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Polit & Beck Chapter 12: Sampling Plans

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Sampling
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The process of selecting representative units of a population. – Avoids Sampling Bias (systematic or random) *the more representative, the more you avoid bias*
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Representative sample
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Main characteristics closesly approximate those of the population
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Population
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an entire aggregate of cases
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Accessible Population
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– Composed of cases from the target population that are accessible to the researcher as study participants – Those whom the research can feasibly
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Target Population
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– the entire population – those whom the researcher wants to generalize or apply results
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Element
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the basic unit of a population about which information is collected- usually humans in nursing research
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Eligibility criteria
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defines population characteristics – Delimitations required to make population homogenous on key variables (e.g., 20-35 y/o NURS Majors)
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How does eligibility criteria Control extrenous (or attribute) variable
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– Eligibility criteria determines who’s in and who’s out rules it out certain people -Found in the method section
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Inclusion Criteria
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AKA eligibility criteria. It determines whether a person qualifies as a member of the population
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Exclusion Criteria
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defined in terms of characteristics that people must not possess For example: excluding people who do not speak English
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Sampling
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the processes of selecting a portion of the population to represent the entire population
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Sample
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Subset of population elements
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Sampling Bias
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the systematic overrepresentation or underrepresentation of some segment of the population in terms of a characteristic relevant to the research question
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Homogeneity
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restricting the selection of subjects to eliminate variability of the confounding variable
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Strata
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subpopulations that are mutually exclusive segments of a population based on specific characteristic For example: a population of RNs in the US could be divided into two strata based on gender
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Difference between Nonprobability verus Probability
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Probability every subject has the same chance in the study *prevents bias* Non probability every subject has the same chance in the study
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Nonprobability Sampling (Quantitative Research)
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– researchers select elements by nonrandom methods – less likely to produce representative samples – convenient sampling – has a lot of bias, may or may not know that
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Primary Methods of Nonprobability Sampling
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– Convenience Sampling +Snowball Sampling -Quota Sampling -Consecutive Sampling -Purposive Sampling
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Convenience Sampling
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uses the most readily available or most convenient group of people for the sample -known as volunteer sample
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Snowball Sampling
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– type of convenience sampling – referrals for potential participants are made by those already in the sample – network sampling
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Quota Sampling
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– divides the population into homogeneous strata to ensure representation of the subgroups in the sample -subjects are sampled by convenience – ex even number of men and woman or ethinicity
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Consecutive sampling
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taking all of the people from an accessible population who meet the eligibility criteria over a specific time interval, or for a specific sample size – ex all the people in the ER who came in between 10-11 or the first 100 people who comes in the ICU
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Strategies to Improve Nonprobability Sampling
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Matching: using info about subject characteristics to form comparable group Random Assignment (Randomization)
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Purposive Judgemental Sampling
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participants are hand picked to be included in the sample based on the researcher’s knowledge about the population
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Random Selection
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a process in which each element in the population has an equal, independent chance of being selected
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Probability Sampling Designs
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– random selection of elements from the population – more likely to produce representative sampling
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Probability Sampling Designs Types
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– Simply Random Sampling -Stratified Random Sampling – Cluster Sampling -Systematic Sampling
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Simple Random Sampling
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Random selection of elements from a sampling frame that enumerates all the elements
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Sampling Frame
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the list of population elements
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Stratified Random Sampling
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divides the population into homogenous subgroups from which elements are selected at random
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Proportionate Sample
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comparison between strata of proportionate saize
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Disproportionate Sample
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comparison between strata of unequal size, usually desired
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Cluster Sampling or Multistage Sampling
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successive selection of random samples from larger to smaller units
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Systematic Sampling
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– the selection of ever kth case from a list – for example every 10th person on the patient list
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Sampling interval
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– the standard distance between the selected elements – Population size is divided into desired sample size
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Sampling Error
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Refers to differences between the population values (e.g. the average age of the popluation) and sample values (e.g., the average age of the sample)
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Sample Size
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– the number of subjects in a sample
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Large Sample Size
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– Larger samples are preferable over small samples – larger samples enhance statistical conclusion validity – tend to be more representative but does not guarantee
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Power Analysis
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– an process to determine how large the sample size should be to test research hypothesis
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Types of Qualitative Sampling
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– Convenience and Snowball Sampling +Volunteer Sampling
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Maximum Variation Sampling
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entails purposely selecting cases with a wide range of variation
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Sampling confirming and disconfirming cases
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selecting cases that enrich and challenge the researchers’ conceptualizations
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Extreme Case Sampling
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selecting the most unusual or extreme case
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Typical case sampling
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selecting case that illustraste what is typical
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Criterion sampling
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Studying cases that meed a predetermined criterion of importance.
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Data Saturation
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Sampling to the point at which no new information is obtained and redundancy is achieved
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Sample size requirements
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# of variables # of group comparisons Instruments and statistics
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Qualitative samples
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Need data rich samples. • What persons in a sample would – Confirm my understanding? – Challenge or modify my understanding? – Enrich or deepen my understanding
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Qualitative samples
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Purposeful or theoretical sampling based on information needed (p. 318) 1. Maximum variation sampling 2. Homogenous sampling 3. Extreme/deviant case sampling 4. Intensity sampling 5. Typical case sampling 6. Theory-based sampling Confirming/disconfirming case
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Data Saturation
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May determine size
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Ethnography
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Key Informants serves as guides and interpreters of culture
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Phenomenology
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Experience and articulate it Works with a small sample
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Grounded Theory
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Theoretical Sampling: in which sampling decisions are guided in an ongoing fashion by the emerging theory Samples about 20-40 people
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Basic Critique of Sampling
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1. Type of sampling 2. Response rate? 3. Nonresponse bias?