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Psychometric properties of Honey & Mumford’s Learning

Styles Questionnaire (LSQ)

Angus Duff*, Tim Duffy

Research into Learning Unit, University of Paisley, Ayr Campus, Ayr KA8 0SR, UK

Received 12 March 2001; received in revised form 27 June 2001; accepted 24 July 2001

Abstract

Honey and Mumford’s Learning Style Questionnaire (LSQ) has been proposed as an alternative for

Kolb’s Learning Style Inventory (LSI) and a later refined version (LSI-1985). The LSQ has been widely

applied in the fields of management training and education. Limited evidence exists concerning the psy-

chometric properties of the LSQ. Participants were 224 undergraduates enrolled in business courses and

164 undergraduates in health studies. Exploratory and confirmatory factor analysis failed to support the

existence of the two bipolar dimensions proposed by Kolb, and four learning styles hypothesised by Honey

and Mumford. An item analysis and pruning exercise failed to raise the internal consistency reliability to a

satisfactory level, or provide adequate model fit to the data. The results of a structural equation model

finds no consistent relationship between scores on the four learning style scales, two bipolar dimensions

and academic performance between the two samples. The tests of factorial invariance provide no support

for the stability or generalizability of the model. It is concluded: the LSQ is not a suitable alternative to the

LSI and LSI-1985; and its use in applied research considering higher education students is premature.

# 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Learning Style Questionnaire; Reliability; Validity; Academic performance

1. Introduction

A learning style is described as being:

. . .a description of the attitudes and behaviour which determine an individual’s preferred way

of learning. (Honey & Mumford, 1992, p. 1)

0191-8869/02/$ - see front matter # 2002 Elsevier Science Ltd. All rights reserved.P I I : S 0 1 9 1 - 8 8 6 9 ( 0 1 ) 0 0 1 4 1 - 6

Personality and Individual Differences 33 (2002) 147–163www.elsevier.com/locate/paid

* Corresponding author. Tel.:+44-01292-886296; fax:+44-01292-886250.

E-mail address: [email protected] (A. Duff).

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Learning style is the composite of characteristic cognitive, affective, and psychological factors

that serves as an indicator of how an individual interacts with and responds to the learning

environment. The study of learning style involves the investigation of individual differences:

people perceive and gain knowledge differently, they form ideas and think differently, and theyact differently. Research on style as an individual trait has been of interest to psychologists for

many years (Jung, 1921; Myers & Briggs, 1962). Kolb’s (1976) Experiential Learning Model

(ELM) is a well-established model that has attracted much interest and application. The ELM

consists of a hypothesised four-stage learning cycle and is based on the work of Lewin (1936).

The hypothesised learning cycle can be entered at any stage but must be followed in sequence.

According to the theory, different individuals may cope better with, or prefer, some parts of the

learning cycle to others. In the learning cycle, or process, learners acquire information by con-

crete experience in the new experience. Second, a stage of reflective observation on the experience

occurs whereby the learner organises the experiential data from a number of perspectives. Third,

a stage of abstract conceptualisation occurs, whereby the learner develops generalisations fromwhich to assist them integrate their observations into sound theories or principles. Finally,

through active conceptualisation, learners use these generalisations as guides to action in new and

more complex situations. This process explains individual differences in learning style in terms of

relative abilities (i.e. level) for performing well (or less well) at various stages of the learning cycle.

That is, the ideal learner will possess maximum abilities for all four stages.

Since Kolb developed his concept of a four-stage process, the process has been developed fur-

ther as two orthogonal dimensions of learning derived from the Learning Style Inventory (LSI;

Kolb, 1976). These two dimensions are labelled prehension, grasping information from experi-

ence (Concrete Experience-Abstract Conceptualisation); and transformation, that is the proces-

sing of information grasped (Reflective Observation-Active Experimentation). This concept

explains differences in terms of two bipolar styles (i.e. the manner) by which each stage in thelearning process is approached and operationalised. These bipolar dimensions are sometimes

described as learning types.

Kolb’s 12-item LSI has been widely applied to measure learning style. Twelve short statements

concerning learning situations are presented and respondents are required to rank-order four

sentence endings that correspond to the four learning styles. Later research finds little factor

analytic support for the four styles and two independent dimensions (Freedman & Stumpf, 1978,

1980; Geller, 1979; Newstead, 1992; Stout & Ruble, 1991a; 1991b). Notably, the ipsative scoring

method guarantees that some scales must be negatively correlated. Similar psychometric pro-

blems exist with a refined version of the instrument, the LSI-1985. These problems are sum-

marised in: Geiger, Boyle, and Pinto (1992, 1993); Loo (1999); Ruble and Stout (1993); Willcoxonand Prosser (1996); Yahya (1998). Critics of the application of Kolb’s LSI maintain that its use

for education research purposes was premature in the sense that the instrument’s psychometric

properties had not been sufficiently assessed.

Honey and Mumford’s (1992) Learning Style Questionnaire (LSQ) has been proposed as an

alternative to Kolb’s LSI. The LSQ was developed to report management trainees’ learning style

preferences and has subsequently been applied to a wide range of subjects, including students in

higher education. Prudent scholarship requires that the LSQ be subjected to critical analysis

before it is used for applied research and correlation studies (Schwab, 1980). The LSQ is a self-

administered inventory consisting of 80 individually rated (1 or 0) items, differing in this respect

148 A. Duff, T. Duffy / Personality and Individual Differences 33 (2002) 147–163

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from Kolb’s ipsative LSI (1976 version). The normative nature of the scale makes the instrument

a potentially attractive alternative to both the LSI and LSI-1985 to educational researchers given

the much-documented problems with ipsative measures (e.g. Cornwell & Dunlap, 1994; Dunlap

& Cornwell, 1994). The vast majority of LSQ items are behavioural, i.e. they describe an actionthat someone might or might not take. Occasionally, an item probes a preference rather than a

manifest behaviour. The LSQ is designed to probe the relative strengths of four different learning

styles: Activist, Reflector, Theorist and Pragmatist. These four styles correspond approximately

to those suggested by Kolb’s (1976) ELM: active experimentation (Activist), reflective observa-

tion (Reflector), abstract conceptualisation (Theorist), and concrete experience (Pragmatist).

Furthermore, the ELM reflects two independent dimensions: Pragmatist–Theorist (prehension)

and Activist–Reflector (transformation).

Table 1 reports the results of previous studies considering the internal consistency reliability of

scores produced by the LSQ. Most studies report alpha coefficients indicating scores produced by

the instrument of moderate internal consistency reliability.

1

Evidence regarding the factor analytic properties of the LSQ is mixed. Allinson and Hayes

(1988), using a sample of managers, factor analysed the scores of the four learning style scales to

yield the two hypothesised orthogonal factors Activist–Reflector and Pragmatist–Theorist. A

subsequent study of UK undergraduate students (Allinson & Hayes, 1990) confirmed the two

independent dimensions. With a sample of UK and Eire managers De Ciantis and Kirton (1996),

attempted to modify the LSQ by means of an item analysis and pruning exercise. As a result of

this exercise, DeCiantis and Kirton produced two bi-polar measures, Activist–Reflector and

Theorist–Pragmatist, containing 45 and 15 items, with internal consistency reliabilities of 0.90

and 0.69, respectively. The modified bi-polar measures of DeCiantis and Kirton were reported as

correlating negligibly (0.08) in accord with theory.

No previous work has utilised confirmatory factor analysis (CFA): Allinson and Hayes (1988,1990); De Ciantis and Kirton (1996); Sims, Veres, and Shake (1989) and Tepper, Tetrault, Braun,

and Romero (1993) all applied exploratory factor analytic techniques to their respective samples.

Table 1

Summary of internal consistency reliability estimates of previous research

Study Participants n= Coefficient

Activist Reflector Theorist Pragmatist

Allinson and Hayes (1988) UK managers 127 0.58 0.74African & Indian managers 40 0.71 0.63

Sims et al. (1989) US business students 270 0.68 0.68 0.78 0.75

Tepper et al. (1993) US undergraduate students 227 0.75 0.76 0.67 0.52

Jackson and Lawty-Jones (1996) UK psychology students 166 ‘‘all between 0.69 and 0.77 (p. 295)’’

De Ciantis and Kirton (1996) UK and Eire managers 185 0.76 0.76 0.67 0.64

1 Nunnally and Bernstein (1994) indicate an alpha coefficient cutoff of 0.7 is necessary for instruments to be used in

applied settings, although 0.8 is a more preferable and stringent cutoff criterion.

A. Duff, T. Duffy / Personality and Individual Differences 33 (2002) 147–163 149

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Mulaik (1987) identifies that exploratory techniques can never answer definitively questions

regarding the latent structure of a set of variables, arguing that exploratory factor analysis (EFA)

can only suggest structures and these require confirmation by CFA. Previous research consider-

ing the psychometric properties of scores produced by the LSQ is therefore limited.Rather more evidence exists of the criterion validity of scores produced by the instrument.

Furnham and Medhurst (1995) report high and consistent correlations’ between students’ scores

on the Pragmatist dimension and positive performance in university seminars. Other work has

considered the relationship between learning style as measured by the LSQ and personality

(Furnham, 1992, 1996; Jackson & Lawty-Jones, 1996). Such investigations have reported con-

siderable correlation between learning styles such as Activist and personality variables such as

extraversion. These studies conclude in general that learning style is a subset of personality. In a

recent study of telephone sales staff, Reflector and Pragmatist learning styles account for a small

but important amount of the variance in measures of work performance (Furnham, Jackson, &

Miller, 1999).In summary, rather mixed evidence exists as to the validity and utility of LSQ data. The

objectives of the present study were: first, to use EFA and CFA to determine if the four proposed

learning styles and two bipolar dimensions are clearly identified; second, to determine the gen-

eralizability and stability of LSQ responses across different student groups and third, to deter-

mine the predictive validity of the LSQ by relating scores on the instrument to examination marks

for the sample of health studies students using structural equation modelling (SEM).

2. Method

2.1. Participants

Scores on the LSQ were validated using participants (n=388) of undergraduate students

enrolled in two different faculties of a regional university in Scotland: business and health studies.

The instrument was administered to all participants at the start of the academic year. Those

enrolled in the business faculty completed the instrument in a classroom environment, the health

studies students completed the instrument at their home and returned the instrument by post. The

distribution of the sample across the faculties was as follows: business, n=224 (males=104,

females=120; average age=24.36 years, minimum age=17 years, maximum age=53 years); health

studies, n=164 (males=15; females=149; average age=34.82 years, minimum age=20 years,

maximum age=62 years). Students enrolled in the business faculty were undertaking taughtmodules requiring attendance on campus on a full-time or part-time basis. Those enrolled in the

health studies faculty were undertaking distance-learning modules requiring no attendance on cam-

pus and were located throughout the UK. As can be seen from the above description, the sample

was demographically diverse enough to generalize to other Western higher educational settings.

2.2. Statistical analyses

The appropriateness of the four styles was evaluated using CFA using the SPSS version of

AMOS v4.0 (Arbuckle, 1999). AMOS is a statistical program to perform structural equation


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modelling, a form of multivariate analysis. CFAs were performed using AMOS to test for the

goodness-of-fit between our obtained data and the hypothesised structure of Honey and Mum-

ford (1992). When undertaking CFA, there are ‘‘vague and sometimes contradictory guidelines

about the desirable amount of data’’ (Marsh & Hau, 1999, p. 252). Marsh, Balla, and Hau (1997)using a Monte Carlo study investigated the effect of varying numbers of indicators (items) per

factor (p/f ratio) on varying sample sizes. Their results support a ‘‘more is better’’ approach to

both sample size and p/f ratio. For a p/f ratio as small as six, a sample size of 50 was adequate.

Therefore, the combined sample size of n=388, is satisfactory for conducting CFAs at the item

level of the LSQ, where the number of items per factor (p/f) equals 20 (Marsh et al., 1997). In

evaluating goodness-of-fit, we present the w2 statistic, the Relative Noncentrality Index (RNI),

the Tucker-Lewis Index (TLI), the ratio of the discrepancy, w2, divided by the degrees of freedom

(w2 /df), the Adjusted-Goodness of Fit Index (AGFI) and an evaluation of parameter estimates to

ensure the solution is proper (Marsh, Balla, & Hau, 1996; McDonald & Marsh, 1990). Although

no precise standards exist to indicate what value of indices are needed for a satisfactory fit, typicalguidelines are that the RNI should exceed 0.9. Various rules-of-thumb ranging from 2 to 5 have

been suggested as cut-offs for CMIN/d.f.. The present study follows the recommendations of

Byrne (1989) that a w2/d.f. ratio of greater than 2.0 represents an inadequate fit.

In the results reported below, discussions as to the best model fit include each of the above mea-

sures. This is in keeping with the Hoyle and Panter’s (1995) recommendations that multiple indica-

tors of overall fit should be selected from ‘‘absolute-fit indexes’’ (such as w2, and the AGFI) and

‘‘incremental fit indexes,’’ which should be selected from ‘‘type-2’’ and ‘‘type-3’’ indexes, such as

the TLI (Tucker & Lewis, 1973) and RNI (McDonald & Marsh, 1990). A type-2 index compares

the lack of fit of a target model with the lack of fit of a baseline model, usually the independence

model. Value estimates the relative improvement per degrees of freedom of the target model over

a baseline model (Hoyle & Panter, 1995). A type-3 index ‘‘indexes the relative reduction in lack of fit asestimated by the noncentral (two of a target model versus a baseline model’’ (Hoyle & Panter, 1995).

The hypothesised bipolar structure is examining by first, an examination of the LSQ scale cor-

relation coefficient matrix; and second, by means of EFA using maximum-likelihood analysis

followed by oblimin rotation. The psychometric requirement for achieving two zero-correlated

bi-polar measures (see Kirton, 1994) would be for each of the four style scales to be negatively

correlated with one other scale and zero correlated with the two remaining scales.

Considering the EFA, principal components were used: first, because this method yields com-

ponent scores that have the same correlation coefficients as the rotated factors; and second, as

component analysis does not unduly capitalise on sampling error as the price for estimating

measuring error (Thompson & Daniel, 1996). A further consideration is to determine the numberof factors to be extracted. Thompson and Daniel (1996) recommend employing a number of dif-

ferent methods to select factors. Accordingly, the present study uses eigenvalues-greater-than-one

rule (Kaiser, 1960), scree tests (Cattell, 1978) and parallel analysis techniques (Horn, 1965).

Separate CFAs were conducted on responses by students from the two groups of business and

health studies. The generalizability and stability of the two-factor and four-factor model is eval-

uated—following Marsh’s (1994) recommendations—by comparing the fit of three models: total

non-invariance (where no parameter estimates were constrained to be equal across groups), factor

parameters invariant (where only factor parameters were constrained to be equal across groups)

and total invariance (where all parameter estimates were constrained to be equal across groups).


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Educational attainment is generally believed to be a function of the learning environment, the

ability of the student and the individual’s learning style. Therefore it could be expected that stu-

dents with a preference for particular learning activities would outperform those with preferences

for other learning activities. To evaluate the relationship between learning style and performance,a structural equation model was constructed for both the health studies students and business

studies students using AMOS v4.0 (Arbuckle, 1999). Each model treats the four learning style

dimensions as observed exogenous predictor variables and academic performance as the observed

endogenous variable. Academic performance is proxied: for health studies students by a percen-

tage grade in a healthcare module (scores ranging from 19 to 79%; mean=58.0%; S.D.=9.4%),

and for business students by a percentage grade in an accounting module (scores ranging from 21

to 68%; mean=47.8%; S.D.=10.1%).2

3. Results

Alpha coefficients were calculated for the scores on the four LSQ scales. The coefficients indi-

cate the scores produced by the LSQ have only modest internal consistency reliability: Activist,

0.681; Reflector, 0.731; Theorist, 0.577; Pragmatist, 0.516. Item analysis of each scale removed 26

items, on a one-by-one basis, in total (four from Reflector, 11 from Theorist, 11 from Pragmatist)

that failed to improve the homogeneity of the scale to which they were assigned by the authors of

the LSQ. This item attrition failed to raise the scales to a level of minimum integrity of 0.70

suggested by Nunnally and Bernstein (1994): Activist, 0.671; Reflector, 0.750; Theorist, 0.661;

Pragmatist, 0.638. The results of the item attrition exercise are shown in the appendix and com-

pared to the results of De Ciantis and Kirton (1996).

Before item analysis, the combined 40-item Activist–Reflector scale yielded an alpha coefficientof 0.789. Item analysis of the combined Activist–Reflector scale created a 27-item scale (=0.817;

Table 2

Learning Style Questionnaire (LSQ) scale correlation matrix

Scale

Scale A P R T A–R T–P

Activist (A) – 0.038 0.464 * 0.105

Pragmatist (P) 0.219 * – 0.023 0.573 *

Reflector (R) 0.480 * 0.082 – 0.148 *

Theorist (T) 0.269 * 0.415 * 0.499 * –

A–R – 0.099

T–P 0.220 * –

The upper triangle represents scales after item analysis. The lower triangle represents original LSQ scale scores before

the scales were re-defined. *P<0.01

2 Like most measures of academic performance the data suffers from a restriction of range. Also, academic perfor-

mance is proxied by the results of only one module. However, the investigation benefits from an analysis of two distinct

samples.


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Appendix). Combining Theorist–Pragmatist into a single 40-item scale, produced an alpha of

0.689, and after item analysis this increased to 0.766 (16 items).

Table 2 reports the correlation coefficient matrix of the four learning styles of the LSQ, both

prior to and after item analysis. As hypothesised, Activist is highly negatively correlated withReflector (prior to item analysis, r=0.48; after item analysis, r=0.46). However, Activist is

also positively correlated with Pragmatist and Theorist.

Theorist is highly correlated with Pragmatist (prior to item analysis, r=0.42; after item analy-

sis, r=0.57). Not only is the correlation coefficient in the opposite direction to that expected in

theory, as Theorist and Pragmatist are considered opposites, it is of a high magnitude. Prior to

item analysis a stronger relationship exists between Theorist and Reflector (r=0.50). However,

after item analysis, the Theorist–Reflector relationship is attenuated (r=0.15). Prior to item

analysis, Activist-Pragmatist and Theorist Reflector are positively correlated (r=0.22); however

after item analysis this is negligible (r=0.10). We conclude, after the item attrition exercise, the

LSQ scale correlation matrix provides some evidence, albeit mixed, to support the two dimen-sions of prehension and transformation.

The results of the EFA are shown in Table 3. Principal component analysis using the eigenva-

lues-greater-than-one rule, extracted 27 factors (accounting for 62.51% of the total variance);

parallel analysis extracted nine factors (accounting for 34.59% of the total variance) and scree

tests extracted five factors (accounting for 25.77% of the total variance). Evidence exists to show

that the scree test is more accurate than the eigenvalue-greater-than-one criterion when both are

tested on artificially generated sample data (Zwick & Velicer, 1982, 1986). The results of the scree

test are shown in Fig. 1. Factor 1 consisted of 24 items with item coefficients greater than 0.25,

Fig. 1. EFA Scree Test.


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Table 3

Factor structure matrix

Item Scale Factor

I II III IV V

15 Reflector 0.577

16 Reflector 0.549

46 Reflector 0.516 0.338

60 Reflector 0.496

25 Reflector 0.474

28 Reflector 0.472 0.322

2 Activist 0.457 0.322

56 Pragmatist 0.415

66 Reflector 0.409 0.319 0.375

29 Reflector 0.382

57 Theorist 0.36331 Reflector 0.329

35 Pragmatist 0.282

38 Activist 0.339

67 Reflector 0.300

48 Activist 0.391

10 Activist 0.271

74 Activist 0.288 –.481

34 Activist 0.340 0.340 0.403

7 Reflector 0.330 0.345

75 Theorist 0.255 0.541

70 Pragmatist 0.253 0.441 0.284 0.444

73 Pragmatist 0.305 0.39241 Reflector 0.316 0.318

42 Theorist 0.567

68 Theorist 0.554

65 Pragmatist 0.500 0.257

14 Theorist 0.494

12 Theorist 0.470

35 Pragmatist 0.470

27 Pragmatist 0.421

61 Theorist 0.415

54 Pragmatist 0.405

80 Pragmatist 0.380

49 Pragmatist 0.360

47 Theorist 0.349


22 Theorist 0.319

77 Theorist 0.319

8 Theorist 0.343

20 Theorist 0.367 0.526


1 Theorist 0.251 0.277 0.424

30 Theorist 0.339

(continued on next page)


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with 19 items from the combined Activist–Reflector dimension, item 10 from the Activist scale

was negatively correlated with the factor. Factor 2 consisted of 19 item coefficients greater than

0.25, with all items from the Theorist–Pragmatist dimension. However, three items (1, 19 and 20)

were negatively correlated with the factor. Factor three, consisted of 19 items, with 13 items from

the Activist–Reflector dimension negatively correlated with the factor. Of the residual six items,

five were from the Theorist–Pragmatist scale and were all positively correlated with the factor.

The remaining item from the third identified factor was from the Activist scale and positively

correlated with the factor. The fourth identified factor consisted of 15 items. Eleven items were

from the Activist scale—each negatively correlated with the factor. Of the residual four items,

Table 3 (continued )

Item Scale Factor

I II III IV V

Item Scale I II III IV V

67 Reflector 0.674

62 Reflector 0.650

58 Activist 0.611

43 Activist 0.572

48 Activist 0.561

23 Activist 0.516

10 Activist 0.508

72 Activist 0.461

11 Pragmatist 0.429

8 Theorist 0.374

79 Activist

00.357

0.32932 Activist 0.320

17 Activist 0.483

4 Activist 0.403

71 Activist 0.378

45 Activist 0.368

64 Activist 0.367

40 Activist 0.351

6 Activist 0.345

24 Activist 0.260


63 Theorist 0.545

21 Pragmatist 0.541

18 Theorist 0.490

78 Theorist 0.487

50 Pragmatist 0.456

51 Theorist 0.453

59 Pragmatist 0.449

44 Pragmatist 0.350

37 Pragmatist 0.346

38 Activist 0.250

% Variance Explained 7.59% 7.23% 4.99% 3.04% 2.92%

Principal components analysis; oblimin rotation; factor structure matrix coefficients less than 0.25 omitted.


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two were from the Reflector scale and again, negatively correlated with the factor. Factor five

consisted of 18 items with factor structure matrix coefficients greater than 0.25. Fifteen items in

this identified factor lie within the Pragmatist–Theorist dimension and each was positively corre-

lated with the factor.Considering the results of the EFA along with the analysis of the LSQ scale correlation matrix,

only limited evidence exists to support the hypothesised two learning dimensions and four learn-

ing styles. The descriptive statistics and results of the confirmatory factor analyses are shown in

Table 4. The fit indices for both the two-factor and four-factor model indicates that the data are a

poor fit to the two hypothesised learning dimensions and four learning styles. Fit indices are

shown for both the two-factor and four-factor models after item analysis. Inspection of the fit

indices after the item attrition exercise indicates both models estimated still failed to fit the data.

The results of the tests of factorial invariance are shown in Table 5. For both analyses, the fit

indices of the total invariance models were worse than the fit indices for the models with invariant

factor parameter estimates that were worse than fit indices for the models with no invarianceconstraints across the two groups. In both cases, models constraining all parameters to be the

same across the two groups of students had poor fit indices.

Table 4

Confirmatory Factor Analyses of the Learning Style Questionnaire (LSQ)

Model Alpha Mean S.D. No. Items

One-factor model 0.783 48.436 8.510 80

w2=11470.967; d.f.=3161; w2/df=3.629; TLI=0.278; RNI=0.288; AGFI=0.539

Two-factor model

Activist–Reflector 0.789 25.204 5.790 40Pragmatist–Theorist 0.689 23.232 5.092 40

w2=6422.316; df=3082; w2/df=2.084; TLI=0.346; RNI=0.362; AGFI=0.633

Two-factor model after item analysis

Activist–Reflector 0.817 18.186 5.016 27

Pragmatist–Theorist 0.769 8.601 3.622 16

w2=2261.967; d.f.=819; w2/df=2.762; TLI=0.461; RNI=0.487; AGFI=0.739

Four-factor model

Activist 0.681 10.55 3.46 20

Pragmatist 0.516 11.79 2.91 20

Reflector 0.731 14.66 3.39 20Theorist 0.577 11.44 3.07 20

w2=6358.546; d.f.=3077; w2/d.f.=2.066; TLI=0.357; RNI=0.374; AGFI=0.642

Four-factor model after item analysis

Activist 0.681 10.55 3.46 20

Pragmatist 0.638 4.76 2.11 9

Reflector 0.750 11.56 3.22 16

Theorist 0.661 4.79 2.24 9

w2=3238.583; d.f.=1373; w2/d.f.=2.359; TLI=0.421; RNI=0.444; AGFI=0.725

TLI, Tucker-Lewis Index; RNI, Relative Noncentrality Index; AGFI, Adjusted-Goodness of Fit Index.


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Table 5

Two-group Analyses of Invariance for Business and Health Studies Students

Model w2 d.f. w

2/df AGFI RNI TLI

Two-factor model

2gp (no inv) 10531.673 6164 1.709 0.564 0.245 0.226

2gp (fl inv) 10733.557 6239 1.720 0.563 0.224 0.213

2gp (tot inv) 11026.942 6319 1.745 0.561 0.187 0.187

Four-factor model

2gp (no inv) 10339.496 6154 1.680 0.585 0.277 0.257

2gp (fl inv) 10554.325 6219 1.697 0.578 0.251 0.239

2gp (tot inv) 12056.621 6353 1.898 0.513 0.015 0.020

TLI, Tucker-Lewis Index; RNI, Relative Noncentrality Index; AGFI, Adjusted-Goodness of Fit Index.

Fig. 2. Business studies students’ learning styles and academic performance.


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The standardised estimates of the SEM for the sample of business students is shown in Fig. 2.

The squared multiple correlation between academic performance (the observed endogenous

variable) and the four learning styles (the observed exogenous variables) is 0.09, indicating 9% of

academic performance is accounted for by its predictors (learning style dimensions). Examiningthe standardised regression weights, the strongest positive predictor variable is Theorist with a

weight of 0.18. Notably, both Pragmatist and Reflector have negative standardised regression

weights of 0.28 and 0.23, respectively.

Fig. 3 displays the results of the standardised estimates of the SEM for the sample of 145 health

studies students. The squared multiple correlation between the observed exogenous predictor

variables (learning style dimensions) and observed endogenous variable (academic performance)

is 0.04, indicating only 4% of academic performance is accounted for by its predictors (learning

style dimensions). Examining the standardised regression weights, the strongest weight is between

the Pragmatist scale and academic performance (0.18). The standardised regression weights for

the Activist and Theorist scales are negligible at

0.01 and 0.05, respectively.

Fig. 3. Health studies students’ learning styles and academic performance.


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Comparing the path diagrams—shown in Figs. 2 and 3—for the business students and the

health studies students respectively, reveals a number of significant differences. Considering the

relationship between the observed exogenous predictor variables (learning style dimensions) and

the observed endogenous variable, the strongest predictor is Pragmatist. However, the standar-dised regression weight for the health studies is 0.18 and for the business students, 0.27. The

strongest positive standardised regression weight between the predictor variable and academic

performance for the business students is Theorist (0.18). Theorist is a poor predictor of academic

performance amongst the health studies students with a standardised regression weight of only

0.05. An analysis of the correlation coefficients between the learning style dimensions shown in

Figs. 2 and 3 reveals a number of significant differences. Notably, the highest correlation between

the learning style dimensions for the health studies students is between Reflector and Pragmatist

(r=0.50); for business studies students this relationship is negligible (r=00.03).

Other notable differences include: Activist–Pragmatist (business r=0.22, health studies

r=

0.21); Reflector–Theorist (business r=0.41, health studies r=0.08); and Activist–Theorist(business r=0.17, health studies r=0.27). In summary, evidence exists to: first, suggest the

relationship between learning style dimensions is different between the sample of business stu-

dents and health studies students; second, indicate that learning style is only a weak predictor of

academic performance; and third, that the learning style predictors of academic performance are

different between the samples of business and health studies students.

4. Conclusion

Results from the multi-analytical research techniques used in the present study with higher

education students from two different faculties and undertaking study in two different deliverymodes indicate that the scores produced by the LSQ have limited reliability and validity for this

population and that the LSQ’s multidimensional structure is not invariant across faculty groups.

Although an item pruning exercise improved the internal consistency reliability evidence, three

(of the four) scales failed to meet a level of minimum internal integrity. The results of the CFAs

on both the two-factor and four-factor models after the item analysis, indicated a poor fit to the

data. In summary, no evidence is found for the four learning styles or two bipolar dimensions

hypothesised by Honey and Mumford (1992). Our study indicates even after modification the

LSQ cannot be safely used with samples of UK undergraduate students.

The use of CFA as a technique, along with supplemental analyses of the invariance of the

parameter estimate across multiple groups, extends the psychometric research surrounding theinstrument. Our study supports the EFAs of De Ciantis and Kirton (1996) sampling UK and Eire

managers.

A further issue concerning the LSQ is that the four learning styles are orthogonal to one

another; that is a person may score high on one and low on the others, or high or low all four. A

problem arises with individuals who have only low or moderate preferences for any particular

learning style, implying a low level of preference for any form learning as conceived by Kolb’s

ELM. Explanations for this could include that the instrument is not sufficient to identify students’

dominant learning style or they do not interact with the learning model of higher education. De

Ciantis and Kirton (1996) contend that the level (abilities) in which individuals engage in each


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stage of the learning process (the learning cycle) is unrelated to their learning style and their

effectiveness in completing all or part of the process, each of which should be assessed separately.

Despite the measurement problems surrounding the LSQ (and LSI/LSI-1985), Kolb’s ELM has

been applied in schools, colleges and universities and in vocational education as well as in man-agement development, where it has gained unquestioned status at the core of the profession’s

pedagogical apparatus (Reynolds, 1997). One of the reasons Kolb’s work has caught on so dra-

matically is that the research on which it is based suggests that persons who choose one or

another occupational field tend strongly to share that learning style. This is useful knowledge for

those who are interested in career choice and pedagogy in professional fields. Hopkins (1993)

identifies a profound difference between measured experience and lived experience—a difference

that makes Kolb’s formulation so deeply problematic. Nelsen and Grinder (1985) identify the

fundamental problem with Kolb’s ELM as the failure to untangle experience from learning and

structure from process. Although Kolb has developed measures of the four learning styles,

thereby operationally distinguishing the structures, no method or operation for investigating theprocesses underlying these structures is presented.

In conclusion, it is suggested that the LSQ is based on a model (the ELM) which is not suffi-

ciently sophisticated to describe the learning that takes place in higher education. The LSQ is

defined in terms of a management trainee’s learning rather than that of a student in higher edu-

cation. Caution should be employed if adopting the LSQ to select appropriate instructional

methods or to categorise individual students. The findings indicate the LSQ is not a suitable

alternative to either the LSI or LSI-1985.

Appendix. Revised scale items after item analysis

Activist–Reflector Theorist–Pragmatist

Present study DeCiantis and

Kirton (1996)

Present study De Ciantis and

Kirton (1996)

2 2 5 5

3 8 8

4 11

6 127 7 14

10 21

13 26

15 15 27 27

16 16 30

17 17 35 35

(continued on next page)


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Activist–Reflector Theorist–Pragmatist

Present study DeCiantis and

Kirton (1996)

Present study De Ciantis and

Kirton (1996)

23 37

24 42 42

25 25 47

28 28 54 54

29 29 56

31 31 61 61

32 65

34 34 68

36 69

38 70 7039 39 73

40 77

41 41 80

43 43

45 45

46 46

48 48

52

55 55

58 5860

62 62

64

66 66

67 67

71

72 72

74 74

79 79

27 items; =0.82 36 items; =0.88 16 items; =0.77 15 items; =0.69

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