GIÁO TRÌNH MARKETING NGHIÊN CỨU - PHẦN 12 pot

Determining How to Select a Sample Ch 12 2 Basic Concepts in Sampling • Population: the entire group under study as defined by research objectives – Researchers define populations in specific terms such as “heads of households located in areas served by the company who are responsible for making the pest control decision.” Ch 12 3 Basic Concepts in Sampling • Sample: a subset of the population that should represent the entire group • Sample unit: the basic level of investigation • Census: an accounting of the complete population Ch 12 4 Basic Concepts in Sampling • Sampling error: any error in a survey that occurs because a sample is used • A sample frame: a master list of the entire population • Sample frame error: the degree to which the sample frame fails to account for all of the population…a telephone book listing does not contain unlisted numbers Ch 12 5 Reasons for Taking a Sample • Practical considerations such as cost and population size • Inability of researcher to analyze huge amounts of data generated by census • Samples can produce precise results Ch 12 6 Two Basic Sampling Methods • Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample • Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown Ch 12 7 Probability Sampling Methods • Simple random sampling • Systematic sampling • Cluster sampling • Stratified sampling Ch 12 8 Probability Sampling Methods Ch 12 9 Probability Sampling: Simple Random Sampling • Simple random sampling: the probability of being selected into the sample is “known” and equal for all members of the population – E.g., Blind Draw Method – Random Numbers Method (see MRI 12.1, p. 335) Ch 12 10 Probability Sampling: Simple Random Sampling – Advantage: • Known and equal chance of selection – Disadvantages: • Complete accounting of population needed • Cumbersome to provide unique designations to every population member [...]... into clusters Ch 12 15 Cluster Sampling • One cluster may be selected to represent the entire area with the one-step area sample • Several clusters may be selected using the two-step area sample Ch 12 16 A Two-Step Cluster Sample • A two-step cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneous Ch 12 17 Stratified... sampling – Skip interval=population list size/sample size Ch 12 11 Probability Sampling Systematic Sampling – Advantages: • Approximate known and equal chance of selection…it is a probability sample plan • Efficiency…do not need to designate every population member • Less expensive…faster than SRS – Disadvantage: • Small loss in sampling precision Ch 12 12 Probability Sampling Cluster Sampling • Cluster sampling:... agree that there is value Freshmen will have less agreement Ch 12 20 Stratified Sampling Ch 12 21 Probability Sampling Stratified Sampling • Stratified sampling: method in which the population is separated into different strata and a sample is taken from each stratum – Proportionate stratified sample – Disproportionate stratified sample Ch 12 22 Probability Sampling Stratified Sampling – Advantage: •... sampling – Judgment sampling – Referral sampling – Quota sampling Ch 12 28 Nonprobability Sampling Ch 12 29 Nonprobability Sampling • May not be representative but they are still used very often Why? – Decision makers want fast, relatively inexpensive answers… nonprobability samples are faster and less costly than probability samples Ch 12 30 Nonprobability Sampling • May not be representative but they... samples will have desired proportion of different respondent classes Ch 12 35 Online Sampling Techniques • Random online intercept sampling: relies on a random selection of Web site visitors • Invitation online sampling: is when potential respondents are alerted that they may fill out a questionnaire that is hosted at a specific Web site Ch 12 36 ... stratum Ch 12 23 Stratified Sampling • Why is stratified sampling more accurate when there are skewed populations? – The less variance in a group, the less sample size it takes to produce a precise answer – Why? If 99% of the population (low variance) agreed on the choice of Brand A, it would be easy to make a precise estimate that the population preferred Brand A even with a small sample size Ch 12 24... proportionate share of the population and freshmen would be sampled more 26 Ch 12 Stratified Sampling • Note that we would expect this question to be answered differently depending on student classification Not only are the means different, variance is less as classification goes up…Seniors tend to agree more than Freshmen! Ch 12 27 Nonprobability Sampling • With nonprobability sampling methods selection... by subgroups… Ch 12 18 Stratified Sampling – Research Question: “To what extent do you value your college degree?” Answers are on a five point scale: 1= “Not valued at all” and 5= “Very highly valued” • We would expect the answers to vary depending on classification Freshmen are likely to value less than seniors We would expect the mean scores to be higher as classification goes up Ch 12 19 Stratified... based upon what 100 or 200 or 300 people say…they don’t feel they need a probability sample Ch 12 31 Nonprobability Sampling • Convenience samples: samples drawn at the convenience of the interviewer – Error occurs in the form of members of the population who are infrequent or nonusers of that location Ch 12 32 Nonprobability Sampling • Judgment samples: samples that require a judgment or an “educated... will have a smaller chance of selection than others Ch 12 33 Nonprobability Sampling • Referral samples (snowball samples): samples which require respondents to provide the names of additional respondents – Members of the population who are less known, disliked, or whose opinions conflict with the respondent have a low probability of being selected Ch 12 34 Nonprobability Sampling • Quota samples: samples . Ch 12 16 Cluster Sampling • One cluster may be selected to represent the entire area with the one-step area sample. • Several clusters may be selected using the two-step area sample. Ch 12. Two-Step Cluster Sample • A two-step cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneous. Ch 12. are unknown Ch 12 7 Probability Sampling Methods • Simple random sampling • Systematic sampling • Cluster sampling • Stratified sampling Ch 12 8 Probability Sampling Methods Ch 12 9 Probability