50 -.54

-.56 -.33 -.53 -.46





Job challenge and autonomy

.69 .63 .58

Challenge and variety Autonomy Job importance

.55 .65 .48

.54 .56 .48

.53 .54 .42

.41 .52 .36

.36 .52 .32

.63 .61 .36

.63 .59 .35

.76 .65 .55 .72 .68

.71 .65 .53 .71 .65

.84 .79 .77 .73 .73

.85 .80 .78 .77 .72

.57 .52 .59

.66 .61 .55

.64 .60 .49

Leadership facilitation and support

.74 .70 .67 .66 .69

Leader trust and support Leader goal facilitation Leader interaction facilitation Psychological influence Hierarchial influence

.71 .67 .67 .71 .68

.75 .77 .68 .65 .68

.71 .77 .68 .64 .67

Workgroup cooperation, friendliness, and warmth Workgroup cooperation Workgroup friendliness and warmth Reputation for effectiveness Esprit de corps

.62 .60 .58 .74

.56 .53 .61 .75

.59 .60 .70

.57 .55 .73

.57 .53 .57

Summary statistics .37-.7S

Range Mean1 ULS ML

.60 .60


.29-.77 .61 .61

.61 .61


.62 .61

Note. NA = Navy personnel, SA = systems analysts, PL = production line, FF = firefighters. ULS = unweighted least squares; ML = maximum likelihood. a Mean was computed on absolute values.

also has some interesting implications. For instance, the PC8 mode] suggests that underlying cognitive structures pertaining to emotionally relevant cognitions of work environments are

Table 8 Squared Correlations ofPCs with Overall Job Satisfaction and Personality Variables Provided by Unweighted Least Sqttares

PC8 Variables





Overall job satisfaction Achievement motivation Rigidity Self-esteem

.79 .04 .00 .06

.88 .06 .10 .01

.75 .11 .04 .06

.73 .05 .00 .03

Note. NA = Navy personnel, SA - systems analysts, PL = production line, FF = firefighters.

more integrated and parsimonious than has been realized. The existing tendencies to compartmentalize environmental perception research by environmental domains such as jobs and leaders have resulted in a large, diffuse set of perceptual constructs that not only lacks a unifying theme but also lacks an integrated theory regarding the substantive impact of the perceived work environment on individuals. We propose that such an integrated theory may exist and that it is possible, in part, to explain the substantive impact of the perceived work environment on individuals. Stated simply, people respond to work environments in terms of how they perceive these environments, and a key substantive concern in perception is the degree to which individuals perceive themselves as being personally benefited as opposed to being personally harmed (hindered) by their environment. This article concludes with discussions of two models that furnish alternative explanations for the correlations among the PC variables. These models do not exhaust potentially viable

MEASUREMENT OF MEANING alternatives to PCg theory, but we consider them to be among the more salient competitors of such theory.

Situational Process Model


not provide the range of data needed to meaningfully examine the factor structure of JS or to compare OJS with PCg. Thus, a test to ascertain whether PCg and OJS are distinguishable awaits future research.

The logic here is that perceptions on the PC variables repre-

As part of this future research we propose a test of an additional model. The theory underlying this model is that PCg and

sent, primarily, the influences of situational processes and thus

an indicator of organizational well-being such as OJS should be

covariation among the PC variables reflects, primarily, covaria-

highly correlated because emotionally relevant cognitions and

tion among workgroup and other contextual processes. Partial support for a situational process model is furnished by prior

the emotions (or feeling states) are components of reciprocally

studies that have reported significant correlations between mea-

interacting, interdependent, nonrecursive, fused processes (cf. Ittelson, Proshansky, Rivlin, & Winkel, 1974; James & Jones,

sures of situational variables and one or more of the PC variables (cf. James et al., 1979, 1981; James & Jones, 1980; James & Sells, 1981; Jones & James, 1979). It is also true, however,

1974, 1980; James &Tetrick, 1986; Lazarus, 1982, 1984; Park, Sims, & Motowidlo, 1986). In regard to a PCe -»• OJS relation,

that many of these studies indicated that perceptions on PC

object is personally beneficial versus detrimental to one's organ-

variables were also related uniquely to person variables (e.g., job involvement) or to Person X Situation interactions, or both.

izational well-being is believed to determine the type and qual-

Thus, it cannot be said that the PC variables are primarily functions of workgroup and other contextual processes. An additional point is that to confirm a situational process model with our data would require that the correlations among situational processes not only be quite high but also that relations involving differing sets of processes be similar. To illustrate, consider the rather flat profile of correlations among the first-order PC factors, reported in Table 4, that had an average absolute value of approximately .66. One might build a case that the meanings associated with jobs, leaders, workgroups,

a cognitive appraisal of the degree to which an event, agent, or

ity of emotion experienced as well as the direction and intensity of the emotion (Lazarus, 1982, 1984). Moreover, the global appraisal of whether the work environment as a whole is personally beneficial versus detrimental is hypothesized to be the primary cause of organizational well-being, which may be characterized by a general feeling state (Isen,

1984) reflecting

primarily positive affect (e.g., happiness, contentment, satisfaction) or primarily negative affect (e.g., depression, melancholy, dissatisfaction; cf. Diener, 1984). The reciprocal OJS -»• PQ causal relation is based on the hypothesis that existing or desired levels of overall organizational well-being may cause indi-

and individual/organizational interfaces should be correlated simply because the environmental attributes that are the objects of perception are correlated (e.g., open-systems theory).

viduals to restructure or redefine cognitions to make them con-

However, it is rather unlikely that such a case would predict a

Although the reciprocal relationship between PQ and OJS has not been tested, a possible implication of the reciprocal in-

flat profile of correlations over diverse environmental attributes, particularly for an average correlation as high as .66. A more cogent explanation of these findings is that each first-order

sistent with the experienced (or desired) level of affect (cf. James &Tetrick, 1986).

fluence of OJS on PCg is noteworthy. That is, the reciprocal OJS -»• PCB causal relation suggests an additional source of co-

factor was generated at least in part by a common, latent, cogni-

variation among the first-order PC factors. This possibility is

tive variable. Of course, a meaningful proportion of the variance in the first-order factors is unique and, by definition, reli-

predicated on the assumption that overall job satisfaction is, in part, a function of individual dispositions such that individuals have a generalized predisposition toward a given level or degree

able, so we must be careful not to overstate our position. In addition, it is likely that covariation among situational processes carried over into the meanings attributed to these processes. Nevertheless, a flat profile of high correlations is pre-

of satisfaction (Pulakos & Schmitt, 1983). If such a predisposition exists, then an individual's global cognitive appraisal of benefit versus harm engendered by the work environment may

cisely what one would expect if a strong, general, cognitive fac-

be a function of that individual's predisposition to be satisfied

tor were present in the data. Thus, these findings are consistent with a cognitive "g-factor theory" and suggest that PCg is useful

or dissatisfied. It follows that the covariation among the first-

for explaining salient portions of variance in work environment perceptions.

Job Satisfaction Model The average correlation (over samples) of .89 between PCe

order PC factors attributable to PC, may be, in part, a function of the reciprocal influence of OJS on PQ. In short, a key (albeit indirect) cause of covariation among the first-order PC factors may be an affective predisposition. To illustrate, negative affectivity (NA) is viewed as a mooddispositional variable associated with negative emotions, such

and OJS (see Table 8) stimulated yet again the question, What is

as dissatisfaction. Individuals that measure high on NA tend to interpret situations in a manner that is consistent with their

the difference between climate and job satisfaction (JS; Guion,

experienced negative emotions (e.g., dissatisfaction). Such indi-

1973; Johannesson, 1973)? Indeed, one reviewer suggested that the second-order factor that we designated PCg may instead be OJS. This same reviewer noted the need to conduct empirical

viduals accentuate the negative aspects of most situations (Watson & Clark, 1984). If these same individuals tend to interpret

tests to ascertain whether second-order factors for PC and JS are empirically distinguishable. We agree, but we could not conduct such a test with these data because a brief, short-form instrument was used to assess OJS. Such an instrument does

the work environment in a manner that is consistent with existing levels of negative affect, then a predisposition toward dissatisfaction may cause them to view their work environment as being detrimental to their overall well-being. The implied low score on PCe would in turn stimulate perceptions that jobs lack



challenge, leaders are unsupportive, roles are stressful, and so on. In this manner, covariation among the perceptions of challenge, support, stress, and so forth, would reflect the indirect influences of a predisposition toward NA. Research pertaining to the possibility of a dispositional source of job satisfaction has been sparse. Nonetheless, the few studies that have been done have suggested that job satisfaction may in part be a function of stable individual characteristics (Arvey, Bouchard, Segal, & Abraham, 1989; Schmitt & Pulakos, 1985; Staw, Bell, & Clausen, 1986; Staw & Ross, 1985). Thus, the possibility that predispositions in affect influence both PCg and the first-order PC factors is a viable question for future research. On the other hand, this is but one of several hypotheses, other hypotheses being that situational factors are ultimately responsible for covariations among first-order PC factors or between repeated measurements on OJS, or both (cf. Gerhart, 1987) and that stable individual values are responsible for the valuations underlying interpretations of environments. Of course, it is possible, if not likely, that PC perceptions and PCBare multiply determined, that is, a function not only of situations, both actual and perceived, and stable individual predispositions (e.g., affect, values), but also of potentially complex interactions among these various sources (cf. James, James, & Ashe, in press). In sum, the presence of alternative models points to the need (and direction) for future research. A key hypothesis to be tested is that a general perceptual factor of personal benefit versus personal detriment furnishes a theme for unifying perceptions of work environments and an integrated theory for explaining, in part, the substantive impact of work environment perceptions on individual outcomes. A series of hypotheses then pertain to the causes of this general perceptual factor, which includes stable individual characteristics (values) and predispositions (affect), situational factors, and various interactions among these potential causes.

References Arvey, R. D., Bouchard, T. J., Jr., Segal, N. L., & Abraham, L. M. (1989). Job satisfaction: Environmental and genetic components. Journal of Applied Psychology 74, 187-192. Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods and Research. 16, 78-117. Bradburn, N. M. (1969). The structure of psychological well-being. Chicago: Aldine. Diener, E. (1984). Subjective well-being. Psychological Bulletin. 95. 542-575. Ekehammer, B. (1974). Interaclionism in personality from a historical perspective. Psychological Bulletin, 81, 1026-1048. Endler, N. S., & Magnusson, D. (1976). Toward an interactional psychology of personality. Psychological Bulletin, S3, 956-974. Gerhart, B. (198 7). How important are dispositional factors as determinants of job satisfaction? Implications for job design and other personnel programs. Journal of Applied Psychology, 72, 366-373. Guion, R. M. (1973). A note on organizational climate. Organizational Behavior and Human Performance, 9, 120-125. Hayduk, L. A. (1987). Structural equation modeling with LISREL. Baltimore, MD: Johns Hopkins University Press. Hertzog, C. K. (1989). Using confirmatory factor analysis for scale development and validation. In M. P. Lawton & A. R. Herzog (Eds.), Special research methods for gerontology (pp. 281-306). Amityville, NY: Bayweod Press.

Isen, A. M. (1984). Toward understanding the role of affect in cognition. In R. S. Wyer, Jr. & T. K. Srull (Eds.), Handbook of social psychology (pp. 179-236). Hillsdale, NJ: Erlbaum. Ittelson, W. H., Proshansky, H. M., Rivlin, L. G., & Winkel, G. H. (1974). An introduction to environmental psychology. New %rk: Holt, Rinehart & Winston. James, L. R. (1982). Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67,219-229. James, L. R., Gent, M. J., Hater, J. J., & Coray, K. E. (1979). Correlates of psychological influence: An illustration of the psychological climate approach to work environment perceptions. Personnel Psychology, 32, 563-588. James, L. R., Hater, J. J., Gent, M. J., & Bruni, J. R. (1978). Psychological climate: Implications from cognitive social learning theory and interactional psychology. Personnel Psychology, 31, 783-813. James, L. R., Hater, J. J., & Jones, A. (1981). Perceptions of psychological influence: A cognitive information processing approach for explaining moderated relationships. Personnel Psychology, 34, 453477. James, L. R., James, L. A., & Ashe, D. (in press). The meaning of organizations enacted, transacted, or imposed. In B. Schneider (Ed.), Frontier Series. James, L. R., & Jones, A. P. (1974). Organizational climate: A review of theory and research. Psychological Bulletin, SI, 1096-1112. James, L. R,, & Jones, A. P. (1980). Perceived job characteristics and job satisfaction: An examination of reciprocal causation. Personnel Psychology, 55,97-135. James, L. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis assumptions, models and data. Beverly Hills, CA: Sage. James, L. R., & Sells, S. B. (1981). Psychological climate: Theoretical perspectives and empirical research. In D. Magnusson (Ed.), Toward apsychology of situations: An interactional perspective (pp. 275-295). Hillsdale, NJ: Erlbaum. James, L. R., & Tetrick, L. E. (1986). Confirmatory analytic tests of three causal models relating job perceptions to job satisfaction. Journal of Applied Psychology, 71, 77-82. Johannesson, R. E. (1973). Some problems in the measurement of organizational climate. Organizational Behavior and Human Performance, 10, 118-144. Johnson, R. A., & Wichern, D. W. (1988). Applied multivariate statistical analysis (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall. Jones, E. E., AGerard, H. B. (1967). Foundations of social psychology. New York: Wiley. Jones, A. P., & James, L. R. (1979). Psychological climate: Dimensions and relationships of individual and aggregated work environment perceptions. Organizational Behavior and Human Performance, 23, 201-250. Joreskog, K. G., & Sorbom, D. (1986). USRKL vi analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Mooresville, IN: Scientific Software. Lazarus, R. S. (1982). Thoughts on the relations between emotion and cognition. American Psychologist, 37, 1019-1024. Lazarus, R. S. (1984). On the primacy of cognition. American Psychologist, 39, 124-129. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer, Lewin, K. (1938). The conceptual representation of the measurement of psychological forces. Durham, NC: Duke University Press. Lewin, K. (1951). Behavior and development as a function of the total situation. In D. Cartwright (Ed.), Field theory >" social science (pp. 238-303). New York: Harper Torchbooks. Locke, E. A. (1976). The nature and causes of job satisfaction. In

MEASUREMENT OF MEANING M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297-1349). Chicago: Rand McNally. Long, J. S. (1983). Confirmatory factor analysis. Beverly Hills, CA: Sage. MacCallum, R. (19S6). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107-120. Mandler, G. (1982). The structure of value: Accounting for taste. In M. S. Clark & S. T. Fiske (Eds.), Affect and cognition (pp. 3-36). Hillsdale, NJ: Erlbaum. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391-410. Mirriam-WebsterDictionary(l915). Springfield, MA: Author. Mischel, W. (1968). Personality and assessment. New Trbrk: Wiley. Osgood, C. E., May, W. H., & Miron, M. S. (1975). Cross-cultural universals of affective meaning. Urbana: University of Illinois Press. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurements of meaning. Urbana: University of Illinois Press. Park, O. S., Sims, H. P., Jr., & Motowidlo, S. J. (1986). Affect in organizations. In H. P. Sims, Jr. & D. A. Gioia and Associates (Eds.), The thinking organization: Dynamics of organizational social cognition (pp. 215-237). San Francisco: Jossey-Bass, Pulakos, E. D., & Schmitt, N. (1983). A longitudinal study of a valence model approach for the prediction of job satisfaction of new employees. Journal of Applied Psychology, 68, 307-312. Reisenzein, R. (1983). The Schacter theory of emotion: Two decades later. Psychological Bulletin, 94, 239-264. Schachter, S., & Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review 69, 379-399. Schmitt, N., & Pulakos, E. D. (1985). Predicting job satisfaction from


life satisfaction: Is there a general satisfaction factor? International Journal of Psychology, 20, 155-167. Schmitt, N., & Stults, D. M. (1986). Methodology review: Analysis of multitrait-multimethod matrices. Applied Psychological Measurement, 10, 1-22. Staw, B. M., Bell, N. E., & Clausen, J. A. (1986). The dispositional approach to job attitudes: A lifetime longitudinal test. Administrative Science Quarterly, 31, 56-77. Staw, B. M., & Ross, J. (1985). Stability in the midst of change: A dispositional approach to job attitudes. Journal of Applied Psychology, 70, 469-480. Stotland, E., & Canon, L. K. (1972). Social psychology and cognitive approach. Philadelphia, PA: Sanders. Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10. Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465-490. Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. Minneapolis: Industrial Relations Center, University of Minnesota. Widaman, K. F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1-26. Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151-175. Received July 26,1988 Revision received January 30, 1989 Accepted February 1,1989 •