# Psy 2200 Exam 2

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1. Correlational Research (4)
• 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
2. Why can't correlations establish causality? (2)
• Dirction of casuality
• -X could cause Y
• - or Y could cause X
• 3rd Variable Problem
• - something else could cause both X and Y
3. Correlational Coefficient (r) (3)
• 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
4. Positive Correlations
• As one goes up, the other goes down
• -go in the same direction
• -direct relationship
5. Negative Correlations
• 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
6. The statistical signficance of r is affected by what 3 factors?
• 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
7. 2 Aspects of Experimental Power
• manipulation: making something happen
• control: keeping everything the same except the manipulated variable
8. 5 Aspects of True Experiments
• 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.
9. Internal Validity (3)
-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
10. Mills' 3 Requirements for Establishing Casuuality
• 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
11. Confounding Variables (confounds, nuisance variables) (2)
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
12. 3 Causes of Confounds
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.
13. 3 Ways to Deal with Confounds
• Be careful

• -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
14. 3 Categories of Participant Confounds
• "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)
15. Selection Bias (2)
sampling people from an unrepresentative sample

• 2 kinds
• -self-selection
• -selection of samples that may differ to begin with
16. History
changes that occur in a very larege group of people such as a nation of culture
17. Maturation
specific developmental or experimental changes that occur in a particular person, or a particular cohort overtime
18. Regression Toward the Mean
• 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
19. Testing Effects
changes in test scores are due to repeated testing rather than manpulation of the IV
20. Mortality (Attrition)
• participants are lost from the study
• - group differences may be due to differences in those remaining and not to the manipulation of the IV
21. Participant Reaction Bias
• people behave differently when they know they are being studied
22. 3 participant expectancies
• 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
23. Experimenter Bias (aka experimenter expectancy) (2)
errors in a study due to the predisposed notions or beliefs of the experimenter

unintentional behaviors on the part of the experimenter
24. Instrumentation
• changes in the DV are produced by changes in the measurement instrument itself
• - a bad scale (unreliable)
• - a bad measure (rater)
• - time of measurement
25. 4 Ways to Minimize Confounding Variables
• 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
26. Testing vs Instrumentation
both deal with data collection

testing sensitizes participants to data collection process

instrumentation changes the data collection process in some way
27. Solving Thereats to Internal Validity (3)
• comparison/control group
• random assignment
• experimental control

*need to think about threats while in the design phase of a study
28. Manipulating the IV (3)
operationally define the construct (concept)

provide informed consent

explain without revealing the hypothesis
29. 2 Types of Manipulation
• straightforward: no deception involved (only type used in psy 2200)
• staged: set up "real situational/psychological state (often uses deception/confederates)
30. Strength of Manipulation
• Weak: run risk of not detecting small, but meaningful, differences between groups
• Strong: maximizes differences between groups and may not reflect reality
31. 3 Types of IVs
• 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
32. Central Limit Theorem
• 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
33. Random Sampling/Selection (2)
the method of selecting a sample from a population

every memeber of the population has an equal chance of being selected for the study
34. Random Assignment (2)
the method of allocating participants to groups

each participant is as likely to be assigned to one group as to another
35. Extraneous Variables
any thing other than the manipulated variable that may affect the outcome (aka control variables/nuisance variables)
36. Measures are based on (3)
• theory
• past research
• intuition
37. 3 Types of Measures
• self-report
• behavioral (covert, overt)
• physiological responses
38. 2 Measurable Outcomes of an Experiment
• outcome of the experimetnal group
• outcome of the comparison group

* comparison group is not necessarily a "control" group
39. Measured outcomes depend upon...
the behavior of the participant in response to manipulation of the IV
40. Sensitivity of DV (2)
must be sensitive enough to detect meaningful differences between groups

41. Ceiling Effect
everyone does well when the DV is not sensitive enough
42. Floor Effect
everyone does poorly if the DV is too sensitive
43. Changes in the DV
• found by comparsion
• -to different subjects
• -to selves in different conditions

The comparsion group determines/drives experimental design.
44. Different Designs Address Different Problems
• 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.
45. 4 treatments for control groups
• "True" control group: no treatment
• Placebo: give shame treatement
• Waiting list: give no treatment right now
• Yoked: same exact conditions except treatment
46. Mundane Realism
extent to which your conditions are physically similar to the "real world"

not of primary importance but affects external validity
47. Experimetnal Realism
extent to which your conditions are psychologically similar to the "real world"

of primary importance: affects internal validity
48. 4 Types of Experimetnal Designs
• 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
49. Comparibility
need to have comparable groups

need to see if effect is larger than or smaller than expected
50. complete counterbalancing (3)
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
51. partial counterbalancing
includes only some of the possible sequences

each sequence must appear equally often

use a balanced Latin square to choose sequences
52. 2 Rules for balanced Latin Squares
every condition occurs equally often in each position

every condition precedes/follows other conditions only once
53. Constructing a Latin Square
• 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
54. Graphs are most useful for prediction because...
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
56. The more sohpisticated the statistical techniques...
...the closer we can come to making casual inferences.
57. Partial Correlation
• 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.
58. One-way/Single-Factor Design
simplest type of design

1 IV, 2 Leveles (experimental, comparison/control)
59. Comparison Groups (Between v Within)
• 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.
60. 4 types of Single Factor Designs
• 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
61. Levels
experimental group + 1 or more comparison group
62. Increasing levels in a Single-factor design
• (e.g., curvilinear relationships: more groups)
63. Increasing IVs in a Single-factor design (3)
facorial design: 2+ IV's/factors

closer to real-world conditions

all levels of 1 IV combined with all levels of other IVs
64. Between-Participant Design (Indpendent Groups)
• -each participant enters the experiment untainted by previous exposure to the IV.

• -takes many more participants
• -problem of equivalent groups
65. Withn-Participant Design (Repeated Measures)
• -need fewer particpants
• -participatints act as their own controls
• -reduces error variance "within" conditions

• -more demanding to participants
• -sequence/order effects
66. Graphing Correlations (3)
generally use scatterplots

shows relationships between 2 variables

can use only 2 datapoints but this restricts the range and may miss non-linear relationships
67. How do graphs improve interpretation? (3)
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 3rd variables
68. Factorial Design Notation
• # 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)
69. Between-participants factorial design
• more than 1 IV, all between-participants
• If 1 IV is a particular variable, called a person x environment factorial design
70. Within-participant factorial design
• more than 1 IV, all within-participants
• aka repeated measures factorial design
71. Mixed 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
72. Increasing IV's and Levels
• 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
73. Factorial Designs Look At
• main effects: effect of each IV in isolation
• interactions: effect of IV's "combined"
74. Calculating Marginal Mean
• 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.
75. Calculating Interaction
• 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.
76. 2 Interaction Rules
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
77. Graphing Interactions
• parallel lines mean no interaction
• nature of relationship for B1 and B2 is the same across levels of A1 and A2
78. Finding Main Effects & Interactions
• ___________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

### Card Set Information

 Author: lotusofavalokitesvara ID: 115062 Filename: Psy 2200 Exam 2 Updated: 2011-11-08 10:57:21 Tags: Research Methods Folders: Description: Research & Methods: ch 6, 9, 10, 11 Show Answers:

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