Psy 2200 Exam 2

• Examining relationships between and among variables
• Seeing if two variables are related to each other
• Useful for
• - description
• - manipulation
• Extent to which 2 measured variables are sytematically related

• Dirction of casuality
• -X could cause Y
• - or Y could cause X
• 3^rd Variable Problem
• - something else could cause both X and Y

• statistic that shows the direction of a relationship between 2 variables
• -show the direction of a relationship: postivite/negative
• -shows the magnitude (strength) of a relationship: relatively weak/relatively strong

• As one goes up, the other goes down
• -go in the same direction
• -direct relationship

• Indicated by absolute value:
• -closer to 1 (-1) = stronger
• -closer to 0 = weaker
• strong relationship: variables are more related
• weak relationship: variables are less related

• sample size
• -# of participants min. r for sig.
• >>10_________.52
• >>100________.16
• >>1000_______.05

• magnitude of correlation: larger r is more likely to be significant
• -conventional rule
• >> .10 = weak correlation
• >> .30 = moderate correlation
• >> .50 = stronge correlation

alpha level: how sure we want to be

• manipulation: making something happen
• control: keeping everything the same except the manipulated variable

• IV is manipulated
• -quantitatively (including high, low & no)
• -qualitatively (e.g., rehearsal vs imagery)

random assignment

extraneous factors are controlled

DV is measured

Basic logic: if DV varies as a result of manipulation of the the IV, than the IV caused the DV to change.

-the basic minimum without which any experiment is uniterpretable

-degree to which you can confidently draw a casual inference from the data (i.e., the IV caused the DV to vary)

- degree to which your study is free from confounds

• Covariation: 2 things go together (change at the same time)
• Temportal Sequence: 1 thing precedes another
• Eliminating Confounds: other things that didn't cause it to happen

any uncontrolled extraneous variable that "covaries" with the independent variable and could provide an alternative explanation of the results

literally means mistaking one thing for another

Participant/subject variables: something about the subjects in the study (e.g., partcipants are selected base on the same trait).

Environmental variables: something about the setting the subjects are in.

Procedural variables: something about the procedure - the way subjects are treated.

• Be careful
• -read as much as possible about your topic to reduce the chance of making mistakes

• Replicate Your Findings
• -use converging operations to study the same topic

• Measure and Control
• -if you can, control it from the start
• -if you can't, measure it and control it statistically

• "People are different"
• individual differences can undermine careful, systematic scientific observation (e.g., selection bias)

• "People change"
• differences within people across time and situations can masquerade as meaningful research findings (e.g., history, maturation and regression toward the mean)

• "The process of studying people changes people"
• many studies have the uninteneded consequence of changing people's behavior in some small way (e.g., tesing effects, mortality, reaction biases and instrumentation)

sampling people from an unrepresentative sample

• 2 kinds
• -self-selection
• -selection of samples that may differ to begin with

changes that occur in a very larege group of people such as a nation of culture

specific developmental or experimental changes that occur in a particular person, or a particular cohort overtime

• extreme high/low scores tend to move closer to the mean
• -may occur when participants have been selected from a study based on extreme scores

changes in test scores are due to repeated testing rather than manpulation of the IV

• participants are lost from the study
• - group differences may be due to differences in those remaining and not to the manipulation of the IV

• people behave differently when they know they are being studied

• demand charactereistics: give hints as to what the experimenter wants
• participant reactence: participants rebel against an experimental manipulation
• evaluation apprehension: people are concered about being judged

errors in a study due to the predisposed notions or beliefs of the experimenter

unintentional behaviors on the part of the experimenter

• changes in the DV are produced by changes in the measurement instrument itself
• - a bad scale (unreliable)
• - a bad measure (rater)
• - time of measurement

• Use random assignment
• -law of large numbers
• -most things are normally distributed

Pretesting: can check baseline differences and control is necessary

Matching: ensures confounding variables are evenly distributed (e.g., gender, IQ)

Experimental Control: keep/hold things constant

both deal with data collection

testing sensitizes participants to data collection process

instrumentation changes the data collection process in some way

• comparison/control group
• random assignment
• experimental control

*need to think about threats while in the design phase of a study

operationally define the construct (concept)

provide informed consent

explain without revealing the hypothesis

• straightforward: no deception involved (only type used in psy 2200)
• staged: set up "real situational/psychological state (often uses deception/confederates)

• Weak: run risk of not detecting small, but meaningful, differences between groups
• Strong: maximizes differences between groups and may not reflect reality

• Situational: features in the environment
• - # of people present
• - level of lighting, volume of music

• Task Variables: different kinds of problems to solve
• - abstract v concrete
• - easy v difficult

• Instructional Variables: different ways to perform a task
• - imagery v rehearsal
• - argue for/against your own position

• the distribution of means will increasingly approximate a normal distribution as the size N of samples increases
• -i.e., the larger the sample size the more normal the distribution will be

the method of selecting a sample from a population

every memeber of the population has an equal chance of being selected for the study

the method of allocating participants to groups

each participant is as likely to be assigned to one group as to another

any thing other than the manipulated variable that may affect the outcome (aka control variables/nuisance variables)

• theory
• past research
• intuition

• self-report
• behavioral (covert, overt)
• physiological responses

• outcome of the experimetnal group
• outcome of the comparison group

* comparison group is not necessarily a "control" group

the behavior of the participant in response to manipulation of the IV

must be sensitive enough to detect meaningful differences between groups

best if graded/continous

everyone does well when the DV is not sensitive enough

everyone does poorly if the DV is too sensitive

• found by comparsion
• -to different subjects
• -to selves in different conditions

The comparsion group determines/drives experimental design.

• Between-participant (independent groups): different groups of particpants are due to different levels of the IV
• Within-participant (repeated measures): a single group of participants is exposed to all levels of the IV.

• "True" control group: no treatment
• Placebo: give shame treatement
• Waiting list: give no treatment right now
• Yoked: same exact conditions except treatment

extent to which your conditions are physically similar to the "real world"

not of primary importance but affects external validity

extent to which your conditions are psychologically similar to the "real world"

of primary importance: affects internal validity

• One-group design: psuedo-experiment
• One-group pre-test/post-test design: repeated measures/within-partcipants
• Post-test only with non-equivalent groups: between-participants
• Pre-test/Post-test design with nonequivalent groups: measure both, treat one, measure both again; ensures groups are similar to begin with

need to have comparable groups

need to see if effect is larger than or smaller than expected

every possible sequence is used at least once

sequences: (# of conditions) e.g., 3! = 3 x 2 x 1 = 6

• more conditions = more sequences
• e.g., 6! 6 x 5 x 4 x 3 x 2 x 1 = 720

includes only some of the possible sequences

each sequence must appear equally often

use a balanced Latin square to choose sequences

every condition occurs equally often in each position

every condition precedes/follows other conditions only once

• 1st row: A, B, L (last), C, L-1, D, L-2, E, L-3, etc.
• 2nd row: increase 1 letter at each position of the 1st row (e.g., A-B, F-A)
• Continue for each row
• Randomly assign each condition to a letter

if the relationship between 2 variables is statistically significant than the score on 1 variable can predict the score of the other

• don't infer casuality
• beware of directionality problem
• think about 3rd variables

...the closer we can come to making casual inferences.

• correlatation between 2 variables with influence of 1 or the other variable statistically removed.
• If X and Y are still related after removing Z the correlation is not due to Z.

simplest type of design

1 IV, 2 Leveles (experimental, comparison/control)

• Between-participant (independent groups): 1 group receives treatment, 1 does not.
• Within-participant (repeated measures): group recieves treatment and also doesn't
• -serves as own controls.

• independent group: between-participant design with random assignment
• repeated mesaures: within-participant design
• matched groups: between-participant design without random assignment
• nonequivalent groups: not a "true" experiement

experimental group + 1 or more comparison group

• 2+ levels give more information
• (e.g., curvilinear relationships: more groups)

facorial design: 2+ IV's/factors

closer to real-world conditions

all levels of 1 IV combined with all levels of other IVs

• Advantage
• -each participant enters the experiment untainted by previous exposure to the IV.

• Disadvantages
• -takes many more participants
• -problem of equivalent groups

• Advantages:
• -need fewer particpants
• -participatints act as their own controls
• -reduces error variance "within" conditions

• Disadvantages
• -more demanding to participants
• -sequence/order effects

generally use scatterplots

shows relationships between 2 variables

can use only 2 datapoints but this restricts the range and may miss non-linear relationships

they help to make informed guesses about whether correlated variables might be casually related

not definitive evidence but can support (or not) a particular casual explination

can help predict hypotheses about possible effects of 3^rd variables

• # of factors noted by # of numbers
• -A x B factorial design (e.g., 2 factors A & B)
• # of levels of each factor noted by the #s themselves
• -3x2 factorial design
• -2 factors: 1 has 3 lvls, 1 has 2 lvls
• -combines to 6 conditions (2x3=6)

• more than 1 IV, all between-participants
• If 1 IV is a particular variable, called a person x environment factorial design

• more than 1 IV, all within-participants
• aka repeated measures factorial design

• more than 1 IV, both between and within participants
• if 1 IV is a participat variable, it's called a person x environment mixed factorial design

• 5 lvls, 3 w/2 factors, 2 w/3 factors
• -72 conditions
• -720 participants needed for between-participants

• large experiments can become unwieldy
• hard to inerpet: lots of main effects and interactions

• main effects: effect of each IV in isolation
• interactions: effect of IV's "combined"

• Marginal mean of B1 (A1B1 + A2B1/2)
• Marginal mean of B2 (A1B2 + A2B2/2)

If the marginal means of B1 and B2 are differnt there may be a main effect.

• E.G.,_A1__A1
• B1___20__40
• B2___30__20
• A1B1=20 A2B1=40 Difference=+20
• A1B2=30 A2B2=20 Difference=-10

if the differences found for B1 and B2 are different there may be an interaction.

If the changes that occur in the means of one row of a factorial table are similar to changes found in the other row(s) the factors are NOT interacting.

If the changes in one row are greater, lesser, or reversed from those in another row there is (i.e., maybe) an interaction

• parallel lines mean no interaction
• nature of relationship for B1 and B2 is the same across levels of A1 and A2

• ___________Main Effects____Interactions
• Look at_____marginal means__cell means
• Compare for_sig. differences___patterns of change
• Look at_____SPSS print-out__SPSS print-out
• Look at _____graphs________non-parallel lines