2. Defining and Measuring Variables Slides Prepared by Alison L. O’Malley Passer Chapter 4

3. Think of something that would not be considered a variable…

4. Variables: Qualitative vs. Quantitative Qualitative Variable levels are categories – values reflect difference in kind E.g., make of car, region of country Quantitative Variable levels exist on a continuum from low to high – values reflect difference in amount E.g., number of siblings, quiz score

5. Variables: Discrete vs. Continuous Discrete Intermediate values are impossible E.g., # of cars owned, # of Oscars won Continuous Intermediate values are possible – precision limited only by our measurement tools E.g., height (62.675... inches), weight In practice, ultimately converted into discrete values

6. The nature of our variables paves the way for how we make sense of them Which type of variable is depicted in (a)? (b)?

7. Independent and Dependent Variables Identify the independent variable and dependent variable in this research question: Is aggressive behavior influenced by alcohol consumption?

8. Independent and Dependent Variables Discuss independent and dependent variables in terms of “cause” and “effect” Note that this causal language pertains only to experimental research designs! Generate an example of an independent variable that cannot be manipulated

9. Constructs Psychological scientists have their work cut out for them, as they tend to be interested in phenomena that are not directly observable. Love? Motivation? Creativity? Love? Motivation? Creativity?

10. Constructs Constructs must be translated into something measurable This process occurs via operationalization Generate an operational definition for aggression Underlying Construct Underlying Construct Measurable Variable Measurable Variable

11. Moderator Variables A moderator variable influences the direction and/or strength of the relationship between two variables IVDV Moderator

12. Moderator Variables E.g., Social support moderates the relationship between stress and turnover The relationship between stress and turnover (i.e., leaving one’s job) is stronger when social support is low vs. when social support is high StressTurnover Social Support

13. Mediator Variables Mediators explain a causal relationship, shedding light on the process by which the IV influences the DV IVDV Mediator

14. Mediator Variables Oishi, Kesebir, & Diener (2011) identified perceived fairness as a mediating variable accounting for the negative relationship between income inequality and happiness High income inequality is associated with low happiness due (in part) to low perceived fairness Income inequality Happiness Perceived fairness

15. Scales of Measurement Measurement: Assignment of numbers to aspects of objects or events according to rules Scale of measurement impacts how you analyze data

16. Scales of Measurement Nominal Ordinal Interval Ratio Least precise Most precise

17. Scales of Measurement Nominal Group objects into categories Group objects into categories Variable levels differ in kind, not in degree Variable levels differ in kind, not in degree E.g., Political party affiliation E.g., Political party affiliation Ordinal Interval Ratio

18. Scales of Measurement 17 Nominal Nominal Ordinal Ordinal Values reflect rank ordering Values reflect rank ordering 1 hour2 hours3 hours4 hours5 hours6 hours7 hours8 hours 1st place2nd place3rd place4th place

19. Scales of Measurement Nominal Ordinal Interval Numbers reflect actual amounts Numbers reflect actual amounts Equal distance between intervals Equal distance between intervals 0 point is arbitrary 0 point is arbitrary E.g., Temperature (in ° Celsius or Fahrenheit) E.g., Temperature (in ° Celsius or Fahrenheit) Ratio

20. Scales of Measurement Nominal Ordinal Interval Ratio Interval scales, but zero point reflects true absence of property Interval scales, but zero point reflects true absence of property Scores can be compared as ratios or percents Scores can be compared as ratios or percents E.g., speed, dollars E.g., speed, dollars

21. Are Our Measures Any Good? Accuracy reflects the degree to which measure aligns with known standard What does accuracy have to do with systematic error (bias)? Accuracy, Reliability, and Validity

22. Are Our Measures Any Good? Reliability refers to the consistency of measurement What does reliability have to do with random measurement error? Accuracy, Reliability, and Validity

23. Are Our Measures Any Good? Several forms of reliability Test-rest Consistency of scores over time Internal consistency Consistency of a measure within itself Assumes multiple items – do the items strongly correlate with each other? Accuracy, Reliability, and Validity

24. Are Our Measures Any Good? Validity addresses the alignment between our construct and the measurement tool we employed to gain insight into the construct Like reliability, validity can be addressed in several ways Accuracy, Reliability, and Validity

25. Are Our Measures Any Good? Face validity Measure appears appropriate to participants E.g., Job applicants perceived that an interviewer asked job-relevant questions Content validity Measure adequately covers the domain of interest E.g., A course exam samples from all of the content students were exposed to in and out of class Accuracy, Reliability, and Validity

26. Are Our Measures Any Good? Criterion validity Measure predicts an outcome E.g., Conscientiousness is a positive predictor of job performance Accuracy, Reliability, and Validity

27. Are Our Measures Any Good? John Pahn Test 1 ______________ _______________ John Pahn Performance Appraisal ____________ _____________ Valid? (Correlated? ) Predictor Criterion John Pahn Test 1 ______________ _______________ Jane Doe Conscientiousness (Personality Test) ______________ _______________ John Pahn Test 1 ______________ _______________ Jane Doe Job Performance Data ______________ _______________ Establishing Criterion Validity

28. Are Our Measures Any Good? Construct validity Measure authentically represents the construct of interest Demonstrated in part via convergent and discriminant validity Convergent example: Scores on new creativity test correlate with scores on established creativity measures Discriminant example: Scores on new creativity test are not correlated with scores on an assertiveness measure Creativity and assertiveness are different constructs!

29. Are Our Measures Any Good? Scholars may differ in terms of how they approach validity and reliability, but they converge on the following ideas: Reliability is a necessary but insufficient condition for validity Construct validity is the most fundamental validity

30. Are Our Measures Any Good? Consider a student who takes the SAT twice, and receives a much higher score the second time. Discuss this scenario in terms of accuracy, reliability, and validity. Accuracy, Reliability, and Validity