R.M.

• 1. Linearity
• 2. Range of scores in sample
• 3. Extreme scores (esp. in small sample)

match the population

Correlation likely smaller than is in defined population

Correlation coefficient likely larger than in the defined population

r²

• low risk of falsely concluding there is some sort of correlation in the population.
• p<.05

• Size of sample
• Size of correlation coefficient

• 1. Ecological validity
• 2. Larger range of scores
• 3. Best method available

Extent to which results can be generalized to other situations

(intended target situation-natural environment)

• Single relational study usually not best method for determining cause and effect
• Open to many alternative explanations

• Third variable problem
• Directionality problem

An unmeasured variable changes along with the identified variable so that cannot determine which is a true cause

• Identified and measured vs. actual variable
• Maalox & memory loss
• Smoking & lung cancer
• Alcohol & risk taking

• x-->Y
• X<--Y
• X<-->Y
• Sugar
• Corporal punishment
• bad parents
• violent tv

• 1. Classic correlational study
• 2. Form groups or conditions

• Simply measure available values of predictor variable and correlate with value of outcome variable
• Extroversion and car sales

• Select, classify, or categorize subjects based on the value of the predictor variable and correlate, orcompare groups/conditions using a test of means (t-test, ANOVA...)
• Subject variables: pysio./psych. Characteristics: gender, age, anxiety, conscientiousness, experiences

ex post facto

A variable not identified by E that systematically varies with identified variable so that the effect of the confounding variable and the effect of the identified variable can not be separated.

• To see if blondes have more fun
• Dr. XY compared fun surfeys in blondes from a modeling agency with those of brunettes from a temp agency.

• 1. Cost of ignoring information
• 2. Likelyhood that X causes Y?

• If cost is high, take seriously even if evidence is notoverwhelming
• If benefits high, and the cost low, reasonable to take advantage of infor

• are changes in x a cause of y?
• Touching toads and then cancer
• Radiation then cancer

its a logical fallacy if this is the only info. considered (post hoc ergo)

X and Y are related

Other explanations for the relationship b/w x and y

Attempt to demonstrate covariation(2), and eliminate 3^rd or confounding variables(3) and directionality problems(1)

• 1. manipulation of X
• 2. comparison of the effects of several levels of X on Y
• 3. Subjects begin study equivalent on all important characteristics for each level of X (internal)
• 4. Control over all other important variables

Evidence for temporal precedence rule, but gives comparison too. 0,1,2,4,8 mg

whether there is a relationship (covariation, correlation) between the IV and DV

• Cant pinpoint cause
• Cant show that X and Y covary

• Each subject contributes score or mean for only one level of the IV or PV and there are at least two levels.
• Usually but not always, comparisons are made between groups of subjects

Each subject contributes score or mean for all levels of the IV or PV snd there are at least two levels.

• 1. List each level of IV or PV
• 2. Ask did each subject contribute data (a mean or score) for all levels (within) of variable or just one(between)

All subjects are treated exactly the same except for X(the IV) (external)

• 1. Eliminate variable completely
• 2. Hold constant or control variables across conditions (potentionally variables)
• 3. Random assignment

S has equal and independent chance of being assigned to any group-a statistical control

• Randomized groups design
• Post-test only control group design

*this is only possible if X can be manipulated

Equal frequency in the long run

Degree to which the findings can be generalized to other subjects, and to other situations (settings, levels of variables, ways of measurement)

• reactivity (measurement)
• People in the sample that dont belong/People notin the sample that who do belong/wrong proportion of people
• Unusual place(situation)

• Selection bias
• mortality ordifferential attrition
• Interaction of selection bias with the treatment

Diffusion, compensatory equalization, compensatory rivalry, resentful demoralization

• Cheap
• Fast

• Differences among cohorts
• Cohort = companion = accomplice

Eliminates cohort differences

• Very time consuming
• Expensive
• Lose subjects
• Age effect may be specific to cohort you are studying

• True experiments (b/w subjects designs)
• Eliminate, hold constant/control, Random Assignment
• Pretests (check equivalent Ss, no selection bias)

• 1. Groups equivalent
• 2. Subjects really change
• 3. Scale attenuation effects:
• ceiling effects and basement (floor) effects

• "hold constant" subjects
• Reassign based on pretest or records

• 1. Matching by ranking
• --pretest or records
• --Rank subjects best to worst
• --divide ranked subjects into clusters according to number of groups
• --randomly assign subjects from each cluster into the groups
• 2. Matching by using a descriptive statistic
• --No random assignment
• --does not control for unknown sources of variance

hold constant the subjects

• no random assignment
• Does not control for unknown sources of variance

• 1. exactly equivalent subjects (3rd characteristic of true experiment)
• a.) perfect matching
• b.)each subj. own control-compared against self, not people from another group(differences across conditions matter-not that some people better than others
• c.)conditions not confounded with subjects (vs. BS designs; treatments ARE confounded with subjects)

• 2. Fewer subjects (more info from each)
• -terms: levels, conditions, groups
• 3. Statistics hve more power

• 1.Show that Y didnt happen until after X (temporal precedence rrule)
• 2. Show that X and Y are related (covariation rule) The cause and effect must be correlated with each other
• 3. Show that other explanations for the relationship between X and Y can be ruled out (internal validity rule)
• 4. Plausible causal link between X and Y

• Not always possible to give all treatments to 1 subject
• Strong possibilities of many threats to internal validity

• 1. Order effects (maturation)
• -order and treatment condition vary together

• 2. Differential carry over effects(testing sort of)
• -Performance on a condition partially dependent on specific prior treatment

different prior treatments, different mental set for each list

influence on order effects (maturation) ex: fatigue

alcohol

• 1. complete
• 2. incomplete

• use all possible orders
• 2 conditions: 2 X 1 = 2
• 4 conditions: 4 X 3 X 2 X 1 = 24
• 6 conditions: 6 X 5 X 4 X 3 X 2 X 1 =72

• 1. random order-each subject different random order
• 2. balanced latin square

create rows that determine order each S gets each condition

• number of rows=number of treatments (conditions)
• number of columns=number of treatments (conditions)

ex: left side body/alphabet, right side/ silent....