Ch. 9 Selecting the Sample.pdf

Selecting the Sample Midterm - Thursday, February 8th - 50 questions Multiple choice True/false - Covers: Chapter 1-9 J. Crew & Coop Case Decision problems & options presented in the case - Need to bring a green scantron AND PENCIL - Will have full class time Basic Concepts in Sampling The populatio...

Selecting the Sample Midterm - Thursday, February 8th - 50 questions Multiple choice True/false - Covers: Chapter 1-9 J. Crew & Coop Case Decision problems & options presented in the case - Need to bring a green scantron AND PENCIL - Will have full class time Basic Concepts in Sampling The population is the entire group under study as defined by research objectives. A census is an accounting of the complete population. It requires information from everyone in the population The U.S. census is taken every 10 years by the U.S. Census Bureau (www.census.gov). Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Basic Concepts in Sampling Sample: a subset of the population that should represent the entire group Sample unit: the basic level of investigation Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Basic Concepts in Sampling A sample frame: a master source of sample units in the population Sampling frame error: the degree to which the sample frame fails to account for all of the population Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Basic Concepts in Sampling Sampling error: any error in a survey that occurs because a sample is used Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Figure Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Reasons for Taking a Sample Practical considerations such as cost and population size Inability to analyze huge amounts of data generated by a census Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Basic Sampling Methods Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample - Probability sampling methods are those that ensure that, if the exact size of the population were known for the moment in time that sampling took place, the probability of any member of the population being selected into the sample could be calculated. Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown - In the case of nonprobability methods there is no way to determine the probability even if the population size is known, because the selection technique is subjective. - Nonprobability sampling is sometimes called “haphazard sampling”, because it is prone to human error and even subconscious biases. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Methods Simple random sampling Systematic sampling Cluster sampling Stratified sampling Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Simple random sampling: the probability of being selected into the sample is “known” and equal for all members of the population The researcher uses random numbers from a computer, random digit dialing, or some other random selection procedure that guarantees each member of the population in the sample frame has an identical chance of being selected into the sample. Probability of selection sample size/population size Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Simple Random Sampling The random device method involves using a procedure or apparatus that assures that every member of the population has the same chance of being included in the sample. Can use a blind draw or random number A common method is random digit dialing (RDD) Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Simple Random Sampling Advantages - This sampling method guarantees that every member of the population has an equal chance of being selected into the sample; therefore, the resulting sample, no matter what the size, will be representative of the population. Disadvantages - To use either the random device or the random numbers approach, it is necessary to uniquely identify and label each and every population member. - In essence, simple random sampling necessarily begins with a complete listing of the population, and current and complete listings are sometimes difficult to obtain. - Incomplete or inaccurate listings of populations, of course, contain sample frame error. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Systematic sampling: way to select a random sample from a directory or list that is much more efficient than simple random sampling Uses a skip interval for selection Using a sample frame that lists members of the population, the researcher selects a random starting point for the first sample member. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Systematic Sampling Advantages - Systematic sampling is probability sampling because it employs a random starting point, which ensures that there is sufficient randomness in the sample to approximate an equal probability of any member of the population being selected into the sample. - Systematic sampling is more efficient than simple random sampling because only one or a very few random numbers need to be drawn at the beginning. Disadvantages - The greatest danger in the use of systematic sampling lies in the listing of the population (sample frame). Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Cluster sampling: method in which the population is divided into subgroups, called “clusters,” each of which could represent the entire population - The groups are similar to each other - The researcher can then randomly select a few clusters and perform a census of each one (one stage). If desired, the researcher can then randomly select more clusters and take samples from each one (two stage). - The greatest danger in cluster sampling is cluster specification error, which occurs when the clusters are not homogeneous. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Area sampling is a form of cluster sampling – the geographic area is divided into clusters. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Area (Cluster) Sampling One-step area sample: the researcher may believe the various geographic areas (clusters) to be sufficiently identical to allow concentrating his or her attention on just one area and then generalizing the results to the full population Two-step area sample: the researcher selects a random sample of areas, and then, he or she decides on a probability method to sample individuals within the chosen areas Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Probability Sampling Stratified Sampling: separates the population into different subgroups and then samples all of these subgroups Is used when working with a “skewed” population May require the calculation of a “weighted mean” A proportionate stratified sample has sample sizes scaled to population size A disproportionate stratified sample has sample sizes not scaled to population size, but may be more statistically efficient. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Figure Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonprobability Sampling With nonprobability sampling methods selection is not based on fairness, equity, or equal chance. – Convenience sampling – Chain referral sampling – Purposive sampling – Quota sampling Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonprobability Sampling Convenience samples: samples drawn at the convenience of the interviewer (such as mall interviews) - The researcher or interviewer uses a high-traffic location, such as a busy pedestrian area or a shopping mall, as the sample frame from which to intercept potential respondents. - Sample frame error occurs in the form of members of the population who are infrequent users or nonusers of that location. - Other types of error may result from the arbitrary way the interviewer selects respondents from the sample frame. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonprobability Sampling Chain referral samples: require respondents to provide the names of prospective respondents - Respondents are asked for the names or identities of others like themselves who might qualify to take part in the survey. - Members of the population who are less well known or disliked, or whose opinions conflict with those of the selected respondents, have a low probability of being selected. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonprobability Sampling Purposive samples: requires a judgment or an “educated guess” as to who should represent the population - The researcher uses his or her judgment or that of some other knowledgeable person to identify who will be in the sample. - Subjectivity and convenience enter the picture, and consequently, certain members of the population will have a smaller chance of selection than others. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Nonprobability Sampling Quota samples: specified percentages of the total sample for various types of individuals to be interviewed - The researcher identifies quota characteristics, such as demographic or product use factors, and uses these to set up quotas for each class of respondent. - The sizes of the quotas are determined by the researcher’s belief about the relative size of each class of respondent in the population. - Often, quota sampling is used as a means of ensuring that convenience samples will include the desired proportion of different respondent classes. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Online Sampling Techniques Online panels: large numbers of individuals who have agreed to participate in online surveys River samples: created via the use of banners, pop-ups, or other online devices that invite website visitors to take part in the survey E-mail list samples: purchased or otherwise procured from someone or some company that has compiled email addresses of opt-in members of the population of interest Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved Steps in a Sample Plan Step Action Description 1 Define the population. Create a precise description of the group under investigation using demographics, buyer behavior, or other relevant constructs. 2 Obtain a sample frame. Gain access to some master source that uniquely identifies all the units in the population with minimal sample frame error. 3 Decide on the sample method. Based on survey objectives and constraints, endeavor to select the best probability sample method, or if appropriate, select a nonprobability sample method that fits the research requirements. 4 Decide on the sample size. If a probability sampling plan is selected, use a formula; to be covered in the following chapter. 5 Draw the sample. Using the chosen sample method, apply the necessary steps to select potential respondents from the sample frame. 6 Validate the sample. Inspect some relevant characteristics of the sample (such as distribution of males and females, age ranges, etc.) to judge how well it matches the known distribution of these characteristics in the population. Copyright © 2020, 2017, 2014 Pearson Education, Inc. All Rights Reserved