Chapter 19: Population and Sampling Procedures
Definition
Population: including all people or items with the characteristics one wishes to understand
Sampling procedures: choosing part of a population to use to test hypotheses about the entire population.
Sampling procedures
- Select a sampling procedure
- Define the sample size
- Identify the sampling frame
- Select the sample units
- Define the population
- Collect data from the sampled
Sampling technique
Non-probability sampling
Probability sampling
method of selecting units from a population using a subjective (i.e. non-random) method.
not all members of the population have an equal chance of being selected
Expert sampling - involves assembling of a sample of respondents with known or demonstrable experience and expertise in some area
Quota sampling - researcher selects a sample group to represent some specific characteristics of the population.
Judgmental sampling - researcher selects units to be sampled based on their knowledge and professional judgment.
Heterogeneity sampling - include all the opinions or views and doesn't consider representing these views proportionately
Purposive sampling - the process of selecting sample by taking subject that is not based on the level or area, but it is taken based on the specific purpose (Arikunto, 2010, p. 183)
Snowball sampling - research participants are asked to assist researchers in identifying other potential subjects
Convenience sampling - samples are selected because they are accessible to the researcher
Advantages: Cheap, efficient, and simple to implement, less time-consuming
Disadvantage: Sample lacks clear generalizability.
Advantages: Less costly, more readily accessible, more convenient
Disadvantages: Highly prone to researcher bias, sample chosen do not represent the population,
Advantages: Assurance of quality response
Disadvantages: Bias selection of sample may occur, time consuming process
Advantages: Experts can provide valuable insights into the root of problems
Disadvantages: Experts may be wrong
Advantages: Simple, quick and easy
Disadvantages: Prone to bias, time-consuming and costly
Advantages: Can get a broad spectrum of ideas
Disadvantages: Time-consuming as it involve diverse range of participants
Advantages: Locate hidden population, people located are population specific
Disadvantages: Community bias, not random, vague population size, hardly representative of the population,
different numbers of a population have an equal chance of selection
Stratified random sampling - dividing the population into subgroups based on variables known about those subgroups, and then taking a simple random sample of each subgroup
Systematic sampling - involving the selection of elements from an ordered sampling frame
k= N/n
Simple random sampling - process of selecting a sample that allows individual in the defined population to have an equal and independent chance of being selected for the sample
Cluster sampling - population is divided into mutually exhaustive subset
Multi-stage sampling - draw a sample from a population using smaller and smaller groups (units) at each stage.
Advantages: Sample easy to select, suitable sampling frame can be identified easily, sample evenly spread over entire reference population
Sample size
Quantitative research
Qualitative research
Roscoe (1975) rule of thumb
Sample sizes larger than 30 and less than 500 are appropriate for most research
Minimum sample size of 30 for each sub-category is usually necessary
In multivariate research, the sample size should be several times (preferably 10 times or more) as large as the number of variables in the study
For simple experimental research, successful research is possible with samples as small as 10 to 20 in size
depends on the size that adequately answers the research questions
5-50 participants as adequate (Dworkin, 2012).
policy of the Archives of Sexual Behavior Journal adheres to the recommendation that 25-30 participants are the minimum sample size
LIEW YIN WEN (75437)
Advantages: Easy to implement
Disadvantages: Standard errors of estimators can be high, vulnerable to sampling error, cumbersome and tedious
Advantages: Control of sample size in strata, increased statistical efficiency, provides data to represent and analyze subgroups
Disadvantages: Increase cost and complexity of sample selection, leading to increased complexity of population estimates, potentially require a larger sample than would other methods
Disadvantages: Assumes size of population can be determined, greater risk of data manipulation
Advantages: Easy, cheap and quick
Disadvantages: Difficult to implement, difficult to analyze, high sampling error
Advantages: Flexible, cost effective and easy to implement.
Disadvantages: High level of subjectivity, less accurate than simple random sampling.