Variables cont.

Psych 231: Research Methods in Psychology

Announcements

Download the class experiment results from the web page and bring to labs this week

Class experiment due dates: First draft: in labs Oct 23 & 24 Final draft: in class Nov. 19th (no labs that

week)

Scales of measurement

Categorical variables Nominal scale

• Consists of a set of categories that have different names.

Ordinal scale• Consists of a set of categories that are organized in an

ordered sequence.

Quantitative variables Small, Med, Lrg,

blue, green,brown,

Scales of measurement

Categorical variables Nominal scale

• Consists of a set of categories that have different names.

Ordinal scale • Consists of a set of categories that are organized in an

ordered sequence.

Quantitative variables Interval scale

Ratio scale

Small, Med, Lrg,

blue, green,brown,

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

20º40º “Not Twice as hot”

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 The amount of temperature increase is the same60º 80º

20º increase

20º increase

Scales of measurement

Categorical variables Nominal scale Ordinal scale

Quantitative variables Interval scale Ratio scale

Categories

Categories with order

Ordered Categories of same size

Scales of measurement

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

Ratio scale: An interval scale with the additional feature of an absolute zero point.

Scales of measurement

Ratio scale

8 cards high

Scales of measurement

Interval scale5 cards high

Scales of measurement

Interval scaleRatio scale8 cards high 5 cards high

0 cards high means ‘no height’

0 cards high means ‘as tall as the table’

Scales of measurement

Categorical 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

• Given a choice, usually prefer highest level of measurement possible

“Best” Scale?

Variables

Independent variables Dependent variables

Measurement• Scales of measurement• Errors in measurement

Extraneous variables Control variables Random variables

Confound variables

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” Validity = measuring what is intended

Reliability = consistency of measurement

reliablevalid

reliable invalid

unreliable

invalid

Reliability

True score + measurement error A reliable measure will have a small amount of

error Many “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 judgment5:00

4:56

VALIDITY

CONSTRUCT

CRITERION-ORIENTED

DISCRIMINANT

CONVERGENTPREDICTIVE

CONCURRENT

FACE

INTERNAL EXTERNAL

Validity

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

: many varieties

VALIDITY

CONSTRUCT

CRITERION-ORIENTED

DISCRIMINANT

CONVERGENTPREDICTIVE

CONCURRENT

FACE

INTERNAL EXTERNAL

Validity: many varieties

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

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

History – an event happens the experiment Maturation – participants get older (and other

changes) Selection – nonrandom selection may lead to biases Mortality – participants drop out or can’t continue Testing – being in the study actually influences how

the participants respond

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

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

“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.