The document discusses different types of research methodology. It describes research as comprising of defining problems, formulating hypotheses, collecting and evaluating data, making deductions, reaching conclusions, and testing conclusions. The purpose of research is to discover answers to questions through scientific procedures. Research can be exploratory, descriptive, diagnostic, or aimed at hypothesis testing. Additional types discussed include descriptive, analytical, applied, quantitative/qualitative, conceptual, empirical, one-time/longitudinal, field/laboratory, clinical, exploratory/formalized, and conclusion oriented.
This document describes descriptive research and survey research methods. Descriptive research aims to describe characteristics of a population without determining causes. Survey research involves asking questions of respondents using methods like questionnaires and interviews. The document outlines approaches like case studies and surveys, and survey designs including cross-sectional, before-after, and longitudinal studies. It also discusses steps to conduct surveys, question formats, data collection modes, and advantages and limitations of interviews and questionnaires.
Experimental method In Research MethodologyRamla Sheikh
The document defines experimental method and discusses its key elements and characteristics. It states that experimental method involves carefully controlling conditions to test hypotheses about causal relationships. The main elements are control, manipulation, observation, and replication. Control involves isolating variables and establishing comparable groups. Manipulation involves changing the independent variable. Observation measures the dependent variable. Replication improves reliability by repeating experiments. The document also discusses types of experimental designs and the steps of conducting experimental research.
This document provides information about conducting a literature review in research. It defines a literature review as a systematic method of identifying and analyzing existing scholarly work on a topic. The purposes of a literature review are discussed, including giving context and justification for the research topic. Key aspects covered include sources to review, such as academic journals and books, as well as strategies for finding and evaluating relevant literature. The document also distinguishes between descriptive and critical literature review approaches. Overall, the text outlines best practices for performing a comprehensive literature review.
The document discusses independent and dependent variables in scientific experiments. It provides examples of experiments that could investigate how different independent variables, like temperature or type of plant, affect a dependent variable, like how quickly ice melts or size of plant growth. The document instructs the reader to identify independent and dependent variables for sample experiments and explain their choices.
This document provides an overview of mixed-methods research. It defines mixed-methods research as involving both quantitative and qualitative research methods in a single study to provide a more complete understanding than either method alone. It discusses the history and examples of mixed-methods research in education. Key aspects covered include different research designs like exploratory, explanatory, and triangulation; sampling strategies; steps in conducting mixed-methods research; and evaluating and ensuring ethics in mixed-methods studies. The document aims to explain what mixed-methods research entails at a high-level.
This document provides an overview of field research. It defines field research as collecting information outside of a laboratory or workplace through interviews and observations in a natural setting. It discusses the importance of field notes, which record observations and experiences. There are different types of field notes, including job notes, field notes proper, methodological notes, and journals/diaries. The document also outlines the process for conducting field research, including assembling a team, conducting site visits, analyzing data, and presenting results.
This document discusses different types of research classification. It covers three types of classification: by purpose, by strategy, and a comparison of quantitative and qualitative methods. For classification by purpose, there are three main types: basic research which seeks new knowledge, applied research which aims to solve practical problems, and action research which examines solutions through a systematic process. For classification by strategy, research can be quantitative which uses statistical analysis or qualitative which uses interpretive methods. Both have different characteristics and designs. The document compares the nature, assumptions, and strengths of quantitative and qualitative approaches.
Quantitative Research: Surveys and ExperimentsMartin Kretzer
- Example lecture of the course "Methods and Theories in Information Systems"
- Target group: students who want to get an impression of the course before joining it
This document discusses experimental and non-experimental research methods. Experimental research involves manipulating an independent variable and measuring its effects on a dependent variable, while controlling for other influences. It uses random assignment to experimental and control groups. Quasi-experimental research has less control and randomization. Non-experimental research observes relationships through methods like surveys and case studies but cannot prove causation. Surveys are a common non-experimental method used in fields like sociology to understand opinions, conditions, and social issues in a timely manner, though they have weaknesses in establishing variable relationships.
Quantitative, qualitive and mixed research designsAras Bozkurt
This document provides an overview of quantitative method design, specifically experimental design. It discusses key concepts in experimental design including random assignment, control over extraneous variables, manipulation of treatment conditions, outcome measures, and threats to validity. It also describes different types of experimental designs including between-group designs like true experiments, quasi-experiments, and factorial designs as well as within-group designs like time series experiments, repeated measures experiments, and single subject experiments. The document provides examples and explanations of how to implement these different experimental designs.
The document discusses research methodology and defines key concepts such as research problem, objectives of research, characteristics of research, scientific method, and hypothesis. It provides details on formulating the research problem, reviewing literature, and formulating a hypothesis. The research process involves defining the problem, reviewing concepts and theories, formulating a hypothesis, designing the research, collecting and analyzing data, and reporting findings. Variables and types of variables in formulating a hypothesis are also explained.
This document discusses various qualitative research methods for collecting and analyzing data. It describes qualitative research as focusing on collecting narrative and visual non-numerical data to understand a phenomenon of interest. It then outlines several common qualitative research approaches like grounded theory, ethnography, phenomenology, narrative research, case studies and the types of data collection methods used in each approach such as interviews, observations, focus groups and document analysis. Finally, it discusses the process of analyzing qualitative data which typically involves preparing, organizing, coding and categorizing the data to identify themes and patterns.
Quantitative and qualitative analysis of dataNisha M S
This document provides an overview of several qualitative and quantitative research methods and analysis techniques. It discusses interpretative phenomenological analysis (IPA) for qualitative analysis, which aims to explore participants' experiences and perspectives while acknowledging the researcher's own biases. It also reviews grounded theory methodology, discourse analysis techniques, and narrative analysis approaches. For quantitative analysis, it outlines organizing data, visual presentation methods, measures of central tendency, measures of variation, and common statistical tests. The document presents steps and considerations for applying these diverse analytical methods to research.
Intellectual Honesty and Research Integrity.pptxsheelu57
Intellectual honesty is an applied method of problem solving, characterized by an unbiased, honest attitude, which can be demonstrated in a number of different ways including:
Ensuring support for chosen ideologies does not interfere with the pursuit of truth;
Relevant facts and information are not purposefully omitted even when such things may contradict one's hypothesis;
Facts are presented in an unbiased manner, and not twisted to give misleading impressions or to support one view over another;
References, or earlier work, are acknowledged where possible, and plagiarism is avoided. practices.
For individuals, research integrity is an aspect of moral character and experience. It involves above all a commitment to intellectual honesty and personal responsibility for one's actions and to a range of practices that characterize responsible research conduct.
Operationalization is the process of defining abstract concepts as measurable variables. It involves translating concepts into concrete, observable actions or measures that can be quantified. The key steps are:
1. Formulating concepts into variables by linking concepts to specific aspects that can be measured.
2. Defining variables operationally by specifying exactly how they will be measured through specific dimensions, elements, or empirical observations.
3. Linking variables to instruments that will capture the data needed to study them, such as surveys, experiments, or tests.
By precisely defining and measuring variables, operationalization increases the rigor and reproducibility of research. It allows concepts to be studied systematically and results to be more robustly analyzed and compared across
This document discusses the process of conducting surveys. It defines what a survey is and lists its key characteristics. The document outlines the main steps in conducting a survey, which include: defining the problem, identifying the target population, choosing the data collection mode, selecting a sample, preparing the instrument, pretesting the instrument, and training interviewers. It also discusses different types of surveys, sampling techniques, question formats, and other considerations for designing an effective survey.
The document discusses key aspects of research design and types of research. It provides definitions and explanations of important concepts in research design including variables, experimental and control groups, and treatments. It also summarizes several major types of rural research such as survey research, case studies, ex-post facto research, and qualitative vs. quantitative research. Finally, it outlines the typical format for a research proposal.
Case study is a strategy for doing research which involves an empirical investigation of a particular contemporary phenomenon with its real life context using multiple sources of evidence.”
Methods of Data Collection in Quantitative Research (Biostatistik)AKak Long
DEFINITION : Quantitative research, is defined as a the systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical or computational techniques.
Quantitative research gathers information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires etc., the results of which can be depicted in the form of numericals.
After careful understanding of these numbers to predict the future of a product or service and make changes accordingly.
Described as the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer research questions, test hypothesis and evaluate outcome.
Importance of data collection:
Helps us search for answers and resolutions
Facilitates and improve decision-making processes and the quality of the decisions made.
#Types of quantitative research.
. Survey research
The collection of data attained by asking individuals questions by either in person, on paper, by phone or online.
2. Correlational research
Measures two variables, understand assess the statistical relationship between them with no influence from any extraneous variable.
3. Casual-comparative research
To find relationship between independent and dependent variables after an action or event has already occurred.
4. Experimental research
Researcher manipulates one variables, and control/randomizes the rest of the variables.
This document provides an overview of case study research methods. It defines a case study as an in-depth analysis of a single entity within its real-world context. The document discusses case study paradigms, types including intrinsic and instrumental, purposes such as explanatory and exploratory, designs including single and multiple case, methodology involving data collection from documentation and interviews, and analysis techniques like pattern matching. It also reviews issues in reporting case studies and lists some merits like understanding contemporary contexts and demerits like lack of generalization.
Quantitative and qualitative research methods differ in important ways. Quantitative research uses statistical analysis of numeric data from standardized instruments, while qualitative research relies on descriptive analysis of text or image data collected from a small number of individuals. The two approaches also differ in how the research problem is identified, how literature is reviewed, how data is collected and analyzed, and how findings are reported. Common quantitative designs include experimental, correlational, and survey designs, while qualitative designs include grounded theory, ethnographic, narrative, and action research designs. The best approach depends on matching the research questions and goals.
1. A meta-analysis systematically combines data from multiple studies to identify patterns among study results, increase statistical power, and resolve uncertainties in areas where individual studies may be too narrow.
2. Key steps include defining the question, reviewing literature and extracting data, computing effect sizes, determining average effect sizes and confidence intervals, and looking for associations that may explain variability among studies.
3. Factors like study quality and publication bias must be considered, as missing or unpublished studies could change conclusions. Meta-analyses aim to synthesize evidence from diverse studies and elucidate general patterns.
Case study research involves an in-depth examination of a bounded system or multiple systems over time through detailed data collection from multiple sources. It provides an in-depth understanding of a case or comparison of several cases. Case studies can be single instrumental studies exploring a single issue, collective studies exploring an issue through multiple cases, or intrinsic studies analyzing a unique case itself. Data collection involves multiple sources like observations, interviews, documents and artifacts. Data is analyzed through holistic, embedded, thematic, cross-case or within-case analysis to interpret the meaning of the case(s).
This presentation discuss various methods of qualitative data analysis. it further digs various methods used in qualitative data analysis in some Ph.D. thesis i.e. practical part
Field research involves direct observation and asking questions to obtain qualitative and quantitative data in a natural setting. It is appropriate for topics best understood in their natural context. There are various roles for observers, from complete participant to complete observer. Field interviews are less structured than surveys. Access to subjects may involve finding sponsors or informants. Purposive sampling is common due to the challenges of probability sampling. Observations are recorded through notes, recordings, photos. Field research can be combined with other data sources like surveys. Examples include studying speeding through photos and radar, police traffic stops through ridealong interviews, and violence in bars through participant observation. Strengths are depth of understanding and flexibility, while weaknesses include lower reliability and generalizability.
Field research involves direct observation and asking questions to obtain qualitative and quantitative data in a natural setting. It allows researchers to get a comprehensive perspective on phenomena like drug dealers or bar behavior. Researchers can take on different levels of participation from complete participant to complete observer. Sampling is usually purposive rather than probability-based. Data collection methods include note taking, recordings, photographs, and structured observations. Field research provides great depth of understanding but has lower reliability and generalizability than other methods due to its personal nature.
This document discusses different types of research classification. It covers three types of classification: by purpose, by strategy, and a comparison of quantitative and qualitative methods. For classification by purpose, there are three main types: basic research which seeks new knowledge, applied research which aims to solve practical problems, and action research which examines solutions through a systematic process. For classification by strategy, research can be quantitative which uses statistical analysis or qualitative which uses interpretive methods. Both have different characteristics and designs. The document compares the nature, assumptions, and strengths of quantitative and qualitative approaches.
Quantitative Research: Surveys and ExperimentsMartin Kretzer
- Example lecture of the course "Methods and Theories in Information Systems"
- Target group: students who want to get an impression of the course before joining it
This document discusses experimental and non-experimental research methods. Experimental research involves manipulating an independent variable and measuring its effects on a dependent variable, while controlling for other influences. It uses random assignment to experimental and control groups. Quasi-experimental research has less control and randomization. Non-experimental research observes relationships through methods like surveys and case studies but cannot prove causation. Surveys are a common non-experimental method used in fields like sociology to understand opinions, conditions, and social issues in a timely manner, though they have weaknesses in establishing variable relationships.
Quantitative, qualitive and mixed research designsAras Bozkurt
This document provides an overview of quantitative method design, specifically experimental design. It discusses key concepts in experimental design including random assignment, control over extraneous variables, manipulation of treatment conditions, outcome measures, and threats to validity. It also describes different types of experimental designs including between-group designs like true experiments, quasi-experiments, and factorial designs as well as within-group designs like time series experiments, repeated measures experiments, and single subject experiments. The document provides examples and explanations of how to implement these different experimental designs.
The document discusses research methodology and defines key concepts such as research problem, objectives of research, characteristics of research, scientific method, and hypothesis. It provides details on formulating the research problem, reviewing literature, and formulating a hypothesis. The research process involves defining the problem, reviewing concepts and theories, formulating a hypothesis, designing the research, collecting and analyzing data, and reporting findings. Variables and types of variables in formulating a hypothesis are also explained.
This document discusses various qualitative research methods for collecting and analyzing data. It describes qualitative research as focusing on collecting narrative and visual non-numerical data to understand a phenomenon of interest. It then outlines several common qualitative research approaches like grounded theory, ethnography, phenomenology, narrative research, case studies and the types of data collection methods used in each approach such as interviews, observations, focus groups and document analysis. Finally, it discusses the process of analyzing qualitative data which typically involves preparing, organizing, coding and categorizing the data to identify themes and patterns.
Quantitative and qualitative analysis of dataNisha M S
This document provides an overview of several qualitative and quantitative research methods and analysis techniques. It discusses interpretative phenomenological analysis (IPA) for qualitative analysis, which aims to explore participants' experiences and perspectives while acknowledging the researcher's own biases. It also reviews grounded theory methodology, discourse analysis techniques, and narrative analysis approaches. For quantitative analysis, it outlines organizing data, visual presentation methods, measures of central tendency, measures of variation, and common statistical tests. The document presents steps and considerations for applying these diverse analytical methods to research.
Intellectual Honesty and Research Integrity.pptxsheelu57
Intellectual honesty is an applied method of problem solving, characterized by an unbiased, honest attitude, which can be demonstrated in a number of different ways including:
Ensuring support for chosen ideologies does not interfere with the pursuit of truth;
Relevant facts and information are not purposefully omitted even when such things may contradict one's hypothesis;
Facts are presented in an unbiased manner, and not twisted to give misleading impressions or to support one view over another;
References, or earlier work, are acknowledged where possible, and plagiarism is avoided. practices.
For individuals, research integrity is an aspect of moral character and experience. It involves above all a commitment to intellectual honesty and personal responsibility for one's actions and to a range of practices that characterize responsible research conduct.
Operationalization is the process of defining abstract concepts as measurable variables. It involves translating concepts into concrete, observable actions or measures that can be quantified. The key steps are:
1. Formulating concepts into variables by linking concepts to specific aspects that can be measured.
2. Defining variables operationally by specifying exactly how they will be measured through specific dimensions, elements, or empirical observations.
3. Linking variables to instruments that will capture the data needed to study them, such as surveys, experiments, or tests.
By precisely defining and measuring variables, operationalization increases the rigor and reproducibility of research. It allows concepts to be studied systematically and results to be more robustly analyzed and compared across
This document discusses the process of conducting surveys. It defines what a survey is and lists its key characteristics. The document outlines the main steps in conducting a survey, which include: defining the problem, identifying the target population, choosing the data collection mode, selecting a sample, preparing the instrument, pretesting the instrument, and training interviewers. It also discusses different types of surveys, sampling techniques, question formats, and other considerations for designing an effective survey.
The document discusses key aspects of research design and types of research. It provides definitions and explanations of important concepts in research design including variables, experimental and control groups, and treatments. It also summarizes several major types of rural research such as survey research, case studies, ex-post facto research, and qualitative vs. quantitative research. Finally, it outlines the typical format for a research proposal.
Case study is a strategy for doing research which involves an empirical investigation of a particular contemporary phenomenon with its real life context using multiple sources of evidence.”
Methods of Data Collection in Quantitative Research (Biostatistik)AKak Long
DEFINITION : Quantitative research, is defined as a the systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical or computational techniques.
Quantitative research gathers information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires etc., the results of which can be depicted in the form of numericals.
After careful understanding of these numbers to predict the future of a product or service and make changes accordingly.
Described as the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer research questions, test hypothesis and evaluate outcome.
Importance of data collection:
Helps us search for answers and resolutions
Facilitates and improve decision-making processes and the quality of the decisions made.
#Types of quantitative research.
. Survey research
The collection of data attained by asking individuals questions by either in person, on paper, by phone or online.
2. Correlational research
Measures two variables, understand assess the statistical relationship between them with no influence from any extraneous variable.
3. Casual-comparative research
To find relationship between independent and dependent variables after an action or event has already occurred.
4. Experimental research
Researcher manipulates one variables, and control/randomizes the rest of the variables.
This document provides an overview of case study research methods. It defines a case study as an in-depth analysis of a single entity within its real-world context. The document discusses case study paradigms, types including intrinsic and instrumental, purposes such as explanatory and exploratory, designs including single and multiple case, methodology involving data collection from documentation and interviews, and analysis techniques like pattern matching. It also reviews issues in reporting case studies and lists some merits like understanding contemporary contexts and demerits like lack of generalization.
Quantitative and qualitative research methods differ in important ways. Quantitative research uses statistical analysis of numeric data from standardized instruments, while qualitative research relies on descriptive analysis of text or image data collected from a small number of individuals. The two approaches also differ in how the research problem is identified, how literature is reviewed, how data is collected and analyzed, and how findings are reported. Common quantitative designs include experimental, correlational, and survey designs, while qualitative designs include grounded theory, ethnographic, narrative, and action research designs. The best approach depends on matching the research questions and goals.
1. A meta-analysis systematically combines data from multiple studies to identify patterns among study results, increase statistical power, and resolve uncertainties in areas where individual studies may be too narrow.
2. Key steps include defining the question, reviewing literature and extracting data, computing effect sizes, determining average effect sizes and confidence intervals, and looking for associations that may explain variability among studies.
3. Factors like study quality and publication bias must be considered, as missing or unpublished studies could change conclusions. Meta-analyses aim to synthesize evidence from diverse studies and elucidate general patterns.
Case study research involves an in-depth examination of a bounded system or multiple systems over time through detailed data collection from multiple sources. It provides an in-depth understanding of a case or comparison of several cases. Case studies can be single instrumental studies exploring a single issue, collective studies exploring an issue through multiple cases, or intrinsic studies analyzing a unique case itself. Data collection involves multiple sources like observations, interviews, documents and artifacts. Data is analyzed through holistic, embedded, thematic, cross-case or within-case analysis to interpret the meaning of the case(s).
This presentation discuss various methods of qualitative data analysis. it further digs various methods used in qualitative data analysis in some Ph.D. thesis i.e. practical part
Field research involves direct observation and asking questions to obtain qualitative and quantitative data in a natural setting. It is appropriate for topics best understood in their natural context. There are various roles for observers, from complete participant to complete observer. Field interviews are less structured than surveys. Access to subjects may involve finding sponsors or informants. Purposive sampling is common due to the challenges of probability sampling. Observations are recorded through notes, recordings, photos. Field research can be combined with other data sources like surveys. Examples include studying speeding through photos and radar, police traffic stops through ridealong interviews, and violence in bars through participant observation. Strengths are depth of understanding and flexibility, while weaknesses include lower reliability and generalizability.
Field research involves direct observation and asking questions to obtain qualitative and quantitative data in a natural setting. It allows researchers to get a comprehensive perspective on phenomena like drug dealers or bar behavior. Researchers can take on different levels of participation from complete participant to complete observer. Sampling is usually purposive rather than probability-based. Data collection methods include note taking, recordings, photographs, and structured observations. Field research provides great depth of understanding but has lower reliability and generalizability than other methods due to its personal nature.
This document outlines different methods for collecting primary data, including observation, interviews, and questionnaires. It discusses the types, advantages, and disadvantages of each method. Observation can be participative or non-participative. Interviews can be structured or unstructured. Questionnaires can be administered in different ways such as mailing, collective administration, or in public places. Both interviews and questionnaires have advantages like flexibility but also disadvantages such as potential bias or low response rates.
This document discusses field observation as a method of data collection in criminal justice research. It involves directly observing phenomena in their natural settings to obtain qualitative and/or quantitative data. Key points covered include defining field observation, its use for understanding settings, behavior and events, different roles for observers, purposive sampling techniques used, methods for recording observations, linking observations to other data sources, examples of shoplifting and seatbelt use studies, and strengths and weaknesses of the method.
This document discusses various information gathering tools for system analysis including review of literature, on-site observation, interviews, and questionnaires. It provides details on each tool such as reviewing procedures manuals and forms to understand current processes, observing users on-site to understand real systems, conducting interviews to understand perceptions and feelings, and distributing questionnaires to gather information from many people simultaneously. The key is to use these tools accurately and methodically to acquire information with minimal disruption to users.
1. Qualitative interviews involve interactions between an interviewer and respondent to explore topics in an unstructured or semi-structured format. This allows researchers to understand human perspectives and lived experiences.
2. Qualitative interviews are used in criminal justice research to understand subjects' perspectives and gather first-hand accounts. They can also explore how people feel about their roles and identities.
3. There are different types of interview structures from fully structured to unstructured, with semi-structured in between, allowing some flexibility to explore emerging themes. The structure influences how in-depth the interviews can be.
This document discusses qualitative research techniques used in marketing research. It covers observation methods, focus groups, and other techniques like in-depth interviews and projective techniques. Specifically, it defines focus groups as small groups guided by a moderator through discussion to gain relevant information. It also describes how focus groups and online focus groups work, their advantages and disadvantages, and when they should and should not be used.
Sources and methods of data collection five-2.pptxetebarkhmichale
7 Habits That Turn Boys into Men
1. Taking Responsibility:
Real men understand the importance of taking responsibility for their actions and decisions. This habit goes beyond simply admitting when they are wrong; it involves owning up to the consequences of their choices and working to make amends. They don't make excuses or blame others for their circumstances. Instead, they face challenges head-on and use their experiences as learning opportunities. By taking responsibility, they build credibility and trust with others, which is foundational for any meaningful relationship. This practice fosters maturity and accountability, setting the stage for personal and professional growth.
2. Developing Self-Discipline:
Self-discipline is a cornerstone habit that men cultivate in all areas of life. They set clear, achievable goals and establish effective routines to reach them. This involves prioritizing long-term benefits over short-term pleasures, such as maintaining a healthy lifestyle, managing finances wisely, or advancing in their careers. By mastering self-discipline, they develop the ability to stay focused and resilient in the face of distractions and setbacks. This habit empowers them to make consistent progress toward their aspirations, ultimately leading to a more fulfilled and purposeful life.
3. Seeking Continuous Growth:
Men are committed to personal growth and lifelong learning. They actively seek new knowledge, skills, and experiences, understanding that growth is a continuous process. They embrace challenges and step out of their comfort zones to push their boundaries. This habit includes reading, attending workshops, learning new hobbies, or seeking mentorship. By constantly pushing themselves to improve, they become more adaptable and innovative, capable of navigating an ever-changing world. This commitment to growth not only enhances their own lives but also positively impacts those around them.
4. Cultivating Emotional Intelligence:
Emotional intelligence is crucial for building strong, meaningful relationships. Men develop self-awareness, allowing them to understand their emotions and the impact they have on others. They regulate their emotions effectively, ensuring that their reactions are appropriate and constructive. By empathizing with others, they build deeper connections and navigate social complexities with ease. Effective communication and mature conflict resolution are key aspects of this habit, fostering an environment of mutual respect and understanding.
5. Showing Respect:
Real men treat others with respect, regardless of their background or status. They listen attentively, value different perspectives, and treat everyone with dignity. This habit is about recognizing the inherent worth of every individual and behaving in ways that reflect this understanding. Respect is earned through actions, not demanded, and men who consistently show respect build strong, supportive networks both personally and professionally
Data Analysis for Marketing - Observation techniquesJodie Caston
This document discusses various observation techniques used in research including structured vs unstructured, disguised vs undisguised, natural vs contrived, personal observation, electronic observation, audit analysis, content analysis, and trace analysis. It provides details on how each technique is implemented and compares their degrees of structure, disguise, natural setting, observation bias, and analysis bias. Advantages of observation techniques include measuring actual behavior rather than intended behavior. Disadvantages include difficulties establishing underlying motives and potential ethical issues with techniques like hidden cameras.
What is qualitative research? Discuss the methods of qualitative research.pdfMd. Sajjat Hossain
Qualitative research involves collecting and analyzing non-numerical data to understand meanings, experiences, and perspectives of research subjects. It uses methods like field observations, focus groups, and case studies. Field observations involve observing research subjects in natural settings either covertly or overtly. Focus groups involve interviewing 6-12 people in a group setting led by a moderator. Qualitative research provides descriptive insights but results are not generalizable.
This document discusses various methods of collecting primary data through observation for research purposes. It outlines two main types of observation: naturalistic observation where the researcher passively observes subjects in natural settings without influencing them, and laboratory observation where settings are controlled. It also describes participant observation where the researcher joins the group being studied and non-participant observation where the group is unaware of observation. Both qualitative and quantitative approaches to observation are covered.
This document discusses qualitative research techniques used in marketing research, including observation, focus groups, interviews, and other methods. Specifically, it covers how to conduct observations and focus groups, the advantages and disadvantages of each, and when each method is best applied. It also discusses other qualitative techniques like in-depth interviews, projective techniques, and physiological measurements. The overall purpose is to introduce various tools that researchers can use to understand consumers by analyzing what people say and do rather than just collecting numeric data.
Survey research is a commonly used method in sociology, political science, and criminal justice research. Surveys can ask people about their victimization experiences, criminal behavior, attitudes and perceptions. Different survey methods include in-person interviews, mail/online surveys, and telephone surveys. Care must be taken in designing survey questions to avoid biases and get accurate responses. While surveys can efficiently gather information from large populations, they only provide superficial coverage of complex topics.
This document discusses methods for collecting qualitative data, including observations, interviews, documents, and audiovisual materials. It describes the process of conducting observations at a research site, including selecting a site, easing into the site, determining what to observe and for how long, and recording descriptive and reflective field notes. The document also discusses interviews, noting the advantages of permitting detailed descriptions but the disadvantages of responses being filtered or deceptive. It outlines types of interviews and conducting them ethically. Finally, it addresses collecting and analyzing documents located at research sites.
CH-4 Constructing an Instrument for Data Collection.pptxjemalmohamed4
This chapter discusses ethical considerations and methods for collecting data. It covers issues related to participants, researchers, and sponsoring organizations. The two major approaches to gathering information are through primary and secondary sources. Primary data is collected directly for the research purpose while secondary data comes from existing sources. Common primary collection methods include observation, interviews, and questionnaires. Observation can be participant or non-participant. Interviews are structured or unstructured. Questionnaires are administered via mail, in groups, or in public places. Secondary sources include government publications, organizations, earlier research, and media.
This document discusses experimental and quasi-experimental designs. It outlines the key components of classical experimental designs, including independent and dependent variables, experimental and control groups, pretesting and posttesting. It also discusses threats to internal and external validity and variations like quasi-experimental designs that use nonequivalent groups or time series when randomization is not possible. Quasi-experiments aim to make groups as comparable as possible through matching or using natural cohorts.
How MCP Could Redefine the Future of Agentic AI A New Lens on Connectivity.pdfdavidandersonofficia
This blog explores how the Model Context Protocol (MCP) could empower small businesses to harness agentic AI, making it easier and cheaper to connect AI agents with diverse data sources. It highlights MCP’s potential to level the playing field, enabling startups to compete with tech giants through seamless AI integration.
Data Modelling For Software Engineers (Devoxx GR 2025).pdfScott Sosna
Really, data modeling? Is that even a thing any more?
The days of formal data modeling are definitely years in the rearview mirror, empowered teams define their data as they see fit, implement, and move on. Done. And we'll deal with short-comings down the road when they arise, that's Agile, let's keep moving forward (to data architects' frustration when trying to make sense of it all after the fact).
But "modeling data" extends beyond what is persisted in a database server: API Payloads, messages, configuration files, document metadata, Redis indexes are forms of data we define and work with regularly.
If I've got your attention, join me to discuss data modeling, this time from a software engineering perspective!
Winning the UX Battle Whitepaper 032725.pdfmike224215
Explore how superior UX design enhances readiness, informs decision-making, and ensures scalability and resilience in mission-critical defense systems.
In the rapidly evolving landscape of defense operations, the quality of user experience (UX) is not merely an enhancement—it's a strategic necessity.
GDG Cincinnati presentation by Ben Hicks, April 16, 2024.
As AI continues to permeate our industry, it's crucial to consider how it will reshape the way both seasoned and new developers learn, code, and create. This presentation offers a candid look at the evolving landscape – the opportunities, challenges, and the imperative for continuous adaptation. Let's explore the good, the bad, and the ugly of AI's influence on development, and discuss how we can best utilize what it has to offer while avoiding the snake oil.
Monday.com vs Productboard: Which Tool Truly Empowers Product Teams?Matthieu Sanogho
In today’s fast-paced digital landscape, choosing the right product management platform is a strategic decision. As teams scale and product complexity grows, having the right tool can significantly impact collaboration, prioritization, and execution.
That’s exactly why I created this straightforward, visual and actionable comparison between Monday.com and Productboard — two of the most talked-about platforms in the product world.
In this presentation, you’ll find:
✅ A side-by-side breakdown of features that matter to product, marketing, and cross-functional teams
📱 Highlights on UX, automations, mobile access, templates, and integrations
🔒 Where each platform excels — and where they fall short (hello onboarding gaps and release management limits 👀)
💰 A transparent look at pricing for growing teams
📊 Real feedback from usage across product, marketing, client success and sales
Whether you're a Product Manager, Team Lead, or Founder evaluating tools to support your roadmap, OKRs, or user feedback loops — this presentation gives you a quick, curated snapshot to support your decision-making.
👀 Curious to see who comes out on top?
👉 Dive into the full comparison
And feel free to share your thoughts or your own experience with these tools!
Workshop: Mastering Enterprise Agility: From Tension to Transformation by Zia...Agile ME
In a world where change is constant, organisations must rise to the challenge of enterprise agility. This session invites you to confront the tensions that hold your organisation back and transform them into opportunities for growth. In small groups, you'll explore real-world tensions through our specially designed tension cards, identifying the challenges you recognise in your own organisation. With courage and curiosity, you’ll then select a tension to work on and choose from proven organisational design patterns that offer practical solutions. Finally, using Beliminal’s Experiment Canvas, you’ll design a purposeful experiment to take back to your workplace—an actionable step toward unleashing potential and embracing change.
This session is a chance to break through old constraints and unlock what’s possible. With BeLiminal's approach, you’ll navigate the complexities of change and empowered to take bold, confident steps toward true enterprise agility.
Privacy and Security in the Age of Generative AI - C4AI.pdfBenjamin Bengfort
From sensitive data leakage to prompt injection and zero-click worms, LLMs and generative models are the new cyber battleground for hackers. As more AI models are deployed in production, data scientists and ML engineers can't ignore these problems. The good news is that we can influence privacy and security in the machine learning lifecycle using data specific techniques. In this talk, we'll review some of the newest security concerns affecting LLMs and deep learning models and learn how to embed privacy into model training with ACLs and differential privacy, secure text generation and function-calling interfaces, and even leverage models to defend other models.
New from BookNet Canada for 2025: Loan StarsBookNet Canada
In this presentation, BookNet Canada’s Kalpna Patel shares what 2024 brought for the Loan Stars program, and what’s in store for 2025.
Read more
- Learn more about Loan Stars: https://www.loanstars.ca/
- Learn more about LibraryData: https://bnctechforum.ca/sessions/new-from-booknet-canada-for-2025-bnc-salesdata-and-bnc-librarydata/
Presented by BookNet Canada on April 15, 2025 with support from the Department of Canadian Heritage.
Start your ride-hailing service fast with our Uber clone app. Launch in weeks with a powerful, customizable platform built for performance, user satisfaction, and business growth from day one.
Deb Gangopadhyay Pioneering Micromobility Innovations as Beam's CTO.pdfdebgangopadhyay25
Deb Gangopadhyay is the Co-founder and President of Beam Mobility, a micromobility startup established in 2018. An alumnus of Yale University, he has been instrumental in advancing Beam's technology and expansion across the Asia-Pacific region.
_Empowering Intelligent Automation with Salesforce Agentforce.pdfDamco solutions
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Artificial Intelligence (AI) in Computer Vision Market Size, Share, and Growt...NehaShaikh73
Artificial Intelligence (AI) in Computer Vision Market size was valued at USD 22.8 billion in 2023 and is poised to grow from USD 27.93 billion in 2024 to USD 141.63 billion by 2032, growing at a CAGR of 22.5% during the forecast period (2025-2032).
Unlocking advanced keyword analysis with machine learning and NLP for SEOsSante J. Achille
Google Search Console is a treasure trove of data that many SEOs underutilise. While paid keyword
research tools have their place, GSC provides the most accurate representation of how users find your
website through organic search. This guide demonstrates how to leverage this often-neglected data using
machine learning and natural language processing techniques to:
• Automatically analyse thousands of search terms.
• Segment them into primary topics and more granular "nuanced topics”.
• Categorise terms in a way that reveals user intent.
• Create actionable editorial guidelines for content creation.
This approach allows for a more sophisticated understanding of your audience's search behaviour,
enabling you to develop highly targeted content strategies based on actual user data rather than third-party
estimates.
Why This Matters for SEOs
Many SEOs lack the data science knowledge and traditional coding skills to perform these tasks.
However, you can implement these advanced techniques regardless of your technical expertise level by
understanding the core principles and leveraging the provided code examples.
With this process, you'll be able to:
• Scale your keyword research beyond manual capacity
• Identify content opportunities others miss
• Create more precisely targeted content
• Measure and improve your content strategy systematically
Read the PDF and learn how to leverage Principle Component Analysis to leverage scale Keyword Analysis using Google Search Console data and how to automate the process with Machine Learning.
H2O.ai Agents : From Theory to Practice - Support PresentationSri Ambati
This is the support slide deck for H2O Agents AI: From Theory to Practice course.
These slides cover AI agent architecture, h2oGPTe capabilities, industry applications across finance, healthcare, telecom, and energy sectors, plus implementation best practices.
They're designed as a helpful reference while following the video course or for quick review of key concepts in agentic AI.
To access the full course and more AI learning resources, visit https://h2o.ai/university/
What comes after world domination with Daniel Stenberg, April 2025Daniel Stenberg
Open Source has in many ways already won. It is used in every product by every company, to a very a large degree. But we are not done. We can improve: we can take this further, we can make our projects better, we can enhance our communities and make sure it is done sustainably. The future is ours.
FinTech&FutureTech Analyst, Governance & Political Commentator, Legal & Ethic...Vladislav Solodkiy
Vladislav (Slava) Solodkiy is a visionary thinker and prolific writer at the intersection of technology, finance, and governance: https://docs.google.com/document/d/1hf1JjU8lg5LCLAUo__f6Np1zse_H8Kh2vrsu0K016-w/edit?usp=sharing
His work challenges conventional wisdom, blending critical analysis with forward-thinking ideas to inspire change. From dissecting the rise of fintech banks to reimagining digital identity and network states, Solodkiy’s writings are a must-read for professionals, investors, and tech enthusiasts alike.
Thought Leader in Fintech and Crypto: early recognition of fintech trends (e.g., "The First Fintech Banks Arrival") and critical analyses of crypto markets.
Governance Innovator: ideas on network states and digital governance (e.g., "Floating Nations: Dream or Dystopia?" at this SlideShare).
Compliance and Risk Expert: knowledge of AML, sanctions, and fraud prevention (e.g., "The Jan Marsalek Wirecard Scandal" at this SlideShare).
Tech Futurist: explorations of AI, nuclear, hydrogen, and space tech (e.g., "Singapore's Ascent" at this Apple Books link).
Critical Political Commentator: perspectives on international politics.
His work is a rich tapestry of insights across multiple domains, - from a thought leader in fintech, governance, and tech, - interesting for professionals, investors, and enthusiasts who value such unique perspectives.
Future of Finance & Technology (FinTech & Beyond): Fintech trends (Neobanks, BaaS, ATMs, PSD2), Crypto & Blockchain (ICOs, crypto-banking challenges), Digital Identity (especially Worldcoin, NansenID), CBDC & Correspondent Banking, Payments, Investment & M&A in tech/fintech, RegTech (AML, Compliance, Sanctions, KYC, High-Risk banking - often discussed with examples like Wirecard/Marsalek and Puerto Rico). Related Aspects: Design Thinking in finance, AI's role in finance.
Governance, Politics & Society (Exploring new models and critiquing existing ones): Govtech, Network States & Metastates, Techno-optimism, Digital Democracy, critiques of concepts like the "Bubble Generation" or "Financial Nihilism", International Politics (France, Germany, UK, USA mentions), Russian Politics & Opposition (Navalny, anti-Putin focus, war impact, sanctions), Ukraine (Diia platform).
Legal Systems, Ethics & Activism (A strong focus on fairness, accountability, and systemic issues): Legal Ethics & Accountability (lawyer conduct, formalism vs. spirit of law), SLAPP suits & silencing critics, challenges for Self-Litigants, AI in Law, E-notary/E-apostille systems, specific case studies (like the detailed conflict surrounding Arival Pte Ltd), broader ethical considerations (e.g., euthanasia, value-based business).
Deep Tech & Future Industries ) Exploring SpaceTech, Nuclear Energy (especially linked to Sam Altman), Hydrogen technology, Defence Tech, often with a focus on Singapore's potential role: https://docs.google.com/document/d/1hf1JjU8lg5LCLAUo__f6Np1zse_H8Kh2vrsu0K016-w/edit?usp=sharing
Join us for the debut of our "Autopilot for Everyone Series", where we dive into the world of AI-powered automation starting with Session 1: "UiPath Autopilot Overview". Explore the fundamentals of implementing autopilots - covering general architecture diagrams, installation, and configuration.
📕 Our session will guide you through:
- Context grounding
- Prebuilt automations designed to enhance efficiency and productivity in your workflows.
- We will conclude with an interactive Q&A session, providing you the opportunity to seek guidance and insights from automation experts.
👉 Register for our next Autopilot for Everyone Series - Session 2 Elevate Your Automation Skills: https://bit.ly/4cD3fYg
This is your chance to get acquainted with the power of the UiPath Business Automation Platform in a welcoming community atmosphere. Don't miss out on sharing your ideas and connecting with fellow automation enthusiasts. Sign up now and be part of the journey to revolutionize your business processes!
This session streamed live on April 15, 2025, 18:00 GST.
Check out our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Observability-as-a-Service: When Platform Engineers meet SREsEric D. Schabell
Monitoring the behavior of a system is essential to ensuring its long-term effectiveness. However, managing an end-to-end observability stack can feel like stepping into quicksand, without a clear plan you’re risking sinking deeper into system complexities.
In this talk, we’ll explore how combining two worlds—developer platforms and observability—can help tackle the feeling of being off the beaten cloud native path. We’ll discuss how to build paved paths, ensuring that adopting new developer tooling feels as seamless as possible. Further, we’ll show how to avoid getting lost in the sea of telemetry data generated by our systems. Implementing the right strategies and centralizing data on a platform ensures both developers and SREs stay on top of things. Practical examples are used to map out creating your very own Internal Developer Platform (IDP) with observability integrated from day 1.
2. OUTLINE
Introduction
Topics Appropriate to Field Research
The Various Roles of the Observer
Asking Questions
Gaining Access to Subjects
Recording Observations
Linking Field Observation and Other Data
Illustrations of Field Research
Strengths and Weaknesses of Field
Research
3. 3
•Field research encompasses two different
methods of obtaining data:
•Direct observation
•Asking questions
•May yield qualitative and quantitative data
•Often no precisely defined hypotheses to be
tested
•Used to make sense out of an ongoing
process
4. 4
•Gives comprehensive perspective – enhances
validity
•Go directly to phenomenon, observe it as
completely as possible
•Especially appropriate for topics best
understood in their natural setting
•Street level drug dealers to distinguish
customers
•Ethnography: Focuses on detailed and
accurate description rather than explanation
5. 5
•Complete participant: Participates fully; true
identity and purpose are not known to
subjects
•Participant-as-observer: Make known your
position as researcher and participate with the
group
•Observer-as-participant: Make known your
position as a researcher; do not actually
participate
•Complete observer: Observes without
becoming a participant
6. 6
•Qualitative Interview: Is based on a set of topics
to be discussed in depth rather than based on the
use of standardized questions
•Field research is often a matter of going where
the action is and simply watching and listening
•Also a matter of asking questions & recording
answers
•Field research interviews are must less
structured than survey interviews
•Ideally set up and conducted just like a normal,
casual conversation
7. 7
•Begins with initial contact: Sponsor, Letter,
Phone Call, Meeting
•Access to formal organizations
•Find a sponsor, write a letter to executive
director, arrange a phone call, arrange a
meeting
•Access to subcultures
•Find an informant (usually person who works
with criminals), use that person as your “in”
•Snowball sampling is useful as informant
identifies others, who identify others, etc.
8. 8
•Controlled probability sampling used rarely;
purposive sampling is common
•Bear in mind two stages of sampling:
•To what extent are the situations available for
observation representative of the general
phenomena you wish to describe and explain?
•Are your actual observations within those
total situations representative of all
observations?
9. 9
•Note taking, tape recording when interviewing
and when making observations (dictation)
•Videotaping or photographs can make records of
“before” and “after” some physical design change
•Field notes: Observations are recorded as
written notes, often in a field journal; first take
sketchy notes and then rewrite your notes in
detail
•Structured observations: Observers mark
closed-ended forms, which produce numeric
measures
10. 10
•Useful to combine field research with surveys
or data from official records
•Baltimore study of the effects of
neighborhood physical characteristics on
residents’ perceptions of crime problems
(Taylor, Shumaker, & Gottfredson, 1985)
•Perceptions: Surveys
•Physical problems: Observations, actual
population and crime information - census
data & crime reports from police records
11. 11
•Counted only when offense is seen; takes place
only in certain locations; crime of stealth and not
confrontation
•Prevalence defined as ratio of shoplifters:
shoppers
•Subjects selected by systematic sampling, e.g.,
every 20th shopper was followed by a field observer
•Other research staff were employed as shoplifters
to measure reliability of observers’ detections
•Could adjust prevalence rate with reliability
figures
12. 12
•Rate of use: # of people wearing: # of cars
observed
•Stationary observers at roadsides rather than
mobile
•Placed at controlled intersections
•Sampled cars on three dimensions: Time of day,
roadway type, observation site; stratified sites by
density of auto ownership (correlated with
population)
•Emphasized marking “U” when uncertain
13. 13
•Alcohol has a disinhibiting effect which can lead to
aggression and subsequent violence
•Researcher set out to learn how situational
factors promote or inhibit violence in Australian
bars/nightclubs
•Observers in pairs stayed 2-6 hours multiple
times at 23 sites, “complete participant” –
narratives written later
•Correlates: Violence in bars frequented by
working-class males; discomfort & boredom,
drinking patterns, management issues (cover, food
availability, bouncers)
14. 14
•Provides great depth of understanding
•Flexibility (no need to prepare much in advance)
•More appropriate to measure behavior than
surveys
•High validity; quant. measures – Incomplete
picture
•Low reliability– Often very personal
•Generalizability – Personal nature may produce
findings that may not be replicated by another
•Precise probability samples can’t normally be
drawn