defining and measuring variables n.
Skip this Video
Loading SlideShow in 5 Seconds..
Defining and Measuring Variables PowerPoint Presentation
Download Presentation
Defining and Measuring Variables

Defining and Measuring Variables

140 Vues Download Presentation
Télécharger la présentation

Defining and Measuring Variables

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

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

  3. 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

  4. 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

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

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

  7. 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

  8. 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?

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

  10. Moderator Variables • A moderator variable influences the direction and/or strength of the relationship between two variables Moderator IV DV

  11. 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 Social Support Stress Turnover

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

  13. 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 Perceived fairness Income inequality Happiness

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

  15. Scales of Measurement • Nominal • Ordinal • Interval • Ratio Least precise Most precise

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

  17. Scales of Measurement • Nominal • Ordinal • Values reflect rank ordering 1st place 2nd place 3rd place 4th place 1 hour 2 hours 3 hours 4 hours 5 hours 6 hours 7 hours 8 hours

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

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

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

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

  22. Are Our Measures Any Good? Accuracy, Reliability, and Validity • 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?

  23. Are Our Measures Any Good? Accuracy, Reliability, and Validity • 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

  24. Are Our Measures Any Good? Accuracy, Reliability, and Validity • 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

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

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

  27. 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!

  28. 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

  29. Are Our Measures Any Good? Accuracy, Reliability, and Validity • 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.