Development and Construct Validation of the Mentor Behavior Scale

Pascale Brodeur, Simon Larose, George Tarabulsy, Bei Feng, and Nadine Forget-DuboisUniversité Laval

Researchers suggest that certain supportive behaviors of mentors could increase thebenefits of school-based mentoring for youth. However, the literature contains fewvalidated instruments to measure these behaviors. In our present study, we aimed toconstruct and validate a tool to measure the supportive behaviors of mentors partici-pating in school-based mentoring programs. The mentor behavior scale (MBS) wasdeveloped drawing on the premises of the mentoring sociomotivational model. Twohundred and fifty-three (253) college students participating in an eight-monthschool-based mentoring program completed an experimental version of the MBS anddifferent measures of the quality of the mentoring relationship at two times during theprogram. The questionnaire has good internal consistency coefficients and adequatefactorial structure, with the exception of the factor autonomy support. Moreover, threedimensions of the MBS predict mentoring relationship quality and the perceived use-fulness of the intervention. Recommendations for the use and improvement of theMSB are proposed.

Keywords: academic mentoring, school-based mentoring, mentor behaviors,questionnaire validation

Development and Construct Validation of the Mentor Behavior Scale

School-based mentoring refers to a non-professional supportive relationship in which amore educated and experienced person (the mentor) advises and supports a student (thementee) in order to facilitate the mentee’s academic and psychosocial integration (Eby,Allen, Evans, Ng, & DuBois, 2007). It is recognized that effective school-based mentor-ing depends strongly on the quality of the relationship that is developed between mentorand mentee (e.g. Galbraith & Cohen, 1995; Hamilton & Hamilton, 1992; Rhodes, 2005,Styles & Morrows, 1992), which in turn is influenced by a number of factors, includingthe mentor’s supportive behaviors. Although these behaviors are considered in many the-oretical mentoring models (Larose & Tarabulsy, 2014; Rhodes, 2005), few researchershave attempted to describe and measure them. In fact, no questionnaire has been devel-oped to date to specifically and exclusively capture the ways in which mentors supportmentees. At best, the available questionnaires (e.g. Cavell, Elledge, Malcolm, Faith, &

Pascale Brodeur, École de psychologie, Groupe de recherche sur l’inadaptation psychosocialechez l’enfant, Université Laval; Simon Larose, Faculté des sciences de l’éducation, Groupe derecherche sur l’inadaptation psychosociale chez l’enfant, Université Laval; George Tarabulsy,Groupe de recherche sur l’inadaptation psychosociale chez l’enfant, Université Laval; Bei Feng,École de psychologie, Groupe de recherche sur l’inadaptation psychosociale chez l’enfant, Univer-sité Laval; Nadine Forget-Dubois, École de psychologie, Groupe de recherche sur l’inadaptationpsychosociale chez l’enfant, Université Laval.Correspondence concerning this article should be addressed to Simon Larose, Groupe de recher-

che sur l’inadaptation psychosociale chez l’enfant, Faculté des sciences de l’éducation, UniversitéLaval, Québec, Canada. E-mail: simon.larose@fse.ulaval.ca

© 2015 Taylor & Francis

Mentoring & Tutoring: Partnership in Learning, 2015http://dx.doi.org/10.1080/13611267.2015.1011037

Hughes, 2009; Cavell & Hughes, 2000; Darling, Hamilton, Toyokawa, & Matsuda,2002; Harris & Nakkula, 2003; Liang, Tracy, Kenny, Brogan, & Gatha, 2010; Lianget al., 2002; Rhodes, Reddy, Roffman, & Grossman, 2005; Zand et al., 2009) were con-ceived to evaluate the mentor’s overall style; the nature of interactions between mentorand mentee; the types of activities they do together; the mentee’s feelings of emotionalcloseness, trust, and support; and/or the mentee’s engagement in the mentoring relation-ship. Moreover, many of these questionnaires have no theoretical grounding, whichgreatly limits the assessment of their validity.

The general objective of our present study was therefore to develop and validate anew questionnaire that specifically measures mentors’ behaviors. The design of this scalewas guided by the premises of the mentoring sociomotivational model (Larose &Tarabulsy, 2014). We believe that this scale can fill the substantial gap in the evaluationof mentors’ practices and more effectively focus the exploration of certain influentialmechanisms at play in the mentor–mentee relationship.

The Mentoring Sociomotivational Model (MSM)

Theoretical models are useful sources of information for guiding the development ofmeasuring instruments. Several current mentoring models are informative on the charac-teristics of the mentee (DuBois, Neville, Parra, & Pugh-Lilly, 2002; Parra, DuBois,Neville, Pugh-Lilly, & Povinelli, 2002; Rhodes, 2005; Rhodes, Spencer, Keller, Liang,& Noam, 2006), the mentor (Parra et al., 2002), and the types of mentoring relationships(DuBois et al., 2002; Parra et al., 2002; Rhodes, 2005; Rhodes et al., 2006) that are asso-ciated with benefits for the mentee. They also inform us on the mechanisms that canexplain these benefits (e.g. improved interactions between the mentee and significantothers and corrective emotional experiences [Rhodes, 2005], mentee’s perceptions ofsocial support and self-esteem [DuBois et al., 2002]), and on the nature of the expectedbenefits of the mentorship (psychosocial, academic, and professional, as addressed by allthe above-mentioned models). However, none of these models describes which behaviorsthe mentor should adopt in the presence of the mentee, with the exception of thementoring sociomotivational model (MSM; Larose & Tarabulsy, 2014).

The MSM draws its inspiration from the sociomotivational model of self (Connell,1990; Connell & Wellborn, 1991), which is based on the premises of self-determinationtheory (SDT; Deci & Ryan, 1985, 1991; Ryan & Deci, 2000). In the MSM, feelings ofcompetence, relatedness, and autonomy (called self-determination here) are defined asbasic psychological needs that must be satisfied to enable mentee adjustment. Four ele-ments of the mentoring intervention are considered essential to satisfy these three psy-chological needs: structure, engagement, autonomy support, and competency support.Structure refers to the manner in which the mentor counsels the mentee and providesfeedback on the mentee’s questions and concerns. It also translates as the mentor’sefforts to reach an understanding with the mentee of the purpose of the encounters andthe types of activities to do together. Engagement refers to the emotional resources thatthe mentor provides to the mentee, by spending quality time with the mentee, andactively listening to and empathizing with the mentee, among others. Autonomy supportmeans that the mentor helps the mentee make choices that are consistent with the men-tee’s own values and goals, and does not exert control over these choices. Competencysupport translates as positive reinforcement in the case of both success and failure. Thisbehavior was proposed by Deci and Ryan (1985) and Ryan and Deci (2000) under

2 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

self-determination theory. Competence support clearly addresses the basic need toperceive oneself as academically competent, and has received much theoretical andempirical support. The presence of these four behaviors would ensure the developmentof a good-quality mentoring relationship, which would help the student satisfy the threebasic psychological needs, namely the need to feel academically competent, to feelconnected to the school community, and to feel that one’s academic choices are self-determined. In line with Connell and Wellborn (1991), the authors of the MSM contendthat if these three needs are satisfied, the mentee can integrate more easily into academic,social, and professional communities.

Empirical Support for the MSM

Structure

A highly controversial topic in the mentoring field is the notion that the mentor’s actionsmust respond exclusively to the mentee’s spontaneous needs, and that prescriptive, pro-grammed, and structured activities should be avoided at all costs. However, Hamiltonand Hamilton (1992) conducted qualitative interviews with mentors in a program foryoung adolescents (Linking Up) and found that the relationships that lasted the longest,were most positive, and were most preferred by the mentees were those that aimed toachieve specific goals and objectives on which a shared understanding had been reached,and not the relationships that had the adolescent’s well-being as the sole aim. Similarly,Langhout, Rhodes, and Osborne (2004) sought to identify effective mentoring relation-ship patterns in Big Brothers Big Sisters (BBBS) of America programs. Using an experi-mental design in a sample of 1,138 young Americans aged from 10 to 16 years, theyshowed that mentoring relationships that were moderately structured (i.e. goal orientedbut not overly structured; took the youth’s opinions, preferences, and characteristics intoaccount; provided the mentee with realistic expectations and constructive feedback;included challenging activities; initiated discussions about goals for the mentoring rela-tionship and problems encountered) and moderately supportive (i.e. provided emotionalsupport for meeting realistic expectations that were explained to the mentee; supportedthe mentee’s efforts and skills development; provided a steady, reliable, enjoyable, andsupportive mentoring presence; included shared participation in enjoyable activities; tol-erated a reasonable level of negative affect; included honest and open communication;and avoided an overly permissive approach) generated more benefits for the youths (i.e.improvement in self-concept and academic competence) in comparison to mentoringrelationships with unconditional support and/or lack of structure and compared to nomentorship.

Other findings support the importance of structure in the mentor–mentee relationship.Larose, Chaloux, Monaghan, and Tarabulsy (2010) evaluated the impact of the workingalliance between teacher mentors and student mentees (25 academically at-risk collegestudents) on students’ academic functioning. The working alliance was defined as a rela-tionship of emotional closeness and trust between the mentee and the mentor, clearexpectations concerning targeted mentorship goals, and clear expectations about theactivities and responsibilities required to achieve these (Bordin, 1994). The resultsshowed that the mentees who developed a more positive alliance with their mentor werebetter adjusted in college than other mentees and control students. In addition, comparedto a group of students who were not academically at-risk, this first group showed, at the

VALIDATION OF THE MENTOR BEHAVIOR SCALE 3

end of the program, superior academic competencies and a greater tendency to seek helpfrom their professors.

Competency Support

Some studies indicate that the mentor’s support for competency is a key component inthe development of mentoring relationship quality (MRQ) and mentee adjustment. Stylesand Morrows (1992) sought to identify the determinants of a satisfactory mentoringrelationship. According to these authors, a satisfactory relationship should include thefollowing: (a) a mutual feeling of attachment, (b) engagement, expressed as a desire tocontinue the relationship, and (c) the mentee’s perception of being supported by the men-tor. Using semi-structured interviews with mentors in four American programs (LinkingLifetimes Programs), they showed that neither activity type nor encounter frequency wasdeterminant for relational satisfaction, but instead the mentor’s behaviors. Moreprecisely, the mentor’s criticisms, judgments, and reprimands of the mentee had a devas-tating effect on satisfaction. In contrast, encouragement, signs of trust, and support forself-esteem contributed to the development of relationships that were perceived as satis-factory. In a younger clientele, King, Vidourek, Davis, and McClellan (2002) evaluatedthe impact of a mentoring program for 32 mentees aged from 9 to 10 years. Under theHealthy Kid Mentoring Program, mentors met with mentees twice a week for fivemonths. In addition to authentic dialog, goal-setting, and academic support, the activitieswere specifically designed to develop self-esteem. In this perspective, the mentors initi-ated activities and discussions designed to support the mentees’ feelings of relatedness,empowerment, and uniqueness. At the end of the program, the mentees showed signifi-cantly higher self-esteem as well as greater feelings of relatedness at school, with family,and with peers compared to control students. As additional benefits, the mentees showedfewer at-risk and delinquent behaviors, and the majority of mentees showed improvedacademic performance. This study allow us to suggest that it is possible and advanta-geous to formally support a feeling of competence in mentees in order to contribute totheir personal, academic, and social adjustment.

Autonomy Support

The role of autonomy support was explored in a seminal study by Morrow and Styles(1995) on the nature and effects of mentors’ intervention style. From semi-structuredinterviews conducted in a sample of 82 dyads participating in a BBBS mentoringprogram, the authors identified two main intervention styles: a developmental and aprescriptive approach. The first approach was characterized by the mentee’s substantialinvolvement in managing the relationship and its limits, including decisions on whatactivities to do together. In contrast, the second approach was hampered by the mentor’sover-involvement, which translated into unilateral control over goal setting and goalmeeting as well as which activities to do. More importantly, the results showed that, ninemonths after the mentoring, 90% of the dyads that were considered developmental con-tinued to meet, whereas only 33% of the dyads considered prescriptive remained active.

The importance of autonomy support was also underscored by the results of a studyby Larose, Tarabulsy, and Cyrenne (2005) who evaluated the impacts of a shortacademic mentoring program (10 hr) initiated by 5 college professors and 40 of their

4 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

students. More precisely, in this quasi-experimental study, Larose, Tarabulsy, andCyrenne showed that students who were exposed to an autonomy-supportive mentor(e.g. the mentor did not tell the student what to do; the mentor did not make decisionsfor the student) reported that they were more socially adjusted and felt more relatednessat college compared to control students. In contrast, students who were paired with amore controlling mentor perceived more adjustment problems (academically, socially,and emotionally) and earned lower grades than control students. These two studiesclearly suggest that non-autonomy-supportive mentors risk having their mentoringrelationship cut short, or even generating negative consequences for their mentees.

Engagement

The majority of mentoring researchers recognize that the mentor’s emotional engagementis a key supportive factor. In fact, this support dimension appears—explicitly or implic-itly—in all the theoretical models of mentoring and in the vast majority of definitions ofmentoring (Johnson, Rose, & Schlosser, 2007; Rhodes, 2000). For example, in his modelof youth mentoring, Rhodes specified that listening and empathy by the mentor areessential conditions for the development of an effective mentoring relationship. In addi-tion to these theoretical propositions, other researchers have showed that a secure emo-tional bond with the mentor is strongly determinant for the mentee’s adjustment. Forexample, in a study in 158 at-risk college students, Soucy and Larose (2000) found thata secure emotional attachment to the mentor, as perceived by the mentee, was positivelyassociated with the mentee’s academic and emotional adjustment, regardless of the men-tee’s perceived security of the parental relationship. In a second study in 102 at-risk col-lege students, Larose, Bernier, and Soucy (2005) demonstrated that for students with amore preoccupied attachment style (according to Main & Goldwyn’s classification,1998), a feeling of security to mentor increased the mentee’s satisfaction with thementorship and favored a continued relationship.

Our Present Study

This review suggests that structure, competence support, autonomy support, and engage-ment are mentor’s behaviors that are positively associated with the quality of the mentor-ship and the mentee’s adjustment. However, in very few of these studies, researchershave specifically or validly measured these behaviors. Several have deduced them fromsemi-structured interviews or the mentee’s feelings and impressions. Moreover, the eval-uation of the mentor’s supportive behaviors has rarely been grounded on a theoreticalmodel. A systematic measure of these behaviors, using a concise and easily-administeredquestionnaire based on the MSM, would enrich the research on mentoring processes,better validate the premises of certain theoretical models, and guide the training ofmentors as well as the application of mentoring practices with mentees.

In our present study therefore, we aimed to develop and validate the mentor behaviorscale (MBS), which was inspired by the premises of MSM. Using data from an evalua-tive study on the impact of an academic mentoring program in a college setting, weexamined the internal consistency of the MBS by calculating the ordinal coefficientalpha for each of the four subscales of the questionnaire. We also evaluated the constructvalidity of the MBS in three ways: by examining its (a) concomitant validity, (b)

VALIDATION OF THE MENTOR BEHAVIOR SCALE 5

factorial validity, and (c) temporal stability. With respect to factorial validity, it waspredicted that the four dimensions of the MBS would be distinct and therefore moder-ately correlated. In terms of concomitant validity, it was expected that the scores on thesubscales and the overall scores on the MBS would correlate positively with MRQ andwith mentees’ perceived usefulness of the mentorship. For temporal stability, it wasexpected that subscale and overall scale scores measured near the start of the programwould be only moderately correlated with the same scores measured four months later.This hypothesis stems from the notion that mentors’ supportive behaviors are not fixed,and are instead learned skills that are context-sensitive, and could therefore be influencedby factors such as training, supervision, and mentee’s behaviors.

Method

The Mentoring Program

The data for the present study were taken from an evaluative study of the Mentoring forthe Integration and Success of Science Students program (MIRES; Larose et al., 2011).The purpose of the MIRES program was to help secondary school graduates perseverein mathematics, science and technology (MST) programs in college. Studentsparticipated in a mentoring relationship throughout the first year of college studies, inwhich they were paired with university students who were completing a degree inscience and engineering. The ultimate goal of the MIRES program was to positivelyinfluence the participants’ academic, professional, and social trajectories.

The mentors who participated in the MIRES program were given an initial trainingof 16 and 12 hr of continuous training. Each mentor was expected to spend 30 hr inmeetings with each mentee, spread out over the school year (from September to April),for which they would receive financial compensation. The activities they did with thementees should help the mentees integrate into their study program, better prepare themto choose a career in MST, improve their understanding of the MST culture, and helpthem develop meaningful relationships with the scientific community. It was suggestedto the mentors that these objectives could be achieved by holding discussions with thementees on program and career choices, talking about how to understand and resolveissues that arise during their studies (e.g. first exams), going on cultural and scientificoutings, and attending public conferences.

Participants, Matching, and Questionnaire Administration

The study participants were 253 mentees who participated in a MIRES program in 2006–2007 or 2007–2008 (experimental groups). They were recruited from students enrolled innatural science, science and art, and computer science programs at two colleges in theQuebec City area, and were randomly assigned to either the MIRES program or a controlgroup. The first cohort of students was recruited when they were taking an exam in thespring to determine their level of English, and the second cohort was recruited by tele-phone just before they entered college. At the time when the students were assigned to theexperimental groups, 88% of the mentees were enrolled in a natural science program, 6%in a science and art program, and 6% in computer science. The average age of the samplewas 17 years, ranging from 16 to 22 years. The mentees were divided equally between the

6 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

two sexes. Each mentee was paired with one of the 57 mentors in the program accordingto sex, and their academic and professional aspirations.

At the time of recruitment, the 57 mentors were attending Laval University inQuebec City, where 68% were undergraduates, 28% were graduate students, and 4%were postgraduate students. The average age of the mentors was 23 years, ranging from19 to 31 years, and 54% were female. The possibility for significant nested effectscaused by mentees having the same mentor was explored by calculating the intra- classcorrelation coefficients (ICC) in the questionnaire data on mentees. The results on theICCs for each questionnaire subscale do not exceed .1, indicating that the data do notneed to be considered as nested.

The MBS was administered in October 2006 and 2007 (Time 1—middle of the 1stsemester) and four months later in February 2007 and 2008 (Time 2—middle of the sec-ond semester), along with the questionnaire to assess the quality of the mentoring rela-tionship. The mentees’ perceptions of the usefulness of the mentorship were measured atTime 2 only.

Development of the MBS

A preliminary version of the MBS was developed as part of the MIRES program, basedon the premises of the MSM. Some of the item formulations were inspired by the Aca-demic Counseling Behavior Scale (Larose, Boivin, & Doyle, 2001), which was createdfor a study of academic counseling. The MBS contained 12 items at that time, dividedequally among four subscales, to which the student and mentor responded to assess thementor’s behaviors during their meetings. More precisely, it was designed to measureengagement, structure, control (vs autonomy support), and competency support. Partici-pants rated the items on a five-point Likert scale (1 = doesn’t apply at all, 2 = doesn’treally apply, 3 = neutral [I’m not sure], 4 = applies somewhat, and 5 = applies verywell). This initial version has not been validated.

Upon reading the first version of the MBS, it appeared that the structure dimension,as defined by the MSM, was not fully covered. More specifically, the structure aspect ofthe mentoring relationship was not addressed, and the items appeared to target only thecounseling and information that was provided to the protégé. Therefore, for the presentstudy, five items adapted from the Working Alliance Inventory-Short Version (WAI-S;Horvath & Greenberg, 1989; Tracey & Kokotovic, 1989, translated by Bachelor &Salamé, [Inventaire d’alliance de travail, french traduction of the Working AllianceInventor, personal communication], 1991), were administered along with the MBS toadd the structure dimension to the original instrument and make it more consistent withthe behavioral dimensions of the MSM.

The WAI-S contains 12 items that measure the quality of the working alliance betweena psychotherapist and a client. As mentioned above, this alliance comprises three dimen-sions: (a) the degree of mutual understanding of targeted goals (Goal), (b) the degree ofmutual understanding of the tasks required to achieve these goals (Task), and (c) the qual-ity of the emotional bond (Bond) between the mentor and mentee (Bordin, 1994). For pur-poses of the study, the WAI-S was adapted for the mentoring relationship by adjustingsome of the terms to correspond more closely with the nature of the mentorship. Forexample, the word therapy was replaced by meeting and therapist was replaced by men-tor. Participants rated the items on a seven-point Likert scale (1 = never, 2 = rarely,3 = occasionally, 4 = sometimes, 5 = often, 6 = very often, and 7 = always).

VALIDATION OF THE MENTOR BEHAVIOR SCALE 7

The characteristics of the WAI contributed to the decision to incorporate five of itsitems in the structure dimension of the original MBS. We took into account the weakfactorial structure of the long and short version of the WAI (Corbière, Bisson, Lauzon,& Ricard, 2006; Hatcher & Gillaspy, 2006; Tracey & Kokotovic, 1989), the strongcorrelations found between the task and goal items in various studies (including our pres-ent study), and the proposal by Corbière et al. (2006) to subdivide the working allianceconcept into two dimensions: a combined goal and task dimension and a bond dimen-sion. The outcome was that three of the eight items of the goal and task dimensions werenot retained: two items referring to disagreement about the mentee’s needs and goals,and one item referring to the benefits of tasks carried out together with the mentor wereconsidered reverse items, and therefore redundant.

Criteria for the Construct Validity of the MBS

Mentoring relationship quality. In line with Corbière et al.’s (2006) proposal tosubdivide the working alliance concept into two dimensions, one of which would bequality of the bond, the four items of the WAI making up this dimension were retainedin the present study to assess MRQ. Recall that for purposes of the study, these itemswere adapted to the mentoring relationship by adjusting some of the terms. The averagescore on these four items represents the relationship quality score.

Scale for perceived usefulness of the mentorship. Perceived usefulness of the men-torship was considered as a predicted variable in the construct validity analysis. Thismeasure of the mentee’s satisfaction with the mentoring experience contains 11 itemsthat assess the instrumental benefits of the mentoring in terms of services received (fiveitems), satisfaction with the conduct of the individual meetings and contact with thementor (five items), and mentor’s availability (one item). These items were developed bya committee of experts in the field of academic mentoring, including the authors of theMSM and the MIRES program, and have demonstrated good validity. Each item is ratedon a seven-point Likert scale ranging from 1 (completely disagree) to 7 (completelyagree). The score for this scale is calculated as the average score on the 11 items.

Statistical Analysis

Normality of Data

The data (MBS and WAI responses by mentees in the MIRES program) used to estimatethe psychometric properties of the instrument did not conform to normal law. Thus, thepeakedness coefficient (kurtosis statistic) for two-thirds of the data exceeded the 2.0threshold, indicating a ceiling effect. The asymmetry coefficients (skewness) for one-quarter of the data were below the −2.0 threshold. Although this low data variance couldbe explained by the mentors’ strict adherence to the program’s guidelines, it was neces-sary to address this statistical issue for both measurement times. To do so, we retainedthree solutions. First, because to all appearances the mentees did not distinguish betweenReponses 1 and 2 on the five-point Likert scale for the MBS (1 = doesn’t apply at all,2 = doesn’t really apply) or on the seven-point WAI scale (1 = never, 2 = rarely), theseoptions were combined to obtain four- and six-point Likert scales, respectively. This

8 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

regrouping improved the kurtosis coefficients and the data skewness. However, becausethese statistics were still too high, such that the majority of the data were distributedalong the four-point Likert scale with non-equidistant values between the choices, it wasdecided to consider these data as ordinal data (categorical) rather than continuous(Newsom, 2010). Consequently, a robust-weighted least squares mean and variance-adjusted (WLSMV; Muthén, du Toit, & Spisic, 1997) test of model fit was applied, usingpolychoric correlation. Finally, item 1 on the MBS was withdrawn due to a kurtosiscoefficient of 40.0 at Time 1 (94% of participants chose the same response) and askewness coefficient of −5.8, indicating a quasi-absence of variance, making thisvariable unusable for standard deviation analyses based on variance, such as factorialanalyses. In Table 1, we present the means, skewness coefficients, and kurtosiscoefficients for the final version of the MBS for Time 1 and Time 2, after withdrawingitem 1 and adjusting the response scales.

Missing Data

The analysis of missing data on the MBS revealed that of the 253 mentees who partici-pated in the program, 11 (4.3%) did not respond at Time 1 (mid-first semester) and 55(21.7%) at Time 2 (mid-second semester). These percentages do not correspond to anattrition rate, because all the mentees who did not respond to the questionnaires at Time1 did so at Time 2. Thus, 187 participants responded at both Time 1 and Time 2. How-ever, another type of missing data must be addressed: the rare cases when a mentee didnot respond to one or a few questionnaire items at a given measurement time. At bothmeasurement times, this proportion did not exceed 1% of all the questionnaire items,and involved less than 10% of the participants.

Given the large percentage of missing data on the MBS at Time 2 (21.7%), particularattention was paid to this analysis. Little’s MCAR Test (Little, 1988) was used to

Table 1Means, Standard Deviation, skewness, and Kurtosis for MSBa Subscales and Global Scale at Time1 and 2

Subscale and global M (SD) Skewness Kurtosis

Time 1Structure 4.56 (.66) −1.83 5.02Engagement 3.69 (.56) −2.20 5.58Autonomy 3.17 (.81) −.81 −.01Competence 3.35 (.66) −1.09 0.93Global scale 4.02 (.53) −1.76 4.73

Time 2Structure 4.61 (.61) −1.33 2.68Engagement 3.67 (.52) −1.71 2.94Autonomy 3.24 (.73) −.74 .03Competence 3.47 (.60) −1.30 1.38Global scale 4.08 (.47) −1.17 2.00

Notes. The Structure subscale is a six-point Likert scale, while the three other subscales are a four-point Likertscale. Therefore the Global scale maximum score was 5.25.aAfter retrieving item 1 of the original MBS and modifying the response scale.

VALIDATION OF THE MENTOR BEHAVIOR SCALE 9

determine whether the pattern of missing data was missing completely at random(MCAR). When Little’s test was applied to a complete data-set that included the vari-ables liable to be associated with the missing data (perceived usefulness of the mentor-ship, MRQ, withdrawal from the program, and number of meetings), the result(χ2 = 882.032, ddl = 674, p = .000) showed that the percentage of missing data at Times1 and 2 (respectively, 4.3 and 21.7%) did not increase completely at random. Data foreach measurement time were then analyzed separately, excluding participants who hadnot responded at the other measurement time. In contrast to the complete data-set, Time1 and Time 2 considered separately showed a completely at random pattern of missingdata (less than 1%), χ2 = 235.288, ddl = 226, p = .322 (Time 1) and χ2 = 125.293,ddl = 122, p = .401 (Time 2).

It is necessary to determine the origin of the bias in the larger amount of missing dataat Time 1 compared to Time 2, because the data that were not missing at random(NMAR) could bias the estimated model parameters. More precisely, the approach con-sisted of determining whether the bias was due to a third variable (missing at random—MAR) or to the missing variable itself (NMAR). In the present data-set, it was deter-mined that the participants who perceived the mentoring experience as less positive wereresponsible for more missing data on the MBS. However, this does not mean that thisbias in the missing data produces an impact on the validated model parameters. To testfor this impact, the procedure proposed by Allison (2001) was used. It consists of a mul-tiple imputation method to test for the effect of modeling NMAR data on the modelparameters. The modeling is performed with auxiliary variables (correlated with theMBS and/or related to a response [or not] to the questionnaire; Graham, 2009), to whicha two-level correction is applied. In this case, the applied correction should reduce theimputed values of the missing data by a factor of .8 and .6, respectively (Allison, 2001).The test for sensitivity of this model to the assumed missing data indicated that the biaswas sufficiently large to distort certain parameters of the tested model. This finding,albeit hypothetical, suggests that the results should be generalized with caution. Thisissue is considered in more detail in the Discussion section.

With respect to the missing data at Time 1 and Time 2 considered separately, whichshowed an MCAR pattern, the full information maximum likelihood method was used toretain the few participants (fewer than 10) who did not respond to one or a few items onthe MBS only. Finally, concerning the perceived usefulness of the mentorship, all men-tees who completed the MBS at Time 2 completed this scale.

Internal Consistency of the MBS

The use of Cronbach’s alpha—as the instrument of choice to measure the internal consis-tency of instrument subscales with a Likert scale—has increasingly been called intoquestion. This method assumes that responses on a Likert scale are continuous. How-ever, it has been demonstrated that Cronbach’s alpha can be underestimated when usedfor scales containing fewer than five points, because the responses are consideredcategorical. To offset this significant limitation, Zumbo, Gadermann, and Zeisser (2007)proposed a new reliability index called the ordinal coefficient alpha (or ordinal alpha),calculated using polychoric correlation matrices. We used this method to estimate theinternal consistency of the MBS scales and compare it with Cronbach’s alpha.

10 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

Construct Validity

Confirmatory factorial analysis. To ensure that the developed instrumentreproduces the factorial structure proposed by the MSM, structural equation analyseswere performed (for a detailed description of this statistical method, see Byrne, 2006).Because specific relationships between the questionnaire items (observed variables) andtheir latent factors were expected and tested, confirmatory factorial analyses (CFA) wereused to compare the questionnaire’s factorial structure to the underlying model. Calcula-tions were performed with Mplus (version 5.2; Muthén & Muthén, 1998–2007) usingthe robust WLSMV estimator, as described above. The following criteria were used toassess the fitness indices obtained (Table 2). The weighted root mean square residual(WRMR) is recommended for models tested with categorical data. Hancock and Mueller(2006) propose that a well-convergent model should have a WRMR less than 1.0. Therobust WLSMV chi-square for the degrees of freedom (χ2/dl) used in Mplus appears toprovide a very good indicator (Flora & Curran, 2004), although there is always a riskwhen using it alone, because it is highly sensitive to sample size. Ideally, it should beless than .3. There is evidence to support that the root mean square error of approxima-tion (RMSEA) is adequate for categorical data (Hutchinson & Olmos, 1998). Ideally, itshould be less than .6 (Hu & Bentler, 1999). The relative fit indices can be valid indica-tors when calculated with the robust WLSMV chi-square. This is the case for the com-parative fit index (CFI) calculated in Mplus, which appears to be consistent with theRMSEA and the WMR, although it must be used with caution because it has not beensystematically examined with categorical data (Newsom, 2010). Finally, the TLI, whichcorresponds to the non-normed fit index (NNFI) for continuous data, is reported as well.The criterion for these last two indices is .95 (Hu & Bentler, 1999).

Concomitant validity and temporal stability. Construct validity was further exam-ined using Pearson correlations (subscale scores and overall scores are distributed ascontinuous variables) between the MBS and MRQ at both measurement times, and theMBS and perceived usefulness of the mentorship at Time 2. Finally, stability over timefor the MBS responses was examined by calculating Pearson correlations between MBSscores obtained at Times 1 and 2.

Results

The results are presented in three parts. The first part includes the results on the internalconsistency analysis of the MBS. The second presents the construct validity in terms offactorial structure. The third presents an assessment of the construct validity according tothe correlations between MBS scores at the different measurement times, and betweenMBS scores and MRQ and perceived usefulness of the mentorship.

Table 2Cut-off Criteria Used For the CFA Fit Indices

Criterion WRMR χ2/dl RMSEA CFI TLI (NNFI)

Good <1 <3 6–.01 95–.99 95–.99Acceptable <5 8–.05 90–.95 90–.95

VALIDATION OF THE MENTOR BEHAVIOR SCALE 11

Internal Consistency of the MBS

In Table 3, we present the internal consistency coefficients for the 15-item MBS, afteritems 1 and 6 were withdrawn. The questionnaire has good internal consistency coeffi-cients for three of the four subscales. The ordinal alphas exceed .7, meeting the criterionproposed by Nunally (1978). However, the autonomy support dimension has an alphabelow .7 at both measurement times.

Due to its inadequate ordinal alpha, particular attention was paid to the autonomysupport dimension. The analysis results showed that item 6 for this factor was negativelyassociated with 9 of the 15 items, which was not predicted by the theory. It is possiblethat this reverse-scored item, which originally measured mentor’s control (My mentortells me what to do, and how to do it), was actually perceived as a form of structure. Infact, the mean score at Time 1 (2.87 out of 5 when reverse-scored, vs. 3.74 and 4.54 foritems 4 and 8, respectively) differed from the scores for the other autonomy supportitems, supporting the hypothesis that it was perceived as a positive aspect of structure. Inany case, based on the preliminary CFA performed with EQS (version 6.1) and thepreliminary analyses of the items, it was deemed relevant to withdraw item 6.

With respect to the item-total correlations, they are all above .3, meeting the widelyused criterion in the literature for a threshold indicating adequate correlation with itsrespective subscale. The CFA was therefore performed on a 15-item model.

Construct Validity

Factorial structure of the MBS. This model has adequate fit indices at Time 1(WRMR = .709; χ2/dl = 2.04; RMSEA = .066; CFI = .963; TLI = .985) and at Time 2(WRMR = .778; χ2/dl = 2.25; RMSEA = .079; CFI = .962; TLI = .978). The correlationsbetween the four factors range from .264 to .917 at Time 1 and from .398 to .788 atTime 2. These correlations are significant at .01, with the exception of those between thefactors autonomy support and competency support at Time 1, which is .05. The satura-tions range from .54 to .87 at Time 1 and are significant at .01. For Time 2, they rangefrom .49 to .93 and are significant at .01 at Time 2. The measurement models arepresented in Figures 1 and 2.

Concomitant validity and temporal stability. In Table 4, we present the correla-tions between MBS scores at the two measurement times. There is a .53 correlationbetween the overall score at Time 1 and at Time 2, p < .001. The correlations

Table 3Internal Consistency Coefficients (Ordinal Alpha and Cronbach’s Alpha) of the MBSa Subscales atTime 1 and 2

Ordinal coefficient alpha Cronbach’s alpha

Subscale Time 1 Time 2 Time 1 Time 2

Structure .92 .92 .87 .87Engagement .80 .70 .63 .53Autonomy .58 .63 .43 .50Competence .82 .86 .72 .73

aAfter retrieving item 1 and item 6 of the original MBS and modifying the response scale.

12 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

between subscale scores measured at Time 1 and Time 2 vary from .35 to .59,p < .001. This indicates that the MBS scores are moderately correlated when the ques-tionnaires are administered four months apart. On the other hand, Tables 4 and 5show that the overall MBS score is highly correlated with MRQ measured at bothtimes (r = .74 for Time 1 and r = .79 for Time 2, p < .001). The subscale scores areslightly lower, with correlations varying from .26 to .77, p < .001. Finally, in Table 5,we show that the overall MBS score at Time 2 is moderately correlated with per-ceived usefulness of the mentorship, which was measured at Time 2 only, r = .41,p < .001. As for the subscale scores, their correlation with perceived usefulness variesfrom .07 (non-significant) to .44, p < .001.

Figure 1. MBS—15-items model, Time 1. Saturations and correlations are all significant at .01,except the one between F3 and F4 that was at .05.

Figure 2. Mentoring behavior scale—15-items model, Time 2. Saturations and correlations areall significant at .01.

VALIDATION OF THE MENTOR BEHAVIOR SCALE 13

Discussion

The aim of our study was to construct and validate a new instrument to measure thebehaviors of mentors intervening in the context of school-based mentoring programs. A17-item questionnaire was developed drawing on the premises of the mentoring soci-omotivational model (Larose & Tarabulsy, 2014) and then empirically tested using datacollected at two measurement times of a college academic mentoring program. The vali-dation process ended up with a 15-item questionnaire (the Mentoring Behavior Scale)covering four dimensions: structure (eight items), engagement (two items), autonomysupport (two items), and competency support (three items). The factorial structure wassupported by CFA, and the instrument showed good psychometric properties in terms ofreliability and construct validity.

The MBS appears to be a reliable instrument, as indicated by the ordinal alphas cal-culated for each of the subscales. Although the factor autonomy support appears to beless well measured at Time 1, this shortcoming is redressed at Time 2, with an increasein the data variance and a satisfactory internal consistency coefficient for this factor. Interms of the factorial structure, it is noteworthy that autonomy support was less stronglyrelated to the three other factors than they were with each other. Aside from the lowerinternal consistency for the autonomy support subscale, the particular definitions of thebehaviors measured by the MBS could explain this difference. The three other behaviorscan be regrouped under an overall attitude that involves establishing a close bond with

Table 4Correlations between MBS Scores at Both Measurement Times, and between Time 1 Scores andMentoring Quality Relationship (MQR) at Time 1

Correlate variable

MBS score at Time 1

Structure Engagement Autonomy Competence Global scale

Structure T2 .49**Engagement T2 .35**Autonomy T2 .59**Competence T2 .46**Global scale T2 .53**MRQ T1 .74** .53** .23** .51** .74**

aAfter retrieving item 1 and 6 of the original MBS and modifying the response scale.**p < .001.

Table 5Correlations between MBSa Scores at Time 2 and Concomitant Variables

Concomitant variable

MBS score at Time 2

Structure Engagement Autonomy Competence Global scale

MRQ T2 .77** .45** .26** .59** .79**Usefulness perception .44** .27** .07 .24** .41**

aAfter retrieving item 1 and 6 of the original MBS and modifying the response scale.**p < .001.

14 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

and caring for the mentee, as well as collaboration and coaching with the aim of helpingthe mentee discover his or her pathway in life. In contrast, autonomy support impliesestablishing a certain distance from the mentee, along with acknowledging the mentee’sunique character and personal responsibility for defining and making choices (Deci &Ryan, 1985; Larose & Tarabulsy, 2014).

Still concerning the instrument’s validity, the moderate correlations that were pre-dicted between MBS scores at Times 1 and 2 were confirmed. In terms of concomitantvalidity, the MBS scores were moderately to strongly correlated with MRQ at the twotimes (Time 1 and Time 2), with the exception of autonomy support, which was onlyweakly correlated. This weak correlation between the MRQ and autonomy support wasunexpected, because studies indicate that the mentor’s control can be harmful to theMRQ (Karcher, Herrera, & Hansen, 2010), and can also shorten the length of the men-torship (possibly due to the harmful effect; Morrow & Styles, 1995). Recall that the fac-tor autonomy support was made up of two items designed to measure the mentor’scontrolling behaviors, and that these items must be reverse-coded. Two hypotheses couldexplain the weak correlation obtained. On the one hand, it is possible that the relation-ship between these two variables was weakened by the slight lack of internal consistencyfor the factor autonomy support. On the other hand, it is probable that one of the twocontrolling behaviors under the factor autonomy support was perceived by some menteesas a desirable form of structure or engagement (Larose, Cyrenne, Garceau, Brodeur, &Tarabulsy, 2010), due to the particular formulation of these items. These controllingbehaviors, as measured by the MBS (In our discussions, my mentor talks more than Ido; My mentor often makes decisions for me), could have been perceived favorably bymentees who were not aiming for autonomy, but were looking mainly for guidelines,supervision, strong investment, and even a take-charge attitude on the mentor’s part. Ifthis is the case, this discrepancy in the mentees’ assessment of these behaviors wouldmake a moderate or strong correlation between the factor autonomy support and MRQimprobable, because some mentees would appreciate these behaviors and others wouldfind them vexing.

The correlations between the perceived usefulness of the mentorship and MBSscores, ranging from weak to moderate at Time 2 with the exception once again ofautonomy support, were expected and can be explained. Structure, a factor that correlatesmoderately with perceived usefulness, consists of a mutual understanding of targetedgoals that would benefit the mentee, how to achieve them, and the quality of the mentor-ing relationship (bond) (Larose & Tarabulsy, 2014). The relational aspects, on the otherhand, which include engagement and competency support, were weakly correlated. It ispossible that, for the mentee, these more affective and less tangible aspects of the rela-tionship would be less associated with the usefulness of the mentorship compared tostructure, which would enable the mentee and mentor to directly address targeted goals,particularly in an academic mentoring program. This hypothesis is consistent withHamilton and Hamilton (1992) proposal, that the relationships that lasted the longest,were most positive, and were most preferred by mentees were those in which a mutualunderstanding had been reached on which goals and objectives to pursue, and not thosethat had the adolescent’s well-being as the sole aim. Langhout et al. (2004) reached asimilar conclusion by showing that moderately structured and supportive mentoring rela-tionships produced more benefits for youth (and therefore potentially higher perceivedusefulness) compared to relationships of unconditional support but little structure.

VALIDATION OF THE MENTOR BEHAVIOR SCALE 15

Finally, the study by Eccles, Roeser, Wigfield, and Freedman-Doan (1999) showed thatstudents were more interested in participating in extracurricular activities when theybelieve they can achieve specific goals.

Autonomy support, for its part, did not correlate with perceived usefulness. It isprobable that the lower internal consistency of this subscale affected the correlationbetween these two variables, but it is also possible that the mentees did not perceiveautonomy support as particularly useful. In fact, a lack of control could be necessary sothat the mentee and mentor could structure the relationship together and develop a good-quality relationship, even though the mentee might not perceive this as making a directcontribution to achieving goals, in contrast to the behaviors addressed in the structuresubscale. However, we could not confirm this hypothesis because, to our knowledge, nostudy to date has compared the impact of these different behaviors on the mentee’s per-ception of the usefulness of the mentorship. In sum, for a formal mentoring program, theMBS can be used to assess mentors’ behaviors four months later. With the exception ofautonomy support, it can also predict both the quality of the mentoring relationship mea-sured at the same time and, to a lesser extent, the mentee’s perception of the usefulnessof the mentorship.

Study Strengths, Advantages of the MBS, and Implications

In our study, we found several strengths that are rarely seen in attempts to develop andvalidate questionnaires, or in studies on mentoring (Larose et al., 2011). First, it is basedon solid theoretical and empirical underpinnings. Next, the construct validity of theMBS from several perspectives as well as the reliability of the instrument were investi-gated, and particular attention was paid to missing data. Thus, rigorous statistical meth-ods suitable for the data in question were applied. Moreover, in a departure frommentoring questionnaires that are limited to addressing overall attitude (e.g. developmen-tal, instrumental, or prescriptive approach), the quality of the mentoring relationship, orthe mentee’s feelings about the mentor, the MBS measures highly specific behaviors. Byexclusively targeting mentors’ behaviors, it avoids the frequent problem whereby studiesconfuse these behaviors with characteristics of the relationship, such as length of therelationship, which activities are done, and the mentee’s feelings about the mentor. Inaddition, these behaviors were measured based on the mentees’ view providing a morevalid measure within a formal mentorship program, because the mentees did not receivetraining and were less subject to social desirability bias. The use of mentees’ perceptionsto measure all the study variables makes a further contribution to the validity of theapproach by ensuring that the constructs of interest and their relationships were properlyassessed. Indeed, it has been demonstrated that different people may perceive a samerelationship very differently. For example, in the study by Larose et al. (2010), the scoresof mentors and mentees on a measure of the working alliance (concerning structure andrelationship quality) were poorly correlated. Similar disparities have been found in clini-cal studies (e.g. Clemence, Hilsenroth, Ackerman, Strassle, & Handler, 2005; Hatcher,Barends, Hansell, & Gutfreund, 1995) that compared the perceptions of the workingalliance by therapists and their clients (in terms of structure, relationship quality, andperceived usefulness). Finally, it was demonstrated that perceptions of received support,compared to reports of provided support, were better at predicting the effects of that sup-port (Eby et al., 2013; Sarason, Sarason, & Pierce, 1990, 1994). These findings support

16 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

the relevance of capturing the perceptions of those who receive services when validatingan instrument to measure those services.

The MBS is a short, rapidly scored instrument that obviates the need for semi-structured interviews or a battery of questionnaires to examine a key determinant for thementoring relationship and its benefits. It would be very suitable for longitudinal studiesthat require a repeated measure that is accessible, easy to use, and rapidly administrated.In the field, it could facilitate follow-up by educators and mentors on the mentoringrelationship and allow optimal oversight of mentors along with continuous adjustmentsthat could help prevent failed mentoring relationships. Because it contains four distinctsubscales, the MBS can help identify mentors’ strengths as well as which behaviors needimproving. Furthermore, because the MBS is correlated with the quality of the mentoringrelationship and the perceived usefulness of the mentorship, it can provide an overviewof these aspects when no other direct measures are available. In addition, the MBScomes in two versions—one for the mentee and one for the mentor—although thementor version has not yet been validated.

Study Limitations, Limitations of the MBS, and Recommendations

Although the data in the MBS reproduce the factorial structure of the MSM, the correla-tions between certain factors (structure vs engagement and structure vs competency sup-port) are high, particularly at Time 1. It would be tempting to conclude that some factorsactually belong to a same construct. However, the correlations become weaker whenmeasured at Time 2, indicating that the longer the time interval between the mentors’training and the administration of the measure, the more the mentors would tend to per-sonalize their approach to the mentee instead of simply applying all the behaviors theyhad learned regardless. Accordingly, we see more variation in the mentors’ behaviorsand more clearly distinguished behaviors as the mentoring relationship evolves. Thisobservation suggests that the factorial validation of the MBS would be even more defini-tive in seasoned mentors.

Aside from the impact of the relationship stage on mentors’ behaviors, it is possiblethat the response scale for the MBS made it difficult to report on fine differences in men-tors’ behaviors. Thus, the two first ratings in the scale were difficult to distinguish(1 = doesn’t apply at all, 2 = doesn’t really apply), whereas the two ratings at the topend of the scale were much more distinct (4 = applies somewhat and 5 = applies verywell). To ensure sufficient variation in the participants’ responses, it would be preferableto adjust the scale as follows: 1 = doesn’t apply at all, 2 = doesn’t apply very much,3 = applies somewhat, 4 = applies well, and 5 = applies very well. In addition, applyingthis response scale to all 15 items of the MBS would simplify the administration andscoring (see final questionnaire in Table 6).

The dimension autonomy support has a slightly insufficient internal consistency coef-ficient, such that it does not satisfactorily predict the MRQ or the perceived usefulnessof the mentorship. This questionnaire subscale contains items that measure a form ofcontrol, and the scores must be reversed in order to capture autonomy support. The useof reverse scores to measure a construct has been criticized, suggesting that reverse itemsshould always be accompanied by items that directly measure the construct of interest.To this, we may add the fact that this factor contains only two items, making it more dif-ficult to measure the construct. It is arguable that, by using the items in the present scale,

VALIDATION OF THE MENTOR BEHAVIOR SCALE 17

autonomy support was not measured from enough perspectives, or with sufficient accu-racy, such that the two items representing it form a homogenous factor. Nevertheless,unlike the removal of item 6, it would be surprising if a significant percentage of thementees had interpreted either of the two items as a form of structure, because in thatcase, it would have been correlated with the MRQ, according to the factorial structure ofthe MBS.

On another issue, the assumed bias in the missing data means that the results shouldbe generalized with caution, apart from the positive experience of the mentorship asassessed by the mentees. The analysis of missing data showed that more mentees whohad a negative mentoring experience did not complete the MBS. In addition, although inthe MIRES program the lack of data variance indicated that the mentors largely adheredto the behaviors they were trained in, this conformity means that the results can only becautiously generalized to mentoring programs where mentors receive little or no training.

The mentors’ perspective was not taken into account in the development and valida-tion of the MBS. Although subject to social desirability bias, the mentors’ responseswould have deepened the understanding of the psychometric properties of the MBS andimproved the validity of the mentees’ version of the questionnaire. Furthermore, three ofthe four subscales of the MBS contained only positive items. It would be beneficial to

Table 6Mentor Behavior Scale

Questions were answered using the following five-point scale:1. Does not apply at all to my situation2. Does not apply much to my situation3. Applies a little to my situation4. Applies well to my situation5. Applies very well to my situation

StructureMy mentor gives me useful advices when I tell him my needs, my worries, and my difficultiesMy mentor likes to give me constructive advices on what I initiateMy mentor gives me information to help me in my actions and in solving my problemsMy mentor and I are working towards mutually agreed upon goalsWe have established a good understanding of the kind of changes that would be good for meMy mentor and I agree about the things I will need to do to help improve my situationWe agree on what is important for me to work onI believe the way we are working on my situation is correct

EngagementMy mentor understands my needs, my worries, and my problemsMy mentor listens attentively to the needs, worries, and achievements I share with him

Autonomy support (scores must be recoded)When meeting, my mentor talks more than I doOften, my mentor takes decisions for me

Competency supportMy mentor values me even after I experience failuresMy mentor often tells me what I do wellMy mentor congratulates me when I do something right

18 BRODEUR, LAROSE, TARABULSY, FENG, AND FORGET-DUBOIS

add items referring to negative aspects of the constructs addressed, because it appearsthat the absence of negative experiences in the relationship is better at predicting rela-tionship length compared to the presence of negative experiences (Rhodes, 2005).

Conclusion

Despite a few shortcomings, this first version of the MBS should be considered valuable.Given that no other current measures of the mentoring relationship are designed tocapture specific behaviors, and very few have been subjected to a detailed and rigorousvalidation, the MBS would offer more benefits than concerns to any program administra-tor who uses it with certain precautions. Because it is founded on a model and theoriesthat are supported by an abundant and available literature, it could be used to train andoversee mentors in order to optimize their interventions with youth who are coping withacademic and/or psychosocial problems. What is more, because it correlates with a num-ber of variables, including the quality of the mentoring relationship, the MBS can makea substantial contribution to prevent harmful mentoring relationships. The MBS canundoubtedly contribute to advance the study, understanding, and management of school-based mentoring programs.

Notes on contributorsPascale Brodeur is a graduate student in Developmental and Clinical Psychology at Laval Univer-sity (Québec City, Canada). Over the past seven years, she has been studying the impact of men-tor’s support behaviors on mentoring relationship quality and protege’s adjustment. She is acontributor to an article on the structure of mentoring in late adolescence appearing in NewDirection for Youth Development.

Simon Larose is a professor in Educational Psychology at Laval University (Québec City, Canada).Over the past 20 years, he has been studying the potentiality of mentoring relationship to promoteacademic success and persistence in adolescence and young adulthood. He is the coauthor of arecent chapter on mentoring academically at-risk students appearing in the Handbook of YouthMentoring.

George Tarabulsy is a full professor of psychology at Laval University (Québec City, Canada). Hisresearch focuses on the development of children in high-risk contexts, within an attachment andemotion regulation paradigm. He has published several recent reports on attachment-based inter-vention that focuses on parental mentoring, for children living in maltreatment conditions.

Bei Feng is a statistician working for the Research unit on children’s psychosocial maladjustment,Laval University (Québec City, Canada), since 2003. Her main interests are genetic analysis, twinanalysis, child behavior development, and adolescents’ academic success. She has a rich experi-ence in statistical research and application.

Nadine Forget-Dubois is a research agent and lab coordinator at Laval University (Québec City,Canada). She has been involved in research on the biological bases of social development for morethan 10 years and has recently coauthored a paper about social reticence in children.

VALIDATION OF THE MENTOR BEHAVIOR SCALE 19

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