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CHAPTER 9

MANAGEMENT RESEARCH Third Edition, 2008 Prof. M. Easterby-Smith, Prof. R. Thorpe, Prof. Paul R. Jackson. CHAPTER 9. Creating Quantitative Data. Learning Objectives. To be able to select an appropriate form of sampling design for the objectives of the research.

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CHAPTER 9

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  1. MANAGEMENT RESEARCH Third Edition, 2008 Prof. M. Easterby-Smith, Prof. R. Thorpe, Prof. Paul R. Jackson CHAPTER 9 Creating Quantitative Data

  2. Learning Objectives • To be able to select an appropriate form of sampling design for the objectives of the research. • To be able to select among alternative sources of quantitative data according to the purpose of the research, taking into account the benefits and drawbacks of each. • To be able to design structured questions and select appropriate forms of measurement scale.

  3. Principles in designing a sample • Representativeness: the characteristics of the sample are the same as those of the population from which it is drawn. Biased samples are different from the population. • Precision: credibility of a sample, which depends on: • Sample size – bigger samples are more precise • Sampling proportion – what proportion of the population is sampled

  4. Achieving a credible sample

  5. Probability Sampling Designs • Simple Random Sampling – every entity has an equal chance of being part of the sample • Stratified Random Sampling – divide the population into strata, and take a random sample from each stratum • Systematic Random Sampling – list the entities in the population, and takes every nth (i.e. 27th) entity • Cluster Sampling – divide the population into clusters and then take samples from each cluster • Multi-Stage Sampling – combines several of the above methods

  6. Non-Probability Sampling Design • Convenience Sampling – selection is based on how easily accessible the sample entities are • Quota Sampling – divide the population into relevant categories and take samples until a target quota is achieved in each category • Purposive Sampling – researcher has a clear idea of what the sample unit should be • Snowball Sampling – use respondents to suggest the names of other relevant respondents to approach

  7. The Value of Sampling Designs • Probability Sampling Designs: the researcher knowsthe relationship between the sample and the population from which it is drawn • Non-Probability Sampling Designs: the researcher can overcome practical problems, where representativeness of the sample is either unnecessary or impossible to achieve

  8. Sources of Quantitative Data - Surveys • Postal Questionnaires – possible to achieve large samples cheaply • Face-to-face Structured Interviews – costly, but can achieve higher quality data • Telephone Structured Interviews – lower cost, and can achieve higher quality data • Web-based Surveys – can achieve large samples cheaply, easy to customise

  9. Sources of Quantitative Data – Observational Data • Types of observation data: • Verbal behaviour – words used to express meaning • Non-Verbal behaviour – vocal & visual ways of conveying meaning • Factors affecting observational data: • Observer effects • Sampling & coding behaviour

  10. Sources of Quantitative Data –Secondary Data/Databases • Financial databases – e.g. daily share prices, income statements, mergers & acquisitions • Issues in using secondary data: • The structure of the database • What data are recorded • Forming indices & derived variables

  11. The process of measurement • Principles in designing structured questions • Measurement scales for recording responses

  12. Principles in designing structured questions • Each item should express only one idea • Avoid jargon & colloquialisms • Use simple expressions • Avoid the use of negatives • Avoid leading questions

  13. Measurement scales for recording responses • Category scale • Nominal scale – categories have no intrinsic ordering • Ordinal Scale – categories have an intrinsic ordering • Likert Scale – a type of ordered category scale • Continuous Scale • Ratio Scale – has a meaningful zero point • Interval Scale – has no meaningful zero point

  14. Further Reading • Gunn (2002) ‘Web-based surveys: changing the survey process’, First Monday, 7 (12). • Couper, M.P., Traugott, M.W. and Lamias, M.J. (2001) ‘Web survey design and administration’, Public Opinion Quarterly, 65 (2): 230-53. • Sapsford, R. (2006) Survey Research, 2nd edn. London: Sage.

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