THE EFFECTS OF COGNITIVE STYLE ON RESEARCH SUPERVISION:

A STUDY OF STUDENT-SUPERVISOR DYADS IN MANAGEMENT EDUCATION

by

1Steven J. ArmstrongCentre for Management & Organisational Learning

University of Hull Business School

Dr. Christopher W. AllinsonLeeds University Business School

The University of LeedsLeeds, LS2 9JT, United Kingdom.

Tel: 44 (0)113 2333637Email: cwa@lubs.leeds.ac.uk

Prof. John HayesLeeds University Business School

The University of LeedsLeeds, LS2 9JT, United Kingdom.

Tel: 44 (0)113 2333632Email: jh@lubs.leeds.ac.uk

1Address for correspondence:Dr. Steven J. Armstrong

Centre for Management & Organisational LearningThe University of Hull Business School

Hull, HU6 7RX,United Kingdom

Tel: 44 (0)1482 465719Fax: 44 (0)1482 466637

Email : stevearmstrong@welton-lincoln.freeserve.co.uk

1

THE EFFECTS OF COGNITIVE STYLE ON RESEARCH SUPERVISION:

A STUDY OF STUDENT-SUPERVISOR DYADS IN MANAGEMENT EDUCATION

ABSTRACT

Whilst attention has been paid to many aspects of teaching and learning in management

education, one facet that has been seriously overlooked is the process of research supervision.

Research at both the graduate and the undergraduate level suggests that the relationship

between the student and the supervisor is a significant predictor of success and failure in

independent research projects. One personality variable that has been shown to be partly

responsible for shaping the overall effectiveness of such relationships is cognitive style,

defined as consistent individual differences in how we perceive, organize and process

information, solve problems, learn and relate to others. This study examined the effects of

differences and similarities in the analytic-intuitive dimension of cognitive style on the

supervision process. Data were collected from both partners in 421 dyadic relationships, each

comprising an academic supervisor and a management student undertaking a major research

project. Findings suggest that analytic supervisors were perceived to be significantly more

nurturing and less dominant than their more intuitive counterparts, indicating a higher degree

of closeness in their relationships. This led to increased liking in the relationship, and

significantly higher performance outcomes for the student. These effects were highest in

dyads whose students and supervisors were more analytic.

Key words: research supervision cognitive style

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INTRODUCTION

Although attention has been paid to many aspects of student learning in management

education, one facet still seriously overlooked is research supervision. The present study

explores the possibility that differences and similarities in the cognitive styles of students and

their research supervisors might have a significant effect on socio-emotional aspects of their

interpersonal working relationships, and ultimately, on performance outcomes.

While research supervision is an important issue for final year undergraduate, MBA and

research students, most of the published research on this topic relates to postgraduate

students. Over many years concern has been rising about completion rates of research

degrees (Burnett, 1999; Bowen & Rudenstine, 1992). In the UK for example, Rudd’s study

(1985) into postgraduate failure revealed that between 40% and 50% of students failed to

successfully complete dissertations in the social sciences. Similar figures were reported in a

later study by Dunkerley & Weeks (1994) who found that out of 1969 candidates, 46%

withdrew. In North America, thesis and dissertation requirements have also been reported to

increase attrition and delay completion of graduate degrees (Garcia et al, 1988). Failure and

completion rates have been very similar to those reported in the UK, with as many as 50% of

students entering graduate programs dropping out before finishing their theses or dissertations

(Jacks et al, 1983; Naylor & Sanford, 1982; Moore, 1985; Elfatouri et al, 1988 - cited in

Garcia et al, 1988). Furthermore, a high proportion of those who do complete their research

degrees take significantly longer than expected (Garcia et al, 1988). Earlier studies that report

similar findings clearly indicate that this is not a new problem (Berelson, 1960; Snell, 1965;

Wilson, 1965; Rudd & Hatch, 1968). The success of students’ independent research projects

has implications beyond student learning. For example, in the UK, the Research Councils

link a University’s eligibility for postgraduate research studentships to completion rates.

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The quality of the supervision process has often been highlighted as one of the main reasons

for these problems (Gant et al, 1980; Zoia, 1981; Dillon & Malott, 1981; Malott, 1986; Garcia

& Malott, 1988) and students themselves often express dissatisfaction with the process

(Hockey, 1991). Aspects of dissatisfaction include the need for more structure and direction

(Acker & Black, 1994), being allocated a supervisor whose interests and knowledge do not

match their own (Macrotest, 1987), and receiving insufficient guidance concerning planning,

organizing and time-scaling (Delamont & Eggleston, 1981; Wright & Lodwick, 1989).

Dissatisfaction rates are generally higher among social science students than in the natural

sciences (Young et al, 1987), despite the fact that supervision itself is often regarded as ‘the

single most important variable affecting the success of the research process in the social

sciences’ (ESRC 1991 p8). Whilst there have been numerous testimonies to its critical

importance, there have also been reports of its exceptional difficulty (Acker et al, 1994). It

has been described as ‘probably the most responsible task undertaken by an academic’

(Burnett, 1977, p17), ‘the most complex and subtle form of teaching in which we engage’

(Brown & Atkins, 1988 p115), and ‘the most advanced level of teaching in our education

system’ (Connell, 1985). As several authors have pointed out, however (Hill et al, 1994;

Hoshmand, 1994), such observations seem curiously at odds with the general dearth of

research on the detailed nature of supervision in the student/supervisor relationship in

educational settings.

This study will focus on the nature of the student/supervisor relationship in undergraduate

independent research projects. There are many similarities between the research supervision

process at undergraduate and postgraduate levels, but there are also some important

differences. For example, while many postgraduate students have had prior experience of

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research supervision, for most undergraduate students the final year research project is usually

their first major piece of student-directed learning. This is normally undertaken over an

extended period of several months and has to be written up and presented as a

thesis/dissertation for examination. The student is allocated a supervisor and has to develop

a relationship with this individual that is, in many ways, very different from the relationships

that they have had with the lecturers who delivered most of the courses on their degree

programme. While they need guidance, they also need to develop sufficient autonomy and

freedom to design and execute their own projects (see Harding 1973 and Cornwall et al 1977).

Undergraduate students may be more inclined than postgraduate students to regard their

supervisor as the unquestioned authority on their topic (Armstrong & Shanker, 1983) and

therefore may experience more problems developing the confidence to act independently.

Hammick and Acker (1998) note how issues of power and control are expressed in

supervisor-undergraduate student dyads.

Choice and motivation are also important (Cook, 1980). Most undergraduate and MBA

students have no choice but to engage in the final year research project, irrespective of

whether they have any inherent interest in this part of their degree programme. Postgraduate

research students, on the other hand, have voluntarily chosen to enrol for a research degree

and therefore are more likely to be self motivated. At the undergraduate level, maintaining

psychological momentum and student motivation may assume even greater importance and

may require supervisors to bring to the relationship much more than subject relevant

expertise.

In an extensive study into the research supervision process for postgraduate students

(Egglestone & Delamomt, 1983), the matching of student to supervisor for effective

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relationships was regarded as being crucially important, but ‘very little attempt has been made

to empirically examine the impact of this relationship on the quality of supervision’ (Kam,

1997, p81.). Whilst students have been found to be rather more interested in personal

compatibility (Hill et al, 1994), the matching process for undergraduate and postgraduate

students in most institutions often begins with a search for compatibility based on their levels

of research interests. Armstrong and Shanker (1983) report in their study of undergraduate

students that 69 percent chose their own supervisors after the end of their second year, when

they were familiar with the interpersonal styles of the members of staff who were available to

act as supervisors. However, a more typical scenario is that undergraduate students are

allocated to supervisors on the basis of the supervisor's subject-expertise or the need to ensure

even workloads between supervisors. Hammick and Acker (1998: 338) report that in their

study they found that 'matching students to supervisor on a personal basis of any kind was not

done'. Rarely is consideration given to preferences for the degree of structure in the process,

for direction versus freedom in supervisory styles, or for other relationship variables that

might be important for effective supervision. Yet relationships with supervisors are also

known to be related to the satisfaction that students experience in their supervision (Harrow &

Loewenthal, 1992; Blumberg, 1973), are known to be critical for successful completion

(Salmon, 1992 - cited in Burgess, 1994; Gollan, 1987) and are regarded by most students as

the single most important aspect of the quality of their research experience (Acket et al, 1994;

Katz & Hartnett, 1976).

Poor interpersonal relationships and lack of rapport between student and supervisor is the

reason most often given for problems encountered (Hill et al, 1994; McAleese and Welsh,

1983). Given that there appears to be wide-spread agreement that successful completion of

research dissertations is critically dependent on the working relationship established between

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student and supervisor (Eggleston & Delamont, 1983), and that personality differences can

result in the type of conflict that is least likely to be resolved (Berger & Buchholz, 1993), the

question remains as to how the matching process between students and their supervisors

might be managed more effectively from the onset.

Blumberg (1978) suggested that successful supervision depends on relationships that are

founded in trust, warmth and honest collaboration, whereas others have argued that “among

the criteria to be used when appointing supervisors is an ability to perceive the students’

problems in a way that can be operationalised and subsequently turned into helpful and

practical solutions” (Eggleston & Delamont, 1983, p 55). Given the fact that it has

previously been found that satisfaction with supervision was more strongly correlated with the

students’ perceptions of the supervisory relationships than with perceived expertise

(Heppner & Handley, 1981) some authors have suggested that matching students and

supervisors on the basis of certain personal characteristics as well as academic compatibility

may be beneficial (Elton & Pope, 1989; McMichael, 1992), with the most appropriate

criterion perhaps being some aspect of ‘working style’ (Welsh, 1983; Phillips & Pugh, 1994;

Hammick & Acker, 1998; Hammick, 1997).

After conducting research into the nature of interpersonal relationships in the related field of

mentoring, Bennetts (1995) concluded that the psychology of the relationship is important and

one area of interest to theorists has been cognitive similarity. Forty years ago, Triandis

(1960) reported that cognitive similarity and commonalities in the ways in which dyad

members communicated and evaluated events increased communication effectiveness and

mutual liking. The relationship between students’ and supervisors’ cognitive (information

processing) styles and the supervision process was examined by Handley (1982) who found

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that satisfaction with supervision and the quality of interpersonal relationships might be

enhanced when there was cognitive similarity. More recent research has confirmed that

whilst cognitive style may indeed significantly affect the success of interpersonal dyadic

relationships (Armstrong, Allinson & Hayes, 1997; Allinson, Armstrong & Hayes, 2001;

Armstrong, Allinson & Hayes, 2002), the idea that these effects can be reduced to a

straightforward matching hypothesis may be too simplistic when considered across different

contexts (Armstrong, 1999; Armstrong, 2001).

This study examines the effects of differences and similarities in the cognitive styles of

students and their research supervisors on the quality of their working relationships and

performance outcomes.

Cognitive Style

Cognitive styles refer to self-consistent modes of functioning which individuals show in their

perceptual and intellectual activities (Witkin et al, 1971), that lead to habitual ways in which

individuals process and evaluate information, solve problems and make decisions (Goldstein

& Blackman, 1978). While cognitive style relates to generalized habits of information

processing , Messick (1984) argues that they are also intimately interwoven with affective,

temperamental and motivational structures as part of the total personality. Riding & Douglas

(1993) suggested that cognitive style is a relatively static and in-built feature of the individual

and Messick (1976) suggests that its influence extends to almost all human activities that

implicate cognition, including social and interpersonal functioning. Where individual

differences in cognitive style occur, Witkin et al (1977) and Witkin & Goodenough (1977)

suggested that they may fundamentally affect the way one individual relates to another.

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In a review of the literature, Armstrong (1999) identified 54 different dimensions on which

cognitive/learning style has been differentiated and a number of different labels have been

given to them. Although certain authors (e.g. Streufert & Nogami, 1989; Globerson &

Zelniker, 1989) argue that the multiplicity of constructs reflects the sheer complexity of

cognition, others (e.g. Messick, 1976; Kogan, 1983; Miller, 1987) have suggested that they

are merely different conceptions of a superordinate dimension, the extremes of which confirm

the dual nature of human consciousness (Robey & Taggart, 1981). When Miller (1987)

attempted to integrate common conceptions of cognitive styles into an information-processing

model of cognition, he also indicated how they could be grouped into super-ordinate

(analytic-holistic) stylistic differences, which represent a long-standing distinction between

contrasting modes of thought (Nickerson et al, 1985):

‘…..Thus, at the analytic pole of this dimension one would expect to find

sharpening; field independence; analytic/verbal codes; high conceptual

differentiation; convergence; serial processing; tight analogies and actuarial

judgement. At the holistic pole would be levelling; field dependence;

analog/visual codes; low conceptual differentiation; divergence; holistic

classification; loose analogies and intuitive judgement’ (p263).

Riding and Cheema (1991) later considered various labels and after studying the descriptions,

correlations, methods of assessment and effect on behaviour, concluded that they may be

grouped into two principal cognitive styles; the Verbal Imagery and the Wholist-Analytic.

With regard to the second of these two groupings, Riding & Sadler-Smith (1992) included the

following labels once again arguing that these ‘are but different conceptions of the same

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dimensions’ (p324), a view later supported by Riding & Cheema, (1993) and Rayner &

Riding (1997):

Converger-diverger - (Hudson, 1966)

Field dependence-field independence (Witkin et al, 1962)

Reflective-impulsive (Kagan, 1965)

Serialist-holist (Pask & Scott, 1972)

Levellers-sharpeners (Holzman & Klein, 1954)

Analyst-wholist (Riding, 1991)

These poles have also been commonly labelled intuitive-analytic (Zeleny, 1975; Doktor,

1978; Agor, 1986; Hammond et al, 1987; Simon, 1987) and these were recently adopted by

Allinson & Hayes (1996) to distinguish between the end-points on their own Cognitive Style

Index instrument which they believe genuinely taps the unitary superordinate dimension of

cognitive style hypothesised by many theorists. According to Allinson & Hayes (1996) an

intuitive person tends to take a broad perspective on a problem, and get an overall ‘feel’ for it,

before reaching a conclusion fairly rapidly. An analytic person tends to take more of a logical,

step-by-step approach before deciding on a solution after a period of reflection. According to

Allinson, Armstrong & Hayes (2001) and Lynch (1986), in the work context, an intuitive

person would tend to be nonconformist, prefer a rapid, open-ended approach to decision

making, rely on random methods of exploration and work best on problems favoring a holistic

approach. An analytic individual, on the other hand, would tend to be compliant, prefer a

structured approach to decision-making, apply systematic methods of investigation and be

especially comfortable when handling problems requiring a step-by-step solution. In short,

each style reflects a particular way of thinking. Neither cognitive style is generally preferable

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to the other, although research suggests that certain styles may be best for particular types of

task (Armstrong, 2000).

Several authors have postulated that these cognitive style differences may be due to

differences in left/right hemispheric specialization of the brain (Ornstein, 1977; Doktor, 1978;

Robey & Taggart, 1981; Entwhistle, 1981; Agor, 1984; Taggart et al, 1985; Wilson, 1988;

Waber, 1989; Sonnier, 1990; Riding et al, 1993). The pioneering work of Sperry (1964) and

Luria (1966) strongly influenced this conceptual connection between neuro-physiology and

cognitive psychology. Their studies demonstrated the human left cerebral hemisphere to be

specialized for primarily analytic, rational and sequential information processing and the right

cerebral hemisphere to be specialized for primarily intuitive, holistic, and simultaneous

information processing. Whilst some now regard this split-brain formulation as an

oversimplification (Rao et al, 1992), others (e.g. Languis, 1998; Languis & Miller, 1992)

continue to report patterns of brain mapping research which are consistent with Luria’s (1980)

theory of brain functioning.

Cognitive Style and Dyadic Interaction

Previous evidence suggests that individual differences in cognitive style may fundamentally

affect the nature of interpersonal relationships (Messick, 1976; Witkin et al, 1977; Witkin &

Goodenough, 1981; Armstrong, 1999). DiStefano (1970) first reported evidence of this when

she studied teachers and students in a high school classroom situation and found that when

they were matched on cognitive style they had positive views of one another, but when they

were mismatched, their views of one another were negative. Witkin et al (1977, p33) reports

a similar study conducted by James (1973) that confirmed the findings of DiStefano (1970),

but also found teachers more inclined to give higher marks to students matching their own

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cognitive styles. Others have continued to report that matching may have a direct effect on

performance (e.g. Brophy & Good, 1974; Pask, 1976; Packer & Bain, 1978; Kolb, 1981; Sein

& Robey, 1991; Dunn, 1987; Dunn et al, 1989; Dunn et al, 1990; Katz, 1990), although some

remain skeptical regarding positive benefits of a matching hypothesis (e.g. Thompson &

Crutchlow, 1993; Messick, 1976; Saracho & Dayton, 1980; Frank & Davis, 1982; Mahlios,

1981; Meredith, 1985; Conwell et al, 1987). Others still, suggest that matching at the very

least has a positive effect on attitudes which may indirectly affect performance (e.g.

DiStefano, 1970; James, 1973; Witkin et al, 1977; McCaulley, 1978; Renninger & Snyder,

1983; Cooper & Miller, 1991; Hayes & Allinson, 1996). For example, relationship

satisfaction has been shown to be positively related to matched styles (McCaulley, 1978;

Renninger & Snyder, 1983; Cooper & Miller, 1991) whereas cognitive dissimilarity is likely

to result in conflict (Kubes, 1992; Rickards & Moger, 1994; Tullet, 1995; Leonard & Straus,

1997) as differences in style yield differences in interests, values and problem-solving

techniques which may handicap a working relationship (Lawrence,1993).

The view that matching cognitive styles has a positive effect on interpersonal working

relationships which may indirectly affect performance is examined in this study.

Interpersonal Relationships

According to Van-Denburg et al, (1992) in dyadic interaction each interpersonal action

represents a distinct combination of two basic dimensions of interpersonal behaviour: control

(dominance-submission) and affiliation (friendliness-hostility). ‘In any transaction

interactants are continually negotiating these two major relationship issues – how friendly or

hostile they will be, and how much in control they will be in their relationship’ (p84). A

considerable amount of research dealing with interpersonal transactions has resulted in the

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two dimensions of dominance and nurturance, the polar extremes of which are commonly

designated as “assertive-nonassertive” and “warm-cold” respectively (Pincus & Wiggins,

1992). A considerable number of factor-analytic studies have consistently shown that these

two primary constructs are necessary for construing interpersonal situations (e.g. Borgatta,

Cottrell & Mann, 1958; Carter, 1955; Kassebaum, Couch & Slater, 1959; LaForge & Suczek,

1955; Leary, 1957; Lorr & McNair, 1963; Norman, 1963; North, 1949; Peterson 1965;

Schutz, 1963; Wish, 1976; Wish, Deutch & Kaplan, 1976) and that these derive from the

work of Sullivan (1953) and Leary (1957). The continuum of behaviours on the ‘dominance’

dimension refers to who is directing and controlling the interaction. Those associated with

‘nurturance’ concern the degree of closeness between individuals in the relationship. It is to

be expected that, in a constructive dyadic relationship, neither partner will dominate to a

significant extent if a healthy exchange of ideas is to be maintained, and the degree of

nurturance will be high.

In addition to these primary dimensions, two behavioral variables, previously shown to be

relevant to the supervisory setting (Allinson, Armstrong & Hayes, 2001), were included in the

study. One was the extent to which ideas were perceived to be generated by supervisors and

students respectively. The other was the extent to which the dyadic partners liked each other.

Idea generation, defined as the perceived rate of ideas generated in meetings by self and/or

other when engaged in dyadic interaction (Armstrong, 1999), is believed to represent an

important aspect of the coaching and problem-solving roles of the supervisor. Idea generation

can also be regarded as an index of productivity in dyadic relationships (Allinson et al, 2001).

When this is perceived to be high for students, it may have the effect of creating a positive

impression in the mind of the supervisor and increasing the supervisors’ liking of

subordinates (Jones and Wortman, 1973; Tedeschi & Melburg, 1984; Wayne & Ferris 1990).

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Conversely, high idea generation on the part of the supervisor has been found to result in an

increase in both the quality of a dyadic relationship and the subordinates liking of their

supervisor (Armstrong et al, 2002).

Liking between partners is considered to be the major currency in which social intercourse is

transacted (Zajonc, 1980), and its influence on the quality of dyadic relationships has been an

important factor in several previous studies (e.g. Liden, 1985; Duchon et al, 1986; Wayne &

Ferris, 1990; McClane, 1991; Day & Crain, 1992). A supervisor’s liking of a subordinate in

a work context has been shown to influence attributions regarding that person’s behavior

(Dobbins & Russel, 1986) and the supervisor’s treatment of that subordinate (Alexander &

Wilkins, 1982). This influences the subordinate’s behaviors which, in turn, affect the

supervisor’s performance evaluation of that subordinate (Dipboye, 1985; Kingstrom &

Mainstone, 1985). Studies have found similar effects in a teaching context. For example,

teachers who like certain students tend to create a more positive climate for them, interact

with them more frequently and provide more feedback to them (Harris & Rosenthal, 1986).

These differences in behavior expressed by the teacher result in a Pygmalion effect (Jussim,

1986) as the students who are liked more, learn more. According to the similarity-attraction

paradigm (Byrne, 1971) one would expect to find that congruent cognitive styles would lead

to increased liking and, therefore, higher quality dyadic relationships as previous authors have

predicted (Graen & Uhl-Bien, 1995; Burke et al, 1994; Turban et al, 1990; Myers, 1980).

Research Hypotheses

The discussions above suggest that congruence between the cognitive styles of supervisors

and students will lead to mutually beneficial attitudes between parties in a relationship, such

as satisfaction with the relationship (Cooper & Miller, 1991), mutual understanding (Witkin,

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1970) and liking (Wayne & Ferris, 1990), and that this may lead to higher performance

outcomes (Katz, 1990; Sein & Robey, 1991). This is perhaps due to dyad members sharing

common personality attributes, having similar modes of communication and having a

tendency to focus spontaneously on the same aspect of a situation, thereby heightening the

enjoyment of their interaction (Witkin et al, 1977). This leads to the following research

hypotheses:

Hypothesis 1: The greater the similarity between supervisor and student cognitive styles, the

more nurturing the supervisory relationship.

Hypothesis 2: The greater the similarity between supervisor and student cognitive styles, the

lower the degree of dominance exerted by either party in the supervisory relationship.

Hypothesis 3: The greater the similarity between supervisor and student cognitive styles, the

more each partner will like the other.

The tendency for intuitive people to generate ideas rapidly and analytic people to prefer a

more leisurely, reflective approach (Zeleny, 1975; Allinson & Hayes, 1996), leads to the

fourth hypothesis.

Hypothesis 4: Intuitive members of dyadic relationships will be perceived to be more

productive than analytic members in terms of their generation of ideas.

Intuitive people are claimed to be more attentive to the views of others (Pascual-Leone,

1989), are sensitive to social cues (Oltman et al, 1975), have a social orientation (Witkin &

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Goodenough, 1981), and encompass a strong interest in people with a preference for being

with others (Witkin & Goodenough, 1977). This leads to the fifth hypothesis.

Hypothesis 5: Intuitive members of a dyad will be perceived to be more nurturing than

analytic members, irrespective of their position as supervisor or student.

Analytic people on the other hand are claimed to have greater skills in cognitive analysis

(Agor, 1986) and consequently tend to have more explicit reasons for the views that they

articulate. They also have a more impersonal nature (Pascual-Leone, 1989). Furthermore,

whilst intuitive people are more likely to shift their opinions to resolve conflicts (Oltman et al,

1975), analytic people tend to be less willing to accommodate their views to those of others

(Kirton, 1976). This leads to the sixth hypothesis.

Hypothesis 6: Analytic members of a dyad will tend to be more dominant than intuitive

members, irrespective of their position as supervisor or student.

The belief that intuitive people have a social orientation and encompass a strong interest in

other people (Witkin & Goodenough, 1977) compared with the more analytic types who have

a more impersonal nature (Pascual-Leone, 1989) and are less adaptive to the views of others

(Lynch, 1986), leads to the seventh hypothesis.

Hypothesis 7: Where there is incongruence, intuitive members of the dyad will be more liked

than analytic members.

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Hayes and Allinson (1998) argue that people learn and perform best where the information

processing requirements of the situation match their cognitive style. In the study reported here,

students are required to undertake a project the published criteria of which state that it must be

an individual piece of research which is problem solving in nature, requiring detailed and

systematic collection and analysis of secondary and primary data. The students must

demonstrate synthesis and evaluation of solutions, and a logical and linear progression through

careful planning and scheduling. This task, which culminates in the submission of a 10,000-

word dissertation after a project duration of eight months, has been deemed to be consonant

with the analytic style of working (Armstrong, 1999). This leads to the eighth hypothesis.

Hypothesis 8: Analytic students working with analytic supervisors will outperform those in

other dyadic combinations.

Previous studies into the quality of supervisor-subordinate interactions suggest that the quality

of the supervision process will be positively related to the extent to which dyad members

share a nurturing relationship (Crouch & Yetton, 1988) and like one another (Wayne & Ferris,

1990), and that this will be reflected in the subordinates’ performance outcomes (Turban et al,

1990). This leads to the ninth hypothesis.

Hypothesis 9: Irrespective of cognitive styles, performance outcomes will be significantly

affected by the extent to which students and supervisors share a nurturing relationship and

like one another.

Recent studies have revealed that females were significantly more analytic than males (e.g.

Doucette et al, 1998; Murphy et al, 1998; Hayes et al, in preparation). The fact that analytic

17

students appear to out-perform intuitive students in Business and Management studies

(Armstrong, 2000) leads to the tenth and final hypothesis.

Hypothesis 10: Performance outcomes will be significantly higher for female management

students compared with male students.

METHOD

Sample

The sampling frame comprised 731 supervisor-undergraduate student dyads engaged in close

project supervision relationships during the academic years 1994/95, 1995/96, 1996/97 and

1997/98 at a University Business School in the UK. The University caters for approximately

16, 000 students, both full time and part time, on a range of courses delivered by six separate

schools, including the Business School that serves approximately 2000 undergraduate, graduate,

and postgraduate students in any one year. The present study involved students from the final

year of a Bachelor of Arts degree in Management and Business Administration, and supervisors

from academic staff in the departments of General, Financial, Legal, Marketing, Economic, and

Human Resource Management. The supervisors were all of Western European origin, as was

91% of the student sample. The remainder was made up of Chinese (3.1%), Indian (1.7%),

Arabic (2.1%) and Israeli (2.1%) students.

Both parties in each of 421 dyads returned questionnaires intended to measure their individual

cognitive styles, an overall response rate of 58 per cent. Response rates for other elements of

the survey varied. For example, both parties in 152 dyads returned questionnaires with data

concerning the primary interpersonal dimensions of dominance and nurturance, representing an

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overall response rate of 21 per cent. These response rates, which remained relatively consistent

across the four years, are considered reasonable for making generalizations in this type of study

and are consistent with previous studies of this nature (e.g. Kam, 1997). Two hundred and three

of the student respondents were women, representing 48 per cent of the overall sample. Thirty-

six different supervisors were engaged in the supervision process, of which seven were women,

representing 19 per cent of the overall sample. Each of these was experienced at supervising

both undergraduate and postgraduate research students and each had received appropriate

research supervision training. Each supervisor typically supervised between three and five of

the final year undergraduate student projects over each of the four years of the study.

Context and Nature of the Supervisory Relationships

As a part of their final year studies, students were required to undertake a major research

project. Each project needed to be problem solving in nature, and related to a current business

issue. The process closely resembled that experienced by postgraduate students in the

University, except that the duration of the research and the length of the undergraduate

thesis/dissertation were shorter. Students were expected to pass through the following stages

between September and June of their final academic year:

Find an organisation to work with.

Identify a suitable problem requiring significant research.

Submit a dissertation proposal for authorisation by their academic supervisor.

Produce a detailed project plan.

Submit a 10, 000-word thesis/dissertation that:

Demonstrates extensive secondary research.

Demonstrates extensive primary research.

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Demonstrates detailed analysis of primary and secondary research data.

Draw conclusions and make recommendations.

Make a presentation and attend an oral examination.

Each student worked under the supervision of an academic member of staff (his/her supervisor),

whom he/she met at least once every two weeks over this eight to nine month period. Research

data for the purpose of this study were collected in the last few weeks of their supervisory

relationships, for each of the four cohorts (1995/6, 1996/7, 1997/8, & 1998/9). The frequency

of meetings increased as needs demanded, much as one would expect within a postgraduate

supervision process. The students’ work ultimately contributed 20 CATS (credit accumulation

transfer scheme) points out of 360 required for graduation (120 per year). Their work was

evaluated by their supervisor and then second evaluated by another supervisor. External

examiners, who worked as senior academics in other University Business Schools, also

scrutinized samples of theses/dissertations.

Measures

Cognitive Style. Cognitive style was assessed in terms of the analytic-intuitive dimension

described earlier. The Cognitive Style Index (Allinson & Hayes, 1996), a self-report

questionnaire, was administered to all participants in the study. Each of its 38 items has a

true-uncertain-false response mode, and scores of 2, 1 or 0 are assigned to each response

depending on the polarity of the item (17 having been reversed to control for acquiescence-

response bias). The nearer the total score to the theoretical maximum of 76, the more

analytical the respondent, and the nearer to the theoretical minimum of 0, the more intuitive

the respondent. Reliability of the CSI is good with test-retest correlation’s ranging from 0.78

to 0.90 across 26 samples and alpha coefficients from 0.78 to 0.92 across four samples

20

(Allinson & Hayes, 1996; Armstrong et al, 1997; Murphy et al 1998; Armstrong, 1999).

Construct validity is indicated by items loading on a single factor in most previous studies,

and significant correlations with various personality dimensions (Allinson & Hayes, 1996),

job level (Allinson, Armstrong & Hayes, 2001; Armstrong, 1999; Allinson & Hayes, 1996)

and national culture (Allinson & Hayes, 2000).

Interpersonal Relationships. Subjects registered their attitudes along self-developed 7-

point semantic differential scales in order to provide an assessment of interpersonal variables.

Item labels for dominance were ‘non assertive-assertive’ and for nurturance were ‘cold-

warm’. Other item labels included ‘low-high’ ideas, and ‘low-high’ degrees of liking. Items

were completed by each respondent with regard to perception of self and perception of dyad

partner. Collecting data from both parties in each relationship in this way avoids the sub-

optimal strategies reported by Kenny and Judd (1986) leading to the potential biases

discussed below. It also avoids the dangers of ecological fallacy highlighted by Baxter (1988)

who suggests that to infer objective information about a dyadic relationship while collecting

data from the individual, combining both partners’ data into a single relationship variable may

be necessary.

Performance. Performance outcomes from the supervision process were determined by

extracting grade points from the University records system for the research dissertations.

These grades were represented on a 16-point scale, where 0 indicates catastrophic failure and

16 indicates maximum possible academic achievement.

21

ANALYSIS AND RESULTS

Interdependence

A potential problem with the study of dyads is interdependence, the tendency for the score of

one person on a particular variable to be influenced by that of the other. The two individuals

may transcend their own identities so that the dyad, not the person, may be considered as the

unit of analysis. Bias can result from a focus on the person when data are non-independent

(Kenny & Judd, 1986). Interdependence is determined by correlating the scores of the two

people in each dyad (Kenny, 1988). The criterion recommended by Kenny and Kashy (1991)

is for the coefficient to be statistically significant at the .20 level, a liberal threshold intended

to ensure beyond reasonable doubt that the independence assumption is not violated. For the

present study, Table 1 shows correlations between supervisor and student perceptions of

supervisor and student dominance and nurturance. It can be seen that all supervisor and

student perceptions can be regarded as interdependent (p < .20) except those relating to

student dominance.

Table 1 about here

These variables were therefore analysed at the dyad, as well as the individual level. Where

interdependence exists, Kenny and Kashy (1991) indicate that this level of analysis should be

carried out by averaging the two scores for each dyad. This approach was adopted in the

present study so that scores representing student nurturance, supervisor nurturance, and

supervisor dominance are each the average of supervisor and student perceptions of these

aspects of the interpersonal relationship. An individual level of analysis was also conducted

22

on these, and other variables in the study. For example, student dominance was analysed in

terms of supervisor and student perceptions separately on account of their independence.

Supervisors’ liking of student and students’ liking of supervisor were assessed simply through

supervisor and student perceptions respectively.

Measurement of Congruence

The notion of congruence is clearly important for the present study, which seeks to determine

the extent to which the degree of similarity in cognitive styles affects the student-supervisor

relationship. One procedure which offers the distinct advantage of conceptual clarity and

relative ease of interpretation, involves splitting the cognitive style index (CSI) variable at a

point close to the mean to create groups of dyads that are matched or mis-matched according

to their cognitive styles and then comparing the means for the dependent variables in each

group using the analysis of variance framework.

In the present study, CSI scores were designated low (intuitive) or high (analytic) according

to whether they were < or > 43 (supervisors) and < or > 44 (students). These thresholds,

which were close to the sample means of 40 and 45 respectively, were chosen primarily to

ensure that sub-samples were large enough for analysis. The resulting groups consisted of

‘analytic supervisors /analytic students’ (n = 70 dyads), ‘analytic supervisors /intuitive

students’ (n = 84 dyads), ‘intuitive supervisors /analytic students’ (n = 145 dyads) and

‘intuitive supervisors /intuitive students’ (n = 122 dyads). Table 2 shows descriptive statistics

for the CSI. In the full sample of dyads, scores were significantly lower (more intuitive) for

supervisors than for students (t = 7.18, d.f. = 840, p < .001). Table 3 shows mean scores and

standard deviations for the interpersonal variables in relation to each of the four groups.

23

Tables 2 & 3 about here

Difference Scores. A complementary analysis involved examining the relationships

between the interpersonal variables (dominance, nurturing and liking) and the difference

between the CSI scores (analytic or intuitive) of the supervisor and student in each dyad to

demonstrate the effect of the magnitude of congruence between supervisor and student

cognitive styles. Table 4 shows correlations between interpersonal variables and CSI

difference scores for cases in which the supervisor’s score is higher (more analytic), and cases

in which it is lower (more intuitive), than that of the student. Supervisor and student CSI

scores were the same in six of the dyads, and these were excluded from this part of the

analysis. Where the supervisor’s CSI score was higher than that of the student, the mean

difference was 12.45 (s.d. = 7.32). Where lower, it was 14.91 (s.d. = 9.91).

Table 4 about here

Hypothesis Testing

Congruence Hypotheses (H1 – H3). The general hypotheses that cognitive similarity

between supervisors and students will result in both parties reporting more nurturance (H1),

less dominance (H2) and higher degrees of liking (H3) in their relationships relative to dyads

where there is incongruence in their cognitive styles were not supported. The research

therefore fails to establish a significant link between congruence in cognitive styles and these

interpersonal variables.

24

The results reported in Table 3 and 4 do, however, indicate a number of other interesting

findings that will be discussed below.

Cognitive Style and Idea Generation (H4). Results in Table 3 reveals that intuitive

students perceived that their analytic supervisors generated significantly more ideas in their

relationship than did analytic students irrespective of whether their supervisors were analytic

or intuitive. There was no direct support however for the hypothesis that dyadic relationships

involving one or more intuitive members will be perceived to be more productive in terms of

the generation of ideas. It is recognized, however, that this would apply only in the shorter

term owing to the fact that the work style of an analytic is to build a foundation over a

relatively long period, and then produce innovations, alternatives or variations. The use of

measures of idea generation with specific reference to the short term may therefore have

produced different results.

A correlational analysis revealed that there was no significant relationship between students’

cognitive styles and perceptions of students’ idea-generation (r = .03, n = 198, p > .05, two

tail). There is, however, a weak correlation between the degree to which supervisors are more

analytic in their cognitive styles and perceptions of their level of idea-generation (r = .14, n =

199, p < .05, 1- tailed).

Cognitive Style and Nurturance (H5). An independent samples t-test on the whole

sample of supervisors revealed that students’ perceptions of supervisor nurturance was

significantly higher (t = 3.10, p < .01) for analytic supervisors (M = 5.65, SD = 1.28, n = 75)

than for intuitive supervisors ( M = 5.03, SD = 1.51, n = 124). The same test carried out on

the whole sample of students revealed that supervisors’ perceptions of student nurturance was

25

also significantly higher (t = 2.05, p < .05) for analytic students ( M = 4.71, SD = 1.43, n =

133) than for intuitive students (M = 4.33, SD = 1.44, n = 117).

Correlations were computed to search for a direct causal relationship between cognitive styles

and students’/supervisors’ perceptions of nurturance shown by their dyadic partners. These

results, reported in Table 5, again indicate that the more analytic the partners, the more they

are perceived to be nurturing in their relationships.

Table 5 about here

The mean scores on student perception of supervisor nurturance and supervisor perception of

student nurturance for each of the four groups are shown in Table 3. Results indicate that

analytic supervisors are perceived by their students to be more nurturant than intuitive

supervisors, irrespective of students’ cognitive styles. Results also indicate that analytic

students are perceived to be more nurturant than their intuitive counterparts.

Cognitive Style and Dominance (H6). Conversely, the results shown in Table 3 reveal

that intuitive supervisors were perceived to be significantly more dominant than analytic

supervisors, regardless of the cognitive styles of their students. In the cases where supervisors

were more analytic than their students, Table 4 reveals that the higher the magnitude of

incongruence between supervisor and student cognitive styles, the less dominant are

supervisors (r = -.21, n = 95, p < .05, 2-tailed) and the less students perceive themselves to be

dominant (r = -.26, n = 62, p < .05, 2-tailed).

26

Correlations for the whole sample revealed that supervisors’ cognitive styles were

significantly related to their levels of dominance (r = -.19, n = 297, p < .001, 2-tailed) in their

relationships. The difference between supervisors’ and students’ cognitive styles was also

significantly related to supervisor’s dominance (r = -.15, n = 297, p < .01, 2-tailed). An

independent samples t-test revealed that dominance was significantly higher (t = -4.30, p

< .001, 2-tailed) for intuitive supervisors (M = 4.94, SD = 0.96, n = 184) than for analytic

supervisors ( M = 4.39, SD = 1.14, n = 113).

For student dominance, scores by students and their supervisors were independent and were

therefore treated separately. Independent samples t-tests for the whole sample of students

revealed that there were no significant differences (t = 0.07, p > .05) in self-perception of

dominance between intuitive (M = 4.59, SD = 1.27, n = 95) and analytic (M = 4.58, SD =

1.28, n = 104) students. Nor were there differences between supervisors’ perception of

dominance between intuitive (M = 3.64, SD = 1.53, n = 117) and analytic (M = 3.50, SD =

1.62, n = 133) students.

In summary, intuitive supervisors are perceived to be more dominant in their relationships

than analytic supervisors, irrespective of the cognitive styles of their students. The degree to

which students were perceived to be dominant in their interpersonal relationships is unrelated

to their cognitive styles.

Cognitive Style and Liking (H7). There is no evidence to support the hypothesis that

where there is incongruence in cognitive style, intuitive members of dyads will be more liked

than analytic members. There are, however, other important and significant findings. For

example, results in Table 3 reveal that analytic students with intuitive supervisors are more

27

liked by their supervisors than intuitive students with analytic supervisors. Those shown in

Table 4 also reinforce these results, which indicate that the more analytic students are than

their supervisors, the more their supervisors like them. Conversely, the more intuitive

students are than their supervisors, the less they are liked. An independent samples t-test also

revealed that analytic students (M = 5.19, SD = 1.32, n = 69) were significantly more liked (t

= -3.29, p = .001) than intuitive students (M = 4.37, SD = 1.40, n = 54). A correlational

analysis for the whole sample also revealed a clear, statistically significant relationship

between supervisors’ liking of students and students’ cognitive styles (r = .36, n = 123, p

= .001, 2-tailed).

The correlation matrix shown in Table 6 also reveals how supervisors liking of their students

was related to other important variables, which is of significant interest in the present study.

Table 6 about here

Cognitive Style and Performance (H8). The analysis of variance (F = 4.46, df = 3,397,

p < .01) and Duncan multiple range tests reported in Table 3 indicate that analytic students

matched with analytic supervisors achieved significantly higher results for their research

dissertations than any of the other three groupings. Hypothesis 8 is therefore supported.

An independent samples t-test also revealed that analytic students achieved significantly

higher grades (t = -2.05, p < .05) for their final year research projects (M = 10.30, SD = 3.07,

n = 209) than intuitive students (M = 9.70, SD = 2.89, n = 192), regardless of the cognitive

styles of their supervisors.

28

Interpersonal Relationships and Performance Outcomes (H9). The matrix in Table 7

clearly indicates that relationship scores for supervisor and student nurturance correlated

positively with dissertation grades awarded, as did supervisors’ liking of their students.

Hypothesis 9 is therefore supported.

Table 7 about here

Gender Differences in Cognitive Style (H10). An independent samples t-test revealed

that female management students (M=44.32, SD=11.72, n=203) were significantly more

analytic (t=2.71, p<.01) than male students (M=41.23, SD=11.66, n=218). This is consistent

with other recent findings (e.g. Doucette et al, 1998;, Murphy et al, 1998; Hayes et al, under

review). However, there was no evidence to suggest that female students out-performed male

students leading to the rejection of hypothesis H10. The effects of cognitive style on

performance were therefore considered to be gender-neutral.

DISCUSSION

The Matching Hypothesis

The idea that similarity in cognitive styles between supervisor and student has a beneficial

effect on the socio-emotional aspects of their interpersonal relationship is not supported. For

example, there was no evidence to support the hypotheses that nurturance or liking increased

or that dominance in the relationships decreased in relation to the degree of congruence in

cognitive styles between supervisor and student. Despite the logical appeal and evidence

from previous research to support the idea that matching cognitive styles in a learning

29

situation would have a positive effect on learning outcomes (e.g. Kolb, 1981; Sein & Robey,

1991; Dunn et al, 1990; Katz, 1990) some authors have remained skeptical (e.g. Meredith,

1985; Conwell et al, 1987; Thompson & Crutchlow, 1993). They suggest that research is

clouded by inconsistent findings and there may be several reasons for this.

One reason may be that previous studies exploring the matching hypothesis have focused on

different aspects of the learning situation but research reviews supporting the hypothesis have

failed to take account of this. For example, some studies have focused on subjects’ attitudes

to their interpersonal relationships (Handley, 1982; Renninger & Snyder, 1983; Cooper &

Miller, 1991; Kubes, 1992; Schroeder & Jackson, 1991; Rickards & Moger, 1994; Erickson,

1993; Tullet, 1995), some have focused on subjects’ performance outcomes (Saracho &

Dayton, 1980; Katz, 1990; Robey, 1991), whilst others have focused on both of these effects

(Menges, 1969; Packer & Bain, 1978). The possibility that the effects of matching cognitive

style on interpersonal behavior, performance outcomes, or behavioral attitudes (which may

indirectly affect performance) may be different is frequently overlooked. Studies are simply

grouped together to form an overall consensus of the matching hypothesis.

Another source of confusion arises from different views people have of whether style is a fixed

characteristic of the individual. Such confusion is also confounded by researchers who

erroneously consider learning style and cognitive style to be interchangeable. One factor that

distinguishes learning style from cognitive style is temporal stability. To clarify the confusion,

Curry (2000) placed the most time stable instruments at the centre of what she refers to as an

‘onion model’ (Curry, 1983; Melear, 1989; Aragon, 1996) and referred to these as cognitive

personality styles. The middle layer of the model is classified as information processing style

which is less time-stable. In this layer she places instruments such as Kolb’s (1985) learning

30

styles inventory. The outermost level is least stable over time and the easiest to alter through

interaction with other variables. In this layer she places well known instruments such as Dunn

and Dunn’s (1989) model and refers to these as learning styles or instructional preferences.

This suggests that learning style may vary from time to time and may be learned and

developed, but cognitive style is considered to be a fixed characteristic of the individual and

intimately connected with personality (Riding & Rayner, 1998). Studies that attempt to

support or refute the matching hypothesis have used a variety of measures drawn from each of

these layers, thereby increasing the likelihood that changes in the independent variable (e.g.

learning style) will distort the outcomes of such studies. Clearly, if an individual’s ‘style’

changes during an educational experience, it will be difficult to draw meaningful conclusions

about the effects of matching the learner to the learning environment, as some studies have

tried to do. Repeated measures of subjects’ cognitive styles over periods from 8 weeks to 30

months using the Cognitive Style Index used in this study revealed high temporal stability and

there were no significant changes over time (Armstrong, 1999).

Other factors leading to conceptual confusion in the literature that may lead to false

assumptions about a matching hypothesis include the nature of the task and the context of the

working relationship. It has been shown that generalizations cannot always be drawn across

populations when there are differences in the nature of the work context (Armstrong, 1999;

Armstrong, 2001). Few studies have attempted to control for interacting and confounding

variables such as task or work context.

31

Socio-emotional Aspects of the Relationship

In the context of research supervision, results of this study suggest that under certain

circumstances, a mis-match in cognitive style between supervisor and student may be

beneficial. For example, results in Tables 3 & 4 revealed that in dyads whose supervisors are

intuitive, levels of nurturance in their relationships were higher when paired with analytic

students. Results also revealed that the more analytic students were than their intuitive

supervisors, the more those students were liked. It appears as though there is a reciprocal

relationship because these students then report significantly higher degrees of nurturance for

their supervisors than do intuitive students. Because the overall research task favours an

analytic approach, it is possible that supervisors perceive the more analytic students as being

easier to supervise than intuitive students. Liking may therefore derive from an efficiency

issue for time-pressured supervisors.

It was also revealed that under mis-matched conditions, analytic supervisors were perceived

as generating significantly more ideas in their relationships and that the level of idea

generation increased with the degree to which they were analytic, resulting in better

performance outcomes (see below). This is consistent with a previous study by Garlinger and

Frank (1986) who found that mis-matching field-dependent (intuitive) learners with field

independent (analytic) teachers was beneficial for performance in educational settings,

possibly because the systematic approach of field independent (analytic) teachers provides

structure and order to the relatively open-ended thinking of field dependent (intuitive)

learners. The field dependent-independent cognitive styles are often broadly equated with

intuition and analysis respectively (Wilson, 1988; Riding & Sadler-Smith, 1992). Garlinger

and Frank (1986) also discovered that matching field dependent learners with field dependent

32

teachers may fail to provide the structure that field dependent learners require. Possibly for a

similar reason, Armstrong et al (1997) found that intuitive students with intuitive supervisors

perceived less empathy in the student-supervisor relationship than students in dyads

representing other combinations of the intuitive and analytic cognitive styles.

Irrespective of the matching hypothesis, certain characteristics associated with particular

cognitive style types were shown to significantly affect the supervision process. For

example, analytic supervisors were significantly less dominant in their relationships than their

intuitive counterparts. Furthermore, both analytic supervisors and analytic students were

perceived to be more nurturing by their dyadic partners, indicating a higher degree of

closeness in their relationships compared with those of their intuitive counterparts. This is

perhaps because people with an analytic orientation tend to organize information into clear-

cut and bounded conceptual groupings, viewing information as a collection of parts and

focusing on just one aspect of the whole at a time (Riding & Pearson, 1994). Being able to

analyze information into clearly bounded parts in this way means that they are able to impose

their own structure on a situation and this allows them to come quickly to the heart of a

problem (Riding, 1996). This may be one reason why those with analytic styles are viewed as

more nurturing because clear boundaries and less shifting of ideas may give students a greater

sense of control over their project. Indeed, clear boundaries, consistency and predictability

may be the characteristics of healthy, intimate and interdependent authority relationships,

particularly in the context of a complex and potentially ambiguous research project. This has

some resonance with both attachment (Bowlby, 1980; Sroufe & Fleeson, 1986) and authority

relations theories where it has been suggested that supervisors and subordinates with

interdependent models (of authority) use the structure and boundaries provided by authority

33

relations without letting themselves and their relations be dictated by those systems (Kahn &

Kram, 1994).

An intuitive student’s characteristic approach to organizing and processing information on the

other hand will be to see it as a whole, and the distinction between the parts is often blurred so

that it is difficult to distinguish the components that make up the whole of a piece of

information (Riding, Glass & Douglas, 1993). These students may feel less overwhelmed and

may experience less stress when some structure is imposed by analytic supervisors, resulting

in clearer conceptual boundaries. A previous study by Saracho & Dayton (1980) also found

some evidence of this, concluding that pupils benefit more from supervision by field

independent (analytic) teachers compared with field dependent (intuitive) ones. This

manifested itself through greater achievement gains measured using a test of basic skills

(McGraw-Hill, 1973) cited in Saracho & Dayton (1980).

Performance Outcomes

Achievement gains indicated by grade points were also affected by cognitive styles in the

present study. Analytic students significantly out-performed intuitive students and, as

hypothesized, analytic students performed significantly better when they were matched with

analytic supervisors. This should be no surprise when one considers that students were

expected to demonstrate logical and linear progression through careful planning and

scheduling over a nine month period, and to engage in tasks requiring detailed and systematic

data collection, evaluation and analysis, before producing a 10,000 word dissertation.

34

Other striking findings were revealed following analyses of a series of mediation effects,

where cognitive style was found to work indirectly through its influence on other variables.

For example, supervisors’ liking of their students correlated positively with students’

cognitive styles. This suggests that the more analytic the student, the more she/he will be

liked by her/his supervisor. As one might expect, it was also revealed that degrees of

nurturance in these dyadic relationships also correlated strongly with supervisors’ liking of

their students. Relationship scores for supervisor and student nurturance correlated positively

with dissertation grades awarded, as did supervisors’ liking of their students. These results,

which are summarized in Tables 6 & 7, suggest that the more analytic the student, the closer

will be their interpersonal relationship, the more she/he will be liked, and therefore, the higher

the grade that student will achieve.

This raises the question of whether students were liked more because they developed a record

of high achievement (by virtue of their more analytic cognitive style), whether they received

more attention and support from their supervisors from the start because they were perceived

to be high performers and students responded to this and lived up to their supervisors’

expectation (the Pygmalion effect, Jussim, 1986) or, whether they achieved higher grades

from their supervisors because they were liked more. It is highly unlikely that the latter is the

case, because all work was ‘blind’ second-marked, and external examiners also carried out

sample third-level marking. Considering the optimum match of analytic students with

analytic supervisors, it is more probable that students’ cognitive styles affect their

supervisors’ attitudes towards them, and those supervisors’ cognitive styles affect the

students’ attitudes towards the subject matter. This is reflected in the overall quality of the

supervisory relationship, and therefore on the level and quality of guidance given in order to

achieve these higher grades.

35

Gender

In discussing the gender-centered perspective of managers, Green and Cassell (1996) suggest

that women are characterized as relatively submissive, nurturing, warm, kind, and selfless and

Loden (1985) argues that they have a lower need for control and are more cooperative than

men. These gendered stereotypical differences also embrace approaches to problem solving

and decision making. For example, it has been reported that men are described as being

analytical and logical (Brenner & Bromer, 1981) and that they symbolize gender-neutral

rationality and decision making (Green & Cassell, 1996). Women on the other hand are

described as intuitive (Brenner & Bromer, 1981). Clares (1999) refers to intuition as one of

the valuable contributions that women bring to management and this may lead to them

adopting a more holistic approach to leadership (Bancroft, 1995). Agor (1986) and Parikh et

al (1994) report studies that found that women are more intuitive than men and the view that

women are less analytical and more intuitive is also often reflected in the popular press. For

example, one article suggested that “So engrained is the idea of female intuition that it is

tempting to think this social stereotype must contain a kernel of truth” (Highfield, 1995).

Results from this study fail to support the widely held view that male managers are

unemotional and analytic problem-solvers, and that women supplement this rational approach

with intuition. Instead, the study revealed that female management students were

significantly more analytic than male management students. This is consistent with other

recent findings (e.g. Doucette et al, 1998; Murphy et al, 1998). Another recent study by

Hayes, Allinson & Armstrong (2004) involving more than 500 managers and more than 1000

non managers also found no direct evidence to support this gendered stereotypic thinking.

36

Instead, the study concluded that the opposite might be true, that women in general are more

analytic than men in general. Findings of the study indicated that those women who occupy

organizational roles that are free from pressures to blend in with male gendered organizational

structures and cultures exhibit a more analytic approach to information processing than

women who occupy roles that are subject to such pressures. The study further concluded that

women may be naturally more analytic than men and that the reason why women managers

appear to be more intuitive than women in general is because women managers are pressured

to adopt male characteristics when processing information.

On the basis that analytic students tend to out-perform intuitive students in management

education (Armstrong, 2000), and that women in general tend to be more analytic than men,

this study hypothesized that female students would out-perform male students. Results of the

study, however, found this not to be the case and the effects of cognitive style on academic

performance in the context of research projects are therefore considered to be gender-neutral.

Limitations of the study

Before moving on to draw conclusions from this study a potential limitation of the research

design needs to be considered. To provide sub-samples large enough for rigorous analysis the

sampling frame in this study comprised undergraduate research students, despite the fact that

a lot of published research on this topic relates to postgraduate students. This was justified on

the basis that the study has more to do with the relationship between the participants than the

level of education within which the participants are engaged. In this regard, the independent

and dependent variables (e.g. cognitive style, liking, dominance, nurturance, performance

etc.) in the study are believed to be as relevant to pre as to postgraduate levels. It is also

37

believed that the research process experienced by students in this study closely resembled that

of postgraduate students, albeit over a shorter time frame. Furthermore, the body of

literature that does deal with undergraduate research projects (e.g. McMichael, 1992; Zoia,

1981; Gant et al, 1980) reveals findings that are consistent with those in the postgraduate

literature concerning student supervisor relationships. Nevertheless, whilst there are many

similarities between the research supervision process at these two levels, the authors concede

that there may also be some important differences. In order to determine whether the reported

results can be generalized to the postgraduate population where high wastage and failure rates

are prevalent, it is recommended that future studies focus on postgraduate research. Dyads in

an experimental group could be created in accordance with the findings of the present study.

The use of control groups would also be desirable. Ethical dilemmas associated with creating

dyads with particular cognitive style characteristics, however, need to be considered very

carefully and a code of practice acceptable to all parties would need to be identified.

A further limitation that might affect the design of future studies arises from the possibility

that treating research supervision as equivalent in different social science disciplines may be

problematic. Many of the past studies conducted to examine reasons for differences in pass

rates and completion rates between the social and the natural sciences (e.g. Young et al, 1987;

ESRC, 1991; Hockey, 1991; Rudd, 1985) relied on data from several disciplines (e.g. Acker,

et al, 1994; Hill et al, 1994; Dunkerley & Weeks, 1994). A further recommendation,

therefore, is that future studies involving postgraduate students should focus exclusively on

management education, as indeed the present study has done. This will probably require a

longitudinal research approach to be adopted.

38

CONCLUSIONS

Evidence reported over several decades reveals that while there is widespread concern about

the research supervision process in general and the quality of the student-supervisor

relationship in particular there has been relatively little research aimed at enhancing our

understanding of these issues. Consequently there have been few meaningful

recommendations concerning the systematic allocation of students to supervisors in order to

improve the quality of the student-supervisor relationship. This study has sought to determine

whether individual differences and similarities in cognitive style affect the interaction

processes and outcomes of supervisor-student dyads engaged in the research supervision

process. Findings revealed that cognitive style affected supervisory relationships in multiple

and important ways and thus provides an important contribution to this field of literature.

Results showed that students whose dominant cognitive styles were analytic achieved

significantly higher grades for their research dissertations. This is perhaps not surprising

because the task categories associated with the long-term solitary task of a research project

involving careful planning and analysis of information, would be expected to be consonant

with an analytic individual’s style of working.

The study also revealed that allocating students to supervisors on the basis of their individual

cognitive styles may not only improve socio-emotional aspects of their relationships, but also

the performance outcomes of the students. The results suggest that assembling research

supervision teams whose members’ dominant cognitive styles are analytic will lead to warmer

interpersonal relationships. A particularly significant finding was that analytic students

matched with analytic supervisors out-performed all other dyadic combinations. This is

39

consistent with the findings of a recent study of working relationships in a related context of

formal mentoring systems in industry (Armstrong et al, 2002). This study found that in dyads

in which the mentor was more analytic, congruence between the cognitive styles of mentors

and their protégés resulted in enhanced psychosocial and career mentoring functions being

reported.

It should not be assumed, however, that these results can be generalized to other relationships

because the analytic style may not be appropriate in all situations or for all types of task. For

example, in contrast to the context of the present study, decision making processes in some

organizations are relatively more dynamic and situations somewhat less structured. This will

have implications for the most effective combination of styles in dyadic relationships, as

expressed by Armstrong (2001). The importance of work situation and task design are

highlighted when the results of this study are compared with findings reported by Allinson,

Armstrong & Hayes (2001). They studied leader-subordinate relationships in a

manufacturing context where the tasks called for a rapid and relatively unconstrained mode of

decision making and found that assigning analytic subordinates to intuitive leaders appeared

to create relatively warm, amiable relationships. In this context, intuitive leaders were

perceived to be less dominating and more nurturing than their analytic colleagues, and they

were more liked and respected by analytic subordinates than analytic leaders were by intuitive

subordinates. The least desirable relationship was where intuitive subordinates were paired

with analytic leaders.

Contrary to expectations, the results of this study also provided some support for the idea that

incongruence may be beneficial. For example, more intuitive students benefit from being

mis-matched with analytic supervisors in terms of socio-emotional aspects of the student-

40

supervisor relationship and performance outcomes. This combination leads to the perception

of a greater number of ideas being generated by the supervisor in order to assist the student in

the supervision process, and also results in an increase in the overall quality of supervision.

In turn, this is likely to result in improvements in the student’s achievement in completing the

research dissertation. Similar patterns to these emerged from a previous study (Hayes &

Allinson, 1996), which concluded that while analytic learners benefit from a match between

their own and their trainer’s cognitive style, intuitive learners might benefit from a mis-match.

This mis-match may lead to improved learning performance of intuitive learners because

analytic supervisors may be more likely to provide the structure they require.

Intuitive students working with intuitive supervisors were shown to be the least favorable

combination when working on this analytic independent research task. When intuitive

supervisors are used in a supervisory team, it is recommended that they be paired with

analytic students because, under these mis-matched conditions, intuitive supervisors become

significantly more nurturing in their relationship than when they are matched with intuitive

students. Furthermore, the intuitive supervisors themselves were shown to like analytic

students more than intuitive students. This combination not only appears to affect each dyad

member’s attitude towards the other, but it also has an indirect but significant effect on

student achievement. Once again, this finding lends support to the idea that incongruence

may be beneficial under some circumstances.

41

Implications

If cognitive style is a relatively fixed characteristic of the individual, as the present authors

suggest, the results of this study indicate that it may be a vitally important basis for the

allocation of supervisors to students. It certainly appears that assembling research

supervision teams whose members’ dominant cognitive styles are analytic will be beneficial

because results suggest that this will lead to warmer interpersonal relationships. It also

appears that assigning analytic students to analytic supervisors will not only result in better

interpersonal relationships, but will also lead to better performance outcomes compared with

other dyadic combinations. The least desirable arrangement may be for intuitive students to

be paired with intuitive supervisors because this leads to less nurturance and less liking in the

relationship. Supervisors in these dyads will also be perceived to be significantly more

dominant, and performance outcomes may suffer. It is therefore recommended that intuitive

students be paired with analytic supervisors wherever possible.

The authors accept that there are, potentially, many factors influencing the interrelationships

of dyadic partners engaged in a research supervision process and that cognitive style is but

one variable. Hackman's (2002) studies that investigated the conditions for team success

suggest that the way a supervisor manages the context within which people work (the

functions the supervisor performs, such as providing a compelling sense of direction, specific

and bounded goals, milestones, performance measures and formative feedback) could be at

least as important as the supervisor's cognitive style. While it might appear that analytic

research supervisors are more inclined to provide these functions for students and manage the

research context in this way, intuitive supervisors might be able to adapt, or be trained to

modify, their preferred way of working to provide this same kind of support. While many

42

subscribe to the view that cognitive style is stable over time (Messick et al 1976; Kogan 1980;

Robertson 1985; Kirton 1989) they also suggest that specific cognitive strategies, though

sometimes at odds with an individual's cognitive style, may be adopted in the short term in

order to perform particular tasks in the most effective way.

The present research has made an incremental contribution towards furthering our

understanding of these complex phenomena. It is hoped that its findings may now pave the

way for further research, leading ultimately, to a more complete understanding of the issues

and their interrelationships.

AUTHORS NOTES:

1. We would like to express our sincere thanks to two anonymous reviewers and the

editor, Cynthia Fukami, for their helpful comments on earlier drafts of this article.

2. Correspondence concerning the article should be addressed to Dr. Steven J.

Armstrong, Centre for Management and Organizational Learning, University of Hull

Business School, Cottingham Road, Hull, HU6 7RX, United Kingdom, or email

stevearmstrong@welton-lincoln.freeserve.co.uk.

43

REFERENCES

Agor, W. H. (1984). Intuitive Management. Englewood Cliffs, N.J: Prentice Hall.

Agor, W. H. (1986). The logic of intuitive decision making: A research-based approach for top

management. New York: Quorum Books.

Acker, S., Hill, T. & Black, E. (1994). Thesis supervision in the social sciences: managed or

negotiated? Higher Education, 28, pp. 483-498.

Aiken, L. S. & West, S. G. (1991). Multiple Regression; Testing and Interpreting Interactions.

London: Sage.

Allinson,C.W. & Hayes, J. (1996). The Cognitive Style Index: a measure of intuition-analysis for

organizational research. Journal of Management Studies, 33:1, 119-135.

Allinson,C.W. & Hayes, J. (2000). Cross-national differences in cognitive style: Implications for

management. International Journal of Human Resource Management, 11:161-170.

Allinson, C. W., Armstrong, S. J. & Hayes, J. (2001). The effect of cognitive style on leader-member

exchange: a study of manager-student dyads. Journal of Occupational and Organizational

Psychology, 74, 201-220.

Armstrong, M. and Shanker, V. (1983) The supervison of undergraduate research: student perception

of the supervisor role, Studies in Higher Education, 8, 3, 177-183.

Armstrong, S. J., Allinson, C. W., & Hayes, J. (1997). The implications of cognitive style for the

management of student-supervisor relationships. Educational Psychology, 17:1,2, 209-217.

Armstrong, S. J. (1999). Cognitive style and dyadic interaction: a study of supervisors and

subordinates engaged in working relationships. Unpublished PhD thesis, University of Leeds,

UK.

Armstrong, S. J. & Priola, V. (2001). Individual cognitive styles and the composition of self-

managing work-teams: An empirical study. Small Group Research, 32, 4, 283-312.

Armstrong, S.J. (2000). The influence of individual cognitive style on performance in management

education. Educational Psychology, 20, 3, 323-340.

Armstrong, S. J. (2001). Cognitive style and dyadic interaction: The matching hypothesis re-visited.

Paper presented to the 6 th Annual ELSIN conference , University of Glamorgan: Wales.

Armstrong, S. J., Allinson, C. W., & Hayes, J. (2002). Formal mentoring systems: An examination of

the effects of mentor/protégé cognitive styles on the mentoring process. Journal of

Management Studies, 39, 8, pp1111-1137.

Baxter, L. A. (1988). Dyadic personal relationships: measurement options. In C.H.Tardy (Ed) A

Handbook for the Study of Human Communications. Norwood, N.J.: Ablex Publishing.

Berger, S. S. & Buchholz, E. S. (1993) On becoming a supervisee: Preparation for learning in a

supervisory relationship. Psychotherapy, 30, 1, pp86-92.

Berelson, B. (1960). Graduate education in the United States. New York: McGraw-Hill.

44

Blumberg, A. (1978). Supervision and Interpersonal Intervention. Journal of Classroom Interaction,

13, 1, pp23-32.

Borgotta, E. F., Cottrell, L. S. & Mann, B. M. (1958). The spectrum of individual interaction

characteristics: An interdimensional analysis. Psychological Reports, 4, 271-319

Brophy, J. E. & Good, T. L. (1974). Teacher student relationships: Causes and consequences. New

York: Holt, Rinehart and Winston.

Brown, G. & Atkins, M (1988). Effective Teaching in Higher Education. London: Methuen.

Burgess, R.G. (1994). Some issues in postgraduate education and training in the social sciences:

Processes and products. In R. G. Burgess (Ed) Postgraduate education and trainingin the

social sciences: Processes and products, London: Jessica Kingsley.

Burnett, P. (1999). The supervision of doctoral dissertations using a collaborative cohort model.

Counselor Education and Supervision, 39, 1, pp. 46-52.

Burnett Report (1977) Report of the vice chancellor’s committee on research and postgraduate study.

St Lucia: University of Queensland. In I. Moses (1984) Supervision of higher degree

students-problem areas and possible solutions. Higher Education Research and Development,

3, 2, 153-165.

Carter, L. (1955). Recording and evaluating the performance of individuals as members of small

groups. In H. P. Hare, E. F. Borgotta, & R. F. Bales (Eds.) Small Groups: Studies in

interaction. New York: Knopf.

Connell, R. (1985). How to supervise PhD’s. The Australian Universities’ review, 28, 2, pp38-41.

Conwell, C. R., Stanley, L., Helgeson, S. L. & Wachowiak, D. G. (1987). The Effect of Matching and

Mismatching Cognitive Style and Science Instruction. Journal of Research in Science

Teaching, 24, 8. 713-722.

Cooper, S. E. & Miller, J. A. (1991). MBTI Learning Style-Teaching Style Discongruences.

Educational and Psychological Measurement. 51, 699-706.

Cornwall, M. G., Schmithals, F. and Jaques, D (Eds.) (1977) What is project orientation? An

overview. In Proceedings of the Seminar on Project Orientation in Higher Education, pp. 1-

16, University of Bremen, March 1976 (University of London, Institute of Education).

Day, D.V. & Crain, E. (1992). The role of affect and ability in initial exchange quality perceptions.

Group and Organisation Management, 17, 380-397.

Dillon, M. J., & Malott, R. W. (1981). Supervising masters theses and doctoral dissertations.

Teaching of Psychology, 8, 195-202.

DiStefano, J. J. (1970). Interpersonal perceptions of field independent and field dependent teachers

and students. Doctoral dissertation, Cornell University. Dissertation Abstracts International,

32, 463A-464A. (University Microfilms, No 70-11, 225).

45

Doktor, R. (1978). Problem solving styles of executives and management scientists. TIMS studies in

the management sciences, 8, 123-134.

Doucette, P.A., Kelleher, W.E., Murphy, H.J. & Young, J.D. (1998). Cognitive Style and Law

Students in Eastern Canada: Preliminary Findings. College Student Journal, 32, 2, 206-214.

Duchon, D., Green, S. G. & Taber, T. D. (1986). Vertical Dyad Linkage: A longitudinal assessment of

antecedents, measures and consequences. Journal of Applied Psychology, 71, 56-60.

Dunn, R. (1987). Research on Instructional Environments: Implications for student achievement and

attitudes. Professional School Psychology. 2, 1, 43-52.

Dunn, R., Beaudry, J. S. & Klavas, A. (1989). Survey of Research on Learning Styles. Educational

Leadership, 46 (6), 50-58.

Dunn, R., Giannitti, M. C., Murray, J. B., Rossi, I., Geisert, G., & Quinn, P. (1990). Grouping

Students for Instruction: Effects of Learning Style on Achievement and Attitudes. The

Journal of Social Psychology, 30, 4, 485-494.

Dunkerley, D. & Weekes, J. (1994) Social science research degrees: Completion times and rates. In

R. G. Burgess (Ed) Postgraduate education and trainingin the social sciences: Processes and

products, London: Jessica Kingsley.

Economic and Social Research Council (1991). Postgraduate training: Guidelines on the provision of

research training for postgraduate research students in the social sciences. Swindon: ESRC.

Egglestone, J. & Delamont, S. (1983). Supervision of students for research degrees.

Birmingham: BERA.

Elton, L. & Pope, M. (1989). Research supervision: The value of collegiality.

Cambridge Journal of Education. 19,3, 267-276.

Entwistle, N. (1981). Styles of Learning and Teaching, Chichester, Wiley.

Frank, B. M. & Davis, J. K. (1982). Effect of Field Independence Match or Mismatch on a

Communication Task. Journal of Educational Psychology, 74, 1, 23-31.

Garcia, M.E., Malott, R.W., Brethower, D. (1988). A system of thesis and dissertation supervision:

Helping graduate students succeed. Teaching of Psychology, 15, pp186-191.

Gant, G. D., Dillon, M. J., & Mallott, R.W. (1980). A behavioural system for supervising

undergraduate research. Teaching of Psychology, 7, 89-92.

Globerson,T., & Zelniker, T. (1989). Cognitive Style and Cognitive Development, Norwood,N.J.:

Ablex Publishing Corporation.

Goldstein, K. M. & Blackman, S. (1978). Cognitive Style: Five approaches and relevant research.

New York, Wiley Interscience.

Gollan, V. (1987). The supervision of research students at the University of Sydney. Sydney,

Australia: Sydney University Postgraduate Representative Association.

Hackman, J.R. (2002). Leading Teams: Setting the Stage for Great Performances, Boston, Mass:

Harvard Business School Press.

46

Hammick, M. (1997) Undergraduate research supervison: a gendered perspective, Pater presented at

the British Educational Research Association Annual Conference, Septembre: University of

York

Hammick, M. and Acker, S. (1998) Undergraduate research supervision: a gender analysis, Studies in

Higher Education, 23, 3, 335-347.

Hammond, K. R., Hamm, R. M., Grassia, J. & Pearson, T. (1987). Direct comparison of the efficacy

of intuitive and analytical cognition in expert judgement. IEEE Transactions on systems, man

and cybernetics, 17, 5, 753-770.

Handley, P. (1982). Relationship Between Supervisors’ and Trainees’ Cognitive Styles and the

Supervision Process. Journal of Counseling Psychology, 29, 5, 508-515.

Harding, A.G. (1973) The objectives and structure of undergraduate projects, British Journal of

Educational Technology, 4, 216-232.

Harrow, J. & Loewenthal, D. (1992). Management research supervision: some users’ perspectives on

roles, interventions and power, Management Education and Development, 23, pt1, pp. 54-64.

Hayes, J. & Allinson, C. W. (1996). The Implications of Learning Styles for Training and

Development: A Discussion of the Matching Hypothesis: British Journal of Management, 7,

63-73.

Hayes, J., Allinson, C. W. & Armstrong, S.J. (2004). Intuition, Women Managers and Gendered

Stereotypes. Personnel Review, 33, 3.

Hayes, J. & Allinson, C. W. (1998). Cognitive style and the theory and practice of individual and

collective learning in organizations. Human Relations, 51, 7, 847-871.

Heppner, P. & Handley, P. (1981). A study of the interpersonal influence process in supervision.

Journal of Counseling Psychology, 28, 5, 437-444.

Hill, T., Acker, S., & Black, E., (1994). Research students and their supervisors in

Education & Psychology. In R.G.Burgess (Ed), Postgraduate education and

training in the social sciences. London: Jessica Kingsley.

Hockey, J. (1991). The social science PhD - A literature review. Studies in Higher

Education. 16,3, 319-332.

Holzman, P. S. & Klein, G. S. (1954). Cognitive system-principles of levelling and sharpening:

individual differences in visual time-error assimilation effects. Journal of Psychology, 37,

105-122.

Hoshmand, L. T. (1994). Supervision of pre-doctoral graduate research: a practice oriented approach.

Counselling Psychologist, 22, 1, 147-161.

Hudson, L. (1968). Frames of mind: Ability perception and self-perception in the arts and sciences.

London: Methuen.

47

Jacks, P., Chubin, D.E., Porter, A.L. & Connolly, T. (1983) The ABC’s of ABD’s: A study of

incomplete doctorates. Improving College and University Teaching, 31, 74-81.

James, C. D. (1973). A cognitive style approach to teacher-pupil interaction and the academic

performance of black children. Unpublished Master’s Thesis, Rutgers University.

Kagan, J. (1965). Individual Differences in the Resolution of Response Uncertainty, Journal of

Personality and Social Psychology, 2, 2, 154-160.

Kam, B.H. (1997). Style and quality in research supervison: the supervisor dependency factor.

Higher Education, 34, 81-103.

Kassebaum, G., Couch, A. S., & Slater, P. E. (1959). The factorial dimensions of the MMPI. Journal

of Consulting Psychology, 23, 226-236.

Katz, J. & Hartnett , R. T. (1976). Scholars in the making. Cambridge. MA.: Ballinger.

Katz, N. (1990). Problem Solving and Time: Functions of Learning Style and Teaching Methods. The

Occupational Therapy Journal of Research , 10, 4, 17-33.

Kenny, D. A. & Judd, C. M. (1986). Consequences of violating the independence assumption in

analysis of variance. Psychological Bulletin, 99, 3, 422-431.

Kenny, D. A. (1988). The analysis of data from two person relationships. In S.Duck (Ed) Handbook

of Personal Relationships. London: John Wiley

Kenny, D. A. & Kashy,D.A. (1991). Analysing interdependence in dyads. In B. M. Montgomery & S.

Duck (Eds.), Studying Interpersonal Interaction, New York: Guilford press.

Kirton, M. (1976). Adaptors and Innovators: A description and measure. Journal of Applied

Psychology, 61, 622-629.

Kogan, N. (1980). Cognitive styles and reading performance. Bulletin of the Orton Society, 30, 63-78.

Kogan, N. (1983). Stylistic variation in childhood and adolescence: creativity, metaphore and

cognitive styles. In P. Mussen (ed). Handbook of Child Psychology, VIII. Cognitive

Development. New York: Wiley, 630-706.

Kolb, D. A. (1981). Learning styles and disciplinary differences. In Chickering, A. W. and associates,

The modern American college, San Francisco: Jossey Bass.

Kubes, M. (1992). Cognitive Style and Interpersonal Behaviour: The Kirton Adaptation-Innovation

and Schutz’s FIRO-B Inventories. Psychology a Journal of Human Behaviour. 29, 2, 33-38.

LaForge, R. & Suczek, R. F. (1955). The Interpersonal Dimensions of Personality: III. An

interpersonal checklist. Journal of Personality, 2, 94-112.

Languis, M.L., (1998). Using knowledge of the brain in educational practice. NASSP Bulletin, 82, pp

38-47.

Languis, M.L. & Miller, D.C. (1992). Luria’s Brain Functioning Theory: A Model for Research in

Cognitive Psychophysiology. Educational Psychology, 27, pp 493-511.

Lawrence, G. (1993). People Types and Tiger Stripes: A practical guide to learning styles. Gainsville,

Fl: Centre for Application of Psychological Type.

48

Leary, T. F. (1957). Interpersonal diagnosis of personality, New York: Ronald Press.

Leonard, D. & Straus, S. (1997). Putting your company’s whole brain to work. Harvard Business

review, 75, 110-121.

Lorr, M. & McNair, D. M. (1963). An interpersonal behavior circle. Journal of Personality and Social

Psychology, 67, 68-75.

Luria, A. R. (1966). Higher Cortical Functions in Man, New York: Basic Books.

Luria, A.R. (1980). Higher Cortical Functions in Man, 2nd ed. New York: Wiley.

Lynch, D. (1986). Is the brain stuff still the right (or left) stuff? Training and Development Journal,

February, 23 – 26.

Mahlios, M. C. (1981). Effects of Teacher-Student Cognitive Style on Patterns of Dyadic Classroom

Interaction. Journal of Experimental Education, 49, 3, 147-157.

Malott, R. W. (1986). Self-management, rule governed behavior, and everyday life. In H. W. Reese &

L. J. Parrot (Eds.), Behavior science: Philosophical, methodological, and empirical advances

(pp. 207-228). Hillsdale, NJ: Lawrence Earlbaum Associates Inc.

McAleese, R., & Welsh, J. (1983). The supervision of post-graduate research students. In J. F.

Egglestone & S. Delamont (Eds) Supervision of Students for Research Degrees. Kendall-

Dixon Pub: London

McCaulley,M. (1978). Application of the MBTI to medicine and other health professions.

Gainesville, FL: Centre for Application of Psychological Type.

McMichael, P. (1992). Tales of the unexpected: supervisors’ and students’ perspectives on short-term

projects and dissertations, Educational Studies, 18, 299-310.

Meredith, G. E. (1985). Transmitter-Facilitator Teaching Style and Focus-Scan Learning Style in HE.

Perceptual and Motor Skills. 61, 545-546.

Messick,S. (1984). The nature of cognitive styles: problems and promise in educational practice,

Educational Psychologist, 19, 2, 59-74.

Messick, S. (1976). Personality consistencies in cognition and creativity. In Messick,S. and

Associates (Eds), Individuality in Learning. San Francisco, CA: Jossey Bass, 4-22.

Miller, A. (1987). Cognitive Styles: an integrated model. Educational Psychology, 7, 4, 251-268.

Moore, R.W. (1985). Winning the PhD game. New York: Dodd, Mead.

Murphy, H. J., Kelleher, W. E., Doucette, P. A., & Young, J. D. (1998). Test-Retest Reliability and

Construct Validity of the Cognitive Style Index for Business Undergraduates. Psychological

Reports, 82, 595 – 600.

Naylor, P.D., & Sanford, T.R. (1982). Intrainstitutional analysis of student retention across student

levels. College and University, 58, 143-158.

Nickerson et al (1985) p7

Ornstein, R. E. (1977). The Psychology of Consciousness, (2nd Ed). New York: Harcourt, Brace,

Jovanovich.

49

Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor

structures in peer-nomination personality ratings. Journal of Abnormal and Social

Psychology, 66, 574 – 583.

North, R. D. (1949). An analysis of the personality dimensions of introversion-extraversion. Journal

of Personality, 17, 352 – 367.

Packer, J. & Bain, J. D. (1978). Cognitive style and teacher-student compatibility. Journal of

Educational Psychology, 70, 5, 864-871.

Parikh, J., Neubaue, F. and Lank, A.G. (1994). Intuition: The New Frontier of Management, Oxford:

Blackwell.

Pascual-Leone, J. (1989). An organismic process model of Witkins Field dependence-independence.

In T. Globerson and T. Zelniker (Eds.), Cognitive style and cognitive development – human

development, 3, 36-70.

Pask,G., & Scott, B. C. (1972). Learning strategies and individual competence. International Journal

of Man-Machine Studies, 4, 217 – 253.

Pask, G. (1976). Styles and strategies of learning. British Journal of Educational Psychology, 46,

128-148.

Peterson, D. R. (1965). The scope and generality of verbally defined personality factors.

Psychological Review, 72, 48 – 59.

Phillips, E. & Pugh, D. S. (1994). How to Get a PhD. Bristol: Open University Press.

Pincus, A. L. & Wiggins, J. S. (1992). An expanded perspective on interpersonal assessment. Journal

of Counseling and Development, 7, 9194.

Rao, H. R., Jacob, V. S., Lin, F., Robey, D. & Huber, G. P. (1992). Hemispheric specialisation,

cognitive differences, and their implications for the design of decision support systems. MIS

Quarterly, 16, 145-151.

Renninger, K. A. & Snyder, S. S. (1983). Effects of Cognitive Style on perceived satisfaction and

performance among students and teachers. Journal of Educational Psychology, 75, 5, 668-

676.

Rickards, T. & Moger, S. (1994). Felix and Oscar revisited: An exploration of the dynamics of a real-

life odd couple work relationship. Journal of Applied Behavioural Science, 30, 108-131

Riding, R. (1991). Cognitive Styles Analysis User Manual. Birmingham: Learning and Training

Technology.

Riding, R. & Cheema, I. (1991). Cognitive styles - an overview and integration, Educational

psychology, 11, 3 & 4, 193-215.

Riding, R. & Douglas, G. (1993). The effect of cognitive style and mode of presentation on learning

performance. British Journal of Educational Psychology, 63, 297 – 307.

Riding, R. & Sadler-Smith, E. (1992). Type of Instructional Material, Cognitive Style and Learning

Performance, Educational Studies, 18, 3, 323-339.

50

Riding, R., Glass,A. & Douglas, G. (1993). Individual Differences in Thinking: cognitive and

neurophysiological perspectives, Educational Psychology, 13, 3 & 4, 267-279.

Riding, R., & Rayner, S. (1998). Cognitive styles and learning strategies. London: David Fulton.

Robey, D. & Taggart, W. (1981). Measuring managers’ minds: The assessment of style in human

information processing. Academy of Management Review, 6, 375 – 383.

Rudd, E. & Hatch, S. (1968). Graduate Study and After. Weidenfield and Nicholson, London.

Rudd, E. (1985). A New Look at Postgraduate Failure. Guildford: SRHE and Slough: NFER-Nelson.

Saracho, O. N. & Dayton, C. M. (1980). Relationship of teachers cognitive style to pupils academic

achievemnet gains. Journal of Educational Psychology, 72, 4, 544-549.

Schutz, W. C. (1963). FIRO: A three dimensional theory of interpersonal behavior. New York: Ronald

Press

Sein, M. K. & Robey, D. (1991). Learning style and the efficacy of computer training methods,

Perceptual and Motor Skills, 72, 243-248.

Sullivan, H. S. (1953). The Interpersonal Theory of Psychiatry. New York: Norton.

Taggart, W., Robey, D. & Kroeck, K. G. (1985). Managerial Decision Styles and Cerebral

Dominance: an empirical study. Journal of Management Studies, 22, 2, 175-192.

Thompson, C. & Crutchlow, E. (1993). Learning Style Research: A critical review of the literature

and implications for nursing education. Journal of Professional Nursing, 9, 1, 34-40.

Triandis, H. C. (1960). Cognitive similarity and communication in a dyad. Human Relations, 13, 175-

183.

Tullett, A. D. (1995). The Adaptive-Innovative (A-I) Cognitive Styles of Male and Female Project

Managers: some implications for the management of change. Journal of Occupational and

Organisational Psychology, 69, 359-365.

Turban, D.B., Jones, A.P. & Rozelle, R.M. (1990) Influence of supervisor liking of a subordinate and

the reward context on the treatment and evaluation of that subordinate. Motivation and

Emotion, 14, 3, 215-233.

Simon, H.A. (1987). Making management decisions: the role of intuition and emotion. In W.H. Agor

(Ed.), Intuition in Organisations (pp. 23-39). Newbury Park, CA: Sage. Snell, J. L. (1965).

The master’s degree. In E. Walters (Ed.), Graduate eduction today (pp. 74-102).

Washington, DC: American Council on Education.

Sonnier, I. L. (1990). Hemisphericity: A key to understanding individual differences among teachers

and learners. Journal of Instructional Psychology, 18, 1, 17-22.

Sperry, R. W. (1964). The Great Cerebral Commissure, Scientific American, Jan, 42-52, Offprint No

174.

Streufert, S. & Nogami, G. Y. (1989). Cognitive style and complexity: Implications for I/O

Psychology; In Cooper,E.L., Robertson,I., (Eds), International Review of Industrial and

Organisational Psychology, 93-143, Chichester: Wiley.

51

Van-Dendurg, T. F., Schmidt, J. A. & Kiesler, D. J. (1992). Interpersonal assessment in counselling

and psychotherapy. Journal of Counselling and Development, 71, 84-90.

Waber,D. (1989). The biological boundaries of cognitive styles: A neurophysiological analysis. In

Globerson, T & Zelniker, T (Eds.), Human Development, Volume 3. Norwood, Jersey: Ablex

Publishing Corporation.

Wayne, S.J. & Ferris, G.R. (1990) Influence tactics, affect, and exchange quality in supervisor-

subordinate interaction: a laboratory experiment and field study. Journal of Applied

Psychology, 75, 487-499.

Welsh, J. (1983). Improving the supervision of postgraduate students. Research in Education, 27, 1 –

8.

Wilson, K. M. (1965). Of time and the doctorate. Atlanta, GA: Southern Regional Education Board.

Wilson,D.K., (1988b). Management Learning: A Grounded Study of the Written Reflections of

Managers of Their Approach to Their Real Time Work Problems. Unpublished Ph.D. Thesis,

University of Lancaster, England.

Wish, M. (1976). Comparisons among multidimensional structures of interpersonal relations.

Multivariate Behavioral Research, 11, 297 – 324.

Wish, M., & Deutsch, M., & Kaplan, S. J. (1976). Perceived dimensions of interpersonal relations.

Journal of Personality and Social Psychology, 33, 409 – 420.

Witkin, H. A., Dyk, R. B., Faterson, H. F., Goodenough, D. R. & Karp, S. A. (1962). Psychological

Differentiation: Studies of development, London John Wiley and sons.

Witkin, H. A., Oltman, P. K., Raskin, E., & Karp,S.A. (1971). A manual for the embedded figures

tests, Palo Alto,. Calif.: Consulting psychologists press.

Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field dependent and field

independent cognitive styles and their educational implications, Review of Educational

Research, 47, 1, 1-64.

Witkin, H. A. & Goodenough, D. R. (1977). Field Dependence and Interpersonal Behaviour,

Psychological Bulletin, 84, 4, 661-689.

Witkin, H. A. & Goodenough, D. R. (1981). Cognitive Style: Essence and Origins, New York:

International Universities Press.

Young, K., Fogarty, M. P., & McRea, S. (1987). The management of doctoral studies in the social

sciences. London; PSI

Zeleny, M. (1975). Managers without management science, Interfaces, 5, 35-42.

Zoia, T. K. (1981). Completing long-term undergraduate projects: Some critical variables.

Unpublished master’s thesis, Western Michigan University, Kalamazoo, MI.

52

Variable r p n

___________________________________________________________________________

Supervisor’s dominance .14* .088 152

Student’s dominance .08 .350 152

Supervisor’s nurturance .28*** .001 152

Student’s nurturance .18 ** .025 152

__________________________________________________________________________

*** p < 0.01, ** p < 0.05, * p < 0.2,

Table 1 - Correlation between supervisor and student perceptions of interpersonal

aspects of their relationship

53

_____________________________________________________________________Group Subjects n Mean SD Range_____________________________________________________________________

Analytic supervisor/ supervisors 70 47.91 4.00 44-56

analytic student students 70 52.43 6.67 44-73

Analytic supervisor/ supervisors 84 48.05 4.11 44-56

intuitive student students 84 32.83 9.29 11-43

Intuitive supervisor/ supervisors 145 31.23 7.73 15-39

analytic student students 145 51.39 4.84 44-64

Intuitive supervisor/ supervisors 122 30.64 7.76 15-39

intuitive student students 122 33.66 8.59 5-43

Full sample supervisors 421 37.19 10.55 15-56

Full sample students 421 42.72 11.78 5-73

_____________________________________________________________________

TABLE 2 - Descriptive statistics for the Cognitive Style Index

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TABLE 3Interaction between supervisors’ and students’ cognitive styles in explaining interpersonal variables

1Analytic supervisor/

analytic student

2Analytic supervisor/

intuitive student

3 Intuitive supervisor/

analytic student

4Intuitive supervisor/

intuitive student

Interpersonal variable n Mean SD n Mean SD n Mean SD n Mean SD df F

Students’ perceptions ofown nurturance

35 5.54 .85 40 5.45 1.01 69 5.49 1.16 55 5.24 .92 3,195 .897

Supervisors’ perceptions ofStudents’ nurturance

43 4.72 1.05 44 4.273 1.45 90 4.872,4 1.45 73 4.233 1.4 3,246 3.75**

Students’ nurturance(Dyad level of analysis)

56 4.96 1.12 57 4.85 1.21 100 5.08 1.28 84 4.75 1.06 3,293 1.24

Supervisors’ perceptions ofown nurturance

43 4.56 1.12 44 4.093 1.07 90 4.942,4 1.41 73 4.143 1.16 3,246 7.62***

Students’ perceptions ofSupervisors nurturance

35 5.803,4 1.13 40 5.783,4 .95 69 5.131,2 1.53 55 4.751,2 1.48 3,195 6.65***

Supervisors’ nurturance(Dyad level of analysis)

56 4.93 1.37 57 4.84 1.20 100 5.034 1.33 84 4.423 1.19 3,293 3.83**

Students’ perceptions ofown dominance

35 4.37 1.29 40 4.50 1.24 69 4.68 1.28 55 4.66 1.29 3,195 0.57

Supervisors’ perceptions ofStudents’ dominance

43 3.19 1.44 44 3.43 1.40 90 3.64 1.69 73 3.77 1.59 3,246 1.42

Students’ dominance(Dyad level of analysis)

--- --- --- --- --- --- --- --- --- --- --- --- --- ---

Supervisors’ perceptions ofown dominance

43 4.473,4 1.12 44 4.393,4 1.26 90 5.021,2 1.14 73 5.031,2 .99 3,246 5.48***

Students’ perceptions ofSupervisors’ dominance

35 4.313,4 1.02 40 4.404 1.26 69 4.811 1.09 55 5.001,2 1.25 3,195 4.87*

Supervisors’ dominance(Dyad level of analysis)

56 4.373,4 1.02 57 4.413,4 1.25 100 4.951,2 0.93 84 4.941,2 1.00 3,293 6.65***

Students’ perceptions ofown idea-generation

35 5.34 1.39 39 5.62 0.99 69 5.51 1.17 55 5.29 1.13 3,194 0.75

Students’ perceptions ofSupervisors’ idea-generation

35 4.862 1.59 40 5.651,3 1.14 69 4.842 1.64 55 5.16 1.62 3,195 3.00*

Students’ liking ofSupervisors

25 5.68 1.46 22 5.41 1.76 40 5.45 1.72 30 5.10 1.54 3,113 0.60

Supervisors’ liking ofStudents

28 5.11 0.92 21 4.193 1.29 41 5.242 1.55 33 4.48 1.48 3.119 3.88*

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Students’ performance outcomes 66 11.032,3,4 2.75 77 10.161 2.59 143 9.961 3.16 115 9.371 3.05 3,397 4.46**

*** p <0.001, ** p < 0.01,* p < 0.05

Subscript to a mean refers to a group whose mean is significantly different (Duncan multiple range test).

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Difference between CSI scores of Student and Student

Student lower Student higher(more intuitive) (more analytic)than supervisor than supervisor

Interpersonal Variable n r n r Students’ perceptions of 62 .04 131 -.05own nurturance

Supervisors’ perceptions of 73 -.08 172 .17*students nurturance

Students’ nurturance 95 .02 196 .09(Dyad level of analysis)

Supervisors’ perceptions of 73 .09 172 .20**own nurturance

Students’ perceptions of 62 .04 131 -.21*supervisors nurturance

Supervisors’ nurturance 95 .08 196 .10 (Dyad level of analysis)

Students’ perceptions of 62 -.26* 131 .07own dominance

Supervisors’ perceptions of 73 -.03 172 -.06students dominance

Students’ dominance --- --- --- ---(Dyad level of analysis)

Supervisors’ perceptions of 73 -.25* 172 -.01own dominance

Students’ perceptions of 62 -.23 131 .04supervisors dominance

Supervisors’ dominance 95 -.21* 196 .04(Dyad level of analysis)

Students’ perceptions of 61 -.06 131 -.08own idea-generation

Students’ perceptions of 62 .14 131 -.16supervisors idea-generation

Students’ liking of 34 -.13 79 -.07supervisors

Supervisors’ liking of 35 -.37* 84 .25*students

Students’ performance 35 -.37* 84 .25*outcomes ** p < 0.01 * p < 0.05

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Table 4 – Correlations between (a) the difference between supervisor and student CSI scores and (b) interpersonal

variables

Supervisor Student

Cognitive Style Cognitive Style

Supervisors’ perceptions of -.10 .21***Y

student nurturance

Student’ perceptions of .32***YY .07

Supervisor nurturance

***p < .001, two tails Yn = 250, YY n = 199

Table 5 – Correlation Matrix for Partners’ Perception of

Nurturance and Cognitive Style

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Variables Supervisors’ Liking of Students

Students’ Cognitive Style +.36***YY

Difference between Supervisors’ and Students’ cognitive styles -.35***YY

Supervisor Nurturance (mean of scores awarded) +.46***YY

Student Nurturance (mean of scores awarded) +.61***YY

Supervisors’ independent perceptions of Students’ nurturance +.72***YY

***P < .001, two tails Y n = 119, YY

n = 123,

Table 6 – Correlation Matrix for Supervisors’ Liking of Students

against Students’ Cognitive Styles and other Interpersonal

Variables

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Variables Dissertation Grade Awarded

Supervisor Nurturance (mean of scores awarded) +.33***Y

Student Nurturance (mean of scores awarded) +.36***Y

Supervisors’ independent perceptions of student nurturance +.39***YY

(Note: Correlation with students’ CSI scores (r = +.244, p < .001, n = 250)

Supervisors’ liking of students +.49***YYY

***P < .001, two tails Y n = 285, YY n = 244, YYY n = 119,

Table 7 – Correlation’s between Dissertation Grade and other

Interpersonal Variables

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