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Variables cont.

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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,

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,

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: 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

- Categorical variables
- Nominal scale
- Ordinal scale

- Quantitative variables
- Interval scale
- Ratio scale

Categories

Categories with order

Ordered Categories of same size

- 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.

Ratio scale

8 cards high

Interval scale

5 cards high

Ratio scale

Interval scale

8 cards high

5 cards high

0 cards high means ‘as tall as the table’

0 cards high means ‘no height’

- 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

“Best” Scale?

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

- Independent variables
- Dependent variables
- Measurement
- Scales of measurement
- Errors in measurement

- Measurement
- Extraneous variables
- Control variables
- Random variables

- Confound variables

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

- 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)

- Does your measure really measure the construct?

- Reliability

Bull’s eye = the “true score”

Bull’s eye = the “true score”

Validity = measuring what is intended

Reliability = consistency of measurement

unreliable

invalid

reliable

invalid

reliablevalid

- True score + measurement error
- A reliable measure will have a small amount of error
- Many “kinds” of 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

- Test the same participants more than once

Reliable

Unreliable

- 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)

- 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

5:00

4:56

VALIDITY

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

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.”

- 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 – 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
- 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
- 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
- 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.