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

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

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

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

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*

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*

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

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*

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*

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*

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*

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