Ch3 Surveys and Sampling .pdf

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University of Maryland
Business and Management
BMGT 230
Kazim Ruhi

Ch3 Surveys and Sampling Wednesday, January 29, 2014 11:46 AM Three Ideas of Sampling 1. Draw a sample. 1. chosen from the population. 2. Samples that over- or underemphasize some characteristics of the population: biased a. we should select sample at random 2. Randomize • difference between the sample and population values is considered as sampling error or sampling variability 3: The Sample Size Is What Matters • How large a random sample do we need for the sample to be reasonably representative of the population? • sample size matters - regardless of the population size ** important! parameter & statistic if the sample estimate the corresponding parameters accurately, the sample is representative. ---- Homework question: researcher went to car dealer and ask every 20th people if they care about environment.What's the population? • Hey! The population is people not people who go to dealers! • people who go to dealers is the Sampling Frame. • sample is the everything 20th people! -- population parameter of interest -- Census everyone Sampling Techniques Nonstatistical sampling --> not random sampling Convenience • Collected in the most convenient manner for the researcher (ask whoever is around) • Bias: Opinions limited to individuals present Voluntary • Individuals choose to be involved.These samples are very susceptible to being biased because different people are motivated to respond or not. Often called “public opinion polls,” these are not considered valid or scientific • Bias: Sample design systematically favors a particular outcome Statistical Sampling --> Individuals in the sample are chosen based on known or calculable probabilities 1. Simple Random Sampling • Every possible sample of a given size has an equal chance of being selected • The simplest way to obtain a sample is to draw names out of a hat • The sample can be obtained using a table of random numbers or computer random number generator -- Sampling Frame • to select sample at random, we need to define a sampling frame • from the whole population, we obtain a List of population - a list of individuals from which the sample is drawn. • Examples • Phone book • Registered voter list • Membership lists • aka Effective population -- 2. Stratified Random Sampling • Divide population into homogenous subgroups (called strata) according to some common characteristic • must determine first: population is dividable • e.g. gender / major • Select a simple random sample from each subgroup • Combine samples from subgroups into one 3. Cluster Sampling • Divide population into several “clusters,” each representative of the population (e.g., county) • here, the population cannot be divided into distinct groups • e.g. when measuring fertilizer use, because there are too many fertilizer brands, types, we measure clusters - counties - • Select a simple random sample of clusters • All items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling technique • e
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