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Chloé Schwizgebel,
Jan Dul,
Wilfred Knol,
Jon Bokrantz,
and
Nicole Franziska Richter
Welcome to Necessary Condition Analysis (NCA).
NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed.
Read more
Welcome to Necessary Condition Analysis (NCA).
NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed.
Read more
Welcome to Necessary Condition Analysis (NCA).
NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed.
Examples of necessary conditions are a student’s GMAT score for admission to a PhD program; a student will not be admitted to a PhD program when his GMAT score is too low. Intelligence for creativity, as creativity will not exist without intelligence, and management commitment for organizational change, as organizational change will not occur without management commitment.
NCA can be used with existing or new data sets and can give novel insights for theory and practice. You can apply NCA as a stand-alone approach, or as part of a multi-method approach complementing multiple linear regression (MLR), structural equation modelling (SEM) or Qualitative Comparative Analysis (QCA).
This course explains the basic elements of NCA and uses illustrative examples on how to perform NCA with R software. Topics include (i) Setting up an NCA study (ii) Run NCA and (iii) Present the results of NCA.
We hope you enjoy the course!
Register for this course and see more details by visiting:
OpenCourser.com/course/nr83g5/necessary
What's inside
Syllabus
Week 1 - Introduction to Necessary Condition Analysis
Professor Jan Dul, founder of NCA, welcomes you and starts off with a quick introduction of necessity logic and Necessary Condition Analysis (NCA). The first week will explain necessity logic, why it is important and how it is different from other sorts of logic such as Boolean and additive logic. Furthermore, the basics of NCA and its benefits are explained. We invite you to go through the videos and readings to improve your understanding of necessity logic and NCA.
Read more
Syllabus
Week 1 - Introduction to Necessary Condition Analysis
Professor Jan Dul, founder of NCA, welcomes you and starts off with a quick introduction of necessity logic and Necessary Condition Analysis (NCA). The first week will explain necessity logic, why it is important and how it is different from other sorts of logic such as Boolean and additive logic. Furthermore, the basics of NCA and its benefits are explained. We invite you to go through the videos and readings to improve your understanding of necessity logic and NCA.
Read more
Week 2 - Setting up an NCA study
In week 2 you will be guided through the process of setting up an NCA study. First, you will deep dive into the formulation of necessary condition hypotheses that can be analyzed with NCA. Next, general research practices of sampling and measurement will be discussed. After this week you will be able to start conducting research with NCA.
Week 3 - Data analysis with NCA
In this module, you will examine how an NCA is ran in R, a programming language for statistical computing and graphics. Key elements will be explained such as the identification of the empty spaces in scatter plots. Once you can run an NCA in R, it is important to be able to interpret the results of the analysis, such as the effect size and the p-value. This week will also provide you an opportunity to practice with NCA.
Week 4 - Reporting the results of NCA
After finishing the first three weeks of this MOOC, you are now able to conduct an NCA. Crucial to every research method is getting across the message of your research. This module will therefore explain how you can convincingly report the results of your NCA study and reflect on the strengths and the weaknesses of the method.
Week 5 - Advanced Topics of NCA
In this final week of the NCA MOOC, you will be challenged with the more advanced topics. The short videos will cover topics like analyzing other corners in the scatter plot, analyzing outliers approach, how to conduct NCA in small N cases study or qualitative research and how is NCA different from QCA. After finishing this week you will have a more enhanced understanding of the analysis and moreover, will be able to start on your own NCA research!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores necessary condition analysis, which is an innovative approach in various research fields
Provides a comprehensive overview of NCA, covering its theoretical foundations, practical applications, and advanced topics
Led by experts Jan Dul, Wilfred Knol, and Nicole Franziska Richter, all of whom are recognized for their significant contributions to NCA
Emphasizes hands-on learning through interactive exercises and practical examples, enabling learners to apply NCA concepts in real-world scenarios
Suitable for researchers, students, and practitioners seeking to enhance their understanding and skills in NCA
Save this course
Save Necessary Condition Analysis (NCA) to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Necessary Condition Analysis (NCA) with these
activities:
Read and Review: Introduction to Data Science by John Stanton
Show steps
This book provides a comprehensive overview of data science, including data collection, analysis, and visualization. It is a good resource for students who want to learn more about the basics of data analysis.
View
Maximizing Value with Automation and Digital...
on Amazon
Show steps
Create a Mind Map of NCA Concepts
Show steps
Creating a mind map of NCA concepts will help you to visualize the relationships between the different concepts and to better understand the overall structure of the theory.
Show steps
-
Review the NCA course materials.
-
Identify the key concepts of NCA.
-
Create a mind map that connects the key concepts.
-
Review the mind map and add any additional concepts or connections that you identify.
Develop Research Questions and Hypotheses
Show steps
Developing research questions and hypotheses is a critical skill for any researcher. This activity will provide you with practice in developing NCA-specific research questions and hypotheses.
Show steps
-
Understand the basics of NCA and its applications.
-
Develop a familiarity with the necessity logic.
-
Formulate a research question that can be addressed with NCA.
-
Develop a hypothesis that can be tested with NCA.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Learn and Use R for NCA
Show steps
R is the primary tool for running necessary condition analysis in scientific research, being able to use R for NCA will allow you to conduct NCA independently on your own data.
Show steps
-
Install R and RStudio on your computer.
-
Learn the basics of R, including data types, data structures, and basic operations.
-
Learn how to use the NCA package in R.
-
Apply your knowledge of R and NCA to analyze a dataset.
Analyzing the Scatter Plots
Show steps
It is important to be able to analyze scatter plots and identify patterns in the data. This is a key skill for NCA, as it will help you to identify necessary conditions and to develop research hypotheses.
Browse courses on
Scatter Plots
Show steps
-
Load the dataset into a statistical software package.
-
Draw a scatter plot of the two variables.
-
Identify the outliers, empty spaces, and patterns in the scatter plot.
-
Based on observations, formulate necessary condition hypothesis.
Practice Data Analysis Exercises
Show steps
Practice data analysis exercises to reinforce your understanding of NCA concepts and techniques.
Browse courses on
Data Analysis
Show steps
-
Review the provided NCA exercise datasets and questions.
-
Analyze the datasets using the NCA methods learned in the course.
-
Check your answers against the provided solutions.
Practice Necessary Condition Analysis (NCA) Problems
Show steps
Practicing NCA problems will help you to develop a better understanding of the concepts and how to apply them to real-world data.
Show steps
-
Find a set of data that you want to analyze using NCA.
-
Formulate a necessary condition hypothesis that you want to test.
-
Run the NCA analysis using the R code provided in the course materials.
-
Interpret the results of the analysis, including the effect size and the p-value.
Record Yourself Explaining a Concept
Show steps
Recording yourself explaining a concept will help you to clarify your understanding of the concept and to identify any areas where you need further study.
Show steps
-
Choose a concept from the NCA course materials.
-
Record yourself explaining the concept in a clear and concise way.
-
Edit the recording and add any additional visuals or audio effects that you think will help to improve the explanation.
-
Share the recording with others and get feedback.
Create an NCA Research Proposal
Show steps
Creating an NCA research proposal will help you to develop a deeper understanding of the research process and to identify the steps that you need to take to conduct a successful NCA study.
Show steps
-
Identify a research question that you want to answer using NCA.
-
Develop a research hypothesis and a research design.
-
Write a research proposal that outlines your research question, hypothesis, design, and methods.
-
Get feedback on your research proposal from your instructor or other experts.
Develop NCA Case Study
Show steps
Create a case study that applies NCA to a real-world problem, demonstrating your understanding of the method's potential.
Browse courses on
Case study
Show steps
-
Identify a research question or problem that can be addressed with NCA.
-
Gather data and prepare it for NCA analysis.
-
Conduct the NCA analysis and interpret the results.
-
Write up your findings in a clear and concise case study.
Read and Review: Introduction to Data Science by John Stanton
Show steps
This book provides a comprehensive overview of data science, including data collection, analysis, and visualization. It is a good resource for students who want to learn more about the basics of data analysis.
View
Maximizing Value with Automation and Digital...
on Amazon
Show steps
Show steps
Show steps
Create a Mind Map of NCA Concepts
Show steps
Creating a mind map of NCA concepts will help you to visualize the relationships between the different concepts and to better understand the overall structure of the theory.
Show steps
Show steps
Show steps
- Review the NCA course materials.
- Identify the key concepts of NCA.
- Create a mind map that connects the key concepts.
- Review the mind map and add any additional concepts or connections that you identify.
Develop Research Questions and Hypotheses
Show steps
Developing research questions and hypotheses is a critical skill for any researcher. This activity will provide you with practice in developing NCA-specific research questions and hypotheses.
Show steps
Show steps
Show steps
- Understand the basics of NCA and its applications.
- Develop a familiarity with the necessity logic.
- Formulate a research question that can be addressed with NCA.
- Develop a hypothesis that can be tested with NCA.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Learn and Use R for NCA
Show steps
R is the primary tool for running necessary condition analysis in scientific research, being able to use R for NCA will allow you to conduct NCA independently on your own data.
Show steps
Show steps
Show steps
- Install R and RStudio on your computer.
- Learn the basics of R, including data types, data structures, and basic operations.
- Learn how to use the NCA package in R.
- Apply your knowledge of R and NCA to analyze a dataset.
Analyzing the Scatter Plots
Show steps
It is important to be able to analyze scatter plots and identify patterns in the data. This is a key skill for NCA, as it will help you to identify necessary conditions and to develop research hypotheses.
Browse courses on
Scatter Plots
Show steps
Show steps
Show steps
- Load the dataset into a statistical software package.
- Draw a scatter plot of the two variables.
- Identify the outliers, empty spaces, and patterns in the scatter plot.
- Based on observations, formulate necessary condition hypothesis.
Practice Data Analysis Exercises
Show steps
Practice data analysis exercises to reinforce your understanding of NCA concepts and techniques.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review the provided NCA exercise datasets and questions.
- Analyze the datasets using the NCA methods learned in the course.
- Check your answers against the provided solutions.
Practice Necessary Condition Analysis (NCA) Problems
Show steps
Practicing NCA problems will help you to develop a better understanding of the concepts and how to apply them to real-world data.
Show steps
Show steps
Show steps
- Find a set of data that you want to analyze using NCA.
- Formulate a necessary condition hypothesis that you want to test.
- Run the NCA analysis using the R code provided in the course materials.
- Interpret the results of the analysis, including the effect size and the p-value.
Record Yourself Explaining a Concept
Show steps
Recording yourself explaining a concept will help you to clarify your understanding of the concept and to identify any areas where you need further study.
Show steps
Show steps
Show steps
- Choose a concept from the NCA course materials.
- Record yourself explaining the concept in a clear and concise way.
- Edit the recording and add any additional visuals or audio effects that you think will help to improve the explanation.
- Share the recording with others and get feedback.
Create an NCA Research Proposal
Show steps
Creating an NCA research proposal will help you to develop a deeper understanding of the research process and to identify the steps that you need to take to conduct a successful NCA study.
Show steps
Show steps
Show steps
- Identify a research question that you want to answer using NCA.
- Develop a research hypothesis and a research design.
- Write a research proposal that outlines your research question, hypothesis, design, and methods.
- Get feedback on your research proposal from your instructor or other experts.
Develop NCA Case Study
Show steps
Create a case study that applies NCA to a real-world problem, demonstrating your understanding of the method's potential.
Browse courses on
Case study
Show steps
Show steps
Show steps
- Identify a research question or problem that can be addressed with NCA.
- Gather data and prepare it for NCA analysis.
- Conduct the NCA analysis and interpret the results.
- Write up your findings in a clear and concise case study.
Career center
Learners who complete Necessary Condition Analysis (NCA) will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and education. NCA can help Statisticians to develop better statistical models and to make more accurate predictions. Additionally, NCA can be used to identify outliers and other anomalies in data. This information can be used to improve the quality of statistical analysis and to make more informed decisions.
Data Analyst
Data Analyst
A Data Analyst uses data to analyze trends and patterns. They also use this information to make recommendations for how to improve business processes. NCA can help Data Analysts to make more accurate predictions by providing them with a better understanding of the relationship between different variables. Additionally, NCA can help to identify outliers and other anomalies in data. This information can be used to improve the quality of data analysis and to make more informed decisions.
Business Analyst
Business Analyst
Business Analysts analyze business processes and make recommendations for how to improve them. They use a variety of tools and techniques to gather and analyze data. NCA can help Business Analysts to identify bottlenecks and other inefficiencies in business processes. Additionally, NCA can be used to develop and evaluate new business processes. This information can be used to improve the efficiency and effectiveness of business operations.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers and markets. They use this information to help businesses make better decisions about products, services, and marketing campaigns. NCA can help Market Researchers to segment markets and to identify target customers. Additionally, NCA can be used to track changes in consumer behavior over time. This information can be used to develop more effective marketing campaigns.
Financial Analyst
Financial Analyst
Financial Analysts provide advice and guidance to businesses and individuals on financial matters. They use their expertise to help clients make sound financial decisions. NCA can help Financial Analysts to develop better financial models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in financial markets. This information can be used to make more informed financial decisions.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics. They use their expertise to help businesses solve problems and achieve their goals. NCA can help Consultants to develop better solutions for their clients. Additionally, NCA can be used to evaluate the effectiveness of consulting interventions. This information can be used to improve the quality of consulting services.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use their expertise to create software that meets the needs of users. NCA can help Software Engineers to develop better software systems. Additionally, NCA can be used to evaluate the effectiveness of software systems. This information can be used to improve the quality of software development and to make more informed decisions about software design.
Data Scientist
Data Scientist
Data Scientists use data to develop new products and services. They also use data to improve the efficiency and effectiveness of existing business processes. NCA can help Data Scientists to develop better statistical models and to make more accurate predictions. Additionally, NCA can be used to identify outliers and other anomalies in data. This information can be used to improve the quality of data analysis and to make more informed decisions.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use data to make predictions about future events. They use a variety of statistical and mathematical techniques to develop models that can be used to make informed decisions. NCA can help Quantitative Researchers to develop better models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in financial markets. This information can be used to make more informed investment decisions.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. They use this information to develop insurance products and to make other financial decisions. NCA can help Actuaries to develop better risk models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in financial markets. This information can be used to make more informed financial decisions.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. They use this information to make predictions about the behavior of biological systems. NCA can help Biostatisticians to develop better models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in biological systems. This information can be used to make more informed decisions about biological research.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems. They use a variety of statistical and mathematical techniques to analyze data and make predictions. NCA can help Epidemiologists to develop better models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in public health. This information can be used to make more informed decisions about public health policy.
Economist
Economist
Economists use economic theory and data to analyze economic issues. They use this information to make predictions about the behavior of the economy. NCA can help Economists to develop better models and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in the economy. This information can be used to make more informed decisions about economic policy.
Psychologist
Psychologist
Psychologists study the mind and behavior. They use a variety of research methods, including NCA, to understand how people think, feel, and behave. NCA can help Psychologists to develop better theories and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in the field of psychology. This information can be used to make more informed decisions about psychological research and practice.
Sociologist
Sociologist
Sociologists study society and how it works. They use a variety of research methods, including NCA, to understand social phenomena. NCA can help Sociologists to develop better theories and to make more accurate predictions. Additionally, NCA can be used to identify risks and opportunities in society. This information can be used to make more informed decisions about social policy.
For more career information including salaries, visit:
OpenCourser.com/course/nr83g5/necessary
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Necessary Condition Analysis (NCA).
Save
This classic work in modal logic provides a rigorous foundation for understanding the concepts of necessity and possibility. It is essential reading for anyone who wants to understand the theoretical underpinnings of NCA.
Provides a rigorous introduction to logic and probability theory. It valuable resource for researchers and students who want to learn more about the foundations of NCA.
Save
Provides a comprehensive overview of reasoning in political science. It discusses a variety of methods for political inquiry, including NCA.
Provides a clear and concise introduction to causal analysis in social research. It great resource for researchers and students who want to learn more about the basic concepts and methods of causal inference.
Provides a comprehensive overview of social research methods. It valuable resource for researchers and students who want to learn more about the different methods for collecting and analyzing data.
Provides a critical assessment of NCA. It valuable resource for anyone who wants to learn more about the strengths and weaknesses of NCA.
Save
Provides a formal theory of conditionals in natural language. It valuable resource for researchers and students who want to learn more about the semantics of conditionals.
Save
Provides a comprehensive overview of the logic of necessity. It valuable resource for researchers and students who want to learn more about the logical foundations of necessity logic and NCA.
Save
Provides a comprehensive overview of causal inference in nonexperimental research. It valuable resource for researchers and students who want to learn more about the different methods for causal inference in observational studies.
Provides a comprehensive overview of reasoning about uncertainty. It valuable resource for researchers and students who want to learn more about the logical foundations of necessity logic and NCA.
Provides a clear and concise introduction to causal inference in statistics. It great resource for researchers and students who want to learn more about the basic concepts and methods of causal inference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nr83g5/necessary
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Level
Introductory
Via
Coursera
Institution
Erasmus University Rotterdam
Instructors
Chloé Schwizgebel
Jan Dul
Wilfred Knol
Jon Bokrantz
Nicole Franziska Richter
Language
English
Good to know
Explores necessary condition analysis, which is an innovative approach in various research fields
Provides a comprehensive overview of NCA, covering its theoretical foundations, practical applications, and advanced topics
Led by experts Jan Dul, Wilfred Knol, and Nicole Franziska Richter, all of whom are recognized for their significant contributions to NCA
Emphasizes hands-on learning through interactive exercises and practical examples, enabling learners to apply NCA concepts in real-world scenarios
Suitable for researchers, students, and practitioners seeking to enhance their understanding and skills in NCA
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