Experiment Basics: Variables

Experiment Basics: Variables
Psych 231: Research Methods in Psychology

Print out the Class experiment (listed on syllabus page) exercise and bring it to labs this week
Class experiment results are now posted, you will discuss these in labs this week or next Group project introduction sections due this week Quiz 5, chapter 4, is due Friday Journal Summary #1 is due in labs next week Reminders

Many kinds of Variables
Independent variables (explanatory) Dependent variables (response) Extraneous variables Control variables Random variables Confound variables Many kinds of Variables

Identifying potential problems
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 (range effects) Identifying potential problems

Reactivity 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 Cooperative “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.” Reactivity

Range effects Floor: 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. Ceiling: 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 Range effects

Variables Independent variables (explanatory)
Dependent variables (response) Extraneous variables 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

Choosing your dependent variable
How to measure your your construct: 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 - agree - undecided - disagree - strongly disagree Is the dependent variable directly observable? Choice/decision Is the dependent variable indirectly observable? Physiological measures (e.g. GSR, heart rate) Behavioral measures (e.g. speed, accuracy) Choosing your dependent variable

Measuring your dependent variables
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

Scales of measurement Categorical variables (qualitative)
Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale 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

Scales of measurement Categorical variables (qualitative)
Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories 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: XXL XL, Lrg, Med, Small, Scales of measurement

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

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 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’ Scales of measurement

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

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? Measuring the true score

Errors in measurement 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

Bull’s eye = the “true score”
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 Measurement error Estimate of true score biased reliable valid unreliable invalid reliable invalid Dartboard analogy

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

Does your measure really measure what it is supposed to measure?
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

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 to the mean – extreme performance is often followed by performance closer to the mean The SI cover jinx 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?” 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

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”

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