Presentation on theme: "Manipulation and Measurement of Variables"— Presentation transcript:
1 Manipulation and Measurement of Variables Psych 231: Research Methods in Psychology
2 AnnouncementsFor labs this week you’ll need to download (and bring to lab):Class experiment packet
3 Choosing your independent variable Choosing the right range (the right levels) of your independent variableReview the literaturedo a pilot experimentconsider the costs, your resources, your limitationsbe realisticpick levels found in the “real world”pick a large enough range to show the effect
4 Potential problemsThese are things that you want to try to avoid by careful selection of the levels of your IV (issues for your DV as well).
5 Demand characteristics Characteristics of the study that may give away the purpose of the experimentMay influence how the participants behave in the studyExamples:Experiment title: The effects of horror movies on moodObvious manipulation: Ten psychology students looking straight upBiased or leading questions: Don’t you think it’s bad to murder unborn children?
6 Experimenter Bias Experimenter bias (expectancy effects) the experimenter may influence the results (intentionally and unintentionally)E.g., Clever HansOne solution is to keep the experimenter “blind” as to what conditions are being testedSingle blind - experimenter doesn’t know the conditionDouble blind - neither the participant nor the experimenter knows the condition
7 Reactivity Knowing that you are being measured just being in an experimental setting, people don’t always respond the way that they “normally” would.CooperativeDefensiveNon-cooperativeCooperative“You seem like a nice person: I’ll help you get the right results”Defensive“I don’t want to look stupid/evil. I’ll do what a smart/good person is expected to do (rather than what I normally would do).”Noncooperative“This experiment is annoying. Let me screw up the results.”
8 Floor effects A value below which a response cannot be made Imagine a task that is so difficult, that none of your participants can do it.As a result the effects of your IV (if there are indeed any) can’t be seen.
9 Ceiling effectsWhen the dependent variable reaches a level that cannot be exceededImagine a task that is so easy, that everybody scores a 100% (imagine accuracy is your measure).So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out”.So you want to pick levels of your IV that result in middle level performance in your DV
10 Measuring your dependent variables: Scales of measurement - the correspondence between the numbers representing the properties that we’re measuringThe scale that you use will (partially) determine what kinds of statistical analyses you can perform
11 Scales of measurementCategorical variablesNominal scale
12 Scales of measurementNominal Scale: Consists of a set of categories that have different names.Measurements on a nominal scale label and categorize observations, but do not make any quantitative distinctions between observations.Example:Eye color:blue,green,brown,hazel
13 Scales of measurement Categorical variables Nominal scale Ordinal scale
14 Scales of measurementOrdinal Scale: Consists of a set of categories that are organized in an ordered sequence.Measurements on an ordinal scale rank observations in terms of size or magnitude.Example:T-shirt size:XXLXL,Lrg,Med,Small,
16 Scales of measurementInterval 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 scale40º20º“Not Twice as hot”
18 Scales of measurementRatio scale: An interval scale with the additional feature of an absolute zero point.With a ratio scale, ratios of numbers DO reflect ratios of magnitude.It is easy to get ratio and interval scales confusedConsider the following example: Measuring your height with playing cards
20 Scales of measurementInterval scale5 cards high
21 Scales of measurement Ratio scale Interval scale 8 cards high 0 cards high means ‘no height’0 cards high means ‘as tall as the table’
22 Scales of measurement Categorical variables Quantitative variables Nominal scaleOrdinal scaleQuantitative variablesInterval scaleRatio scale“Best Scale?”:Given a choice, usually prefer highest level of measurement possible
23 Errors in measurement Reliability Validity if you measure the same thing twice (or have two measures of the same thing) do you get the same values?Validitydoes your measure really measure what it is supposed to measure?Does our measure really measure the construct?Is there bias in our measurement?
24 Reliability & Validity Reliability =consistencyValidity = measuring what is intendedunreliable reliable reliable invalid invalid valid
25 Reliability & Validity Example: How can we measure intelligence?
26 Reliability True score + measurement error A reliable measure will have a small amount of errorMultiple “kinds” of reliability
27 Reliability Test-restest reliability Test the same participants more than onceMeasurement from the same person at two different timesShould be consistent across different administrationsSensitive to type of measure
28 Reliability Internal consistency reliability Multiple items testing the same constructExtent to which scores on the items of a measure correlate with each otherCronbach’s alpha (α)Split-half reliabilityCorrelation of score on one half of the measure with the other half (randomly determined)
29 Reliability Inter-rater reliability Extent to which raters agree in their observationsAre the raters consistent?At least 2 raters observe behaviorNeed a second opinionRequires some training in judgment
30 ValidityDoes your measure really measure what it is supposed to measure?There are many “kinds” of validity
31 VALIDITY CONSTRUCT INTERNAL EXTERNAL CRITERION-ORIENTED FACE PREDICTIVECONVERGENTDISCRIMINANTCONCURRENT
32 Construct ValidityUsually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct
33 Face ValidityAt 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.”
34 External ValidityAre experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”
35 External Validity Variable representativeness relevant variables for the behavior studied along which the sample may varySubject representativenesscharacteristics of sample and target population along these relevant variablesSetting representativenessecological validity
36 Internal Validity The precision of the results Did the change result from the changes in the DV or does it come from something else?
37 Threats to internal validity History – an event happens the experimentMaturation – participants get older (and other changes)Selection – nonrandom selection may lead to biasesMortality – participants drop out or can’t continueTesting – being in the study actually influences how the participants respond
38 “Debugging your study” Pilot studiesA trial run throughDon’t plan to publish these results, just try out the methodsManipulation checksAn attempt to directly measure whether the IV variable really affects the DV.Look for correlations with other measures of the desired effects.
39 Next time Read chapters 8. Remember: For labs this week you’ll need to download:Class experiment packet