Module 3: Concepts and Measures

Overview

In this module, we will examine the role of concepts in social science, considering first what concepts are and why conceptualization is so important to social science research, and then moving to consider how we can critique conceptual definitions. With this foundation, we discuss how political scientists seek to measure concepts, considering the role of precision in measurement and questions to ask as we seek to critique measures.

As always, to help you better understand and engage with the ideas that we are covering in the modules ahead, I strongly encourage you to discuss the course material in the class discussion boards.

Objectives

When you have finished this module, you should be able to do the following:

  1. Explain what conceptual definitions are and why they are so important;
  2. Create strategies to assess conceptual definitions in social science research;
  3. Explain what operationalization is and why it matters;
  4. Operationalize concepts at different levels of measurement; and
  5. Create strategies to assess measures in social science research.

Module Instructions

  1. Read Chapters 4 and 5 of the 3rd edition of our textbook or Chapter 4 of the 4th edition of our textbook. Create self-study flashcards for the chapter.
  2. Watch mini-lectures “Conceptual definitions”, “Assessing conceptual definitions”, “Operationalization”, “Levels of measurement”, and “Assessing measures.”
  3. Complete Learning Activity.

Key Terms and Concepts

  • Concept
  • Multidimensional concept
  • Variable
  • Indicator
  • Random error
  • Non-random error
  • Reliability
  • Measurement validity
  • Values
  • Nominal level variable
  • Ordinal level variable
  • Mutually exclusive response categories
  • Interval level variable
  • Feeling thermometer
  • Exhaustive response categories
  • Index

Required Readings

  1. Chapters 4 and 5 in Berdahl, Loleen and Keith Archer. Explorations: Conducting Empirical Research in Canadian Political Science (Third Edition). Oxford University Press OR Chapter 4 in Berdahl, Loleen and Jason Roy. Explorations: Conducting Empirical Research in Canadian Political Science (Fourth Edition). Oxford University Press.


 

Learning Material

Conceptual Definitions

Assessing Conceptual Definitions

Moving From Concepts to Measures

Levels of Measurement

Assessing Measures

Foundations Summary

Learning Activity

Thinking Through Complex Conceptual Definitions

  1. Read the PBS article “When it comes to defining ‘terrorism,’ there is no consensus” ( https://www.pbs.org/newshour/nation/defining-terrorism-consensus ). In 300-500 words, discuss why a concept like terrorism is hard to define, and explain how the selection of a definition has consequences for research. If you were researching the topic, what would you consider in selecting a conceptual definition? In your response, use at least two terms covered in the module (readings and/or videos), and be sure that all terminology is used correctly. Proofread carefully.
  2. Post your response in your Learning Activity Discussion Board.
  3. Provide a constructive response to at least one of your fellow group members’ posts. A constructive response is one that (a) uses supportive language to (b) identify for the author an area in which the work can be strengthened. For example, it may identify an issue where the wording is unclear or a point where terminology is used incorrectly, or suggest ideas for examples or ways to strengthen the argument, or let the author know of questions that the work raised for them. A constructive response goes beyond ‘I agree’ or ‘that is interesting’ to assist the author in improving the work. It should provide feedback that is intended to assist the author of the learning activity in improving their work.

Reminder: At the end of Module 4, you are required to select one learning activity for submission from Modules 1-4. You can use the feedback that you receive in the group forum to revise your selected learning activity prior to submission.

Glossary

Concept: an abstract idea that represents or symbolizes a quality

Exhaustive response categories: inclusion of all possible response options in a measure

Feeling thermometer: a type of measure in which the metaphor of a thermometer is used to assist respondents to identify their beliefs, feelings, or thoughts as relatively “warm” or relatively “cold”; often used with a 100 point scale

Index: a complex measure that combines responses from more than one question in creation of a new measure

Indicator: a variable that is used to represent the presence of a quality

Interval level variable: level of measurement in which all values have a numerical category, the categories are ranked and there is a consistent range between each value

Measurement validity: the extent to which the measurement of a particular concept matches its operational definition

Multidimensional concept: a concept that comprises different elements or dimensions, each of which must be captured in the definition and measurement

Mutually exclusive response categories: responses cannot be in more than one category

Nominal level variable: level of measurement in which the variables are given numerical values (1, 2, 3, etc.) that represent a difference in kind rather than in degree (e.g., 2 is not higher than one but simply different)

Non-random error: inaccuracies caused by factors that are systematic or intentional; for example, systematic error that occurs because people lie

Ordinal: level of measurement in which the values represent differences and can be ranked from lowest to highest (e.g., the level of agreement on an agree/disagree questions)

Random errors: inaccuracies caused by factors that are not systematic or intentional; found in all samples because the full range of possible respondents cannot be included; amount of random error can be estimated in a probability sample

Reliability: the extent to which the measurement of a variable yields consistent results.

Values: categories that capture the variance between the observable characteristics of phenomena

Variable: the observable characteristics of phenomena that can take on more than one value; specific and concrete measurements of a concept

Note: Unless otherwise stated, glossary source is Berdahl, Loleen and Keith Archer. 2015. Explorations: Conducting Empirical Research in Canadian Political Science (Third Edition). Oxford University Press.

References

Andersen, Chris. 2017. “Who can call themselves Metis?” The Walrus. https://thewalrus.ca/who-can-call-themselves-metis/

Berdahl, Loleen and Keith Archer. 2015. Explorations: Conducting Empirical Research in Canadian Political Science (Third Edition). Oxford University Press.

Barrington, Lowell W. 1997. “"Nation" and "Nationalism": The Misuse of Key Concepts in Political Science.” PS: Political Science and Politics 30 (4), pp. 712-716.

Collier, David and Levitsky, Steven. 1997. “Democracy with Adjectives: Conceptual Innovation in Comparative Research.” World Politics, Vol. 49, pp. 430-451.

Gerring, John. 1999. “What Makes a Concept Good? A Criterial Framework for Understanding Concept Formation in the Social Sciences.” Polity 31 (3), 357-93.   

Le Roy, Michael and Michael Corbett. 2009.  Research Methods in Political Science: An Introduction. Nelson Education.

Miller, Bernhard. Miller B. (2007) Making Measures Capture Concepts: Tools for Securing Correspondence between Theoretical Ideas and Observations. In Gschwend T., Schimmelfennig F. (eds) Research Design in Political Science. Palgrave Macmillan, London

Paxton, Pamela. 2000. “Women’s Suffrage in the Measurement of Democracy: Problems of Operationalization.” Studies in Comparative International Development 35(3): 92-111.

Peters, B. Guy. 2013. Strategies for Comparative Research in Political Science. Macmillan International Higher Education.

Rubin, Allen. 2008. Practitioner’s Guide to Using Research for Evidence-Based Practice. New Jersey: John Wiley & Sons Inc.

Salkind, Neil J. 2007. Statistics for People Who (Think They) Hate Statistics. Thousand Oaks: Sage.

Sinnott-Armstrong, Walter and Ram Neta. 2015. Think Again: How to Reason and Argue. Coursera.org