Exploratory Factor Analysis

Atiqah Ismail Exploratory Factor Analysis 2011

MKT3004

ANALYTICAL TECHNIQUES FOR MARKETING

EXPLORATORY FACTOR ANALYSIS

NEWCASTLE UNIVERSITY

2011

BY: ATIQAH ISMAIL

NEWCASTLE UNIVERSITY BUSINESS SCHOOL

TUTOR: MITCHELL NESS

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1. INTRODUCTION

Factor analysis provides a set of techniques aimed to condense the dimensionality of an

original set of metric variables into a new, smaller set of composite variables which can

explain the interrelationships between the original set of metric variables, with minimum loss

of information (Hair et al., 2010).

Social studies often involve multi-item scales or constructs consisting large number of

variables with complex, multidimensional relationships. Factor analysis helps to analyse the

structure of interrelationships among those large number of variables by identifying

dimensions or factors. The main aim of this paper is to apply and conduct exploratory factor

analysis (EFA) on a scale that measures students’ attitudes to the importance of supermarket

features, to identify the underlying dimensions of the scale, evaluate its goodness of fit, and

to interpret the derived factors.

The following section will introduce the theory of factor analysis. Subsequently, section three

will illustrate an application of factor analysis in establishing the dimensionality of a scale

based on a research by Mai and Ness (2006). Section four will explain and discuss the

research methodology for the attitudinal measures to the importance of supermarket features.

This is followed by the presentation of the empirical results. Section six will evaluate the

marketing implications of the results. Finally, section seven will conclude with summary,

evaluation of the study and research recommendations.

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2. THEORY

2.1. Objective of Factor Analysis

Factor analysis identifies and allows interpretation of the underlying structure of the original

data. In particular it seeks to discover if the observed variables can be explained largely in

terms of a much smaller number of variables called factors.

The two main purpose of factor analysis is data reduction and theory development; data

reduction seeks to reduce the number of variables into a smaller number of factors, whereas,

theory development is concerned with identifying structures underlying the correlations

between variables in order to classify highly correlated variables into factors (Neill, 2010).

2.2. Data Requirement

Theory development is concerned with variance-covariance matrix (see Appendix A) which

represents the variances of variables and associations between pairs of variables.

The basis of factor analysis is the correlation matrix (see Appendix B), within which the

correlation of each variable with itself and other variables are represented. Therefore, data for

factor analysis must be suitable for correlation analysis. Firstly, the original data is required

to be metric or ratio data, or at least at an interval level. Secondly, data is required to be

correlated.

2.3. The Factor Analysis Model

Factor analysis is based on the Common Factor Model (Figure 1, Equation 1) which proposes

that each original observed variable (x’s) is influenced by underlying non-observable

common factors (F’s) and non-observable unique factor (e’s) (DeCoster, 1998).

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F1

F2

x1

x2

x3

x4

x5

e1

e2

e3

e4

e5


Figure 1 The Common factor Model

Adapted from: DeCoster (1998)

Hence, it assumes that each p original observed variables (x’s) are the linear combinations of

k non-observable common factors (F’s) and the non-observable unique factors (e’s).

x p=b p 1F 1+b p 2F 2++b p k F k+e pEquation (1)

Therefore, the basic model assumes that:

The original observed variables and the common factors are standardised to have a

mean of zero and a variance of unity,

The covariances between common factors are zero so that they are uncorrelated,

Common and unique factors are not correlated,

Covariances between unique factors are zero so that pairs of unique factors are not

correlated.

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3. APPLICATION TO MARKETING

In marketing research factor analysis can be applied to facilitate the identification of factors

or dimension of scales, to use factor input as variable for subsequent analysis, and to confirm

the dimensionality of existing scales.

This section will use a study by Mai and Ness (2006) to illustrate the application of EFA in

establishing the dimensionality of a scale.

Aim of the Study

Mai and Ness (2006) aims to investigate mail-order shopper’s characteristics, attitudes,

preferences and behaviour. The survey was implemented as a national survey in the United

Kingdom (UK). The study employed a mail survey on customer contacts supplied by five

mail-order specialty food companies. The study employed a stratified random sampling

method based upon the relative sizes of the firms’ contact lists. The survey yielded 1,028

valid responses for factor analysis.

EFA was applied to establish the dimensionality of a scale to measure satisfaction with mail-

order specialty food, based on 8 features of mail-order shopping, in the form of 8-item five-

point satisfaction scale.

Description of Data and Measures

The original variable consisted of an 8-item five-point scale concerned with the levels of

transaction satisfaction (1 = Very satisfied, 5 = Very dissatisfied). The scale is assumed to be

a metric measure.

Mai and Ness (2006) applied EFA to the 8-item five-point scale of transaction satisfaction.

The analysis employed principal components with Varimax rotation, and the extraction

criterion was to derive factors with eigenvalues greater than unity.

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Results

Two factors were derived from the analysis (see Appendix C). Both factors account for 59%

of total variance, while communalities are generally respectable, apart from those for

catalogue presentation (0.410) and price (0.411). The first factor is most associated with, in

descending order of magnitude, ordering process (0.819), payment terms (0.801), delivery

service (0.762) and catalogue presentation (0.537) and is defined as “Service satisfaction”.

The second factor is most associated with, in descending order of magnitude, product

selection (0.833), product quality (0.827), price (0.553), and enquiry service (0.520) and is

defined as “Product satisfaction”.

Implications

The two-dimensions of the scale indicate that customer satisfaction is associated with both

service and product features of mail-order specialty food, however, satisfaction with service

transaction appears to be more important. Hence, the implication for mail-order firms is that

they need to expand their vision beyond food delivered by post, and consider in-store

specialty food that it is the high-level of customer care and service which differentiate the

mail-order product from the in-store equivalent. Satisfaction and re-purchase likelihood are

dependent on integrated features of both product and service aspects of the mail order

business

3.1. Other Applications

Factor analysis can also be used to confirm or evaluate the suitability of an existing scale to a

new body of study. For example, Dobson and Ness (2009) adopted attitude to food shopping

scale from Chethamrongchai and Davies (2000) and attitudes to time scale from Davies and

Madran (1997) to be used in their study. Dobson and Ness employed factor analysis to

evaluate the suitability of the adopted scales to their current study of students’ attitudes to

food shopping and students’ attitudes to time. Factor analysis can therefore help researchers

in employing suitable scales in their study and hence produce a more reliable and accurate

output.

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Factor analysis can establish factors for a basis for a further research, where, subsequently,

Dobson and Ness used the evaluated measure and the post hoc factor structure from factor

analysis to form the basis of cluster analysis in exploring the existence of student segments.

4. METHODOLOGY

Data Explanation

The study employed a questionnaire survey designed to include nominal measures of

shopping behaviour, a scale of students’ attitudes to the importance of supermarket features,

and nominal measures of students’ characteristics. The survey adopted face-to-face

interviews with full-time undergraduate Newcastle University students. A convenience quota

sampling method was used to approximate student representation by gender and faculty.

Subsequently the survey yielded 731 valid responses.

Measures

The scale consists of fourteen five-point scales concerned with measuring the importance of

supermarket store features to students (1 = Not at all important, 5 = Very important). Specific

measures are identified in Appendix D.

Confirmation that Data are Metric

The use of ordinal scale (Appendix E) with explicit numerical scores and equal intervals of

unity (1) between descriptors implies that the scale is assumed to be interval and thus a metric

measure.

Confirmation that Data are Correlated

The confirmation that data are inter-correlated is indicated by a KMO index of 0.697 (0.7

rounded), classified by Kaiser (1974, cited in Ness, 2011) as “middling”. Additionally, the

Bartlett’s test of sphericity results in the rejection of the null hypothesis, indicating that data

are inter-correlated at the 5% significance level (χ2 (91) = 1848.233, p<.000).

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Explanation of factor Method

The analysis employed principal components with Varimax rotation, and the extraction

criterion was to derive factors with eigenvalues greater than unity (1). The analysis yielded a

5-factor solution (Table 1). Factor scores were generated for each respondent for subsequent

analysis. The analysis was conducted IBM SPSS 19.0 (SPSS, 2008).

5. RESULTS

Table 1 Varimax Rotated Factor Matrix for Importance of Store Attributes

Store FeatureFactor Number

h2

1 2 3 4 5

Convenient location .181 .156 .114 .069 -.769 .665Parking facilities -.013 .123 .132 .161 .781 .668Pleasant atmosphere .008 -.010 .594 .402 -.187 .550Well-known brands .010 .173 .769 .020 .099 .632Own label products .601 .044 .132 -.255 .211 .490High quality products .047 .066 .734 .100 .037 .557Value for money .767 .078 .098 .084 -.148 .634Low prices .833 -.067 -.097 .077 -.123 .729Special offers .757 .054 -.017 .095 -.090 .594Friendly, helpful staff .103 -.026 .201 .805 -.114 .714Check-out speed .021 .555 .043 .531 .074 .597Methods of payment .077 .824 .162 .024 -.013 .711Cash-back facilities .003 .846 .055 .006 -.027 .719Other facilities -.020 .088 .087 .560 .291 .414

Eigenvalue 2.754 2.219 1.370 1.312 1.019

Variance % 19.669 15.853 9.787 9.368 7.278

Cumulative variance % 19.669 35.522 45.310 54.678 61.956

Notes

1. h2 refers to communality

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Report and Evaluation: Goodness of Fit

Goodness of fit is evaluated using total variance explained and communalities (h2). The

minimum acceptable value for communalities was set at 0.5 (Hair et al., 2010), the minimum

threshold for the inclusion of factor loadings consistent with the sample size 731 was set at

0.300 (Hair et al., 2010). Total variance explained and communalities with values greater

than 0.6 are considered to be ‘respectable’ for social science data (Ness, 2011).

Goodness of fit resulted to acceptable total variance of 62% and a generally respectable

communalities, apart from variable ‘other facilities’ (.414). The communality for ‘other

facilities’ (.414) is very weak. It indicates that only 41% of the variance in ‘other facilities’ is

explained by the 5 factors, and may indicate that this attribute do not fit well with the factor

solution. However this was retained because it loaded significantly on one factor.

All measures load uniquely and significantly on at least one factor, hence contribute to the

validity of the measure. Goodness of fit indicates an acceptable fit of 14 features consisting of

1 very weak, 3 weak, 6 respectable, and 4 strong communalities. Goodness of fit is also

supported by an acceptable total variance of 61.956 indicating that 62% of all original

variables are explained by the 5 factors. The 14 original variables are replaced by 5 factors in

which the degree of data reduction is 64% and information loss of 34%.

Interpretation of Results

Factor 1 is most strongly associated, in descending order of importance, with ‘low prices’

(.833), ‘value for money’ (.767) and ‘special offers’ (.757). It is therefore interpreted as

“Economy” factor.

Factor 2 is most strongly associated, in descending order of importance, with ‘cash-back

facilities’ (.846) and ‘method of payment’ (.824). It is therefore interpreted as “Payment

facilities” factor.

Factor 3 is most strongly associated, in descending order of importance, with ‘wide range of

well-known brands’ (.769) and ‘high quality products’ (.734). It is therefore interpreted as

“Range in quality product” factor.

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Factor 4 is most strongly associated with ‘friendly, helpful staff’ (.805) and therefore it is

interpreted as “Friendly staff” factor.

Factor 5 is most strongly associated, in descending order of importance, with ‘car-parking

facilities’ (.781) and ‘inconvenient location’ (.769). It is therefore interpreted as

“Accessibility” factor.

Hence, in descending order of magnitude factors are economy, payment facilities, range in

quality product, friendly staff, and accessibility. Therefore, these factors represent the relative

importance of supermarket features to students.

6. MARKETING IMPLICATIONS OF RESULTS

Factor analysis was conducted to develop a measure for students’ attitudes to the importance

of supermarket features. The factor structure could provide strategic direction to managerial

decision-making for supermarkets seeking to cater the student segments.

Managers could shape their promotional strategies based on the identified dimensions.

Promotional strategies can be adapted to effectively attract students by focusing its core

message on economy, such as discounts or availability of quality product-range in-store. An

example of a promotional campaign can be a ‘buy 4 for the price of 3’ on its quality product

range.

Identification of “Accessibility” factor may assist managerial decision-making in locating a

new supermarket branch. For example, strategically locating near public transport facilities or

selecting a location which provides abundant space for car-parking facilities. “Economy”

factor may assist operational decision-making in the strategic selection of suppliers, for

example, selecting suppliers which enable the supermarket to provide economy and range in

quality product in its offering, efficiently and effectively. Other managerial strategies may

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also include staff motivation and human resource training, in contributing to reliable shop-

floor staff.

However, it is important to note that the resulting factors merely indicate the importance of

supermarket features. They do not indicate students’ preferences.

7. SUMMARY AND CONCLUSION

The purpose of this paper is to identify the dimensions underlying the measure of students’

attitudes to the importance of supermarket features. The study employed a questionnaire

survey using face-to-face interviews with full-time undergraduate Newcastle University

students. Factor analysis was conducted on 14 original variables to identify the structure of

the measure using Varimax rotation with an extraction criterion to derive factors with

eigenvalues greater than unity. The analysis resulted in a five-factor solution defined as,

‘economy’, ‘payment facilities’, ‘range in quality product’, ‘friendly staff’, and

‘accessibility’. Goodness of fit resulted to acceptable total variance of 62% and a generally

respectable communalities.

However, the representativeness of the findings is limited to full-time undergraduate

Newcastle University students. This does not represent the whole student population

students, hence suggests the need for further research to extend the sample frame to a more

representative student population. For example, inclusion of undergraduate and post graduate

university students from other regions of the UK in the sample frame. The potential

significance of a wider representativeness could enable an extension of the study to include

subsequent research, such as cluster analysis, to allow the identification of student segments

and potentially allows segment profiling. This can ultimately allow more refined marketing

strategies. Moreover, the study is an exploratory research, hence merits further research.

Nevertheless, the significance of the findings can contribute valuable implication to

marketers in strategic decision-making, in establishing promotional strategies and other

managerial decision. This finding is particularly valuable, to supermarkets in Newcastle city

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centre where university campus (Newcastle University and Northumbria University) and

students’ residence are within proximity of the city.

The study is also limited to the importance of supermarket features, it does not reflect

students’ preferences and the extent to which these features influence their supermarket

selection, and hence the scope for further research is indeed significant.

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Documents

Exploratory Factor Analysis