Experiment Basics: Variables

Psych 231: Research Methods in Psychology

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Many kinds of Variables

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

Control variables Random variables

Confound variables

Many kinds of Variables

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

Control variables Random variables

Confound variables

Measuring your dependent variables

Scales of measurement Errors in measurement

Reliability & Validity Sampling error

Example: Measuring intelligence?

Measuring the true score

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?

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)

Dartboard analogy

Bull’s eye = the “true score”

Dartboard analogy

Bull’s eye = the “true score” Reliability = consistency

Validity = measuring what is intended

reliablevalid

reliable invalid

unreliable

invalid

Measurement error

Estimate of true score biased

Reliability

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 Unreliable

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

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

5:004:56

Validity

Does your measure really measure what it is supposed to measure? There are many “kinds” of validity

VALIDITY

CONSTRUCT

CRITERION-ORIENTED

DISCRIMINANT

CONVERGENTPREDICTIVE

CONCURRENT

FACE

INTERNAL EXTERNAL

Many kinds of Validity

VALIDITY

CONSTRUCT

CRITERION-ORIENTED

DISCRIMINANT

CONVERGENTPREDICTIVE

CONCURRENT

FACE

INTERNAL EXTERNAL

Many kinds of Validity

Face 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.”

Construct Validity

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

Internal Validity

Did the change in the DV result from the changes in the IV or does it come from something else?

The precision of the results

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

External 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 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

Measuring your dependent variables

Scales of measurement Errors in measurement

Reliability & Validity Sampling error

Sampling

Population

Everybody that the research is targeted to be about

The subset of the population that actually participates in the research

Sample

Errors in measurement Sampling error

Sampling

Sample

Inferential statistics used to generalize back

Sampling to make data collection manageable

Population

Allows us to quantify the Sampling error

Sampling

Goals of “good” sampling:– 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 Methods

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

Simple random sampling

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

Systematic sampling

Selecting every nth person

Cluster sampling

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

Convenience sampling

Use the participants who are easy to get

Quota sampling

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

individuals

Variables

Independent variables Dependent variables

Measurement• Scales of measurement• Errors in measurement

Extraneous variables Control variables Random variables

Confound 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

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?

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

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?

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

List 2

Our results

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.

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

List 2Men

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

List 1Women

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

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

BlueGreenRed

PurpleYellowGreenPurpleBlueRed

YellowBlueRed

Green

IVDV

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

BlueGreenRedPurpleYellowGreenPurpleBlueRedYellowBlueRedGreen

BlueGreenRed

PurpleYellowGreenPurpleBlueRed

YellowBlueRed

Green

“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.