This document provides an overview of different approaches to data sampling, collection, and testing. It discusses various units of measurement that can be used and describes structured, semi-structured, and mixed methods for data collection. Structured approaches use close-ended tools like surveys to collect quantifiable data, while semi-structured use open-ended questions in interviews and discussions to gather qualitative insights. Mixed methods combine approaches. The document provides examples of when each approach would be most applicable and addresses common questions about differences between methods.
Here are some data collection methods that can be used if you don't have direct access to the population of interest:
- Remote key informant interviews: Conduct interviews over the phone or online platforms with people who have knowledge about the population/area but are not directly part of it, such as local leaders, aid workers, journalists etc.
- Secondary data review: Analyze existing data from government, UN agencies, NGO reports, media articles, academic studies etc. to understand the situation without primary data collection.
- Remote household surveys: Use phone surveys to interview households. This requires having a sample frame of phone numbers for the population.
- Online/mobile surveys: Distribute surveys virtually through websites/apps if the
This document provides an overview of research design and methodology. It discusses key components of methodology including selecting the overall research method, data collection approaches, and sampling strategy. Both quantitative and qualitative research methods are described, as well as mixed methods. Common data collection techniques are explained for structured, semi-structured, and mixed methods approaches. Considerations for selecting an appropriate research method based on objectives, resources, and other factors are also outlined. The document aims to help researchers design a methodology that will feasibly achieve their intended research scope and objectives.
This document provides an overview of research design methodology, including different types of research methods, data collection approaches, and frequently asked questions. It discusses quantitative, qualitative, and mixed methods approaches. Specifically, it describes structured surveys, semi-structured discussions, observations, and sequential or concurrent mixed methods. Considerations for selecting a methodology include the research objectives, available time and resources, and required scope and quality of findings. The document aims to help researchers design the overall strategy for data collection and analysis.
This document provides an overview of research design methodology, including different types of research methods, data collection approaches, and frequently asked questions. It discusses quantitative, qualitative, and mixed methods approaches. Specifically, it describes structured surveys, semi-structured discussions, observations, and sequential or concurrent mixed methods. Considerations for selecting a methodology include the research objectives, available time and resources, and required scope and quality of findings. The document aims to help researchers design a methodology that effectively addresses their research questions.
Qualitative research is a scientific method of investigation that seeks to understand and explain social phenomena through analysis of people's experiences. It involves collecting data in natural settings through methods like interviews, observations and focus groups. The data collected consists of words and images rather than numbers. Three common qualitative methods are participant observation, in-depth interviews, and focus groups. Qualitative research is characterized by its inductive approach, focus on meanings and experiences, flexible design, and interpretation of findings. It aims to gain an in-depth understanding of issues rather than generalizing to a population.
This document provides an overview of quantitative and qualitative research methods. It distinguishes between quantitative and qualitative research, describing how they differ in terms of data collection methods, sampling strategies, and analysis approaches. Quantitative research uses structured and closed-ended questioning, probability sampling, and statistical analysis to generalize to populations. Qualitative research employs semi-structured interviews, non-probability sampling, and inductive analysis to understand perspectives without generalization. Mixed methods combines quantitative and qualitative data collection and analysis to provide a comprehensive understanding of research problems.
This document discusses qualitative research methods. Qualitative research seeks to understand a research problem from the perspectives of the local population. It provides information about human behaviors, beliefs, opinions and relationships. When used with quantitative methods, qualitative research can help interpret complex realities and data. Some key aspects of qualitative research include unstructured response options, no statistical tests, and less generalizable but more valid and reliable results depending on the researcher's skill.
Qualitative research seeks to understand a research problem from the perspectives of the local population involved. It provides information about the human side of issues by identifying factors like social norms, beliefs, and relationships. When used with quantitative methods, qualitative research can help interpret complex realities and data. Valid qualitative research comprehensively collects data through methods like interviews and observation from participants selected through strategies such as purposive and snowball sampling. It analyzes data appropriately and corroborates findings through techniques including member checking and triangulation.
Here are some data collection methods that can be used if you don't have direct access to the population of interest:
- Remote key informant interviews: Conduct interviews over the phone or online platforms with people who have knowledge about the population/area but are not directly part of it, such as local leaders, aid workers, journalists etc.
- Secondary data review: Analyze existing data from government, UN agencies, NGO reports, media articles, academic studies etc. to understand the situation without primary data collection.
- Remote household surveys: Use phone surveys to interview households. This requires having a sample frame of phone numbers for the population.
- Online/mobile surveys: Distribute surveys virtually through websites/apps if the
This document provides an overview of research design and methodology. It discusses key components of methodology including selecting the overall research method, data collection approaches, and sampling strategy. Both quantitative and qualitative research methods are described, as well as mixed methods. Common data collection techniques are explained for structured, semi-structured, and mixed methods approaches. Considerations for selecting an appropriate research method based on objectives, resources, and other factors are also outlined. The document aims to help researchers design a methodology that will feasibly achieve their intended research scope and objectives.
This document provides an overview of research design methodology, including different types of research methods, data collection approaches, and frequently asked questions. It discusses quantitative, qualitative, and mixed methods approaches. Specifically, it describes structured surveys, semi-structured discussions, observations, and sequential or concurrent mixed methods. Considerations for selecting a methodology include the research objectives, available time and resources, and required scope and quality of findings. The document aims to help researchers design the overall strategy for data collection and analysis.
This document provides an overview of research design methodology, including different types of research methods, data collection approaches, and frequently asked questions. It discusses quantitative, qualitative, and mixed methods approaches. Specifically, it describes structured surveys, semi-structured discussions, observations, and sequential or concurrent mixed methods. Considerations for selecting a methodology include the research objectives, available time and resources, and required scope and quality of findings. The document aims to help researchers design a methodology that effectively addresses their research questions.
Qualitative research is a scientific method of investigation that seeks to understand and explain social phenomena through analysis of people's experiences. It involves collecting data in natural settings through methods like interviews, observations and focus groups. The data collected consists of words and images rather than numbers. Three common qualitative methods are participant observation, in-depth interviews, and focus groups. Qualitative research is characterized by its inductive approach, focus on meanings and experiences, flexible design, and interpretation of findings. It aims to gain an in-depth understanding of issues rather than generalizing to a population.
This document provides an overview of quantitative and qualitative research methods. It distinguishes between quantitative and qualitative research, describing how they differ in terms of data collection methods, sampling strategies, and analysis approaches. Quantitative research uses structured and closed-ended questioning, probability sampling, and statistical analysis to generalize to populations. Qualitative research employs semi-structured interviews, non-probability sampling, and inductive analysis to understand perspectives without generalization. Mixed methods combines quantitative and qualitative data collection and analysis to provide a comprehensive understanding of research problems.
This document discusses qualitative research methods. Qualitative research seeks to understand a research problem from the perspectives of the local population. It provides information about human behaviors, beliefs, opinions and relationships. When used with quantitative methods, qualitative research can help interpret complex realities and data. Some key aspects of qualitative research include unstructured response options, no statistical tests, and less generalizable but more valid and reliable results depending on the researcher's skill.
Qualitative research seeks to understand a research problem from the perspectives of the local population involved. It provides information about the human side of issues by identifying factors like social norms, beliefs, and relationships. When used with quantitative methods, qualitative research can help interpret complex realities and data. Valid qualitative research comprehensively collects data through methods like interviews and observation from participants selected through strategies such as purposive and snowball sampling. It analyzes data appropriately and corroborates findings through techniques including member checking and triangulation.
The descriptive method is used to gather information about existing conditions and explore causes of phenomena. It involves collecting data to test hypotheses or answer questions about the current status of what is being studied. Information can be obtained through personal interviews, surveys, observation, use of measurement devices, case studies, surveys, developmental studies, longitudinal studies, cross-sectional studies, and documentary analysis. While the descriptive method provides useful information, it has limitations including lack of breadth, difficulty generalizing findings, and potential for bias. Researchers must take care to properly employ the method and avoid simply information gathering rather than true research.
This chapter discusses research methodologies used in health communication, including qualitative and quantitative approaches. It defines key terms like prevalence, incidence, epidemiological studies. Quantitative research involves collecting and analyzing statistical data from representative samples, using methods like surveys, correlation analysis, and regression analysis. Qualitative research uses smaller purposive samples for in-depth insights through techniques like observation, interviews and focus groups. Both methodologies can be combined for effective formative research. The chapter emphasizes the importance of research in planning health communication programs.
The descriptive method is used to gather information about existing conditions and explore phenomena. It involves collecting data through methods like interviews, surveys, observation, and analysis of documents and records to describe situations and answer questions about current status. While it provides expansive data useful for understanding problems, limitations include lack of depth in large surveys and inability to determine causation. Researchers must take care to avoid misusing the method and ensure results are reliable and trustworthy.
The document provides an overview of research methodology. It discusses key aspects of the research process including developing research questions and hypotheses, research design, data collection methods, and data analysis. Some common data collection methods described are surveys, experiments, case studies, observations, interviews, and focus groups. The document also covers qualitative and quantitative research approaches, as well as descriptive, explanatory, and exploratory research.
Quantitative and Qualitative Approaches.pdfssuser504dda
This document provides an overview of quantitative and qualitative research approaches. It defines quantitative research as deductive, using numeric data from large samples to test hypotheses and analyze relationships between variables objectively. Qualitative research is defined as inductive, relying on words from smaller samples to understand participant experiences subjectively and identify themes in the data. The key differences between the two approaches are described in terms of identifying research problems, reviewing literature, specifying research purposes and questions, collecting and analyzing data, and reporting results. The document also discusses research design and types of quantitative, qualitative, and mixed methods designs.
This document outlines and compares quantitative and qualitative research methods. Quantitative methods focus on measuring responses from large sample sizes using tools like surveys and statistical analysis, while qualitative methods use descriptive data collection like interviews and observations to understand differing perspectives on reality. It also discusses participatory methods which involve workshops and diagrams to collaboratively understand issues. Examples of each method type are provided. The document then outlines the typical phases of research including idea generation, problem definition, design, data collection, analysis, interpretation, and communication of results.
Data Collection and Sampling Techniques Demo ppt.pptxChristianAlcaide2
This document discusses data collection and sampling techniques used in research. It defines primary and secondary data, as well as qualitative and quantitative data. Several primary data collection methods are described, including surveys, interviews, focus groups, observation, experiments, diaries, and case studies. Secondary data collection involves obtaining published information from sources like books, journals, and government records. The document also explains probability sampling techniques like simple random sampling, systematic sampling, stratified random sampling and cluster sampling. Non-probability sampling techniques include quota sampling, snowball sampling, convenience sampling, and purposive sampling.
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.
This document discusses research methods and designs. It defines quantitative and qualitative research methods. Quantitative methods use numerical data and statistics, while qualitative methods focus on patterns and human experiences. The document also outlines different types of research designs, including descriptive, experimental, quasi-experimental, correlational, narrative, phenomenological, grounded theory, historical, case study and ethnographic. It provides examples of how to create a research method and an example research method related to constraint identification and classification.
Share MED3-DATA COLLECTION AND PRESENTATION(METHODS OF DATA) PPT.pptxShenaCanoCover
This document discusses various methods for collecting data in research. It describes two main types of data - quantitative and qualitative. Quantitative data deals with numerical values while qualitative data involves non-numerical values like opinions. Some common data collection methods are surveys, interviews, focus groups, observations, experiments, case studies, and document analysis. Each method has advantages and limitations depending on the research question, and often multiple methods are used together to provide a comprehensive understanding. Overall, systematic data collection ensures valid and reliable data to accurately inform conclusions.
Share MED3-DATA COLLECTION AND PRESENTATION(METHODS OF DATA) PPT.pptxShenaCanoCover
This document discusses various methods for collecting data in research. It describes two main types of data - quantitative and qualitative. Quantitative data deals with numerical values while qualitative data involves non-numerical values like opinions. Some common data collection methods are surveys, interviews, focus groups, observations, experiments, case studies, and document analysis. Each method has strengths and weaknesses for collecting different types of information. Using multiple methods can provide a more comprehensive understanding of the topic being researched.
PR1 M5 Understanding Data and Ways how to Systematically Collect Data.pdfLEONILAMIRANDA2
This document discusses qualitative research methods for systematically collecting data. It describes various non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling. Sample size in qualitative research typically continues until information redundancy or saturation occurs, with rules of thumb based on the research approach, data collection method, and length of interviews. Qualitative data collection methods are time-consuming so samples are usually smaller, but the information is richer with deeper insight into the phenomenon studied. Data analysis involves examining, categorizing, and recombining evidence to address the study's initial propositions.
PR1 M5 Understanding Data and Ways how to Systematically Collect Data.pdfLEONILAMIRANDA2
This document discusses qualitative research methods for systematically collecting data. It describes various non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling. Sample size in qualitative research typically continues until information redundancy or saturation occurs, with rules of thumb based on the research approach, data collection method, and length of interviews. Qualitative data collection methods are time-consuming so samples are usually smaller, but the information is richer with deeper insight into the phenomenon studied. Data analysis involves examining, categorizing, and recombining evidence to address the study's initial propositions.
Class 6 research quality in qualitative methods 3 2-17tjcarter
This document discusses key ethical issues and methodological considerations for conducting Scholarship of Teaching and Learning (SoTL) research. It outlines assumptions of qualitative research designs, including that they seek to understand meaning and experience rather than generate generalized knowledge. It also discusses eight stages of formative research to generate options and assess interventions. The document emphasizes rigor in qualitative research through credibility, transferability, dependability, and confirmability. It explores mixed methods approaches and priorities for integrating qualitative and quantitative methods.
This document discusses various methods for collecting data, both primary and secondary. It defines data as units of information that are measured, collected, analyzed and used for data visualizations. There are two main types of data collection methods discussed:
Primary methods involve directly collecting original data and include observation, surveys, interviews and questionnaires. Observation allows collecting large quantities of data in an inexpensive way but requires extensive training. Surveys can be conducted in-person or online and collect standardized information from a sample. Interviews are conducted one-on-one and allow collecting more in-depth information.
Secondary methods involve using existing data collected by others. Common secondary sources include publications, reports, and data available online. While cheaper and faster
This document discusses various qualitative research methods used in descriptive research, including different types of observation techniques (naturalistic observation, participant observation, structured observation, field experiments), recording behaviors both qualitatively and quantitatively, unobtrusive measures like physical traces and archival data, and the importance of research methods in psychology for understanding behavior, testing hypotheses, and improving lives. It also compares stratified random sampling and snowball sampling.
This document discusses various methods for collecting data in research studies. It outlines the differences between quantitative and qualitative research methods. Some key methods discussed include interviews, focus groups, observation, questionnaires, and secondary data collection. Interviews can be structured, unstructured, or semi-structured. Focus groups involve a moderator guiding discussion among similar participants. Observation methods include controlled observation, naturalistic observation, and participant observation. Questionnaires can be self-administered or involve personal interviews. Secondary data is existing unpublished or published information from various sources. The document provides guidance on using these different techniques for collecting both primary and secondary data.
Qualitative research second copy correctedMenaal Kaushal
The document provides an overview of qualitative research including:
- The difference between qualitative and quantitative studies
- The scope and uses of qualitative research such as exploring phenomena and generating hypotheses
- Common qualitative research methods like participant observation, in-depth interviews, and focus group discussions
- Steps in conducting qualitative research from establishing research questions to data analysis
- Types of qualitative sampling, data forms, and analysis which are iterative and focus on themes rather than numbers
The document serves as an introduction to key concepts in qualitative research methodology.
The document provides an overview of a course on qualitative research methods. It discusses key topics that will be covered in the lectures, including what qualitative research is, different qualitative research strategies and how to implement them, methods for collecting data through observation and interviews, and analyzing qualitative data. The lectures will cover theory, qualitative research strategies and processes, data collection techniques, and critiques of qualitative research approaches.
This document discusses various methods for collecting data in research. It describes qualitative and quantitative data, as well as primary and secondary data. Some key data collection methods covered include experiments, surveys, interviews/focus groups, observation, literature reviews, and case studies. For each method, the document discusses what it is, its pros and cons, and how to implement the methodology. The goal of the document is to provide an overview of different approaches to gathering raw facts and evidence for research.
The descriptive method is used to gather information about existing conditions and explore causes of phenomena. It involves collecting data to test hypotheses or answer questions about the current status of what is being studied. Information can be obtained through personal interviews, surveys, observation, use of measurement devices, case studies, surveys, developmental studies, longitudinal studies, cross-sectional studies, and documentary analysis. While the descriptive method provides useful information, it has limitations including lack of breadth, difficulty generalizing findings, and potential for bias. Researchers must take care to properly employ the method and avoid simply information gathering rather than true research.
This chapter discusses research methodologies used in health communication, including qualitative and quantitative approaches. It defines key terms like prevalence, incidence, epidemiological studies. Quantitative research involves collecting and analyzing statistical data from representative samples, using methods like surveys, correlation analysis, and regression analysis. Qualitative research uses smaller purposive samples for in-depth insights through techniques like observation, interviews and focus groups. Both methodologies can be combined for effective formative research. The chapter emphasizes the importance of research in planning health communication programs.
The descriptive method is used to gather information about existing conditions and explore phenomena. It involves collecting data through methods like interviews, surveys, observation, and analysis of documents and records to describe situations and answer questions about current status. While it provides expansive data useful for understanding problems, limitations include lack of depth in large surveys and inability to determine causation. Researchers must take care to avoid misusing the method and ensure results are reliable and trustworthy.
The document provides an overview of research methodology. It discusses key aspects of the research process including developing research questions and hypotheses, research design, data collection methods, and data analysis. Some common data collection methods described are surveys, experiments, case studies, observations, interviews, and focus groups. The document also covers qualitative and quantitative research approaches, as well as descriptive, explanatory, and exploratory research.
Quantitative and Qualitative Approaches.pdfssuser504dda
This document provides an overview of quantitative and qualitative research approaches. It defines quantitative research as deductive, using numeric data from large samples to test hypotheses and analyze relationships between variables objectively. Qualitative research is defined as inductive, relying on words from smaller samples to understand participant experiences subjectively and identify themes in the data. The key differences between the two approaches are described in terms of identifying research problems, reviewing literature, specifying research purposes and questions, collecting and analyzing data, and reporting results. The document also discusses research design and types of quantitative, qualitative, and mixed methods designs.
This document outlines and compares quantitative and qualitative research methods. Quantitative methods focus on measuring responses from large sample sizes using tools like surveys and statistical analysis, while qualitative methods use descriptive data collection like interviews and observations to understand differing perspectives on reality. It also discusses participatory methods which involve workshops and diagrams to collaboratively understand issues. Examples of each method type are provided. The document then outlines the typical phases of research including idea generation, problem definition, design, data collection, analysis, interpretation, and communication of results.
Data Collection and Sampling Techniques Demo ppt.pptxChristianAlcaide2
This document discusses data collection and sampling techniques used in research. It defines primary and secondary data, as well as qualitative and quantitative data. Several primary data collection methods are described, including surveys, interviews, focus groups, observation, experiments, diaries, and case studies. Secondary data collection involves obtaining published information from sources like books, journals, and government records. The document also explains probability sampling techniques like simple random sampling, systematic sampling, stratified random sampling and cluster sampling. Non-probability sampling techniques include quota sampling, snowball sampling, convenience sampling, and purposive sampling.
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.
This document discusses research methods and designs. It defines quantitative and qualitative research methods. Quantitative methods use numerical data and statistics, while qualitative methods focus on patterns and human experiences. The document also outlines different types of research designs, including descriptive, experimental, quasi-experimental, correlational, narrative, phenomenological, grounded theory, historical, case study and ethnographic. It provides examples of how to create a research method and an example research method related to constraint identification and classification.
Share MED3-DATA COLLECTION AND PRESENTATION(METHODS OF DATA) PPT.pptxShenaCanoCover
This document discusses various methods for collecting data in research. It describes two main types of data - quantitative and qualitative. Quantitative data deals with numerical values while qualitative data involves non-numerical values like opinions. Some common data collection methods are surveys, interviews, focus groups, observations, experiments, case studies, and document analysis. Each method has advantages and limitations depending on the research question, and often multiple methods are used together to provide a comprehensive understanding. Overall, systematic data collection ensures valid and reliable data to accurately inform conclusions.
Share MED3-DATA COLLECTION AND PRESENTATION(METHODS OF DATA) PPT.pptxShenaCanoCover
This document discusses various methods for collecting data in research. It describes two main types of data - quantitative and qualitative. Quantitative data deals with numerical values while qualitative data involves non-numerical values like opinions. Some common data collection methods are surveys, interviews, focus groups, observations, experiments, case studies, and document analysis. Each method has strengths and weaknesses for collecting different types of information. Using multiple methods can provide a more comprehensive understanding of the topic being researched.
PR1 M5 Understanding Data and Ways how to Systematically Collect Data.pdfLEONILAMIRANDA2
This document discusses qualitative research methods for systematically collecting data. It describes various non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling. Sample size in qualitative research typically continues until information redundancy or saturation occurs, with rules of thumb based on the research approach, data collection method, and length of interviews. Qualitative data collection methods are time-consuming so samples are usually smaller, but the information is richer with deeper insight into the phenomenon studied. Data analysis involves examining, categorizing, and recombining evidence to address the study's initial propositions.
PR1 M5 Understanding Data and Ways how to Systematically Collect Data.pdfLEONILAMIRANDA2
This document discusses qualitative research methods for systematically collecting data. It describes various non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling. Sample size in qualitative research typically continues until information redundancy or saturation occurs, with rules of thumb based on the research approach, data collection method, and length of interviews. Qualitative data collection methods are time-consuming so samples are usually smaller, but the information is richer with deeper insight into the phenomenon studied. Data analysis involves examining, categorizing, and recombining evidence to address the study's initial propositions.
Class 6 research quality in qualitative methods 3 2-17tjcarter
This document discusses key ethical issues and methodological considerations for conducting Scholarship of Teaching and Learning (SoTL) research. It outlines assumptions of qualitative research designs, including that they seek to understand meaning and experience rather than generate generalized knowledge. It also discusses eight stages of formative research to generate options and assess interventions. The document emphasizes rigor in qualitative research through credibility, transferability, dependability, and confirmability. It explores mixed methods approaches and priorities for integrating qualitative and quantitative methods.
This document discusses various methods for collecting data, both primary and secondary. It defines data as units of information that are measured, collected, analyzed and used for data visualizations. There are two main types of data collection methods discussed:
Primary methods involve directly collecting original data and include observation, surveys, interviews and questionnaires. Observation allows collecting large quantities of data in an inexpensive way but requires extensive training. Surveys can be conducted in-person or online and collect standardized information from a sample. Interviews are conducted one-on-one and allow collecting more in-depth information.
Secondary methods involve using existing data collected by others. Common secondary sources include publications, reports, and data available online. While cheaper and faster
This document discusses various qualitative research methods used in descriptive research, including different types of observation techniques (naturalistic observation, participant observation, structured observation, field experiments), recording behaviors both qualitatively and quantitatively, unobtrusive measures like physical traces and archival data, and the importance of research methods in psychology for understanding behavior, testing hypotheses, and improving lives. It also compares stratified random sampling and snowball sampling.
This document discusses various methods for collecting data in research studies. It outlines the differences between quantitative and qualitative research methods. Some key methods discussed include interviews, focus groups, observation, questionnaires, and secondary data collection. Interviews can be structured, unstructured, or semi-structured. Focus groups involve a moderator guiding discussion among similar participants. Observation methods include controlled observation, naturalistic observation, and participant observation. Questionnaires can be self-administered or involve personal interviews. Secondary data is existing unpublished or published information from various sources. The document provides guidance on using these different techniques for collecting both primary and secondary data.
Qualitative research second copy correctedMenaal Kaushal
The document provides an overview of qualitative research including:
- The difference between qualitative and quantitative studies
- The scope and uses of qualitative research such as exploring phenomena and generating hypotheses
- Common qualitative research methods like participant observation, in-depth interviews, and focus group discussions
- Steps in conducting qualitative research from establishing research questions to data analysis
- Types of qualitative sampling, data forms, and analysis which are iterative and focus on themes rather than numbers
The document serves as an introduction to key concepts in qualitative research methodology.
The document provides an overview of a course on qualitative research methods. It discusses key topics that will be covered in the lectures, including what qualitative research is, different qualitative research strategies and how to implement them, methods for collecting data through observation and interviews, and analyzing qualitative data. The lectures will cover theory, qualitative research strategies and processes, data collection techniques, and critiques of qualitative research approaches.
This document discusses various methods for collecting data in research. It describes qualitative and quantitative data, as well as primary and secondary data. Some key data collection methods covered include experiments, surveys, interviews/focus groups, observation, literature reviews, and case studies. For each method, the document discusses what it is, its pros and cons, and how to implement the methodology. The goal of the document is to provide an overview of different approaches to gathering raw facts and evidence for research.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
2. Session
Contents
1. Unit of measurement
2. Types of data collection approaches
(structured)
3. Types of data collection approaches (semi-
structured)
4. Types of data collection approaches (mixed
methods)
5. Frequently Asked Questions (FAQs)
4. What is it?
The unit that will be used to record,
measure and analyse observations/
information collected
Examples?
Individual
Family
Household
Community/ group
Town/ village
Facility
Cow
5. Remember…
Unit will impact the time, resources needed to collect and analyse information
Unit will define the depth of information possible and scope of analysis
Depth
of
information
Location level
Household level
Individual level
Community/Group level
Time / Cost / Access
7. 1. The structured survey approach
Information collected through an interview, a discussion, a conversation
Using structured, close-ended data collection tools
Collection of quantifiable information
Cross-sectional or longitudinal
Types of data collection methods?
Household (HH) survey – collecting data at HH level, to understand experiences and characteristics of HHs within population of interest
Individual survey – collecting data at individual level, to help understand situation and characteristics of individuals within the population of
interest can include some HH level indicators if needed
Key informant interview – collecting data at community. location or group level from a key informant (KIs) i.e. an individual whose informal/
formal position gives him specific knowledge about other people, processes, or events that is more extensive, detailed, or privileged than
other individuals in their group/ community/ location
Group discussion – collecting data at community, location or group level from a group of representatives e.g. KIs
8. 1. The structured survey approach- Applicability
When should you use this approach?
To measure prevalence provide a quantifiable, numeric description of
the trends, behaviours, experiences, attitudes or opinions of a population
To generalize findings to a wider population probability sample
statistically representative information
Need prevalence data, understanding of scale of crisis but probability
sampling not possible non-probability sample indicative information
Types of research cycles this approach is commonly used for?
Multi-sector needs assessments
In-depth thematic needs assessments e.g. WASH Cluster needs assessment
Longitudinal studies
Third party monitoring (impact evaluation, outcome monitoring, post-
distribution monitoring, etc.)
9. 2. The structured experimental approach
What is it?
Similar to survey approach
But relies on experimental survey design
control vs. treatment group
Types of data collection methods?
Household (HH) survey – collecting data at HH level, to understand experiences and
characteristics of HHs within population of interest
Individual survey – collecting data at individual level, to help understand situation and
characteristics of individuals within the population of interest can include some HH level
indicators if needed
10. 2. The structured experimental approach - Applicability
When should you use this approach?
To measure prevalence and evaluate the outcomes or impact of a medium to large-scale intervention on
the population of interest
Generalize findings to a wider population probability sample statistically representative
information
Types of research cycles this approach is commonly used for?
Outcome monitoring
Impact evaluations
Etc.
11. 3. The structured observation approach (Description)
What is it?
Information collected through observation rather than
conversation
Using structured, close-ended checklists to collect
quantifiable information
Looking for specific object, behaviour or event against a
checklist e.g. Household using soap? Damage to health
center? Students participating in classroom?
Can be used as part of experimental approach
Types of data collection methods?
Participant observation – researcher participates in
context (e.g. anthropologists)
Direct observation – researchers observes context (e.g.
psychologists or clinical research)
12. 3. The structured observation approach - Applicability
When should you use this approach?
Serves similar purpose as survey approach
Depends on research objectives observation vs.
conversation?
Types of research cycles this approach is commonly used for?
Could be same as survey approach
Could be same as experimental approach
14. 4. The semi-structured discussion approach
What is it?
Information collected through detailed, narrative interviews, group discussions
Using semi-structured (NOT UNSTRUCTURED) data collection tools
open-ended questions, probes
Purposefully selected participants
Types of data collection methods?
Individual interview – collecting data at individual level, to help understand situation and characteristics of individuals within the population
of interest can include some HH level indicators if needed
Key informant interview – collecting data at community. location or group level from a key informant (KIs) i.e. an individual whose informal/
formal position gives him specific knowledge about other people, processes, or events that is more extensive, detailed, or privileged than
other individuals in their group/ community/ location
Group discussion – collecting data at community, location or group level from a group of representatives e.g. Kis
Focus group discussion – bringing together people from similar backgrounds or experiences to discuss a specific topic of interest; data
collected at community, location or group level
15. 4. The semi-structured discussion approach - Applicability
When should you use this approach?
To gather detailed insights about the
experiences, perspectives of specific population
group or location
To provide a qualitative description of the
experiences, trends, attitudes or opinions of a
population
Types of research cycles this approach is
commonly used for?
In-depth assessments where there is limited
prior understanding of a situation e.g. access to
cash among refugees & migrants in Libya
Participatory mapping exercises (mapping FGDs
or KI interviews)
16. ‘Most Significant Change’ data collection technique
A very specific type of participatory,
discussion-based data collection
method used for monitoring &
evaluation
Invites participants (through KI
interviews, individual interviews or
FGDs) to explain the most significant
changes brought about in their lives
by a project over a given period of time,
in key domains of change
Useful for third party monitoring or
impact evaluation research cycles
17. 5. The semi-structured observation approach
Similar to structured observation approach
But two key differences:
Structured observation Semi-structured observation
1. Differences in data
collection methods
Information collected using a
structured set of questions,
usually to identify specific object,
behaviour or event against a
checklist
Information collected based on a
short set of open-ended
questions for observations e.g.
movement patterns of refugees
in and out of camps during a
sustained period of time
2. Differences in purpose Provide a quantifiable, numeric
description of the trends,
behaviours, experiences, etc. of
a population
Gather detailed insights about
the behaviours, experiences of
a specific population group or
location, and to understand, by
observation, how things are
done and what issues exist
19. 6. Sequential mixed methods data collection
Method used to sequentially elaborate or expand on the findings of one type of
research method with another
• Identify coping
strategies
Qualitative
• Measure
prevalence of
identified coping
strategies
Quantitative
1. Exploratory sequential approach
• Measure
prevalence of
known coping
strategies
Quantitative
• Understand and
contextualize
observed trends
in prevalence
Qualitative
2. Explanatory sequential approach
• Identify coping
strategies
Qualitative
• Measure
prevalence of
identified coping
strategies
Quantitative
• Understand and
contextualize
observed trends
in prevalence
Qualitative
3. The “ideal” sequential approach
20. 7. Concurrent mixed methods approach
Method used to merge or converge the findings from different research methods collected at the
same time
Alternative to sequential approach if time constraints sequential better practice if time and
resources allow
Concurrent mixed methods serves two key purposes:
Triangulation strategy
Convergence
of information
collected?
Divergence of
information
collected?
Embedded strategy
Primary
method:
quant
(What?
Where?)
Secondary
method:
qual (How?
Why?)
Key findings &
conclusions
21. Case study data collection technique
Using a combination of different data
collection methods to zoom in to a
specific issue, area or group
A component within a research
cycle, not a research cycle by itself
Useful to collect detailed information
on an event, activity, process, group
e.g. zoom in to one specific type of
intervention in an area within a larger
DFID-funded humanitarian programme
23. FAQs (1)
What is the difference between a key informant interview and an individual interview?
Isn’t the key informant also technically an individual?
The differences lies in the unit of measurement individual experiences (individual interview) vs.
community/ village/ institution experiences (KI interview)
For semi-structured data collection, when is it recommended to use FGDs over KI or
individual interviews?
This depends on two things
Research objectives and type of information needed e.g. Variety of opinions and
experiences useful? Specific information needed from an expert? Topics sensitive to discuss
in group setting?
Logistical constraints e.g. Large number of individuals to be reached within a short
timeframe?
24. FAQs (2)
Is it possible to have two different units of measurement in the same questionnaire?
Ideally, should be avoided, but there are some exceptions:
Individual information within a household survey (e.g. child attendance roster)
Household information within an individual survey (e.g. household size or income indicators)
Individual information within a village/ community/ location level interview (e.g. KI’s displacement status and experiences,
if KI also part of the affected population)
Household information within a village/ community/ location level interview (e.g. KI estimates # or % of households
affected by a specific situation in a village)
What if my population of interest includes minors (i.e. individuals <18 years of age)? Can I collect data
from minors?
Only if absolutely necessary to meet objectives of the research
Only if required information cannot be collected from adult respondents e.g. parents or caregivers
Ideally, only from respondents >15 years
Only if the required protocols are being followed
Will de discussed later in this training
27. What is remote data collection?
Remote data collection is a means of gathering data without a
physical presence in the data collection location and without
direct, in-person contact with the population of interest
When is it useful?
When it is not possible to conduct in-person visits to the
locations / populations of interest because of reasons such as:
Disease outbreak (e.g. COVID-19)
Time or resource constraints (e.g. not enough budget to hire
enumerators to cover all areas for face to face interviews)
Access constraints due to:
Security concerns
COVID-19 travel restrictions
Physical access barriers such as lack of infrastructure
Severe weather conditions which limits travel
possibilities, etc.
Etc.
28. Pros and cons of remote data collection
Pros Cons
Planning efficiency
More time and resource efficient; if
necessary logistics already in place,
could be fairly straightforward to
deploy
Challenging and time consuming to
set up correctly (e.g. identifying
respondents, organizing necessary
logistics, etc.), difficult to apply
stratification in sampling; challenging
to monitor progress
Implementation efficiency
Easier to implement even with
limited time, access and resources
(assuming planning and design is
done robustly)
Higher likelihood of low response
rates; limited means of verifying
responses/ data quality assurance;
more challenging to build trust with the
respondents; difficult to deploy long
or complicated questionnaires
Coverage
Ensures maximum possible
coverage of areas and population of
interest despite access constraints
Difficult to have the “full picture” as it
could introduce potential sampling
biases (e.g. based on phone network
coverage) and results in exclusions/
oversight of certain population
groups or areas
29. Some types of remote data collection methods (1)
1. Phone-based (individual, household, community level)
Most relevant for: needs assessments, post distribution monitoring (PDMs),
humanitarian situation monitoring (HSM)
Representative sampling could be possible
2. REACH “Area of Knowledge” methodology (face-to-
face data collection in alternate location)
Most relevant for: community-level needs assessments or HSM
Representative sampling not relevant (requires identifying the
respondent most likely to have the required knowledge)
3. Internet-based data collection
Tools include: social media, web-based surveys, online discussion
platforms, chatbots (WFP mVAM), etc.
Most relevant for: community-level needs assessments or HSM (KI
interviews or group discussions), PDMs (individual perception surveys)
Representative sampling could be possible (but extremely difficult to
implement e.g. would need email address database and usually low
response rates)
30. Some types of remote data collection methods (2)
4. Remote sensing
Only relevant if aim is to gain an understanding based on specific physical
characteristics of an area (e.g. agriculture and vegetation health analysis, shelter
damage assessment, flood impact assessment, etc.)
Representative sampling or even census could be possible
5. Secondary data review and “expert” consultations
Most relevant for: needs analysis or HSM
Only feasible if relevant and «reliable» data sources already exist
6. Paper form submissions
Only applicable if respondents have no movement restrictions and are able to
send paper forms back through required means
Logistically difficult, not the most time and resource efficient
Most relevant for: community-level needs assessments or HSM (KI
interviews), PDMs (individual perception surveys)
Representative sampling could be possible (but extremely difficult to
implement e.g. would need postal address database and expect very low
response rates)
Editor's Notes
HH survey has two components: (1) a short background and demographics module (which includes a detailed roster of each household member’s age, sex, marital status and relationship status to the head of household) and (2) a detailed module exploring the key indicators and variables relevant to the topic of research. In some cases, a third module is also included which records individual-level data within the household, for e.g. information on education background and current status of each child member of school-going age within the household.
FGDs are useful to:
gain insight into how a specific group thinks about an issue
collect anecdotal evidence
gather a wide range of opinions and ideas through a few discussions only, and
identify and understand inconsistencies and variations that exist in a particular community in terms of perceptions, experiences and practices.