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Experiment Basics: Variables

This guide covers the importance of variables such as independent, dependent, and extraneous in psychological experiments. Learn to identify and avoid biases, floor and ceiling effects, demand characteristics, and more.

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Experiment Basics: Variables

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

  2. Labs: • For labs download the Class Experiment Exercise from the lab webpage • Reminder APA title page exercise is due in labs this week (pg 109) • My office hours: cancelled tomorrow Announcements

  3. Independent variables (explanatory) • Dependent variables (response) • Extraneous variables • Control variables • Random variables • Confound variables Many kinds of Variables

  4. These are things that you want to try to avoid by careful selection of the levels of your IV (may be issues for your DV as well). • Floor and ceiling effects • Demand characteristics • Experimenter bias • Reactivity Identifying potential problems

  5. A value below which a response cannot be made • As a result the effects of your IV (if there are indeed any) can’t be seen. • Imagine a task that is so difficult, that none of your participants can do it. Floor effects

  6. When the dependent variable reaches a level that cannot be exceeded • So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out” • Imagine a task that is so easy, that everybody scores a 100% • To avoid floor and ceiling effects you want to pick levels of your IV that result in middle level performance in your DV Ceiling effects

  7. Characteristics of the study that may give away the purpose of the experiment • May influence how the participants behave in the study • Examples: • Experiment title: The effects of horror movies on mood • Obvious manipulation: Ten psychology students looking straight up • Biased or leading questions: Don’t you think it’s bad to murder unborn children? Demand characteristics

  8. Experimenter bias (expectancy effects) • The experimenter may influence the results (intentionally and unintentionally) • E.g., Clever Hans • One solution is to keep the experimenter (as well as the participants) “blind” as to what conditions are being tested Experimenter Bias

  9. Knowing that you are being measured • Just being in an experimental setting, people don’t always respond the way that they “normally” would. • Cooperative • Defensive • Non-cooperative Reactivity

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

  11. 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

  12. 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 • Is the dependent variable indirectly observable? • Physiological measures (e.g. GSR, heart rate) • Behavioral measures (e.g. speed, accuracy) Choosing your dependent variable

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

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

  15. 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

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

  17. 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

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

  19. 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

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

  21. 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

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

  23. 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

  24. Ratio scale 8 cards high Scales of measurement

  25. Interval scale 5 cards high Scales of measurement

  26. 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

  27. 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

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

  29. 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

  30. 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

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

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

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

  34. 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

  35. 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

  36. 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

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

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

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

  40. 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

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

  42. 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

  43. Experimenter bias & reactivity • History – an event happens the experiment • Maturation – participants get older (and other changes) • Selection – nonrandom selection may lead to biases • Mortality (attrition) – participants drop out or can’t continue • Regression to the mean – extreme performance is often followed by performance closer to the mean • The SI cover jinx Threats to internal validity

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

  45. 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

  46. Scales of measurement • Errors in measurement • Reliability & Validity • Sampling error Measuring your dependent variables

  47. Population • Errors in measurement • Sampling error Everybody that the research is targeted to be about The subset of the population that actually participates in the research Sample Sampling

  48. Sampling to make data collection manageable Inferential statistics used to generalize back Population Sample • Allows us to quantify the Sampling error Sampling

  49. Goals of “good” sampling: • Maximize Representativeness: • To what extent do the characteristics of those in the sample reflect those in the population • Reduce Bias: • A systematic difference between those in the sample and those in the population • Key tool: Random selection Sampling

  50. Have some element of random selection Susceptible to biased selection • Probability sampling • Simple random sampling • Systematic sampling • Stratified sampling • Non-probability sampling • Convenience sampling • Quota sampling Sampling Methods

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