Experiment Basics: Variables Psych 231: Research Methods in Psychology

Reminders Journal Summary 1 due in labs this week Don’t forget Quiz 6 (due Fri) Reminders

Many kinds of Variables Independent variables (explanatory) Dependent variables (response) Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables Many kinds of Variables

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

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

Scales of measurement Categorical variables Quantitative variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order Ordered Categories of same size 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 Kelvin scale of Thermodynamic temperature 0oK = -273.15oC = -479.67oF Scales of measurement

Ratio scale 8 cards high Scales of measurement

Interval scale 5 cards high Scales of measurement

Scales of measurement Ratio scale Interval scale 8 cards high 0 cards high means ‘as tall as the table’ 0 cards high means ‘no height’ Can say ”8 cards tall is 2 cards taller than 6 cards tall” Can say “8 cards tall is twice as tall as 4 cards tall” Can say ”8 cards tall is 2 cards taller than 6 cards tall” Can NOT say “8 cards tall is twice as tall as 4 cards tall” Scales of measurement

Scales of measurement Categorical variables Quantitative 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

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

Measuring the true score 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? Internet IQ tests: Are they valid? (The Guardian Nov. 2013) Measuring the true score

Errors in measurement In search of the “true score” Reliability Do you get the same value with multiple measurements? Consistency – getting roughly the same results under similar conditions 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” for the construct e.g., a person’s Intelligence Dart Throw = a measurement e.g., trying to measure that person’s Intelligence Dartboard analogy

Dartboard analogy unreliable invalid - The dots are spread out Reliability = consistency Validity = measuring what is intended Bull’s eye = the “true score” for the construct Measurement error Estimate of true score Estimate of true score = average of all of the measurements unreliable invalid - The dots are spread out - The & are different Dartboard analogy

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

Errors in measurement In search of the “true score” Reliability Do you get the same value with multiple measurements? Consistency – getting roughly the same results under similar conditions Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement

Reliability True score + measurement error A reliable measure will have a small amount of error Multiple “kinds” of reliability Test-retest Internal consistency Inter-rater reliability 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 Unreliable Reliability

Reliability 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

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 Not very funny Funny 5:00 4:56 Reliability

Errors in measurement In search of the “true score” Reliability Do you get the same value with multiple measurements? Consistency – getting roughly the same results under similar conditions Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error) Errors in measurement

Does your measure really measure what it is supposed to measure (the construct)? There are many “kinds” of validity Validity

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

Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED “The degree to which a study provides causal information about behavior.” “The degree to which the results of a study apply to individuals and realistic behaviors outside of the study.” PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of 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

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

Internal Validity The precision of the results Did the change in the DV result from the changes in the IV or does it come from something else? Internal Validity

Threats to internal validity 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 toward the mean – extreme performance is often followed by performance closer to the mean The SI cover jinx | Madden Curse Threats to internal validity

Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?” Example: Measuring driving while distracted External Validity

External Validity 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

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

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

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

Sampling Goals of “good” sampling: Key tool: Random selection 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

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

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

Selecting every nth person Systematic sampling

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

Use the participants who are easy to get Convenience sampling

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

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

Extraneous Variables 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

Colors and words 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

Blue Green Red Purple Yellow List 1

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 List 2

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

List 2 Men List 1 Women Blue Green Red Purple Yellow Blue Green Red Matched Mis-Matched

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 Blue Green Red Purple Yellow IV DV Confound Co-vary together

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 Blue Green Red Purple Yellow

“Debugging your study” 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”