Card Set Information
Psych 242 exam 1
How much does one variable change across observations & what is typical or average
How two or more variables change together
relationships among observed variables and presume the relationship is consistent and universal.
Include assumed causes & unobserved mechanisms that
how and why variables are related (whether consistently lawful or not)
what will occur in a specific situation based on prior observations and/ or the logical implications of theories.
What makes some theories better? (more plausible)
Make unique & specific predictions
Consistent with established theories
Explain the most data
Each assumption has a probability of bring wrong
More assumptions=more probability of being wrong
Unless assumptions verified by better prediction, explanation, consistency
5 Limitations of Personal Experience
: 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.
: 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.
How research overcomes these limitations
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
a word apply to?
-No objective answer & evidence is irrelevant
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
observable or measurable
Either a particular variable or composed of multiple variables
To test theories, we must
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.
An operational definition should...
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.
-Limits ability for explanation
Why are they used so often?
-Still allows us to describe relationships
Why is specificity so important?
Allows for evaluation of validity
Allows others to replicate the work
Reduces opportunity for fraud or expectancy bias
Refining our constructs
Concepts are like theories
Concepts should be modified in light of data
-Reduces the influence of other variables
-Scores better representation the underlying concept
-More complex to use
-Units of measurement no longer have concrete meaning.
Observed score= True score + Random error + Systematic error
R: Random, inconsistent influences on measurements
Lack of control or specifying in the method
S: Systematic, consistent influences on measurements
Calibration error in equipment or consistent error by observer
Same amount and direction for each observation
Random Error & Reliability
Lots of R can reduce
How to reduce random error?
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
How do we assess Validity?
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
How do we improve Validity?
Pick a new operational definition
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