Quantitative data group 3 ppt
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Collecting, analyzing, and interpreting qualitative data
This slides is made to complete assignment of Research Methodology courses B class
Check our website https://bclassof2018.wixsite.com/website
Application of Secondary Data in Epidemiological Study, Design Protocol and S...
Application of Secondary Data in Epidemiological Study, Design Protocol and S...Mohammad Aslam Shaiekh
This document provides an overview of secondary data analysis in epidemiological studies. It discusses the sources and types of secondary data, as well as the advantages and disadvantages of using secondary data. The document also outlines the steps involved in secondary data analysis, including determining the research question, locating and evaluating relevant data sources, and analyzing the data. Additionally, it provides guidelines for designing secondary data analysis protocols and conducting statistical analysis of secondary data, including addressing biases through techniques like propensity score matching and sensitivity analysis.Glossary
There are several types of research discussed in the document. Applied research aims to solve specific organizational problems, while basic research seeks to understand problems and potential solutions. Descriptive research describes characteristics of variables, and exploratory research investigates problems where little is known. Industry research involves collecting data on a specific industry to understand trends. Quantitative research measures things precisely to test theories, while qualitative research provides an in-depth understanding through various sources like interviews. There are also different sampling methods, variables in research, steps to data mining, levels of information sources, and rating scales used in organizational research.
Collecting, analyzing and interpreting data
This document discusses the process of collecting, analyzing, and interpreting data in research. It involves:
1) Collecting appropriate data through selected methods and tools based on the research question, respondents, objectives, and available resources.
2) Analyzing raw data by coding and tabulating it into categories for further analysis, attempting to classify it into purposeful categories. Software can help with large amounts of data.
3) Interpreting relationships or differences found in the analysis to determine validity of conclusions and explain findings, with the goal of answering the original research questions.
Statistics collection of data
Data collection is the process of systematically gathering and measuring information to answer questions and evaluate outcomes. There are three main sources of data: secondary data collected by others, internal data from within an organization, and primary data collected through questioning and observation. Questionnaires can be structured, with predetermined questions and responses, or unstructured, allowing respondents to answer freely in their own words. Properly designing questionnaires requires skill, as researchers must determine what information is needed, the questionnaire type and format, question content and response format, sequencing, and whether it will be disguised or structured. Factors like cover letters, question number, logical arrangement, simplicity, sensitivity, instructions, footnotes, objectivity, calculations, pre-testing, cross-
Methods of data collection
There are two types of data: primary and secondary. Primary data is collected directly by the researcher through methods like questionnaires, observations, interviews, and surveys. Secondary data is previously collected data from sources like government publications, journals, and reports.
Data collection methods for primary data include questionnaires, observations made without controlling the situation, interviews between a researcher and participant, and surveys administered through enumerators. Secondary data comes from published sources like government documents and unpublished sources from individuals and organizations.
After collection, data must be processed which includes editing, coding, classification, and tabulation to organize it for analysis. Different types of analysis are then used like descriptive, correlation, multivariate, and inferential analysis. Hypotheses are
Methods of qualitative data analysis
Data is a source of great information which can enable informed decision making for businesses. Data is divided into Quantitative Data and Qualitative Data. Qualitative data refers to those non-numerical, explanatory data. Herein, we will have a detailed look into the various methods of qualitative data analysis.
Initial analysis of data metpen
This document discusses data analysis in qualitative research. It defines data analysis as systematically examining research materials like interviews, field notes, and other sources to increase understanding and present findings. The document outlines that data analysis involves identifying patterns and relationships. It also discusses different types of analysis for quantitative and qualitative data, including techniques like frequency distributions, measures of central tendency, and thematic analysis. Steps in data analysis are preparing, tabulating, processing data according to the research approach. Cross tabulation is presented as a method to examine relationships between multiple variables.
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Collecting, analyzing, and interpreting qualitative data
This slides is made to complete assignment of Research Methodology courses B class
Check our website https://bclassof2018.wixsite.com/website
Application of Secondary Data in Epidemiological Study, Design Protocol and S...
Application of Secondary Data in Epidemiological Study, Design Protocol and S...Mohammad Aslam Shaiekh
This document provides an overview of secondary data analysis in epidemiological studies. It discusses the sources and types of secondary data, as well as the advantages and disadvantages of using secondary data. The document also outlines the steps involved in secondary data analysis, including determining the research question, locating and evaluating relevant data sources, and analyzing the data. Additionally, it provides guidelines for designing secondary data analysis protocols and conducting statistical analysis of secondary data, including addressing biases through techniques like propensity score matching and sensitivity analysis.Glossary
There are several types of research discussed in the document. Applied research aims to solve specific organizational problems, while basic research seeks to understand problems and potential solutions. Descriptive research describes characteristics of variables, and exploratory research investigates problems where little is known. Industry research involves collecting data on a specific industry to understand trends. Quantitative research measures things precisely to test theories, while qualitative research provides an in-depth understanding through various sources like interviews. There are also different sampling methods, variables in research, steps to data mining, levels of information sources, and rating scales used in organizational research.
Collecting, analyzing and interpreting data
This document discusses the process of collecting, analyzing, and interpreting data in research. It involves:
1) Collecting appropriate data through selected methods and tools based on the research question, respondents, objectives, and available resources.
2) Analyzing raw data by coding and tabulating it into categories for further analysis, attempting to classify it into purposeful categories. Software can help with large amounts of data.
3) Interpreting relationships or differences found in the analysis to determine validity of conclusions and explain findings, with the goal of answering the original research questions.
Statistics collection of data
Data collection is the process of systematically gathering and measuring information to answer questions and evaluate outcomes. There are three main sources of data: secondary data collected by others, internal data from within an organization, and primary data collected through questioning and observation. Questionnaires can be structured, with predetermined questions and responses, or unstructured, allowing respondents to answer freely in their own words. Properly designing questionnaires requires skill, as researchers must determine what information is needed, the questionnaire type and format, question content and response format, sequencing, and whether it will be disguised or structured. Factors like cover letters, question number, logical arrangement, simplicity, sensitivity, instructions, footnotes, objectivity, calculations, pre-testing, cross-
Methods of data collection
There are two types of data: primary and secondary. Primary data is collected directly by the researcher through methods like questionnaires, observations, interviews, and surveys. Secondary data is previously collected data from sources like government publications, journals, and reports.
Data collection methods for primary data include questionnaires, observations made without controlling the situation, interviews between a researcher and participant, and surveys administered through enumerators. Secondary data comes from published sources like government documents and unpublished sources from individuals and organizations.
After collection, data must be processed which includes editing, coding, classification, and tabulation to organize it for analysis. Different types of analysis are then used like descriptive, correlation, multivariate, and inferential analysis. Hypotheses are
Methods of qualitative data analysis
Data is a source of great information which can enable informed decision making for businesses. Data is divided into Quantitative Data and Qualitative Data. Qualitative data refers to those non-numerical, explanatory data. Herein, we will have a detailed look into the various methods of qualitative data analysis.
Initial analysis of data metpen
This document discusses data analysis in qualitative research. It defines data analysis as systematically examining research materials like interviews, field notes, and other sources to increase understanding and present findings. The document outlines that data analysis involves identifying patterns and relationships. It also discusses different types of analysis for quantitative and qualitative data, including techniques like frequency distributions, measures of central tendency, and thematic analysis. Steps in data analysis are preparing, tabulating, processing data according to the research approach. Cross tabulation is presented as a method to examine relationships between multiple variables.
.Analyzing data
This document discusses techniques for qualitative and quantitative data analysis. Qualitative data analysis deals with non-numerical data like words or text, while quantitative data uses numerical data. Some qualitative techniques include analyzing unstructured observations, interviews, records and documents to identify and sort relevant text segments. Quantitative techniques include descriptive statistics like frequencies, measures of central tendency, and variability for descriptive research, and correlational techniques, multiple regression, discriminant analysis and factor analysis for multivariate research. Different research designs require different analysis methods such as t-tests, analysis of variance, and factorial analysis of variance.
Analyzing data (chapter 9)
There are different techniques for analyzing qualitative, descriptive, and correlational research data. Qualitative analysis reduces data to essential parts through categorizing segments of text or using existing categories. Descriptive research reports frequencies verbally and with graphs from frequency tables. Correlational techniques analyze descriptive research data to determine if relationships between variables are meaningful by obtaining relevant correlations.
CHARACTERISTICS OF QUANTITAIVE RESEARCH
The document describes quantitative research and its key characteristics. It defines quantitative research as objective and systematic examination of numerical data to describe and explain phenomena. It then lists 5 key characteristics: 1) large sample sizes, 2) collection of numerical data, 3) structured data collection methods like questionnaires, 4) data analysis using statistical software to produce descriptive and inferential statistics, and 5) highly reliable and reusable outcomes typically presented using tables and graphs. Examples are provided for some of the characteristics.
Lecture 6 qualitative data analysis
1. Qualitative data analysis involves coding texts to identify patterns, which turns qualitative data into quantitative codes. The purpose is to produce findings by analyzing data, interpreting patterns, and presenting conclusions.
2. Analyzing qualitative data is challenging due to the massive amounts of information collected. The process involves reducing the volume of data, identifying significant patterns, and developing a framework to communicate what the data reveals.
3. Rigorous analysis depends on gathering high-quality data, the credibility of the researcher, and a philosophical belief in qualitative inquiry. Common stages of analysis include familiarization, coding, identifying themes, re-coding, developing categories, exploring relationships, and reporting findings.
Data analysis aug-11
The document discusses various methods for analyzing and interpreting data. It describes descriptive analysis which helps summarize data patterns. Statistical analysis techniques like clustering, regression, and cohorts are explained. Inferential analysis makes judgments about differences between groups. Qualitative and quantitative methods are outlined for interpreting data through coding and establishing relationships. The purpose of data analysis and interpretation is to answer research questions and determine trends to support decision making.
Qualitiative data analysis: data triangulation
As the second part of the lecture on qualitative data analysis we discussed the need to cross-validate the collected insights. In this presentation I show what are the different approaches to data triangulation and how I applied them in my research work.
How to handle discrepancies while you collect data for systemic review – pubrica
1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
Continue Reading: https://bit.ly/36i7iYo
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Secondary data
Research methodology - Secondary data: characteristics, importance, sources, advantages and disadvantages
SECONDARY DATA
This document discusses secondary data - data originally collected by someone other than the user. It defines secondary data and lists common sources like censuses and government/organizational records. The purposes of secondary data are extracting relevant information, fact finding, model building, and data mining. Criteria for evaluating secondary data include specifications, error, currency, objectives, nature, and dependability. Secondary data is advantageous as it is economical, time saving, and helps focus primary data collection. However, disadvantages are that secondary data may not fit the research factors and accuracy is unknown. Secondary data can be used to identify problems, better define problems, develop research approaches, formulate research designs, and help interpret primary data.
Collection & Editing of data
The document discusses data collection and editing. It defines statistical and grouped data and explains primary and secondary data collection methods. Primary data is collected directly from sources while secondary data has already been collected. Methods of collecting primary data include personal investigation, through investigators, questionnaires, and telephone. Secondary data comes from official and semi-official sources as well as publications. The document then defines data editing as reviewing and adjusting collected data to ensure quality. Interactive, selective, and macro editing methods are described.
research design
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.
Qualitative Data Analysis (Strategies)
Qualitative data analysis strategies include transcribing data into a form that can be analyzed, segmenting and coding the data to identify themes and concepts, categorizing codes to group similar ideas, relating categories to determine connections between them, prioritizing categories to create a hierarchy, enumerating themes to quantify frequency, memoing reflective notes and determining next steps, and diagramming to understand complex relationships within the data.
Qualitative data analysis
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
Data Collection Process And Integrity
The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
Chapter 15 Social Research
The document provides an overview of quantitative and qualitative data analysis methods. It discusses the differences between quantitative and qualitative data/analysis, as well as various statistical and coding techniques used in each method. For quantitative analysis, it covers descriptive statistics, inferential statistics, univariate analysis including measures of central tendency and variation, bivariate analysis including crosstabulation and correlation, and multivariate analysis including elaboration models. For qualitative analysis, it discusses social anthropological versus interpretivist approaches, the relationship between data and ideas, strengths and weaknesses, and typical analysis steps including coding, data reduction, and conclusion drawing.
Secondary data
This document discusses secondary data, which is data that is obtained from published or unpublished sources rather than being originally collected. Secondary data includes sources like census data used by researchers other than the original collecting agency. Secondary data comes from published sources like government reports and unpublished sources like studies. While convenient, secondary data users must ensure the data is suitable, adequate, reliable, accurate, consistent, complete, and homogeneous for their purposes. Both primary and secondary data have advantages and limitations depending on the inquiry's nature, resources, time constraints, and accuracy needs.
Data & data collection
This document discusses different methods for collecting and analyzing quantitative and qualitative data in research. It describes the following key points:
- Quantitative data involves numerical data that can be statistically analyzed, while qualitative data involves non-numerical data like text.
- Common statistical analyses for quantitative data include descriptive statistics like frequencies, means, and variability measures. Correlational research examines relationships between variables. Experimental research compares means between groups using t-tests or analyzes variance between groups using ANOVA.
- Qualitative data analysis involves deriving categories from text and identifying patterns. It requires intuition to understand the data.
- The document outlines various multivariate techniques like regression, discriminant analysis, and factor analysis that can analyze multiple
EDUCATIONAL RESEARCH II (II Bimetsre Abril Agosto 2011)
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Introduction To Statistics
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
Research methods module 4
The document discusses various methods of primary data collection including observation, surveys, interviews and questionnaires. It provides details on each method, such as observational methods involving non-verbal analysis and linguistic analysis. Questionnaire design is also covered, noting the different types of closed-ended, open-ended and mixed questionnaires. Key steps in designing a questionnaire include deciding what information is needed, formulating question types, wording, and pre-testing the questionnaire. Secondary data sources and their characteristics are also briefly mentioned.
THE Data Collection (RESEARCH METHODOLOGY)
This document discusses various methods for collecting data, both primary and secondary. It defines data as units of information that are measured, collected, analyzed and used for data visualizations. There are two main types of data collection methods discussed:
Primary methods involve directly collecting original data and include observation, surveys, interviews and questionnaires. Observation allows collecting large quantities of data in an inexpensive way but requires extensive training. Surveys can be conducted in-person or online and collect standardized information from a sample. Interviews are conducted one-on-one and allow collecting more in-depth information.
Secondary methods involve using existing data collected by others. Common secondary sources include publications, reports, and data available online. While cheaper and faster
Practical Research 1 about quantitative and qualitative methods
Quantitative methods involve collecting and analyzing numerical data using statistical techniques. This may include polls, questionnaires, and manipulating existing statistical data. Quantitative techniques provide systematic and powerful analysis based on quantitative data. Quantitative data can be counted or measured numerically, while qualitative data includes non-numerical responses. The role of the researcher is to design rigorous quantitative studies and ensure valid and reliable results.
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.Analyzing data
This document discusses techniques for qualitative and quantitative data analysis. Qualitative data analysis deals with non-numerical data like words or text, while quantitative data uses numerical data. Some qualitative techniques include analyzing unstructured observations, interviews, records and documents to identify and sort relevant text segments. Quantitative techniques include descriptive statistics like frequencies, measures of central tendency, and variability for descriptive research, and correlational techniques, multiple regression, discriminant analysis and factor analysis for multivariate research. Different research designs require different analysis methods such as t-tests, analysis of variance, and factorial analysis of variance.
Analyzing data (chapter 9)
There are different techniques for analyzing qualitative, descriptive, and correlational research data. Qualitative analysis reduces data to essential parts through categorizing segments of text or using existing categories. Descriptive research reports frequencies verbally and with graphs from frequency tables. Correlational techniques analyze descriptive research data to determine if relationships between variables are meaningful by obtaining relevant correlations.
CHARACTERISTICS OF QUANTITAIVE RESEARCH
The document describes quantitative research and its key characteristics. It defines quantitative research as objective and systematic examination of numerical data to describe and explain phenomena. It then lists 5 key characteristics: 1) large sample sizes, 2) collection of numerical data, 3) structured data collection methods like questionnaires, 4) data analysis using statistical software to produce descriptive and inferential statistics, and 5) highly reliable and reusable outcomes typically presented using tables and graphs. Examples are provided for some of the characteristics.
Lecture 6 qualitative data analysis
1. Qualitative data analysis involves coding texts to identify patterns, which turns qualitative data into quantitative codes. The purpose is to produce findings by analyzing data, interpreting patterns, and presenting conclusions.
2. Analyzing qualitative data is challenging due to the massive amounts of information collected. The process involves reducing the volume of data, identifying significant patterns, and developing a framework to communicate what the data reveals.
3. Rigorous analysis depends on gathering high-quality data, the credibility of the researcher, and a philosophical belief in qualitative inquiry. Common stages of analysis include familiarization, coding, identifying themes, re-coding, developing categories, exploring relationships, and reporting findings.
Data analysis aug-11
The document discusses various methods for analyzing and interpreting data. It describes descriptive analysis which helps summarize data patterns. Statistical analysis techniques like clustering, regression, and cohorts are explained. Inferential analysis makes judgments about differences between groups. Qualitative and quantitative methods are outlined for interpreting data through coding and establishing relationships. The purpose of data analysis and interpretation is to answer research questions and determine trends to support decision making.
Qualitiative data analysis: data triangulation
As the second part of the lecture on qualitative data analysis we discussed the need to cross-validate the collected insights. In this presentation I show what are the different approaches to data triangulation and how I applied them in my research work.
How to handle discrepancies while you collect data for systemic review – pubrica
1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
Continue Reading: https://bit.ly/36i7iYo
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Secondary data
Research methodology - Secondary data: characteristics, importance, sources, advantages and disadvantages
SECONDARY DATA
This document discusses secondary data - data originally collected by someone other than the user. It defines secondary data and lists common sources like censuses and government/organizational records. The purposes of secondary data are extracting relevant information, fact finding, model building, and data mining. Criteria for evaluating secondary data include specifications, error, currency, objectives, nature, and dependability. Secondary data is advantageous as it is economical, time saving, and helps focus primary data collection. However, disadvantages are that secondary data may not fit the research factors and accuracy is unknown. Secondary data can be used to identify problems, better define problems, develop research approaches, formulate research designs, and help interpret primary data.
Collection & Editing of data
The document discusses data collection and editing. It defines statistical and grouped data and explains primary and secondary data collection methods. Primary data is collected directly from sources while secondary data has already been collected. Methods of collecting primary data include personal investigation, through investigators, questionnaires, and telephone. Secondary data comes from official and semi-official sources as well as publications. The document then defines data editing as reviewing and adjusting collected data to ensure quality. Interactive, selective, and macro editing methods are described.
research design
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.
Qualitative Data Analysis (Strategies)
Qualitative data analysis strategies include transcribing data into a form that can be analyzed, segmenting and coding the data to identify themes and concepts, categorizing codes to group similar ideas, relating categories to determine connections between them, prioritizing categories to create a hierarchy, enumerating themes to quantify frequency, memoing reflective notes and determining next steps, and diagramming to understand complex relationships within the data.
Qualitative data analysis
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
Data Collection Process And Integrity
The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
Chapter 15 Social Research
The document provides an overview of quantitative and qualitative data analysis methods. It discusses the differences between quantitative and qualitative data/analysis, as well as various statistical and coding techniques used in each method. For quantitative analysis, it covers descriptive statistics, inferential statistics, univariate analysis including measures of central tendency and variation, bivariate analysis including crosstabulation and correlation, and multivariate analysis including elaboration models. For qualitative analysis, it discusses social anthropological versus interpretivist approaches, the relationship between data and ideas, strengths and weaknesses, and typical analysis steps including coding, data reduction, and conclusion drawing.
Secondary data
This document discusses secondary data, which is data that is obtained from published or unpublished sources rather than being originally collected. Secondary data includes sources like census data used by researchers other than the original collecting agency. Secondary data comes from published sources like government reports and unpublished sources like studies. While convenient, secondary data users must ensure the data is suitable, adequate, reliable, accurate, consistent, complete, and homogeneous for their purposes. Both primary and secondary data have advantages and limitations depending on the inquiry's nature, resources, time constraints, and accuracy needs.
Data & data collection
This document discusses different methods for collecting and analyzing quantitative and qualitative data in research. It describes the following key points:
- Quantitative data involves numerical data that can be statistically analyzed, while qualitative data involves non-numerical data like text.
- Common statistical analyses for quantitative data include descriptive statistics like frequencies, means, and variability measures. Correlational research examines relationships between variables. Experimental research compares means between groups using t-tests or analyzes variance between groups using ANOVA.
- Qualitative data analysis involves deriving categories from text and identifying patterns. It requires intuition to understand the data.
- The document outlines various multivariate techniques like regression, discriminant analysis, and factor analysis that can analyze multiple
EDUCATIONAL RESEARCH II (II Bimetsre Abril Agosto 2011)
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Lic. Alba Bitalina Vargas Saritama
Ciclo: Séptimo
Bimestre: Segundo
Introduction To Statistics
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
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How to handle discrepancies while you collect data for systemic review – pubrica
How to handle discrepancies while you collect data for systemic review – pubrica
EDUCATIONAL RESEARCH II (II Bimetsre Abril Agosto 2011)
EDUCATIONAL RESEARCH II (II Bimetsre Abril Agosto 2011)
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The document discusses various methods of primary data collection including observation, surveys, interviews and questionnaires. It provides details on each method, such as observational methods involving non-verbal analysis and linguistic analysis. Questionnaire design is also covered, noting the different types of closed-ended, open-ended and mixed questionnaires. Key steps in designing a questionnaire include deciding what information is needed, formulating question types, wording, and pre-testing the questionnaire. Secondary data sources and their characteristics are also briefly mentioned.
THE Data Collection (RESEARCH METHODOLOGY)
This document discusses various methods for collecting data, both primary and secondary. It defines data as units of information that are measured, collected, analyzed and used for data visualizations. There are two main types of data collection methods discussed:
Primary methods involve directly collecting original data and include observation, surveys, interviews and questionnaires. Observation allows collecting large quantities of data in an inexpensive way but requires extensive training. Surveys can be conducted in-person or online and collect standardized information from a sample. Interviews are conducted one-on-one and allow collecting more in-depth information.
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Quantitative data group 3 ppt
- 1. Group 3: Auliatuz Zahrotul Jannah Alfa Fitri Fajriyati Irma Imroatina Collecting, Analizing and Interpreting Data Quantitative Research Methodology
- 2. Table of Contents Analizing Collecting Interpreting 01 03 02
- 3. Collecting Data Quantitative is defined as the procedure of collecting, measuring and analyzing accurate insights for research using standard validated techniques. A researcher can evaluate their hypothesis on the basis of collected data. 01
- 4. Methods Used or Quantitative Data Collection
- 5. Methods used for quantitative data collection 1. Probability sampling - Simple random sampling: - Systematic random sampling - Stratified random sampling 2. Interviews - Telephone interviews - Face-to-face interviews - Telephone interviews - Face-to-face interviews 3. Surveys/questionnaires - Web-based questionnaire - Mail Questionnaire 4. Observation - Structured observation 5. Document Review - Public Records: - Personal Documents - Physical Evidence
- 6. Analizing Data Quantitative Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision- making. 02
- 7. Quantitative Data : Analysis Methods MaxDiff analysis Conjoint analysis TURF analysis Cross- tabulation Gap analysis Trend analysis SWOT analysis Text analysis
- 8. Steps to Conduct Quantitative Data Analysis Relate measurement scales with variables Connect descriptive statistics with data Decide a measurement scale: Select appropriate tables to represent data and analyze collected data
- 9. Interpreting Data Quantitative Data interpretation refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion. The interpretation of data assigns a meaning to the information analyzed and determines its signification and implications. 03
- 10. Statistical Methods Used in Analyzing Quantitative Data Mean Standard deviation Frequency distribution
- 11. Source : ● https://www.questionpro.com/blog/data- collection/amp/ ● https://www.questionpro.com/blog/quantitative- data-collection-methods/amp/ ● https://www.datapine.com/blog/data-analysis- methods-and-techniques/ ● https://www.google.com/amp/s/www.questionpro. com/blog/quantitative-data/amp/ ● https://www.datapine.com/blog/data- interpretation-methods-benefits-problems/
- 12. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Thanks