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Experiment Basics: Variables

Experiment Basics: Variables

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Experiment Basics: Variables

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

  2. Journal summary 1 due in labs this week • See link on syllabus Announcements

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

  4. 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

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

  6. 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

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

  8. 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

  9. 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

  10. Do the research results generalize to other individuals, methods, or settings? External Validity

  11. 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 • Is your sample size large enough? • Is there bias in your sampling procedure? • Setting representativeness • Ecological validity - are the properties of the research setting similar to those outside the lab • Do the materials, methods, & setting approximate the ‘real life’ situation? • Often confused with external validity (they are related concepts, and sound similar) External Validity

  12. Independent variables • Dependent variables • Measurement • Scales of measurement • Errors in measurement • Extraneous variables • Control variables • Random variables • Confound variables Variables

  13. 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

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

  15. 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

  16. 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

  17. Every individual has a equal and independent chance of being selected from the population Simple random sampling

  18. Selecting every nth person Systematic sampling

  19. Step 1: Identify groups (clusters) • Step 2: randomly select from each group Cluster sampling

  20. Use the participants who are easy to get Convenience sampling

  21. Step 1: identify the specific subgroups • Step 2: take from each group until desired number of individuals Quota sampling

  22. Independent variables • Dependent variables • Measurement • Scales of measurement • Errors in measurement • Extraneous variables • Control variables • Random variables • Confound variables Variables

  23. 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

  24. 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

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

  26. 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?

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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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”

  33. This week: • Journal summary 1 due in labs • Next week: • In lab turning in Methods, Appendix (stimuli), and IRB form for group projects Reminders