a word that refers to your method/process of selecting respondents or people to answer questions meant to yield (produce/provide) data for a research study.
Population
is the entirety of the group including all the members that forms a set of data. Ex.(City, School,Barangay)
Sample
contains a few members of the population. They were taken to represent the characteristics or traits of the whole population.
Sampling Errors
crops up if the selection does not take place in the way it is planned.
Sample size
defined as the number of observations used for determining the estimations of a given population.
Sampling Frame
a list of the items or people forming a population from which a sample is taken.
THE BEGINNING OF SAMPLING COULD BE TRACED BACK TO THE EARLY POLITICAL ACTIVITIES OF THE AMERICANS IN 1920 WHEN LITERARY DIGEST DID A PIONEERING SURVEY ABOUT THE AMERICAN CITIZENS’ FAVORITE AMONG THE 1920 PRESIDENTIAL CANDIDATES
Literary Digest
was an influential American general interest weekly magazine published by Funk & Wagnalls. Founded by Isaac Kaufmann Funk in 1890
SAMPLING OF THE AMERICANS IN 1920
This was the very first survey that served as the impetus (the force that makes something happen) for the discovery by academic researchers of other sampling strategies that they categorized into two classes
PROBABILITY SAMPLING
an UNBIASED SAMPLING
PROBABILITY SAMPLING
EVERY MEMBER OF THE POPULATION HAS THE CHANCE OF BEING SELECTED.
Non – probability Sampling
Not every member of the population has the equal chance of being selected
Non – probability Sampling
It can rely on subjective judgement of the researcher.
PROBABILITY SAMPLING
IT INVOLVES PRINCIPLE OR RANDOMIZATION OR CHANCE.
Simple random Sampling
This method involves randomly selecting a sample from the population without any bias.
Systematic Sampling
This method involves selecting every nth member of the population after a random starting point is chosen
Simple random Sampling
It’s the most basic and straightforward form of probability sampling
Stratified Sampling
This method involves dividing the population into subgroups or strata and selecting a random sample from each stratum
Cluster Sampling
This method involves dividing the population into groups or clusters and then randomly selecting some of those clusters.
Stratified Sampling
This technique is useful when the population is heterogeneous(mixed) and you want to ensure that the sample is representative of different subgroups
Cluster Sampling
This technique is useful when the population is spread out over a large geographical area. But It is not possible or practical to survey everyone.
Simple Random Sampling
The names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
Cluster Sampling
The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You can’t travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.
Stratified Sampling
The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
Systematic Sampling
All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people
QUOTA SAMPLING
You resort to ____ sampling when you think you know the characteristics of the target population.
Voluntary sampling
Since the subject you expect to participate in the sample selection are the ones volunteering to constitute the sample, there is no need for you to do any selection process.
Snowball sampling
this sampling method does not give a specific set of samples
Availability sampling
The willingness of a person as your subject to interact with you counts a lot in this non-probability sampling method.
Convenience Sampling
Also known as accidental, opportunity or grab sampling
QUOTA SAMPLING
a specific set of persons whom you believe to have the characteristics of the target population involved in the study is your way of showing that the sample you have choosen closely represents the target population.
Purposive Sampling
Samples are chosen based on the goals of the study. They may be chosen based on their knowledge of the study being conducted or if they satisfy the traits or conditions set by the researcher.
Convenience Sampling
Selecting a sample based on the availability of the member and/or proximity to the researcher.
Voluntary sampling
the subjects you expect to participate in the sample selection are the ones volunteering to constitute the sample, there is no need for you to do any selection process
Snowball sampling
a study involving unspecified group of people
Snowball sampling
Dealing with varied groups of people such as street children, mendicants, drug dependents, call center workers, informal settlers, street vendors, and the like is possible in this kind of non-probability sampling. Free to obtain data from any group, freely expanding and accumulating at a certain place, you tend to increase the number of people you want to form from the sample of your study (HARDING 2013)
Snowball sampling
Participants in the study were tasked to recruit other members for the study.
Search for the Literature
stage of a review where you devote much of your time in looking for sources of knowledge, data to answer or research questions or to support your assumptions about your research topic.
general references
that will direct you to the location of other sources
Primary sources
directly report or present a person’s own experiences