1. Defining, Measuring and Manipulating Variables

2. 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

3. 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

4. Categorical Data  Nonparametric statistical procedures  Chi Square test of Independence  Chi Square test of Goodness- of-Fit

5. Magnitude  Data that rank in order along a continuum of the variable being measured.  Ex: time to finish NYC marathon:  1 st place: 2:09:59  2 nd place: 2:13:51  3 rd place: 2:34:27  Difference between ranks is not the same.  Ordinal scale of measurement.  There is no absolute zero value  Nonparametric statistics: Wilcoxon tests

6. 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.

7. 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

8. 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)

9. 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.

10. Reliability Types  Test/Retest reliability  Alternate forms reliability  Split-half reliability  Interrater reliability  See next slide for definitions…

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

12. Alternate-forms Reliability  Administering 2 tests, but the tests are slightly altered from each other (parallel-forms reliability).  Measures how performance on the 1 st test is correlated with performance on the alternate 2 nd 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

13. 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.

14. 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

15. 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

16. 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

17. 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.

18. 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

19. 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

20. 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