Chapter 12: Selecting a Sample Flashcards
Sampling
- process of selecting “units” from a population of interest
- by studying the sample we may fairly generalize our results back to the population from which they were chosen
Target Population
total group of individuals from which the sample might be drawn
Generalizability
extent to which we can apply the findings of our research to the target population we are interested in
Sampling Bias
- one of the problems that can occur when selecting the sample
- sampling has to reflect the characteristics of the target population
Sampling Design/Strategy
the way you select the individuals that will be part of your sample
Sampling Frame
a list identifying each individual in the study population
Sample Statistics
findings based on the information obtained from your respondents (sample)
In Quantitative Research
- quantify data and generalize results from a sample to the population of interest
- to measure the incidence of various views and opinions in a chosen sample
- sometimes followed by qualitative research which is used to explore some findings further
- usually large number of cases representing the population
- random selection
In Qualitative Research
- gain understanding of underlying reasons and motivations
- provide insights into the setting of a problem, generating ideas and/or hypotheses for later quantitative research
- uncover prevalent trends in thought and opinion
- usually a small number of non-representative cases
- respondents are selected to fulfill a given quota
Quantitative Research: Random Sampling Techniques
- simple random sampling
- systematic random sampling
- stratified random sampling (proportional, disproportional)
- cluster random sampling (one-stage, two-stage)
Quantitative Research: Non-Random Sampling Techniques
- convenience sampling
- quota sampling
- purposive sampling
- snowball sampling
Steps in Random Sampling
- define population
- choose sample size
- list population
- assign numbers to the units
- find random numbers
- select your sample
Advantages of Random Sampling
- highly representative sample of the population
- reduced potential human bias in selection of the sample
- allows generalizations with external validity
Disadvantages
- can only be carried out if the list of the population is available and complete
- challenging to access that list
- some populations are expensive and time consuming to contact
Steps in Systematic Random Sampling
- defining the population
- choosing your sample size
- listing the population
- assigning numbers to the cases
- calculating the sampling fraction
- selecting the first unit
- selecting your sample
Advantages Systematic Random Assignment
- reduce the potential for human bias in the selection of cases
- allows statistical conclusions
- systematic procedure can be viewed as superior because it improves the potential for the units to be more evenly spread over the population
Disadvantages
- only if a complete list of the population is available
- list has to have some kind of standardised arrangement
Stratifies Random Sampling
when interested in particular strata (meaning groups) within the population
Stratifies Random Sampling: Steps
- defining the population
- choosing the relevant stratification
- listing the population
- listing the population according the chosen stratification
- choosing your sample size
- calculating a proportionate stratification
- using a simple random or systematic sample to select your sample
Proportionate Stratified Random Sample
size of each strata is proportionate to the population size of the strata when looked across the entire population
Disproportionate Stratified Random Sample
different strata do not have the same sampling fractions as each other
Cluster Random Sampling
- researcher selects groups or clusters and then from each cluster the researcher selects the individual subjects by either simple random or systematic random sampling
One-step Stage Cluster Sample
when the researcher includes all individuals from all the randomly selected clusters as sample
Two-step Cluster Sample
when the researcher only selects a number of individuals from each cluster by using simple or systematics random sampling
Non-Random Sampling: Convenience Sampling
subjects are selected because of their convenient accessibility and proximity to the researcher
Non-Random Sampling: Quota Sampling
researcher selects people according to some fixed quota
Non-Random Sampling: purposive sampling
sample is selected based on knowledge of a population and the purpose of the study
Non-Random Sampling: Snowball Sampling
used by the researcher to identify potential subjects in studies where subjects are hard to locate
The Calculation of Sample Size
- what level of confidence do you want to test your result
- with what degree of accuracy do you wish to estimate the population parameters?
- what is the estimated level or variation, with respect if the main variable you are studying in the population?
Sampling in Qualitative Research
- aim to explore diversity, therefore concepts like sample size and sampling strategy are not essential when selecting a sample
- non-probability (random) sampling designs can also be used in qualitative research with two main differences
a. in qualitative research you do not have a predetermined sample
b. in quantitative research you want to hace a random sample and in qualitative research you want to have the subjects that can provide you with the best information
“Saturation Point” in Qualitative Research
- when in the process of collecting data you are or getting any new information ir it is negligible
- the stage determined the sample size
- it is a subjective concept. each researcher decides when she/he reached that point
- more applicable to studies where you collect information a one-to-one basis
