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In a study of personal alarms, women were asked to rate eight personal alarms using the following 15 statements:

1 Feels comfortable in the hand.

2 Could be easily kept in the pocket.

3 Would fit easily into a handbag.

4 Could be easily worn on the person.

5 Could be carried to be very handy when needed.

6 Could be set off almost as a reflex action.

7 Would be difficult for an attacker to take it off me.

8 Could keep a very firm grip on it if attacked.

9 An attacker might be frightened that I might attack him with it.

10 Would be difficult for an attacker to switch off.

11 Solidly built.

12 Would be difficult to break.

13 Looks as if it would give off a very loud noise.

14 An attacker might have second thoughts about attacking me if he saw me with it.

15 I would be embarrassed to carry it around with me.

The question was ‘Could these 15 variables be reduced to a smaller number of derived variables, known as factors, in such a way that too much information was not lost?’. Factor analysis enabled these 15 variables to be reduced to four underlying dimensions, or factors that women used to evaluate the alarms. Factor 1 seemed to measure a dimension of size, on a continuum of small to large. Factor 2 tapped into aspects of the appearance of a personal alarm. Factor 3 revealed robustness characteristics, with factor 4 related to hand feel.

 
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Analyse the Benetton data (taken from Exercise 4, Chapter 20). Consider only the following variables: awareness, attitude, preference, intention and loyalty towards Benetton.

a Analyse these data using principal components analysis, using the varimax rotation procedure.

b Interpret the factors extracted.

c Calculate factor scores for each participant.

d If surrogate variables were to be selected, which ones would you select?

e Examine the model fit.

f Analyse the data using common factor analysis, and answer parts b to e.

Exercise 4

In a survey pre-test, data were obtained from 45 participants on Benetton clothes. These data are given in the table overleaf, which gives the usage, gender, awareness, attitude, preference, intention and loyalty towards Benetton of a sample of Benetton users. Usage was coded as 1, 2 or 3, representing light, medium or heavy users. Gender was coded as 1 for females and 2 for males. Awareness, attitude, preference, intention and loyalty were measured on a seven-point Likert-type scale (1 = very unfavourable, 7 = very favourable). Note that five participants have missing values, which are denoted by 9.
Analyse the Benetton data to answer the following questions. In each case, formulate the null and alternative hypotheses and conduct the appropriate statistical test(s):

a Obtain a frequency distribution for each of the following variables and calculate the relevant statistics: awareness, attitude, preference, intention and loyalty towards Benetton.

b Conduct a cross-tabulation of the usage with gender. Interpret the results.

c Does the awareness for Benetton exceed 3.0?

d Do males and females differ in their awareness of Benetton? Their attitude towards Benetton? Their loyalty to Benetton?

e Do the participants in the pre-test have a higher level of awareness than loyalty?

f Does awareness of Benetton follow a normal distribution?

g Is the distribution of preference for Benetton normal?

h Assume that awareness towards Benetton was measured on an ordinal scale rather than an interval scale. Do males and females differ in their awareness of Benetton?

i Assume that loyalty towards Benetton was measured on an ordinal scale rather than an interval scale. Do males and females differ in their loyalty towards Benetton?

j Assume that attitude and loyalty towards Benetton were measured on an ordinal scale rather than an interval scale. Do the participants have a greater awareness of Benetton than loyalty to Benetton?

 
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Driving nuts for Beetles

Consumer preferences for cars need to be continually tracked to identify changing demands and specifications. However, there is one car that is quite an exception – the Volkswagen Beetle. More than 22 million of the original model were built between 1938 and 2003. Surveys have been conducted in different countries to determine the reasons why people purchased Beetles. Principal components analysis of the variables measuring the reasons for owning Beetles have consistently revealed one factor – fanatical loyalty. The company had long wished for the car’s natural death, but without any effect. The noisy and cramped ‘bug’ inspired devotion in drivers across the generations. Now old bugs are being sought across the globe. VW reintroduced the brand in 1998 as the ‘New Beetle’, which has won several distinguished automotive awards.

 
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Factors predicting unethical marketing research practices

Unethical employee behaviour was identified as a root cause for the global banking and financial mess of 2008–9. If companies want ethical employees, then they themselves must conform to high ethical standards. This also applies to the marketing research industry. In order to identify organisational variables that are determinants of the incidence of unethical marketing research practices, a sample of 420 marketing professionals was surveyed. These marketing professionals were asked to provide responses on several scales, and to provide evaluations of incidence of 15 research practices that have been found to pose research ethics problems. One of these scales included 11 items pertaining to the extent that ethical problems plagued the organisation, and what top management’s actions were towards ethical situations. A principal components analysis with varimax rotation indicated that the data could be represented by two factors. These two factors were then used in a multiple regression, along with four other predictor variables. They were found to be the two best predictors of unethical marketing research practices.

To simplify the table, only varimax rotated loadings of 0.40 or greater are reported. Each was rated on a five-point scale, with 1 = ‘strongly agree’ and 5 = ‘strongly disagree’.

 
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