This document discusses different methods for selecting participants in qualitative and quantitative research. It describes probability sampling, where every member of the population has an equal chance of being selected, and non-probability sampling, where selection is not random. Some common types of probability sampling mentioned are simple random sampling, systematic sampling, and stratified sampling. Examples of non-probability sampling include convenience sampling, quota sampling, and snowball sampling. The document emphasizes that the sampling method should be properly discussed and explained in research to select representative samples.
By the end of this presentation you should be able to:
Describe the justification of qualitative Sampling Techniques
Understand different types of Sampling Techniques
Introduction to quantitative and qualitative researchLiz FitzGerald
This presentation, delivered in an Open University CALRG Building Knowledge session, gives a preliminary introduction to both quantitative and qualitative research approaches. There has been widespread debate when considering the relative merits of quantitative and qualitative strategies for research. Positions taken by individual researchers vary considerably, from those who see the two strategies as entirely separate, polar opposites that are based upon alternative views of the world, to those who are happy to mix these strategies within their research projects. We consider the different strengths, weaknesses and suitability of different approaches and draw upon some examples to highlight their use within educational technology.
By the end of this presentation you should be able to:
Describe the justification of qualitative Sampling Techniques
Understand different types of Sampling Techniques
Introduction to quantitative and qualitative researchLiz FitzGerald
This presentation, delivered in an Open University CALRG Building Knowledge session, gives a preliminary introduction to both quantitative and qualitative research approaches. There has been widespread debate when considering the relative merits of quantitative and qualitative strategies for research. Positions taken by individual researchers vary considerably, from those who see the two strategies as entirely separate, polar opposites that are based upon alternative views of the world, to those who are happy to mix these strategies within their research projects. We consider the different strengths, weaknesses and suitability of different approaches and draw upon some examples to highlight their use within educational technology.
Practical Research 1 for SHS
Lesson 1: The Importance of Research in Daily life
Content
1. Differentiate Inquiry from Research
2. Share research experiences and knowledge
3. Explain the importance of research in daily life.
You can watch here https://www.youtube.com/watch?v=bY8lFadJia8&t=1357s
Chapter 2: Ethical Principles of Research Monte Christo
Practical Research 1 :This course develops critical thinking and problem-solving skills through qualitative research.
This power point made possible by : Prof. JOBIEN S.DAYAO, MA, Prof. Roel Jumawan MTP,MAEM AND Prof. Penn T.Larena ,CPS,MPA
Data collection is a one of the major important topic in research study, It should be clear and understandable to all students, especially in graduate studies
DATA GATHERING IS PART OF THE PROCESS IN DOING A RESEARCH. THIS PRESENTATION IS PART OF THE REQUIREMENTS IN COMPLETING THE COURSE EDUCATIONAL RESEARCH UNDER THE MASTER OF ARTS IN HOME ECONOMICS, A GRADUATE STUDY PROGRAM OF ZAMBOANGA STATE COLLEGE OF MARINE SCIENCES AND TECHNOLOGY , ZAMBOANGA CITY.
Practical Research 1 for SHS
Lesson 1: The Importance of Research in Daily life
Content
1. Differentiate Inquiry from Research
2. Share research experiences and knowledge
3. Explain the importance of research in daily life.
You can watch here https://www.youtube.com/watch?v=bY8lFadJia8&t=1357s
Chapter 2: Ethical Principles of Research Monte Christo
Practical Research 1 :This course develops critical thinking and problem-solving skills through qualitative research.
This power point made possible by : Prof. JOBIEN S.DAYAO, MA, Prof. Roel Jumawan MTP,MAEM AND Prof. Penn T.Larena ,CPS,MPA
Data collection is a one of the major important topic in research study, It should be clear and understandable to all students, especially in graduate studies
DATA GATHERING IS PART OF THE PROCESS IN DOING A RESEARCH. THIS PRESENTATION IS PART OF THE REQUIREMENTS IN COMPLETING THE COURSE EDUCATIONAL RESEARCH UNDER THE MASTER OF ARTS IN HOME ECONOMICS, A GRADUATE STUDY PROGRAM OF ZAMBOANGA STATE COLLEGE OF MARINE SCIENCES AND TECHNOLOGY , ZAMBOANGA CITY.
a PowerPoint about research analysis on the diversity of a certain organisms in a specific place and their abundance and environmental factors that could possibly affect their existence in the area
this document also includes the presentation of my group and a comprehensive analysis on lichen life in the baranggay
unfortunately it's not the final research for this paper so all the details are not yet to include tho alot of important information were included so that a general understanding of he topic is expected to be explained very well including all the important details
Sampling for Quantities & Qualitative Research Abeer AlNajjar.docxanhlodge
Sampling for Quantities & Qualitative Research
Abeer AlNajjar
1
Population
Target group (universe in texts)
Census (to study every member of a population)
because measuring every member of a population usually is not feasible most researchers employ a Sample
Sample ( a subgroup of the population)
2
Communication researchers are interested in a population (also called a universe when applied to texts) of communicators, all the people who posses a particular characteristic, or, in the case of those who study texts, all the messages that share a characteristic of interest.
The population of interest to researchers (often called the target group) might be members of a business, communication majors at a university, all students at a university, all people living in a city, all eligible voters in a country.
Texts ( editorials published in a specific newspaper for a week, or a large universe such as every editorial published In every newspaper in the UAE, or even larger such as all persuasive messages).
The best way to generalize to a population is to study every member of a population (Census)
If every member is studied, we know, by definition, the population’s response at the point in time the study was done
Sample
The results from the sample are then generalized back to (used to represent) the population
Representative sample ( population validity)
Its similarity to its parent population
3
The results from the sample are then generalized back to (used to represent) the population). For such generalization to be valid (demonstrate population validity), the sample must be representative of its population. That is, it must accurately approximate the population.
Types of sampling
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
Nonrandom sampling (nonprobability sampling)
Is what ever researchers do instead of using procedures that ensure that each member of a population has an equal chance of being selected
Sampling error
Is a number that express how much the characteristic of a sample probably differ from the characteristics of a population
5
There are 2 different types of sampling procedures, and differ in terms of how confident we are about the ability of the selected sample to represent the population from which it is drawn
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
By giving everyone an equal chance , random sampling eliminates the danger of researchers biasing the selection process because of their own opinions or desires. By eliminating bias, random sampling provides the best assurance that the same characteristics of the population exist in the sample, and, therefore, that the sample represents the population.
Nonrandom sampling: it sometimes is .
Project Monitorig and Evaluation_Data Collection Methods is a Presentation by William Afani Paul for a Project MEAL Masterclass by Excellence Foundation for South Sudan
This session is designed to equip participants with essential knowledge and skills in monitoring and evaluating projects effectively.
During this masterclass, participants will delve into the fundamental concepts, tools, and techniques of project monitoring and evaluation. Through interactive discussions, case studies, and practical exercises, attendees will gain a comprehensive understanding of MEAL principles and their application in diverse project contexts.
Key Objectives
Understand the importance of project monitoring and evaluation in ensuring project success.
Learn how to develop and implement effective monitoring and evaluation frameworks.
Explore various data collection methods and analysis techniques for monitoring and evaluation purposes.
Gain insights into utilizing monitoring and evaluation findings to inform decision-making and improve project outcomes.
Learning Outcomes: By the end of the masterclass, participants will able to:
Define key concepts related to project monitoring and evaluation.
Develop a monitoring and evaluation plan tailored to specific project requirements.
Apply appropriate data collection methods and tools for monitoring and evaluation activities.
Utilize monitoring and evaluation findings to enhance project performance and impact.
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.
A Strategic Approach: GenAI in EducationPeter 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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2. Method of Selection
Selecting participants or respondents
and the type of sample has many ways to
consider depending on how you use the
information.
In research, SAMPLING is a word that
refers to the method or process of selecting
respondents or people to answer questions
meant to yield data for a research study.
(Paris 2013)
3. The word population is a technical term
in research which means a big group of people
from where you choose the sample or the
chosen set of people to represent the
population. Sampling frame, on the other hand
is the list of the members of population to which
you want to generalize or apply your findings
about the sample, and sampling unit is the
term referring to every individual in the
population (Emmel 2013; Lapan 2013)
4. HISTORY
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.
This was the very first survey that served as the
impetus for the discovery by academic researchers
of other sampling strategies that they categorized
into two classes: PROBABILITY SAMPLING and
NON-PROBABILITY SAMPLING. (Babbie 2013)
5. Selecting Participants
Probability Sampling
An equal chance of
participation in the
sampling or selection
process is given to
every member.
It is also known as
SCIENTIFIC
SAMPLING.
Non-Probability Sampling
Disregards random
selection of subjects.
The subjects are
chosen based on their
availability or the
purpose of the study.
It is also known as
NON-SCIENTIFIC
SAMPLING.
6. Types of Probability Sampling
Simple Random Sampling – choosing of
respondents from a population.
Systematic Sampling - picking out from
the list every 5th or every 8th member listed
in the sampling frame until the completion
of the desired total number of respondents
7. Stratified Sampling – the group
compromising the sample is chosen in a
way that such group is liable to subdivision
during the data analysis stage.
Cluster Sampling – selecting respondents
in clusters, rather than in separate
individuals.
8. Types of Non-probability Sampling
Convenience Sampling – It is also known
as accidental or incidental sampling.
Quota Sampling – choosing specific
samples that you know correspond to the
population in terms of one, two or more
characteristics.
Voluntary Sampling – selecting people
who are very much willing to participate as
respondents.
9. Availability Sampling- picking out people who
are easy to find or locate and willing to
establish contact with you.
Snowball sampling – selecting samples from
several alternative sample like drug
dependents, human traffickers, street
children and other wayward.
Purposive sampling- choosing respondents
whom you have judged as people with good
background or with great enthusiasm.
10. Expert Sampling- in this method, the
researcher draws the sample from a list of
experts in the field.
Heterogeneity/ Diversity sampling – a type of
sampling where you deliberately choose
members so that all views are represented.
However, those views may or may not be
represented proportionally.
Modal Instance Sampling – the most “typical”
members are chosen from a set.
11. In selecting sample of a study, the following
elements must be properly discussed:
- The total population and its parameters
- The sample and its statistics
- The sampling method with references to
support it.
- Explanation and discussion of sampling
method
- Explanation of how the sampling was done.
- An enumeration of qualifying criteria and the
profiles of the subjects and or respondents
12. References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312514/
- Joan Sargeant, PhD.
Practical Research 1 & 2, Esther L. Baraceros, pp. 92-97,
126-131.
Practical Research, Amadeo Pangilinan Cristobal, Jr. , Ed. D
And Maura Consolacion De La Cruz-Cristobal, Ed. D., pp. 173 -
177
http://www.statisticshowto.com/non-probability-sampling/
-Non-Probability Sampling: Definition, Types was last modified: March
17th, 2017 by Andale
http://www.statisticshowto.com/probability-sampling/
- Probability Sampling: Definition,Types, Advantages and
Disadvantages was last modified: March 4th, 2017 by Andale
Editor's Notes
In the course of planning phase, we’ve come to think of which participants or community members will be best to acquire the information that we need particularly if we get the issue or problem within our vicinity or organization. We go through questions and other things to consider before making a final decision in selecting participants.
The sampling, as well as the research results, is expected to speak about the entire population. Unless this does not refer to the population, in general, the sample-selection procedure has no scientific value.
PROBABILITY SAMPLING OR UNBIASED SAMPLING- a method that makes you base your selection of respondents on pure chance. In this case, everybody in the population participates. All are given equal opportunity or chance to form the sample that is capable of reflecting that characteristics of the whole population from where such sample was drawn. (Tuckman 2013; Emmel 2013; De Vaus 2013; Picardie 2014)
NON PROBABILITY SAMPLING – is a process of selecting respondents in which not all members of the entire population are given a chance of being selected as samples.
SIMPLE RANDOM SAMPLING -
STRATIFIED SAMPLING EXAMPLE – the population is first divided into different strata, and then the sampling follows. A researcher will study the common effects of smoking on high school students. The researcher decides to select equal numbers of students from the freshman, sophomore, junior and senior levels.
CLUSTER SAMPLING EXAMPLE- is used in large-scale studies, where population is geographically spread out. Sampling procedures may be difficult and time-consuming.
EXAMPLE: A researcher wants to interview 100 teachers across the country. It will be difficult and expensive on their part to have respondents in 100 cities or provinces. Cluster sampling is helpful for the researcher who randomly selects the regions (first cluster), then select the schools (second cluster), and then the number of teachers.
Example of accidental sampling - A researcher intends to study the elementary students of a particular school, and has determined the desired sample size. Due to study’s constraints, the elementary pupils who are present at the time of the researcher’s visit to the school will be chosen respondents.
Example of quota sampling – a researcher wants to survey the employees of a company regarding thoughts on the company’s new policies. The researcher intends to have representatives from all departments in his sample, but one department is so small that doing random sampling might result in that department not being represented. The researcher then sets a quota of respondents from that department to ensure their inclusion in the sample.
Snowball Sampling
In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are not likely to be able to find good lists of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them.
Example of purposive sampling- In a study about honor students, the researcher uses a list of honor students and chooses the necessary number of respondents, to the exclusion of all other students.
Modal Instance Sampling
In statistics, the mode is the most frequently occurring value in a distribution. In sampling, when we do a modal instance sample, we are sampling the most frequent case, or the "typical" case. In a lot of informal public opinion polls, for instance, they interview a "typical" voter. There are a number of problems with this sampling approach. First, how do we know what the "typical" or "modal" case is? We could say that the modal voter is a person who is of average age, educational level, and income in the population. But, it's not clear that using the averages of these is the fairest (consider the skewed distribution of income, for instance). And, how do you know that those three variables -- age, education, income -- are the only or even the most relevant for classifying the typical voter? What if religion or ethnicity is an important discriminator? Clearly, modal instance sampling is only sensible for informal sampling contexts.
Expert Sampling
Expert sampling involves the assembling of a sample of persons with known or demonstrable experience and expertise in some area. Often, we convene such a sample under the auspices of a "panel of experts." There are actually two reasons you might do expert sampling. First, because it would be the best way to elicit the views of persons who have specific expertise. In this case, expert sampling is essentially just a specific subcase of purposive sampling. But the other reason you might use expert sampling is to provide evidence for the validity of another sampling approach you've chosen. For instance, let's say you do modal instance sampling and are concerned that the criteria you used for defining the modal instance are subject to criticism. You might convene an expert panel consisting of persons with acknowledged experience and insight into that field or topic and ask them to examine your modal definitions and comment on their appropriateness and validity. The advantage of doing this is that you aren't out on your own trying to defend your decisions -- you have some acknowledged experts to back you. The disadvantage is that even the experts can be, and often are, wrong.
Quota Sampling
In quota sampling, you select people nonrandomly according to some fixed quota. There are two types of quota sampling: proportional and non proportional. In proportional quota samplingyou want to represent the major characteristics of the population by sampling a proportional amount of each. For instance, if you know the population has 40% women and 60% men, and that you want a total sample size of 100, you will continue sampling until you get those percentages and then you will stop. So, if you've already got the 40 women for your sample, but not the sixty men, you will continue to sample men but even if legitimate women respondents come along, you will not sample them because you have already "met your quota." The problem here (as in much purposive sampling) is that you have to decide the specific characteristics on which you will base the quota. Will it be by gender, age, education race, religion, etc.?
Nonproportional quota sampling is a bit less restrictive. In this method, you specify the minimum number of sampled units you want in each category. here, you're not concerned with having numbers that match the proportions in the population. Instead, you simply want to have enough to assure that you will be able to talk about even small groups in the population. This method is the nonprobabilistic analogue of stratified random sampling in that it is typically used to assure that smaller groups are adequately represented in your sample.
Heterogeneity Sampling
We sample for heterogeneity when we want to include all opinions or views, and we aren't concerned about representing these views proportionately. Another term for this is sampling for diversity. In many brainstorming or nominal group processes (including concept mapping), we would use some form of heterogeneity sampling because our primary interest is in getting broad spectrum of ideas, not identifying the "average" or "modal instance" ones. In effect, what we would like to be sampling is not people, but ideas. We imagine that there is a universe of all possible ideas relevant to some topic and that we want to sample this population, not the population of people who have the ideas. Clearly, in order to get all of the ideas, and especially the "outlier" or unusual ones, we have to include a broad and diverse range of participants. Heterogeneity sampling is, in this sense, almost the opposite of modal instance sampling.