COMPLETE UNDERSTANDINGOF Quantitative Research
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The document discusses quantitative research methods, defining it as research that involves counting and statistical analysis to get clear, accurate results. It provides steps for conducting a questionnaire survey, including defining the problem, sample size, developing the questionnaire, and using scales like Likert scales. Quantitative research uses mathematical and statistical techniques to analyze collected data.
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Some Research Concepts
This document discusses statistical concepts and measurement, types of variables, and key terms for data analysis. It addresses:
- How variables are measured and the levels of measurement (nominal, ordinal, interval, ratio).
- The difference between discrete and continuous variables.
- Key terms like population, sample, parameter, and statistic and how inferences are made about populations based on statistics from samples.
- The components of theories, including propositions, nomological networks, and hypotheses testing.
- Important sources for PhD students, including familiarity with key concepts and links to additional documents.
Types of Statistics
This document discusses different types of statistics used in research. Descriptive statistics are used to organize and summarize data using tables, graphs, and measures. Inferential statistics allow inferences about populations based on samples through techniques like surveys and polls. The key difference is that descriptive statistics describe samples while inferential statistics allow conclusions about populations beyond the current data.
statistical analysis of questionnaires
This document provides an overview of statistical analysis of questionnaire data. It discusses topics like questionnaire construction, data entry, reliability analysis using Cronbach's alpha, descriptive statistics for Likert scale items including frequencies, medians, interquartile ranges and box plots. It also covers composite scale analysis using means, standard deviations and comparisons between groups. An example is provided on assessing student satisfaction regarding teaching using 4 questionnaire items from 60 students. Results would be reported using tables and figures with interpretations.
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This chapter discusses three approaches to research on instructional effectiveness: what works, when does it work, and how does it work. It also discusses criteria for selecting good experimental comparisons, including experimental control, random assignment, and appropriate measures. The chapter addresses how to interpret findings of no effect from experimental comparisons, and how to interpret research statistics in terms of statistical and practical significance. Finally, it provides guidance on how to identify relevant research, focusing on similarity of learners, experimental research design, replication of results, measures of application, and consideration of practical significance.
How to conduct meta analysis
This document outlines the steps for conducting a meta-analysis, including identifying relevant studies, evaluating study quality, abstracting data, performing statistical analysis using fixed and random effects models, and conducting sensitivity analysis and addressing publication bias. The objectives are to synthesize previous studies, provide a summary estimate, and identify different types of models for calculating summary effects and interpreting meta-analysis results.
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Qualitative vs quantitative data - infographic
Qualitative data is non-numerical information such as words, pictures, and observations that cannot be counted, while quantitative data uses numbers and can be counted. Qualitative data is used to understand social interactions and identify patterns through methods like content analysis and thematic analysis, while quantitative data answers questions about numbers, amounts, and frequencies and is used to test hypotheses and draw conclusions about large populations through statistical analysis methods. The key difference between the two data types is whether the information can be expressed numerically.
The Scientific Process
The scientific method is a systematic process of empirical investigation used to solve problems. It involves making observations, inferring conclusions, formulating hypotheses, controlling variables in experiments, interpreting data, and communicating results. The key steps are identifying a problem, formulating hypotheses, testing hypotheses through experiments, collecting and analyzing data, and drawing conclusions. Important skills for scientists include observation, controlling variables, and interpreting experimental results. Values like patience, honesty, and curiosity are also important for scientists.
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Some Research Concepts
This document discusses statistical concepts and measurement, types of variables, and key terms for data analysis. It addresses:
- How variables are measured and the levels of measurement (nominal, ordinal, interval, ratio).
- The difference between discrete and continuous variables.
- Key terms like population, sample, parameter, and statistic and how inferences are made about populations based on statistics from samples.
- The components of theories, including propositions, nomological networks, and hypotheses testing.
- Important sources for PhD students, including familiarity with key concepts and links to additional documents.
Types of Statistics
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statistical analysis of questionnaires
This document provides an overview of statistical analysis of questionnaire data. It discusses topics like questionnaire construction, data entry, reliability analysis using Cronbach's alpha, descriptive statistics for Likert scale items including frequencies, medians, interquartile ranges and box plots. It also covers composite scale analysis using means, standard deviations and comparisons between groups. An example is provided on assessing student satisfaction regarding teaching using 4 questionnaire items from 60 students. Results would be reported using tables and figures with interpretations.
Chapter 3 e learning summary
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Qualitative data is non-numerical information such as words, pictures, and observations that cannot be counted, while quantitative data uses numbers and can be counted. Qualitative data is used to understand social interactions and identify patterns through methods like content analysis and thematic analysis, while quantitative data answers questions about numbers, amounts, and frequencies and is used to test hypotheses and draw conclusions about large populations through statistical analysis methods. The key difference between the two data types is whether the information can be expressed numerically.
The Scientific Process
The scientific method is a systematic process of empirical investigation used to solve problems. It involves making observations, inferring conclusions, formulating hypotheses, controlling variables in experiments, interpreting data, and communicating results. The key steps are identifying a problem, formulating hypotheses, testing hypotheses through experiments, collecting and analyzing data, and drawing conclusions. Important skills for scientists include observation, controlling variables, and interpreting experimental results. Values like patience, honesty, and curiosity are also important for scientists.
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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.
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The document outlines the key components of findings, presentation, and discussion sections of a research study report. It notes that findings should present objective results through descriptive and inferential statistics. These findings are then discussed in a narrative, using tables and figures as needed, to explain their meaning and significance. The discussion also compares results to previous studies and addresses reliability and validity. The conclusions summarize what was learned and if research goals were met, while implications and recommendations suggest applications and directions for future work.
Correlational research
Correlational research investigates relationships between two variables without determining cause and effect. It can help explain behaviors and predict outcomes. The process involves selecting variables and participants, choosing valid and reliable instruments, collecting data on multiple variables for each subject, and computing a correlation coefficient to determine the strength and direction of relationships. Correlation coefficients range from 0 to 1, with higher values indicating stronger linear relationships and positive or negative signs indicating the direction of relationships between variables.
Hypothesis
The document discusses different statistical techniques used to test hypotheses, including chi-square tests, correlation coefficients, and regression analysis. Chi-square tests are used to determine if categorical data shows dependency between two classifications or if they are independent. Correlation coefficients measure the degree and direction of relationship between two variables. Regression analysis studies the functional relationship between variables and can be used to predict unknown dependent variable values from known independent variables.
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- The arithmetic mean, which is the sum of a list of numbers divided by the total number of items. It provides an overall trend of data.
- Frequency distributions which show how many evaluations fall into various categories using tables, histograms or pie charts.
- Bar graphs which present categorical and numeric variables in class intervals through bars to show patterns.
- Standard deviation which measures how spread out numbers are from the mean. A low standard deviation means numbers align with the mean.
- T-tests and Pearson's correlation coefficient which measure relationships between variables, and the chi-square test which compares expected to observed categorical variable frequencies.
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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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The document provides guidance to students on completing sections of a written project report on memory. It outlines the requirements and deadlines for the Introduction, Method, and Conclusion sections. For the Introduction, it describes including background research on memory models and relating previous studies. The Method section must describe the design, participants, materials, and procedures in enough detail to replicate the study. It also provides details on what to include for the hypothesis, independent and dependent variables, sampling method, materials, procedure, and controlling for confounding variables. The deadline for the final coursework is March 1st.
Practical Research 2.pptx
This document discusses quantitative research methods. It defines quantitative research as research that uses numerical data and statistical analysis to characterize phenomena. Some key points made include:
- Quantitative research emphasizes measurements and statistical analysis of data collected through surveys and questionnaires.
- It aims to identify relationships between variables and test models or designs.
- It typically uses organized research instruments to collect large, representative data samples that can be replicated.
- Findings are presented numerically in tables, charts and figures and analyzed statistically.
Practical Research 2: Nature and Inquiry about research
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The document provides guidance on developing a quantitative research design and data analysis plan for a faculty learning community project. It recommends identifying the research question, goals, objectives and intervention before data collection. It also advises choosing appropriate data collection methods and statistical tests that align with the research question and variables. The document emphasizes preparing "table shells" with planned statistical tests to envision the final data analysis and check that the research design will provide the intended results.
Statistics- Statistical Investigations Workshop 1
This document guides students through a statistics workshop on using a statistical investigation process called "data detective". It involves the following steps:
1. Defining a problem or question. An example problem is given about determining student interest in buying school lunches.
2. Planning how to collect data to solve the problem. An example plan is given to survey students about lunch preferences.
3. Collecting data by surveying classmates.
4. Analyzing the data through bar graphs showing survey results. Example graphs are provided.
5. Drawing a conclusion by looking at the graphs and determining if the problem was solved. Students are then asked to conduct their own investigation.
WPIPosterPresentation24x36
The document describes a study that tested whether feedback could help everyday people better interpret data visualizations. Participants were asked to compare sections of pie charts and bar charts and estimate percentages. Some participants received feedback on their estimates of pie charts. Those who received feedback improved at interpreting pie charts over the course of the experiment compared to those who did not receive feedback, suggesting feedback can help develop data literacy skills.
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.
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This document discusses various aspects of data analysis. It outlines the basic steps in research and data analysis, including identifying the problem, collecting data, analyzing and interpreting results. Both qualitative and quantitative data analysis methods are covered. Descriptive statistics are used to summarize data through measures like frequencies and central tendency. Inferential statistics allow generalization to populations through hypothesis testing using techniques like t-tests and chi-square tests. The document provides an overview of common statistical analysis methods and selecting the appropriate tests.
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This document discusses hypothesis testing and its importance in research. It defines statistical significance as a relationship between variables that is not due to chance. A hypothesis is a statement assumed to be true that must be tested. There are two types of hypotheses: the null hypothesis states there is no relationship, while the alternative hypothesis states there is a relationship. The document outlines the steps in hypothesis testing, including stating hypotheses, choosing a statistical test, determining significance levels, computing test values, and deciding whether to reject or fail to reject the null hypothesis based on a comparison of critical and calculated values. It also discusses types I and II errors and parametric vs. non-parametric tests.
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The document outlines the key components of findings, presentation, and discussion sections of a research study report. It notes that findings should present objective results through descriptive and inferential statistics. These findings are then discussed in a narrative, using tables and figures as needed, to explain their meaning and significance. The discussion also compares results to previous studies and addresses reliability and validity. The conclusions summarize what was learned and if research goals were met, while implications and recommendations suggest applications and directions for future work.
Correlational research
Correlational research investigates relationships between two variables without determining cause and effect. It can help explain behaviors and predict outcomes. The process involves selecting variables and participants, choosing valid and reliable instruments, collecting data on multiple variables for each subject, and computing a correlation coefficient to determine the strength and direction of relationships. Correlation coefficients range from 0 to 1, with higher values indicating stronger linear relationships and positive or negative signs indicating the direction of relationships between variables.
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The document discusses different statistical techniques used to test hypotheses, including chi-square tests, correlation coefficients, and regression analysis. Chi-square tests are used to determine if categorical data shows dependency between two classifications or if they are independent. Correlation coefficients measure the degree and direction of relationship between two variables. Regression analysis studies the functional relationship between variables and can be used to predict unknown dependent variable values from known independent variables.
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Quantitative analysis
This document summarizes quantitative data analysis techniques for summarizing data from samples and generalizing to populations. It discusses variables, simple and effect statistics, statistical models, and precision of estimates. Key points covered include describing data distribution through plots and statistics, common effect statistics for different variable types and models, ensuring model fit, and interpreting precision, significance, and probability to generalize from samples.
Basic understanding of scientific principles and data analysis for the herbalist
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Practical Research 2 Chapter 3: Common Statistical Tools
This document provides an overview of common statistical tools including:
- The arithmetic mean, which is the sum of a list of numbers divided by the total number of items. It provides an overall trend of data.
- Frequency distributions which show how many evaluations fall into various categories using tables, histograms or pie charts.
- Bar graphs which present categorical and numeric variables in class intervals through bars to show patterns.
- Standard deviation which measures how spread out numbers are from the mean. A low standard deviation means numbers align with the mean.
- T-tests and Pearson's correlation coefficient which measure relationships between variables, and the chi-square test which compares expected to observed categorical variable frequencies.
Power, effect size, and Issues in NHST
The document discusses effect size, power analysis, and alternatives to null hypothesis significance testing (NHST). It provides formulas for computing effect sizes like Cohen's d and explains Cohen's standards for small, medium, and large effect sizes. The document also defines statistical power as the probability of detecting an effect if one exists and explains how power is influenced by effect size, sample size, significance level, and whether the null hypothesis is true or false. Examples are given showing how effect size, sample size, and significance level each impact statistical power.
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.
Writing up your coursework
The document provides guidance to students on completing sections of a written project report on memory. It outlines the requirements and deadlines for the Introduction, Method, and Conclusion sections. For the Introduction, it describes including background research on memory models and relating previous studies. The Method section must describe the design, participants, materials, and procedures in enough detail to replicate the study. It also provides details on what to include for the hypothesis, independent and dependent variables, sampling method, materials, procedure, and controlling for confounding variables. The deadline for the final coursework is March 1st.
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Content List
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Definition
What is Quantitative Research
Characteristics of Quantitative Research
Quantitative Research: Key Advantages
Quantitative Research Methodology
7 Best Practices to Conduct Quantitative Research
Basic Research Design for Quantitative Studies
Strengths of Using Quantitative Methods
Limitations of Using Quantitative Methods
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Definition
What is Qualitative Research?
Characteristics of Qualitative Research
Three Key Elements of Qualitative Research
Quantitative Research: Key Advantages
Basic Research Design for Qualitative Studies
Strengths of Using Qualitative Methods
Limitations of Using Qualitative Methods
5 Key Types of Qualitative Research
Qualitative Research Methods: The Top 4 Techniques
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References
https://libguides.usc.edu/writingguide/quantitative#:~:text=Quantitative%20research%20deals%20in%20numbers,%2C%20free%2Dflowing%20manner%5D
https://www.scribbr.com/methodology/qualitative-quantitative-research/
https://www.enago.com/academy/qualitative-vs-quantitative-research/
https://www.investopedia.com/terms/q/quantitativeanalysis.asp
https://ideascale.com/blog/what-is-quantitative-research/
https://ideascale.com/blog/what-is-qualitative-research/
Media research
The document discusses various aspects of media research methodology. It outlines the steps involved including defining the research problem, developing objectives and hypotheses, reviewing literature, deciding the research design, collecting and analyzing data, and developing conclusions. It also discusses different research approaches and methods that can be used including experimental, exploratory, descriptive, case studies, surveys, and content analysis. The key aspects of ensuring rigorous media research are emphasized.
Edited assignment in research
The document discusses quantitative research methods. It begins by defining quantitative data as pieces of information that can be counted, often from large random samples. Both qualitative and quantitative methods are then described as complementary approaches. Key points about quantitative research include: it aims to determine relationships between variables; designs are descriptive or experimental; it focuses on numbers, logic and objectivity rather than divergent reasoning; and characteristics include using structured instruments, representative large samples, reliability, clearly defined questions, and numerical data. The strengths are broader generalization while weaknesses include less detail and flexibility.
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Research Process design
Research process involves a systematic way that focuses on being objective and gathering a multitude of information for analysis so that the researcher can come to a conclusion. Basic steps that should be considered and research process definition pdf and marketing research process 5 steps
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COMPLETE UNDERSTANDINGOF Quantitative Research
- 2. INTRODUCTION. DEFINITION. QUESTIONNAIRE SURVEY-STEPS. QUESTIONNAIRES. A FINE EXAMPLE OF QUANTITATIVE. SCALE. CONCLUSION.
- 3. Typically involves counting. Often statistical tests are applied to detect or confirm problems. Purpose is to get clear cut, accurate results. Questionnaire is one of the popular tools for quantitative research.
- 4. The range of mathematical and statistical techniques used to analyse data (www.answers.com)
- 5. Define the problem. Use previous studies & advice of an expert. Define population and sample size. Develop a questionnaire.
- 6. A form containing a set of questions, especially one addressed to a statistically significant number of subjects as a way of gathering information for a survey. (answers.com). Broad coverage. Quick response. Filling at a convenient time.
- 8. Different scales are there to measure the figures. Lickert scale in which each response assigned a number
- 9. Quantitative research is very important to find out the real facts and figures. It is about explaining the natural phenomena by collecting data. Which are analysed by mathematically based method.