SAMPLING Chapter 7. DESIGNING A SAMPLING STRATEGY The major interest in sampling has to do with the generalizability of a research study’s findings Sampling.

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SAMPLING Chapter 7

DESIGNING A SAMPLING STRATEGY The major interest in sampling has to do with the generalizability of a research study’s findings Sampling is common in research studies but is not always necessary

Defining the Sample Components and the Population A sample is a subset of a population Sampling units – the people from whom data are actually collected A key issue is defining a sample precisely so that it is clear what population the sample is supposed to represent

Evaluating the Sample’s Generalizability Can the findings from a sample be generalized to the population from which the sample was drawn? Sampling error – the difference between the characteristics of a sample and the characteristics of the population from which the sample was selected Target population – the population to which generalizations are made

Assessing the Diversity of the Population Researchers should not assume that phenomenon studied are identical across all people Studies should be replicated in different settings and different populations to assess the generalizability of findings across different groups of people

Considering a Census Census – studying the entire population of interest Why social workers do not often conduct a census –Very expensive –Very time consuming

SAMPLING METHODS Sampling Frame – the list from which elements of the population were selected Probability Sampling – relies on random, or chance, selection procedures where the likelihood (or probability) of being selected is known Nonprobabilty Sampling – the probability or chance of being selected is not known; systematic selection bias is assumed

Probability Sampling Methods The probability of selection is known, and systematic sampling bias is ruled out Sampling error is still a concern –The degree of difference between a sample and the population from which it was drawn Four Random Methods (1) Simple random (3) Stratified (2) Systematic (4) Cluster

Simple Random Sampling Procedures for sample selection are strictly based on chance –Flip of a coin –Lottery –Random numbers table Each case has an equal chance of being selected

Systematic Random Sampling Simple random sampling procedures are applied to pick the first case from a list and then every n th case on the list is selected Ideal when cases are arranged sequentially and a tangible list of names is not available (e.g., case records stored in a filing cabinet)

Stratified Random Sampling Simple random sampling procedures are applied to strata – subgroups defined by a particular characteristic of the sample –Proportionate Sampling – reduces sampling error in the sample’s distribution of a particular variable –Disproportionate Sampling – permits “over sampling” of minority groups to allow for meaningful group comparisons

Cluster Random Sampling Simple random sample procedures are applied to clusters, or naturally occurring groups or aggregates – schools, social agencies, neighborhoods Ideal when a sampling frame is not available and cases are spread out across a large geographic areas or many organizations

Nonprobability Sampling Methods Random procedures are NOT used Sampling bias is present, and samples are not considered representative of the populations from which they were drawn –Findings do not generalize Four types (1) Availability (3) Quota (2) Purposive (4) Snowball

Availability Sampling Cases are selected based on their availability to the researcher Also called haphazard, accidental, or convenience sampling Useful for exploratory or preliminary research when one is trying to gain an initial sense of attitudes or an idea about a new setting

Quota Sampling Available cases are selected according to defined quotas – subgroups are defined by a particular characteristics of the sample A slight improvement over availability sampling since sample proportions match the population on a particular feature (quota) The sample is not representative of the population

Purposive Sampling Sample elements are selected based on –elective criteria that define a unique group –targeting knowledgeable individuals (key informants) Ideal for case study research Sampling continues until –Completeness: data are comprehensive –Saturation: little or no new knowledge is added

Snowball Sampling Select one member of a population, and after speaking to him/her ask that person to identify others in the population Ideal for studying “hard to reach” populations (e.g., homeless, criminals, prostitutes) Targeted incentives may be used to ensure diversity in the sample

LESSONS ABOUT SAMPLE QUALITY Features of Quality Samples –The population from which the sample was drawn is clearly specified –The sampling method (procedures) are clearly specified –High response rate –Researcher limits discussion about implications of their findings to the population from which they actually sampled

DETERMINING SAMPLE SIZE Smaller Sample Yields higher sample error With homogeneous populations For simple analysis When strong relationship between variablesis expected Larger Sample Yields lower sample error With heterogeneous population For complex analysis When weak relationship between variables is expected

SUMMARY Sampling is a powerful tool for social work research The major aim is to draw a sample that is representative of the larger population

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