biostatist

Sampling techniques

Population : all the people living in an area, frequently of a country.
Common speech
In statistics Population is any collection of individuals in which we may interested, where these individuals may be anything.

Important statistical terms

Population: a set which includes all measurements of interest to the researcher (The collection of all responses, measurements, or counts that are of interest) Sample: A subset of the population

If we are interested in:

population
Characteristics of Iraqi people
All people in Iraq
Treatment of diabetics
all diabeticsics
Failure rate in 3rd year of college of medicine
Height of males in 3rd year of college of medicine – Baghdad university


If we are interested in the toss of two coins then the population is


Can we choose your class for:1- The opinion about the possibility of organizing alternative activities in Baghdad. 2- A poll about the opinion on different political leaders.3- The opinion about the possible choices for a end- of year trip with student of your class

Imagine that we are going to make studies on: Percentage of Iraqi population that had access to internet. The population we would to ask is bigger than 30 million - Time Money at time of interview we miss some people It is better to choose sample in appropriate way so that we can obtain later conclusion

-- the selection methods for elements of population (sampling methods) Sample size - Reliability degree of the conclusions that we can obtain, this is, an estimation of an error that we are going to have (in term of probability).


A sampleA finite part of a statistical population whoseproperties are studied to gain informationabout the whole– A set of respondents selected from a largerpopulation for the purpose of a survey orexperiment.

Sampling The act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population.


Random sampling Stratified sampling Cluster sampling Systematic sampling other types of sample technique
Probability sampling
Non- probability sampling
Convenience sampling Purposive sampling snowball Quota sample



Target Population: The population to be studied/ to which the investigator wants to generalize his results Sampling Unit: smallest unit from which sample can be selected Sampling frame List of all the sampling units from which sample is drawn Sampling scheme Method of selecting sampling units from sampling frame

Non probability samples

Probability of being chosen is unknown Cheaper- but unable to generalise potential for bias

Probability samples

Random sampling Each subject has a known probability of being selected Allows application of statistical sampling theory to results to: Generalise Test hypotheses

Conclusions

Probability samples are the best Ensure Representativeness Precision

Simple random sample: It requires: Sample frame: a numerical list of all observations (or units) composing the population Sample fraction: sample size to the total population
Lottery method Computer generated random sampling Random number table (random digit)

Simple random sampling

Systematic random sampling – samples according to a ruleE.g., every fifth person is chosenProblems: same as simple random. Rule must not lead to bias. Systematic sampling

Systematic sampling

Cluster sampling
Cluster: a group of sampling units close to each other i.e. crowding together in the same area or neighborhood

Section 4

Section 5
Section 3
Section 2
Section 1
Cluster sampling

Stratified sampling Multi-stage sampling

Stratified sampling – break the sample into various subgroups or strata and sample from them.Must have good knowledge of strata

Nonprobability sampling

Qualitative researchers are not as concerned about representativeness Relevance to the research topic Importance of context Sample size does not have to be determined in advance. Selection of cases gradually over time Important: many statistics assume random sampling



Types of nonprobability samplingConvenience sampling (haphazard, accidental) – sample whoever is available.Used by both quantitative and qualitative researchersProblems no representativenessIt is haphazard, can be very biasedNot random (avoid using word)

Purposive sampling - Use judgment and deliberate effort to pick individuals who meet a specific criteria. Especially good for exploratory or field research.Appropriate for at least 3 situations.1. select cases that are especially informative.E.g., college coaches and championships2. desired population for the study is rare or very difficult to locate.E.g., prostitutes3. case studies analysis – find important individuals and study them in depth.

Purposive sampling - Use judgment and deliberate effort to pick individuals who meet a specific criteria. Especially good for exploratory or field research.Appropriate for at least 3 situations.1. select cases that are especially informative.E.g., college coaches and championships2. desired population for the study is rare or very difficult to locate.E.g., prostitutes3. case studies analysis – find important individuals and study them in depth.


Systematic error (or bias) Inaccurate response (information bias) Selection bias Sampling error (random error)
Errors in sample