Marketing 6 chapter 14 sampling fundamentals


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Marketing 6 chapter 14 sampling fundamentals

  1. 1. Marketing 6 Chapter 14 Sampling Fundamentals Submitted by: Franklin K. Go Raymond A. Gonzales Angelo R. Cantor Submitted to:Mr. Abelito T. Quiwa, MBA
  2. 2. Learning Objectives: 1. Distinguish between a census and a sample. 2. Know the difference between sampling and non-sampling errors. 3. Learn the concept of sampling process. 4. Describe probability and non-probability sampling procedures. 5. Determine sample size with ad-hoc methods. 6. Learn to deal with non-response problems. 7. Understand sampling in international context.Sample or CensusA researcher is interested in the characteristics of the population wherein his potential targetmarket may reside. The respondents are asked to input necessary information through surveyscalled census.When a Census is AppropriateA census is appropriate if the sample size is small. Every individual may be included in thepopulation.When a Sample is AppropriateA sample is appropriate if the population is large, the time allotted in obtaining the informationfrom the population is long and the cost is high.Error in SamplingError in sampling occurs if there is a gap between the true value and the observed value of thevariable interest of the population. • Sampling Error occurs if the error lies in the sample parameter and the sample statistics because of sampling. • Non-sampling Error occurs if the error lies in the population.
  3. 3. Sample ProcessFactors that should be considered when the decision to use a sample is made: • Various steps included in sampling: • Major activities with sampling process: 1. Identify the target population. 2. Determine the sampling frame. 3. Resolving the Differences. 4. Selecting a sampling procedure. 5. Determining the relevant sample size. 6. Obtaining information from respondents. 7. Dealing with the non-response public. 8. Generating information for decision-making purposes.Determining the Target Population
  4. 4. Sampling is necessary to get information about the population. Inaccurate definition of thepopulation results to inaccurate data collection. This leads to the wrong question beinganswered. For instance, in Houston, in order for car dealers to know the prospective car buyers,the population should consist of adults with driver’s licenses. In order for toy stores to knowtheir potential customers, they need to identify the number of children in every household of thepopulation in. • How do you define children? Are they below 10 years, 13 years or 16 years old? • How do you define Houston? Does it include only the metropolitan area or are the suburban areas included as well? • Who in the household is going to provide the information?The following should be considered in determining the target population: 1. Look to the research objectives 2. Consider alternatives 3. Know your market 4. Consider the appropriate sampling unit 5. Specify clearly what is excluded 6. Don’t overdefine 7. Should be reproducible 8. Consider convenienceDetermining the Sampling FrameIt is important to distinguish the difference between the population and the sample frame. Thesampling frame is a list of the population with which the sample is taken from. They may bemagazine subscribers, retail stores or college students. Even maps may serve as a list.Tasks to do in determining the sample frame: 1. Create lists  The hardest way to obtain a sample is creating a list. The researcher needs to identify which among the population in an area should be included. 2. Create lists for telephone interviewing  Telephone directories are often used to generate samples because the population is narrowed down to one entry per household and also eliminates those who do not have telephones. 3. Dealing with population sampling frame difference  The following are three types of problems when dealing with population sampling frame difference: 1. Subset problem  Occurs when sampling frame is smaller than the population. 2. Superset problem.  Occurs when the sampling frame is larger than the population. 3. Intersection problem.  Occurs when some elements of the population are omitted from the sampling frame.
  5. 5. Selecting a Sampling ProcedureThere are many ways of selecting a sampling procedure. First, the researcher must selectbetween Bayesian Sampling Procedure and Traditional Sampling Procedure. Then, theresearcher must decide whether the sample may have a replacement or not.Probability SamplingProbability sampling involves four considerations. First, the target population must be specified.Second, the methods for selecting sample needs to be developed. Third, the sample size must bedetermined. Last, the non-response problem must be addressed.Selecting the Probability SampleMethods used to select a probability sample: 1. Simple Random Sampling.  Each population member has equal probability of being selected. 2. Accuracy Cost Trade-off  The ratio of accuracy over cost. In general, the higher the cost, the higher the accuracy. 3. Stratified Sampling  Similar to the Random sampling but increases the accuracy higher than the cost increase. 4. Proportional Stratified Sampling  The number of sample units is proportional to the number of population. 5. Directly Proportional Stratified Sampling  The population is grouped based on categories like a population of 600 is divided into 400 for brand loyalty and 200 for variety seeking. Samples are taken from each division. If a sample size of 60 is desired, a 10% directly proportional stratified random sampling is employed. 10% Directly Consumer Group Proportional Type Size Stratified Random Size Brand 400 40 Loyal Variety 200 20 Seeking Total 600 60 6. Inversely Proportional Stratified Sampling  If in a population of 600, 200 are heavy drinkers and 400 are light drinkers, If the researcher values the opinion of heavy drinkers more than the light drinkers, more
  6. 6. people shall be sampled from the desired group. In such cases, the researcher can use inversely proportional stratified sampling. If a sample size of 60 is desired, a 10% inversely proportional stratified random sampling is employed. 10% Inversely Consumer Group Proportional Type Size Stratified Random Size Brand 400 40 Loyal Variety 200 20 Seeking Total 600 60 7. Disproportional Stratified Sampling  The sample size is not proportional to the group size. This usually occurs when multiple groups are compared. 8. Cluster Sampling  This is done by decreasing cost at a faster rate than accuracy. 9. Systematic Sampling  This is done by systematically spreading the sample size through the list of population members.Multistage DesignIt is often used when samples in multiple areas are desired. Cities in Ajax Country Cumulative City Population PopulationConcorde 15000 1-15000Mountain 15001- 10000 View 25000 25001- Filmore 60000 85000 85001- Austin 5000 90000 90001- Cooper 2000 92000 92001-Douglas 5000 97000 Rural 97001- 3000 Area 100000
  7. 7. Non-probability Sampling  The cost and trouble of developing a sampling frame are eliminated unlike the probability sampling wherein probability and theory guides the researcher in obtaining data from samples. The following are stages where non-probability sampling is used: 1. Exploratory stage of a research project 2. Pretesting a questionnaire 3. Dealing with homogeneous population 4. When researcher lacks statistical knowledge 5. When operational ease is required  The following are the four types of non-probability sampling: 1. Judgment sampling  Expert use of judgment to identify representative samples.  The situations where judgment sampling is useful are, first, probability sampling is not feasible and is expensive. Second, if the sample size is very small. Third, it is useful to obtain a deliberately biased example. 2. Snowball Sampling  Appropriate for small, specialized population. 3. Convenience Sampling  Used to obtain information quickly and inexpensive. 4. Quota Sampling  It is a judgmental sampling with constraints which includes a minimum number from each specified subgroup in the population. The following are the characteristics of Quota Sampling: 1. It is often based on demographic data such as geographic location, age, sex and income. 2. It eliminates gross biases that could be part of judgment sampling. 3. There are serious biases that are not controlled by the quota.Determining the Sample SizeHow large should the sample be? Although this question is direct, answering it is not easy.  Web-Based Samples – frequently used because of the ease that it brings in collecting data. It has several benefits such as speed, flexibility and economy.Non-response ProblemsSampling is done to obtain necessary data from the population. Unfortunately, some of thepopulations are not cooperative. The following are reasons why some of the population becomenon-respondents: 1. Refuse to respond 2. Lack of ability to respond 3. Not at home
  8. 8. 4. InaccessibleWhat can be done about the non-response problems? 1. Improve the research design to reduce non-responses. • For telephone interviews, gain initial interest through interviewer skills and design the proper placements of questions. • For mail surveys, motivate respondents through incentives. 2. Repeat the contact one or more times to reduce non-responses (callbacks). • It is necessary to do as much callbacks as possible to reduce the number of non- responses. 3. Attempt to estimate the non-response bias. • Make extra effort to interview the sub sample to reduce non-response bias. • Give incentives such as worthwhile gifts to entice respondents.Shopping Center SamplingShoppers are intercepted and interviewed personally or through surveys. This method is calledstore-intercept interviews.Shopping Center SelectionSelecting a shopping center is essential because the respondents will be those who live in thenearby area. The standard of living of the neighborhood will affect how the sampling will takeplace and the types of samples that will be obtained.Sample Locations within a CenterThis is randomly selecting samples through shopping center visits.Time SamplingTime segments are devised when obtaining sample data because of the fact that the time peoplego shopping vary from one another.Sampling People versus Shopping VisitsResearchers should ask the respondents on how often they shop. The respondents are weightedbased on the frequency of visits when getting the average. Those who visit twice are weighed1/2, those who visit thrice are weighted 1/3 and so on. Another approach is to use quotas. Forexample, shoppers aged 25 to 45 tend to make more visits than those who are younger and older.Another is the employment status. Unemployed people tend to shop more than those who areemployed. Quotas are set so that the number of samples is proportional to the number of thepopulation.