Ebook Marketing research: An applied approach – Part 2

Ebook Marketing research: An applied approach – Part 2 presents the following content: Chapter 15 Sampling: final and initial sample size determination; Chapter 16 Survey fieldwork; Chapter 17 Data preparation; Chapter 18 Frequency distribution, crosstabulation and hypothesis testing; Chapter 19 Analysis of variance and covariance; Chapter 20 Correlation and regression; Chapter 21 Discriminant analysis; Chapter 22 Factor analysis; Chapter... Đề tài Hoàn thiện công tác quản trị nhân sự tại Công ty TNHH Mộc Khải Tuyên được nghiên cứu nhằm giúp công ty TNHH Mộc Khải Tuyên làm rõ được thực trạng công tác quản trị nhân sự trong công ty như thế nào từ đó đề ra các giải pháp giúp công ty hoàn thiện công tác quản trị nhân sự tốt hơn trong thời gian tới.

MKRS_C15.QXD 14/6/05 4:49 pm Page 381 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 CHAPTER Stage Problem definition Stage Research approach developed 15 Sampling: final and initial sample size determination Objectives After reading this chapter, you should be able to: define key concepts and symbols pertinent to sampling; Stage Research design developed understand the concepts of the sampling distribution, statistical inference and standard error; discuss the statistical approach to determining sample size Stage Fieldwork or data collection based on simple random sampling and the construction of confidence intervals; derive the formulas to determine statistically the sample size for estimating means and proportions; Stage Data preparation and analysis discuss the non-response issues in sampling and the procedures for improving response rates and adjusting for non-response; understand the difficulty of statistically determining the sample size in international marketing research; Stage Report preparation and presentation identify the ethical issues related to sample size determination, particularly the estimation of population variance Making a sample too big wastes resources, making it too small diminishes the value of findings – a dilemma resolved only with the judicious use of sampling theory MKRS_C15.QXD 14/6/05 4:49 pm Page 382 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Chapter 15 • Sampling: final and initial sample size determination Overview This chapter focuses on the determination of sample size in simple random sampling We define various concepts and symbols and discuss the properties of the sampling distribution Additionally, we describe statistical approaches to sample size determination based on confidence intervals We present the formulas for calculating the sample size with these approaches and illustrate their use We briefly discuss the extension to determining sample size in other probability sampling designs The sample size determined statistically is the final or net sample size; that is, it represents the completed number of interviews or observations To obtain this final sample size, however, a much larger number of potential respondents have to be contacted initially We describe the adjustments that need to be made to the statistically determined sample size to account for incidence and completion rates and calculate the initial sample size We also cover the non-response issues in sampling, with a focus on improving response rates and adjusting for non-response We discuss the difficulty of statistically determining the sample size in international marketing research and identify the relevant ethical issues Statistical determination of sample size requires knowledge of the normal distribution and the use of normal probability tables The normal distribution is bell-shaped and symmetrical Its mean, median and mode are identical (see Chapter 18) Information on the normal distribution and the use of normal probability tables is presented in Appendix 15A The following example illustrates the statistical aspects of sampling example Has there been a shift in opinion? The sample size used in opinion polls commissioned and published by most national newspapers is influenced by statistical considerations The allowance for sampling error may be limited to around three percentage points The table that follows can be used to determine the allowances that should be made for sampling error These intervals indicate the range (plus or minus the figure shown) within which the results of repeated samplings in the same time period could be expected to vary, 95% of the time, assuming that the sample procedure, survey execution and questionnaire used were the same Recommended allowance for sampling error of a percentage In percentage points (at 95% confidence level for a sample size of 385) Percentage near 10 Percentage near 20 Percentage near 30 Percentage near 40 Percentage near 50 Percentage near 60 Percentage near 70 Percentage near 80 Percentage near 90 4 5 4 The table should be used as follows If a reported percentage is 43 (e.g 43% of Norwegian Chief Executives believe their company will suffer from staff shortages in the next 12 months), look at the row labelled ‘percentages near 40’ The number in this row is 5, so the 43% obtained in the sample is subject to a sampling error of ±5 percentage points Another way of saying this is that very probably (95 times out of 100) the average of repeated samplings would be somewhere between 38% and 48% The reader can be 95% confident 382 MKRS_C15.QXD 14/6/05 4:49 pm Page 383 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Definitions and symbols that in the total population of Norwegian Chief Executives between 38% and 48% believe their company will suffer from staff shortages in the next 12 months, with the most likely figure being 43% The fortunes of political parties measured through opinion polls are regularly reported in newspapers throughout Europe The next time that you read a report of a political opinion poll, examine the sample size used, the confidence level assumed and the stated margin of error When comparing the results of a poll with a previous poll, consider whether a particular political party or politician has really grown or slumped in popularity, or the reported changes can be accounted for within the set margin of error as summarised in this example ■ Definitions and symbols Confidence intervals and other statistical concepts that play a central role in sample size determination are defined in the following list ■ ■ ■ ■ ■ ■ Parameter A parameter is a summary description of a fixed characteristic or measure of the target population A parameter denotes the true value that would be obtained if a census rather than a sample was undertaken Statistic A statistic is a summary description of a characteristic or measure of the sample The sample statistic is used as an estimate of the population parameter Finite population correction The finite population correction (fpc) is a correction for overestimation of the variance of a population parameter – for example, a mean or proportion – when the sample size is 10% or more of the population size Precision level When estimating a population parameter by using a sample statistic, the precision level is the desired size of the estimating interval This is the maximum permissible difference between the sample statistic and the population parameter Confidence interval The confidence interval is the range into which the true population parameter will fall, assuming a given level of confidence Confidence level The confidence level is the probability that a confidence interval will include the population parameter The symbols used in statistical notation for describing population and sample characteristics are summarised in Table 15.1 Table 15.1 Symbols for population and sample variables Variable Population Mean µ Sample – X Proportion π p Variance σ2 s2 Standard deviation σ s Size N n Standard error of the mean σ –x S –x Standard error of the proportion σp Sp X–µ σ – X–X S σ µ S X Standardised variate (z) Coefficient of variation (C) 383 MKRS_C15.QXD 14/6/05 4:49 pm Page 384 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Chapter 15 • Sampling: final and initial sample size determination The sampling distribution Sampling distribution The distribution of the values of a sample statistic computed for each possible sample that could be drawn from the target population under a specified sampling plan Statistical inference The process of generalising the sample results to a target population The sampling distribution is the distribution of the values of a sample statistic computed for each possible sample that could be drawn from the target population under a specified sampling plan.1 Suppose that a simple random sample of five hospitals is to be drawn from a population of 20 hospitals There are (20 × 19 × 18 × 17 × 16)/ (1 × × × × ), or 15,504 different samples of size that can be drawn The relative frequency distribution of the values of the mean of these 15,504 different samples would specify the sampling distribution of the mean An important task in marketing research is to calculate statistics, such as the sample mean and sample proportion, and use them to estimate the corresponding true population values This process of generalising the sample results to a target population is referred to as statistical inference In practice, a single sample of predetermined size is selected, and the sample statistics (such as mean and proportion) are computed Theoretically, to estimate the population parameter from the sample statistic, every possible sample that could have been drawn should be examined If all possible samples were actually to be drawn, the distribution of the statistic would be the sampling distribution Although in practice only one sample is actually drawn, the concept of a sampling distribution is still relevant It enables us to use probability theory to make inferences about the population values The important properties of the sampling distribution of the mean, and the corresponding properties for the proportion, for large samples (30 or more) are as follows: The sampling distribution of the mean is a normal distribution (see Appendix 15A) Strictly speaking, the sampling distribution of a proportion is a binomial For large samples (n = 30 or more), however, it can be approximated by the normal distribution n – The mean of the sampling distribution of the mean X = Σ Xi /n or of the ( ( )) i=l Standard error The standard deviation of the sampling distribution of the mean or proportion proportion (p) equals the corresponding population parameter value, µ or π, respectively The standard deviation is called the standard error of the mean or the proportion to indicate that it refers to a sampling distribution of the mean or the proportion and not to a sample or a population The formulas are: Mean Proportion σ σx- = ––– n σp = π(l – π) –––––––– n Often the population standard deviation, σ, is not known In these cases, it can be estimated from the sample by using the following formula: n Σ (Xi – X–)2 i=l –––––––––––– n–1 s= or n n s= 384 Σ Xi2 (Σ X ) – –––––– i=l i n –––––––––––– n–l i=l MKRS_C15.QXD 14/6/05 4:49 pm Page 385 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Statistical approaches to determining sample size In cases where σ is estimated by s, the standard error of the mean becomes – s est σX = ––– n where ‘est.’ denotes that s has been used as an estimate of σ Assuming no measurement error, the reliability of an estimate of a population parameter can be assessed in terms of its standard error Likewise, the standard error of the proportion can be estimated by using the sample proportion p as an estimator of the population proportion, π, as p (l – p) ––––––– n The area under the sampling distribution between any two points can be calculated in terms of z values The z value for a point is the number of standard errors a point is away from the mean The z values may be computed as follows: – Xµ z = –––– σX– est Sp = z value The number of standard errors a point is away from the mean For example, the areas under one side of the curve between the mean and points that have z values of 1.0, 2.0 and 3.0 are, respectively, 0.3413, 0.4772 and 0.4986 (See Table in the Appendix of Statistical Tables.) In the case of proportion, the computation of z values is similar When the sample size is 10% or more of the population size, the standard error formulas will overestimate the standard deviation of the population mean or proportion Hence, these should be adjusted by a finite population correction factor defined by N–n ––––– N–l In this case, σ σX– = ––– n N–n ––––– N–l Statistical approaches to determining sample size Several qualitative factors should also be taken into consideration when determining the sample size (see Chapter 14) These include the importance of the decision, the nature of the research, the number of variables, the nature of the analysis, sample sizes used in similar studies, incidence rates (the occurrence of behaviour or characteristics in a population), completion rates and resource constraints The statistically determined sample size is the net or final sample size: the sample remaining after eliminating potential respondents who not qualify or who not complete the interview Depending on incidence and completion rates, the size of the initial sample may have to be much larger In commercial marketing research, limits on time, money and expert resources can exert an overriding influence on sample size determination In the GlobalCash Project, the sample size was determined based on these considerations The statistical approach to determining sample size that we consider is based on traditional statistical inference.2 In this approach the precision level is specified in advance The confidence interval approach to sample size determination is based on the construction of confidence intervals around the sample means or proportions 385 MKRS_C15.QXD 14/6/05 4:49 pm Page 386 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Chapter 15 • Sampling: final and initial sample size determination Ensuring a 99% confidence level in the probability of a tossed pancake returning to the pan using the standard error formula As an example, suppose that a researcher has taken a simple random sample of 300 households to estimate the monthly amount invested in savings schemes and found that the mean household monthly investment for the sample is €182 Past studies indicate that the population standard deviation σ can be assumed to be €55 We want to find an interval within which a fixed proportion of the sample means would fall Suppose that we want to determine an interval around the population mean that will include 95% of the sample means, based on samples of 300 households The 95% could be divided into two equal parts, half below and half above the mean, as shown in Figure 15.1 – Calculation of the confidence interval involves determining a distance below (X L ) – – and above (X U)the population mean (X ), which contains a specified area of the normal curve – – The z values corresponding to X L and X U may be calculated as – XL – µ zL = –––––– σX– – XU – µ zU = –––––– σX– 0.475 Figure 15.1 The 95% confidence interval 386 XL 0.475 X XU MKRS_C15.QXD 14/6/05 4:49 pm Page 387 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Statistical approaches to determining sample size – where zL = – z and zU = + z Therefore, the lower value of X is – X L = µ – zσX– – and the upper value of X is – X U = µ + zσX– – Note that µ is estimated by X The confidence interval is given by – X ± zσX– We can now set a 95% confidence interval around the sample mean of €182 As a first step, we compute the standard error of the mean: σ = ––––– 55 = 3.18 σX– = ––– n 300 From Table in the Appendix of Statistical Tables, it can be seen that the central 95% of the normal distribution lies within ±1.96 z values The 95% confidence interval is given by – X ± 1.96σX– = 182.00 ± 1.96 (3.18) = 182.00 ± 6.23 Thus, the 95% confidence interval ranges from €175.77 to €188.23 The probability of finding the true population mean to be within €175.77 and €188.23 is 95% Sample size determination: means The approach used here to construct a confidence interval can be adapted to determine the sample size that will result in a desired confidence interval.3 Suppose that the researcher wants to estimate the monthly household savings investment more precisely so that the estimate will be within ± €5.00 of the true population value What should be the size of the sample? The following steps, summarised in Table 15.2, will lead to an answer Specify the level of precision This is the maximum permissible difference (D) between the sample mean and the population mean In our example, D = ± €5.00 Specify the level of confidence Suppose that a 95% confidence level is desired Determine the z value associated with the confidence level using Table in the Appendix of Statistical Tables For a 95% confidence level, the probability that the population mean will fall outside one end of the interval is 0.025 (0.05/2) The associated z value is 1.96 Determine the standard deviation of the population This may be known from secondary sources lf not, it might be estimated by conducting a pilot study Alternatively, it might be estimated on the basis of the researcher’s judgement For example, the range of a normally distributed variable is approximately equal to ± standard deviations, and one can thus estimate the standard deviation by dividing the range by The researcher can often estimate the range based on knowledge of the phenomenon Determine the sample size using the formula for the standard error of the mean – X–µ z = ––––– σX– D = ––– σX– 387 MKRS_C15.QXD 14/6/05 4:49 pm Page 388 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Chapter 15 • Sampling: final and initial sample size determination or D σX– = –– z or σ = D – ––– n z or σ2 × z n = –––––– D2 In our example, 552(1.96)2 n = ––––––––– 52 = 464.83 = 465 (rounded to the next highest integer) It can be seen from the formula for sample size that sample size increases with an increase in the population variability, the degree of confidence, and the precision level required of the estimate If the resulting sample size represents 10% or more of the population, the finite population correction (fpc) should be applied The required sample size should then be calculated from the formula nN nc = –––––––– N+n–1 where n = sample size without fpc nc = sample size with fpc If the population standard deviation, σ, is unknown and an estimate is used, it should be re-estimated once the sample has been drawn The sample standard deviation, s, is used as an estimate of σ A revised confidence interval should then be calculated to determine the precision level actually obtained Suppose that the value of 55.00 used for σ was an estimate because the true value was unknown A sample of n = 465 is drawn, and these observations generate a – mean X of 180.00 and a sample standard deviation s of 50.00 The revised confidence interval is then 50.0 – X ± zsX– = 180.00 ± 1.96 × ––––– 465 = 180.00 ± 4.55 or 175.45 ≤ µ ≤ 184.55 Note that the confidence interval obtained is narrower than planned, because the population standard deviation was overestimated, as judged by the sample standard deviation In some cases, precision is specified in relative rather than absolute terms In other words, it may be specified that the estimate be within plus or minus R percentage points of the mean Symbolically, D = Rµ 388 MKRS_C15.QXD 14/6/05 4:49 pm Page 389 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Statistical approaches to determining sample size In these cases, the sample size may be determined by σ2 × z2 n = ––––––– D2 C × z2 = ––––––– R2 where the coefficient of variation C = σ/µ would have to be estimated The population size, N, does not directly affect the size of the sample, except when the finite population correction factor has to be applied Although this may be counter-intuitive, upon reflection it makes sense For example, if all the population elements are identical on the characteristics of interest, then a sample size of one will be sufficient to estimate the mean perfectly This is true whether there are 50, 500, 5,000 or 50,000 elements in the population What directly affects the sample size is the variability of the characteristic in the population This variability enters into the sample size calculation by way of population variance σ or sample variance s Sample size determination: proportions If the statistic of interest is a proportion rather than a mean, the approach to sample size determination is similar Suppose that the researcher is interested in estimating the proportion of households possessing a debit card The following steps should be followed.4 Specify the level of precision Suppose that the desired precision is such that the allowable interval is set as D = p – π = ±0.05 Specify the level of confidence Suppose that a 95% confidence level is desired Determine the z value associated with the confidence level As explained in the case of estimating the mean, this will be z = 1.96 Estimate the population proportion π As explained earlier, the population proportion may be estimated from secondary sources, or from a pilot study, or may be based on the judgement of the researcher Suppose that based on secondary data the researcher estimates that 64% of the households in the target population possess a debit card Hence, π = 0.64 Determine the sample size using the formula for the standard error of the proportion p–π σp= –––––– z D = –– z = π(l – π) –––––––– n or π(l – π)z n = ––––––––– D2 In our example, 0.64(1 – 0.64)(1.96)2 n = ––––––––––––––––––– (0.05)2 = 354.04 = 355 (rounded to the next highest integer) 389 MKRS_C15.QXD 14/6/05 4:49 pm Page 390 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Chapter 15 • Sampling: final and initial sample size determination If the resulting sample size represents 10% or more of the population, the finite population correction (fpc) should be applied The required sample size should then be calculated from the formula nN nc = –––––––– N+n–1 where n = sample size without fpc nc = sample size with fpc If the estimate of π turns out to be poor, the confidence interval will be more or less precise than desired Suppose that after the sample has been taken, the proportion p is calculated to have a value of 0.55 The confidence interval is then re-estimated by employing sp to estimate the unknown σp as p ± zsp where p(l – p) Sp = ––––––– n In our example – 0.55) Sp = 0.55(1 –––––––––––– 355 = 0.0264 The confidence interval, then, is 0.55 ± 1.96(0.0264) = 0.55 ± 0.052 which is wider than that specified This is because the sample standard deviation based on p = 0.55 was larger than the estimate of the population standard deviation based on π = 0.64 If a wider interval than specified is unacceptable, the sample size can be determined to reflect the maximum possible variation in the population This occurs when the product is the greatest, which happens when π is set at 0.5 This result can also be seen intuitively Since one half of the population has one value of the characteristic and the other half the other value, more evidence would be required to obtain a valid inference than if the situation was more clear cut and the majority had one particular value In our example, this leads to a sample size of 0.5(0.5)(1.96)2 n = ––––––––––––– (0.05)2 = 384.16 = 385 (rounded to the next higher integer) Sometimes, precision is specified in relative rather than absolute terms In other words, it may be specified that the estimate be within plus or minus R percentage points of the population proportion Symbolically, D = Rπ In such a case, the sample size may be determined by z 2(l – π) n = ––––––– R2π 390 MKRS_ZO3.QXD 17/6/05 2:52 pm Page 746 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Subject Index open coding 146, 210–11 opening questions 342 operational data 109–10, 112, 127, 732 operational equivalence 678, 732 operationalised variables 136, 732 opinion polls 238, 382–3, 398–9 opinion research (in Hungary) 13 optical scanning 244, 247, 429–30, 441 optimising partioning method 603, 608, 732 oral presentation (reports) 655–6 order bias 336, 732 ordinal interaction 501–2, 732 ordinal scales 293–4, 295–6, 732 ordinary least squares (OLS) 633 orthogonal arrays 628, 631 orthogonal rotation 582, 732 out-of-range data values 430 outdoor advertising 73 paired comparison scaling 298–300, 318, 732 paired samples 469, 474–5, 478–80, 732 paired samples t tests 474–5, 732 paired t tests 437, 447, 469 pairwise approach (conjoint analysis) 630–1 pairwise deletion 431–2, 732 pairwise tables 628 Pakistan/Pakistanis 664 pan-cultural analysis 439, 732 pan-European banking 22 pan-European research 37 pan-European view (of executives) 227 panels 68–9, 78, 98–9, 732 pantry audits 245, 732 paradigm 44–5, 136, 732 parallel threshold method 603, 732 parallel translation 679, 732 parameters 383, 391 parameters estimation 522–3 parametric tests 468–75, 479–80, 732 part-worth functions 628, 633, 635–6 part correlation coefficients 517–18, 539, 732 partial correlation 516–18, 539 partial correlation coefficient 732 partial F tests 529, 533 partial regression coefficient 529–31 participant observation 142 participation (action research) 149 Pearson correlation coefficient 512–15 perceived respondent anonymity 238, 732 percentage of variance 574, 580 perception data 619–20 perceptual maps 617 perfume 48–9 personal alarms 572 746 Personal Digital Assistants (PDAs) 252–3, 351 personal hygiene 173, 179, 239, 333, 350 personal interviews 224–5, 228–30, 233–41, 249–50, 330, 348, 395, 675, 705, 707 personal observation 244, 246–8, 732 phi coefficient 465–6, 732 physical form of rating scale 309–10 physical geography 114 physical stimuli (surveys) 235 picture response technique 190, 732 ‘pictures’ 703, 704 pie charts 652, 732 piggybacking 350 pilot-testing 345–6, 348, 349, 732 point-of-purchase (POP) 144, 259, 262 political differences 114–15 political polls 383, 398–9 pooled within-groups correlation matrix 550, 553, 555, 560–1 population definition errors 75–6 populations 357, 732 position bias 336, 732 positive statements 341 positivism 136, 138–42, 167, 185, 732 post-coding 428, 441 post-test-only control group design 268, 271–2, 732 postal interviews see mail interviews power, balance of 151 power of a statistical test 455, 733 pre-coding 335, 344, 428, 733 pre-experimental designs 268–70, 274, 733 pre-test–post-test control group design 268, 271, 733 precision levels 383 predicted value 520, 522, 727 prediction accuracy 527–8 prediction matrix 549 predictive validity 315, 733 predictors (relative importance) 539 preference data 620–1, 625–6 rankings 618 transitivity of 299–300, 736 preparation of reports see reports pricing research/analysis 9, 617 primary data 41, 78–9, 84–5, 87, 104, 132–3, 225, 245, 704, 733 primary research 16, 84 primary scales 293–7 principal axis factoring 578, 733 principal components analysis 539, 577–8, 584–6, 588, 626, 733 prior notification 394 probability 401–2, 403, 456–7 probability proportionate to size 371–2, 733 probability sampling 79, 360, 362–3, 367–75, 377, 392, 675, 700, 733 probing 162, 180, 183, 240, 409, 733 problem-solving research 7–9, 148, 733 problem audits 39–40, 733 problem definition 14, 30–2, 35, 37–44, 733 problem fields 45 problem formulation cluster analysis 599–600 conjoint analysis 629–30 discriminant analysis 550–1, 558–9 factor analysis 575–6 multidimensional scaling 618–19 problem identification research 7–8, 733 product development 686 product moment correlation 512–15, 733 product placement 275–6 product research 9, 20–1 product testing 73 production blocking 169 products attribute claims 498 scanning system 112–13 used/not used (monitoring) 113 professional appearance (reports) 650 professional buyer (business-tobusiness) 691, 693–6 profile analysis 304, 306 profiling clusters 606–7 projective techniques 136, 138, 159, 161–2, 187–97, 733 promotion components 584–6 promotions research 8, properties values 402–3 property types 115 proportionate stratified sampling 370 proportions 389–91, 473–4 proposals, research 35–7, 734 protocol analysis 195, 302–3, 346 psycho-galvanometer 244–5, 247–8, 733 psychographics 98, 118, 733 psychological data 118, 123 public records 704, 705 public sector 702 published external secondary data 91–4 Punjabi 664 pupilometers 244, 245, 733 purchases, business-to-business and consumer (differences) 69–704 purchasing decision (number involved in) 691–3 Q-sort scaling 298, 302–3, 318, 733 qualitative data analysis 146, 206–20 qualitative interviews 79 MKRS_ZO3.QXD 17/6/05 2:52 pm Page 747 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Subject Index qualitative research 15–17, 43, 733 action research 148–50 American style 136–8, 170–1, 673 approaches 130–55 brainstorming 169–70 classification of techniques 158–60 comparisons 132–4, 192–3 data analysis 201–22 ethics 151, 159, 195–6 ethnic minorities 664 ethnographic research 142–5 European style 137–8, 171, 673 grounded theory 145–7 industrial group discussions 170 international marketing research 170–3, 193–5, 673–4, 664, 665 Internet/computer applications 152, 174–5, 197 nature of 133–4 philosophy and 136–42 quantitative and (differences) 132–4 rationale for using 134–6 see also depth interviews; focus groups; projective techniques quality control (fieldworkers) 413 quality of interviewing/data 415 quantitative observation 224, 242, 733 advantages/disadvantages 248 classified by administration 244–6 comparative evaluation 246–8 ethics 250–2 international marketing research 249 Internet/computer applications 252–4 quantitative research 15, 16–17, 700, 733 qualitative and (differences) 132–4 QuarkXpress 350 quasi-experimental designs 268, 269, 272–4, 733 questioning errors 76 questionnaire design 15, 225, 324–5 checklist 346–8 ethics 349–50 form and layout 344, 347 individual question content 331, 345 information needed 329–30, 346 international marketing research 348 Internet/computer applications 350 interviewing methods 330, 346 order of questions 342–4 process 326–9 question structure 335–7, 347 question wording 338–41, 347 refusal rate (lowering) 394 respondent inability/unwillingness (overcoming) 332–4, 347 questionnaires 136, 733 administration 394 checking 422–3 definition 326 objectives 326 pilot-testing 345–6, 348 reproduction of 344–5, 348 self-completion 373, 707 translation 679–80 questions diversity of (surveys) 235 fixed-response alternatives 225 individual (content) 331m 346 order of 342–4, 347 structure 335–7, 347 training survey fieldworkers 409–11 wording 338–41, 347 quota sampling 364–6, 374, 376–7, 733 R-square 618, 624 random-effects model 494 random error 313, 624, 733 random sampling 700 random sampling error 74, 77, 79, 358, 733 randomisation 267, 733 randomised block design 269, 275–6, 733 range 450–1, 733 rank order scaling 298, 300–1, 733 rapport building (surveys) 240–1 ratio scales 293–5, 297, 733 readers (of reports) 649 real-time groups 174, 175, 197 reality 139 rebates 588–9 recommendations (reports) 648–9 recording answers 411 recording errors 76 recruitment industry 703 refusals 74, 394–5 regional shopping centres 537–8 regression analysis 439, 486, 487, 510–11, 637, 733 ANOVA/ANCOVA 487, 541 bivariate regression 440, 519–28 cross-validation 540 discriminant analysis and 548–9 dummy variables 540–1 Internet/computer applications 542 multicollinearity 538–9 multiple 438, 511, 528–37 predictors 539 stepwise regression 537–8 regression coefficients 520 regression line 521 relationship development 691, 702–4 relationships (business-to-business) 689–91, 695, 696, 702, 703, 705 relative importance weights 628, 636–7 relevance (information) 59 reliability 87, 140, 734 cluster analysis 607–8 conjoint analysis 637 multi-item scales 311–16 multidimensional scaling 624 repeated measures ANOVA 503–4, 734 repertory grid technique 185–7 replacement 396 reports 36, 216, 643 ethics 657–8 follow-up 656–7 format 646–9 importance of 645 international marketing research 657 Internet/computer applications 658–9 oral presentation 655–6 preparation 18–19, 644–54 presentation 18–19, 644–6, 655–6 writing 649–54 research approach 16, 37–41, 44–9, 136 brief 32–5, 734 business-to-business 694–5, 701 follow-up 656–7 market share 701 methodology 16–17, 21 organisation 36 proposals 35–7, 734 questions 35, 47, 48–9, 734 research design 35, 37–8, 140–1, 734 acid test 73 classification 62–73 definition 58 development 16 errors (potential sources) 74–7 ethics 78–9 international 77–8 Internet/computer applications 79 outdoor advertising reach 73 perspectives on 59–62 reports 648 sample survey 702–3 secondary data collection 87, 88 researcher 134, 139, 147, 326, 327 qualitative 202–6 supportive role 22 residuals 533–7, 574, 734 respondent-moderator focus group 169 respondent error 697, 698 respondent selection errors 76 respondents 152, 326, 327 anonymity see anonymity articulate 333 effort required 333 inability/unwillingness 332–4, 347, 697 informed/uninformed 332 interview experience 412 memory 332–3 names/telephone numbers 414 rapport building 240–1 rights 78 unsatisfactory (discarding) 423–4 747 MKRS_ZO3.QXD 17/6/05 2:52 pm Page 748 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Subject Index response errors 75–6, 326, 697–8, 700, 734 response latency 244, 245, 734 response rates 237–8, 393–5, 414, 700, 708, 734 results (in reports) 648 retail audits 101–2, 144, 245 retail sector 143, 144 revenue loss (telecom operators) 706–7 role playing 161, 191, 734 rotation of factors 579, 581–2, 587–8 Royal Statistical Society 238 runs tests 437, 468, 476, 734 Russia 283, 317, 663, 672 sales analysis salespeople 511, 556 sample size 76–7, 432, 734 adjusting statistically determined 392–3 cluster sampling 392 definitions/symbols 383 determining 360–1, 381–3 ethics 398–9 international marketing research 398 Internet/computer applications 399 multiple characteristics 391 non-response issues 393–7 normal distribution 401–3 sampling distribution 384–5 simple random sampling 392 statistical approaches 385–91 stratified sampling 392 systematic sampling 392 sample survey research design 702–3 samples 357–8, 734 control 235–6, 734 sampling 355–6, 692 adequacy (KMO measure) 574 classification of techniques 362–3 control 413, 734 design process 358–62 distribution 384–5, 734 errors 76–7, 357, 377, 697 ethics 377 frame 233, 236, 238, 359, 700, 701, 702, 707, 734 frame errors 76, 359, 361–2 international marketing research 376 Internet/computer applications 377–8 methods 700 non-probability 362, 363–7, 373–4 probability 362–3, 367–74, 375 sample or census 357–8 technique selection 360 unit 358–9, 413, 734 with/without replacement 360, 734 scalar equivalence 678, 734 scale descriptors 318 748 scale transformation 434–5, 734 scaling 291–3, 734 comparative techniques 298–303 comparison of techniques 297–8 development/evaluation of scales 311–16 ethics 318 international marketing research 317, 677–8 Internet/computer applications 318 itemised rating scales 304–10 mathematically derived scales 316–17 meaning of 293 non-comparative techniques 303–4 preference data 625–6 verbal protocols 302–3, 737 scanner data 100–1, 734 scanner diary panels 97, 100, 734 scanner services 97, 99–101, 102 scanning devices 111–14, 734 scattergrams 520–2, 534, 563 Scheffé’s test 503 schematic figures 653–4 schools 372 Scotland 406, 433 scree plot 574, 580, 581 secondary data 15–17, 41, 52, 77–9, 83–4, 703, 704, 705, 734 advantages/uses 86–7 classification 90–1 computerised databases 94–6 definition 85–6 disadvantages 87 ethics 104 evaluation criteria 87–90 external 72, 91–4 internal 72, 90–1, 104, 105, 109–11 international marketing research 103, 105, 671–3 Internet/computer applications 104–5 syndicated sources 96–102 segmentation 9, 117–18, 597, 610–11 see also market segmentation selecting survey fieldworkers 407–8 selection bias 266–7, 270–2, 734 selective coding 146, 212 self-completion questionnaires 373, 707 semantic differential scales 186, 298, 303, 305–6, 317, 734 semi-structured interviews 72–3 semiconductor industry 705–6 sensitive information 135, 239–40, 334, 350 sensitive subjects 152 sentence completion 189–90, 734 sequential sampling 374, 734 services 11–14 shape (measures) 452 share purchase 260–1 shoppers 144, 473 shopping centres 537–8 SIC codes 692 sign tests 438, 469, 479, 734 significance of association 525–7 discriminant function 555 of interaction effect 496–8, 734 level of 455, 729 of main effects 496, 498, 735 of overall effect 495–6, 497, 735 testing 490–1, 524, 532–3, 581 silent probe 410 similarity/distance coefficient matrix 598 similarity judgements 618 simple correlation coefficients 512–15, 525, 532, 539 simple random numbers 712–13 simple random sampling (SRS) 367–8, 369, 374–5, 378, 392, 700, 735 simulated test markets 282–3, 735 Singapore 349, 637 single cross-sectional research 66, 67, 73, 702–3, 735 single linkage 601, 735 site quality indicators 116–17 skewness 452, 735 SNAP 218, 252, 254, 351, 421, 424–5, 441, 442, 481 snowball sampling 366–7, 374, 376, 700, 735 soap 179, 188 social change 148 social desirability surveys 238–9, 735 social frames of reference 219 social problems 350 Social Research Association 251 social values 202–3, 206, 220 Society of Competitive Professionals (SCIP) 704 socio-cultural environment 50, 283 soft drinks 51, 67, 284, 616 Solomon four-group design 268, 272, 735 South Korea 498–9, 669 Soviet Union 317 spatial maps 617–18, 620–6, 627 Spearman’s rho 518 special-purpose databases 95, 735 specific components of problems 43, 735 spectator (buying role) 692 speed (surveys) 232, 241 sphericity, Bartlett’s test of 574 SphinxSurvey 441 split-half reliability 314, 580, 735 sponsorship 331, 356 sport 77–8, 166, 285–6, 292, 356, 372 MKRS_ZO3.QXD 17/6/05 2:52 pm Page 749 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Subject Index spreadsheets 212–13, 426–7 square of part correlation coefficient 539 square of partial correlation coefficient 539 square of simple correlation coefficient 539 SSbetween 488, 489, 490 SSwithin 488, 489, 490 SSy 488, 489, 490 SSI-SNAP 378 stacked bar charts 653, 654 standard deviation 384, 387–8, 398, 451, 524, 735 standard error 384–7, 389, 520, 524, 735 standard test markets 281–2, 283, 735 standardisation 224, 435, 438–9, 523, 735 standardised discriminant function coefficients 550, 555–6, 565 standardised regression coefficients 520, 523–4, 530–1, 539 standardised services 11, 13, 735 Stapel scale 298, 303, 306–7, 735 static group designs 268, 270, 735 statistical approach (sample) 385–93 statistical control 268, 735 statistical designs 268–9, 274–8, 735 statistical inference 384, 385, 735 statistical regression 266, 735 statistical significance 539 statistical tables 712–22 statistical techniques 437–8, 454 statistically adjusting data 432–5 statistics 383 cluster analysis 598 conjoint analysis 628 cross-tabulation 463–8 discriminant analysis 549–50 factor analysis 574–5 frequency distribution 449–52 government/non-government data 92–4 multidimensional scaling 622 one-way ANOVA 487–8 stepwise discriminant analysis 552, 565–7, 586, 735 stepwise regression 537–8, 539, 735 stepwise solution 537 stimuli construction 627, 630–2 story completion 190, 197, 735 strategic clusters 608 strategic planning 704 stratified sampling 369–71, 374–5, 392, 735 street interviews 151, 229, 233–41, 249, 676 strenth of association 525–7, 531–2 stress 618, 622, 624 structural environment 669 structure correlations 550, 554, 561 structured data collection 224, 735 structured observation 242, 735 structured questions 335–7, 423, 735 Student-Newman-Keuls test 503 Student’s t statistic 469 sub-sampling (non-respondents) 396 subconscious feelings 135 subjective estimates 396 subjectivity 147 submission letters (reports) 647 substitute variables 584 substitution 396, 735 sugging 24, 250, 325, 414, 735 sum of squared errors 520 summated scale 304, 305, 314, 341 SUMMO 2000 study 73 supermarkets 17, 72–3, 124, 268, 396–7 supervising survey fieldworkers 413–14 suppliers importance of individual consumers 691, 698–702 internal/external 11–14 supply chain management 693 suppressed association 461–2 surrogate information errors 75 surrogate variables 584, 735 survey fieldwork 405 data collection process 407 ethics 416 evaluation 414–15 international marketing research 415 Internet/computer applications 416 nature of 406–7 selecting fielworkers 407–8 supervising fieldworkers 413–14 training fieldworkers 409–13 validation of 414 Survey Guardian 197 survey techniques 224–5, 736 comparative evaluation 233–41 ethics 250–2 international marketing research 249–50, 675–7 Internet/computer applications 252–4 mail interviews 225, 230–2, 249–50 personal interviews 225, 228–30, 249–50 selection of 241–2, 249–50 telephone interviews 225–8, 249–50 surveys 97, 98, 706, 736 data 119–20, 124 Swedish Tourism Trade Association 92–3 Sylheti/Sylhet region 664 symbols 263–4, 383 symmetric lambda 467, 736 syndicated services 13, 96–102, 736 syndicated sources (secondary data) 96–102, 105, 736 systematic error 313, 316, 736 systematic sampling 368–9, 374–5, 392, 700, 736 t distribution 469, 717–19, 736 t statistic 469, 520, 736 t tests 437, 469–74, 486–7, 736 tab houses 14 tables (in reports) 650–2 tacit knowledge 690–1 Taiwan 349, 637 Target Group Index 88, 672 target markets 117–18, 134, 166, 204, 206, 699 target populations 140, 358–9, 700, 736 taste 51 tau b statistic 467, 736 tau c statistic 467, 736 technical competence (of interviewer) 695 technological environment 669 teenagers 134–5 telecom operators (revenue loss) 706–7 telephone interviews 32, 225–8, 233–41, 249–50, 330, 395, 675, 706–7 telescoping 332, 697, 698, 736 television 244, 498–9, 558, 559 advertising 167, 224, 270, 273, 283, 285–6, 475 Tennis magazine survey 369 terminating interviews 411 territorial maps 563, 564, 736 test-retest reliability 313, 316, 624, 637, 736 test marketing 268, 281–3, 598, 736 test markets 281–2, 736 test for significance 490–1, 524, 532–3, 581 test statistics 454, 455–6, 736 test units 262, 263, 266, 736 testing effects 265–6, 736 Thailand 349, 616, 637–8 ‘Thank You’ booklet 251, 411 thematic apperception tests 190 thematic maps 114, 736 theoretical sampling 140, 146, 166, 215, 217, 736 theory 140–1, 216, 736 role 45–6, 136 third-person technique 191, 736 three-group discriminant analysis 559–65 3GSM World Congress 685, 686 three variable cross-tabulation 460–3 threshold effect 46 Thurstone case V procedure 301 time 34, 36, 89, 152, 280, 414 749 MKRS_ZO3.QXD 17/6/05 2:52 pm Page 750 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Subject Index time factor (purchase decision) 691, 696–8 time order of occurrence variables 261, 280 time series design 268, 272–3, 736 title (of graphs) 651 title page (reports) 647 toiletries 131 toothpaste 575–6 topic guides 164–5, 180, 183, 736 total correlation matrix 550 total error 74, 76, 77, 79, 358, 736 trace analysis 246–8, 736 tracking surveys 362 trade fairs 666 training survey fieldworkers 409–13 transcribing/transcripts 152, 168, 208, 216, 429–30, 736 transitivity of preference 299–300, 736 translation 679–80 travel 462 treatment 486, 737 treatment effect 262, 264, 266–7, 270 tree graphs 598 trend analysis 396–7, 737 true experimental designs 268–72, 274, 737 true feelings 693, 694 true score model 312, 737 Tukey’s alternate procedure test 503 two-group discriminant analysis 438, 547–8, 551–8, 737 two-sample median test 477, 737 two-stage cluster sampling 371m 372 two-tailed tests 454, 737 two-way ANOVA 497 two-way focus groups 169 two independent samples t tests 471–4, 476–8, 479 type I errors 455, 737 type II errors 455, 737 750 U statistic 550 unaided recall 697 unbalanced scales 308, 310 undisguised observation 242–3 unfolding 618, 625 unforced rating scales 308, 310 unit definition 701 United Nations 93 United States 170–1, 415, 608, 675 qualitative research 136, 137, 138 univariate techniques 17, 437, 737 Universal Product Code (UPC) 100 unsatisfactory responses 423–4 unstructured observation 242, 737 unstructured questions 335, 348–9, 423, 428–9, 707, 737 unweighted least squares 578 unwillingness errors 76, 697–8 unwillingness to answer 332–4, 347 Urdu 664 users (buying role) 692 vacuum cleaners 20–1 validation samples 361–2, 540, 551–2, 557, 737 survey fieldwork 414 validity 140–2, 147, 161, 215, 737 cluster analysis 607–8 conjoint analysis 637 discriminant analysis 557–8, 563–5 in experimentation 264 multi-item scales 311–12, 314–16 multidimensional scaling 624 values 139 social 202–3, 206, 220 see also cultural values variability measures 450–2, 730 variable interdependence 438 variable re-specification 433–4, 737 variance 451, 494–5, 737 analysis see analysis of variance method (clustering) 602, 737 percentage 574, 580 variation, coefficient of 451–2 varimax procedure 582, 737 verbal content 485 verbal description (rating scales) 309, 310, 317 verbal models 46, 737 verbal protocols 302–3, 737 verification 214–15, 216 videos/video recorders 161–2, 167–8, 174, 196, 656 viewing laboratories 171–2, 737 vision 4–5 visual aids 237, 655 voice pitch analysis 244, 245, 737 volume tracking data 100, 101, 737 Ward’s procedure 602, 603–5, 737 web interviews 707, 708–9 webcams 197 websites 44, 104, 126–7 weighting 397, 432–3, 737 wholesaler audits 101–2, 245 Wilcoxon matched-pairs signed-ranks tests 468, 478–9, 737 Wilcoxon tests 438, 477 Wilks’ lambda 550, 553, 555 560 within-country analysis 439 word association 188–9, 192, 737 working opportunities 114 workshops 695 World Wide Web 104, 233, 377 writing reports 649–54 Xiao Kang 376 yoghurt 485–6, 487 young people 3–4, 670–1 z tests 437, 470–1, 737 z values 385, 386–7, 737 zero order correlation matrix 536 MKRS_ZO4.QXD 17/6/05 2:52 pm Page 751 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Name Index NAME INDEX Armes, Tim 88–9 Aufderheide, Bernd 666 Authers, Alex 167–8 Aziz, Khalid 655 Backinsell, David 243 Bairfelt, S 703 Bannister, Lucy 172 Biddle, Mathew 122 Birks, David 19 Birks, James 19 Bretschneider, Rudolf 669 Broadbent, Maryan 123–4 Brown, Colin 251 Clegg, Alicia 136 Connell, Stephen 706, 707 Cooper, Peter 138 Corbin, J.M 211 Crick, Tim 124 de Boer, Siebe-Geert 73 Dibb, S 687 Dowding, Pat 361, 413 Emsden, Gavin 73 Estefan, Gloria 284 Evans, Malcolm 219 Featherstone, Shirley 251 Fenwick, Trevor 104 Field, Georgia 150 Field, Richard 694 Ford, David 687, 689, 690, 691 Forestier-Walker, Miranda 22 Glaser, B 145 Goodyear, Mary 665 Gordon, Wendy 20, 21–2, 161 Grant, Andrew 23 Guttman, Justin 184 Hammer, M.C 284 Helton, Alicia 705 Hemingway, Marsha 182 Henderson, Ian 124 Hummerston, Arno 658 James, Meril 23 Jenkins, David 325 Jowell, Roger 251 Kelly, George 185 Kessels, Nicole 109 Kirby, Robert 679 Kirkby, Robin 86 Kohl, Helmut 68 Lewin, Kurt 148 Likert, Rensis 304 Littler, D 688 Macer, Tim 192, 218, 351, 378, 442 McNeil, Ruth 692 McPhee, Neil 694 Maklan, Stan 150 March, Wendy 132 Mendoza, David 680 Merriden, Trevor 126 Metcalf, Peter 142 Miller, Jeff 233 Monk, Virginia 226, 227 Murray, Anne 131 Necchi, Richard 676 Pearson, Karl 512 Price, Kevin 144 Redding, Phillip 655 Roddick, Anita 20 Southorn, Carola 123 Stapel, Jan 306 Stewart, Rod 284 Stollan, Martin 671 Strauss, A 145, 211 Stuart, Cristina 655 Turner, Tina 284 Valentine, Virginia 219 van der Herberg, Bert L.J van der Kooi, Marcel 73 van Meurs, Lex 73 Walton, paul 167 Ward, Greg 123 Webster, Frederick E., Jr 688, 691 Whelan, Ron 377, 378 Wilkinson, T 703 Wills, Peter 351 Wilson, D.F 688 Wind, Y 691 Wübbenhorst, Klaus 23 751 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 752 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Company Index C O M PA N Y I N D E X A.C.Nielsen 2, 3, 99–100, 101, 105, 244, 282, 286 ABN AMRO 117, 275, 295, 296, 297, 331, 427 ACE Fieldwork 251 Added Value Company, The 166 Adidas 78, 298 Air France 84, 89 AIT 127 Allied Domecq 57, 644–5 Armani Asics 78 Audit Bureau of Circulation (ABC) 88 Avon Products 511 Aziz Corporation 655 Bally 629 Banco Bilbao Vizcaya 295 Banco Central Hispanoamerican 10 Bank Brussels Lambert 295 Bass 282 Becks 619, 620, 621–3, 625–6 BehaviorScan 105 Benetton 158–9 Birks (kiln construction commpany) 19 BMW 318, 505, 699 Body Shop 20 Boots 123, 616–17 BPRI 686 British Market Research Bureau 672 Budapest Bank 295, 297 Budvar 299, 619, 620, 622–3, 625–6 Budweiser 619, 620, 622–3, 625–6 Buena Vista Entertainment 229 Burberry 376 Burger King 243 Burke 233, 273, 277–8 Cadbury 376 Calvin Klein Canon 317, 356 Carlsberg 299, 356, 619, 620, 622–3, 625–6 Caterpillar 630 Center Parcs 109 Chanel 48–9 Chase Manhattan Bank 644 Cheeseborough Ponds 282 Citibank 295, 296, 472, 644 Citrosusco Paulisto 49 Claritas 124 Clarke Research 157–8 Clausthaler 528 752 Coca-Cola 86, 105, 284, 297, 298, 299, 331, 356, 528–9, 616 Comcon 672 Compaq 252 Corona 619, 620, 622–3, 624, 625–6 Coutts Design 144 Credit Lyonnais 295 Heinz 49 Hewlett-Packard 86, 252, 566–7 Hindustan Lever Limited (HLL) 666 Hitachi 48 Hitachi Europe 696 Holsten 299, 619, 620, 622–3, 624–6 Home Shopping Budapest 518 Dalgety Animal Feeds 366–7 Davies Riley Smith Maclay 172 Den Danske Bank 295, 296–7 Deutsche Bank 126, 295, 296–7 Diesel Disney Video Company 229 Disneyland Paris 166 DKNY Dolland Aitchison 157–8 Dr Martens 629 Dr Pepper 616 Dresdner Bank 303, 304, 305–7, 341 Dunn and Bradstreet 105 Dutch Air Miles 117 Dyson 20–1 IBM 86, 689, 701 ICI Agricultural Products 638–9 ICR 84 ID Magasin 17 Intel 86, 132 IPSOS 249, 671 Eckerd Drug Company 259, 262 Equifax 124 Ericsson 685–6 Euromonitor 92, 93 Excel 368, 378, 441, 481, 506, 542, 567, 590, 612 Experian 115–17, 119, 124, 127, 378 Ferrari 165 FESSEL-GfK 669 Ford 23 Fuji Film 356 Fuld and Company 86 Futures Group, The (TFG) 86 Gallup Organisation 44, 67 Gap General Electric 86 General Motors 356 GfK 23, 101, 105, 659, 669 GIA 23 Gobi International 680 Great Universal Stores (GUS) 122 Grolsch 299, 619, 620, 622–3, 625–6 Gucci Häagen-Dazs 438–9, 596 Harp 619, 620, 621–3, 625–6 Heineken 22, 151 JVC 356 Kantor 325 Kao Corporation 637 Kaufhof 498–9 Kellogg 8, 298 Kelly’s 699 Kentucky Fried Chicken 376 Kodak 30 Land Rover 18–19, 680 Lever Brothers 637 Lion Corporation 637 LMN 117 L’Oréal 131 McDonald’s 30, 356 Mambo Mars 30, 283 MasterCard 356 Media Centre, The 88 Mercator 254, 424, 481 Mercury Asset Management 110 Microsoft 86, 252, 350, 441, 481, 655, 705 Miller Freeman 92 Millward Brown Intelliquest 25 Minitab 286, 441, 480–1, 506, 542, 555, 567, 590, 612 Minolta 317 Mintel 105 Mitsubishi 317 MORI 67 Motorola 86 Muller 485–6, 487 Nestlé 73, 663 Network Research 226 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 753 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 Company Index New Solutions 167–8 Next Nielsen, A.C see A.C.Nielsen Nike 78, 285–6, 299, 376 Okobank Finland 295, 296–7 OMD Denmark Omnimas 228 Pegram Walters International 57, 134 Peperami 191 Pepsi 134–5, 284, 297, 298, 616 Perrier 528 Peugeot 699 Philips 356 Pillsbury Company 66 Prada Presentation Company, The 655–6 Procter & Gamble 66, 86, 143, 188, 282, 283 Propaganda 703 Quirks.com 105 Ralph Lauren Reebok 78 Reed Elsevier 699 Renault Company 127 Research International 415 Research Services Ltd 679 Retail Marketing (In-Store) Services 14 Ricoh 317 RISC 677 Rolex 376 Rover 18–19, 680 Royal Ahold 2–3, 4, 17 Royal Bank of Scotland 123–4 Saatchi & Saatchi 518 Sage 123 Sainsbury’s 122 San Miguel 619, 620, 622–3, 625–6 SAP 127 SAS 286, 441, 480, 506, 542, 555, 567, 590, 612, 639 Scottish and Newcastle 109 7-Eleven 111–12 7-Up 616 SevenHR 703 Shell 117 Snickers 356 SpeakEasy Training 655 Sports Council 372 SPSS 122, 218, 441–2, 480, 506, 542, 555, 567, 590, 612, 639 Staropramen 282 Stella Artois 299, 619, 620, 622–3, 624, 625–6 Stussy Sun Life of Canada 123 Tango 616 Taylor Nelson AGB 7, 123, 676 Taylor Nelson Sofres 105, 659 Techneos 252 Tesco 122, 124 Texas Instruments 705–6 3Com 252 Timberland 629 Umbro 356 Unilever 50, 665–6 Value Engineers, The 167 Versace Virgin Cola 298 Visa 41 Wall Street Journal 44 Xerox 317 Yankelovich and Partners 528 753 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 754 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 755 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 756 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 757 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 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21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 MKRS_ZO5.QXD 17/6/05 2:53 pm Page 759 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 MKRS_ZO6.QXD 17/6/05 2:53 pm Page 760 33a8d66 6e7d7dc9e13 dd1 05b1 1d31 bb1a 3455 1df2b0 cb9 7186 bc6 d16a 369ee5 b ee72a4a6 c95e 8b44 261 c11b4da31 9ff705 b88da 47d8 4df733 b53a c07db5dfacc 1510e98 0f4 50b60aa5d5a6890 d04 084e1 69f91b0a 0746aa f8db6ad4b36 3cb2aa f7241 c66a 32f777 f8d7 cb0bb287 f89ee b3cc87 25aa013 8eb5 ef5 3e30 c2eaa3 b4 e02a5a6fa 70b0 7f7 fcd90 ba65b61b8 f12 3f1 9667 d8f652fe56 cf4 b7e8a dcc6c3 27fc8c5 9ff18a6 cc5 b550e f27 2207e 2890 e7004 6d87 71b5cc78 c4cc78 b7b5 3ed 7c671 77c6ed c0d9 cb4e3df6 d9b4 f27 9f2 4b01 e9147a 384db32 2798e 50c0f8e b6 be2c8 01b1fb0070 8e12 c6de 961 c5f1c0 06855 d27 b368 f5d3200 457bf86 82875 7da9aa76 fc2 ed63 f83 0eaf0 c38 74ebfb6 7e9c8ed f16 f6dc82 6b51 078e7 60f49c 65a914d4973 444e2 d79a7 58d43b2e 6adbb6da 6d7 cb1 d692 8950 8de5 27b9 8e614 08e5183 8cb468 07e5 f69d5b5 f32e 0b59 dd6 d94 9422a0 b5 cc7e 452e d3c3d3a4 8f c8c0 747 d2d9 988b26a4d181 f8d1ae03e7 8f6a 3d5a4 0036 f14 74f03bfa68a33 1f 24180d1943 19c5b53 60e51 00c27f5c0 6601 be5b55b9 1eb2 908e5 cb1a159e 6e2b bd19 f0b1a72 c4971 21fb1e8 ee703 c88 1d05 b4f370 b27a4 cb9a 76d3 8fc7fa3 9f9 6e4c1 25a430 5bfc91 dc8 7d41 6036 0fb00fca063 6038aae 4774 0cfd0a7 b33ab4d c075 cc2 f31a 7f7 245 c7a5fca8 f749 3b20 d1be27aa69 d40 c7a2 f7f36b3f0ae f35 e190ac1c9 6f6 f10 748 f84c4d3a 7aaad61 9ff8ef2 9806 c05 43c99b8a 20c9a1df4 b83b8 d125 48d1f8 da85e1 7f2 45c47e48 f5 cf18c4a38b4fb6219a 69980 133a2 49 IMPORTANT: READ CAREFULLY WARNING: BY OPENING THE PACKAGE YOU AGREE TO BE BOUND BY THE TERMS OF THE LICENCE AGREEMENT BELOW If You not agree to these terms then promptly return the entire publication (this licence and all software, written materials, packaging and any other components received with it) with Your sales receipt to Your supplier for a full refund This is a legally binding agreement between You (the user or purchaser) and Pearson Education Limited By retaining this licence, any software media or accompanying written materials or carrying out any of the permitted activities You agree to be bound by the terms of the licence agreement below SINGLE USER LICENCE AGREEMENT ❏ ● ● ● ❏ YOU ARE PERMITTED TO: YOU MAY NOT: Use (load into temporary memory or permanent storage) a single copy of the software on only one computer at a time If this computer is linked to a network then the software may only be installed in a manner such that it is not accessible to other machines on the network ● Rent or lease the software or any part of the publication ● Copy any part of the documentation, except where specifically indicated otherwise ● Make copies of the software, other than for backup purposes Make one copy of the software solely for backup purposes or copy it to a single hard disk, provided you keep the original solely for back up purposes ● Reverse engineer, decompile or disassemble the software ● Use the software on more than one computer at a time ● Install the software on any networked computer in a way that could allow access to it from more than one machine on the network ● Use the software in any way not specified above without the prior written consent of Pearson Education Limited Transfer the software from one computer to another provided that you only use it on one computer at a time ONE COPY ONLY This licence is for a single user copy of the software PEARSON EDUCATION LIMITED RESERVES THE RIGHT TO TERMINATE THIS LICENCE BY WRITTEN NOTICE AND TO TAKE ACTION TO RECOVER ANY DAMAGES SUFFERED BY PEARSON EDUCATION LIMITED IF YOU BREACH ANY PROVISION OF THIS AGREEMENT Pearson Education Limited owns the software You only own the disk on which the software is supplied LIMITED WARRANTY Pearson Education Limited warrants that the diskette or CD rom on which the software is supplied are free from defects in materials and workmanship under normal use for ninety (90) days from the date You receive them This warranty is limited to You and is not transferable Pearson Education Limited does not warrant that the functions of the software meet Your requirements or that the media is compatible with any computer system on which it is used or that the operation of the software will be unlimited or error free You assume responsibility for selecting the software to achieve Your intended results and for the installation of, the use of and the results obtained from the software The entire liability of Pearson Education Limited and its suppliers and your only remedy shall be replacement of the components that not meet this warranty free of charge This limited warranty is void if any damage has resulted from accident, abuse, misapplication, service or modification by someone other than Pearson Education Limited In no event shall Pearson Education Limited or its suppliers be liable for any damages whatsoever arising out of installation of the software, even if advised of the possibility of such damages Pearson Education Limited will not be liable for any loss or damage of any nature suffered by any party as a result of reliance upon or reproduction of or any errors in the content of the publication Pearson Education Limited does not limit its liability for death or personal injury caused by its negligence This licence agreement shall be governed by and interpreted and construed in accordance with English law