A related nonprobability approach is purposive sampling, which involves sampling with a specific purpose in mind. A researcher who uses a purposive sampling ins more likely to get the opinions of the target population; however, he or she is also likely to overweight subgroups in the population that are more readity accessible. Modal instance sampling involves sampling the most frequent ( or typical ) case. Expert sampling involves targeting a sample of persons with known or demonstrable experience and expertise in a given area. For example, a researcher wishing to determine the specific effect of the Americans with Disabilities Act on the day-to-day operations of a facility might survey facility managers who are responsible for implementing such policies. In quota sampling, individuals are selected non randomly according to a predetermined quota. Consider the ticket holder example discussed earlier. The researcher might set out to capture a sample in which45 percent are season ticket holders and 55 percent hold tickets to an individual game. In nonrandom quota sampling, the researcher might contact fans until the percentage for one or the other category for the sample is met. If, for instance, the researcher desires to collect a sample of 1,000 fans and, of the first 600 fans sampled, 450 were season ticket holders, the researcher might then continue by surveying only single-event ticket holders until the final sample of 1,000 is reached. In contrast, researchers who want to include all opinions or views, even at the expense of losing proportionality, can turn to heterogeneity sampling. In essence, heterogeneity sampling, or diversity sampling, is almost the opposite of modal instance sampling, because the goal is to obtain all opinions rather than just the most popular, or typical, opinion. Finally, snowball sampling is particularly useful when a researcher is trying to reach a population that is inaccessible or hard to find. In snowball sampling, the researcher identifies an individual who meets the criteria for inclusion in the study, then asks him or her to recommend others who also meet the criteria. For example, a sport management researcher who wishes to analyze the experiences of sport managers in their first year after graduation might contact recent graduates of a particular sport management program who are new employees in a sport organization and ask them to distribute surveys to similar individuals in their new organization.
A related nonprobability approach is purposive sampling, which involves sampling with a specific purpose in mind. A researcher who uses a purposive sampling ins more likely to get the opinions of the target population; however, he or she is also likely to overweight subgroups in the population that are more readity accessible. Modal instance sampling involves sampling the most frequent ( or typical ) case. Expert sampling involves targeting a sample of persons with known or demonstrable experience and expertise in a given area. For example, a researcher wishing to determine the specific effect of the Americans with Disabilities Act on the day-to-day operations of a facility might survey facility managers who are responsible for implementing such policies. In quota sampling, individuals are selected non randomly according to a predetermined quota. Consider the ticket holder example discussed earlier. The researcher might set out to capture a sample in which45 percent are season ticket holders and 55 percent hold tickets to an individual game. In nonrandom quota sampling, the researcher might contact fans until the percentage for one or the other category for the sample is met. If, for instance, the researcher desires to collect a sample of 1,000 fans and, of the first 600 fans sampled, 450 were season ticket holders, the researcher might then continue by surveying only single-event ticket holders until the final sample of 1,000 is reached. In contrast, researchers who want to include all opinions or views, even at the expense of losing proportionality, can turn to heterogeneity sampling. In essence, heterogeneity sampling, or diversity sampling, is almost the opposite of modal instance sampling, because the goal is to obtain all opinions rather than just the most popular, or typical, opinion. Finally, snowball sampling is particularly useful when a researcher is trying to reach a population that is inaccessible or hard to find. In snowball sampling, the researcher identifies an individual who meets the criteria for inclusion in the study, then asks him or her to recommend others who also meet the criteria. For example, a sport management researcher who wishes to analyze the experiences of sport managers in their first year after graduation might contact recent graduates of a particular sport management program who are new employees in a sport organization and ask them to distribute surveys to similar individuals in their new organization.
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A related nonprobability approach is purposive sampling, which involves sampling with a specific purpose in mind. A researcher who uses a purposive sampling ins more likely to get the opinions of the target population; however, he or she is also likely to overweight subgroups in the population that are more readity accessible. Modal instance sampling involves sampling the most frequent ( or typical ) case. Expert sampling involves targeting a sample of persons with known or demonstrable experience and expertise in a given area. For example, a researcher wishing to determine the specific effect of the Americans with Disabilities Act on the day-to-day operations of a facility might survey facility managers who are responsible for implementing such policies. In quota sampling, individuals are selected non randomly according to a predetermined quota. Consider the ticket holder example discussed earlier. The researcher might set out to capture a sample in which45 percent are season ticket holders and 55 percent hold tickets to an individual game. In nonrandom quota sampling, the researcher might contact fans until the percentage for one or the other category for the sample is met. If, for instance, the researcher desires to collect a sample of 1,000 fans and, of the first 600 fans sampled, 450 were season ticket holders, the researcher might then continue by surveying only single-event ticket holders until the final sample of 1,000 is reached. In contrast, researchers who want to include all opinions or views, even at the expense of losing proportionality, can turn to heterogeneity sampling. In essence, heterogeneity sampling, or diversity sampling, is almost the opposite of modal instance sampling, because the goal is to obtain all opinions rather than just the most popular, or typical, opinion. Finally, snowball sampling is particularly useful when a researcher is trying to reach a population that is inaccessible or hard to find. In snowball sampling, the researcher identifies an individual who meets the criteria for inclusion in the study, then asks him or her to recommend others who also meet the criteria. For example, a sport management researcher who wishes to analyze the experiences of sport managers in their first year after graduation might contact recent graduates of a particular sport management program who are new employees in a sport organization and ask them to distribute surveys to similar individuals in their new organization.
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