using multi-source data to understand the unfolding of good & bad mentoring over time
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Using Multi-Source Data to Understand the Unfolding of Good & Bad Mentoring Over Time. Lillian T. Eby University of Georgia Marcus M. Butts University of Texas-Arlington. Methodological Criticisms of Mentoring Research. Mostly cross-sectional designs (Allen et al., 2008) - PowerPoint PPT PresentationTRANSCRIPT
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Using Multi-Source Data to Understand the Unfolding of
Good & Bad Mentoring Over Time
Lillian T. EbyUniversity of Georgia
Marcus M. ButtsUniversity of Texas-Arlington
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Mostly cross-sectional designs (Allen et al., 2008) Multi-source data is uncommon (Allen et al., 2008)
Concerning because we know that relationships are both dyadic & dynamic (e.g., Kram, 1985; Levinger, 1979)
Methodological Criticisms of Mentoring Research
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Presumption that mentoring is a universally positive experience But research evidence to the contrary (e.g., Eby et al.,
2000, 2010; Ragins & Scandura, 1997) Most mentoring relationships are marked by
both positive & negative experiences (Eby, 2007; Scandura, 1997)
Need to consider role of time Does “bad beget bad” & “good beget good”? How does this play out over time?
Conceptual Criticisms of Mentoring Research
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223 in-tact mentor-protégé dyads Two waves of data collection from all
participants Psychometrically sound multi-item measures of
“good” (Ragins & McFarlin, 1990; Ragins & Scandura, 1997) and “bad” mentoring (Eby et al, 2000, 2010)
Context: healthcare organization, supervisory mentoring relationships, all areas of U.S.
Methodology
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Contemporaneous Correlations
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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Contemporaneous Correlations
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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Contemporaneous Correlations
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
Trending toward greater
consistency as relationship length
increases
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Lagged Correlations: Good Begets Good
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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Lagged Correlations: Bad Begets Bad
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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Contemporaneous Correlations Between Good & Bad
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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Lagged Correlations Between Good & Bad
P good Y1 P good Y2 P bad Y1 P bad Y2
M good Y1 .18* .15* -.12 -.11
M good Y2 .14* .25* -.11 -.20*
M bad Y1 -.21* -.18* .14* .15*
M bad Y2 -.22 -.35* .16* .31*
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It’s important to include both the mentor’s & protégé’s perspective
Studying mentoring over time may lead to new insights
Examining the good and bad aspects of mentoring provides a more complete (and realistic) picture of dyadic relational processes
Take-Aways