This document discusses data collection techniques for research. It defines key terms like primary and secondary data sources, population and sampling. It covers various sampling methods like simple random sampling, stratified sampling and cluster sampling. It also discusses different data collection instruments like questionnaires, interviews, opinionnaires and projective methods. It provides examples of each technique and notes their advantages and limitations.
Non- Probability Sampling & Its MethodsArpit Surana
A detailed explanation of non-probability sampling and its methods have been covered. There are 4 types of non- probability sampling methods:
1. convenience sampling
2. purposive sampling
3. quota sampling (both controlled and uncontrolled)
4. snowball sampling (all 3 ways of performing)
Meaning with adequate examples, pros and cons have been covered
For and query or further information, Kindly contact:
Arpit Surana
https://www.linkedin.com/in/arpitsurana116/
arpitsurana116116@gmail.com
What is snowball sampling describe in detail?WaqarRaees
It is more effective for the the researcher of snowball sampling What is snowball sampling describe in detail? snowball sampling advantage and disadvantages are discribed.
More important for the Bs English students application of snowball sampling describe
Non- Probability Sampling & Its MethodsArpit Surana
A detailed explanation of non-probability sampling and its methods have been covered. There are 4 types of non- probability sampling methods:
1. convenience sampling
2. purposive sampling
3. quota sampling (both controlled and uncontrolled)
4. snowball sampling (all 3 ways of performing)
Meaning with adequate examples, pros and cons have been covered
For and query or further information, Kindly contact:
Arpit Surana
https://www.linkedin.com/in/arpitsurana116/
arpitsurana116116@gmail.com
What is snowball sampling describe in detail?WaqarRaees
It is more effective for the the researcher of snowball sampling What is snowball sampling describe in detail? snowball sampling advantage and disadvantages are discribed.
More important for the Bs English students application of snowball sampling describe
By the end of this presentation you should be able to:
Describe the justification of qualitative Sampling Techniques
Understand different types of Sampling Techniques
In many different types of researches we are interested in learning about large groups of people who all have something in common that is called 'target population' Researchers commonly study traits or characteristics (parameters) of populations in their studies. It is more or less impossible to study the whole population therefore researches need to select a sample or sub-group of the population that is likely to be representative of the target population. Therefore, the researcher would select individuals from which to collect the data which is called sample. Sampling is the method of selecting individuals from the population. The method of sampling is a key factor for generalizing the results of sample into a population. There are two main methods of sampling including probable and non-probable sampling techniques. In probable sampling method the sample, should be as representative as possible of the population which leads to more confident to generalize the results to the target population.
Another important question that must be answered in all sample surveys is "How many participants should be chosen for a survey"? An under-sized study can be a waste of resources since it may not produce useful results while an over-sized study uses more resources than necessary. Determining the sample size should be based on type of research and its objectives as well as required statistical methods. There are different methods for determining the sample size applying various formulas to calculate a sample size.
Exploratory Research Design - Meaning and MethodsSundar B N
This ppt contains Exploratory Research Design which covers Introduction to Exploratory Research, Meaning of Exploratory Research, Techniques of Exploratory Research, Examples of Exploratory Research, Methods of Designing Exploratory Research
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Description about;
what is research design, need of research design, importance, how it is helpful,definition of research design,classification of research design, types of research design, likewise
exploratory research, conclusive research design, descriptive research, casual research, cross sectional research, longitudinal research.
how many types of research design brief notes and knowledge about all types of research design.
By the end of this presentation you should be able to:
Describe the justification of qualitative Sampling Techniques
Understand different types of Sampling Techniques
In many different types of researches we are interested in learning about large groups of people who all have something in common that is called 'target population' Researchers commonly study traits or characteristics (parameters) of populations in their studies. It is more or less impossible to study the whole population therefore researches need to select a sample or sub-group of the population that is likely to be representative of the target population. Therefore, the researcher would select individuals from which to collect the data which is called sample. Sampling is the method of selecting individuals from the population. The method of sampling is a key factor for generalizing the results of sample into a population. There are two main methods of sampling including probable and non-probable sampling techniques. In probable sampling method the sample, should be as representative as possible of the population which leads to more confident to generalize the results to the target population.
Another important question that must be answered in all sample surveys is "How many participants should be chosen for a survey"? An under-sized study can be a waste of resources since it may not produce useful results while an over-sized study uses more resources than necessary. Determining the sample size should be based on type of research and its objectives as well as required statistical methods. There are different methods for determining the sample size applying various formulas to calculate a sample size.
Exploratory Research Design - Meaning and MethodsSundar B N
This ppt contains Exploratory Research Design which covers Introduction to Exploratory Research, Meaning of Exploratory Research, Techniques of Exploratory Research, Examples of Exploratory Research, Methods of Designing Exploratory Research
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Description about;
what is research design, need of research design, importance, how it is helpful,definition of research design,classification of research design, types of research design, likewise
exploratory research, conclusive research design, descriptive research, casual research, cross sectional research, longitudinal research.
how many types of research design brief notes and knowledge about all types of research design.
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.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
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.
Different Kind of Landforms and Water forms.
I don't own any pictures and informations. This presentation is for Educational Purpose ONLY and compiled by Roiden Fernandez!
Follow me on twitter @roidenfredrich!
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
2. At the end of this lesson, the student should be able to:
1. recognize the importance of data gathering;
2. distinguish primary from secondary data sources;
3. define population and sampling;
4. define census and sample;
5. identify the various data collection techniques and sources
of data;
6. describe the various instruments for data gathering;
7. cite the advantages of the use of such instruments;
8. recognize the limitations of certain research instruments;
3. 1.
Primary sources of data - are those that
provide information that are collected for the first
time as part of a research project.
- are tangible
materials that provide a description of a historical
event and were produced shortly after the event
took place.
Example: Newspaper stories, personal letters, public
documents, eyewitness, verbal accounts, court
decisions, and personal diaries
-
4. 2. Secondary sources - are those that provide
data which have been collected previously and
reported by some individual other than the present
researcher
- borrowed knowledge from other
sources.
5. - refers to the processes whereby a sub-group is
picked out from a larger group and then use this subgroup as a basis for making judgments about the
larger group.
- called a sample
- referred to as population
6.
Population - is a
whether individuals, animals, objects, or events that
conform to specific criteria and to which one intend to
generalize the results of the research (McMillan, 1998; Wood
& Haber, 1998).
A census is a study that collects data from all members of
the population.
7.
Target population is the group or set of items or
individuals from which or about which
representative information is originally desired.
Sampling population is the population from
which a sample is actually drawn.
A sample is a set of elements, or a single
element, from which data are obtained
8.
Researchers generally use sampling because of
budget, time, and manpower constraints. Such constraints
prevent them from undertaking a complete study of the
total target population.
1. Reduced cost
2. Greater speed
3. Greater scope
4. Greater accuracy
9.
A probability sampling method - is any method of sampling
that utilizes some form of random selection.
In order to have a random selection method, you must set up
some process or procedure that assures that the different
units in your population have equal probabilities of being
chosen.
10.
1. Simple random sampling - is a process of selecting a sample from a
set of all sampling units, giving each unit in the frame an equal chance of
being included in the sample.
Two ways of randomly selecting samples:
- lottery method
- using table of random numbers - contains columns of digits that have been
mechanically generated, usually by a computer, to assume a random order.
2. Systematic sampling - refers to the process of selecting every kth
sampling unit of the population after the first sampling unit is selected at
random from the first k sampling units
11.
3. Stratified sampling - involves dividing the population into two or
more strata and then taking either a simple random (stratified random
sampling) or a systematic sample (stratified systematic sampling) from
each stratum.
4. Cluster sampling - is a method of selecting a sample of distinct
groups of clusters of smaller units called elements.
- A cluster refers to any intact group of similar
characteristics.
5. Multistage sampling -is a complex form of cluster sampling.
Cluster sampling is a type of sampling which involves dividing the
population into groups (or clusters).
- Using all the sample elements in all the selected
clusters may be prohibitively expensive or not necessary.
- the researcher randomly selects elements from
each cluster.
12. Two stages of Multistage sampling
First stage - Constructing the clusters
Second stage - Deciding what elements within the cluster
to use.
The technique is used frequently when a complete list of
all members of the population does not exist and is
inappropriate.
13.
Despite the accepted superiority of probability sampling
designs, the researcher is sometimes faced with the
problem of whether he would use nonprobability
sampling or not.
This is especially true when probability sampling
becomes expensive or when precise representatives are
not necessary.
14. Types of Nonprobability Sampling:
1.
Convenience sampling
- is selecting sampling units that are easily
(conveniently) available to the researcher.
- It is used in exploratory research where the
researcher is interested in getting an inexpensive approximation of the truth.
- used during preliminary research efforts to get a
gross estimate of the results, without incurring the cost or time required to
select a random sample.
2.
Judgment sampling or purposive sampling
- is selecting
units to be observed on the basis of our judgment about which one will be
useful or representative. The researcher selects the sample based on
judgment.
15. 3.
Quota sampling - is selecting samples on the basis of
pre-specified characteristics, so that the total sample will
have the same distribution of characteristics as assumed to
exist in the population being studied. The researcher first
identifies the stratums and their proportions as they are
represented in the population.
4.
Dimensional sampling - is a multi-dimensional
extension of quota sampling.
- In this sampling
procedure, instead of a large size, a small size is selected. It is
emphasized that all areas of interest should cover at least one case.
16.
5. Voluntary sampling - is a special type of sampling in which
6. Snowball sampling or sometimes called networking
sampling - researcher first identifies few individuals for the sample
subjects/cases are informed about the subject matter willingly or
voluntarily participate in the study. This sampling is useful especially if
one dealing with information on sensitive or delicate issues.
and uses them as informants. On the basis of their information, the
researcher collects the name of more persons bearing similar
characteristics.
Useful when one wants to consider possible respondents who are not
normally visible.
Used when the desired sample characteristic is rare
For example, study of drug addicts in a university, or a study of socioeconomic conditions of teacher-retirees, or a study of patients with AIDS
17.
1. Questionnaire - is often referred to as a “lazy man’s way of gaining
information”. It is also said that it is the most used and abused of data-gathering
devices. However, a carefully prepared questionnaire can yield better data.
2. Interview Method - is one of the data-gathering techniques in research. It
is defined as a face-to-face interaction between two persons. The one who asks
questions is called the interviewer and the one who supplies the information
asked for is called the interviewee or respondent.
scheduled-structured interview,
nonscheduled-structured interview
nonscheduled interview
18.
3. Opinionnaire - is an instrument that attempts to obtain the measured
attitude or belief of an individual. The opinionnaire is usually used to infer
attitude-expressed opinion of an individual.
This may be done by: directly asking how one feels about the subject
In asking an individual directly how one feels about the subject, we may use
either semantic differential scale or the Likert scale.
4. Projective methods - involve some sort of imaginative activity on the
part of the individual in interpreting ambiguous stimuli.
Projective methods were first used by psychologists wherein tests administered
provide a comprehensive picture of an individual’s personality
structure, emotional needs, conflicts and other feelings. In these
tests, responses of the individual are not taken on face value but are based on
some pre-established psychological conceptualization. The use of
pictures, verbal techniques, and play techniques are mostly used in
projective methods
19. 5. Observation - is a process whereby the
researcher watches the research situation.
This
data-collecting technique is mostly used when the
respondents are unwillingly to express themselves
verbally.
Observation
may be natural or contrived; disguised or
undisguised; structured or unstructured; direct or
indirect.