Variables, measurement and causation. Variable Any personal or physical characteristic that......
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Transcript of Variables, measurement and causation. Variable Any personal or physical characteristic that......
Variables, measurement and causation
Variable• Any personal or physical characteristic that..
.– Can change– The change must be measurable
• Examples of “variables”
– Height: a physical characteristic• It changes and changes can be measured with an instrument
– Weight: a physical characteristic• It changes and changes can be measured with an instrument
– Temperature: a physical characteristic• It changes and changes can be measured with a thermometer
– Knowledge about a topic: a personal characteristic• It changes and changes can be measured with a test
• When we measure a variable, we obtain its “value”, “score” or “level”.– These terms are interchangeable
• Categorical variables– Nominal: Mutually exclusive groups or “categories”
• Gender (M/F), color (Red/White/Black), etc.– Ordinal: Implied rank order
• Example: Low/medium/high• Sometimes treated as continuous variables
• Continuous variables: Can take on an infinite number of values– Can specify the precise distance between adjoining scores or
values (distance between 1 & 2 cm. same as between 8 & 9 cm.)– Length, height, weight, temperature, etc.– There are “interval” and “ratio” continuous variables. The
difference (presence/absence of an absolute zero) is not important.
Measuring variables
Issues in Measurement
• Validity: Method of measuring a variable (scaling and categorization) accurately reflects how the “real” world works
• Reliability: System of measuring is “reproducible”– Consistently yields the same value for a variable even
when measured by others
• Operational definition: precise method that must be followed to obtain the value of a variable, according to the system of measurement in use
Causation and hypotheses
• Conditional statement of cause and effect– If [causal variable], then [effect variable]– Changes in height cause changes in weight
• Causal variable is called the “independent” variable– Usual position on the left (e.g., height)
• Effect variable is called the “dependent” variable– Usual position on the right (e.g., weight)
• One and two-tailed hypotheses– Two-tailed simply predicts change: changes in calorie intake cause
changes in weight– One-tailed predicts change and specifies its direction:
Consuming more calories increases a person’s weight• Direction of a relationship
– Positive: Scores on the variables rise and fall together– Negative: Scores on the variables move in opposite directions
Hypotheses
H1: Increased pressure on the accelerator increases speed (+ relationship)
H2: Increased pressure on the brake reduces speed (- relationship)
CauseEffect (speed)
measured with a speedometer
Independent variable
Dependent variable measured with a pressure gauge or by changes in angle of pedal
Causation v. correlation
• Causation
– Must demonstrate more than just an association between variables (“correlation”)
– In nature and society, many variables that have nothing to do with each other are mathematically correlated
• Lunar cycles and homicide
– Must begin with a plausible hypothesis
• Narrowly specified, measurable variables
• Based on a thorough literature review– Nothing is “new”
• Causal order
– Cause (change in an independent variable) must precede effect (change in a dependent variable)
Testing a hypothesis - example
• Hypothesis: Poverty Crime– Which is the independent variable?– How would we measure it?– Which is the dependent variable?– How would we measure it?
• Are these variables “associated”? Do they “vary” or change together?• A simple way of analyzing a relationship between two variables is to
make both categorical– Measure poverty: Low or High income– Measure crime: Arrest record or No record
• From the same population, draw equal-size random samples of persons at each value of the independent variable– 100 Low income– 100 High income
• Then code each case for its scores or values on the variables
Low
Income
High
Income
No prior
Arrest
20 80
Prior
Arrest
80 20
Is a criminal record associated with one’s economic situation?
BUT - is it possible that criminal record is NOT associated with one’s economic situation? Could there be another independent variable at work?
Income
Arrest
An alternative hypothesis...
• It only LOOKS like Poverty Crime• The REAL cause of crime is poor education
– Poverty seems to be the cause because poor people get poor education, and wealthy people get good education
– So whenever we measure poverty, we are really measuring one’s education level
• Is this true?– Make educational level a categorical variable: Less than high
school, High school +– Code each case (all 200 persons) for education
• Repeat the original test, but this time “control” for the new independent variable: educational level– We “stratify” our sample into two groups – one for each value of
the “control” variable
Less than high school High school and above
Poverty Crime – or – Education Crime?
Low
Income
High
Income
No prior
Arrest
20 40
Prior
Arrest
30 20
Low
Income
High
Income
No prior
Arrest
10 30
Prior
Arrest
40 10
Low
Income
High
Income
No prior
Arrest
20 80
Prior
Arrest
80 20
Original tableOne result
Income Education Crime
Low
Income
High
Income
No prior
Arrest
20 20
Prior
Arrest
30 30
Low
Income
High
Income
No prior
Arrest
30 30
Prior
Arrest
20 20
Less than high school High school and above
What is the REAL cause of crime – Poverty or Education?Could the causal chain be complex?
Low
Income
High
Income
No prior
Arrest
20 80
Prior
Arrest
80 20
Original tableAnotherresult