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### Variables cont.

Psych 231: Research Methods in Psychology

- 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)

blue, bring to labs this week

green,

brown,

Lrg,

Small,

Med,

- 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.

- Nominal scale
- Quantitative variables

blue, bring to labs this week

green,

brown,

Lrg,

Small,

Med,

- 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.

- Nominal scale
- Quantitative variables
- Interval scale
- Ratio scale

- Interval Scale bring to labs this week: 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- Categorical variables bring to labs this week
- 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. bring to labs this week
- 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.

Ratio scale bring to labs this week

Interval scale

8 cards high

5 cards high

0 cards high means ‘as tall as the table’

0 cards high means ‘no height’

Scales of measurement- Categorical variables bring to labs this week
- 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

- Independent variables bring to labs this week
- Dependent variables
- Measurement
- Scales of measurement
- Errors in measurement

- Measurement
- Extraneous variables
- Control variables
- Random variables

- Confound variables

Example: Measuring intelligence? bring to labs this week

- 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

- In search of the “ bring to labs this weektrue 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)

- Does your measure really measure the construct?

- Reliability

Bull’s eye = the “ bring to labs this weektrue score”

Dartboard analogyBull’s eye = the “ bring to labs this weektrue score”

Validity = measuring what is intended

Reliability = consistency of measurement

unreliable

invalid

reliable

invalid

reliablevalid

Dartboard analogy- True score bring to labs this week + measurement error
- A reliable measure will have a small amount of error
- Many “kinds” of reliability

- Test-restest reliability bring to labs this week
- Test the same participants more than once
- Measurement from the same person at two different times
- Should be consistent across different administrations

- Test the same participants more than once

Reliable

Unreliable

Reliability- Internal consistency reliability bring to labs this week
- 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)

- Inter-rater reliability bring to labs this week
- At least 2 raters observe behavior
- Extent to which raters agree in their observations
- Are the raters consistent?

- Requires some training in judgment

5:00

4:56

ReliabilityVALIDITY bring to labs this week

CONSTRUCT

INTERNAL

EXTERNAL

FACE

CRITERION-

ORIENTED

PREDICTIVE

CONVERGENT

CONCURRENT

DISCRIMINANT

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

Validity

: many varieties

VALIDITY bring to labs this week

CONSTRUCT

INTERNAL

EXTERNAL

FACE

CRITERION-

ORIENTED

PREDICTIVE

CONVERGENT

CONCURRENT

DISCRIMINANT

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

Validity: many varieties

- 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

- 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

- History or does it come from something else? – 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

- Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”

- Variable representativeness does the process of control put too much limitation on the “way things really work?”
- 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

- Control variables does the process of control put too much limitation on the “way things really work?”
- 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.

- Randomization
- 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

- Pilot studies does the process of control put too much limitation on the “way things really work?”
- 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.

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