Sampling brm chap-4
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This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
Sampling brm chap-4
- 1. Sample Design Dr. Shriram S. Dawkhar. Associate Professor, Sinhgad Institute of Business Administration & Research, Kondhwa, Pune.
- 2. ➢ The following order concerning various steps provides a useful procedural guideline regarding the research process. 1. Formulating the research problem 2. Literature survey (use of Library) 3. Formulation of hypothesis 4. Preparing the research design 5.Determining sample design © Shriram Dawkhar, May- 2010 (Updated - Nov-2019)
- 3. 6. Collecting the data.(The evidence collected by research process) 7. Analysis of data 8. Hypothesis Testing 9. Generalization and interpretation & 10. Preparation of the report or presentation of the result. (i.e. formal write up or conclusions.) © Shriram Dawkhar, May- 2010 (Updated - Nov-2019)
- 6. Determine Sample Design • Universe or Population – All the items under consideration in any field of inquiry constitute a ‘Universe’ or ‘Population’. • Census • A complete enumeration / counting of all the items in to population is known as a census inquiry. • Census inquiry is not possible in practice because of quite often researcher select only a few items from the universe for the study purposes. This items so selected constitute what is technically called a ‘sample’.
- 7. Determine Sample Design • Population: • refers to entire group of people, events or things of interest that the researchers wishes to investigate. • Sampling: Sampling is the process of selecting a sufficient number of elements from the population, so that the study of the sample & understanding of its properties or characteristics would make it possible for us to generalize such properties or characteristics to the population
- 8. Determine Sample Design • Sample : • A sample is a group of units / element selected from a larger group – the population. • Sampling frame: The listing of all accessible population from which you will draw your sample is called sampling frame.
- 9. Determine Sample Design • Sample Design – A sample design is a definite plan determined before any data are actually collected for obtaining a sample from a given population.
- 10. Need of Sampling • Lower Cost • Greater accuracy of results. • Greater speed of data collection • Availability of Population elements • Sample Versus Census.
- 11. Characteristics of good sample design / Sampling • 1) Sample design must result in a truly representative sample. • 2) Sample design must be such which result in a small sampling error. • 3) Sample design must be viable in the context of funds available for the research study.
- 12. Characteristics of good sample design • 4) Sampling design must be such so that systematic bias can be controlled in a better way. • 5) Sample should be such that the result of the sample study can be applied, in general, with a universe with a reasonable level of confidence.
- 14. The Sampling Design Process Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process
- 16. DETERMINATION OF SIZE OF SAMPLE • Common Misconceptions – The sample should be a proportion (often 5 or 10 per cent) of the population; – The sample should total about 500; – Any increase in the sample size will increase the precision of the sample results. ◙ No such rule-of-thumb method is adequate.
- 17. Sample Sizes Used in Marketing Research Studies Type of Study Minimum Size Typical Range Problem identification research (e.g. market potential) 500 1,000-2,500 Problem-solving research (e.g. pricing) 200 300-500 Product tests 200 300-500 Test marketing studies 200 300-500 TV, radio, or print advertising (per commercial or ad tested) 150 200-300 Test-market audits 10 stores 10-20 stores Focus groups 2 groups 4-12 groups
- 18. SIZE OF SAMPLE • Most researchers find it difficult to determine the size of the sample. • Krejcie and Morgan (1970) have prepared a table.
- 19. TABLE FOR DETERMINING SAMPLE SIZE FROM A GIVEN POPULATION N S N S N S 10 10 460 210 2600 335 20 19 500 217 2800 338 30 28 550 226 3000 341 40 36 600 234 3500 341 50 44 700 248 4000 351 60 52 800 260 4500 354 70 59 900 269 5000 357 80 66 1000 278 6000 361 90 73 1200 291 7000 361 100 80 1300 297 8000 367 120 92 1400 302 9000 368 140 103 1500 306 10000 370 160 113 1600 310 15000 375 180 123 1700 313 20000 377 200 123 1800 317 30000 379 250 152 1900 320 40000 380 300 169 2000 322 50000 381 360 186 2200 327 75000 382 400 196 2400 331 100000 384 N=Population S=Sample Size
- 20. There is only one method of determining sample size that allows the researcher to PREDETERMINE the accuracy of the sample results… The Confidence Interval Method of Determining Sample Size
- 21. Sample Size Formula - Proportion • The sample size formula for estimating a proportion (also called a percentage or share):
- 22. The Central Limit Theorem allows us to use the logic of the Normal Curve Distribution • Since 95% of samples drawn from a population will fall within + 1.96 x Sample error (this logic is based upon our understanding of the normal curve) we can make the following statement: ….
- 23. Practical Considerations in Sample Size Determination • How to estimate variability (p and q shares) in the population • Expect the worst case (p=50%; q=50%) • Estimate variability: results of previous studies or conduct a pilot study
- 24. Practical Considerations in Sample Size Determination • How to determine the amount of desired sample error • Researchers should work with managers to make this decision. How much error is the manager willing to tolerate (less error = more accuracy)? • Convention is + 5% • The more important the decision, the less should be the acceptable level of the sample error
- 25. Practical Considerations in Sample Size Determination • How to decide on the level of confidence desired • Researchers should work with managers to make this decision. The higher the desired confidence level, the larger the sample size needed • Convention is 95% confidence level (z=1.96 which is + 1.96 s.d.’s ) • The more important the decision, the more likely the manager will want more confidence. For example, a 99% confidence level has a z=2.58.
- 26. Sample Size… • Many numerical techniques for determining sample sizes are available , but suffice it to say that the larger the sample size is, the more accurate we can expect the sample estimates to be.
- 27. Types of Error Sampling Nonsampling 10-27 Errors in sampling
- 28. Sources of Error in Sampling • Sampling Errors – error caused by the act of taking a sample. – They cause sample results to be different from the results of census. • Nonsampling errors – errors not related to selecting the sample. – They can be present even in a census.
- 29. Sampling and Non-Sampling Errors… • Two major types of error can arise when a sample of observations is taken from a population: • sampling error and non sampling error. • Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample. Random and we have no control over. • Non sampling errors are more serious and are due to mistakes made in the acquisition of data or due to the sample observations being selected improperly. Most likely caused by poor planning, sloppy work, act of the Goddess of Statistics, etc.
- 30. Sampling Error… • Sampling error refers to differences between the sample and the population that exist only because of the observations that happened to be selected for the sample. • Increasing the sample size will reduce this type of error.
- 31. Sampling errors • Random sampling error – the deviation between the sample statistic and the population parameter caused by chance in selecting a random sample. – this is only component of the margin of error • Bad Sampling Methods – Convenience Sampling – Voluntary Response • Undercoverage – when some members of the population are left out of the process of choosing the sample.
- 32. Undercoverage • sampling frame is the list of individuals from where the samples are actually chosen. – If the sampling frame leaves out certain classes of people, random sample from that frame will be biased.
- 33. Example- Undercoverage • We used a telephone book to randomly choose numbers to dial and ask “What brand of soap do you use most often?” – Population: All Indian adults – Sampling Frame: All adults with listed phone numbers – Error: Undercoverage • By using the telephone book, we have left out all those people who do not have phones and all the people who have unlisted phone numbers.
- 34. Reducing sampling error • If sampling principles are applied carefully within the constraints of available resources, sampling error can be kept to a minimum.
- 35. Nonsampling errors • Processing errors- mistakes in mechanical tasks, such as doing arithmetic or entering responses into a computer. • Response errors – occurs when a subject gives an incorrect response. – i.e. not understanding a question, lying about a question. • Nonresponse – the failure to obtain data from a selected individual in the survey.
- 36. Nonresponse • One of the most serious types of nonsampling errors. • Can happen for a variety of reasons. – Most nonresponse happens because some subjects can’t be contacted or because some subjects who are contacted refuse to participate. • Can cause bias, which easily overwhelm the random sampling error. – Different groups have different rates of nonresponse.
- 37. Reducing non-sampling errors • Can be minimised by adopting any of the following approaches: – using an up-to-date and accurate sampling frame. – careful selection of the time the survey is conducted. – planning for follow up of non-respondents. – careful questionnaire design. – providing thorough training and periodic retraining of interviewers and processing staff.
- 38. Reducing non-sampling errors – cont’d - designing good systems to capture errors that occur during the process of collecting data, sometimes called Data Quality Assurance Systems.
- 39. Classification of Sampling Techniques Sampling Techniques Nonprobability Sampling Techniques Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Systematic Sampling Stratified Sampling Cluster Sampling Other Sampling Techniques Simple Random Sampling
- 40. Non-Probability Sampling Methods ◼ Convenience Sample : The sampling procedure used to obtain those units or people most conveniently available. ✓Subjects selected because it is easy to access them. • No reason tied to purposes of research. ▪Students in your class, people on State Street, friends ◼ Why: speed and cost ◼ External validity? ◼ Internal validity ◼ Is it ever justified?
- 41. ◼ Advantages ◼ Very low cost ◼ Extensively used/understood ◼ No need for list of population elements ◼ Disadvantages ◼ Variability and bias cannot be measured or controlled ◼ Projecting data beyond sample not justified.
- 42. Convenience Sampling Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected because they happen to be in the right place at the right time. – use of students, and members of social organizations – mall intercept interviews without qualifying the respondents – department stores using charge account lists – “people on the street” interviews
- 43. ◼ Judgment or Purposive Sample ◼ The sampling procedure in which an experienced researcher selects the sample based on some appropriate characteristic of sample members… to serve a purpose. ➢Subjects selected for a good reason tied to purposes of research ➢ Small samples < 30, not large enough for power of probability sampling. ➢ Nature of research requires small sample ➢ Choose subjects with appropriate variability in what you are studying ➢ Hard-to-get populations that cannot be found through screening general population
- 44. ◼ Advantages ◼ Moderate cost ◼ Commonly used/understood ◼ Sample will meet a specific objective ◼ Disadvantages ◼ Bias! ◼ Projecting data beyond sample not justified.
- 45. Judgmental Sampling Judgmental sampling is a form of convenience sampling in which the population elements are selected based on the judgment of the researcher. – test markets – purchase engineers selected in industrial marketing research – expert witnesses used in court
- 46. ◼ Quota Sample ◼ The sampling procedure that ensure that a certain characteristic of a population sample will be represented to the exact extent that the investigator desires. ◼ Specific number of sample unit (Quota)
- 48. ◼ Advantages ◼ moderate cost ◼ Very extensively used/understood ◼ No need for list of population elements ◼ Introduces some elements of stratification ◼ Disadvantages ◼ Variability and bias cannot be measured or controlled (classification of subjects) ◼ Projecting data beyond sample not justified.
- 49. Quota Sampling Quota sampling may be viewed as two-stage restricted judgmental sampling. – The first stage consists of developing control categories, or quotas, of population elements. – In the second stage, sample elements are selected based on convenience or judgment. Population Sample composition composition Control Characteristic Percentage Percentage Number Sex Male 48 48 480 Female 52 52 520 ____ ____ ____ 100 100 1000
- 50. ◼ Snowball sampling ◼ The sampling procedure in which the initial respondents are chosen by probability or non-probability methods, and then additional respondents are obtained by information provided by the initial respondents
- 51. ◼ Advantages ◼ low cost ◼ Useful in specific circumstances ◼ Useful for locating rare populations ◼ Disadvantages ◼ Bias because sampling units not independent ◼ Projecting data beyond sample not justified.
- 52. Panel Sampling • The same units or elements are measured on subsequent occasion. • E.g. : Some households – to know consumption pattern & after six months same house holds.
- 53. Master Samples • A master sample is one form which repeated sub-samples can be taken as and when required from the same area of population.
- 54. Probability Sampling • Please refer class notes provided during class.