Leadership styles, mentoring functions received, and job-related stress

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376 J. J. SOSIK AND V. M. GODSHALK and Avolio, 1997). PLS o€ers several bene®ts over traditional techniques such as ANOVA or regression. These bene®ts are discussed in Appendix A. We used PLS-Graph (version 2.91.02.08), a graphics-based program for performing PLS analysis in this study. PLS generates estimates of standardized regression coecients (i.e., path coecients) for the model paths, which can then be used to measure the relationships between latent variables. A jackkni®ng procedure called blindfolding was used to compute standard errors and assess signi®cance of estimates of path coecients (Wold, 1985). The blindfolding procedure omits a part of the data matrix for a particular construct and then estimates the model parameters (e.g., path coecients) associated with that construct. This process is repeated as often as the omission distance, which refers to how many data points in the data matrix are skipped before omitting one data point. An omission distance of 9 was employed (Sambamurthy and Chin, 1994). A conservative hurdle rate of p 5 0.001 was used to indicate signi®cance. Full sample data (n ˆ 204) was utilized to test Hypothesis 1a through Hypothesis 3, in terms of the unmoderated model. To test Hypothesis 4 in terms of the moderating e€ect of mentoring functions received, we split the sample into two sub-samples (i.e., low and high levels of mentoring functions received). Partitioning of the data into these sub-samples is discussed below. This approach parallels more traditional moderated regression by testing a model separately for each sub-sample (Duxbury and Higgins, 1991). Unpaired t-tests were used to test for di€erences in standardized regression coecients across the sub-samples (see Duxbury and Higgins, 1991). In PLS, constructs may be modelled using re¯ective or formative indicators. Re¯ective indicators are suggested when they arise from the construct, because of which they tend to covary (e.g., components of transformational leadership). Formative indicators are suggested when they combine to form a construct (e.g., aspects of socio-economic status) (Falk and Miller, 1992). Modelling of constructs examined in the present study is discussed below. Measurement of constructs Information was obtained from participants and from their mentors. Multiple-item measures were used to assess mentor leadership behaviors and proteÂ geÂ perceptions of mentoring functions received and job-related stress. Items for the measures are listed in Appendix B. Mentor's leadership behaviors Mentor leadership style/behavior was measured using items from the Multifactor Leadership Questionnaire (MLQ-5X; Bass and Avolio, 1997). While previous versions of the MLQ have been criticized for failure to empirically generate the factor structure proposed by Bass and Avolio (1994) to underlie transformational leadership (e.g., Bycio, Hackett and Allen, 1995; Yukl, 1994), research on the MLQ-Form 5X (e.g., Avolio, Bass and Jung, 1997; Bass and Avolio, 1997; Bass, 1998) has been shown it to be a psychometrically sound instrument. Mentors were asked to judge how frequently they exhibited speci®c behaviors measured by the MLQ-5X. Each behavior was measured on a ®ve-point frequency scale ranging from not at all (0) to frequently, if not always (4). Laissez-faire leadership was measured using two items from the MLQ-5X. Contingent reward leadership was measured using three items from the MLQ-5X. The following four-item scales was used as indicators of transformational leadership: (a) idealized in¯uenceÐbehavior (a ˆ 0.75), (b) inspirational motivation (a ˆ 0.72), (c) intellectual stimulation (a ˆ 0.72), and (d) individualized consideration (a ˆ 0.64). Copyright # 2000 John Wiley & Sons, Ltd. J. Organiz. Behav. 21, 365±390 (2000)
LEADERSHIP, MENTORING AND STRESS 377 Mentoring functions received To assess the degree of mentoring received by the proteÂ geÂ , we used two 10-item scales from Noe (1988) as indicators of mentoring functions: (a) career development (a ˆ 0.86), and (b) psychosocial support (a ˆ 0.87). ProteÂ geÂ s were asked to indicate their extent of agreement with each item using a ®ve-point scale ranging from disagree strongly (1) to agree strongly (5). Hypothesis 4, which pertained to the moderating e€ect of mentoring functions received, was tested by creating two sub-samples based on a median split: low mentoring functions received sub-sample (scale score 53.85; n ˆ 103) and high mentoring functions received sub-sample (scale score 43.85, n ˆ 101). Job-related stress Job-related stress perceived by the proteÂ geÂ was measured using six items from Parasuraman (1977, `Sources and outcomes of organizational stress: a multidimensional study of the antecedents, and attitudinal and behavioral indices of job stress'. Unpublished doctoral dissertation, State University of New York at Bu€alo). ProteÂ geÂ s were asked to judge how often they experienced situations described in each item. Each situation was measured on a ®ve-point frequency scale ranging from never occurs (1) to constantly occurs (5). The indicators of laissez-faire, contingent reward, and transformational leadership were expected to covary (see Bass, 1998), as were the indicators of mentoring functions received and job-related stress, thereby indicating that they arise from their respective constructs. Therefore, these indicators were modelled as re¯ective. Control variables Theoretical work on mentoring (e.g., Kram, 1985; Murray, 1991) suggests that proteÂ geÂ experience (i.e., age, job level, job tenure, education level), industry, and mentor gender can a€ect mentor behavior, mentoring functions received, and proteÂ geÂ job-related stress. To control for these potential e€ects, age, job level, job tenure, education level, industry, and mentor gender were entered into the PLS model as covariates. Each covariate was modelled as a single-item indicator. PLS is insensitive to how the indicators of single-indicator constructs are modelled. Results Table 2 presents the scale means, standard deviations, and product moment correlations among the measures. PLS generates statistics to test the reliability and validity of latent constructs with two or more re¯ective indicators. Reliability was assessed by ®rst examining the factor loadings of indicators: a common rule of thumb is that the factor loadings should exceed 0.7 since this implies that less than half of the indicator's variance is due to error (Fornell and Larcker, 1981). Next, we computed each construct's composite scale reliability (Fornell and Larcker, 1981), a measure of internal consistency similar to Cronbach's alpha. Fornell and Larcker (1981) recommended using a criterion cut-o€ of 0.7 or more. Also, the average variance extracted by the construct from indicators was examined. For this criterion, Fornell and Larcker (1981) recommended using a cut-o€ of 0.5 or more. Copyright # 2000 John Wiley & Sons, Ltd. J. Organiz. Behav. 21, 365±390 (2000)
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