Sampaling
- 2. You have to first know what you are looking for - this is not always so easy. If your new chocolate bar isn’t selling well, you don’t automatically do market research on the “taste” - because maybe the reason has to do with the packaging. Problem Definition
- 3. hypothesis After the problem has been defined; (Step 1), and an exploratory investigation (Step 2), has been conducted, it is possible to then formulate a Hypothesis (Step 3)
- 4. “A tentative explanation about the relationship between variables as a starting point for further testing.” The way of thinking about how something works - and using your original “guess” as a starting point for further investigation HypothesisHypothesis
- 5. The selection of areas considered reasonably typical of the total market, and introducing a new product to these areas with a total marketing campaign to determine consumer response before marketing the product nationally. Test Marketing
- 6. SampleSample Sample is a process of selecting a subset of rationalised number of members of the population of the study and collecting data about their attributes These limited members are called sampling units Based on the data gathered on the sample the analyst draws conclusion about the population
- 7. What is a sample ? A subset of some of the units in the population A subgroup of the population
- 8. Example population size = 1000 ( blue collar) Sample size = 200 (chosen for studying performance of blue collar)
- 9. Why sampling ? Too expensive to test the entire population Impossible to test entire population Testing the entire population often produces errors May give accurate results Enables to researchers to make estimates of some unknown characteristics of population in question High scope of accuracy and reliability
- 10. Sampling Methods Probability Non-Probability Based on probability theory. Focus on volunteers, easily available units, or those that just happen to be present when the research is done. Every unit of the population of interest must be identified, and all units must have a known, non-zero chance of being selected into the sample. Useful for quick and cheap studies, for case studies, for qualitative research, for pilot studies, and for developing hypotheses for future research.
- 11. Probability Simple random sample Systematic random sampling Stratified random sampling Cluster sampling
- 12. Simple random sampling Population = n Sample = n All possible sample= n A random number table is a list of numbers, composed of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. Numbers in the list are arranged so that each digit has no predictable relationship to the digits that preceded it or to the digits that followed it. In short, the digits are arranged randomly. The numbers in a random number table are random numbers Link - http://stattrek.com/Tables/Random.aspx
- 13. Simple random sampling 25 Random Numbers 068 057 036 098 014 015 012 022 094 080 094 052 077 076 006 013 018 002 051 080 066 035 000 004 044 * This table of 25 random numbers was produced according to the following specifications: Numbers were randomly selected from within the range of 0 to 100. Duplicate numbers were allowed.
- 14. Systematic Random sampling • most appropriate practical method for sampling • for instance - to select every Xth item from the list.
- 15. Systematic Random sampling There is a population of 2000 and sample size is 100. apply the systematic random sampling. Identify Population size = N Sample size = n= 100 Interval =k=? k= N/n = 2000/100 = 20 Now Select a random number –x between 1 and k. Suppose first xth number is 12 Then next number is = x+k = 12+20 = 32 so on
- 16. Question • Population of people going to night-clubs is on an average about 250-300 people in a city. Number of night clubs are 30. • A researcher wants to select people for interview among this population .Apply systematic random sampling to identify the people.
- 17. Stratified sampling • Constructed by classifying the population in sub- populations (or strata), base on some well- known characteristics of the population, such as age, gender or socio-economic status. • The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods.
- 18. Example Sample size = n = 30 Population size =N= 8000 Population is divided into three strata ; N1= 4000 , N2= 2400 , N3= 1600 Find the sample sizes for the different strata by using proportional allocation : For Proportional Allocation – P1 = 30 (4000/8000) = 15 P2 and P3 = ??
- 19. Question In the class 12th , pupils are offered Maths, Physics or Chemistry homework. 28 choose Maths homework 47 choose Physics homework 25 choose Chemistry homework. If you wanted to check the homework of any 20 students, how many of each would you choose? ..
- 20. Cluster • Suitable for conducting research studies that cover large geographic area. • Once the cluster is formed the researcher can either go for one stage, two stages, or multi stage cluster sampling.
- 21. Example Suppose that the Department of Agriculture wishes to investigate the use of pesticides by farmers in England. A cluster sample could be taken by identifying the different counties in England as clusters. A sample of these counties (clusters) would then be chosen at random, so all farmers in those counties selected would be included in the sample. It is easier to visit several farmers in the same county than it is to travel to each farm in a random sample to observe the use of pesticides.
- 22. Non-Probability • Convenience sample • Purposive sample • Quota sample
- 23. Convenience An exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient.
- 24. Purposive The researcher selects the units with some purpose in mind, for example, students who live in dorms on campus, or experts on urban development.
- 25. Quota widely used in opinion polling and market research. Interviewers are each given a quota of subjects of specified type to attempt to recruit for example, an interviewer might be told to go out and select 20 adult men and 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing.
- 26. Differences between Probability Sampling and Non-Probability Probability (Random) Sampling Non-Probability (Non-Random) Sampling Allows use of statistics, tests hypotheses Exploratory research, generates hypotheses Can estimate population parameters Population parameters are not of interest Eliminates bias Adequacy of the sample can't be known Must have random selection of units Cheaper, easier, quicker to carry out
- 27. 27 What is a Good Sample? Issue Criterion Population Definition Consistency of target population and study population Truly representative sample Sampling Method To select any member of study population equally likely Precision of Estimate Estimate precise enough to inform decision making May results in a small sampling error.
- 28. Steps in Sampling Design Type of universe: The universe can be finite or infinite. In finite universe the number of items is certain, but in case of an infinite universe (city population, factory workers etc,) the number of items is infinite, i.e., we cannot have any idea about the total number of items ( number of stars). Sampling Unit: Sampling Unit may be geographical one such as state, district, village, etc., or a construction unit such as house flat., it may be individual. 28
- 29. Steps in Sampling Design (contd..) Source list: It is also known as ‘sampling frame’ from which sample is to be drawn. It contains the names of all items of a universe (incase of finite universe only). If the source list is not available, the researcher has to prepare it. Such list should be comprehensive, correct, reliable and appropriate. It is extremely important for the source list to be as representative of the population as possible. 29
- 30. Steps in Sampling Design (contd..) Size of sample: The sample size should neither be excessively large, nor too small. It should be optimum. An optimum sample is one which fulfills the requirements of efficiency, appropriate representation, reliability and flexibility. While deciding the sample size, researcher must determine the desired precision as also an acceptable confidence level for the estimate.
- 31. Steps in Sampling Design (contd..) Parameters of interest: In determining the sample design, one must consider the question of the specific population parameters which are of interest. For instance, we may be interested in estimating the proportion of persons with some characteristic in the population, or we may be interested in knowing some average or the other measure concerning the population. 31
- 32. Steps in Sampling Design (contd..) Sampling procedure: Finally, the researcher must decide the type of sample he will use i.e., he must decide about the technique to be used in selecting the items for the sample. In fact, this technique or procedure stands for the sample design itself. There are several sample design out of which the researcher must choose one for his study. 32
- 33. Definition A set of routine procedures to continuously collect, monitor, and present internal and external information on company performance and opportunities in the marketplace. Marketing Information Systems
- 34. For some companies, market knowledge comes in on a regular basis. Some stuff is “Data”, and some is “Information” Data = statistics, opinions in surveys, facts, predictions etc. Information = data RELEVANT to the Marketing Manager in making decisions Marketing Information Systems
- 35. All of this depends on the ability of the company to use technology to help it be better than the competition Marketing Information Systems
- 36. THANK YOU