ThiQar college of Medicine Family & Community medicine dept ThiQar college of Medicine Family & Community medicine dept. Biostatistics Lecture Third stage by: Dr. Muslim N. Saeed December 22nd ,2016

Sampling techniques

Common speech Population : all the people living in an area, frequently of a country. 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 – ThiQar university

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 sample A finite part of a statistical population whose properties are studied to gain information about the whole population. – A set of respondents selected from a larger population for the purpose of a survey or experiment.

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.

Probability sampling Random sampling Stratified sampling Cluster sampling Systematic sampling other types of sample technique 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 sampling Systematic random sampling – samples according to a rule E.g., every fifth person is chosen Problems: same as simple random. Rule must not lead to bias.

Systematic sampling

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

Cluster sampling Section 1 Section 2 Section 3 Section 5 Section 4

Must have good knowledge of strata 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 sampling Convenience sampling (haphazard, accidental) – sample whoever is available. Used by both quantitative and qualitative researchers Problems no representativeness It is haphazard, can be very biased Not random.

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 championships 2. desired population for the study is rare or very difficult to locate. E.g., prostitutes 3. case studies analysis – find important individuals and study them in depth.

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