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What is science?

What is science?

Science, a rigorous and systematic endeavor, seeks to build a comprehensive understanding of the natural world and our place within it. It's a never-ending quest to:

  • Gather Knowledge: Using observation, experimentation, and analysis, science builds a vast and ever-growing body of knowledge.
  • Test Ideas: Developing hypotheses and conducting experiments are crucial to test their validity and refine our understanding.
  • Refine Understanding: Science is a dynamic process, constantly evolving with new evidence leading to revisions and advancements.

What are the main features of science?

  • Evidence-Based: Science relies on verifiable evidence gathered through observation and experimentation.
  • Objectivity: It strives for objectivity in its methods and conclusions, minimizing bias to ensure reliable findings.
  • Repeatability: Scientific findings are expected to be repeatable by other researchers following the same methods, fostering trust and verification.

What are important sub-areas in science?

The vast domain of science can be broadly categorized into three major branches:

  • Natural Sciences: Explore the physical universe, encompassing physics, chemistry, biology, astronomy, geology, and ecology.
  • Social Sciences: Investigate human behavior and societies, including psychology, sociology, anthropology, economics, and political science.
  • Formal Sciences: Deal with abstract systems and structures, including mathematics, logic, and computer science.

What are key concepts in science?

  • The Scientific Method: A structured process for research, guiding scientists through observation, hypothesis development, experimentation, analysis, and conclusion.
  • Theories: Well-substantiated explanations of some aspect of the natural world, supported by evidence and open to revision as new information emerges.
  • Laws of Nature: Universal principles that describe how things consistently work in the natural world.
  • Models: Simplified representations of a system or phenomenon that aid in understanding complex processes.

Who are influential figures in science?

  • Galileo Galilei: Championed the experimental method and challenged prevailing astronomical beliefs through observation.
  • Isaac Newton: Revolutionized physics with his laws of motion and universal gravitation, laying the foundation for classical mechanics.
  • Marie Curie: Pioneered research on radioactivity, becoming the first woman to win a Nobel Prize and the first person to win it twice.
  • Charles Darwin: Developed the theory of evolution by natural selection, fundamentally changing our understanding of life on Earth.
  • Albert Einstein: Revolutionized our perception of space, time, and gravity with his theory of relativity, forever altering our understanding of the universe.

Why is science important?

  • Understanding the World: Science provides a framework for understanding the natural world, from the tiniest subatomic particles to the vast expanse of the cosmos.
  • Technological Advancements: Scientific discoveries fuel technological innovations that improve our lives in countless ways, from medicine to communication.
  • Problem-Solving: The scientific approach, emphasizing systematic investigation and analysis, can be applied to tackle complex problems across various fields.
  • Improved Healthcare: Scientific advancements lead to new medical treatments, vaccines, and diagnostics, promoting a healthier future for all.

How is science applied in practice?

  • Space Exploration: Understanding the universe, searching for life on other planets, and developing technologies for space travel.
  • Medicine: Developing new drugs, vaccines, and treatments for diseases, constantly improving healthcare and life expectancy.
  • Climate Change Mitigation: Conducting
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What is research methods?

What is research methods?

Research methods are a crucial toolkit used across various disciplines. It's the art and science of:

  • Extracting Meaningful Insights: Transforming raw data into knowledge by choosing appropriate methods for data collection and analysis.
  • Designing Effective Studies: Developing research plans that answer specific questions in a reliable and unbiased way.
  • Evaluating Research: Critically assessing the strengths and weaknesses of research studies to interpret their findings accurately.

What are the main features of research methods?

  • Data-Driven Decisions: Research methods equip researchers with the skills to base conclusions on evidence, not just intuition.
  • Problem-Solving: They help formulate research questions, identify relevant data, and analyze it to find solutions to complex issues.
  • Rigorous and Systematic: Research methods emphasize well-defined procedures for data collection and analysis to ensure the credibility of findings.

What are important sub-areas in research methods?

  • Quantitative Research: Focuses on numerical data collection and analysis using statistical techniques (e.g., surveys, experiments).
  • Qualitative Research: Explores experiences, meanings, and social phenomena through non-numerical methods (e.g., interviews, focus groups).
  • Mixed Methods: Combines both quantitative and qualitative approaches for a more comprehensive understanding of a research topic.
  • Data Analysis: The process of cleaning, organizing, interpreting, and visualizing data to extract meaningful insights.
  • Research Design: Choosing the appropriate research strategy (e.g., experiment, survey, case study) based on the research question.

What are key concepts in research methods?

  • Variables: The elements you're measuring or analyzing in your research (e.g., age, income, satisfaction level).
  • Data Collection: The process of gathering information relevant to your research question.
  • Data Analysis: Methods used to organize, summarize, and interpret data to draw conclusions.
  • Validity: The extent to which a research study measures what it intends to measure.
  • Reliability: The consistency and trustworthiness of research findings if the study were repeated under similar conditions.
  • Ethics: Ensuring research is conducted with respect for participants' rights and well-being.

Who are influential figures in research methods?

  • Sir Francis Galton: A pioneer in statistics and research design, known for his work on correlation and regression analysis.
  • John W. Tukey: Championed exploratory data analysis and visualization techniques.
  • W. Edwards Deming: An advocate for data-driven decision making in quality management.
  • Jane Addams: A social reformer and sociologist who used qualitative research methods to study poverty and social issues.
  • Howard S. Becker: A sociologist who emphasized the importance of participant observation in qualitative research.

Why are research methods important?

  • Unveiling the Truth: Research methods help us discover facts, understand relationships, and build knowledge across all disciplines.
  • Informed Decisions: Individuals and organizations can make better choices based on evidence gathered through research methods.
  • Problem-Solving and Innovation: Research methodologies are crucial for identifying problems, developing solutions, and driving innovation.
  • Evaluation and Improvement: Research methods allow us to evaluate the effectiveness of programs, policies, and interventions and make necessary improvements.

How are research methods applied in practice?

  • Business Research: Understanding customer preferences, market trends, and competitor analysis for informed business decisions.
  • Scientific Research: Designing experiments, collecting data, and analyzing results to test hypotheses and develop scientific
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What is research design?

What is research design?

While research design is a fundamental aspect of research methods. It focuses on the planning and structuring of an investigation to answer a specific research question effectively.

What are the main features of research design?

  • Purposeful Approach: Choosing the most appropriate design (e.g., experiment, survey, case study) to address the research question.
  • Control and Bias: Designing a study that minimizes bias and allows for drawing valid conclusions.
  • Ethical Considerations: Ensuring the research design adheres to ethical guidelines for participant selection and data collection.

What are important sub-areas in research design?

  • Quantitative Designs:

    • Experimental Design: Manipulating variables to observe cause-and-effect relationships.
    • Survey Research: Collecting data from a large sample through questionnaires or interviews.
    • Quasi-Experimental Design: Similar to experiments but with less control over variables.

  • Qualitative Designs:

    • Case Studies: In-depth exploration of a single individual, group, or event.
    • Ethnography: Immersive study of a culture or social group through observation and participation.
    • Phenomenological Research: Understanding the lived experiences of individuals from their perspective.

  • Mixed Methods Design: Combining quantitative and qualitative approaches for a more holistic understanding.

What are key concepts in research design?

  • Research Question: The specific question the study aims to answer.
  • Variables: The elements you're measuring or analyzing in your research (e.g., age, income, satisfaction level).
  • Independent and Dependent Variables: In experiments, the independent variable is manipulated to observe its effect on the dependent variable.
  • Validity: The extent to which a research design measures what it intends to measure.
  • Reliability: The consistency of the research design if the study were repeated under similar conditions.
  • Sample and Population: The sample is the group you're studying, representing the larger population you're interested in.

Who are influential figures in research design?

  • Sir Ronald Fisher: A pioneer of experimental design and statistical analysis.
  • Donald Campbell: Developed influential frameworks for evaluating research designs.
  • John W. Creswell: A prominent researcher known for his work on mixed methods research design.
  • Robert K. Yin: A leading figure in case study research methodology.
  • Anselm Strauss: A sociologist who contributed significantly to qualitative research design, particularly grounded theory.

Why is research design important?

  • Foundation for Reliable Findings: A well-designed research study ensures the data collected is relevant and leads to trustworthy conclusions.
  • Optimizing Resource Allocation: Designing an efficient study helps manage resources (time, money, personnel) effectively.
  • Addressing Bias: A strong research design minimizes bias and allows for more objective conclusions.
  • Replication and Generalizability: A solid design facilitates the replication of the study by others and the generalizability of findings to a wider population.

How is research design applied in practice?

  • All Research Fields: Research design is crucial for any study, from scientific research and social science investigations to business research and educational research.
  • Public Policy Development: Informing policy decisions by designing studies that evaluate the effectiveness of existing policies or potential interventions.
  • Program Evaluation: Research design plays a key role in assessing the impact of programs and interventions in various domains.
  • Marketing and Product
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What is statistics?

What is statistics?

Statistics is the science of data, encompassing its collection, analysis, interpretation, and communication to extract knowledge and inform decision-making.

This definition focuses on the core aspects of the field:

  • Data-driven: Statistics revolves around analyzing and interpreting data, not just manipulating numbers.
  • Knowledge extraction: The goal is to gain insights and understanding from data, not just generate summaries.
  • Decision-making: Statistics informs and empowers informed choices in various settings.

Statistics has a wide application:

1. Design and Inference:

  • Designing studies: Statisticians use statistical principles to design experiments, surveys, and observational studies that allow for reliable inferences.
  • Drawing conclusions: Statistical methods help estimate population parameters from sample data, accounting for uncertainty and variability.

2. Modeling and Analysis:

  • Identifying relationships: Statistical models reveal patterns and relationships among variables, aiding in understanding complex systems.
  • Quantitative analysis: Various statistical techniques, from regression to machine learning, enable deep analysis of data structures and trends.

3. Interpretation and Communication:

  • Meaningful conclusions: Statisticians go beyond numbers to draw meaningful and context-specific conclusions from their analyses.
  • Effective communication: Clear and concise communication of findings, including visualizations, is crucial for informing stakeholders and advancing knowledge.

Applications across disciplines:

These core principles of statistics find diverse applications in various academic fields:

  • Social sciences: Understanding societal patterns, testing hypotheses about human behavior, and evaluating policy interventions.
  • Natural sciences: Analyzing experimental data, modeling physical phenomena, and drawing inferences about natural processes.
  • Business and economics: Forecasting market trends, evaluating business strategies, and guiding investment decisions.
  • Medicine and public health: Analyzing clinical trials, identifying risk factors for disease, and informing healthcare policies.

Ultimately, statistics plays a crucial role in numerous academic disciplines, serving as a powerful tool for extracting knowledge, informing decisions, and advancing human understanding.

  • 2115 reads
Statistics: Magazines for encountering Statistics

Statistics: Magazines for encountering Statistics

Startmagazine: Introduction to Statistics
Recognizing commonly used statistical symbols: greek, latin and mathematical
Stats for students: Simple steps for passing your statistics courses

Stats for students: Simple steps for passing your statistics courses

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Stats of studentsTheory of statistics

  • The first years that you follow statistics, it is often a case of taking knowledge for granted and simply trying to pass the courses. Don't worry if you don't understand everything right away: in later years it will fall into place and you will see the importance of the theory you had to know before.
  • The book you need to study may be difficult to understand at first. Be patient: later in your studies, the effort you put in now will pay off.
  • Be a Gestalt Scientist! In other words, recognize that the whole of statistics is greater than the sum of its parts. It is very easy to get hung up on nit-picking details and fail to see the forest because of the trees
  • Tip: Precise use of language is important in research. Try to reproduce the theory verbatim (ie. learn by heart) where possible. With that, you don't have to understand it yet, you show that you've been working on it, you can't go wrong by using the wrong word and you practice for later reporting of research.
  • Tip: Keep study material, handouts, sheets, and other publications from your teacher for future reference.

Formulas of statistics

  • The direct relationship between data and results consists of mathematical formulas. These follow their own logic, are written in their own language and can therefore be complex to comprehend.
  • If you don't understand the math behind statistics, you don't understand statistics. This does not have to be a problem, because statistics is an applied science from which you can also get excellent results without understanding. None of your teachers will understand all the statistical formulas.
  • Please note: you will have to know and understand a number of formulas, so that you can demonstrate that you know the principle of how statistics work. Which formulas you need to know differs from subject to subject and lecturer to lecturer, but in general these are relatively simple formulas that occur frequently and your lecturer will tell you (often several times) that you should know this formula.
  • Tip: if you want to recognize statistical symbols you can use: Recognizing commonly used statistical symbols
  • Tip: have fun with LaTeX! LaTeX code gives us a simple way to write out mathematical formulas and make them look professional. Play with LaTeX. Wit that, you can include used formulas in your own papers and you learn to understand how a formula is built up – which greatly benefits your understanding and remembering that formula. See also (in Dutch): How to create formulas like a pro on JoHo WorldSupporter?
  • Tip: Are you interested in a career in sciences or programming? Then take your formulas seriously and go through them again after your course.

Practice of statistics

Selecting data

  • Your teacher will regularly use a dataset for lessons during the first years of your studying. It is instructive (and can be a lot of fun) to set up your own research for once with real data that is also used by other researchers.
  • Tip: scientific articles often indicate which datasets have been used for the research. There is a good chance that those datasets are valid. Sometimes there are also studies that determine which datasets are more valid for the topic you want to study than others. Make use of datasets other researchers point out.
  • Tip: Do you want an interesting research result? You can use the same method and question, but use an alternative dataset, and/or alternative variables, and/or alternative location, and/or alternative time span. This allows you to validate or falsify the results of earlier research.
  • Tip: for datasets you can look at Discovering datasets for statistical research

Operationalize

  • For the operationalization, it is usually sufficient to indicate the following three things:
    • What is the concept you want to study?
    • Which variable does that concept represent?
    • Which indicators do you select for those variables?
  • It is smart to argue that a variable is valid, or why you choose that indicator.
  • For example, if you want to know whether someone is currently a father or mother (concept), you can search the variables for how many children the respondent has (variable) and then select on the indicators greater than 0, or is not 0 (indicators). Where possible, use the terms 'concept', 'variable', 'indicator' and 'valid' in your communication. For example, as follows: “The variable [variable name] is a valid measure of the concept [concept name] (if applicable: source). The value [description of the value] is an indicator of [what you want to measure].” (ie.: The variable "Number of children" is a valid measure of the concept of parenthood. A value greater than 0 is an indicator of whether someone is currently a father or mother.)

Running analyses and drawing conclusions

  • The choice of your analyses depends, among other things, on what your research goal is, which methods are often used in the existing literature, and practical issues and limitations.
  • The more you learn, the more independently you can choose research methods that suit your research goal. In the beginning, follow the lecturer – at the end of your studies you will have a toolbox with which you can vary in your research yourself.
  • Try to link up as much as possible with research methods that are used in the existing literature, because otherwise you could be comparing apples with oranges. Deviating can sometimes lead to interesting results, but discuss this with your teacher first.
  • For as long as you need, keep a step-by-step plan at hand on how you can best run your analysis and achieve results. For every analysis you run, there is a step-by-step explanation of how to perform it; if you do not find it in your study literature, it can often be found quickly on the internet.
  • Tip: Practice a lot with statistics, so that you can show results quickly. You cannot learn statistics by just reading about it.
  • Tip: The measurement level of the variables you use (ratio, interval, ordinal, nominal) largely determines the research method you can use. Show your audience that you recognize this.
  • Tip: conclusions from statistical analyses will never be certain, but at the most likely. There is usually a standard formulation for each research method with which you can express the conclusions from that analysis and at the same time indicate that it is not certain. Use that standard wording when communicating about results from your analysis.
  • Tip: see explanation for various analyses: Introduction to statistics
  • 2712 reads
Statistics: Magazines for understanding statistics

Statistics: Magazines for understanding statistics

Startmagazine: Introduction to Statistics
Understanding data: distributions, connections and gatherings
Understanding reliability and validity
Statistics Magazine: Understanding statistical samples
Understanding distributions in statistics
Understanding variability, variance and standard deviation
Understanding inferential statistics
Understanding type-I and type-II errors
Understanding effect size, proportion of explained variance and power of tests to your significant results
Statistiek: samenvattingen en studiehulp - Thema
  • 2673 reads
Statistics: Magazines for applying statistics

Statistics: Magazines for applying statistics

Applying z-tests and t-tests
Applying correlation, regression and linear regression
Applying spearman's correlation
Applying multiple regression
Applying logistic regression
Statistiek: samenvattingen en studiehulp - Thema
  • 2965 reads

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Icon Wat is correlatie?
Wat is correlatie?

Correlatie beschrijft de statistische samenhang tussen twee variabelen. Het geeft aan in hoeverre de waarden van de ene variabele gepaard gaan met de waarden van de andere variabele. Correlatiecoëfficiënten variëren tussen -1 en +1:

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Wat is causaliteit?

Causaliteit, ook wel oorzakelijkheid genoemd, beschrijft de relatie tussen oorzaak en gevolg. Het is het idee dat een gebeurtenis (de oorzaak) een andere gebeurtenis (het gevolg) teweegbrengt. Causaliteit is een centraal concept in veel wetenschappelijke disciplines, van natuurkunde tot sociologie. ...

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Wat is confounding?

Confounding, ook wel verstoring genoemd, is een statistisch fenomeen dat optreedt wanneer er een extra variabele in een onderzoek aanwezig is die niet wordt gecontroleerd, maar die de relatie tussen de onafhankelijke en afhankelijke variabelen beïnvloedt. Dit kan leiden tot verwarrende resultat...

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Science and research: The best concepts summarized

Science and research: The best concepts summarized Table of contents

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Icon What is science?
What is science?

Science, a rigorous and systematic endeavor, seeks to build a comprehensive understanding of the natural world and our place within it. It's a never-ending quest to:

  • Gather Knowledge: Using observation, experimentation, and analysis, science builds a vast and ever-growing body of knowledge....

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What is academic research?

Academic research is the cornerstone of higher education, equipping researchers with the skills to:

  • Ask Meaningful Questions: Identify gaps in knowledge and formulate research questions that drive inquiry.
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UvA Methodologiewinkel Wiki

Hulp nodig bij Statistiek en Onderzoeksmethoden? Check out de Methodologiewinkel van de Universiteit van Amsterdam! Op deze wiki vind je informatie over de statistische aspecten van wetenschappelijk onderzoek. Deze wiki is gemaakt door researchmaster studenten van de opleiding psychologie. Zij hebbe...

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How can you check the quality of your scientific article?

Here are some tips on how to check the quality of your academic writing, tailored to different levels of experience: Beginners:

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Op deze pagina verzamel ik alle samenvattingen, oefententamens, SPSS-instructies en college aantekeningen van Toetsende Statistiek die ik heb gevonden op WorldSupporter! Hier vind je alle oefententamens:

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