Experiment Basics Variables Psych 231 Research Methods in
Experiment Basics: Variables Psych 231: Research Methods in Psychology
n n n Independent variables (explanatory) Dependent variables (response) Extraneous variables n n n Control variables Random variables Confound variables Variables
n n 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). n Consider our class experiment n n Conceptual level: Memory Operational level: Recall test n n n 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
n How to measure your construct: n 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-undecided-disagreestrongly disagree n Is the dependent variable directly observable? • Choice/decision (sometimes timed) n Is the dependent variable indirectly observable? • Physiological measures (e. g. GSR, heart rate) • Behavioral measures (e. g. speed, accuracy) Choosing your dependent variable
n n Scales of measurement Errors in measurement Measuring your dependent variables
n n Scales of measurement Errors in measurement Measuring your dependent variables
n Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring n The scale that you use will (partially) determine what kinds of statistical analyses you can perform Measuring your dependent variables
n Categorical variables (qualitative) n n Nominal scale Quantitative variables Scales of measurement
n Nominal Scale: Consists of a set of categories that have different names. n n n Label and categorize observations, Do not make any quantitative distinctions between observations. Example: • Eye color: blue, green, brown, hazel Scales of measurement
n Categorical variables (qualitative) n n n Nominal scale Ordinal scale Categories Quantitative variables n n Interval scale Ratio scale Scales of measurement
n Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. n Rank observations in terms of size or magnitude. n Example: • T-shirt size: Small, Med, Lrg, XL, Scales of measurement XXL
n Categorical variables n n n Nominal scale Ordinal scale Categories with order Quantitative variables n n Interval scale Ratio scale Scales of measurement
n Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. n Example: Fahrenheit temperature scale n With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. n 20º 40º 20º increase 60º 80º 20º increase The amount of temperature increase is the same However, Ratios of magnitudes are not meaningful. 40º “Not Twice as hot” 20º Scales of measurement
n Categorical variables n n n Nominal scale Ordinal scale Quantitative variables n n Interval scale Ratio scale Categories with order Ordered Categories of same size Scales of measurement
n Ratio scale: An interval scale with the additional feature of an absolute zero point. n Ratios of numbers DO reflect ratios of magnitude. n 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 Interval scale 8 cards high 5 cards high 0 cards high means ‘no height’ Scales of measurement 0 cards high means ‘as tall as the table’
n Categorical variables n n n Nominal scale Ordinal scale Quantitative variables n n Interval scale Ratio scale Categories with order Ordered Categories of same size with zero point “Best” Scale? • Given a choice, usually prefer highest level of measurement possible Scales of measurement
n n Scales of measurement Errors in measurement n Reliability & Validity Measuring your dependent variables
Example: Measuring intelligence? n n 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
n In search of the “true score” n Reliability • Do you get the same value with multiple measurements? n Validity • Does your measure really measure the construct? • Is there bias in our measurement? (systematic error) Errors in measurement
Bull’s eye = the “true score” Dartboard analogy
Bull’s eye = the “true score” Reliability = consistency Validity = measuring what is intended unreliable invalid Dartboard analogy reliable valid
n True score + measurement error n n A reliable measure will have a small amount of error Multiple “kinds” of reliability Reliability
n Test-restest reliability n 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
n Internal consistency reliability n n 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
n Inter-rater reliability n n At least 2 raters observe behavior Extent to which raters agree in their observations • Are the raters consistent? n Requires some training in judgment 4: 56 Reliability 5: 00
n Does your measure really measure what it is supposed to measure? n 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
n 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
n Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct Construct Validity
n The precision of the results n Did the change in the DV result from the changes in the IV or does it come from something else? Internal Validity
n History – an event happens the experiment n Maturation – participants get older (and other changes) n Selection – nonrandom selection may lead to biases n Mortality – participants drop out or can’t continue n Testing – being in the study actually influences how the participants respond Threats to internal validity
n Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work? ” External Validity
n Variable representativeness n n Subject representativeness n n Relevant variables for the behavior studied along which the sample may vary Characteristics of sample and target population along these relevant variables Setting representativeness n Ecological validity - are the properties of the research setting similar to those outside the lab External Validity
n n n Independent variables (explanatory) Dependent variables (response) Extraneous variables n n n Control variables Random variables Confound variables Variables
n Control variables n n Holding things constant - Controls for excessive random variability Random variables – may freely vary, to spread variability equally across all experimental conditions n Randomization • A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. n Confound variables n n 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
n Divide into two groups: n n men women n Instructions: Read aloud the COLOR that the words are presented in. When done raise your hand. n Women first. Men please close your eyes. Okay ready? n Colors and words
Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 1
n n n 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?
Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green List 2
n So why the difference between the results for men versus women? n Is this support for a theory that proposes: n n “Women are good color identifiers, men are not” Why or why not? Let’s look at the two lists. Our results
Matched List 1 List 2 Women Men Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green Mis-Matched
n Blue Green Red Purple Yellow Green Purple Blue Red Yellow Blue Red Green n n What resulted in the performance difference? n Our manipulated independent variable (men vs. women) n The other variable match/mis-match? Because the two variables are perfectly correlated we can’t tell This is the problem with confounds IV Co-vary together Confound DV 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 n What DIDN’T result in the performance difference? n Extraneous variables n Control • # of words on the list • The actual words that were printed n Random • Age of the men and women in the groups n 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
n Pilot studies n n n A trial run through Don’t plan to publish these results, just try out the methods Manipulation checks n n 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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