1. Psych 231: Research Methods in Psychology
Errors in Measurement
Psych 231: Research Methods in Psychology

2. Class Experiment Turn in your class experiment results
Pass the results over
Pass the consent forms over
Class Experiment

3. Variables Independent variables Dependent variables
Measurement
Scales of measurement
Errors in measurement
Extraneous variables
Control variables
Random variables
Confound variables
Variables

4. Reliability & Validity
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?
Reliability & Validity

5. Errors in measurement Reliability Validity
If you measure the same thing twice (or have two measures of the same thing) do you get the same values?
Validity
Does your measure really measure what it is supposed to measure (the construct)?
Is there bias in our measurement?
Errors in measurement

6. Dartboard analogy reliable valid unreliable invalid reliable invalid
Reliability = consistency
Validity = measuring what is intended
Bull’s eye = the “true score”
reliable valid
unreliable
invalid
reliable
invalid
Dartboard analogy

7. Reliability True score + measurement error
A reliable measure will have a small amount of error
Multiple “kinds” of reliability
Reliability

8. 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

9. 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

10. 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
Reliability

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

12. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION-
ORIENTED
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity

13. Many kinds of Validity VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE
CRITERION-
ORIENTED
PREDICTIVE
CONVERGENT
CONCURRENT
DISCRIMINANT
Many kinds of Validity

14. 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

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

16. 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

17. 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
Threats to internal validity

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

19. 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

20. 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

21. “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”

22. Sampling Why do we do we use sampling methods?
Typically don’t have the resources to test everybody, so we test a subset
Sampling

23. Sampling Everybody that the research is targeted to be about
Population
Everybody that the research is targeted to be about
The subset of the population that actually participates in the research
Sample
Sampling

24. Sampling Population Sample Sampling to make data collection manageable
Inferential statistics used to generalize back
Sampling to make data collection manageable
Sample
Sampling

25. Sampling Why do we do we use sampling methods?
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
Sampling

26. 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

27. Simple random sampling
Every individual has a equal and independent chance of being selected from the population
Simple random sampling

28. Selecting every nth person
Systematic sampling

29. Stratified sampling Step 1: Identify groups (strata)
Step 2: randomly select from each group
Stratified sampling

30. Use the participants who are easy to get
Convenience sampling

31. Quota sampling Step 1: identify the specific subgroups
Step 2: take from each group until desired number of individuals
Quota sampling