Week 2 (Sampling) Flashcards
What is a representative sample
The sample contains sub-groups of people in direct proportion to their prevalence in the general population
-Means sample accurately reflects the characteristics of the population
What is external validity
The extent to which the results of one study can be generalised across settings, time, and populations
What is “sampling bias” and a “biased sample”
-A sampling bias is the systematic tendency to over or under-represent some categories in a sample.
-A biased sample is a sample in which members of a sub-group of the larger population are over or under-represented.
What does Standard error (SE) measure
How well the means are similar to the population mean, and our sample is likely to be an accurate reflection of that population.
What does a small standard error indicate
That sample means are similar to the population mean, and our sample is likely to be an accurate reflection of that population.
How to calculate standard error
Standard deviation divided by the square root of the number of samples.
Probability sampling
-Everyone in the population has a specific/known chance of being selected
-Requires a clearly defined population we can have access to
-They are most likely representative of the population, so we should use these samples whenever feasible
-It can reduce the amount of sampling error that exists in a study
Different types of probability sampling
-Simple random sampling
-Systematic random sampling
-Stratified sampling
-Cluster sampling
Simple random sampling
-A sample is chosen randomly from the population so everyone has an equal chance of being selected.
-Reduces sampling error by choosing from all members of the population to represent the population
-Sample units are selected randomly
-Can be selected using computers, or manually
Systematic random sampling
-Method that requires selecting samples based on a system of intervals in a numbered population.
-Random starting point but with a fixed, periodic interval
-Sample selected by taking every nth case from a list of the target population
-Sample units are selected systematically via a single technique
Stratified sampling
-Introduces an extra step before sampling the population: determining groups “strata” within our population whose proportions we want our sample to reflect to enhance it’s representativeness
-Involves dividing the population into groups
-The proportion of a group in the sample should be equal to the proportion of that group in the population
-Reduces bias by equating proportions in the sample and the population
Cluster sampling
-Clusters of individuals are identified, and then a subset of clusters is randomly chosen to sample from.
-The sample is chosen randomly from clusters identified in the population.
-Makes it easier to choose members randomly from smaller clusters to better represent the population
-Can ignore segments of the population that are not in the clusters chosen for the sample
Non probability sampling
-The chance for each unit in our population to be selected is unknown and is not the same
-Individuals not chosen randomly
Types of non-probability sampling
-Convenience sampling
-Purposive sampling
-Quota sampling
-Snowball sampling
Convenience sampling
-A sample is chosen from the people who are available to participate in the research
-Also referred to as “availability sampling” or “volunteer sample”
