Qualitative data analysis involves analyzing words, observations, images and symbols to answer research questions. There are several common methods for analyzing qualitative data, including content analysis, narrative analysis, framework analysis, and discourse analysis. These methods involve coding the data by categorizing words and phrases into themes, then identifying patterns and relationships between themes to summarize the findings.
Data Analysis & Interpretation and Report WritingSOMASUNDARAM T
Statistical Methods for Data Analysis (Only Theory), Meaning of Interpretation, Technique of Interpretation, Significance of Report Writing, Steps, Layout of Research Report, Types of Research Reports, Precautions while writing research reports
The document discusses defining a research problem. It states that a research problem exists when an individual or group faces two possible courses of action with unequal outcomes in an environment defined by uncontrolled variables. The document also outlines criteria for selecting a good research problem, such as it being original, solvable and feasible. It describes techniques for defining a problem, such as understanding its nature, surveying literature, and clearly stating assumptions. Finally, it notes problems can originate from contemporary interest, one's own interest, or gaps in existing research.
Data analysis is important for structuring findings from data collection to acquire meaningful insights and base critical decisions on. Proper data validation and editing checks that data are valid, consistent, and secure before processing by looking for errors and omissions. Tabulation compresses complex data into rows and columns to simplify, facilitate comparison, identify patterns, and reveal relationships. Tables and graphs are then prepared to communicate the analyzed data.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
This document provides an overview of quantitative data analysis techniques used in sociology. It defines key terms like univariate analysis, bivariate analysis, and multivariate analysis. Univariate analysis examines one variable at a time through measures like frequency distributions, averages, and standard deviation. Bivariate analysis examines the relationship between two variables using cross-tabulation tables. Multivariate analysis examines relationships between multiple variables simultaneously. The document also discusses data coding, codebook construction, and ethical considerations in quantitative data analysis.
The document outlines the key components of the research process which include: defining the research problem and objectives, formulating hypotheses, developing a theoretical or conceptual framework, stating assumptions, reviewing related literature, designing the research, collecting and analyzing data, and presenting conclusions and recommendations. It provides more details on defining the research problem, objectives and hypotheses, describing the different types of hypotheses, and explaining the purpose of the theoretical framework and assumptions in research.
Data Analysis & Interpretation and Report WritingSOMASUNDARAM T
Statistical Methods for Data Analysis (Only Theory), Meaning of Interpretation, Technique of Interpretation, Significance of Report Writing, Steps, Layout of Research Report, Types of Research Reports, Precautions while writing research reports
The document discusses defining a research problem. It states that a research problem exists when an individual or group faces two possible courses of action with unequal outcomes in an environment defined by uncontrolled variables. The document also outlines criteria for selecting a good research problem, such as it being original, solvable and feasible. It describes techniques for defining a problem, such as understanding its nature, surveying literature, and clearly stating assumptions. Finally, it notes problems can originate from contemporary interest, one's own interest, or gaps in existing research.
Data analysis is important for structuring findings from data collection to acquire meaningful insights and base critical decisions on. Proper data validation and editing checks that data are valid, consistent, and secure before processing by looking for errors and omissions. Tabulation compresses complex data into rows and columns to simplify, facilitate comparison, identify patterns, and reveal relationships. Tables and graphs are then prepared to communicate the analyzed data.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
This document provides an overview of quantitative data analysis techniques used in sociology. It defines key terms like univariate analysis, bivariate analysis, and multivariate analysis. Univariate analysis examines one variable at a time through measures like frequency distributions, averages, and standard deviation. Bivariate analysis examines the relationship between two variables using cross-tabulation tables. Multivariate analysis examines relationships between multiple variables simultaneously. The document also discusses data coding, codebook construction, and ethical considerations in quantitative data analysis.
The document outlines the key components of the research process which include: defining the research problem and objectives, formulating hypotheses, developing a theoretical or conceptual framework, stating assumptions, reviewing related literature, designing the research, collecting and analyzing data, and presenting conclusions and recommendations. It provides more details on defining the research problem, objectives and hypotheses, describing the different types of hypotheses, and explaining the purpose of the theoretical framework and assumptions in research.
This document outlines the research process. It defines key concepts like methodology, research, and characteristics of research such as being empirical, systematic, and analytical. The document discusses the aims of research such as achieving new insights or testing hypotheses. It distinguishes between research methods and methodology, and discusses qualitative and quantitative approaches. It also covers types of research like descriptive vs analytical and applied vs fundamental. Finally, it outlines the steps in the research process such as formulating a problem, selecting a topic, and considering factors like interest, relevance, and availability of data.
The document discusses theoretical and conceptual frameworks. It defines a theoretical framework as providing rationale for relationships between variables in a study. A conceptual framework outlines possible approaches to an idea. Developing a framework involves selecting concepts, identifying relationships between them, defining concepts operationally, and formulating a theoretical rationale through literature review. Frameworks guide research by informing hypothesis development and data analysis. Descriptive theories classify phenomena while explanatory theories specify relationships and predictive theories predict relationships. Frameworks must be clearly identified and consistent with the research topic.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Kinds and Classification of Research
Some Hindrances of Scientific Inquiry
The scientific Method of research
Principles of Scientific Method of Research
This document discusses guidelines for selecting and developing a research problem or topic. It defines a research problem and lists the characteristics of a good problem as being specific, measurable, achievable, realistic, and time-bound. The elements of a research problem are identified as the aim or purpose, subject matter, place, time period, and population. Finally, 18 guidelines for selecting a research topic are provided such as choosing a topic of interest, ensuring it is researchable and can be completed in a reasonable time frame, and that it contributes new knowledge and solutions.
A PRESENTATION ON RESEARCH METHODS: SELECTION OF A RESEARCH TOPIC, FORMULATING A HYPOTHESIS, PHILOSOPHICAL ISSUES IN RESEARCH, QUANTITATIVE VS QUALITATIVE DEBATE & SELECTION OF A RESEARCH METHOD
This document provides guidance on writing a research proposal. It discusses that a research proposal communicates the research problem, significance, and planned procedures to solve the problem. It is often required to present a brief plan before data collection, by a university, or for funding. The document outlines the key components of a strong research proposal, including an abstract, statement of the problem, significance, background, objectives, methods, work plan, personnel, facilities, and budget. It emphasizes developing clear objectives and thorough methods, justification of decisions, and arranging feedback on the proposal draft before final submission.
The document provides an overview of research methodology. It defines research and describes the objectives and characteristics of research. It discusses the scientific method, including basic postulates and criteria for good research. It also outlines the research process, including defining the research problem and reviewing literature. The summary covers the key aspects of research methodology discussed in the document such as the meaning of research, objectives of research, characteristics of the scientific method, criteria for good research, and steps in the research process.
Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
Topic 1 introduction to quantitative researchAudrey Antee
This document provides an introduction to quantitative research. It defines quantitative research as collecting and analyzing numerical data to explore, describe, explain, or predict trends. Quantitative research aims for objectivity and controls outside factors. It states hypotheses and uses statistics to analyze results. The document outlines reasons for quantitative research such as exploration, description, explanation, prediction, and evaluation. It also describes common types of quantitative research designs and the key components of measurement, sampling, research design, and statistical procedures.
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
The document discusses various types of research including descriptive research, which describes characteristics without determining causes, and analytical research, which evaluates facts and information. It also discusses applied research which aims to solve immediate problems, and basic research which improves scientific understanding without specific applications. Additional types discussed are quantitative research using measurement, qualitative research investigating human behavior, conceptual research developing new ideas, empirical research using observation, and historical research studying past events.
The document outlines the typical steps involved in the research process: 1) formulating the research problem, 2) conducting an extensive literature review, 3) developing hypotheses, 4) preparing a research design, 5) determining sample design, 6) collecting data, 7) executing the project, 8) analyzing data, 9) testing hypotheses, 10) generalizing and interpreting results, and 11) preparing a report. Some key aspects include formulating a specific topic or research question, reviewing prior studies and hypotheses, developing a methodology for collecting and analyzing data, drawing conclusions, and communicating findings.
This document discusses research methods and methodologies. It defines research methods as how to accomplish research tasks through procedures to initiate, carry out, and complete projects. Research methodology provides principles for organizing, planning, designing, and conducting research. The document then outlines different types of studies including deductive vs inductive, exploratory vs explanatory, descriptive vs analytical, basic vs applied, quantitative vs qualitative, one-time vs longitudinal, laboratory vs on-field, and test vs diagnostic research. It concludes by listing characteristics of good business research such as having a clearly defined purpose, detailed research process, thorough planning, high ethics, limitations revealed, adequate analysis, unambiguous findings, and justified conclusions.
This document outlines the key steps in the research methodology process. It defines research as a systematic effort to gain new knowledge. The main steps include: reviewing existing literature, identifying problems, setting objectives and hypotheses, planning the methodology, executing the research, analyzing data, drawing inferences, and disseminating findings. It also discusses defining the research problem precisely, formulating objectives, conducting a literature review to learn from past studies, and concluding the research by summarizing the findings and their significance.
Planning the analysis and interpretation of resseaech dataramil12345
The document outlines the researcher's plan for analyzing qualitative and quantitative data collected in a study. It discusses analyzing both types of data, including describing data, identifying typical and atypical findings, and answering research questions. Methods for analyzing qualitative data include historical analysis, inductive analysis, deductive analysis, and content analysis. Quantitative data analysis relies on statistical techniques to summarize data, identify similarities and differences between groups, and test hypotheses. Common statistical analyses include descriptive analysis, univariate analysis, bivariate analysis, multivariate analysis, and comparative analysis. The document also provides guidance on choosing appropriate statistical tests based on research questions, data type, and hypotheses.
This document discusses guidelines for selecting a research problem and formulating hypotheses. It defines key terms like research problem, assumption, hypothesis, and title. It provides guidelines for writing titles, selecting research topics, formulating general and specific research problems. It also discusses the different forms hypotheses can take and their purposes and functions in research.
This document discusses three common data collection methods: observation, interview, and questionnaire. Observation involves personally watching and interacting with research subjects and can be participatory or non-participatory. Interviews are verbal conversations with research participants that can be structured, unstructured, or semi-structured. Questionnaires are paper surveys containing a list of questions for respondents to answer in writing.
Data Analysis, Presentation and Interpretation of DataRoqui Malijan
The document defines and describes various types of data analysis techniques:
- Descriptive statistics summarize and describe data through methods like frequency distributions and descriptive graphs.
- Bivariate analysis examines the relationship between two variables.
- Multivariate analysis studies more than two variables simultaneously.
- Comparative analysis examines similarities and differences between alternatives.
- Evaluation assesses subjects using defined criteria to aid decision making.
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.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding, categorizing, comparing and interpreting collected data to find meanings and implications. The researcher's perspective influences the analysis. It also describes techniques for qualitative data analysis like becoming familiar with the data, providing in-depth descriptions, and categorizing data into themes. Ensuring credibility involves considering factors like the researcher's observations and biases. The document also contrasts qualitative data analysis with quantitative analysis.
This document discusses data collection methods. It begins by defining data collection as the systematic process of gathering observations or measurements. It then outlines the main steps in data collection: 1) defining the research aim, 2) choosing a data collection method such as experiments, surveys, interviews etc., and 3) planning data collection procedures such as sampling and standardizing. It also discusses different measurement scales such as nominal, ordinal, interval and ratio scales that are used to quantify variables. Finally, it covers scaling techniques including comparative scales like paired comparisons and ranking as well as non-comparative scales like Likert scales.
This document outlines the research process. It defines key concepts like methodology, research, and characteristics of research such as being empirical, systematic, and analytical. The document discusses the aims of research such as achieving new insights or testing hypotheses. It distinguishes between research methods and methodology, and discusses qualitative and quantitative approaches. It also covers types of research like descriptive vs analytical and applied vs fundamental. Finally, it outlines the steps in the research process such as formulating a problem, selecting a topic, and considering factors like interest, relevance, and availability of data.
The document discusses theoretical and conceptual frameworks. It defines a theoretical framework as providing rationale for relationships between variables in a study. A conceptual framework outlines possible approaches to an idea. Developing a framework involves selecting concepts, identifying relationships between them, defining concepts operationally, and formulating a theoretical rationale through literature review. Frameworks guide research by informing hypothesis development and data analysis. Descriptive theories classify phenomena while explanatory theories specify relationships and predictive theories predict relationships. Frameworks must be clearly identified and consistent with the research topic.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Kinds and Classification of Research
Some Hindrances of Scientific Inquiry
The scientific Method of research
Principles of Scientific Method of Research
This document discusses guidelines for selecting and developing a research problem or topic. It defines a research problem and lists the characteristics of a good problem as being specific, measurable, achievable, realistic, and time-bound. The elements of a research problem are identified as the aim or purpose, subject matter, place, time period, and population. Finally, 18 guidelines for selecting a research topic are provided such as choosing a topic of interest, ensuring it is researchable and can be completed in a reasonable time frame, and that it contributes new knowledge and solutions.
A PRESENTATION ON RESEARCH METHODS: SELECTION OF A RESEARCH TOPIC, FORMULATING A HYPOTHESIS, PHILOSOPHICAL ISSUES IN RESEARCH, QUANTITATIVE VS QUALITATIVE DEBATE & SELECTION OF A RESEARCH METHOD
This document provides guidance on writing a research proposal. It discusses that a research proposal communicates the research problem, significance, and planned procedures to solve the problem. It is often required to present a brief plan before data collection, by a university, or for funding. The document outlines the key components of a strong research proposal, including an abstract, statement of the problem, significance, background, objectives, methods, work plan, personnel, facilities, and budget. It emphasizes developing clear objectives and thorough methods, justification of decisions, and arranging feedback on the proposal draft before final submission.
The document provides an overview of research methodology. It defines research and describes the objectives and characteristics of research. It discusses the scientific method, including basic postulates and criteria for good research. It also outlines the research process, including defining the research problem and reviewing literature. The summary covers the key aspects of research methodology discussed in the document such as the meaning of research, objectives of research, characteristics of the scientific method, criteria for good research, and steps in the research process.
Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
Topic 1 introduction to quantitative researchAudrey Antee
This document provides an introduction to quantitative research. It defines quantitative research as collecting and analyzing numerical data to explore, describe, explain, or predict trends. Quantitative research aims for objectivity and controls outside factors. It states hypotheses and uses statistics to analyze results. The document outlines reasons for quantitative research such as exploration, description, explanation, prediction, and evaluation. It also describes common types of quantitative research designs and the key components of measurement, sampling, research design, and statistical procedures.
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
The document discusses various types of research including descriptive research, which describes characteristics without determining causes, and analytical research, which evaluates facts and information. It also discusses applied research which aims to solve immediate problems, and basic research which improves scientific understanding without specific applications. Additional types discussed are quantitative research using measurement, qualitative research investigating human behavior, conceptual research developing new ideas, empirical research using observation, and historical research studying past events.
The document outlines the typical steps involved in the research process: 1) formulating the research problem, 2) conducting an extensive literature review, 3) developing hypotheses, 4) preparing a research design, 5) determining sample design, 6) collecting data, 7) executing the project, 8) analyzing data, 9) testing hypotheses, 10) generalizing and interpreting results, and 11) preparing a report. Some key aspects include formulating a specific topic or research question, reviewing prior studies and hypotheses, developing a methodology for collecting and analyzing data, drawing conclusions, and communicating findings.
This document discusses research methods and methodologies. It defines research methods as how to accomplish research tasks through procedures to initiate, carry out, and complete projects. Research methodology provides principles for organizing, planning, designing, and conducting research. The document then outlines different types of studies including deductive vs inductive, exploratory vs explanatory, descriptive vs analytical, basic vs applied, quantitative vs qualitative, one-time vs longitudinal, laboratory vs on-field, and test vs diagnostic research. It concludes by listing characteristics of good business research such as having a clearly defined purpose, detailed research process, thorough planning, high ethics, limitations revealed, adequate analysis, unambiguous findings, and justified conclusions.
This document outlines the key steps in the research methodology process. It defines research as a systematic effort to gain new knowledge. The main steps include: reviewing existing literature, identifying problems, setting objectives and hypotheses, planning the methodology, executing the research, analyzing data, drawing inferences, and disseminating findings. It also discusses defining the research problem precisely, formulating objectives, conducting a literature review to learn from past studies, and concluding the research by summarizing the findings and their significance.
Planning the analysis and interpretation of resseaech dataramil12345
The document outlines the researcher's plan for analyzing qualitative and quantitative data collected in a study. It discusses analyzing both types of data, including describing data, identifying typical and atypical findings, and answering research questions. Methods for analyzing qualitative data include historical analysis, inductive analysis, deductive analysis, and content analysis. Quantitative data analysis relies on statistical techniques to summarize data, identify similarities and differences between groups, and test hypotheses. Common statistical analyses include descriptive analysis, univariate analysis, bivariate analysis, multivariate analysis, and comparative analysis. The document also provides guidance on choosing appropriate statistical tests based on research questions, data type, and hypotheses.
This document discusses guidelines for selecting a research problem and formulating hypotheses. It defines key terms like research problem, assumption, hypothesis, and title. It provides guidelines for writing titles, selecting research topics, formulating general and specific research problems. It also discusses the different forms hypotheses can take and their purposes and functions in research.
This document discusses three common data collection methods: observation, interview, and questionnaire. Observation involves personally watching and interacting with research subjects and can be participatory or non-participatory. Interviews are verbal conversations with research participants that can be structured, unstructured, or semi-structured. Questionnaires are paper surveys containing a list of questions for respondents to answer in writing.
Data Analysis, Presentation and Interpretation of DataRoqui Malijan
The document defines and describes various types of data analysis techniques:
- Descriptive statistics summarize and describe data through methods like frequency distributions and descriptive graphs.
- Bivariate analysis examines the relationship between two variables.
- Multivariate analysis studies more than two variables simultaneously.
- Comparative analysis examines similarities and differences between alternatives.
- Evaluation assesses subjects using defined criteria to aid decision making.
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.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding, categorizing, comparing and interpreting collected data to find meanings and implications. The researcher's perspective influences the analysis. It also describes techniques for qualitative data analysis like becoming familiar with the data, providing in-depth descriptions, and categorizing data into themes. Ensuring credibility involves considering factors like the researcher's observations and biases. The document also contrasts qualitative data analysis with quantitative analysis.
This document discusses data collection methods. It begins by defining data collection as the systematic process of gathering observations or measurements. It then outlines the main steps in data collection: 1) defining the research aim, 2) choosing a data collection method such as experiments, surveys, interviews etc., and 3) planning data collection procedures such as sampling and standardizing. It also discusses different measurement scales such as nominal, ordinal, interval and ratio scales that are used to quantify variables. Finally, it covers scaling techniques including comparative scales like paired comparisons and ranking as well as non-comparative scales like Likert scales.
Quantitative data refers to numerical data that can be analyzed statistically. This document discusses various types of quantitative data like counts, measurements, and projections. It also describes common methods for analyzing quantitative data such as surveys, cross-tabulation, trend analysis, and gap analysis. The advantages of quantitative data include conducting in-depth research with minimum bias and accurate results. However, quantitative data also has limitations like providing restricted information and results depending on the question types used to collect the data.
Tools Of Data Collection, Questionnaire, Data Analysis, Types Of Data Analysis, Interviews, Data Presentations, Types of data Presentations, Audio Video Recordings, dichotomous check list type questions, rating scale questions, rank order questions, structured interviews, unstructured interviews, semi structured interviews, advantages and disadvantages of interviews, Types of data in research, data validation, data coding , data entering. Textual data Presentations, tabular data Presentations, graphical data Presentations, bar graph, pie or circle graph, line graph
This document discusses data analysis, interpretation, and presentation. It covers analyzing both qualitative and quantitative data to describe, summarize, identify relationships, compare and forecast outcomes. The type of analysis depends on the scale of measurement - nominal, ordinal, or numerical. Qualitative analysis involves identifying patterns, categorizing data, and critical incidents. Findings can be presented visually through graphs or by using notations, stories, and summaries. The goal is to move from describing what is found in the data to explaining why.
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Qualitative data analysis involves three main steps: 1) developing and applying codes to categorize themes and ideas in the data, 2) identifying patterns and relationships between codes, and 3) summarizing the findings. There are three types of coding - open, axial, and selective - which are used to organize the raw data and link the emerging categories. While there are no universally applicable techniques, common methods for interpreting qualitative data include identifying frequently used words and phrases, comparing primary and secondary sources, and searching for missing information. The last stage involves linking the findings back to the original hypotheses or objectives.
The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis
This document provides an overview of research methodology concepts including:
1. It defines research and discusses the characteristics of scientific methods and research objectives.
2. It covers developing hypotheses, research design, levels of measurement, and scaling techniques.
3. It describes different types of scaling including comparative, non-comparative, continuous rating, itemized rating, Likert, semantic differential, and Stapel scales.
1. The document discusses various topics related to data processing and analysis including defining data and information, the steps of data processing, types of data processing, what data analysis is, important types of data analysis methods, and qualitative study design and data analysis approaches.
2. It provides details on data editing, coding, classification, entry, validation, and tabulation as steps in data processing. Common statistical packages, tools, and software for data analysis are also outlined.
3. Qualitative research methods and coding systems are explained as well as qualitative data analysis software packages that can be used.
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
The document discusses key aspects of research design including:
1) Research design determines the framework and methods for a study including data collection and analysis.
2) Key decisions in research design include determining primary or secondary data sources, qualitative or quantitative data, specific methods for data collection like surveys or experiments, and approaches for data analysis.
3) A strong research design considers reliability, validity, neutrality, and generalizability and sets up a study for success through a coherent plan.
This document provides guidance on analyzing qualitative data through a systematic process of content analysis. It outlines 5 main steps: 1) Get to know the data by reading it thoroughly multiple times, 2) Focus the analysis by identifying key questions to answer, 3) Categorize the data by identifying themes and patterns to organize into coherent categories, 4) Identify patterns and connections within and between categories through within-category descriptions, identifying relatively important categories, and relationships between categories, and 5) Interpret the findings by bringing together the key points and lessons learned to attach meaning and significance. The document also provides tips for organizing and managing qualitative data.
Quantitative search and_qualitative_research by mubarakHafiza Abas
The document discusses quantitative and qualitative research methods. Quantitative research aims to quantify data by using structured tools like questionnaires to collect numerical data from large samples that can be statistically analyzed. It focuses on objectively testing hypotheses. Qualitative research collects non-numerical data like descriptions through methods such as interviews and observations to understand peoples' experiences. It focuses on exploring topics in-depth. The document also compares surveys and questionnaires, describing their differences and advantages and disadvantages of various survey methods.
This document discusses different approaches to analyzing qualitative and quantitative data from research. It addresses questions like what types of data are common, how to find meanings and patterns, and how to display results effectively. The document provides an overview of quantitative data analysis methods like statistical tests and summarizing data in tables and charts. It also discusses qualitative data analysis, including reducing and organizing text data, coding, conceptualizing, and interpreting meanings. The goal is to help researchers choose appropriate analysis methods based on their research questions, methodological approach, and type of data collected.
This document provides an overview of data analysis using SPSS. It discusses key concepts like variables, measurement scales, data types, statistical terminology, and the steps involved in data analysis using SPSS. The document defines nominal, ordinal, interval and ratio scales of measurement. It also describes the nature of data as categorical or metric, and the types of categorical and metric data. Furthermore, it outlines tasks like data preparation, coding, cleaning and the appropriate use of statistical tools for analysis in SPSS.
The document discusses different types of data and levels of measurement. It describes qualitative data as consisting of attributes, labels or non-numerical entries, while quantitative data contains numerical measurements or counts. Four levels of measurement are introduced: nominal involving categories, ordinal allowing ordering, interval where differences are meaningful, and ratio where values can be expressed as multiples. Examples are provided to demonstrate classifying different data sets by type and level of measurement.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
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Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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2. Qualitative data analysis works a little differently from quantitative
data, primarily because qualitative data is made up of words,
observations, images, and even symbols. Deriving absolute meaning
from such data is nearly impossible; hence, it is mostly used for
exploratory research.
Analyzing Qualitative Data
Analyzing Qualitative Data
4. Content analysis: This is one of the most common methods to analyze
qualitative data. It is used to analyze documented information in the
form of texts, media, or even physical items. When to use this method
depends on the research questions. Content analysis is usually used to
analyze responses from interviewees.
Qualitative Data Analysis
Qualitative Data Analysis
Methods
Methods
5. Narrative analysis: This method is used to analyze content from
various sources, such as interviews of respondents, observations
from the field, or surveys. It focuses on using the stories and
experiences shared by people to answer the research questions.
Qualitative Data Analysis
Qualitative Data Analysis
Methods
Methods
6. Framework analysis. This is a more advanced method that
consists of several stages such as familiarization, identifying a
thematic framework, coding, charting, mapping, and
interpretation.
Qualitative Data Analysis
Qualitative Data Analysis
Methods
Methods
7. Discourse analysis: Like narrative analysis, discourse analysis is used to
analyze interactions with people. However, it focuses on analyzing the social
context in which the communication between the researcher and the
respondent occurred. Discourse analysis also looks at the respondent’s
day-to-day environment and uses that information during analysis.
Qualitative Data Analysis
Qualitative Data Analysis
Methods
Methods
8. Grounded theory: This refers to using qualitative data to explain why a
certain phenomenon happened. It does this by studying a variety of similar
cases in different settings and using the data to derive causal explanations.
Researchers may alter the explanations or create new ones as they study
more cases until they arrive at an explanation that fits all cases.
Qualitative Data Analysis
Qualitative Data Analysis
Methods
Methods
9. Coding can be explained as
he categorization of data. A ‘code’
can be a word or a short phrase
that represents a theme or an
idea.
Step 1: Developing and
Applying Codes.
The analytical and critical thinking skills of the
researcher plays significant role in data
analysis in qualitative studies. Therefore, no
qualitative study can be repeated to generate
the same results.
Step 2: Identifying
themes, patterns and
relationships.
Qualitative data analysis can also be
Qualitative data analysis can also be
conducted through the following
conducted through the following
three
three steps:
steps:
10. At this last stage, you need to link research findings
to hypotheses or research aims and objectives.
When writing the data analysis chapter, you can
use noteworthy quotations from the transcript in
order to highlight major themes within findings and
possible contradictions.
Step 3: Summarizing
the data.
Qualitative data analysis can also be
Qualitative data analysis can also be
conducted through the following
conducted through the following
three
three steps:
steps:
11. Step 1: Developing and Applying
Step 1: Developing and Applying
Codes
Codes
WHAT IS CODE?
Code may be a word or short
phrase that symbolically assigns
a cumulative prominent and
sense-capturing portion of a text
or visual data.
12. Step 1: Developing and Applying
Step 1: Developing and Applying
Codes
Codes
There are three types of coding:
1. Open coding. The initial organization of raw data to try to
make sense of it.
2. Axial coding. Interconnecting and linking the categories of
codes.
3. Selective coding. Formulating the story through
connecting the categories.
13. Step 1: Developing and Applying
Step 1: Developing and Applying
Codes
Codes
There are three types of coding:
Coding can be done manually or using qualitative data
analysis software such as
NVivo, Atlas ti 6.0, Hyper RESEARCH 2.8, Max QDA and others.
14. Step 1: Developing and Applying
Step 1: Developing and Applying
Codes
Codes
https://www.youtube.com/watch?v=6_gZuEm3Op0
15. Step 2: Identifying themes, patterns
Step 2: Identifying themes, patterns
and relationships.
and relationships.
most popular and effective methods of
qualitative data interpretation
Word and phrase repetitions – scanning primary data
for words and phrases most commonly used by
respondents, as well as, words and phrases used with
unusual emotions;
16. Step 2: Identifying themes, patterns
Step 2: Identifying themes, patterns
and relationships.
and relationships.
most popular and effective methods of
qualitative data interpretation
Primary and secondary data comparisons – comparing
the findings of interview/focus group/observation/any
other qualitative data collection method with the
findings of the literature review and discussing
differences between them;
17. Step 2: Identifying themes, patterns
Step 2: Identifying themes, patterns
and relationships.
and relationships.
most popular and effective methods of
qualitative data interpretation
Search for missing information – discussions
about which aspects of the issue was not
mentioned by respondents, although you
expected them to be mentioned;
18. Step 2: Identifying themes, patterns
Step 2: Identifying themes, patterns
and relationships.
and relationships.
most popular and effective methods of
qualitative data interpretation
Metaphors and analogues – comparing primary
research findings to phenomena from a different
area and discussing similarities and differences.
34. Types of Data
Types of Data
Qualitative or Categorical Data is
data that can’t be measured or
counted in the form of numbers.
These types of data are sorted by
category, not by number.
Qualitative or Categorical
Gender ( Male, Female)
Hair color ( Black, Brown, Gray, etc)
Nationality (Indian, American, Chinese,
etc)
These data consist of audio, images,
symbols, or text. The gender of a person,
i.e., male, female, or others, is qualitative
data.
Example
35. Types of Data
Types of Data
Nominal values represent discrete units and
are used to label variables that have no
quantitative value. Just think of them as
“labels.” Note that nominal data that has no
order. Therefore, if you would change the order
of its values, the meaning would not change.
Nominal Data
Example
Qualitative or Categorical
36. Types of Data
Types of Data
Ordinal data have natural ordering where
a number is present in some kind of order
by their position on the scale. These data
are used for observation like customer
satisfaction, happiness, etc., but we can’t
do any arithmetical tasks on them.
Ordinal Data
Example
Qualitative or Categorical
37. Types of Data
Types of Data
Quantitative data is also known as
numerical data which represents the
numerical value (i.e., how much, how often,
how many). Numerical data gives
information about the quantities of a
specific thing. Quantitative data can be used
for statistical manipulation.
Quantitative or numerical
Example
Height or weight of a person or
object
Room Temperature
Scores and Marks (Ex: 59, 80, 60, etc.)
Time
38. Types of Data
Types of Data
Discrete data can take only discrete
values. Discrete information contains
only a finite number of possible values.
Those values cannot be subdivided
meaningfully. Here, things can be
counted in whole numbers.
Discrete
Example
Quantitative or numerical
39. Types of Data
Types of Data
Continuous data represent
measurements and therefore their values
can’t be counted but they can be
measured. An example would be the
height of a person, which you can describe
by using intervals on the real number line.
Continuous
Example
Quantitative or numerical
40. Types of Data
Types of Data
It represents ordered data that is measured
along a numerical scale with equal distances
between the adjacent units. These equal
distances are also referred to as intervals. So
a variable contains interval data if it has
ordered numeric values with the exact
differences known between them.
Interval
Example
Quantitative or numerical
41. Types of Data
Types of Data
Like Interval data, ratio data are also
ordered with the same difference
between the individual units. However,
they also have a meaningful zero so
they cannot take negative values.
Ratio
Example
Quantitative or numerical
The temperature on a Kelvin scale
(0 degrees represent the total
absence of thermal energy)
Height ( zero is the starting point)
weight, length
42. Analyzing Quantitative Data
Analyzing Quantitative Data
Data Preparation
The first stage of analyzing data is data
preparation, where the aim is to convert raw data
into something meaningful and readable. It
includes four steps.
43. The purpose of data validation is to find
out, as far as possible, whether the data
collection was done as per the pre-set
standards and without any bias. It is a
four-step process, which includes…
Step 1: Data Validation
Step 1: Data Validation
Step 1: Data Validation
44. Typically, large data sets include errors. For
example, respondents may fill the fields
incorrectly or skip them accidentally. To make
sure that there are no such errors, the
researcher should conduct basic data checks,
check for outliers, and edit the raw research
data to identify and clear out any data points
that may hamper the accuracy of the results
Step 2: Data Editing
Step 2: Data Editing
45. This is one of the most important steps
in data preparation. It refers to
grouping and assigning values to
responses from the survey.
Step 3: Data Coding
Step 3: Data Coding
46. For example, if a researcher has interviewed 1,000
people and now wants to find the average age of
the respondents, the researcher will create age
buckets and categorize the age of each of the
respondents as per these codes. (For example,
respondents between 13-15 years old would have
their age coded as 0, 16-18 as 1,
18-20 as 2, etc.)
Step 3: Data Coding
Step 3: Data Coding
47. Quantitative Data Analysis
Quantitative Data Analysis
Methods
Methods
After these steps, the data is ready for
analysis. The two most commonly used
quantitative data analysis methods are
descriptive statistics and inferential
statistics.
48. Descriptive Statistics
Descriptive Statistics
Typically descriptive statistics (also known as descriptive analysis)
is the first level of analysis. It helps researchers summarize the
data and find patterns. A few commonly used descriptive statistics
are:
Mean: numerical average of a set of values.
Median: midpoint of a set of numerical values.
Mode: most common value among a set of values.
49. Descriptive Statistics
Descriptive Statistics
Percentage: used to express how a value or group of
respondents within the data relates to a larger group of
respondents.
Frequency: the number of times a value is found.
Range: the highest and lowest value in a set of values.