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HLTD04H3 (42)

Ch 11 Notes

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Health Studies
Toba Bryant

Ch 11 Sampling - Biases in sampling due to o Not all students available at the same time, may have exclusions o The decisions about whom to approach may be influenced by judgments. o Anyone not enrolled in that specific course will get excluded. - Element/ unit- a single case in the population, usually a person. - Population-all cases about which one seeks knowledge. To which the researchers conclusions are meant to apply. - Sampling frame: the list of elements from which the sample will be selected. - Sample- the elements selected for investigation, a subset of population. – may involve probability or non probability - Representative sample- a sample that is microcosm of the population. Represents its essential characteristics. Used in probability sampling process. - Probability sample- a sample selected using a random process. Gives everyone equal chance to be picked. Minimizes sampling error. - Non-probability sampling- a sample selected using non random method. Giving some units a better chance to be selected. - Sampling error- errors of estimation that occur if there is a difference between the characteristics of sample and a population from which it was selected. Can occur even in random sampling. - Non-response- refusal of a unit to participate in a study, can’t be contacted, or don’t supply the required data. - Census: data collected from all elements in a population. o The enumeration of all members of a population of a nation state- national census. - Sampling ratio: n/N (sample size /total population) - Sampling interval: every 20 participant. - Three sources of bias: o Not using a random method to pick the sample. Human judgment will affect the selection process. Avoided by probability sampling. o The sampling frame or list of potential subjects is inadequate- if excludes cases, even random sampling cant help o Some people in the sample refuse to participate or cannot be contacted- non response, sample may be non representative. - Sampling error o Important to be aware of sampling error, ex: one too many in on category o Important to know that sampling error can’t be completely eliminated. o Can be reduced by probability sampling by the known probability of error - Types of probability sample: o Simple random sample- most basic form. Each unit has equal probability of inclusion.  Key points: • Define the population • Select or devise a comprehensive sampling frame. • Decide sample size • List all students in population and assign them random numbers • Using a table of random numbers select n. • The students that match the n number get included. o No opportunity for bias & no issues with participant availability. • The use of table of random numbers- • Simple random sampling without replacement- not using a same participant twice. o Systematic sample:  Selected directly from sampling frame, without using random sampling.  Periodicity- no inherent ordering or pattern in the sampling frame o Stratified random sampling  Stratifying the population/ dividing it in subgroups, by criterion and selecting a simple random sample or systematic sample from each of the resulting strata.  Ensures that the sample is distributed in the same way as in the population in terms of stratifying criterion.  Is feasible only when it is relatively easy to identify and allocate units to strata.  More then one stratifying criterion can be used. o Multi staged cluster sampling  The primary sampling unit is aggregate of units of population.  Involves cluster and subunits within those clusters.  More economic in large populations.  No adequate sampling frame required.  Entails stratification  Allows interviews to be far more geographically concentrated. - Qualities of probability sample o Allows one to make inferences from the sample to the population from which it was selected. o Can be generalized. - Sample size: o Effected by cost and time o Absolute and relative sample size  Absolute size is important, not the proportion of the population it comprises.  Increasing the size of sample increases the precision of the estimates derived from it.  Large sample can’t guarantee precision.  Large samples decreasing sampling error o Non- Response  Refusal of some people, from participating in study.  Response rate: Percentage of the sample that participate in the study.  Response rate= Number of usable questionnaires x 100 Total sample- unsuitable or uncontactable Members of the sample  there is a decline in response rate  Things like: subject matter of research, type of respondent and level of effort expended on improving the number of cooperating respondents affect response rates.  Mailed questionnaires: prone to non response  Following up with people who don’t respond is a good way to get response o Heterogeneity of the population  When population heterogeneous, samples drawn are likely to be highly varied. Should have larger sample size to maximize accurate representation.  When homogeneous, variation is less. o Kind of analysis:  Contingency table: showing relationship between two variables.  In a 2x2 table- here are four cells in which the cases can fall. - Types of non-probability sampling o Includes all forms of sampling that’s not conducted according to probability sampling methods. o Quota sample- considered as good as probability sampling.  Convenience sampling- when elements are readily available. • Good for pilot studies. • May not be used to test newly create scales for reliability or to generate ideas for further research.  Snowball sampling- a form of convenience sampling • Contact one small group of people who are relevant to the research topic, and then use them to establish contacts with others. • It’s not random. • Very unlikely to be representative of the population. • Used in qualitative studies. • Best used when researcher wants to focus on the relationships between people and tracing connections.  Quota sampling • Infrequently used in academic social research. • Used in commercial research. • Aim to produce a sample that reflects a population in terms of the relative proportion of people in different categories. • Is not carried out r
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