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Chapter 5

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University of Toronto Scarborough
Nussbaum D

Chapter 5 Sample: a group of units selected from a larger group that is known as the population Survey research: research in which information is obtained from a sample of individuals through their responses to questions about themselves or others Selecting Research Participants You can study an entire population of interest by conducting a census: research in which information is obtained through responses that all available members of an entire population give to questions  Unfortunately, this is very time consuming and takes a lot of effort Sample Planning Define the Population In behavioural research, it has been found that:  Students, disabled persons, the elderly, and adult samples tend to report similar (not identical) levels of happiness.  The same is true of men and women, as well as white and African Americans  However, in countries around the world, happiness levels are very different.  Satisfaction with life is also different across cultures. This means cannot generalize happiness finding from population to population. Cross-population generalizability: exists when findings about one group, population, or setting hold true for other groups, populations, or settings  Also called external validity Define Sample Components Population: entire set of individuals or other entities to which study findings are to be generalized Elements: the individual members of the population whose characteristics are to be measured Sampling frame: a list of all elements in a population Representative sample: a sample that “looks like” the population from which it was selected in all respects potentially relevant to the study.  In an unrepresentative sample, some characteristics are overrepresented or underrepresentative. Random selection of elements maximizes sample representativeness. Sample representativeness depends on the amount of sampling error: the difference between the characteristics of a sample and the characteristics of the population from which it was selected.  The larger the sampling error, the less representative the sample and the less generalizable the findings obtained from that sample. Estimating Sampling Error Inferential statistics: mathematical tool for estimating 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. Sampling distribution: hypothetical distribution of a statistic across all the random samples that could be drawn from a population  For many statistics, including the mean, the graph has a „normal shape‟ Random sampling error: differences between the population and the sample that are due only to chance factors (random error) not to systematic sampling error.  May or may not result in an unrepresentative sample Sample statistic: the value of a statistic, such as a mean, computed from sample data Population parameter: the value of a statistic, such as a mean, computed using the data for the entire population; a sample statistic is an estimate of a population parameter In a normal distribution, a predictable proportion of cases falls within certain ranges. Inferential statistics takes advantages of this feature and allows researchers to estimate how likely it is that, given a particular sample, the true population value will be within some range of statistics. Sampling methods Probability sampling methods: allow us to know in advance how likely it is that any element of a population will be selected for the sample Nonprobability sampling methods: do not allow us to know in advance the likelihood of selecting each element Probability of selection: the likelihood that an element will be selected from the population for inclusion in the sample  In a census of all elements of a population, the probability that any particular element will be selected is 1.0. If half of the elements in the population are sampled on the basis of chance, the probability of selection for each element is one half, or 0.5. As the size of the sample decreases as a proportion of the population, so does the probability of selection. Random sampling (cases are selected only on the basis of chance) is not totally haphazard. Researchers actually have to be very methodical to ensure a completely random selection. Probability Sampling Methods Probability sampling methods are those in which the probability of selection is known and is not zero (to make sure there is some chance of selecting element). These methods randomly select elements, meaning that they have no systematic bias (), nothing but chance determines which elements are selected for the study.  This makes them much more desirable than nonprobability samples when the goal is to generalize to a larger population. Simple Random Sampling Def.: every sample element is selected only on the basis of chance, through a random process  Requires some procedure that generates numbers or otherwise identifies cases strictly on the basis of chance o One type of procedure is random-digit dialing: random dialing by a machine of numbers within designated phone prefixes, creating a random sample Systematic Random Sampling Def.: Variant of simple random sampling. The first element is selected randomly from a list or from sequential files, and then every nth element is selected.  Have to be cautious of periodicity: the sequence varies in some regular, periodic pattern.  If the sampling interval (number of cases from one sampled case to another) is a number, the same as the periodic pattern, all the cases selected will be in the same position. o In reality, periodicity and the sampling interval are rarely the same. Stratified Random Sampling Def.: uses information known about the total population prior to sampling to make the sampling process more efficient.  All elements in the population (i.e. the sampling frame) are distinguished according to their value on some relevant characteristic. That characteristic forms the sampling strata. Then the elements are sampled randomly from within these strata. Using this method requires more information prior to sampling than is the case with simple random sampling. All of the elements must be able to be categorized in only one stratum so the size of each stratum must be known. This method is more efficient than drawing a simple random sample because it ensures appropriate representation of elements across strata. It is commonly used in national surveys. Proportionate stratified sampling: ensures that the sample is selected so that the distribution of characteristics in the sample matches the distribution of the corresponding characteristics in the population. Disproportionate stratified sampling: sampling in which elements are selected from strata in different proportions from those that appear in the population Cluster Sampling Def.: sampling in which elements are selected in two or more stages  Useful when a sampling frame of elements is not available, as often is the case for large populations across a wide geographic area. Cluster: naturally occurring, mixed aggregate of elements of the population, with each element appearing in one and only one cluster.  Schools can serve as clusters for sampling students Drawing a cluster sample is two stepped: 1. The researcher draws a random sample of naturally occurring clusters. A list of clusters should be much easier to obtain than a list of all individuals in each cluster in the population. 2. The researcher draws a random sample of elements from each selected cluster a. This is a fraction of the total clusters are involved so getting a sampling frame should be easier Nonprobability Sampling Methods Availability sampling: sampling in which elements are selected on the basis of convenience  Also known as haphazard, accidental, or convenience sampling Quota Sampling Def.: quotas are set to ensure that the sample re
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