This document provides guidance on selecting and constructing data collection instruments for evaluations. It discusses various data collection strategies and characteristics of good measures. Both quantitative and qualitative approaches are covered. A number of specific tools for collecting data are also described, including structured and semi-structured surveys, interviews, focus groups, observation, diaries/journals, expert judgment, and the Delphi technique. Guidelines are provided for effectively implementing each of these tools.
This document provides an overview of strategies and tools for collecting data in evaluations. It discusses both quantitative and qualitative approaches. Key points covered include:
- Data collection strategies depend on factors like the type of information needed, data sources, and resources available.
- Both structured and semi-structured approaches have advantages and disadvantages depending on the evaluation needs.
- Common data collection tools include surveys, interviews, focus groups, observation, records review, and expert judgment. The appropriate tool depends on the situation.
- It is best to use multiple data collection methods and triangulate data from different sources to increase accuracy. Thorough planning and testing of tools is also important.
This document discusses various methods and tools for data collection in research. It describes primary and secondary data sources, as well as quantitative and qualitative data. Several data collection methods are outlined, including observation, interviews, questionnaires, and library or laboratory research. The document emphasizes that there is no single best method and researchers must consider their purpose, respondents, resources, and the type of data needed. Both structured and semi-structured approaches are described. The importance of sampling techniques and using multiple data collection methods, such as triangulation, is also highlighted.
This document provides an overview of strategies and tools for collecting data in program evaluations. It discusses both quantitative and qualitative approaches. Key points include:
- Data collection strategies depend on factors like the information needed, data sources, resources, and intended analysis. Both structured and semi-structured approaches are described.
- When collecting original data, it is important to pre-test instruments and establish clear protocols. Using multiple methods, like surveys, interviews, and observations, allows for triangulation.
- Common tools include records/secondary data, observations, surveys/interviews, focus groups, diaries, expert judgment, and the Delphi technique. Each has advantages and challenges depending on the evaluation needs.
- Choosing the right
The document discusses various strategies and tools for collecting data in evaluations. It describes both quantitative and qualitative approaches, and notes that the best approach depends on factors like the information needed, resources available, and complexity of the data. It provides guidelines for collecting data and discusses the advantages and challenges of various tools, including surveys, interviews, focus groups, observation, diaries, expert judgment, and more. The goal is to choose appropriate and multiple methods to ensure accurate and comprehensive data collection.
According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.0
A community needs assessment identifies the strengths and resources available in the community to meet the needs of children, youth, and families. The assessment focuses on the capabilities of the community, including its citizens, agencies, and organizations.
Data can come from internal or external sources. Internal sources include company reports and records, while external sources are outside the organization, like information obtained from other companies. There are various methods for collecting primary data, like interviews, surveys, observation, and experiments. Secondary data has already been previously collected and can come from internal sources within an organization or external sources outside the organization. Data can be structured, semi-structured, or unstructured, and varies in its level of organization and ability to be stored in a relational database. Key characteristics of good data include accuracy, validity, reliability, timeliness, completeness, availability, and accessibility.
1. The document discusses primary and secondary data, methods of data collection, and data classification.
2. Primary data is original data collected directly by researchers, while secondary data was previously collected by others. Primary data collection methods include surveys, interviews, and experiments, while secondary data comes from existing sources like publications and records.
3. Data classification organizes data into categories to aid usage and protection, with the goals of making data easily retrievable, meeting compliance requirements, and increasing awareness of data sensitivity.
This document provides an overview of strategies and tools for collecting data in evaluations. It discusses both quantitative and qualitative approaches. Key points covered include:
- Data collection strategies depend on factors like the type of information needed, data sources, and resources available.
- Both structured and semi-structured approaches have advantages and disadvantages depending on the evaluation needs.
- Common data collection tools include surveys, interviews, focus groups, observation, records review, and expert judgment. The appropriate tool depends on the situation.
- It is best to use multiple data collection methods and triangulate data from different sources to increase accuracy. Thorough planning and testing of tools is also important.
This document discusses various methods and tools for data collection in research. It describes primary and secondary data sources, as well as quantitative and qualitative data. Several data collection methods are outlined, including observation, interviews, questionnaires, and library or laboratory research. The document emphasizes that there is no single best method and researchers must consider their purpose, respondents, resources, and the type of data needed. Both structured and semi-structured approaches are described. The importance of sampling techniques and using multiple data collection methods, such as triangulation, is also highlighted.
This document provides an overview of strategies and tools for collecting data in program evaluations. It discusses both quantitative and qualitative approaches. Key points include:
- Data collection strategies depend on factors like the information needed, data sources, resources, and intended analysis. Both structured and semi-structured approaches are described.
- When collecting original data, it is important to pre-test instruments and establish clear protocols. Using multiple methods, like surveys, interviews, and observations, allows for triangulation.
- Common tools include records/secondary data, observations, surveys/interviews, focus groups, diaries, expert judgment, and the Delphi technique. Each has advantages and challenges depending on the evaluation needs.
- Choosing the right
The document discusses various strategies and tools for collecting data in evaluations. It describes both quantitative and qualitative approaches, and notes that the best approach depends on factors like the information needed, resources available, and complexity of the data. It provides guidelines for collecting data and discusses the advantages and challenges of various tools, including surveys, interviews, focus groups, observation, diaries, expert judgment, and more. The goal is to choose appropriate and multiple methods to ensure accurate and comprehensive data collection.
According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.08% during the forecast period, reaching USD 12290.99 million by 2028.According to the World Supply Chain Finance Report, the global Supply Chain Finance market size was valued at USD 7298.46 million in 2022 and is expected to expand at a CAGR (Compound Annual Growth Rate) of 9.0
A community needs assessment identifies the strengths and resources available in the community to meet the needs of children, youth, and families. The assessment focuses on the capabilities of the community, including its citizens, agencies, and organizations.
Data can come from internal or external sources. Internal sources include company reports and records, while external sources are outside the organization, like information obtained from other companies. There are various methods for collecting primary data, like interviews, surveys, observation, and experiments. Secondary data has already been previously collected and can come from internal sources within an organization or external sources outside the organization. Data can be structured, semi-structured, or unstructured, and varies in its level of organization and ability to be stored in a relational database. Key characteristics of good data include accuracy, validity, reliability, timeliness, completeness, availability, and accessibility.
1. The document discusses primary and secondary data, methods of data collection, and data classification.
2. Primary data is original data collected directly by researchers, while secondary data was previously collected by others. Primary data collection methods include surveys, interviews, and experiments, while secondary data comes from existing sources like publications and records.
3. Data classification organizes data into categories to aid usage and protection, with the goals of making data easily retrievable, meeting compliance requirements, and increasing awareness of data sensitivity.
1. Data can come from various sources like numbers, words, images, facts or ideas. It is needed to answer queries and forms the basis of analysis.
2. Primary data is original and collected specifically for a purpose, while secondary data already exists and is collected economically.
3. Key primary collection methods include observation, questionnaires, experiments, stimulation, interviews, and projective techniques. Secondary data comes from internal company sources or external personal and public sources.
This document discusses various methods for collecting data in research. It describes primary and secondary data, with primary data collected directly by the researcher and secondary data collected previously. Methods for primary data collection include observation, interviews using questionnaires or in-person, and other methods like surveys. Observation involves directly observing behaviors without questions while interviews are verbal exchanges. Questionnaires are surveys distributed for self-completion. The document also reviews collecting secondary data from published sources and ensuring its reliability, suitability, and adequacy for the research purpose. It concludes that researchers should judiciously select collection methods based on the study nature, funds, time, and required precision.
This document discusses research instruments and methods for data collection. It describes common tools for gathering data such as questionnaires, interviews, and observation. It also outlines characteristics of good research instruments and different types of surveys including census, sample, and pilot surveys. Finally, it discusses data sources, collection methods, and types of data.
This chapter discusses types and sources of data used in research as well as methods of data collection. There are two primary types of data: primary data collected specifically for the research purpose, and secondary data not collected for the specific research but still relevant. Data can also be classified by whether it relates to past/future behavior or attitudes/perceptions. Both primary and secondary data are needed to understand current and predict future market trends. Common primary data collection methods include observation, surveys, experiments, and qualitative methods. Secondary and primary data both have limitations such as outdatedness, inaccuracy, and ambiguity. The choice of data collection method also impacts the limitations, such as surveys not being able to answer "why" and qualitative research difficulty to
The document discusses various methods for collecting primary and secondary data for research purposes. It describes primary data collection methods like observation, interviews, schedules, and questionnaires which involve directly gathering original data from respondents. It also explains secondary data research which uses already existing data collected by others. Some key secondary data sources mentioned include internal organizational records, government data, publications, and electronic databases. The characteristics of useful data like relevance, quality, timeliness and completeness are also summarized.
This document discusses research methodology and data collection. It covers primary and secondary data collection methods like observation, interviews, questionnaires, and reviews secondary sources. Specific methods of collecting primary data discussed include personal interviews, telephone interviews, questionnaires, and other techniques like warranty cards and consumer panels. The document also discusses organizing and graphically representing data through diagrams, graphs, tables and charts to better understand information and findings.
This document discusses research methodology and data collection methods. It covers primary and secondary data collection, methods of collecting primary data including observation, interviews, questionnaires, and other methods. It also discusses collecting secondary data and factors to consider when selecting a data collection method such as the nature of the study, time and costs. Specific data collection techniques are described like personal interviews, telephone interviews, mail questionnaires, and using existing data sources.
This document discusses various methods for collecting data in research. It describes primary and secondary data collection. Methods of primary data collection include observation, interviews using questionnaires. Observation involves systematically recording verbal and non-verbal behavior. Interviews can be conducted in-person or by phone and require carefully prepared questions. Questionnaires are often mailed to respondents who self-report answers. Secondary data involves using existing data from sources like government reports and publications. The researcher must ensure the reliability and suitability of secondary data for their study.
This document discusses research methodology and data collection methods. It covers primary and secondary data collection, methods of collecting primary data including observation, interviews, questionnaires, and other methods. It discusses advantages and disadvantages of primary and secondary data. Specific data collection methods like observation, interviews, questionnaires are explained in detail along with the steps involved. Sources of secondary data and factors to consider when using secondary data are also outlined.
Research process quantitative and qualitativeEMERENSIA X
The document outlines the steps in conducting qualitative research, including: 1) identifying a broad research problem area and objectives; 2) reviewing literature to gain preliminary information; 3) entering the research setting and contacting key informants; 4) selecting a small, qualitative sample and semi-structured data collection tools; 5) collecting data through interviews and observations while building rapport; 6) organizing and analyzing data through techniques like coding and thematic analysis; and 7) disseminating findings in publications or presentations.
This document discusses primary and secondary data. Primary data is originally collected field research through surveys, interviews, or observations. It provides original information but can be expensive and time-consuming to collect. Secondary data is previously collected data that someone else has analyzed, such as data from public records or published sources. It is quicker and cheaper to obtain but may be less accurate or reliable than primary data. The type of data used depends on the research goals, budget, time constraints, and desired accuracy. Both primary and secondary data can be used for similar research purposes as long as limitations are considered.
biostatistics for eving degree N&M recentstudent2.pptFatima117039
This document discusses different methods for collecting data, including observation, interviews, questionnaires, and using documentary sources. It provides details on each method, such as how to conduct interviews and structure questionnaires. It also covers the advantages and disadvantages of each approach to collecting data. The key methods discussed are observation, interviews, questionnaires administered in-person or via mail, and obtaining data from existing documentation and records.
There are four main types of research data based on collection methods:
1) Observational data collected through observation
2) Experimental data collected through intervention to measure change
3) Simulation data generated by imitating real-world processes using models
4) Derived data created by transforming existing data points
Data collection involves gathering information systematically to answer research questions. It is required for academic research, ongoing projects, and developing new products/services. Data can be qualitative, quantitative, or mixed. It can also be primary data collected directly or secondary data obtained from other sources. The type of data determines the appropriate collection method to use.
This document discusses sources of data for business decisions. It outlines primary and secondary sources of data. Primary data is collected directly through observation, surveys, or experiments. Secondary data comes from internal sources like sales reports or external sources like government publications, industry associations, and international organizations. Some advantages of secondary data are that it is economical, time-saving, and helps improve understanding. However, disadvantages are that the data may not exactly fit the study, accuracy is unknown, and it could be outdated.
Different Methods of Collection of DataP. Veeresha
Data collection is a term used to describe a process of preparing and collecting data.
Data are the basic inputs to any decision making process in any fields like education, business, industries…. etc
The primary data are those which are collected afresh and for the first time, and thus happen to be original in character. It is real time data and which are collected by the researcher himself.
Secondary data means data that are already available i.e., they refer to the data which have already been collected and analyzed by someone else.
This document discusses different methods for collecting data for research studies. It describes the types of qualitative and quantitative data and some common sources of primary and secondary data like clinical observations, questionnaires, and administrative databases. Some specific data collection methods are explained like personal interviews, self-administered questionnaires, use of medical records, observation, and physical or chemical measures. Criteria for selecting a data collection method include appropriateness, feasibility, cost, validity, reliability, and responsiveness. The document also covers creating a data collection sheet and entering data in an analyzable format with consideration of variable type and unit of analysis.
This document discusses the psychodynamic approach to creativity. It outlines some key assumptions of the psychodynamic approach, including that childhood experiences greatly shape adult emotions and behaviors, and that unconscious drives and desires find expression through creative works. The document explains that Freud saw creativity arising from tensions between conscious reality and unconscious wishes, and that creative works allow unconscious desires to be expressed in socially acceptable ways through sublimation. It provides examples of how defense mechanisms in childhood like repression can relate to behaviors in adulthood. Finally, it discusses views of Freud, Kris, Jung and others on how unconscious processes and archetypes contribute to creative expression.
This document provides background information and objectives for a study assessing the effectiveness of talking therapy on ineffective coping, depression, and suicidal ideation among individuals in the LGBT group in Madhya Pradesh, India. The study aims to evaluate coping skills, depression, suicidal ideation, and HIV risk before and after talking therapy sessions. The study hypothesizes that ineffective coping, depression and suicidal ideation will be associated with demographic variables and will decrease significantly after talking therapy.
1. Data can come from various sources like numbers, words, images, facts or ideas. It is needed to answer queries and forms the basis of analysis.
2. Primary data is original and collected specifically for a purpose, while secondary data already exists and is collected economically.
3. Key primary collection methods include observation, questionnaires, experiments, stimulation, interviews, and projective techniques. Secondary data comes from internal company sources or external personal and public sources.
This document discusses various methods for collecting data in research. It describes primary and secondary data, with primary data collected directly by the researcher and secondary data collected previously. Methods for primary data collection include observation, interviews using questionnaires or in-person, and other methods like surveys. Observation involves directly observing behaviors without questions while interviews are verbal exchanges. Questionnaires are surveys distributed for self-completion. The document also reviews collecting secondary data from published sources and ensuring its reliability, suitability, and adequacy for the research purpose. It concludes that researchers should judiciously select collection methods based on the study nature, funds, time, and required precision.
This document discusses research instruments and methods for data collection. It describes common tools for gathering data such as questionnaires, interviews, and observation. It also outlines characteristics of good research instruments and different types of surveys including census, sample, and pilot surveys. Finally, it discusses data sources, collection methods, and types of data.
This chapter discusses types and sources of data used in research as well as methods of data collection. There are two primary types of data: primary data collected specifically for the research purpose, and secondary data not collected for the specific research but still relevant. Data can also be classified by whether it relates to past/future behavior or attitudes/perceptions. Both primary and secondary data are needed to understand current and predict future market trends. Common primary data collection methods include observation, surveys, experiments, and qualitative methods. Secondary and primary data both have limitations such as outdatedness, inaccuracy, and ambiguity. The choice of data collection method also impacts the limitations, such as surveys not being able to answer "why" and qualitative research difficulty to
The document discusses various methods for collecting primary and secondary data for research purposes. It describes primary data collection methods like observation, interviews, schedules, and questionnaires which involve directly gathering original data from respondents. It also explains secondary data research which uses already existing data collected by others. Some key secondary data sources mentioned include internal organizational records, government data, publications, and electronic databases. The characteristics of useful data like relevance, quality, timeliness and completeness are also summarized.
This document discusses research methodology and data collection. It covers primary and secondary data collection methods like observation, interviews, questionnaires, and reviews secondary sources. Specific methods of collecting primary data discussed include personal interviews, telephone interviews, questionnaires, and other techniques like warranty cards and consumer panels. The document also discusses organizing and graphically representing data through diagrams, graphs, tables and charts to better understand information and findings.
This document discusses research methodology and data collection methods. It covers primary and secondary data collection, methods of collecting primary data including observation, interviews, questionnaires, and other methods. It also discusses collecting secondary data and factors to consider when selecting a data collection method such as the nature of the study, time and costs. Specific data collection techniques are described like personal interviews, telephone interviews, mail questionnaires, and using existing data sources.
This document discusses various methods for collecting data in research. It describes primary and secondary data collection. Methods of primary data collection include observation, interviews using questionnaires. Observation involves systematically recording verbal and non-verbal behavior. Interviews can be conducted in-person or by phone and require carefully prepared questions. Questionnaires are often mailed to respondents who self-report answers. Secondary data involves using existing data from sources like government reports and publications. The researcher must ensure the reliability and suitability of secondary data for their study.
This document discusses research methodology and data collection methods. It covers primary and secondary data collection, methods of collecting primary data including observation, interviews, questionnaires, and other methods. It discusses advantages and disadvantages of primary and secondary data. Specific data collection methods like observation, interviews, questionnaires are explained in detail along with the steps involved. Sources of secondary data and factors to consider when using secondary data are also outlined.
Research process quantitative and qualitativeEMERENSIA X
The document outlines the steps in conducting qualitative research, including: 1) identifying a broad research problem area and objectives; 2) reviewing literature to gain preliminary information; 3) entering the research setting and contacting key informants; 4) selecting a small, qualitative sample and semi-structured data collection tools; 5) collecting data through interviews and observations while building rapport; 6) organizing and analyzing data through techniques like coding and thematic analysis; and 7) disseminating findings in publications or presentations.
This document discusses primary and secondary data. Primary data is originally collected field research through surveys, interviews, or observations. It provides original information but can be expensive and time-consuming to collect. Secondary data is previously collected data that someone else has analyzed, such as data from public records or published sources. It is quicker and cheaper to obtain but may be less accurate or reliable than primary data. The type of data used depends on the research goals, budget, time constraints, and desired accuracy. Both primary and secondary data can be used for similar research purposes as long as limitations are considered.
biostatistics for eving degree N&M recentstudent2.pptFatima117039
This document discusses different methods for collecting data, including observation, interviews, questionnaires, and using documentary sources. It provides details on each method, such as how to conduct interviews and structure questionnaires. It also covers the advantages and disadvantages of each approach to collecting data. The key methods discussed are observation, interviews, questionnaires administered in-person or via mail, and obtaining data from existing documentation and records.
There are four main types of research data based on collection methods:
1) Observational data collected through observation
2) Experimental data collected through intervention to measure change
3) Simulation data generated by imitating real-world processes using models
4) Derived data created by transforming existing data points
Data collection involves gathering information systematically to answer research questions. It is required for academic research, ongoing projects, and developing new products/services. Data can be qualitative, quantitative, or mixed. It can also be primary data collected directly or secondary data obtained from other sources. The type of data determines the appropriate collection method to use.
This document discusses sources of data for business decisions. It outlines primary and secondary sources of data. Primary data is collected directly through observation, surveys, or experiments. Secondary data comes from internal sources like sales reports or external sources like government publications, industry associations, and international organizations. Some advantages of secondary data are that it is economical, time-saving, and helps improve understanding. However, disadvantages are that the data may not exactly fit the study, accuracy is unknown, and it could be outdated.
Different Methods of Collection of DataP. Veeresha
Data collection is a term used to describe a process of preparing and collecting data.
Data are the basic inputs to any decision making process in any fields like education, business, industries…. etc
The primary data are those which are collected afresh and for the first time, and thus happen to be original in character. It is real time data and which are collected by the researcher himself.
Secondary data means data that are already available i.e., they refer to the data which have already been collected and analyzed by someone else.
This document discusses different methods for collecting data for research studies. It describes the types of qualitative and quantitative data and some common sources of primary and secondary data like clinical observations, questionnaires, and administrative databases. Some specific data collection methods are explained like personal interviews, self-administered questionnaires, use of medical records, observation, and physical or chemical measures. Criteria for selecting a data collection method include appropriateness, feasibility, cost, validity, reliability, and responsiveness. The document also covers creating a data collection sheet and entering data in an analyzable format with consideration of variable type and unit of analysis.
This document discusses the psychodynamic approach to creativity. It outlines some key assumptions of the psychodynamic approach, including that childhood experiences greatly shape adult emotions and behaviors, and that unconscious drives and desires find expression through creative works. The document explains that Freud saw creativity arising from tensions between conscious reality and unconscious wishes, and that creative works allow unconscious desires to be expressed in socially acceptable ways through sublimation. It provides examples of how defense mechanisms in childhood like repression can relate to behaviors in adulthood. Finally, it discusses views of Freud, Kris, Jung and others on how unconscious processes and archetypes contribute to creative expression.
This document provides background information and objectives for a study assessing the effectiveness of talking therapy on ineffective coping, depression, and suicidal ideation among individuals in the LGBT group in Madhya Pradesh, India. The study aims to evaluate coping skills, depression, suicidal ideation, and HIV risk before and after talking therapy sessions. The study hypothesizes that ineffective coping, depression and suicidal ideation will be associated with demographic variables and will decrease significantly after talking therapy.
The document provides an overview of several theories of human development, including Freud's psychosexual stages, Erikson's psychosocial stages, and Piaget's cognitive development theory. It discusses the key concepts of each theory, such as Freud's oral, anal, phallic, latent, and genital stages. For Erikson's theory, it outlines the 8 psychosocial stages from trust vs. mistrust in infancy to intimacy vs. isolation in young adulthood. The document also briefly discusses evaluating Freud's theory and provides definitions of developmental, grand, mini, and emergent theories.
Erik Erikson's Stages of Psychosocial Development.pptxAdwinAnandVerma
Erik Erikson proposed eight stages of psychosocial development from infancy to late adulthood. Each stage involves a psychosocial crisis that can result in a healthy or unhealthy personality outcome. The stages involve developing trust, autonomy, initiative, industry, identity, intimacy, generativity, and integrity. Successful completion of each stage results in virtues like hope, will, purpose, competence, fidelity, love, care and wisdom. The stages involve psychological and social challenges as individuals learn how to interact with others.
This document discusses managing stress and the fight or flight response. It defines stress and lists common stressors people experience at different life stages. When faced with stress, the body releases hormones like adrenaline and cortisol to prepare for fighting or fleeing. Over time, prolonged stress can negatively impact health and increase risks for diseases. The document recommends ways to manage stress, such as fixing controllable stressors, accepting uncontrollable ones, and protecting the body. Suggested techniques include exercise, deep breathing, meditation, progressive muscle relaxation, stretching, massage, and humor.
This document discusses methods of data collection. It defines primary and secondary data and describes common collection techniques. Primary data is originally collected for the research purpose, such as through surveys, interviews, or observation. Secondary data is previously collected for another purpose, from sources like published reports, literature, or the internet. Both have advantages and disadvantages - primary data directly answers research questions but at higher cost, while secondary data is lower cost but may not be targeted or timely. Quantitative and qualitative techniques are also discussed.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills