Psychology

How much does one variable change across observations & what is typical or average

How two or more variables change together

Describe relationships among observed variables and presume the relationship is consistent and universal.

• Include assumed causes & unobserved mechanisms that explain how and why variables are related (whether consistently lawful or not)
• Guess

predict what will occur in a specific situation based on prior observations and/ or the logical implications of theories.

• Make unique & specific predictions
• Consistent with established theories
• Explain the most data
• Simplicity (parsimony)

• Each assumption has a probability of bring wrong
• More assumptions=more probability of being wrong
• Unless assumptions verified by better prediction, explanation, consistency

• Expectation Bias: our expectations tend to influence the way we perceive and interpret events.
• The Confirmation Bias: We tend to only use experience and info that are consistent with our current ideas.
• Limited Data: We make inferences from very little info.
• No Baserate/Comparison Group: We often lack and overlook the importance of comparisons, when inferring a relationship between variables.
• Lack of Control: We don't systematically control the variables, which is needed to infer a causal relationship.

• Defines variables in terms of specific, objective criteria.
• Uses settings where observations is optimal.
• Evaluates the reliability and validity of observations.
• Records and replicates observations.
• Collects larger, random sample & estimates chance.
• Uses appropriate comparisons
• Manipulates variables and uses statistical controls to test competing causal explations.

• What is the complete idealized essence of a thing?
• -Concepts are mystical ideals that may or may not exist
• -What should a word apply to?
• -No objective answer & evidence is irrelevant

• What are the real world properties of a thing?
• -Concepts correspond to the actual world.
• -They must be connected to what is observable.
• -Abstract ideas are shaped and reshaped empirical data

• An actual thing/event in the physical world
• Directly observable and measurable

• Idea or concept
• Not directly observable or measurable
• Either a particular variable or composed of multiple variables

• Define the abstract concepts in terms of observable variables.
• Then create operational definitions
• -The precise operations, methods, and procedures used to assign a quantitative value or a qualitative category to each observation.

• Specify exactly what was measured and how.
• Allow for placing each observation on a numerical scale along 1 or more dimensions.

• Continuous vs categories
• Presence vs Absence of a particular thing is Quantitative
• Numerical value does not always translate into exact number of "units"

• Abstract variables composed of multiple variables that cannot be separated.
• Inherently "Confounded"
• -Limits ability for explanation
• Why are they used so often?
• -Still allows us to describe relationships

• VERY IMPORTANT
• Increases reliability
• Allows for evaluation of validity
• Allows others to replicate the work
• Reduces opportunity for fraud or expectancy bias

• Concepts are like theories
• Concepts should be modified in light of data

• Advantages
• -Reduces the influence of other variables
• -Scores better representation the underlying concept
• Disadvantages
• -More complex to use
• -Units of measurement no longer have concrete meaning.

• O=T+R+S
• Observed score= True score + Random error + Systematic error

Lack of control or specifying in the method

• Calibration error in equipment or consistent error by observer
• Same amount and direction for each observation

Lots of R can reduce reliability.

• To reduce amount of R in each observation:
• -Identify and eliminate possible random influences
• To reduce the effects of R on overall results:
• -Measure each observation multiple times and take average
• -Collect a large sample of observations

• Measuring what you think
• If unreliable, then its invalid
• -Lots of R mean O unequal to T
• -Reliability is rarely perfect
• But, reliable measures can still be invalid
• -Due to systematic error
• -Reliability is just the first step in ensuring validity

• Predictive Validity
• Convergent
• Discriminate

• Does it predict scores on previously validated measures of the same thing?
• Why not just use the other measure?

Should not correlate with variables that are supposed to be unrelated

• Pick a new operational definition
• Improve reliability
• Create a Latent Variable

• We only know the observed score
• We can only estimate R and S
• W never know for sure that O=T
• So, we must take steps to minimize any potential error and its impact