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AFFECTIVE AND COGNITIVE COMPONENTS OF JOB SATISFACTION: SCALE DEVELOPMENT AND INITIAL VALIDATION Jeremy Kyle Tekell

Thesis Prepared for the Degree of MASTER OF SCIENCE

UNIVERSITY OF NORTH TEXAS August 2008

APPROVED: Michael M. Beyerlein, Major Professor Daniel J. Taylor, Committee Member Joseph W. Huff, Committee Member Linda Marshall, Chair of the Department of Psychology Sandra L. Terrell, Dean of the Robert B. Toulouse School of Graduate Studies

Tekell, Jeremy Kyle. Affective and cognitive components of job satisfaction: Scale development and initial validiation. Master of Science (Psychology), August 2008, 37 pp., 15 tables, references, 63 titles. Job satisfaction is one of the most commonly studied variables in the organizational literature. It is related to a multitude of employee-relevant variables including but not limited to performance, organizational commitment, and intent to quit. This study examined two new instruments measuring the components of affect and cognition as they relate to job satisfaction. It further proposed including an evaluative (or true attitudinal) component to improve the prediction of job satisfaction. Results provide some evidence of both two and three factor structures of affect and cognition. This study found minimal support for the inclusion of evaluation in the measurement of job satisfaction. Affect was found to be the single best predictor of job satisfaction, regardless of the satisfaction measure used. Further development is needed to define the factor structures of affect and cognition as well as the role of these factors and evaluation in the prediction of job satisfaction.

Copyright 2008 by Jeremy Kyle Tekell

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ACKNOWLEDGEMENTS Many people deserve great thanks for helping me finish this study. I could not have done any of this without my committee members. Their patience, flexibility, and encouragement made this possible. I would also like to thank my friends and classmates that constantly pushed me forward. Most notably among these are Sarah Bodner, Cheryl Harris, Jon Turner, and Terence Yeoh. Thank you all for always asking about my thesis. Finally, without my wife Bri there would be no thesis. Thank you all for pushing me to finish and not letting me quit.

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TABLE OF CONTENTS Page ACKNOWLEDGEMENTS........................................................................................................... iii LIST OF TABLES...........................................................................................................................v Chapters 1.

INTRODUCTION ...................................................................................................1 Affect ...........................................................................................................2 Cognition......................................................................................................5 Affect and Cognition....................................................................................6 Evaluation ....................................................................................................8

2. METHOD

..............................................................................................................11 Participants.................................................................................................11 Procedures..................................................................................................11 Study Variables..........................................................................................12

3. RESULTS

..............................................................................................................14 Exploratory Factor Analysis ......................................................................16 Forced Factors............................................................................................19 Validity ......................................................................................................23 Reliability Analysis....................................................................................23 Multiple Regression ...................................................................................25

4. DISCUSSION

........................................................................................................27

Limitations and Directions for Future Research........................................30 REFERENCES ..............................................................................................................................33

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LIST OF TABLES Page 1.

Trait PA and NA Mean Meta-Analytic Correlations with Outcomes..................................4

2.

Table of Correlations among Affective Items ...................................................................15

3.

Affective Items by Step .....................................................................................................16

4.

Correlations among Cognitive Items .................................................................................17

5.

Cognitive Items by Step.....................................................................................................19

6.

Confirmatory Factor Analysis for Affect with 1 Forced Factor ........................................20

7.

Confirmatory Factor Analysis for Affect with 2 Forced Factors.......................................21

8.

Confirmatory Factor Analysis for Affect with 3 Forced Factors.......................................21

9.

Confirmatory Factor Analysis for Cognition with 1 Forced Factor ..................................22

10.

Confirmatory Factor Analysis for Cognition with 2 Forced Factors.................................22

11.

Confirmatory Factor Analysis for Cognition with 3 Forced Factors.................................23

12.

Reliability Analysis for Affective Items ............................................................................24

13.

Reliability Analysis for Affective Items ............................................................................25

14.

Job Evaluation Means and Standard Deviations................................................................29

15.

Job Evaluation Item Correlations with Study Variables....................................................30

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CHAPTER 1 INTRODUCTION Affect, cognition, and evaluation predicting satisfaction. Job satisfaction is related to several major employee outcomes. Job satisfaction has been shown to be related to performance (Iaffaldano & Muchinsky, 1985; Judge, Thorenson, Bono, & Patton, 2001), commitment (Meyer & Allen, 1997; Meyer, Allen, & Smith, 1993), absenteeism (Tharenou, 1993), and turnover (Mobley, Griffeth, Hand, & Meglino, 1979). Despite the fact that job satisfaction is related to all of these employee variables, the strength of the relationships between them leaves much to be desired. One example of this appears in a meta-analytic study by Iaffaldano and Muchinsky (1985) who found only small correlations between job satisfaction and job performance (r = .17). A similar review by Judge et al. (2001) found the satisfaction-performance relationship to be much stronger but still moderate at best (r = .30). A second example of weak relations between variables appears in a study by Mobley et al. (1979) who found the job satisfactionturnover correlation ranged from small (r = -.13) to moderate (r = -.37) in their review of the literature. These small correlations might be explained by reexamining the composition of job satisfaction and its measures. Early job satisfaction studies conceptualized the construct affectively as “morale” or the general feelings of employees (Child, 1941). Locke (1976) classically defined job satisfaction as “a pleasurable or positive emotional state resulting from the appraisal of one's job or job experiences” (p. 1300). Thus early definitions emphasizing the affective conceptualization of job satisfaction stated that employees are satisfied of dissatisfied based upon the feelings (affect) generated by their experiences at work. While Locke’s definition was affective in nature it also suggested a cognitive component.

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This cognitive component supposedly weighs different aspects of the job and makes a comparison to some alternative (Cohen et al., 1972; Sheppard et al., 1988). Accordingly, as later job satisfaction studies found fault with the affective conceptualization they began to classify job satisfaction as an attitude – “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor”– rather than affect (Eagly & Chaiken, 1993, p. 1). Traditionally, attitudes have been modeled as consisting of affective, behavioral, and cognitive components (Bagozzi, 1978). However, current job satisfaction researchers generally agree that affective and cognitive components are the primary parts of attitudes while behavior is considered an outcome (Brief, 1998a; Brief 1998b; Fisher, 2000; Weiss 2002a; Weiss 2002b).

Affect The affective component of attitudes accounts for the feelings or emotions people associate with their job or attitude object as well as the valence of those feelings (Bagozzi, 1978). Positive affect (PA) reflects the extent to which a person feels enthusiastic, active, and alert.” (Watson, Clark, & Tellegen, 1988, p. 1063). It is sometimes described as enjoying life and feeling fully engaged (Weiss & Cropanzo, 1996). High PA individuals tend to be extroverted, outgoing, and energetic (Watson, Clark, MacIntyre, & Hamaker, 1992; Yik & Russell, 2001). Not surprisingly, these individuals also display more social behavior (Watson et al., 1988) as PA has been linked to extroversion (Watson et al., 1992). Individuals high in PA also tend to be more satisfied with work and life in general as well as being sensitive to the frequency of rewards, suggesting they may orient towards the positive aspects of life (Watson et al., 1988). Conversely, individuals high in negative affect (NA) are generally uncomfortable or otherwise orient towards life’s negative aspects (Watson & Clark, 1984). NA reflects the extent

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to which people experience “a general dimension of subjective distress and unpleasant engagement” that may take the form of many emotional states, “including anger, contempt, disgust, guilt, fear, and nervousness” (Watson et al., 1988, p. 1063). People high in NA report more physical complaints (Schaubroeck, Ganster, & Fox, 1992; Watson 1988a), as well as more stress (Brief et al., 1988; Schaubroeck, Ganster, & Fox, 1992; Watson 1988b). These findings indicate that “high NA individuals may view their lives as a series of stresses or hassles, regardless of what actually happens to them” (Watson, 1988b, p. 1028). PA and NA are independent factors. Though PA and NA logically seem to be opposites, research has consistently shown them to be, at least, reasonably independent (Diener & Emmons, 1984; Egloff, 1998; Schaubroeck et al., 1992; Tellegen, Watson, & Clark, 1999; Watson et al., 1988; Watson & Walker, 1996). Watson (1988) found PA and NA to be highly independent of one another sharing no more than 5% in common variance. Likewise, Watson et al. (1988) found PA and NA to be “largely uncorrelated” and independent of one another (Watson et al., 1988, p. 1063). A recent meta-analysis found further support for the independence of PA and NA finding the constructs were “not entirely distinct, yet certainly not redundant with one another” (Thorensen, Kaplan, Barskey, Warren, & Chermont, 2003, p. 931). Still other research has suggested that PA and NA are negatively related based upon the timeframe measured (see for example Weiss & Cropanzo, 1996). In one such example, Green et al. (1993) found PA and NA to be strongly negatively correlated across short periods of time. Likewise, Diener and Emmons (1994) found the constructs to be negatively related when measured as a state. However, Diener and Emmons (1994) also found that as the time between measures was increased from days to weeks PA and NA became increasingly independent of one another. Similarly, Watson (1988b) found that PA and NA were relatively independent of one another

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regardless of the specified time frame (p. 139). However, Watson’s (1988b) findings agree with those of Diener and Emmons (1994) in that “PA and NA do become more independent over time” (p. 134). Despite the objections of Green et al. (1993), support for a bipolar affective structure remains questionable at best; and for the purposes of this study PA and NA will be assumed to be independent constructs. Many studies have concluded that PA and NA are differentially predictive of employeerelevant variables (Judge & Ilies, 2004; Mak & Mueller, 2000; Thorenson et al., 2003; Watson, 1988a; Weiss & Cropanzano, 1996; Watson & Walker, 1996; Watson et al., 1988). A recent metaanalysis (see Table 1) found strong support for “the discriminant validity of PA and NA in the organizational context” (Thorenson et al., 2003, p. 932). Trait PA was significantly related to job satisfaction, emotional exhaustion, depersonalization, and personal accomplishment (Thorenson et al., 2003). Trait NA was significantly related to job satisfaction, emotional exhaustion, depersonalization, personal accomplishment, and turnover intentions (Thorenson et al., 2003). One interesting result of their study however, is that PA appears to be more predictive of positive outcomes whereas NA appears to be more predictive of negative outcomes (Thorenson et al., 2003). Thus, PA and NA seem to hold up well as independent constructs. Table 1 Trait PA and NA Mean Meta-Analytic Correlations with Outcomes Variable

Trait PA

Trait NA

Job Satisfaction

.33*

-.37*

Emotional Exhaustion

-.32*

.52*

Depersonalization

-.25*

.39*

Personal Accomplishment

.47*

-.27*

--

.24*

Turnover Intentions ρ < .05 4

Cognition While affect is an important part of job satisfaction, cognitions play a significant role as well. Cognitions are often characterized as the content of thoughts or beliefs about an attitude object or statement of fact in question, usually in comparison to a standard or expectation (Bagozzi, 1978; Campbell, 1976; Crites et al., 1994; Organ & Near, 1985; Weiss, 2002b; Weiss & Cropanzano, 1996). Fore example, if an employee expects a certain level of autonomy in the way he works and is being micromanaged, the discrepancy between expected and perceived autonomy may lead to thoughts of dissatisfaction. They may be thought of as the rational, calculating part of attitudes that rely on unemotional comparisons (Hulin & Judge, 2003, Moorman, 1993; Organ & Near, 1985). According to expectancy-value type models cognitions develop attitudes through some algebraic function (Cohen, Fishbein, & Ahtola, 1972; Sheppard, Hatwick, & Warshaw, 1988). As such, cognitions assess both the valence (positive and negative) and the magnitude (relative importance) of various attitudinal attributes as well as how those attributes compare to the attributes of alternatives (Cohen et al., 1972; Sheppard et al., 1988). Meta-analyses have shown these types of models to be quite effective at predicting a number of attitudinal intentions as well as the relationships between those intentions and their subsequent behaviors (Kim & Hunter, 1993; Fishbein & Ajzen, 1972; Sheppard et al.). What we do know about cognition is that it helps to develop attitudes as a function of accessible information (Salancik & Pfeffer, 1978). Salient (easily accessible) information has the biggest influence in decision-making (Salancik & Pfeffer, 1978), which may minimize the role of cognition, as it tends to be slightly less accessible than affect (Verplanken, Hofstee, & Janssen, 1998, Zajonc, 1980). However cognition does predict behavior irrespective of affect and vice

5

versa (Carr, Schmidt, Ford, & DeShon, 2003; Crites et al., 1994; Norman, 1975; Thoresen et al., 2003; Trafimow et al., 2004). Cognition determines both meaning and importance of various characteristics, values, conditions, facets and outcomes (Hulin, & Judge, 2003; Moorman, 1993; Organ & Near, 1985; Salancik & Pfeffer, 1978). Similarly, cognitions consider the appropriateness of response options based on the interpretation of cues or attributes of the attitude object (Crites et al., 1994; Salancik & Pfeffer, 1978). The social context provides “a direct construction of meaning…and acceptable reasons for action” as well as the consequences of the desired course of action (Salancik & Pfeffer, 1978, p. 227). This does not mean that cognitions are not influenced to some extent by affect.

Affect and Cognition An examination of the literature shows no shortage of studies finding that affect and cognition influence one another (Forgas, 1995; Forgas & George, 2001; Jundt, & Hinsz, 2002; Millar & Tesser, 1986; Tsui & Barry, 1985; Varma, Denisi, & Peters, 1996; Weiss, 2002a; Zajonc, 1980). It is likely that “affective processes and cognitive processes operate in parallel and may not be seen as separable and sequential” (Edwards, 1990, p. 213). Affect influences individuals’ cognitive processes such that their resulting behaviors may be either affectively or cognitively driven (Millar & Tesser, 1986). In one such example, Millar and Tesser (1986) used affective and cognitive primes (subtle directions) on participants who were told they would be given a puzzle that would help develop their analytical ability. Participants in the instrumental (cognitive) behavior conditions were told the second part of the experiment was a puzzle test of analytical ability while participants in the consumatory (affective) behavior conditions were told the second part of the experiment was a test of social sensitivity (Millar & Tesser, 1986). They

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found that affectively priming participants predicted affectively driven (consumatory) but not cognitively driven (instrumental) behaviors. Likewise cognitively priming participants predicted cognitively driven (instrumental) but not affectively driven (consumatory) behaviors (Millar & Tesser, 1986). Studies since then have found that affect and cognition appear to have some overlap when they are measured separately, which may account for some of the Millar and Tesser (1986) interaction (Crites et al., 1994; Thorenson et al., 2003). The two most prominent explanations for affective-cognitive interactions are the affect infusion model and the affective events theory (Forgas, 1995; Weiss & Cropanzo, 1996). According to the affect infusion model affective information gets weighted as part of the judgment process and in turn, influences that judgment (Forgas, 1995). In other words, people are cognitive misers who use their current affective state as a cue that provides a type of short cut in processing the information present when evaluating the target object (Forgas, 1995). The affect infusion model posits that affect asserts a selective influence on cognitive processes such that cognitions are sufficiently altered to reflect the newly introduced affective information (Forgas, 1995). Thus, affect is interpreted as new information to be cognitively processed. Conversely, the affective events theory interprets affect and cognition as different types of information that are reacted to differently rather than integrated. The affective events theory posits that affect will lead to more “spontaneous affectively-driven behavior such as acts of good or bad citizenship” while cognitions will lead to more “planned judgment-driven behaviors such as a decision to quit a job” (Fisher & Ashkanasy, 2000, p. 124; Weiss & Cropanzo, 1996). Affect is treated as a product of the work environment that, over time, will not only cause affectively driven behavior but also influence cognitive comparisons such that it influences judgment driven behaviors as well (Fisher & Ashkanasy, 2000; Weiss & Cropanzo, 1996). To this end the

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affective events theory suggests that some behaviors are affectively primed, while others are cognitively primed but influenced by affect. However, the resulting behaviors from these situations may be markedly different from one other. It should come as no surprise that, cognitions seem to be most often influenced by affect in individuals who demonstrate very high levels of positive or negative affect (Bolger & Schilling, 1991; Judge & Ilies, 2004; Tsui & Barry, 1985). It is likely that these people value their feelings more than thoughts – or are overwhelmed by the accessibility of affect – and tend to allow their affective reactions to attitude objects greater influence on their decisions (Salancik& Pfeffer, 1978; Verplanken et al., 1998; Zajonc, 1980).

Evaluation Still more recently, researchers have supported including an evaluative component in addition to the standard affective and cognitive components of attitudes (Millar, & Tesser, 1986; Weiss, 2002a; Weiss, 2002b). Evaluation may be thought of as a judgment of some degree of favor or disfavor towards a target object that represent one’s true attitude toward that object (Bargh, Chaiken, Govender, & Pratto, 1992). Evaluations occur automatically every day (i.e., hearing the word “Hi” in the hallway at work and immediately evaluating it as a positive gesture), and are often activated with the simple reference to or presence of an attitude object, such as your boss (Bargh et al., 1992). Researchers generally acknowledge attitudes as evaluative judgments (Brief, 1998a; Brief, 1998b; Brief & Weiss, 2002; Crites et al., 1994; Eagly & Chaiken, 1993; Fisher, 2000, Forgas & George, 2001; Weiss 2002a; Weiss, 2002b; Weiss & Cropanzo, 1996; Weiss, Nicholas, & Daus, 1999) that have at least an affective and cognitive component (see Fisher, 2000 for review). Several researchers have described affect and cognition

8

as having evaluative components (Aijen & Fishbein, 1972, Crites et al., 1994; Hulin & Judge, 2003) suggesting an underlying evaluative component which may explain the overlap in the constructs (Crites et al.; Thorenson et al., 2003). However, in order to by-pass the affective and cognitive distinctions and get to their underlying evaluative component, attitudes are commonly measured with semantic differential scales (for example; good – bad; like – dislike; pleasant – unpleasant; favorable – unfavorable) that force people to make a judgment about the attitude object (Crites et al., 1994; Eagly & Chaiken, 1993). As such, evaluation is a summary attitudinal judgment about an attitude object which may prove to be a more viable predictor of job satisfaction and other attitudes than affect or cognition alone. Evaluative judgments may be arrived at through either affect or cognition (Crites et al., 1994). Evaluations may be better predicted by either affect or cognition depending on whether the individual places more emphasis on affect or cognition (Crites et al.). Evaluation is best predicted by affect when affectively cued just as it is best predicted by cognitions when cognitively cued (Crites et al.). Though evaluation is related to both affect and cognition, it represents a distinct construct representing the true attitudinal measure (Tekell, Yeoh, & Huff, 2006). These “evaluations of the job, may be more salient and accessible” than standard affective and cognitive components of job attitudes typically measured with social attitudes, and thus be related to and/or improve the prediction of attitudes such as job satisfaction(Hulin & Judge, 2003, p. 258). Hypotheses H1: Affective and cognitive components will form two factor structures. H2: The affective and cognitive items will predict job satisfaction.

9

H3: Affect, cognition, and evaluation will predict job satisfaction better than affect and cognition alone.

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CHAPTER 2 METHOD Participants Two samples were used in this study. The first sample included a total of 217 graduate and undergraduate students from a large public university in the Southwest. The mean age of respondents was 23.8 years. The sample featured mostly female (73%) respondents who reported working an average of 26 hours per week. Average position tenure was 1.50 years whereas average tenure with the organization was 1.71 years. The second sample included a total of 260 graduate and undergraduate students from a large public university in the Southwest were sampled. The mean age of respondents was 23.6 years. The sample was composed mostly of females (74%) and reported working an average of 26 hours per week. Average position tenure was 1.57 years whereas average tenure with the organization was 1.77 years.

Procedures Participants in both samples were given paper-and-pencil questionnaires during normal class sessions and were asked to complete the packets either inside or outside of class. Participants returned the surveys through intercampus mail or directly to my office. Respondents in the second sample received the questionnaire during class sessions and were instructed to complete it outside of their classes. Surveys were returned directly to my office or via intercampus mail. Students in both samples were given sealed envelopes containing surveys with 40 of the same organizational citizenship behavior and performance items completed by participants, to give to their direct supervisors. Supervisors returned completed surveys via U.S. mail in postage-paid envelopes. The data from the first, smaller, sample were used in the

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exploratory factor analysis to generate a factor structure. All subsequent analyses were run using the second, larger, sample. This was done in order to show generalization of factor structures as well as providing initial validity and predictive ability of the variables.

Study Variables Job Satisfaction Job satisfaction was measured using the single-item Faces scale (Kunin, 1955), modified slightly for a more androgynous appearance, and the three general satisfaction items from the Job Diagnostic Survey (JDS, Hackman & Oldham, 1980). The JDS items have been show to be reliable having an overall coefficient alpha value of .77 (Munz , Huelsman, Konold, & McKinney, 1996). The Faces scale (Kunin, 1955) asks participants to rate which of the 9 face options best corresponds to how they feel about their job. The JDS (Hackman & Oldham, 1980) items ask participants to rate each item from 1 (strongly disagree) to 7 (strongly agree). Both scales and items were coded per the original authors’ recommendations.

Affective Component of Job Satisfaction A slightly modified 18-item measure (based on items from Crites, Fabrigar, & Petty, 1994) was used to assess the affective component of job satisfaction. Participants rated 18 adjectives from 1 (Strongly describes) to 5 (Not applicable to my job) how well each term described their current job.

Cognitive Component of Job Satisfaction A slightly modified 18-item measure (based on items from Crites, Fabrigar, & Petty,

12

1994) was used to assess the cognitive component of job satisfaction. Participants rated 18 adjectives from 1 (Strongly describes) to 5 (Not applicable to my job) how well each term reflected their thoughts or beliefs associated with their current job.

Job Evaluation A 4-item measure (Crites, Fabrigar, & Petty, 1994) was used to assess participants overall evaluation of their jobs. Participants rated each if the 4 pairs of terms on a 5 point positivenegative continuum with 1 being most negative and 5 being most positive to indicate their overall favorable or unfavorable rating of their current job. Reliability estimates for this instrument have been shown to be good with coefficient alpha values ranging from .90 to .95 (Crites et al., 1994, p. 626).

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CHAPTER 3 RESULTS The data from the first sample were analyzed using exploratory factor analysis. Factor analysis is simply “discovering which variables in the set form coherent subsets that are relatively independent of one another” (Tabachnick & Fidell, 2001, p. 582). That is to say that one may distinguish groups of variables that measure different things based on their respective correlations with one another and combine those variables into sets called factors (Tabachnick & Fidell, 2001). These factors are simply unobserved variables that represent the sets of observed variables that correlate with them (Crocker & Algina, 1986). In exploratory factor analysis, the researcher begins by including all items the researcher thinks may be useful in measuring the construct, or constructs, of interest (Tabachnick & Fidell, 2001). These items are correlated with and grouped according to their patterns of correlations, called factor loadings, into relatively independent sets called factors (Crocker & Algina, 1986; Tabachnick & Fidell, 2001). The number of factors may be determined by correlations found in the variables, called exploratory factor analysis, or predetermined in an attempt to test theories of factor structures, called confirmatory factor analysis (Tabachnick & Fidell, 2001). Frequently simple correlation among variables is not enough to create an easily interpretable factor structure, which may require rotation. Rotation is simply a “process by which the solution is made more interpretable without changing its underlying mathematical properties” (Tabachnick & Fidell, 2001, p. 584). Once the factor structure has been rotated it is ready for interpretation. Exploratory factory analysis utilizes the correlations found among the variables to determine the number of factors. Confirmatory factor analysis examines the covariance of items based on a predetermined number and structure of factors.

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An exploratory factor analysis (EFA) was run for the affective component items and the cognitive component items, respectively. The EFAs were conducted using the Statistics Package for the Social Sciences (SPSS) version 13. These EFAs were run with maximum likelihood extraction and varimax rotation, to provide a more interpretable structure given the correlations (see Table 2) among the items (Tabachnick & Fidel, 2001). Table 2 Table of Correlations among Affective Items Item

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

1

--

2

.35

--

3

.64

.31

--

4

.23

.66

.19

--

5

.67

.43

.80

.23

--

6

.49

.46

.61

.36

.75

--

7

.20

.44

.13

.36

.12

.13

--

8

.43

.23

.58

.16

.65

.59

-.03

--

9

.30

.46

.30

.35

.29

.37

.51

.21

--

10

.63

.44

.71

.29

.77

.74

.16

.68

.28

--

11

.45

.62

.47

.52

.50

.60

.40

.37

.61

.50

--

12

.46

.37

.46

.34

.46

.49

.08

.48

.40

.49

.59

--

13

.26

.28

.29

.18

.29

.25

.45

.09

.21

.24

.23

-.03

--

14

.32

.67

.29

.51

.34

.34

.45

.21

.48

.32

.64

.46

.24

--

15

.25

.27

.31

.25

.30

.26

.43

.12

.19

.29

.27

.02

.70

.21

16

.28

.65

.25

.53

.32

.35

.53

.15

.55

.34

.54

.29

.21

.61

.26

--

17

.23

.43

.21

.42

.22

.23

.60

.08

.49

.25

.43

.20

.27

.47

.22

.64

--

18

.37

.39

.41

.30

.46

.52

.13

.42

.25

.56

.47

.40

.34

.41

.42

.27

.11

18

--

--

Items that did not load above .32 on any one factor were removed, in line with previous research recommendations (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). Additionally, any items that loaded above .32 on more than one item (called cross loading) were also removed(Crocker & Algina, 1986; Tabachnick & Fidel, 2001). Lastly, any items with communalities below.32 were removed (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). This process was repeated until a 15

clean factor structure emerged. Next, confirmatory factor analyses (CFA) were run using the second sample, in SPSS, to test the factor structure created with the exploratory factor analyses, along with alternatives to determine the factor structure that best fit the data. Finally, the affect, cognition and evaluation variables were used in a regression analysis to predict job satisfaction as characterized by the JDS and Faces measures.

Exploratory Factor Analysis From the 18 affective items, four exploratory factor analyses were conducted before a clean factor structure emerged. The first exploratory factor analysis produced three factors with eigenvalues > 1, accounting for 61.43% of the variance. The rotated model was relatively clean as only 4 items cross loaded, and were subsequently dropped (see Table 3). Table 3 Affective Items by Step EFA 1

2

3

4

Factors

Deleted/Reason

Retained

3

Bored-Crossloading Hateful-Crossloading Dissatisfied-Crossloading Tense-Crossloading

Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Relaxed Disgusted Calm Angry Distressed Accepted

3

Accepted-Crossloading

Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Relaxed Disgusted Calm Calm Distressed

3

Relaxed-two item factor Calm-two item factor

Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Disgusted Calm Distressed

Final Structure

Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Disgusted Calm Distressed

2

No items were dropped for having commonalities below 0.32, or for not loading on factors 16

(Crocker & Algina, 1986; Tabachnick & Fidel, 2001). A second EFA was run, using identical procedures, to again produce 3 factors with eigenvalues >1, accounting for 62.02% of the variance. The rotated model was relatively clean as there was only 1 cross loading item, which was subsequently removed (see Table 4). No items in the analysis had communalities below 0.32 or failed to load on a factor (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). Table 4 Correlations among Cognitive Items Item

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

1

--

2

.38

--

3

.13

.12

--

4

.25

.37

.13

--

5

.43

.72

.12

.44

--

6

.19

.03

-.01

-.08

.04

--

7

.33

.32

.30

.40

.34

.06

--

8

.26

.29

.41

.35

.23

.-04

.34

--

9

.25

.62

.20

.36

.66

.00

.29

.23

--

10

.15

.25

.42

.23

.25

.16

.46

.52

.26

--

11

.55

.41

.23

.29

.46

.18

.33

.33

.37

.26

--

12

.25

.16

.42

.25

.28

-.14

.35

.32

.21

.18

.28

--

13

.14

.40

.34

.21

.40

.03

.52

.44

.40

.58

.20

.16

--

14

.45

.41

.28

.44

.49

.09

.44

.39

.46

.29

.39

.43

.27

--

15

.08

.12

.66

.07

.12

-.03

.32

.42

.20

.33

.18

.47

.40

.21

--

16

.19

.21

.55

.21

.21

-.04

.41

.49

.24

.37

.20

.40

.43

.32

.73

--

17

.38

.29

.08

.31

.35

.11

.32

.22

.33

.16

.58

.16

.18

.30

.08

.10

--

18

.24

.36

.44

.30

.41

-.04

.52

.48

.44

.60

.26

.29

.72

.38

.47

.51

.18

A third EFA was run, using identical procedures, to again produce 3 factors with eigenvalues >1, accounting for 63.70% of the variance. The rotated model was clean as no items were removed for cross loading, having communalities below 0.32, or failing to load on a factor (Crocker & 17

18

--

Algina, 1986; Tabachnick & Fidel, 2001). However, one factor was removed as only items 13 (relaxed) and 15 (calm) loaded on it. This is in line with the recommendations of Tabachnick and Fidel (2001), who suggest that interpreting factors with two or fewer variables loading on them are too "hazardous" to interpret with any degree of certainty (p. 266). Finally, a fourth EFA was run using identical procedures. The resulting rotated model generated two factors with eigenvalues > 1, and accounted for 61.28% of the variance. No items were removed for failure to load, cross loading, or having a communality below 0.32 from this clean two factor structure. For the cognitive component items, a series of EFAs were run with maximum likelihood extraction and varimax rotation, to provide a more interpretable structure given the correlations (see Table 4) among the items (Tabachnick & Fidel, 2001). Items that did not load above .32 on any one factor were removed, in line with previous research recommendations (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). Additionally, any items that loaded above .32 on more than one item (called cross loading) were also removed (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). Lastly, any items with communalities below.32 were removed (Crocker & Algina, 1986; Tabachnick & Fidel, 2001). This process was repeated until a clean factor structure emerged. The first EFA produced 5 factors accounting for 55.83% of the variance. The rotates model was muddled with 1 item (average) dropped for having a low communality and 7 items dropped for cross loading (see Table 5). No items were removed for failing to load on a factor. A second EFA was run using identical procedures to generate 3 factors with eigenvalues > 1, accounting for 61.73% of variance. This rotated model was relatively clean with 1 item (safe) dropped for having a low communality and no items dropped for cross-loading or failing to load. A third exploratory factor analysis was run using identical procedures to produce 2 factors with

18

eigenvalues > 1, accounting for 55.85% of the variance. This rotated model was relatively clean as only 1 item (foolish) was dropped for having a low communality and 1 item (useless) was dropped for cross loading. A fourth and final EFA was run using identical procedures to produce 2 factors with eigenvalues > 1, accounting for 65.35% of the variance. This rotated model was clean. None of the remaining 6 items were dropped for having low comunalities, cross loading, or failing to load on a factor. Table 5 Cognitive Items by Step EFA

1

Factors

Deleted/Reason

5

Average – low com Ideal– cross loading Perfect – cross loading Sensible– cross loading Superior– cross loading Inadequate– cross loading Flawed– cross loading Worthless– cross loading

Retained Useful Beneficial Valuable Safe Harmful Foolish Useless Unsafe Unhealthy

Useful Beneficial Valuable 2

3

Safe– low com

Harmful Foolish Useless Unsafe Unhealthy

3

2

4

2

Foolish– low com Useless – cross loading

Useful Beneficial Valuable Harmful Unsafe Unhealthy Useful Beneficial Valuable Harmful Unsafe Unhealthy

Forced Factors Using the second sample, affective items retained in the initial EFA were run through three analyses that forced 1, 2, or 3 factors using rotation and extraction methods identical to those found in the previous exploratory analyses. The first analysis, which forced a single factor

19

with an eigenvalue > 1, accounted for 45.51% of the variance and produced X2(44) = 455.79, p < .01. This solution generated comunalities below .32 on 9 items (see Table 6). Table 6 Confirmatory Factor Analysis for Affect with 1 Forced Factor Variable Name Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Disgusted Angry Distressed

Factor 1 .731 .849 .377 .921 .841 .746 .452 .888 .400 .452 .371

The second analysis, which forced two factors with eigenvalues >1, accounted for, 62.18% of the variance and produced a X2(34) = 92.95, p < .01. This solution produced acceptable communalities for all affective items and no cross loading items (see Table 7). The third analysis, which forced three factors with eigenvalues >1, accounted for, 65.25% of the variance and produced a X2(25) = 40.17, p = .28 this solution produced acceptable comunalities for all items and one crossloading item (see Table 8). Three more analyses were run using identical procedures to assess the two factor model fit of the cognitive items. Cognitive items retained in the initial EFA were run through three analyses that forced 1, 2, or 3 factors using maximum likelihood extraction and varimax rotation, to account for correlations (see Table 4) among the items (Tabachnick & Fidel, 2001).

20

Table 7 Confirmatory Factor Analysis for Affect with 2 Forced Factors Factor

Variable N ame Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Disgusted Angry Distressed

1

2

.697 .847 .175 .907 .774 .747 .255 .840 .195 .181 .133

.225 .173 .615 .226 .320 .127 .632 .271 .637 .853 .730

Table 8 Confirmatory Factor Analysis for Affect with 3 Forced Factors Variable Name Warm Delighted Sad Joyful Satisfied Excited Annoyed Happy Disgusted Angry Distressed

1 .706 .865 .165 .902 .774 .738 .258 .836 .187 .177 .132

Factor 2 .236 .178 .615 .231 .318 .128 .639 .271 .636 .846 .745

3 -.215 -.175 .191 .004 .347 .127 -.078 .148 .073 .030 -.132

The first analysis, which forced a single factor with an eigenvalue > 1, generated communalities

21

below .32 for 3 of the 6 items (see Table 9). The one-factor solution accounted for 33.77% of the variance and produced a X2(9) = 330.86, p < .01. Table 9 Confirmatory Factor Analysis for Cognition with 1 Forced Factor Variable

Factor 1

Useful

.160

Beneficial

.171

Valuable

.191

Harmful

.737

Unsafe

.874

Unhealthy

.793

The second analysis, which forced two factors with eigenvalues >1, generated acceptable communalities for all 6 cognitive items (see Table10). The two-factor rotated solution accounted for 65.35% of the variance with no cross loading items and a X2(4) = 2.53, p = .64. Table 10 Confirmatory Factor Analysis for Cognition with 2 Forced Factors Variable

Factor 1

2

Useful

.830

.057

Beneficial

.852

.068

Valuable

.722

.103

Harmful

.063

.722

Unsafe

.010

.916

Unhealthy

.155

.760

The third analysis, which forced three factors with eigenvalues > 1, generated acceptable communalities for all items (see Table 11). The three-factor rotated solution accounted for 22

67.50% of the variance, no cross loading items. No items loaded on factor 3 and a X2 analyses were not possible due to a negative degrees freedom. Table 11 Confirmatory Factor Analysis for Cognition with 3 Forced Factors Variables

Factor 1

2

3

Useful

.054

.825

.039

Beneficial

.062

.852

.097

Valuable

.101

.736

-.110

Harmful

.745

.072

-.175

Unsafe

.886

.017

-.003

Unhealthy

.787

.147

.218

Validity In order to establish a basis of validity of these measures they were first correlated with measures of general satisfaction (Hackman & Oldham, 1980), the single item Faces measure (Kunin, 1955), and job evaluation (Crites et al., 1994). The analysis showed the affective items correlated with JDS general satisfaction, Faces, and job evaluation at .74, .72, and .35 respectively. The identical analysis was run for the cognitive items where the second sample showed cognitive items correlated with JDS general satisfaction, Faces, and job evaluation at .64, .62 , and .29 respectively.

Reliability Analysis A reliability analysis was run for the retained affective and cognitive items in order to determine how well the items fit together. The scale had a mean of 6.00 and standard deviation of 23

7.82. The 11 retained affective items showed good reliability with an overall coefficient alpha value of .90. Item-Total correlations ranged from .50 (distressed) to .77 (joyful). All items showed that the overall reliability would be compromised if the item was removed (see Table 12). Table 12 Reliability Analysis for Affective Items Scale Mean if Item Deleted

Scale Corrected Squared Variance i f Item-Total Multiple Item Deleted Correlation Correlation

Cronbach's Alpha if Item Deleted

Joyful

4.59

48.836

.773

.804

.880

Satisfied

4.34

49.289

.733

.693

.882

Delighted

4.61

49.979

.709

.703

.884

Happy

4.31

49.374

.772

.744

.880

Excited

4.64

51.927

.579

.537

.891

Warm

4.57

49.441

.640

.537

.888

Angry

8.24

51.834

.598

.596

.890

Distressed

8.50

52.742

.502

.481

.896

Disgusted

8.09

52.316

.554

.437

.892

Sad

8.00

54.102

.509

.413

.895

Annoyed

8.95

51.674

.546

.426

.893

The reliability analysis for the cognitive items showed less reliability. The scale had a mean of 4.67 and standard deviation of 4.32. The 6 retained cognitive items had an overall coefficient alpha value of .75. Item-Total correlations ranged from .45 (harmful) to .53 (unhealthy). All items showed that the overall reliability would be compromised if the item was removed (see Table 13).

24

Table 13 Reliability Analysis for Cognitive Items Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected ItemTotal Correlation

Squared Multiple Correlation

Cronbach's Alpha if Item Deleted

Useful

2.028

13.701

.484

.550

.712

Beneficial

1.983

13.539

.496

.567

.708

Valuable

2.174

13.502

.480

.450

.713

Harmful

5.631

13.761

.451

.460

.720

Unsafe

5.743

13.709

.466

.599

.716

Unhealthy

5.791

13.291

.532

.525

.698

Multiple Regression A series of multiple regressions were run in order to test the final hypothesis. Both the Faces (Kunin, 1955) and JDS general satisfaction (Hackman & Oldham, 1980) were used as outcome variables. In the first multiple regression the retained affect and cognition items were used to predict JDS general satisfaction. This model was significant such that F(2,257) = 173.53, p < .01. The overall R2 = .76 indicating the affect and cognition variables accounted for considerable variance in the model. Semi-partial correlations revealed affect accounted for the majority of this variance (rxy = .50) while cognition accounted for the remainder (rxy = .16). The second multiple regression was identical to the first except that job evaluation was added to the predictors. This model was significant such that F(3,256) = 124.14, p < .01. The overall R2 = .77 indicating the affect, cognition, and evaluation variables accounted for considerable variance in the model. Semi-partial correlations again revealed affect accounted for the majority of this variance (rxy = .45) while cognition (rxy = .14) accounted for nearly the same amount as before and evaluation (rxy = .14) accounted for the remainder. The third multiple regression used the

25

affect and cognition items as predictors of job satisfaction as measured by the Faces (Kunin, 1955) instrument. This model was significant such that F(2,257) = 148.16, p < .01. The overall R2 = .73 indicating the affect and cognition variables accounted for considerable variance in the model. Semi-partial correlations revealed affect accounted for the majority of this variance (rxy = .49) while cognition accounted for the remainder (rxy = .14). The fourth, and final, multiple regression was identical to the third except evaluation was added to the predictors. This model was significant such that F(3,256) = 103.76, p < .01. The overall R2 = .74 indicating the affect, cognition, and evaluation variables accounted for considerable variance in the model. Semipartial correlations again revealed affect accounted for the majority of this variance (rxy = .45) while cognition (rxy = .13) accounted for nearly the same amount as before and evaluation (rxy = .12) accounted for the remainder.

26

CHAPTER 4 DISCUSSION The initial exploratory factor analyses for the affective and cognitive items fell into two distinct factors, in support of Hypothesis 1. This is not surprising given the body of literature showing a two factor structure (see for review Thorensen et al., 2003). Of the eleven affective items retained, 6 items loaded on the positive factors and 5 items loaded on the negative factor. The cognitive items showed a similar pattern, with the 6 cognitive items retained falling into groups of 3 and 3 for the positive and negative factors respectively. This provides an even distribution of items across the factors. Having an equal number of items per factor avoids weighting the scale in the direction of any one factor and provides balance to the measure. The relatively even distribution of items across factors is encouraging, given that this is the first analysis of the items. Another positive aspect of the study was found in the confirmatory factor analysis X2 goodness of fit measure. The significant X2 measures for both the affective and cognitive items were significant indicating poor fit. However, their relatively small values indicate that the measures may not need much improvement in order to reach acceptable fit. Considering these results were obtained using a second, independent, sample they provide some evidence that the scales are on the right track. The addition and refining of a few new items may greatly improve the scales fit in the future. Forcing different numbers of factors with the affective items produced another interesting result. The two factor structure that emerged in the first sample immediately shifted towards a 3 factor structure in the second sample for affective items. Affective items the forced onto a 3 factor structure accounted for the most variance of the models tested, improving by 4%. The

27

forced 3 factor structure was the only model that showed adequate fit, as evidenced by the significant X2 value. However, his result, may be a function of using the X2 goodness of fit measure with the larger sample (N1 = 217, N2 = 260) as discussed by Klein 2005. The fit of the 3 factor structure is interesting considering the third factor consisted of only one item for the affective scale (satisfied). This suggests that satisfaction and affect are different factors. Additionally, the item forming the third factor cross loaded on the other two factors. Considering all aspects of the confirmatory analysis, the two factor structure may not be completely inappropriate, but further research is needed into the possible third factor. The issue of model fit is further called into question when the validity coefficients are examined. The cognitive and affective items showed moderate to strong validity coefficients, showing support for Hypothesis 2, with regard to the job satisfaction variables. This result is consistent with the results commonly found in the literature (Thorenson et al., 2003; Weiss & Cropanzano, 1996; Watson & Walker, 1996). The interesting results from the validity section of the study lie with the evaluation component. Both, affect and cognition had small validity coefficients with regard to evaluation. Evaluation is thought to be an attitudinal judgment formed with the use of affective and cognitive components (see Fisher, 2000 for review). However, in this instance, neither affect or cognition seems to account for much variance in the evaluation variable. The viability of the evaluation variable is called into question once the regression analyses were run. Affect and cognition showed themselves to be significant predictors of job satisfaction, in support of Hypothesis 2. However, adding evaluation provided for a very small increase in the overall variance in predicting general and faces satisfaction (2% and 1% increases respectively). Such small increases in the overall variance accounted for are very unlikely to be

28

practically significant and likely result simply from having another variable in the prediction equation. Accounting for such a small amount of variance also suggests that evaluation and satisfaction are quantifiably different constructs. Thus, if evaluation is a true attitudinal measure, as suggested by Crites et al. (1994), then job satisfaction may not be an attitude, but rather something else that has yet to be defined. Given the previous research showing evaluation as a viable component of job satisfaction (Crites et al., 1994; Hulin & Judge, 2003; Tekell, Yeoh, & Huff, 2006), post hoc analyses of the individual evaluation items were run. The four job evaluation items from Crites et al. (1994) were examined for consistency of means and standard deviations (see Table 14). They were also correlated with the affective, cognitive, evaluative, and satisfaction (JDS, FACES) measures (see Table 14). Table 14 Job Evaluation Means and Standard Deviations Job Evaluation 1

Job Evaluation 2

Job Evaluation 3

Job Evaluation 4

Mean

3.358

3.366

3.300

3.229

Median

4.000

4.000

4.000

3.000

Std. Deviation

1.2931

1.3088

1.3121

1.2787

The evaluation items were consistent showing no dramatic differences in means or standard deviations across items. Accordingly, they correlated strongly with the overall evaluation score, as expected (see Table 15). However, across all other variables the items consistently correlated at a much lower level (Table 15). No one evaluation item differentiated itself from the others. This serves as further evidence against including evaluation in the measurement of satisfaction.

29

Table 15 Job Evaluation Item Correlations with Study Variables Variable

Eval 1

Eval 2

Eval 3

Eval 4

Faces

Gensat

Job Eval

Ascale

Eval 1

--

Eval 2

.85*

--

Eval 3

.91*

.84*

--

Eval 4

.76*

.75*

.79*

--

Faces

.34*

.33*

.37*

.34*

--

Gensat

.36*

.32*

.40*

.39*

.76*

--

Job Eval

.95*

.93*

.95*

.89*

.37*

.40*

--

Ascale

.30*

.30*

.34*

.36*

.72*

.74*

.35*

--

Cscale

.24*

.29*

.27*

.28*

.55*

.57*

.29*

.60*

Cscale

--

* Correlation is significant at the 0.01 level (2-tailed).

Limitations and Directions for Future Research There are several limitation to the current study. Any of which, could have generated enough error to skew the results. First and foremost, the student sample may not generalize to some organizations as these jobs are not career choices. Additionally, the assumption of normality was violated. The data were not normal which may have caused problems with the interpretation of the EFA, CFA, and regression analyses. Another limitation of the study was that as the data were cleaned, missing data in both samples were replaced with medians. This caused a restriction in range which may have further skewed the results of this study. However, this effect of the restriction in range should be minimal, given the small amount of missing data (no more than 6.9%). In order to minimize these effects future research should strive to utilize truly random sampling at multiple time intervals. Methodology aside, future research is needed to better determine the role of evaluation in the prediction of job attitudes. Previous research has indicated that evaluation may be a useful 30

tool in predicting job attitudes (Crites et al., 1994; Hulin & Judge, 2003; Tekell, Yeoh, & Huff, 2006). However, this study showed evaluation to add nothing practically significant to the prediction of the general or faces variety of satisfaction. Furthermore, the results show job evaluation and job satisfaction to be very different constructs. Evaluation may be a valuable tool in closing the gaps in attitude prediction. However, without further exploration of this variable's contribution remains, at best, muddled. In addition to the evaluation variable the affective and cognitive scales require further refinement. Additional items and assessment methods should be created and tested to build upon the base of the current scales. These two scales show themselves to be predictive of job satisfaction but they may be predictive of job attitudes other than job satisfaction. Adding additional items may improve prediction or show areas for improvement. Researchers should further explore the exact factor structure of these scales as well. The 3 factor structure suggested by the second sample may be in some way related to evaluation. However, in order to confirm or rule out sample specific results additional items should be added and tested on larger, more representative samples. Further research is needed not only in the area of affective and cognitive structure but also in their functions and predictive abilities. This study did not address the relationships and predictive abilities of the positive and negative factors within the affective and cognitive scales. The individual positive and negative factors within each scale may have unique predictive abilities, or provide some function within the attitude. Future research should include factor level analyses in order to determine the full value of the scales in both their present and refined forms. Also of note, the affective and cognitive items used were based on the semantic differential scales used by Crites et al. (1994). These scales carry the inherent problem of using

31

end points that are not wholly opposite one another. Crites et al. (1994) offered a caution on this in their write up of their instrument, saying that too often items focus on some other aspect or tone of the evaluation. As additional items are added to the affective and cognitive scales found here, researchers should try to ensure that every item has truly opposite end points and avoid focusing on a single aspect or tone. Finally, future research should examine the affective items themselves. Affect accounted for the majority of the variance in every regression model. This is not an unheard of result and may be caused by participants allowing their affective reactions to attitude objects greater influence on their decisions (Salancik& Pfeffer, 1978; Verplanken et al., 1998; Zajonc, 1980). This may also be a case of unintended affect priming causing affect to be more accessible to the participants in this particular sample (Millar & Tesser, 1986; Weiss & Croponzano, 1996). Further research needs to be conducted in order to determine if this effect was sample specific or evidence of a larger trend in the prediction of attitudes.

32

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Affective and cognitive components of job - UNT Digital Library

AFFECTIVE AND COGNITIVE COMPONENTS OF JOB SATISFACTION: SCALE DEVELOPMENT AND INITIAL VALIDATION Jeremy Kyle Tekell Thesis Prepared for the Degree of...

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