The document discusses different types of non-probability sampling techniques. It provides descriptions of convenience sampling, purposive sampling, quota sampling, dimensional sampling, voluntary sampling, and snowball sampling. It also discusses determining sample size and calculating sample size using formulas that take into account population size, margin of error, confidence level, and standard deviation.
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
This presentation describes the steps in designing a questionnaire. Also includes video clips for the process in evaluating the questionnaires for its reliability analysis.
this is an presentation regarding samples in research methodology in qualitative and quantitative approaches . this will be very useful basically this presentation most significant for university students those who are following and learning for the research methodology. in this i have discussed
what is sampling
why samples for research
sampling methods
size of sample
types of sample
advantages of sample
disadvantages of sample
process
sampling frame
time factor
sampling problems...
Research techniques; samling and ethics eltAbdo90nussair
Advance Research Techniques; How to make samples Abdurrahman Abdalla .. كيف تؤخد العينة في طرق البحث المتقدم .. إعداد عبدالرحمن المهدي نصير جامعة الشرق الادنى - قبرص الشمالية
What is sampling?
Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population.
Characteristics of a good sample
-True representative
-Free from bias
-Accurate
-Comprehensive
-Approachable
-Good size
-Feasible
-Goal orientation
-Practical and economical
Sampling Error
A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population.
and many more things about the sampling technique.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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Non probability sampling
1. Reporter: Ma. Cora
Antonette E. Yu
Purposive
sampling
Convenience
sampling
Purposive
sampling
Quota
sampling
Voluntary
sampling
Snowball
sampling
Dimension
sampling
Determination of
sample size
Non- probability
Sampling
2. Non-Probability Sampling
Non-probability sampling is a sampling technique where the
odds of any member being selected for a sample cannot be
calculated. It’s the opposite of probability sampling, where
you can calculate the odds. In addition, probability sampling
involves random selection, while non-probability sampling
does not–it relies on the subjective judgment of the
researcher.
The odds do not have to be equal for a method to be considered probability
sampling. For example, one person could have a 10% chance of being selected
and another person could have a 50% chance of being selected. It’s non-
probability sampling when you can’t calculate the odds at all.
One major disadvantage of non-probability sampling is that it’s impossible to
know how well you are representing the population. Plus, you can’t
calculate confidence intervals and margins of error. This is the major reason
why, if at all possible, you should consider probability sampling methods first.
4. Convenience Sampling
Convenience sampling (also called accidental sampling
or grab sampling) is where you include people who are easy to
reach. For example, you could survey people from your
workplace or school, a club you belong to, or you could go to a
local mall and survey local shoppers. Although convenience
sampling is, like the name suggests–convenient–it runs a high
risk that your sample will not be representative of the
population. Sometimes, a convenience sample is the only way
you can drum up participants. According to UC Davis, it could
be “a matter of taking what you can get”.
Convenience sampling does have its uses, especially
when you need to conduct a study quickly or you are on a
shoestring budget. It is also one of the only methods you can use when
you can’t get a list of all the members of a population.
For example, let’s say you were conducting a survey for a company who
wanted to know what SM Supermall employees think of their wages. It’s
unlikely you’ll be able to get a list of employees, so you may have to resort
to standing outside of SM and grabbing whichever employees come out of
the door (hence the name “grab sampling”).
5. purposive Sampling
A purposive sample is where a researcher selects a sample
based on their knowledge about the study and population.
The participants are selected based on the purpose of
the sample, hence the name.
In this method of sampling the choice of sample items
depends exclusively on the judgment of the investigators.
That is, the investigators exercises their judgment in the
choice and includes those items in the sample.
6. Quota Sampling
Quota sampling is a type of judgment
sampling and is perhaps the most
commonly used sampling technique in
non-probability category. In a quota
sample, quotas are set up according to
some specified characteristics.
For example, in radio listening survey, the
interviewers may be told to interview 500
people living in a certain areas and that
out of every 100 persons interviewed 60
are housewife, 25 farmers and 15 children
under the age of 18.With in these quotas
the interviewer is free to select the people
to be interviewed.
7. Dimensional sampling
Dimensional Sampling is an extension to quota sampling. The
researcher takes into account several characteristics (e.g. gender,
income, residence and education. The researcher must ensure that
there is at least one person in the study representing each of the chosen
characteristics.
For example, it may be important to
include the responses of people of
different ages, then ensures that the
sample includes respondents from each of
the groups thus identified.
8. Voluntary sampling
A voluntary sample is one of the main types of non-
probability sampling methods. A voluntary sample is made
up of people who self-select into the survey. Often, these
folks have a strong interest in the main topic of the survey.
For example, in the recent
international pageant here in
the Philippines, social media
are busy looking for the next
woman to be crowned Miss
Universe 2017 through an on-
line poll.
9. Snowball sampling
Snowball sampling is known as network
or chain referral sampling.
The researcher identifies a small
number of respondents who possess a
specific set of characteristics of
interest. Then these respondents will
provide others who possess the same
characteristics set. This can be useful
for a population where sampling is
difficult (ex. Gang members, rape
victims or drug addicts)
10. Determination of sample size
The sample size is an important feature of any empirical study in
which the goal is to make inferences about a population from a sample.
In practice, the sample size used in study is determined based of the
expense of data collection, and the need to have sufficient statistical
power.
Different opinions have been expressed by experts for the selection
of sample size (i.e 5%,10% or 25% of the population). There are no
hard and fast rule can be laid down. However, according to the law of
large number, the largest the sample size, the better the estimation, or
the larger the sample, the closer the ‘true’ value of the population. It
may also be pointed out that the sample size should neither be too large
nor too small. It should be 'optimum' (efficiency, representativeness,
reliability and flexibility).
11. Calculating the Sample Size (ss)
Before we can calculate a sample size, we need to determine a few things
about the target population and the sample we need:
1. Population Size (N) — How many total people fit your demographic? For
instance, if you want to know about mothers living in the US, your population size
would be the total number of mothers living in the US. Don’t worry if you are unsure
about this number. It is common for the population to be unknown or approximated.
2. Margin of Error (e) — No sample will be perfect, so you need to decide
how much error to allow. The confidence interval determines how much higher or lower
than the population mean you are willing to let your sample mean fall.
If you’ve ever seen a political poll on the news, you’ve seen a confidence interval. It will
look something like this: “68% of voters said yes to Proposition Z, with a margin of
error of +/- 5%.”
3. Confidence Level (Z-score) — How confident do you want to be that the
actual mean falls within your confidence interval? The most common confidence
intervals are 90% confident, 95% confident, and 99% confident.
12. 4. Standard of Deviation (sd) — How much variance do you expect in
your responses? Since we haven’t actually administered our survey
yet, the safe decision is to use .5 – this is the most forgiving number
and ensures that your sample will be large enough.
Your confidence level corresponds to a Z-score. This is a constant value
needed for this equation. Here are the z-scores for the most common
confidence levels:
• 90% – Z Score = 1.645
• 95% – Z Score = 1.96
• 99% – Z Score = 2.326
Formula:
Sample Size = (Z-score)² * sd*(1-sd) / (e)²
assuming you chose a 95%)) confidence level, .5 standard deviation, and a
margin of error (confidence interval) of +/- 5%.
ss = ((1.96)² x .5(1-.5)/ (.05)²
= (3.8416 x .25) / .0025
= .9604 / .0025
= 384.16
385 respondents are needed
13. n = ___N_ __
1+ Ne2
Where n = sample size desired;
N = population size; and
e = desired margin or sampling error
Using this formula, what would be the sample size if the total population (N) is
2,000 and the margin of sampling error you allow is 5%?
The sample size maybe computed this way:
n = ____2000_____
1+(2000)(.05)2
n = 2000
1+5
n = 2000
6
n = 333 respondents
When using this formula, the population is assumed to be normally
distributed. When the population is small or poor, this does not apply.
14. * Sample size for specified marginof errors
• Assumption of normal approximation is poor and therefore the
sample size formula does not apply.