development of the talent development environment questionnaire for sport

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Development of the Talent Development EnvironmentQuestionnaire for SportRussell J. J. Martindale a , Dave Collins b , John C. K. Wang c , Michael McNeill c , Kok SonkLee d , John Sproule e & Tony Westbury aa School of Life Sciences, Edinburgh Napier University , Edinburgh, UKb Institute of Coaching and Performance, University of Central Lancashire , Preston, UKc National Institute of Education, Nanyang Technological University , Singapored Ministry of Education , Singaporee Department of Physical Education , Sport and Leisure Studies, The University ofEdinburgh , Edinburgh, UKPublished online: 06 Aug 2010.

To cite this article: Russell J. J. Martindale , Dave Collins , John C. K. Wang , Michael McNeill , Kok Sonk Lee , John Sproule& Tony Westbury (2010) Development of the Talent Development Environment Questionnaire for Sport, Journal of SportsSciences, 28:11, 1209-1221, DOI: 10.1080/02640414.2010.495993

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Development of the Talent Development Environment Questionnairefor Sport

RUSSELL J. J. MARTINDALE1, DAVE COLLINS2, JOHN C. K. WANG3,

MICHAEL MCNEILL3, KOK SONK LEE4, JOHN SPROULE5, & TONY WESTBURY1

1School of Life Sciences, Edinburgh Napier University, Edinburgh, UK, 2Institute of Coaching and Performance, University of

Central Lancashire, Preston, UK, 3National Institute of Education, Nanyang Technological University, Singapore, 4Ministry

of Education, Singapore, and 5Department of Physical Education, Sport and Leisure Studies, The University of Edinburgh,

Edinburgh, UK

(Accepted 21 May 2010)

AbstractAs sporting challenge at the elite level becomes ever harder, maximizing effectiveness of the talent development pathway iscrucial. Reflecting this need, this paper describes the development of the Talent Development Environment Questionnaire,which has been designed to facilitate the development of sporting potential to world-class standard. The questionnairemeasures the experiences of developing athletes in relation to empirically identified ‘‘key features’’ of effective talentdevelopment environments. The first phase involved the generation of questionnaire items with clear content and facevalidity. The second phase explored the factor structure and reliability. This was carried out with 590 developing athletesthrough application of exploratory factor analysis with oblique rotation, principal axis factoring extraction and cronbachalpha tests. This yielded a 59-item, seven-factor structure with good internal consistency (0.616–0.978). The TalentDevelopment Environment Questionnaire appears to be a promising psychometric instrument that can potentially be usefulfor education and formative review in applied settings, and as a measurement tool in talent development research.

Keywords: Talent development, measurement, questionnaire

Introduction

There is an increasing interest in optimizing the talent

pathway in sport. For example, UKSport has recently

committed large resources to the creation of a

sustainable, genuine world-class sporting system (UK-

Sport, 2008). Perhaps the most ‘‘simplistic’’ philoso-

phy is a focus on identification, spending time testing

and searching for special talent in an attempt to find

that needle in a haystack. However, research in recent

years suggests this is not the most productive,

sustainable or even ethical methodology, particularly

at younger ages (Abbott, Collins, Martindale, &

Sowerby, 2002). As an alternative, researchers have

begun to emphasize the development rather than the

identification of talent. This is a sensible approach

because the environment and the way in which it

shapes, challenges, and supports developing talent is

essential for success (Bloom, 1985; Cote, 1999;

Csikszentmihalyi, Whalen, Wong, & Rathunde, 1993;

Durand-Bush & Salmela, 2002; Gould, Dieffenbach,

& Moffett, 2002; Martindale, Collins, & Abraham,

2007; Martindale, Collins, & Daubney, 2005).

A focus on process

Understanding effective processes is particularly

important within the context of talent development

because it can be such a long-term investment. From

beginning to end, a successful system may take a

number of years to produce winners at an elite level.

Consider, for example, the theory of deliberate

practice, which states that at least 10,000 hours of

deliberate practice is required to attain expertise

(Ericsson, Krampe, & Tesch-Romer, 1993). Thus,

evaluation and promotion of good practice based

solely on outcome, while essential information,

provides insufficient timely feedback on the reasons

for success or, even more crucially, on what might be

improved. As such, minor adjustments or decisions

about the allocation of resources and efforts have no

formal, evidence-based way of being monitored for

Correspondence: R. J. J. Martindale, School of Life Sciences, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.

E-mail: [email protected]

Journal of Sports Sciences, September 2010; 28(11): 1209–1221

ISSN 0264-0414 print/ISSN 1466-447X online � 2010 Taylor & Francis

DOI: 10.1080/02640414.2010.495993

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effectiveness. Furthermore, outcomes take no ac-

count of the quality of the people that are coming

into the process, and therefore a measure of success

on output alone may be skewed by serendipitous

recruitment, luck or other confounding factors (cf.

Bailey & Morley, 2006).

Accordingly, a clear understanding of the key

processes, in conjunction with a method by which

these processes can be evaluated, will enable the

broader and coherent application of evidence-based

practice. This, in turn, will help coaches to gain a

clear understanding of key priorities and goals, gain

regular feedback, monitor change, and formatively

evaluate when and where necessary.

Evolving a monitoring tool – holistic or stage focused?

With regard to the ‘‘process’’ of development, there

is some evidence to support a stage model, such as

the stages of development that an athlete may go

through to reach elite status (e.g. Bloom, 1985; Cote,

1999; Durand-Bush & Salmela, 2002). However,

this may represent an oversimplification, since, at the

very least, progression through these stages is both

idiosyncratic and culturally mediated (Bailey et al.,

2009). Acknowledgement of such complexity within

the development process and the consequent em-

phasis on individualized, non-linear progression (e.g.

Abbott, Button, Pepping, & Collins, 2005; Simon-

ton, 1999; Toms, 2005) highlights the need to be

mindful in the application of generic staged progres-

sion. For example, Bloom (1985) found that move-

ment between stages was not determined by

chronological age or some predetermined cut-off

point, but rather was characterized by certain tasks

being completed, relationships or attitudes devel-

oped, or learning achieved. With this in mind, it is

important to recognize that an effective monitoring

tool (such as the Talent Development Environment

Questionnaire) would best be designed for application

to ‘‘excellence environments’’ with athletes who have

certain attributes, as opposed to athletes within a

certain age range per se. Thus, although the use of this

questionnaire is delimited by the age range adopted in

its development, application in practice makes it more

important to consider the aims of the environment and

the characteristics of the athlete, rather than their

placement on a theoretically defined model.

Evolving a monitoring tool – generic or context

specific?

While context-specific differences will undeniably

exist across talent development domains, many

researchers have identified a significant number of

important but generic features of effective develop-

ment environments (e.g. Abbott & Collins, 2004;

Bloom, 1985; Gould et al., 2002). For example, the

psychology of effective learning highlights that

characteristics such as intrinsic motivation are

important regardless of the sport or culture (e.g.

Gould et al., 2002; Sproule, Wang, Morgan,

McNeill, & McMorris, 2007). Furthermore, given

the holistic nature of development, it is important to

recognize that it is likely that a number of these

generic factors apply to a broad range of perfor-

mance, development, and lifestyle issues throughout

the development process, perhaps reducing the

significance of the specific nature of the sport or

culture (e.g. support networks, coach–athlete com-

munication).

To put the aims of this research into perspective,

there is enough evidence to suggest a number of

important generic features for effective talent devel-

opment environments. As such, we believe it is

worthwhile developing a tool that evaluates these,

particularly as it may then impact usefully in a wide

range of applied sport settings. Of course, we

recognize the potential for sport, gender or culturally

specific requirements, which could usefully be

examined in future research. Nonetheless, there are

distinct advantages, and indeed precedents, in first

testing for the existence of psychometrically robust

generic constructs. For example, self-concept used

to be considered unidimensional and was measured

as such (e.g. Rosenberg, 1965), until work by Marsh

and colleagues (Marsh, Byrne, & Shavelson, 1988)

highlighted its multidimensional nature. Accord-

ingly, Marsh evolved a raft of instruments to measure

each of the relevant domains encompassed within the

multidimensional model self-concept. Notably, how-

ever, this process started as a more general but

multifaceted self-description questionnaire (Marsh,

1990) until research highlighted the need to evolve

very domain-specific measures, giving rise to tools

such as the Physical Self-Description Questionnaire

(Marsh, 1996). Based on these precedents, and the

advantages of developing a cross-domain tool, we

pursued a generic solution to the talent development

environment problem.

Evolving a monitoring tool – where next?

On the basis of this theoretical backdrop, the

development of our monitoring tool was framed

specifically to measure the key holistic and generic

processes involved in the effective development of

‘‘talented’’ athletes. While a more complete overview

of this literature is not within the scope of this paper,

a structured set of generic talent development

guidelines has emerged through the assimilation of

past evidence that provides a broad and integrated

picture of what is known about the development of

talent (for a review, see Martindale et al., 2005).

1210 R. J. J. Martindale et al.

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Although the evidence base presented in Martin-

dale and colleagues’ (2005) review offers a useful

start, to enhance the ability of coaches to utilize

knowledge with more ease and confidence, it is

important that a tool with sound psychometric

properties is developed to provide a validated

process-based marker of progression. With the aim

of meeting these specific needs, the purpose of this

paper is to describe the development of the Talent

Development Environment Questionnaire, which

has been designed to facilitate the development of

sporting potential to world-class standard. This

description takes the form of two phases. First, the

process of item generation and justification is

described, followed by the presentation of the

exploratory factor analysis and associated reliability

properties. It is important to note that ethical

approval was gained for each stage of the work

through the relevant institutions.

Methods

Evidence base for item generation

The purpose of Phase 1 was to construct a

questionnaire that assessed the key holistic and

generic features of effective development practice.

The identification of general content was carried out

through adherence to standard guidelines (AERA,

APA, & NCME, 1999), using a triangulated

approach, systematic analysis, and checking proce-

dures, as commonly used in the development of

questionnaires (e.g. Johnston, Leung, Fielding, Tin,

& Ho, 2003; Terry, Lane, Lane, & Keohane, 1999;

Walker & Fraser, 2005; Zervas, Stavrou, & Psy-

chountaki, 2007). This process initially involved a

review and content analysis of empirically based

literature considered relevant to talent development

environments (see Martindale et al., 2005). The

inclusion of research was based on satisfaction of one

or more of the following criteria: (1) the aims of

effective talent development; (2) the needs and

experiences of young developing athletes; and (3)

the design and operation of environments that

provide for the realization of potential. Next, 16

talent development coaches working within the UK

were interviewed and asked to put forward the aims

and associated methods that they considered to be

key for effective talent development. Subsequent

data were analysed both inductively and then

deductively against results emerging from the initial

review (see Martindale et al., 2007). Forty-three

developing athletes were then interviewed and asked

to put forward their perception of the experiences

and environment that had facilitated their develop-

ment. Again, data were analysed inductively and

then deductively against the backdrop of the results

of the preceding stages to provide a triangulated set

of key features of effective talent development (cf.

Martindale & Mortimer, 2010). Broadly, four main

areas consistently emerged: (1) long-term aims and

methods; (2) wide-ranging, coherent messages and

support; (3) an emphasis on development not early

‘‘success’’; and (4) individualized and ongoing

development. As such, we were confident that the

basis for item generation had been both empirically

established (AERA, APA, & NCME, 1999) and

ecologically grounded.

Item generation

Building from this established and triangulated

stance, a four-step approach was used to develop

and select items (Johnston et al., 2003). First, an

initial list of 135 items was developed from the

foundations that emerged from the three-stage

process outlined above.

As the second stage, a panel of experts were asked to

assess the preliminary questions and themes, and

provide structured comments with respect to face

validity, content validity, comprehensibility, and

comprehensiveness within a series of workshops. This

panel consisted of four qualified (chartered and/or

BASES accredited) practising sport psychologists, all

experienced academics, two of whom were academic

professionals with extensive experience of question-

naire development. Furthermore, 12 individuals,

across two individual and two team sports, who had

formal responsibilities for talent development within

their sport in the UK, were also consulted through a

similar nominal group technique (O’Neil & Jackson,

1983). In both cases, experts worked in sub-groups of

two (psychologists) or four (talent coaches). The sub-

groups each took responsibility for assessing different

sets of themes and their associated items, and making

recommendations for change as deemed necessary.

Changes were made if agreed by the rest of the panel.

This process led to some grammatical changes, and

the reduction of the items to 106.

Third, two separate groups of developing athletes

were asked to complete the questionnaire anon-

ymously and comment on the comprehensibility,

relevance, and similarity of the items in the ques-

tionnaire (cf. Johnston et al., 2003). Participants

identified items that, according to their understand-

ing, were closest in relevance and meaning to the

themes they were describing. The first group consisted

of 32 rugby players aged 16–19 years and the second of

50 rugby players aged 13–20 years. Test leaders were

used to provide a safe and confidential environment in

which the questionnaires could be filled out and

subsequently reviewed. After the questionnaires were

completed, discussion took place in relation to any

missing answers, and feedback was solicited from the

Talent Development Environment Questionnaire 1211

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players and test leaders regarding the comprehension

and clarity of all questions. To maximize comprehen-

sibility among the athletes while retaining content

validity, the questionnaire was reduced to 68 items.

This was a result of minimizing repetitive or ambig-

uous items, based on the feedback gained from the

discussion sessions.

This level of item reduction is typical in the

development of many questionnaires. For example,

Terry et al. (1999) ‘‘lost’’ 15% of their initial pool of

items through an expert content validity check, and a

further 40% of items in an effort to maximize

comprehensibility for adolescent use. However, the

sizeable reduction in items was still seen as a

potential cause for concern. Accordingly, content

and face validity were subjected to an additional

check. This final stage of the process involved the 68

questionnaire items and underlying questionnaire

rationale being sent to 10 expert development

coaches for feedback based on face validity, content

validity, comprehensibility, and comprehensiveness.

No subsequent changes were required.

Questionnaire structure

Before the exploratory factor analysis, the Talent

Development Environment Questionnaire consisted

of 68 items and utilized a 6-point Likert scale. A

Likert scale of between 3 and 9 points is considered

appropriate (Bass, Cascio, & O’Connor, 1974) for

such questionnaires, although there has been wide

discrepancy of advice over time (Chang, 1994). As

such, the questionnaire provided a 3-point range of

discrimination for both positive and negative choices

(i.e. ‘‘strongly agree’’, ‘‘agree’’, ‘‘agree a little bit’’,

‘‘disagree a little bit’’, ‘‘disagree’’, and ‘‘strongly

disagree’’), which ensured that athletes could not

‘‘sit on the fence’’ selecting an option such as

‘‘neither agree nor disagree’’. Also, given that the

nature of the questions related to the extent to which

athletes had experienced something, a neutral

response was considered inappropriate. Finally,

there were 15 negatively worded questions to counter

acquiescence (Ray, 1979), and the instructions to the

athletes outlined the confidentiality of their answers

and the need for honesty and concentration when

filling out the questionnaire. The initial form of the

questionnaire included an instruction page and a

section for demographic information, followed by 68

items and took approximately 15 min to complete.

Study 1: Exploratory factor analysis

Sample size

A number of academics recommend using 300 or

more cases (Comrey & Lee, 1992; Kass & Tinsley,

1979; Tabachnick & Fidell, 2001) to ensure a subject

to item ratio of at least 4:1 (Fabrigar, Wegener,

MacCallum, & Strahan, 1999). For the present

investigation, the Kasier-Meyer-Olkin measure pro-

vided another check for sampling adequacy (Hutch-

eson & Sofroniou, 1999), alongside other important

data screening techniques used to check the appro-

priateness of the data, such as Bartlett’s test of

sphericity to test for an adequate level of correlation

between items, and tests of multicollinearity to

examine the possibility of items being too well

correlated. However, using the criteria outlined

above, Stevens (1992) suggests 0.298 as the level of

loading to be considered significantly correlated with

a factor, where cross loading items are dropped.

Participants

Five hundred and ninety athletes (mean age 14.5

years, s¼ 1.4 years, range 13–21 years) volunteered

to participate. Consent was gained from the coaches

(and parents in the event of the athletes being under

16) and from the athletes themselves if they were

over 16. Participants were purposefully sampled in

line with the characteristics of performer and

‘‘excellence’’ environment for which the question-

naire is designed. As such, participants had been

recruited as junior athletes with identified potential

to become senior elites by virtue of their commit-

ment and selection into formally established devel-

opment environments (e.g. regional elite player

development squad, professional sport club acad-

emy). Furthermore, to keep in line with the generic

aims of the research, broad sampling was used to

include multiple sports, males and females, a wide

age range, and two cultures within the context of

‘‘westernized’’ sport academy structures.

Data analysis

The Statistical Package for Social Sciences (SPSS,

version 14) was used to examine the factor structure

of the Talent Development Environment Question-

naire. The emerging factor structure provided insight

into the underpinning latent factors and allowed

important items to be retained and interpreted. The

specific approach used was principal axis factoring

extraction, a factor analysis procedure that seeks the

least number of factors that account for the common

variance of a set of variables only.

To improve the interpretation of the data, an

oblique with direct oblimin rotation was selected due

to the likely correlation between factors and the

naturalistic nature of the data (Field, 2006). Theo-

retically, this type of rotation should render a more

accurate and reproducible solution in such circum-

stances (Costello & Osborne, 2005), and provide


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insight into these potential inter-relationships. As

there is no widely preferred oblique rotation, the

default delta and kappa values were used (Fabrigar,

et al., 1999) to standardize the extent to which

factors were allowed to correlate.

The criteria used for the number of factors to be

retained included the scree test (Cattell, 1966), a

preference for simple, clean structures over complex

ones (Costello & Osborne, 2005; Thurstone, 1947),

the magnitude of the Kaiser-Guttman eigenvalue

(minimum required over 1.0; Cattell, 1966), and the

interpretability of the groups (Harman, 1976). This

combination was employed because no single tech-

nique has been shown to be accurate over a wide

array of circumstances (Fabrigar et al., 1999; Ford,

MacCallum, & Tait, 1986).

Results

Bartlett’s test of sphericity was significant

(X2¼ 33486.23; d.f.¼ 2278; P5 0.001), indicating

that there was adequate correlation between the

variables and therefore that the exploratory factor

analysis was appropriate. The Kaiser-Meyer-Olkin

measure of sampling test revealed significant results

(0.986; P5 0.001), providing further evidence that

the sample size was adequate for factor analysis

(Sharma, 1996).

The communalities of the items ranged from

0.219 to 0.883, providing support for the use of

multiple criteria factor extraction. Indeed, significant

attention was given to the scree plot and the

subsequent search for the cleanest factor structure,

due to the difficulty in pinpointing the inflexion in

the curve. Consequently, the cleanest factor was

assessed through three criteria: (1) item loadings

above 0.298; (2) no or fewest cross-loadings; and (3)

no factors with fewer than 3 items (Costello &

Osborne, 2005). This identified a seven-factor

structure with eigenvalues ranging from 33.76 to

0.979, accounting for 64% of the total explained

variance. Factor loadings ranged from 0.287 to 0.653

across the seven factors (see Table I) where variance

of 0.01 was allowed to ensure no unnecessarily lost

items (Field, 2006). Nine items were dropped for

statistical reasons, either due to low loadings or

cross-loading (see Talent Development Environ-

ment Questionnaire in Appendix).

Furthermore, to ensure that statistical rationale

did not override sense, discussions took place

between the researchers to assess the extent to which

dropped items may impact on content validity and/or

interpretation of the factors. It was decided that none

of the dropped items negatively affected the ques-

tionnaire on these grounds, and that the psycho-

metric strength gained from their loss was more

useful. As an exemplar, one question dropped on

Table I. Factor loadings from the exploratory factor analysis.

Item

#

Factor

1

Factor

2

Factor

3

Factor

4

Factor

5

Factor

6

Factor

7

Qu10 0.653

Qu12 0.604

Qu54 0.578

Qu20 0.556

Qu21 0.540

Qu3 0.483

Qu53 0.464

Qu16 0.457

Qu56 0.457

Qu1 0.449

Qu49 0.435

Qu58 0.426

Qu68 0.399

Qu2 0.398

Qu59 0.398

Qu40 0.388

Qu7 0.384

Qu61 0.383

Qu63 0.375

Qu37 0.360

Qu55 0.351

Qu22 0.351

Qu27 70.344

Qu24 0.320

Qu13 0.500

Qu9 0.455

Qu31 0.422

Qu47 0.311

Qu51 0.287

Qu42 0.482

Qu23 0.444

Qu52 0.441

Qu18 0.425

Qu28 0.406

Qu60 0.348

Qu36 0.348

Qu35 70.604

Qu19 70.565

Qu33 70.473

Qu32 70.375

Qu30 0.660

Qu8 0.624

Qu48 0.504

Qu29 0.393

Qu38 0.386

Qu26 0.357

Qu65 0.328

Qu5 0.317

Qu4 0.480

Qu39 0.348

Qu34 0.329

Qu25 0.326

Qu64 0.443

Qu46 0.393

Qu66 0.385

Qu44 0.357

Qu14 0.349

Qu67 0.319

Qu43 0.316


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statistical grounds included ‘‘strength and condition-

ing training is specifically incorporated into my

programme, which is helping me get fit and strong

for my sport’’, which was from a factor that also

included similar questions such as ‘‘My development

plan incorporates a variety of physical preparation

such as fitness, flexibility, agility, coordination,

balance, strength training, etc.’’ and ‘‘My coach

plans training to incorporate a wide variety of useful

skills and attributes, for example, techniques, physi-

cal attributes, tactical skills, mental skills, decision

making’’.

Relationships between the factors

Graham and colleagues (Graham, Guthrie, &

Thompson, 2003) recommended considering both

the pattern matrix (Table I) and the structure matrix.

While the pattern matrix reveals the unique con-

tribution of a variable to a factor, the structure matrix

also reveals shared variance. The latter can highlight

the extent to which there are any more subtle

interrelationships between factors. On a theoretical

level, dependence between factors does not cause

concern (Field, 2006) and, where relationships exist,

this actually allows more meaningful interpretation

than an orthogonal representation. This is particu-

larly useful and justified within such a naturalistic,

interrelated, and complex area as talent development

environments.

Reflecting these ideas, and following procedures

outlined in Field (2006), it was concluded that the

data revealed a relationship between Factors 1, 3, 5,

and 7 and also a relationship between Factors 4 and

6, while Factor 2 was relatively independent.

Although it is important to note that even though

more subtle relationships may exist between some

factors, we concluded that the questionnaire would

be best used in its full seven-factor form. The full

description and interpretation of these factors is

presented in the Discussion and Appendix.

Study 2: Reliability

Participants

The participants and data set used in Study 1 were

used again to test the internal consistency of the

scale. As such, the reliability analysis utilized 590

athletes (mean age 14.5 years, s¼ 1.4 years, range

13–21 years).

Data analysis

Cronbach’s alpha coefficients were calculated to

test the internal consistency of the questionnaire

and its factors. In line with standard assessment

(Tabachnick & Fidell, 2001), values of 0.7 or above

were considered good and 0.6 or above considered

adequate for any factor with a small number of items

due to the underestimation of scale item inter-

correlation that can occur in this case (Nunnally &

Bernstein, 1994).

Results

Internal consistency estimates for the scales ranged

from 0.616 to 0.978. Specifically, the mean Cron-

bach’s alpha was 0.805, with Factor 1 to Factor 7

scoring 0.978, 0.616, 0.913, 0.730, 0.899, 0.618,

and 0.881 respectively. This means that the under-

lying factors’ reliability ranged from adequate to

excellent (Tabachnick & Fidell, 2001). As such, the

seven subscales of the questionnaire can be used

reliably in future research or real-world application.

Discussion

Nature of the Talent Development Environment

Questionnaire

The aim of this paper was to construct a practical and

reliable measure of effective talent development

processes. Specifically, the Talent Development

Environment Questionnaire focuses on the key

holistic and generic processes involved in the

effective long-term development of ‘‘talented’’ ath-

letes. The content of the questionnaire was devel-

oped from a rigorous triangulation of evidence,

including a review of current literature, expert

opinion, and athlete experience. Furthermore, sev-

eral additional expert panels and athlete groups

helped to refine it and formulate the final 68-item

structure before the factor analysis and reliability

analysis took place. The factor analysis yielded a 59-

item, seven-factor solution with sound reliability.

Although factor analysis identifies latent factors

within a group of items, it does not provide an

interpretation of the meaning of those identified

themes. An accepted practice in psychometrics is to

identify this meaning by consideration of the pivotal

items (those which load most heavily) within any

factor. Where the content of these pivotal items is

consistent with the hypothesized conceptual struc-

ture, it can provide evidence of a valid interpretation

(Hawthorne, Richardson, & Osborne, 1999). Based

on these guidelines, the seven factors were inter-

preted as follows. This interpretation is also related

to previous literature to exemplify each feature’s

relevance.

Factor 1: Long-Term Development Focus. Twenty-four

items related to the extent to which development

opportunities afforded to athletes were specifically


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designed to facilitate long-term success (e.g. ongoing

opportunities, rounded development, clear expecta-

tions, and links to senior progression). Interestingly,

the importance of a consistent and coherent long-

term priority is supported in almost all the talent

development literature by the idea that certain

developmental experiences are required; forming

the foundations for future progressions, as opposed

to preparation for short-term outcome success per se

(e.g. Abbott et al., 2005; Bailey & Morley, 2006;

Bloom, 1985; Cote, 1999; Cote, MacDonald, Baker,

& Abernethy, 2006; Csikszentmihalyi et al., 1993;

Simonton, 1999). Furthermore, even in later periods

of development, certain activities may be required for

advanced future performance levels, which may

hinder short-term performance capability (e.g. Dur-

and-Bush & Salmela, 2002; Martindale et al., 2005,

2007).

In addition, the items in Factor 1 also related to

the attitudes, psychological skills, and understanding

required for long-term progression (e.g. responsi-

bility, dedication, coping skills, and understanding).

Again, these attributes have long been associated as

discriminating between performance levels (e.g.

Gould, Eklund, & Jackson, 1993; Talbot-Honeck &

Orlick, 1998), as well as being indicative of superior

capability for learning and development (e.g. Abbott

& Collins, 2004; Abbott et al., 2002, 2005; Entwistle

& Kozeki, 1985; Ericsson et al., 1993; Gould et al.,

2002; Knowles, Holton, & Swanson, 1998) and the

required management of lifestyle (e.g. Bull, Sham-

brook, James, & Brooks, 2005; De Knop, Wylleman,

Van Houcke, & Bollaert, 1999; Sinclair & Orlick,

1993).

Factor 2: Quality Preparation. Five items related to

the extent to which clear guidance and opportunities

are in place to provide and reinforce quality practice

through training, recovery, and competition experi-

ences. The idea of the need for quality practice is not

new. The theory of deliberate practice (Ericsson

et al., 1993) highlights the need for practice to be

effortful and specifically designed to improve perfor-

mance through goal setting, feedback, and opportu-

nities for repetition. Furthermore, research in motor

learning has long highlighted the importance of the

quality of training structure, feedback, and instruc-

tional style in the development of robust and useful

skill sets (e.g. Williams & Hodges, 2004). Also, the

crucial nature of quality competition experiences for

elite development is highlighted by a number of

researchers (e.g. Bloom, 1985; Cote, 1999; Douglas

& Martindale, 2008; Durand-Bush & Salmela, 2002;

Van Aken, 2005), as is the need for quality recovery

methods, particularly given the dangers to develop-

ing athletes from burnout, de-motivation, over-

training, and over-playing (e.g. Durand-Bush &

Salmela, 2002; Gould, Feltz, Horn, & Weiss, 1982;

Martindale et al., 2007; Polman & Houlahan, 2004;

Van Aken, 2005).

Factor 3: Communication. Seven items related to the

extent to which the coach communicates effectively

with the athlete in both formal and informal settings.

More specifically, the nature of goal setting, review

and feedback, development planning, and emphasis

on progression to senior level were considered

important. The need for good coach communication

in sport has long been highlighted (see Burke, 1997).

The quality of coach–athlete relationships (Reis,

Capobianco, & Tsai, 2002), intrinsic motivation of

athletes (Mageau & Vallerand, 2003), and coach–

athlete effectiveness (Jowett & Cockerill, 2002)

depend on it. Indeed, Vealey (2005) has highlighted

communication as the vehicle for effective coaching,

and Martindale et al. (2007) highlight the need for

both informal and formal communication systems to

maximize effectiveness.

Factor 4: Understanding the Athlete. Four items related

to the extent to which the coach understands the

athlete in depth, at a holistic level, and has developed

a strong professional relationship with them. Sig-

nificant work such as that of Bloom (1985) has

highlighted the need for a close coach–athlete

relationship in the development years and beyond.

This work also highlights the complex, holistic

nature of development, whereby a whole host of

cognitive, physical, social, and performance-based

developments can trigger successful progression. In

similar fashion, the work of Simonton (1999) and

Vaeyens and colleagues (Vaeyens, Lenoir, Williams,

& Philippaerts, 2008) emphasizes the individualized

and emergent nature of performance capability and

development, stressing the need for consideration of

athletes’ needs on an individual and ongoing basis.

Thus, understanding the athlete and their world is

key to providing the right support at the right time.

Furthermore, motivational literature highlights the

crucial role that individual differences, as well as

external factors, play in developing and maintaining

motivation; for example, parental support style

(Wolfeden & Holt, 2005), goal orientation (Duda

& Nicholls, 1992), self-perceptions of competence,

autonomy (Deci & Ryan, 1985), and expectations

(Lepper & Greene, 1975).

Factor 5: Support Network. Eight items related to the

extent to which a coherent, approachable, and wide-

ranging support network is available to help support

and develop the athlete in all areas. Many

instrumental talent development studies have high-

lighted the need for a strong support system

throughout development (Bloom, 1985; Cote,


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1999; Csikszentmihalyi et al., 1993; Durand-Bush &

Salmela, 2002). Indeed, Rees and colleagues (Rees,

Ingledew, & Hardy, 1999) reported a significant

relationship between quality support and perfor-

mance, while poor perceived support can lead to

poor coping mechanisms and stress (Lafferty &

Dorrell, 2006). Four types of support have been

recognized as important, namely emotional, esteem,

informational, and tangible support (Rees & Hardy,

2000). However, it is clear that the relative impor-

tance and role of different support networks change

as athletes develop and encounter different chal-

lenges, highlighting the need to set up a range of

accessible support networks throughout the sporting

lifespan (Gould et al., 2002).

Factor 6: Challenging and Supportive Environment.

Four items related to the extent to which athletes are

challenged appropriately by development experi-

ences and supported through them (e.g. available

support, links to higher level athletes, and de-

emphasis of winning). This concept of providing a

challenging yet supportive environment emerged

strongly from the work by Csikszentmihalyi et al.

(1993) with talented teenagers. Challenging compe-

tition and training environments are necessary to

facilitate development to the highest level, but this is

not to say the traditional ‘‘school of hard knocks’’ is

appropriate. As with any development opportunities,

challenges can be facilitative or debilitative depend-

ing how they are handled. For example, while the

standard and pressure of higher level (e.g. elite,

professional, adult, etc.) training and playing oppor-

tunities are essential for the development of athletes,

only in combination with quality review processes,

goal setting, and support, however, will such an

approach consistently act to support the transition to

senior level (Douglas & Martindale, 2008). Further-

more, this is not something relevant only to advanced

athletes. Even the informal play environment of

mixed age groups has been shown to be highly

beneficial to development (Cote et al., 2006). Once

again, however, it seems that challenging environ-

ments combined with healthy support and de-

emphasis of winning (e.g. Durand-Bush & Salmela,

2002) lead to less stress, heightened intrinsic

motivation, and a strong desire for self-referenced

improvement – essential for long-term development

and success.

Factor 7: Long-Term Development Fundamentals.

Seven items related to the extent to which key

features of the foundations for further development

are considered; for example, ongoing opportunities,

avoidance of early specialization, parental support,

and athlete autonomy. The nature of development

(e.g. Abbott et al., 2005) shows that relative

performance level per se may fluctuate significantly

through the development years for a whole host of

reasons (e.g. maturation, injury, motivation, oppor-

tunities), and as such it is essential that athletes

understand this and the challenges it brings, and are

treated with their individual needs in mind. Further-

more, the crucial importance of self-motivation in

this pursuit, athletes must be involved in decisions

where, opportunities to develop long term stay open.

Importantly, for such opportunities to be most

effective, research highlighting the long term benefits

of diversification and delayed specialization needs to

be considered (e.g. Baker & Cote, 2006). Further-

more, the important role of parents must not be

underestimated even through the development years

and beyond (e.g. Bloom, 1985; Cote, 1999; Gould

et al., 2002; Wolfenden & Holt, 2005). As such, it is

essential they are kept in the loop and utilized

positively.

The depth of empirical support for the underlying

factors attests to the face validity of the question-

naire. This is also apparent through the empirical

foundations on which it is based. Indeed, the

rigorous process through which content and face

validity were triangulated (comprehensive literature

review, expert coaches, current athletes) is strong as

measured against recommended psychometric de-

velopment standards (AERA, APA, & NCME,

1999). Furthermore, the systematic process through

which the items were developed and evolved,

incorporating several expert panels, pilot tests,

refinements, and checks, can also be considered a

strength when evaluated against measures of good

practice (Johnston et al., 2003). While the founda-

tions of the questionnaire’s ecological validity are

robust, however, further checks would be useful to

strengthen and confirm the causative nature of its

properties. Common techniques to assess the

strength of ecological validity include discriminant

function analysis and longitudinal intervention stu-

dies. Plans are currently underway to address both

these steps.

Limitations and de-limitations of the Talent Development

Environment Questionnaire

While the development of the questionnaire has been

implemented through well-established procedures, it

is important to raise some potential limitations

within the context of the aims of its initial develop-

ment. First, it has been developed as a generic tool:

emphasizing the generic environmental features

useful for facilitating the development of excellence

across sports, stage/age, gender, and culture. How-

ever, we recognize that there will be context-specific

requirements within talent development. In fact,

research highlights specific needs associated with


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stage of elite development (e.g. Cote, 1999), sport

(e.g. Hodges & Starkes, 1996), gender (e.g. Eccles &

Harold, 1991), culture (e.g. Sproule et al., 2007),

and even location (e.g. Cote et al., 2006). Indeed,

exploration of these specific issues would be very

welcome research developments, and may identify

the need for context-specific instruments to evolve

from the generic version, as is the case with other

psychometric instruments, for example self-concept

(Marsh, 1990) and attentional skills (Nideffer,

1976). Although at the very least, confirmatory factor

analysis would need to establish the applicability of

the questionnaire across domains.

Second, while the aim of the Talent Development

Environment Questionnaire is to evaluate and

monitor on a generic level, it is important to

recognize that it is de-limited by the development

process itself. For example, only ‘‘westernized’’

academy style structures have been used in the

recruitment of participants in this study (albeit across

two cultures), within a certain age range (13–21

years). While these criteria were purposefully used to

recruit participants in an attempt to maintain a broad

brush, they still act to define boundaries of its use

alongside the athlete and environment criteria out-

lined in the Methods section.

Potential uses for the Talent Development Environment

Questionnaire

There are a number of potential applied uses for the

questionnaire. These include evaluating practice,

gaining formative feedback and aiding reflective

practice (Chivers & Darling, 1999), monitoring and

reinforcing changes to development procedures

(Siedentop, 1978), gaining insight into athletes’

perceptions and understanding (Morgan, Kingston,

& Sproule, 2005), clarifying athletes’ expectations

and understanding (Leary, 1996), and educating and

disseminating knowledge regarding effective practice

(Kitson, Harvey, & McCormack, 1998). Of course,

as always there are issues of impression management

associated with the use of questionnaires, particularly

where ‘‘evaluation’’ is involved (Buckley & Williams,

2002; Davies, 1985). Thus, to avoid such problems it

is important that the questionnaire be used with clear

lines of anonymity, explanation of the importance of

honesty, and help with understanding where appro-

priate. In fact, the questionnaire may be most

effective as a formative and individualized assess-

ment tool to aid reflective practice, understanding,

and ongoing improvements rather than for summa-

tive evaluation.

Furthermore, this tool can potentially be used by

researchers to assess the effect of interventions on

the quality of talent development environments and

athletes’ perceptions. Although more work is

required to establish the temporal stability of the

questionnaire, it may also be useful to further

investigate the nature of talent development en-

vironments and any context-specific issues that

may be apparent. However, given the robust

questionnaire development process and ensuing

level of psychometric properties, the Talent Devel-

opment Environment Questionnaire can be con-

sidered a tool that has potential for aiding both

talent development practice and research in its

current form.

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Appendix: Talent Development Environment Questionnaire factors and associated items.

Scale Items

Factor 1: Long-Term Development

Focus (24 items)

10. My coach is good at helping me to understand my strengths and

weaknesses in my sport

12. My coach is good at helping me to understand what I am doing and

why I am doing it

54. My coach emphasizes the need for constant work on fundamental and

basic skills

20. The more experienced I get the more my coach encourages me to take

responsibility for my own development and learning

21. My development plan incorporates a variety of physical preparation such

as fitness, flexibility, agility, coordination, balance, strength training, etc.

3. If I got injured I believe I would continue to receive a good standard

of support

53. I am constantly reminded that my personal dedication and desire to be

successful will be the key to how good a performer I become

16. My coach constantly reminds me what he/she expects of me

56. My coach is a positive supporting influence on me

1. My coaches care more about helping me to become a professional/top-

level performer, than they do about having a winning team/performer

right now

49. My coach plans training to incorporate a wide variety of useful skills and

attributes, for example, techniques, physical attributes, tactical skills,

mental skills, decision making

58. My training is specifically designed to help me develop effectively in the

long term

68. My coach emphasizes that what I do in training and competition is far

more important than winning

2. I am being trained to be ready for almost anything that is thrown at me

in sport and life

59. I spend most of my time developing skills and attributes that my coach

tells me I will need if I am to compete successfully at the top/professional

level

40. My training sessions are normally beneficial and challenging

7. Me and my sports mates are told how we can help each other develop

further in the sport

61. My coach allows me to learn through making my own mistakes

63. I am encouraged to keep perspective by balancing any frustrations I may

have in one area with thinking about good progress in others (e.g. slow

skill development but good strength gains or poor performances but good

technical development)

37. Organization is a high priority to those who develop my training

programme

55. There are people who help me/teach me how to deal positively with

any nerves or worries that I experience (e.g. coaches, parents,

psychologists)

22. If it didn’t work out for me here, there are other good opportunities that

would help me to keep progressing in my sport

27. Developing performers are often written off before they have had a

chance to show their real potential

24. My coaches and those who support me give me straight answers to my

questions

Factor 2: Quality Preparation (5 items) 13. I struggle to get good-quality competition experiences at the level I

require

9. I am rarely encouraged to plan for how I would deal with things that

might go wrong

31. The guidelines in my sport regarding what I need to do to progress are

not very clear

47. I am not taught that much about how to balance training, competing,

and recovery

51. I feel pressure from my mates in sport to do things differently from what

my coaches are asking of me

Factor 3: Communication (7 items) 42. I regularly set goals with my coach that are specific to my individual

development

(continued)


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Appendix: Talent (continued)

Scale Items

23. My coach and I regularly talk about things I need to do to progress to

the top level in my sport (e.g. training ethos, competition performances,

physically, mentally, technically, tactically)

52. My coach often talks to me about the connections/overlap between

different aspects of my training (e.g. technical, tactical, physical, and

mental development)

18. My coach and I talk about what current and/or past world-class

performers did to be successful

28. My coach and I often try to identify what my next big test will be before

it happens

60. My coach explains how my training and competition programme work

together to help me develop

36. Feedback I get from my coaches almost always relates directly to

my goals

Factor 4: Understanding the Athlete (4 items) 35. My coach rarely talks to me about my well-being

19. My coach doesn’t appear to be that interested in my life outside of sport

33. My coach rarely takes the time to talk to other coaches who work with me

32. I don’t get much help to develop my mental toughness in sport effectively

Factor 5: Support Network (8 items) 30. Currently, I have access to a variety of different types of professionals

to help my sports development (e.g. physiotherapist, sport psychologist,

strength trainer, nutritionist, lifestyle advisor)

8. I can pop in to see my coach or other support staff whenever I need to

(e.g. physiotherapist, psychologist, strength trainer, nutritionist, lifestyle

advisor)

48. My coaches talk regularly to the other people who support me in my sport

about what I am trying to achieve (e.g. physiotherapist, sport psychologist,

nutritionist, strength and conditioning coach, lifestyle advisor)

29. My training programmes are developed specifically to my needs

38. My coaches ensure that my school/university/college understands about

me and my training/competitions

26. Those who help me in my sport seem to be on the same wavelength as

each other when it comes to what is best for me (e.g. coaches,

physiotherapists, sport psychologists, strength trainers, nutritionists,

lifestyle advisors)

65. My coaches and others who support me in sport are approachable (e.g.

physiotherapist, sport psychologist, strength trainer, nutritionist,

lifestyle advisor)

5. All the different aspects of my development are organized into a realistic

timetable for me

Factor 6: Challenging and Supportive Environment (4 items) 4. My school/college/university doesn’t really support me with my sport

when I need it

39. I am regularly told that winning and losing just now does not indicate

how successful I will be in the future

34. I have the opportunity to train with performers who are at a level I am

aspiring to

25. I don’t often get any help from more experienced performers

Factor 7: Long-Term Development Fundamentals (7 items) 64. I would be given good opportunities even if I experienced a dip in

performance

46. I am encouraged to participate in other sports and/or cross train

66. I often have the opportunity to talk about how more experienced

performers have handled the challenges I face

44. My coaches make time to talk to my parents about me and what I am

trying to achieve

14. The advice my parents give me fits well with the advice I get from my

coaches

67. My progress and personal performance is reviewed regularly on an

individual basis

43. I am involved in most decisions about my sport development

Appendix: (Continued).


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development of the talent development environment questionnaire for sport

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