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Manipulation and Measurement of Variables Psych 231: Research Methods in Psychology For labs this week you’ll need to download (and bring to lab): Class experiment packet Announcements Independent variables Dependent variables How do we measure these? Extraneous variables How do we manipulate these? Control variables Random variables Confound variables Variables Independent variables Dependent variables How do we measure these? Extraneous variables How do we manipulate these? Control variables Random variables Confound variables Variables Methods of manipulation Straightforward • Stimulus manipulation - different conditions use different stimuli Abstract vs. concrete words • Instructional manipulation – different groups are given different instructions Has an “a” vs. “ISU related” Staged • Event manipulation – manipulate characteristics of the context, setting, etc. Subject (Participant)– there are (pre-existing mostly) differences between the subjects in the different conditions • leads to a quasi-experiment Manipulating your independent variable Choosing the right levels of your independent variable Review the literature Do a pilot experiment Consider the costs, your resources, your limitations Be realistic Pick levels found in the “real world” Pick a large enough range to show the effect • And in the middle of the range Choosing your independent variable 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). Demand characteristics Experimenter bias Reactivity Floor and ceiling effects Identifying potential problems 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 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 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 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 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 Independent variables Dependent variables How do we measure these? Extraneous variables How do we manipulate these? Control variables Random variables Confound variables Variables 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). Dependent Variables How to measure your your construct: Can the participant provide self-report? • Introspection • Rating scales 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 Scales of measurement Errors in measurement Measuring your dependent variables 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 Categorical variables Nominal scale Quantitative variables Scales of measurement Nominal Scale: Consists of a set of categories that have different names. Label and categorize observations, Do not make any quantitative distinctions between observations. Example: • Eye color: blue, green, brown, hazel Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Scales of measurement Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. Rank observations in terms of size or magnitude. Example: • T-shirt size: Small, Med, Lrg, XL, Scales of measurement XXL Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Scales of measurement Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. Ratios of magnitudes are not meaningful. • Example: Fahrenheit temperature scale 40º 20º “Not Twice as hot” Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Scales of measurement Ratio scale: An interval scale with the additional feature of an absolute zero point. 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 Scales of measurement Ratio scale 8 cards high Scales of measurement Interval scale 5 cards high Scales of measurement Ratio scale 8 cards high 0 cards high means ‘no height’ Interval scale 5 cards high 0 cards high means ‘as tall as the table’ Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale “Best” Scale? • Given a choice, usually prefer highest level of measurement possible Scales of measurement Scales of measurement Errors in measurement Measuring your dependent variables Example: Measuring intelligence? Reliability & Validity 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? Reliability If you measure the same thing twice (or have two measures of the same thing) do you get the same values? Validity Does your measure really measure what it is supposed to measure? • Does our measure really measure the construct? • Is there bias in our measurement? Errors in measurement Reliability = consistency Validity = measuring what is intended unreliable invalid reliable invalid Reliability & Validity reliable valid True score + measurement error A reliable measure will have a small amount of error Multiple “kinds” of reliability Reliability 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 Reliability Unreliable 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 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 Reliability Does your measure really measure what it is supposed to measure? There are many “kinds” of validity Validity VALIDITY CONSTRUCT INTERNAL CRITERIONORIENTED FACE PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity EXTERNAL VALIDITY CONSTRUCT INTERNAL CRITERIONORIENTED FACE PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity EXTERNAL Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity 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 Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” External Validity Variable representativeness Subject representativeness Relevant variables for the behavior studied along which the sample may vary 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 The precision of the results Did the change result from the changes in the DV or does it come from something else? Internal Validity 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 Control variables Holding things constant - Controls for excessive random variability Extraneous Variables Random variables – may freely vary, to spread variability equally across all experimental conditions Randomization • A procedures that assure that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. • On average, the extraneous variable is not confounded with our manipulated variable. Extraneous Variables Can you keep them constant? Should you make them random variables? Two things to watch out for: Experimenter bias Demand characteristics Control your extraneous variable(s) Confound variables Other variables, that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) Confound Variables 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” Read chapters 8. Remember: For labs this week you’ll need to download: Class experiment packet Next time