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Experiment Basics: Variables

Experiment Basics: Variables. Psych 231: Research Methods in Psychology. Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables. Variables. The variables that are measured by the experimenter

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Experiment Basics: Variables

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  1. Experiment Basics: Variables Psych 231: Research Methods in Psychology

  2. Independent variables (explanatory) • Dependent variables (response) • Extraneous variables • Control variables • Random variables • Confound variables Variables

  3. The variables that are measured by the experimenter • They are “dependent” on the independent variables (if there is a relationship between the IV and DV as the hypothesis predicts). • Consider our class experiment • Conceptual level:Memory • Operational level: Recall test • Present list of words, participants make a judgment for each word • 15 sec. of filler (counting backwards by 3’s) • Measure the accuracy of recall Dependent Variables

  4. How to measure your your construct: • Can the participant provide self-report? • Introspection – specially trained observers of their own thought processes, method fell out of favor in early 1900’s • Rating scales – strongly agree-agree-undecided-disagree-strongly disagree • Is the dependent variable directly observable? • Choice/decision (sometimes timed) • Is the dependent variable indirectly observable? • Physiological measures (e.g. GSR, heart rate) • Behavioral measures (e.g. speed, accuracy) Choosing your dependent variable

  5. Scales of measurement • Errors in measurement Measuring your dependent variables

  6. Scales of measurement • Errors in measurement Measuring your dependent variables

  7. Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring • The scale that you use will (partially) determine what kinds of statistical analyses you can perform Measuring your dependent variables

  8. Categorical variables (qualitative) • Quantitative variables • Nominal scale Scales of measurement

  9. brown, blue, green, hazel • Label and categorize observations, • Do not make any quantitative distinctions between observations. • Example: • Eye color: • Nominal Scale: Consists of a set of categories that have different names. Scales of measurement

  10. Categorical variables (qualitative) • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Scales of measurement

  11. Small, Med, Lrg, XL, XXL • Rank observations in terms of size or magnitude. • Example: • T-shirt size: • Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. Scales of measurement

  12. Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Categories with order Scales of measurement

  13. Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. • Example: Fahrenheit temperature scale • With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. • However, Ratios of magnitudes are not meaningful. 20º 40º 20º increase The amount of temperature increase is the same 60º 80º 20º increase 40º “Not Twice as hot” 20º Scales of measurement

  14. Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Categories with order Ordered Categories of same size Scales of measurement

  15. Ratios of numbers DO reflect ratios of magnitude. • It is easy to get ratio and interval scales confused • Example: Measuring your height with playing cards • Ratio scale: An interval scale with the additional feature of an absolute zero point. Scales of measurement

  16. Ratio scale 8 cards high Scales of measurement

  17. Interval scale 5 cards high Scales of measurement

  18. Ratio scale Interval scale 8 cards high 5 cards high 0 cards high means ‘as tall as the table’ 0 cards high means ‘no height’ Scales of measurement

  19. Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale Categories Categories with order Ordered Categories of same size Ordered Categories of same size with zero point “Best” Scale? • Given a choice, usually prefer highest level of measurement possible Scales of measurement

  20. Scales of measurement • Errors in measurement • Reliability & Validity Measuring your dependent variables

  21. Example: Measuring intelligence? • How do we measure the construct? • How good is our measure? • How does it compare to other measures of the construct? • Is it a self-consistent measure? Measuring the true score

  22. In search of the “true score” • Reliability • Do you get the same value with multiple measurements? • Validity • Does your measure really measure the construct? • Is there bias in our measurement? (systematic error) Errors in measurement

  23. Bull’s eye = the “true score” Dartboard analogy

  24. Bull’s eye = the “true score” Reliability = consistency Validity = measuring what is intended reliablevalid unreliable invalid reliable invalid Dartboard analogy

  25. True score + measurement error • A reliable measure will have a small amount of error • Multiple “kinds” of reliability Reliability

  26. Test-restest reliability • Test the same participants more than once • Measurement from the same person at two different times • Should be consistent across different administrations Reliable Unreliable Reliability

  27. Internal consistency reliability • Multiple items testing the same construct • Extent to which scores on the items of a measure correlate with each other • Cronbach’s alpha (α) • Split-half reliability • Correlation of score on one half of the measure with the other half (randomly determined) Reliability

  28. Inter-rater reliability • At least 2 raters observe behavior • Extent to which raters agree in their observations • Are the raters consistent? • Requires some training in judgment 5:00 4:56 Reliability

  29. Does your measure really measure what it is supposed to measure? • There are many “kinds” of validity Validity

  30. VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity

  31. VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity

  32. At the surface level, does it look as if the measure is testing the construct? “This guy seems smart to me, and he got a high score on my IQ measure.” Face Validity

  33. Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity

  34. Did the change in the DV result from the changes in the IV or does it come from something else? • The precision of the results Internal Validity

  35. History – an event happens the experiment • Maturation – participants get older (and other changes) • Selection – nonrandom selection may lead to biases • Mortality – participants drop out or can’t continue • Testing – being in the study actually influences how the participants respond Threats to internal validity

  36. Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” External Validity

  37. Variable representativeness • Relevant variables for the behavior studied along which the sample may vary • Subject representativeness • Characteristics of sample and target population along these relevant variables • Setting representativeness • Ecological validity - are the properties of the research setting similar to those outside the lab External Validity

  38. Independent variables (explanatory) • Dependent variables (response) • Extraneous variables • Control variables • Random variables • Confound variables Variables

  39. Control variables • Holding things constant - Controls for excessive random variability • Random variables – may freely vary, to spread variability equally across all experimental conditions • Randomization • A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. • Confound variables • Variables that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) • Co-varys with both the dependent AND an independent variable Extraneous Variables

  40. Divide into two groups: • men • women • Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. • Women first. Men please close your eyes. • Okay ready? Colors and words

  41. Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1

  42. Okay, now it is the men’s turn. • Remember the instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. • Okay ready?

  43. Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2

  44. So why the difference between the results for men versus women? • Is this support for a theory that proposes: • “Women are good color identifiers, men are not” • Why or why not? Let’s look at the two lists. Our results

  45. List 2Men List 1Women Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Matched Mis-Matched

  46. IV DV Co-vary together Confound • What resulted in the performance difference? • Our manipulated independent variable (men vs. women) • The other variable match/mis-match? • Because the two variables are perfectly correlated we can’t tell • This is the problem with confounds Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green

  47. What DIDN’T result in the performance difference? • Extraneous variables • Control • # of words on the list • The actual words that were printed • Random • Age of the men and women in the groups • These are not confounds, because they don’t co-vary with the IV Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green

  48. Pilot studies • A trial run through • Don’t plan to publish these results, just try out the methods • Manipulation checks • An attempt to directly measure whether the IV variable really affects the DV. • Look for correlations with other measures of the desired effects. “Debugging your study”

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