PSYC - Ch 5 Flashcards
Population
Population - group sharing common characteristics
Target population/population of interest - the group defined by researchers interests
Accessible population - easily available segment of target population, samples are typically selected from this
Sample
Sample - subset of the population, ppl selected to participate in the study
Researchers want the sample to be a good/similar representation of the population so that they can generalize the results to the population.
Representative Sample
Sample with the same characteristics as the population
All members of the population have an equal or specified chance of being included in the sample
The more representative the sample, the more confidence we have that the results can be generalized to the target population
Biased Samples and Sampling Bias
Bias is a major threat to sample representativeness
In a biased sample, the characteristics are different from those in the population ie: older/smarter than the target population
Biased sample results from selection (or sampling) bias - Some members of the target population have a much higher probability of being included in the sample compared to other members
Convenience sampling
Biased as you sample only those who are easy to contact
Self-Selection/Volunteering Sampling
Biased as you only take those who volunteer
Sample Size
A larger sample will be more representative
Law of large numbers - the larger the sample size, the more likely the values are similar to those of the population
Minimum oof 10 participants for statistical purposes
Power Analysis
To determine the sample size needed to obtain the expected results with a given degree of confidence
Sampling Methods
Probability Sampling
Non-Probability Sampling
Probability Sampling
Exact size of the population must be known; must be possible to list all the individuals
Each individual in the population must have a specific (e.g., equal) and known probability of selection
The selection process must be unbiased; must be a random process
Non-Probability Sampling
Exact size of the population is NOT known, and it is NOT possible to list all the individuals in the population
The probability each individual has to be selected in the sample is UNKNOWN
The selection process is NOT unbiased; greater risk of producing a biased sample than probability sampling
Types of probability Sampling
Simple random sampling
Systematic random sampling
Stratified random sampling
Proportionate Stratified random sampling
Cluster random sampling
Multistage random sampling
Can be unrealistic - lost of time/effort, not practical/possible, need a list of population members
Simple random sampling
Each individual has an EQUAL chance of selection
Choice of one individual does not influence the probability of another individual - INDEPENDENT
Sampling with replacement - individual selected is recorded and returned to the population (replaced)
Sampling without replacement - removes each selected individual from the population
ISSUES with simple random sampling - chance determines each selection - possible (though unlikely) to get a distorted sample
Systematic Random Sampling
Sample members are selected according to a random starting point and a fixed, periodic interval
- Entire population is enumerated in a list
- Random starting point
- Every nth person
E.g., Select a random sample of 100 participants from a population of 50,000.
Place target population in a list, do 50,000/100 = 500, randomly pick a number from 1 to 500, e.g.,
342, start with 342 and pick every 500th person on the list after that number
Ensures a high degree of representativeness, it may violate the principle of independence
Stratified Random Sampling
Population divided into subgroups (strata); equal numbers are then randomly selected from each of the subgroups.
Guarantees that each subgroup will have adequate representation
Ensures all subgroups are equally represented in your sample
Useful when your goal is to make comparisons among subgroups
BUT - does not adequately represent proportions found in population and individuals in the population have different probabilities of being selected
Proportionate Stratified Random Sampling
The population is subdivided into strata.
* Number of participants from each stratum is
selected randomly.
* The proportions in the sample correspond to the proportions in the population.
ie: pop - 60% men, 40% women - we choose 60 en and 40 women
Ensures the sample will be representative of the population
Requires a lot of work and may make it difficult or impossible to compare subgroups within strata
BCluster Random Sampling
Clusters (preexisting groups) instead of individuals are randomly selected from a list of the population
An easy method for obtaining a large, relatively random sample
BUT selections are not independent (e.g., students in one classroom are more likely to be similar to each other than students in another classroom)
Multistage Random Sampling
Random sampling at several stages
Ex: Canadian university professors’ opinions on student literacy
* Stage 1: Randomly select universities across Canada
* Stage 2: Randomly select departments within universities
* Stage 3: Randomly select professors within departments
Effective in choosing a sample that is representative of a widely dispersed population (e.g., political polling)
Cost- and time-effective
But there could be issues with non- independence
Types of Non-Probability Sampling
Convenience Sampling
Quota Sampling
Purposive Sampling
Snowball Sampling
Convenience Sampling
Participants chosen based on availability and willingness - “first come first served”
ie: man on the street, booth at fair
Easy but weak - sample is probably biased
Less expensive and time consuming than probability sampling methods
To help curb bias, select a reasonably representative sample and clearly describe the selection process
Quota Sampling
Subgroups are identified and quotas are established for individuals from each subgroup
Can reflect proportions in population, but not randomly selected
Allows a researcher to control the composition of a convenience sample
Easy, economical, time-efficient but the sample probably is biased
*Reflects proportions in population,
but not randomly selected
Purposive Sampling
Known as judgmental, selective, or subjective sampling
Researcher targets a particular group of individuals
E.g.: To study smoking cessation, select only smokers; advertise at tabacco shop, not grocery store
Snowball Sampling
Begin with someone who meets the criteria for inclusion in your study
* Then ask them to recommend others who they may know also meet the criteria
* Most used in hard-to-reach populations
