Measures of Correlation and Contingency from Coursera

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Learn With PsycLearn Essentials

This module introduces you to your PsycLearn Essentials course. Find out what’s included in this course and how to navigate the modules and lessons. You’ll also learn valuable study tips for successful learning.

Measures of Correlation and Contingency

This course provides a comprehensive exploration of statistical relationships, focusing on the principles and applications of correlation and contingency tables. Students will learn to identify appropriate scenarios for using correlation, understand its logic, and describe its direction and strength. The course also covers the use of contingency tables, teaching students to recognize patterns of association within them.

The Meaning of Correlation and Contingency

What kinds of relationships are there between variables? As you will see in this module, the answer depends on the kinds of variables. How do we envision those relationships? This module explains what we can do with a data set to begin to see the relationship, if any, between two variables? And if there is a relationship, how do we interpret it? This module delves into what we can and cannot conclude when we observe a relationship between two variables.

Correlation and Regression

In this module, we look in more detail at how correlation is used to examine the relationship between two variables, along with how related regression procedures can be used to specifically characterize how the value of one variable can be used to predict the value of the other.

Contingency Table Analysis

In this module, we look in more detail at how contingency tables are used to examine the relationship between two nominal- or ordinal-level variables, along with some measures of the strength of a contingency relationship.

Conclusion

PsycLearn Essentials APA Student Resources

This module provides a variety of information and tools from the American Psychological Association (APA) that will help inspire you as you complete your coursework and plan your career goals. Explore APA resources on various psychological issues and scholarly research and writing; a list of sites providing valuable resources on diversity, equity, and inclusion in psychology education and in the professional community; resources on a career in psychology; and links to career opportunities at the APA.

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Provides APA student resources, which may be useful for students completing coursework and planning their careers

Explores statistical relationships, which are fundamental to research and data analysis in psychology

Presented by the American Psychological Association, a leading organization in the field of psychology

Examines contingency table analysis, which is used to examine the relationship between two nominal- or ordinal-level variables

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 Measures of Correlation and Contingency with these activities:

Review Basic Statistics Concepts

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Reinforce foundational statistical concepts like variables, distributions, and hypothesis testing to better understand correlation and contingency.

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Review definitions of variables and data types.
Practice calculating descriptive statistics (mean, median, mode).
Review the concept of statistical significance.

Review 'Statistics for the Behavioral Sciences'

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Solidify understanding of statistical principles and their application in behavioral sciences.

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Read chapters on correlation and regression.
Work through practice problems related to correlation coefficients.
Review the assumptions underlying correlation and regression.

Calculate Correlation Coefficients

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Practice calculating Pearson's r and Spearman's rho to improve proficiency in quantifying relationships between variables.

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Find datasets with two continuous variables.
Calculate Pearson's r using a statistical software package.
Calculate Spearman's rho for ordinal data.
Interpret the strength and direction of the correlations.

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Create a Contingency Table Analysis Report

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Reinforce understanding of contingency tables by creating a report analyzing a real-world dataset.

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Find a dataset with two categorical variables.
Create a contingency table using statistical software.
Calculate chi-square statistic and interpret the results.
Write a report summarizing the findings and their implications.

Review 'Discovering Statistics Using IBM SPSS Statistics'

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Learn how to perform correlation and contingency analyses using SPSS.

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Read chapters on correlation and contingency tables in SPSS.
Follow the examples in the book to perform analyses on sample datasets.
Practice interpreting the SPSS output.

Correlation and Contingency in Research

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Apply knowledge of correlation and contingency to analyze a research question using real-world data.

Browse courses on Correlation

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Identify a research question involving two variables.
Find a relevant dataset.
Perform appropriate correlation or contingency analysis.
Interpret the results and draw conclusions.
Write a research report summarizing the findings.

Tutor Students in Correlation and Contingency

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Solidify your understanding by explaining correlation and contingency concepts to other students.

Browse courses on Correlation

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Offer tutoring sessions to students struggling with the material.
Prepare explanations and examples to illustrate key concepts.
Answer questions and provide feedback on practice problems.

Career center

Learners who complete Measures of Correlation and Contingency will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians develop and apply statistical theories and methods to collect, analyze, and interpret quantitative data. A statistician will regularly employ correlation and contingency analysis techniques. This course, covering in depth the principles and applications of correlation and contingency tables, is directly relevant to the daily work of a statistician. The course material discussing the logic, direction, and strength of correlation is helpful. The emphasis on recognizing patterns of association in contingency tables is also essential.

See salaries and explore the career path for Statistician

Research Analyst

A research analyst collects, analyzes, and interprets data to support research projects. This role benefits from understanding correlation and contingency, which are fundamental statistical concepts covered in this course. The course will help you to identify relationships between different variables and to analyze contingency tables to find significant patterns. This will serve any research analyst. This course provides a solid introduction to the statistical methods that is beneficial to a research analyst.

See salaries and explore the career path for Research Analyst

Market Research Analyst

A market research analyst studies consumer behavior to provide insights that guide business strategy. This role involves understanding relationships between various factors, such as demographics and consumer preferences. This course, focusing on measures of correlation and contingency, is very helpful to analyze the data and determine the strength and direction of these relationships. This course will help you learn to identify patterns with contingency tables, which is often at the heart of evaluating market segments. A market research analyst must use data to draw conclusions. This course will help build a foundation in key data analysis tools.

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Data Scientist

Data scientists use statistical techniques to analyze complex data sets and extract actionable insights. A data scientist must be adept at identifying correlations between variables, and this course specifically teaches about these techniques. The course's focus on contingency tables will further help a data scientist to recognize patterns and relationships in their data. This course helps you learn how to determine when correlation and other statistical methods are appropriate, so it is helpful in a data scientist role.

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Psychometrician

Psychometricians specialize in the design and analysis of psychological tests and assessments. They heavily rely on measures of correlation to establish the reliability and validity of these instruments, and this course covers these measures. The course helps you understand the logic behind identifying appropriate scenarios for using correlation. You will also learn to describe strength and direction. Contingency table analysis can be useful when examining relationships between categorical variables in psychological measurements. A psychometrician would find this course particularly helpful.

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Epidemiologist

Epidemiologists investigate patterns and causes of health-related events, using statistical methods. Understanding correlation and contingency is crucial in this role to find risk factors and associations. This course, which covers these topics thoroughly, is beneficial to an epidemiologist. This course will help you to identify relationships and use contingency tables to analyze data. An important part of the work of an epidemiologist is understanding patterns of disease, and this course provides the relevant skills.

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Business Intelligence Analyst

Business intelligence analysts transform raw data into insights that drive business decisions. This position requires using statistical methods, and this course in measures of correlation and contingency is relevant to the position. The course emphasizes recognizing patterns of association within contingency tables, and this is essential for a business intelligence analyst. This course will help you in the role by improving your abilities to identify relationships in business data. This course is helpful both in its presentation of correlation and its presentation of contingency tables.

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Survey Researcher

Survey researchers design, administer, and analyze surveys. This role uses measures of correlation and contingency to understand relationships between responses and demographics. This course is relevant to this role, as it covers these statistical techniques in depth. This course will be helpful in particular in learning how to interpret relationships between variables and how to use contingency tables to identify patterns in survey data. A survey researcher could make practical use of the concepts in this course.

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Quantitative Analyst

Quantitative analysts apply mathematical and statistical models to financial data. This position benefits significantly from a solid understanding of correlation, which is a technique directly taught by this course. The ability to discern correlation relationships is key to many quantitative analysts. While financial modeling may involve more advanced topics, the core principles of correlation analysis and understanding contingency tables will help build a foundation for this role. The course material on the direction and strength of correlations is important.

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Biostatistician

Biostatisticians use statistical methods to solve problems in biology and health. The techniques of correlation and contingency, which are the focus of the course, are used in this position. A biostatistician uses these techniques to understand the relationship between variables. This course will also assist in the analysis of patterns within contingency tables. This course may be helpful for biostatisticians.

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Risk Analyst

Risk analysts assess potential risks and recommend strategies to mitigate them. The role often uses statistical techniques to identify correlations and relationships between variables. A risk analyst will need to understand relationships, and this course helps to explain the meaning of correlation. The module on contingency table analysis can help you to identify and interpret patterns in risk scenarios. This course can be useful when approaching a risk analyst role.

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Financial Analyst

Financial analysts provide guidance to businesses and individuals making investment decisions. They can benefit from understanding relationships between market variables. While the course does not delve into finance in particular, an understanding of the core principles of correlation and its meaning, as covered in this course, may be useful for this kind of work. The course module on contingency table analysis may also be beneficial when examining categorical data. This course may be useful to those considering a financial analyst role.

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Social Science Researcher

A social science researcher investigates human behavior and social structures, often using statistical methods. The course provides helpful training in the use of correlation and contingency tables, which are important techniques in social science research. The analysis of contingency tables, as taught in this course, will help researchers recognize patterns and relationships. A social science researcher must understand how to apply correlation, and this course provides a good introduction to this. This course may be useful for social science research roles.

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Policy Analyst

Policy analysts research and analyze policy issues to make recommendations in government or the non-profit sector. While a policy analyst performs diverse tasks, analyzing data from reports and studies is often central to their work. This course, teaching the applications of correlation and contingency tables, can be helpful in understanding data. This course may be useful for those who want to work as a policy analyst.

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Human Resources Analyst

A human resources analyst uses data to understand employee trends and improve HR practices. The course's material on correlation and contingency can be helpful in understanding relationships between different HR factors. The course can also help you understand how to identify patterns in contingency tables. This course may be useful to those wishing to become human resources analysts.

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Measures of Correlation and Contingency

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