Determining How to Select a Sample

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

Basic Concepts in Sampling Sample: a subset of the population that should represent the entire group Sample unit: the basic level of investigation (e.g. a household, a person, a company). Census: an accounting of the complete population Ch 12

Basic Concepts in Sampling Sampling error: any error in a survey that occurs because a sample is used Caused by (1) the sample selection method; and (2) the size of the sample. A sample frame: a master list of the entire population Ch 12

Basic Concepts in Sampling 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

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

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

Probability Sampling Methods Simple random sampling Systematic sampling Cluster sampling Stratified sampling Ch 12

Probability Sampling Methods Ch 12

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

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 Ch 12

Simple Random Sampling Random digit dialing (RDD) Used in telephone surveys to overcome the problems of unlisted and new telephone numbers. Telephone numbers are generated randomly with the aid of a computer. May result in a large number of calls to nonexisting telephone numbers. Ch 12

Simple Random Sampling Plus-one dialing procedure Numbers are selected from a telephone directory and a digit “1” is added to each number to determine which telephone number is then dialed. Alternatively, the last digit can be substituted with a random number. Ch 12

Probability Sampling Systematic Sampling Systematic sampling: way to select a random sample from a directory or list that is much more efficient than simple random sampling Skip interval=population list size/sample size Ch 12

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 (e.g. unlisted members, not up to date list). Ch 12

Probability Sampling Cluster Sampling Cluster sampling: method in which the population is divided into groups, any of which can be considered a representative sample Area sampling Ch 12

Cluster Sampling In cluster sampling the population is divided into subgroups, called “clusters.” Each cluster should represent the population. Area sampling is a form of cluster sampling – the geographic area is divided into clusters. Ch 12

Cluster Sampling One cluster may be selected to represent the entire area with the one-step area sample. Select one area randomly and perform a census of its members. Several clusters may be selected using the two-step area sample. Ch 12

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

Probability Sampling Cluster Sampling Advantage: Economic efficiency…faster and less expensive than SRS Disadvantage: Cluster specification error…the more homogeneous the clusters, the more precise the sample results Ch 12

Stratified Sampling When the researcher knows the answers to the research question are likely to vary by subgroups… Ch 12

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

Stratified Sampling Research Question: “To what extent do you value your college degree?” We would also expect there to be more agreement (less variance) as classification goes up. That is, seniors should pretty much agree that there is value. Freshmen will have less agreement. Ch 12

Stratified Sampling Ch 12

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

Probability Sampling Stratified Sampling Advantage: More accurate overall sample of skewed population…see next slide for WHY Disadvantage: More complex sampling plan requiring different sample size for each stratum Ch 12

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

Stratified Sampling But, if 33% chose Brand A, and 23% chose B, and so on (high variance) it would be difficult to make a precise estimate of the population’s preferred brand…it would take a larger sample size… Ch 12

Stratified Sampling Stratified sampling allows the researcher to allocate more sample size to strata with much variance and less sample size to strata with less variance. Thus, for the same sample size, more precision is achieved. This is normally accomplished by disproportionate sampling. Seniors would be sampled LESS than their proportionate share of the population and freshmen would be sampled more. 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

Nonprobability Sampling With nonprobability sampling methods selection is not based on fairness, equity, or equal chance. Convenience sampling Judgment sampling Referral sampling Quota sampling Ch 12

Nonprobability Sampling Ch 12

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

Nonprobability Sampling May not be representative but they are still used very often. Why? Decision makers can make a decision based upon what 100 or 200 or 300 people say…they don’t feel they need a probability sample. Ch 12

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

Nonprobability Sampling Judgment samples: samples that require a judgment or an “educated guess” as to who should represent the population Subjectivity enters in here, and certain members will have a smaller chance of selection than others Ch 12

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

Nonprobability Sampling Quota samples: samples that use a specific quota of certain types of individuals to be interviewed Often used to ensure that convenience samples will have desired proportion of different respondent classes Ch 12

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

Online Sampling Techniques Online panel sampling: refers to consumer or other respondent panels that are set up by marketing research companies for the explicit purpose of conducting surveys with representative samples Other online sampling approaches Ch 12

Developing a Sample Plan Sample plan: definite sequence of steps that the researcher goes through in order to draw and ultimately arrive at the final sample Ch 12

Developing a Sample Plan Define the relevant population. Obtain a listing of the population. The incidence rate is the percentage of people on a list who qualify as members of the population Design the sample plan (size and method). Ch 12

Developing a Sample Plan Draw the sample. Substitution methods: Drop-down substitution Oversampling Resampling Ch 12

Developing a Sample Plan Validate the sample. Sample validation is a process in which the researcher inspects some characteristic(s) of the sample to judge how well it represents the population. Resample, if necessary. Ch 12