# final information

40 views2 pages
Published on 16 Apr 2011
School
UTSC
Department
Health Studies
Course
HLTB15H3
HLTA10
Tutorial- final review
Snowball sampling- survey people and ask them to refer others to get infor mation
Accidental sampling- drawn from population that is readily available and convenient
Non response bias- people who have been asked dont do it or stop half way through
Selection bias- group may not be representative of entire population
Sampling bias- the people you are selecting from dont have an equal chance of being included
Observer bias- any sor t of bias that the researcher introduces (i.e. asking questions in a leading
manner, or preconceived notions)
Multivariate analysis-
Unstructured interviews- they allow participant to say more about the subject at hand
Secondary data- cancer registries are an example
Primary data- data is collected by the researcher
Secondary data – data is collected from an organization, etc., that has already conducted the
research
Qualitative research in terms of sampling:
To determine sample size enough people from the population to avoid sampling bias and not
represent full diversity/heterogeneity of the population you are looking at; when you have a very
small sample size, one person (outlier) can change and skew the whole thing
-you know youve reached your sample size when you have reached satura tion (you know
youve reached this when you stop getting new data from participants)
-often you dont define your sample size at the end because you can build it up
Quantitative- decide sample size at beginning of research using a mathematical calculation
-takes into account variation, accuracy, conf idence
Random/Probability Sampling Design examples- simple random sampling (each element has
independent and equal chance of being selected), stratified sampling (stratify population based
on specific characteristics), cluster sampling (sampling pop is divided into groups or clusters and
elements from each group is selected)
Non Random sampling Design examples- accidental, pur posive, snowball, quota sampling (you
determine before hand what you have to sample – you have a quota and its your goal’ when the
quota is reached, data collection is complete therefore not random)
Research Tools
Observational research: participant observation (researcher participates with or without their
knowing; observe and take notes and use this as their data- qualitative), non participant
observation (researchers obs erve situation, but is not participating)
www.notesolution.com
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## Document Summary

Snowball sampling- survey people and ask them to refer others to get information. Accidental sampling- drawn from population that is readily available and convenient. Non response bias- people who have been asked don"t do it or stop half way through. Selection bias- group may not be representative of entire population. Sampling bias- the people you are selecting from don"t have an equal chance of being included. Observer bias- any sort of bias that the researcher introduces (i. e. asking questions in a leading manner, or preconceived notions) Unstructured interviews- they allow participant to say more about the subject at hand. Primary data- data is collected by the researcher. Secondary data data is collected from an organization, etc. , that has already conducted the research. You know you"ve reached your sample size when you have reached saturation (you know you"ve reached this when you stop getting new data from participants)