Sampling: Sampling & its Types | Simple Random, Convenience, Systematic, Cluster, Stratified

Summary

This short animated video explains the concept of sampling and its importance in research. It covers key topics such as the difference between population and sample, and introduces two main types of sampling: probability and nonprobability sampling. Various methods within these categories are discussed, including random, systematic, cluster, stratified, convenience, snowball, and quota sampling. The video also highlights the pros and cons of each approach, making it easier for researchers to choose the appropriate method. Viewers are encouraged to follow Distributed Learning on social media for more educational content.

Takeaways

• 😊 Sampling is the process of selecting a small group from a larger population for research.
• 📊 A sample represents the entire population and is used when collecting data from every individual is impossible.
• 🌍 Population refers to the entire group from which the sample is drawn, based on the scope of the study.
• 💉 Probability sampling ensures that every member of the population has an equal chance of being selected.
• 🎲 Nonprobability sampling involves selecting individuals based on convenience or criteria, not random selection.
• 🔄 Simple random sampling uses chance and randomness, ensuring every member of the population has an equal chance of being chosen.
• 📏 Systematic sampling selects the first element randomly, then every nth element from the population.
• 🔀 Cluster sampling divides the population into clusters and randomly selects an entire cluster for study.
• 📊 Stratified sampling divides the population into strata (groups) based on certain criteria and selects samples from each stratum.
• 🌐 Nonprobability sampling includes methods like convenience, snowball, quota, and purposive sampling, which are easier but may lack statistical validity.

Q & A

What is the main difference between population and sample in research?

The population refers to the entire group being studied, while the sample is a smaller group selected from the population to represent it in research.

Why is sampling used in research instead of studying the entire population?

Sampling is used to reduce the cost, workload, and difficulty of gathering data from an entire population. It allows researchers to infer information about the whole group from a smaller, manageable subset.

What are the two main categories of sampling methods mentioned in the video?

The two main categories of sampling methods are probability sampling and nonprobability sampling.

What is probability sampling, and how is it different from nonprobability sampling?

Probability sampling ensures that every member of the population has an equal chance of being selected, while nonprobability sampling does not guarantee that each individual has a chance to be included and often relies on non-random criteria.

Can you explain the concept of simple random sampling?

Simple random sampling is a technique where every member of the population has an equal chance of being selected. Selection is entirely based on chance and randomness, removing bias from the process.

What is systematic sampling, and how does it work?

In systematic sampling, the first element is chosen randomly, and then every nth element is selected from the list or sequence, following a systematic pattern.

What distinguishes cluster sampling from stratified sampling?

Cluster sampling involves dividing the population into externally homogeneous but internally heterogeneous groups (clusters), and then randomly selecting entire clusters for study. Stratified sampling divides the population into strata based on specific characteristics and selects samples from each stratum.

What is convenience sampling, and why is it used?

Convenience sampling selects individuals who are easiest to reach or most accessible to the researcher. It is often used because it is quick and inexpensive, but it does not guarantee that the sample represents the entire population.

What is snowball sampling, and in what situations might it be used?

Snowball sampling involves asking selected participants to refer others for the study, creating a 'snowball' effect. This method is useful when studying hard-to-reach populations or in social network research.

What is the purpose of purposive or judgmental sampling?

Purposive sampling involves selecting samples based on the researcher's judgment and experience. It is often used in qualitative research or when seeking specific individuals with relevant knowledge or characteristics.

Outlines

00:00

📊 Understanding Sampling in Research

This paragraph introduces the concept of sampling in research. It explains that a sample is a small group selected from a population to represent the entire population, making it feasible to gather data without needing to study every individual. The paragraph illustrates the importance of sampling with a real-life example of COVID-19 vaccine trials, where a subset of infected individuals is selected for clinical tests. It also defines population as the larger group from which the sample is drawn, emphasizing the efficiency of sampling in research.

05:03

🎯 Types of Sampling Methods

This section explains two main types of sampling: probability sampling and nonprobability sampling. Probability sampling gives every population member an equal chance of being selected, as illustrated with the coin flip example. Nonprobability sampling, on the other hand, is based on convenience or other criteria, not random selection. The focus here is on reducing bias and ensuring that samples are representative of the entire population, making the research process cost-effective and easier to manage.

10:04

🎲 Probability Sampling Methods Explained

In this paragraph, different probability sampling methods are outlined, including simple random sampling, systematic sampling, cluster sampling, and stratified sampling. Simple random sampling ensures that each individual has an equal chance of being selected, reducing bias. Systematic sampling selects every nth element from a list. Cluster sampling involves dividing a population into clusters and studying all elements within a chosen cluster. Stratified sampling divides the population into strata based on specific characteristics (like age or gender) and selects random samples from each stratum.

📊 Nonprobability Sampling Methods

This paragraph shifts focus to nonprobability sampling methods, which include convenience sampling, snowball sampling, quota sampling, and purposive sampling. Convenience sampling involves selecting individuals based on ease of access, while snowball sampling relies on participants referring others. Quota sampling targets specific groups based on predefined characteristics like age or gender. Purposive sampling is based on the researcher’s judgment and experience in selecting participants. The paragraph highlights how these methods are less random and cannot be used to make statistical inferences about the entire population.

Keywords

💡Sampling

Sampling is the process of selecting a subset of individuals from a larger population to represent the entire group in a research study. This concept is central to the video as it discusses different methods researchers use to gather data without surveying every individual in a population. For example, the video explains how clinical trials for vaccines during the COVID-19 pandemic used sampling to test on a small group rather than millions of people.

💡Population

A population refers to the entire group from which a sample is drawn for research. The video emphasizes that the population is the broader group of interest in a study, and a sample is a small, manageable portion of it. For instance, in vaccine trials, the population includes all individuals potentially affected by COVID-19, but only a subset is selected for testing.

💡Probability Sampling

Probability sampling is a technique where every member of a population has an equal and known chance of being selected. The video highlights this as a key sampling method based on the theory of probability, ensuring that the selection process is random and unbiased. An example given is flipping a coin, where there is a 50/50 chance of landing heads or tails, illustrating the equal chances for selection.

💡Nonprobability Sampling

Nonprobability sampling refers to selecting individuals based on convenience or other non-random criteria, which means not every individual has a chance to be included. The video explains that this method is easier and cheaper but may not provide a statistically valid representation of the entire population. It contrasts this with probability sampling, which is more rigorous.

💡Simple Random Sampling

Simple random sampling is a probability sampling technique where every individual in the population has an equal chance of being selected, purely based on chance. The video explains how this method removes bias and randomness determines who is selected, such as picking names out of a hat from a diverse population of people with different ages, genders, and nationalities.

💡Systematic Sampling

Systematic sampling involves selecting the first element randomly and then choosing every nth element in a sequence. The video explains how this method, while still random, follows a specific pattern, making it different from simple random sampling. For instance, in a population of people, every third person might be selected after randomly picking the first individual.

💡Cluster Sampling

Cluster sampling is a method where the population is divided into clusters, and then entire clusters are randomly selected for study. The video illustrates how researchers might group people by external similarities (e.g., age or location) but still have diverse internal characteristics, allowing for more efficient study of specific segments of a population.

💡Stratified Sampling

Stratified sampling divides a population into strata based on shared characteristics, such as age, gender, or location, and then selects individuals randomly from each group. The video explains how this method ensures that each subgroup is represented in the sample. For example, dividing a population into males, females, and different age groups ensures diversity in the research sample.

💡Convenience Sampling

Convenience sampling is a nonprobability sampling technique where participants are selected based on ease of access. The video describes this as a quick and inexpensive way to gather data, but it cautions that it may not represent the entire population accurately. An example provided is choosing individuals who happen to be nearby or most accessible to the researcher.

💡Snowball Sampling

Snowball sampling involves recruiting participants and then asking them to refer others to the study. The video compares this to a snowball that grows larger as it rolls downhill, with each participant helping to expand the sample. This technique is particularly useful when studying hidden or hard-to-reach populations, but it may not provide a representative sample.

Highlights

Explanation of the concept of sampling and its importance in research.

Difference between population and sample: Sample is a small group selected from the population.

In research, it's often impractical to collect data from the entire population, so a sample is used.

Sampling allows researchers to infer information about the population by studying the sample, saving costs and time.

Introduction of two main types of sampling: Probability sampling and Nonprobability sampling.

Probability sampling ensures every member of a population has an equal chance of being selected.

Nonprobability sampling involves random selection based on convenience or other criteria.

Explanation of Simple Random Sampling: Each member of the study population has an equal chance of being selected.

Introduction to Systematic Sampling: A method where every Nth element is selected after choosing the first randomly.

Cluster Sampling: The population is divided into clusters, and samples are taken from randomly selected clusters.

Stratified Sampling: Population is divided into strata (based on age, geography, gender), and a sample is randomly chosen from each stratum.

Nonprobability sampling methods are cheaper and easier but lack statistical validity for the entire population.

Convenience Sampling: Involves selecting samples based on ease of access.

Snowball Sampling: Existing participants refer new participants, making the sample grow like a snowball.

Quota Sampling: A tailored sample is taken, representing different population characteristics.

Transcripts

this short animated video explains the concept of  sampling and the different types of sampling that  

are used in research so don't go anywhere else  just sit back relax and enjoy the video hello  

and welcome to yet another video series from  distributed learning your one-stop solution for  

all your learning needs so before we understand  sampling and its type let us first understand the  

difference between these two terms population and  sample the sample is a small group selected from  

population to represent the entire population so  when you conduct research about a group of people  

it is almost impossible to collect data from  every person in that group instead you select  

a sample and the sample is a group of individuals  who will actually participate in the research and  

will represent the entire population sample is  basically a subset of population that is based  

on the fact that it is drawn from the population  now what is population operation is a group from  

which the sample is drawn exact population  will depend upon the scope of the study let  

us understand with some real-life examples here  millions around the world are infected because  

of this covert in nineteen and many companies are  in progress of developing vaccines and then in the  

process they are also doing some clinical trials  so they will select a small portion of people from  

different background probably age gender and those  who are infected with covered 19 as a sample and  

then will perform a study on these individual  because it's not possible to conduct tests on  

millions of individuals at one time so that is  a difference between the population and sample  

now it understand what is sampling sampling  is a method that allows researchers to infer  

information about population based on the results  from sample without having to investigate every  

individual so reducing the number of individuals  in a study reduces your cost You've workload and  

may make it easier to obtain a high-quality  information to draw a valid conclusion from  

your result you have to carefully decide how will  you select a sample that is the representative of  

the group as a whole so broadly speaking they are  two different categories of sampling one is the  

probability sampling and next is a nonprobability  sampling probability sampling so probability  

sampling is based on the fact that every member  of population has known an equal chance of being  

selected this method is based on the theory of  probability for example when you flip a coin  

there are a 50/50 chance of getting a head or  tail and if you flip a coin once more again the  

chance of getting a head or tail is 1550 even if  you flip a coin a hundred times the next time when  

you flip a coin the chance of getting a head or  tail will still be 50 or 50 and in that case you  

still want to investigate the coin and why it  is coming up head or tail every time the bottom  

line of random selection process here is that  equal probability and the independence of events  

when we talk about the nonprobability sampling it  involves random selection based on conveyance and  

other criteria allowing you to easily collect  initial data so the main focus is that it is  

flowing you to select samples based on conveyance  probabilities sampling so probability sampling is  

based on the fact that every member of population  has known an equal chance of being selected there  

are four types of probability sampling the first  is the simple random sampling so this probe simple  

random sampling is a technique in which every  member of study population has equal chance  

of being selected in the selection of items  completely depends on chance and randomness  

and therefore this technique is also known as  method of chance let us assume that we have this  

population which represents a broad category  of people of different age sex nationality and  

profession and if we have to apply this simple  random technique we will select a sample from  

this population based on randomness and my chance  so the sample that we have are from these four  

individuals that we have selected so these are  selected based on randomness and by chance the  

logic behind using this simple random technique  is to remove the bias on the selection process  

next is the systematic sampling in systematic  sampling the first element is selected randomly  

from the list or from the sequential files and  then every endeth element is selected let us  

assume again that we have this population which  again represent the broad category of people and  

if we have to apply this technique of systematic  sampling we will select sample from this on a  

based on the fact that we will select first  sample randomly it could be anyone and then  

we will apply the innate element from this list so  in this scale we will pick the third element and  

remember from the population every third member on  the sequence will picked from the population will  

present the systematic sampling this method is  different from simple random sampling since every  

possible sample of an earth element is not likely  to be equal clusters sampling so cluster sampling  

is a sampling procedure that involves randomly  selecting a particular cluster of an element from  

a group of population and subsequently selecting  every element of a selected cluster for study  

so with cluster sampling the researchers will  divide the population into separate groups called  

clusters which could be groups of externally  homogeneous but internally heterogeneous groups  

so then a simple random sample of a cluster  is selected from a population it is assumed  

that we have this population which represents  a broad category of people of different age sex  

nationality and profession and if we have to apply  this sampling technique they have been divided the  

entire population into different groups which is  externally homogenous but internally heterogeneous  

group called clusters and after identifying a  particular cluster that we will use for using  

this thing we will pick all the elements of that  selected cluster to be included in the study

stratified sampling so the probability sampling  procedure that different walls dividing the  

entire population into groups or strata defined  by presence of certain critics like your age maybe  

based on geography like north south east west or  maybe male or female and then randomly selecting  

sample from each strata so let us assume that we  have against this population which represents a  

broad category of people and we divide this entire  population into different strata so let us first  

divide this into different strata in this case  we are dividing waisting based on mailing female  

Adele elderly people and the three status now we  will select a sample from this different three  

status based on selecting one person from each  strata at least so this is the stratified sampling  

so did you note the difference between the cluster  sampling in the States stratified sampling so with  

stratified sampling sample includes a ribbon  from each stratum but with Crystal sampling  

samples include element only from one selected  cluster there is a difference between stratified  

sampling and the cluster sampling nonprobability  sampling so nonprobability sampling individuals  

are selected based on non-random criteria and not  every individual has a chance of being included  

in the study this type of sampling is easier  and cheaper to access but you can't use it to  

make a valid statistical inference about the whole  population there are few types of nonprobability  

sampling first is a convenience sampling it  involves selecting sample based on convenience  

a convenience sample simply includes individuals  who happens to be most accessible to the this is  

easy and inexpensive way to gather the initial  data but there is no way to tell if the sample  

is a true representation of entire population  so it can't produced in a rice result it is  

also known as accidental sampling the next type of  nonprobability sampling is the snowball sampling  

here you select samples and ask them to refer  them to refer you to others it is also called  

known as snowball sampling because in theory  once you have a ball rolling it picks up more  

and more snow along the way and becomes large  and larger it is also known as Network sampling

so nonprobability sampling we have another  category like quota sampling so quota sampling  

means to take very tailored sample that  is in proportion to some characteristic  

or traits of a population for example you  divide a publishin by state they live in  

income or education level or may be a male or  female this method of sampling is often used  

by market researchers where interviewers are  given a quota of subject of a specific type to  

attempt to recruit for example an interviewer  might be told to go to and select 20 adult men  

20 adult women 10 teenage girls and then 10  teenage boys so that they could interview them  

about their television viewing another type of  category is purposive or judgmental sampling it  

involves selecting samples based on or his own  judgment this technique relies on judgment of  

the researchers who choose the sample based on  his own experience this approach is often used  

by media when canvassing the public for opinions  in the qualitative research so that is all I have  

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