Defining, Measuring and Manipulating Variables

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Defining, Measuring and Manipulating Variables
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Operational Definition

• The activities of the researcher in measuring and
  manipulating a variable.
• Caffeine consumption of 1 cup of coffee 1 hour
  before experiment.
• Anxiety measured by galvanic skin response.
• Sleep deprivation and test performance
• sleep deprivation awake for 24 hours
• Exam performance 50 questions, 1 point each
  question

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Identity

• Data points that are different, receive different
  scores.
• Ex types of cereal
• Corn Flakes 12
• Cheerios 5
• Raisin Bran 9
• The numerical values serve to divide the data
  into categories.
• Nominal scale of measurement.
• Categorical variables
• Ex ethnicity, gender, religion
• There is no absolute zero value no ethnicity, no
  gender

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Categorical Data

• Nonparametric statistical procedures
• Chi Square test of Independence
• Chi Square test of Goodness- of-Fit

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Magnitude

• Data that rank in order along a continuum of the
  variable being measured.
• Ex time to finish NYC marathon
• 1st place 20959
• 2nd place 21351
• 3rd place 23427
• Difference between ranks is not the same.
• Ordinal scale of measurement.
• There is no absolute zero value
• Nonparametric statistics Wilcoxon tests

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Equal Unit Size

• Data that have an equal unit size vary by the
  same difference throughout the scale.
• Ex temperature
• Interval scale of measurement.
• Do not have an absolute zero 0 degrees is still
  a temperature.
• Capable of performing math operations on interval
  data.

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Absolute Zero

• Data assigned a zero indicates the absence of a
  variable being measured.
• Ex words recalled score is zero if no words
  are recalled
• Ratio scale of measurement.
• Data can be described in terms of proportions or
  ratios.
• Ex 16 words recall is twice the number of 8
  words recall.
• Statistics t-tests, ANOVAs, correlation
  coefficients

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Discrete vs. Continuous Variables

• Discrete variables
• Whole number units or categories
• Values are distinct and detached from each other.
  
• Ex gender, religion, number of children
• Continuous variable
• Allow for fractional values.
• Fall on a continuum.
• Ex weight (75.45 lbs), reaction time (23.41
  seconds)

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Reliability

• Consistency or stability of a measuring
  instrument or measures of behavior.
• Observed score true score measurement error
• Measured using correlation coefficients
• r 0 to 1
• As error increases, reliability scores drop below
  1.00.

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Reliability Types

• Test/Retest reliability
• Alternate forms reliability
• Split-half reliability
• Interrater reliability
• See next slide for definitions

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Test/Retest Reliability

• Giving the same test again over a short time
  interval.
• Measures how performance on the 1st test is
  correlated with performance on the 2nd test.
• if the correlation is high, then the test is
  reliable.
• Measures the stability of a test over time.
• Problems
• Practice effects

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Alternate-forms Reliability

• Administering 2 tests, but the tests are slightly
  altered from each other (parallel-forms
  reliability).
• Measures how performance on the 1st test is
  correlated with performance on the alternate 2nd
  test.
• if the correlation is high, then the tests are
  reliable.
• Measures the stability of a test over time.
• Difficult to create 2 tests that are truly
  parallel.
• Practice effects

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Split-half Reliability

• One test is divided into 2 parts.
• Measures how scores on ½ of the test correlate
  with scores on the other ½ of the test.
• If correlation is high, then test is reliable.
• Does not measure stability of a test over time.
• Difficult to determine how to divide the test.
• Usually divided by odd number and even number
  questions
• Ensures that easy and difficult questions are not
  compared with each other.

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Interrater Reliability (Interobserver Agreement)

• Measures the extent to which 2 or more raters
  agree on observations.
• Based on agreement between raters.
• If the raters data are reliable, then the
  agreement should be high.
• When low interrater reliability is observed
• Check protocol
• Check measuring instruments
• Retrain raters

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Validity

• The truth of a measure or observation.
• Ex Validity test for new machine to measure
  heart rate
• Correlate the results obtained with the new
  machine with the results obtained with existing
  machines.
• 4 types of validity
• Criterion validity
• Construct validity
• External validity
• Internal validity

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Criterion Validity

• Validates a measure by checking it against a
  standard measure (or criterion).
• Making predictions about one aspect of behavior
  based on another measure of behavior.
• Ex SAT scores correlates with freshman year GPA
  in college.
• Predictive Validity

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Construct Validity

• Degree to which IV and DV measure what they
  intend to measure.
• Coke vs. Root Beer vs. Pepsi example
• Confounding variables reduce the construct
  validity of a study.
• Minimize invalidity by using operational
  definitions and adhering to a protocol during
  study.

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External Validity

• Extent to which the observations can be
  generalized to other settings and populations.
• Ex Stroop effect
• Replications whether the observations can be
  repeated under different circumstances.
• Provide an insight into the generality of
  observations

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Internal Validity

• Experiments aim to determine cause-effect
  relations in the world.
• Internal Validity
• Extent to which we can make causal statements
  about the relationship between variables.
• Confounding variables reduce the internal
  validity of a study.
• Cannot infer causality

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Reliability and Validity

• Study can be reliable, but not valid
• Rorschach test
• But if a study is valid, it is also reliable.
• Beck Depression Inventory