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Chapter 5

Chapter 5.docx

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David Nussbaum

Chapter 5 Sampling and survey research Selecting research participants 1. Studying entire population a. Census 2. Studying sample of the population a. Sample Sample planning 1. Answer two questions a. What population will you select cases? b. What method will you use to select cases from this population? Define the population 2. Cannot generalize findings from one population to another population a. E.g. from China to Canada 3. Therefore must define population Define sample components 1. Population a. The entire set of individuals or other entities to which study findings are to be generalized 2. Elements a. The individual members of the population whose characteristics are to be measured 3. Sampling frame a. A list of all elements in a population 4. Representative sample a. A sample that looks like the population from which it was selected in all respects potentially relevant to the study b. In an unrepresentative sample, some characteristics are over or underrepresented c. Random selection of elements maximizes sample representativeness 5. Sample generalizability depends on sampling error a. The difference between the characteristics of a sample and characteristics of the population b. The larger the sampling error, the less representative the sample c. Thus the less generalizable the findings obtained form that sample Estimating sampling error 1. Tool for calculating sampling error is called inferential statistics a. How likely it is that a statistical result based on data from a random sample is representative of the population from which the sample is assumed to have been selected 2. Sampling distributions for many statistics have a normal distribution 3. Shape is produced by random sampling error because of chance a. Differences between the population and the sample that are only due to chance factors and not to systematic sampling error b. May or may not result in an unrepresentative sample c. The magnitude of sampling error due to chance factors can be estimated statistically 4. Any sample distribution is only one of many possible sample distributions of the populations 5. Sample statistic a. The value of a statistic computed from sample data 6. Population parameter a. The value of a statistic computer using the data for the entire population b. The sample statistic is an estimate of a population parameter 7. The more cases in the randomly selected sample, the more confident we can be in a given sample estimate a. i.e. the confidence limits will be smaller (the extremes of the bell curve) Sampling methods 1. Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample are called probability sampling methods a. You can only make a statistical estimate of sampling error for a probability based sample 2. Non-probability sampling methods do not allow us know in advance the probability of an element being selected 3. Probability of selection a. The likelihood that an element will be selected from the population for inclusion in the sample b. As the size of the sample decreases as a proportion of the population, so does the probability of selection Probability sampling methods 1. A random sample of 1000 from a population of 1 million is better than a random sample of 100 from a population of 10,000 2. The four most common methods for drawing random samples are a. Simple random sampling b. Systematic random sampling c. Stratified random sampling d. Cluster sampling Simple random sampling 1. Generates numbers based on chance 2. The probability of selection in a true simple random sample is equal for each element Systematic random sampling 1. Variant of simple random sampling 2. First element is randomly selected from a list, then every nth element is selected 3. Convenient for drawing random samples when population elements are arranged sequentially 4. Disadvantage a. Possibility of periodicity i. Sequence varies in some regular pattern that occurs periodically 1. E.g. house selected as every nth element is always the northwest corner Stratified random sampling 1. Uses information known about the total population prior to sampling to make the sampling process more efficient 2. All elements in the population are distinguished according to their value on some relevant characteristic a. This characteristic forms the sampling strata i. Elements are sample randomly from within each strata 3. Example: a. Race are the population of interest i. Each race forms a strata 1. From there randomly sample from each strata 4. This method is more efficient than drawing a simple random sample because it ensures appropriate representation of elements across strata 5. Two types of stratified sampling a. Proportionate i. Sample is selected so that the distribution of characteristics in the sample matches the distribution of corresponding characteristics in the population b. Disproportionate i. Sample does not match the distribution of the population Cluster sampling 1. Useful when a sampling frame of elements is not available 2. Applicable often for large populations spread across a wide geographic area 3. Cluster is a naturally occurring mixed aggregate of elements of the population a. With each element appearing in one cluster i. Example: 1. Country is large cluster a. States are smaller clusters (select for random states) i. Cities are smaller clusters within states (select for random cities) 1. Schools are smaller clusters within cities (select for random schools) a. Elements randomly sampled from within schools Nonprobability sampling methods 1. Availability sampling a. Does not use random selection procedure b. Do not know if a sample is representative of the larger po
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