4. (a) Applicants for a position in a rm were asked to score themselves, from 0 to 10, through a questionnaire on the following ten characteristics: 0 Ambition 0 Appearance 0 Drive 0 Experience 0 Honesty o Likeability 0 Potential 0 Salesmanship o Self-condence o Suitability. When reviewing the questionnaire, because many correlations between the variables are high, it was felt that some of the variables might be confusing, and/or some variables might be redundant. Therefore, a factor analysis was conducted to determine if any underlying factors could be extracted. Figure 3 (spread over the next two pages] presents selected SPSS output from a factor analysis with principal components extraction, using the varimax rotation procedure. Interpret the output. In your analysis, be sure to address at least the following: 0 Explain how you determine the number of factors and interpret the extracted factors. 0 Explain qualitatively and quantitatively how the t of the factor analysis model should be examined. 0 Briey discuss for what modelling purpose(s) any extracted factors could be used. (20 marks) (b) How may 'operational data' held by organisations help them to build up an understanding of customer behaviour? Support your answer with examples. (10 marks) Page 9 of 11 UL201'0542 Figure 3 KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 782 Bartlett's Test of Approx. Chi-Square 377.094 Sphericity di 45 Sig 000 Communalities Initial Extraction Appearance 1.000 432 Likeability 1.000 .841 Self-confidence 1.000 889 Honesty 1.000 .851 Salesmanship 1.000 888 Experience 1.000 .848 Drive 1.000 .810 Ambition 1.000 910 Potential 1.000 840 Suitability 1.000 864 d: Principal Component Extraction Method: Princi Analysis. Initial Eigenvalues Component Total 6 of Variance Cumulative % 1 5.336 53.362 53.362 2 1.656 16.562 69.924 3 1.181 11.811 81.735 4 689 6.891 38.626 5 336 3.359 91.985 6 .287 2.873 94.858 192 1.925 6.783 8 .132 1.319 98.102 117 1.169 99.271 10 073 .729 100.000 1.0- Honesty Likeability Component 2 0.5- CPotential Suitability Selfconfiden 0.0 OOO Ambition Drive Salesmanship 0.5- -1.0 -1.0 -05 0.0 0.5 1910 0.5 0.0 -0.5 -10 Component 1 Component 3 Page 10 of 11 UL20/0542Figure 3 (continued) Rotated Component Matrix Component 2 Appearance 454 443 171 Likeability .170 .875 217 Self-confidence 914 206 .109 Honesty 178 .879 -.217 Salesmanship 897 150 248 Experience 055 .036 918 Drive .801 192 363 Ambition 933 .138 144 Potential 703 .458 .368 Suitability 353 123 851 Component Score Coefficient Matrix Component 2 3 Appearance 040 180 023 Likeability -.184 529 108 Self-confidence 329 .087 .233 Honesty -.101 525 -.152 Salesmanship 270 -.115 -.018 Experience -.141 ..026 538 Drive 200 .064 069 Ambition .306 -.133 087 Potential 097 132 .094 Suitability .050 005 444 Reproduced Correlations Self- Salesmanshi Appearance Likeability confidence Honesty Experience Drive Ambition Potential Suitability Reproduced Correlation Appearance 432 .502 488 433 516 166 511 510 685 361 Likeability 502 841 a .312 752 337 177 383 311 600 352 Self-confidence 488 312 389a .367 823 057 732 .865 697 256 Honesty 433 752 .367 851 a 237 -.221 .233 256 448 ..013 Salesmanship 16 .337 823 .237 888a .271 837 .893 790 .546 Experience 166 .177 ..057 -.221 .271 848a 370 178 360 797 Drive 511 .383 .732 .233 837 370 810 826 785 615 Ambition .510 .311 865 .256 893 178 826 310a 772 469 Potential 585 .600 697 .448 790 .360 785 772 B40a 618 Suitability 361 352 256 -.013 546 797 615 469 .618 8643 END OF PAPER Page 11 of 11 UL20/0542
