General Information about the Test 50 MC questions 7 from book sections not touched on in class: major ideas and findings, not specific names of researchers.

General Information about the Test

50 MC questions

7 from book sections not touched on in class: major ideas and findings, not specific names of researchers (e.g., “Ambady and Rosenthal found that judgments of teacher behavior . . . could also be used to predict teacher effectiveness”)

You will need to know major figures (e.g., Eysenck, Epstein)

Definitions of personality (2)Methods (correlation, factor analysis, interaction) (16)Trait/situation controversy (11)Personality stability and change (7)Trait theories and the Big 5 (12)Interpersonal theory (2)

You will not need to memorize the specific graphs, but you will need to know how to “read” graphs of the types we’ve gone over in class.

Explain how range affects the graph of the data.

Correlation: r = .60

Correlation: r = -.29Effect of restricting range

I'm still a little confused on how to find a main effect.

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Low E High E

Mo

od

Alone With others

The relationship between x and y depends on z: “Interaction”

Interactionism in personality: The relationship between p and behavior depends on the situation

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Low nAch High nAch

Tas

k d

esir

abil

ity

Easy Hard

The relationship between x and y depends on z: “Interaction”

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abil

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Easy Hard

Main effects? Interaction?

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Easy Hard

Main effects? Interaction?

How does the matrix data turn into factor data? The numbers on the graph of correlations are different from the numbers that appear on the structure matrix where you have the factors. Why do they change?

I was just wanting you to go over the correlational matrix and how to get the main factors from the matrix because I really do not understand how to read the correlational and factor matrix.

1 2 3 4 5 6 7

1. I did what had to be done - .10 .75 -.05 .03 .12 .00

2. I learned to live with it - -.02 .52 .61 -.07-.08

3. I tried to get rid of it - .17 .00 .09.15

4. I accepted that it was there - .71 .11 .08

5. I tried to see it in a different light - .06-.04

6. I slept more than usual - .59

7. I daydreamed about other things -

Factor: A B C D

1. I did what had to be done .62 .15 .01 -.12

2. I learned to live with it .03 -.08 .49 .08

3. I tried to get rid of it .54 .04 -.20 .16

4. I accepted that it was there .10 .11 .56 .03

5. I tried to see it in a different light .07 .08 .50 .02

6. I slept more than usual -.02 .72 .12 -.13

7. I daydreamed about other things .08 .48 .08 .08

Correlations

1 .217* -.081 -.197* .590** -.102 .544**

.016 .375 .029 .000 .262 .000

123 123 122 123 123 122 122

.217* 1 .415** .451** .008 .546** .028

.016 .000 .000 .929 .000 .758

123 123 122 123 123 122 122

-.081 .415** 1 .535** -.024 .525** .107

.375 .000 .000 .791 .000 .240

122 122 122 122 122 122 122

-.197* .451** .535** 1 -.257** .674** -.143

.029 .000 .000 .004 .000 .117

123 123 122 123 123 122 122

.590** .008 -.024 -.257** 1 -.254** .697**

.000 .929 .791 .004 .005 .000

123 123 122 123 123 122 122

-.102 .546** .525** .674** -.254** 1 -.101

.262 .000 .000 .000 .005 .267

122 122 122 122 122 122 122

.544** .028 .107 -.143 .697** -.101 1

.000 .758 .240 .117 .000 .267

122 122 122 122 122 122 122

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

att1_0

fear1_0

guilt1_0

host1_0

jov1_0

sad1_0

sass1_0

att1_0 fear1_0 guilt1_0 host1_0 jov1_0 sad1_0 sass1_0

Correlation is significant at the 0.05 level (2-tailed).*.

Correlation is significant at the 0.01 level (2-tailed).**.

Structure Matrix

.841 -.216

.801 -.268

.648 -.014

.639 .068

-.202 .864

-.052 .790

-.071 .698

sad1_0

host1_0

guilt1_0

fear1_0

jov1_0

sass1_0

att1_0

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Factor

Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization.

Concerning the table displaying the correlations of OCEAN scores at 6 six years ago and present, why aren't the spouses' ratings of C and A present? Was there not enough information to calculate the correlations?

MODEL 1Personality traits do not change:

Correlations (p. 49)

Over 6 years . . . Men Women Spouses

O .81 .73 .80C .76 .71E .86 .73 .77A .59 .55N .81 .81 .83

The most open, conscientious, extraverted, agreeable, and neurotic people will also tend to be highest on those personality traits 6 years later . . . at least, that’s the way they see themselves . . . are spousal ratings any better?

Consistency also increases with age throughout the adult lifespan and peaks at around age 50: cumulative continuity principle

Does the big 5 test allow us to make accurate predictions about future behavior and behavior in certain circumstances/situations?

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-0.05

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O C E A N

Co

rrel

atio

n:

Ave

rag

e co

nta

cts

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O C E A N

Mea

ns:

L

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*

r = -.23

Eff

ec

t s

ize

(r)

C and

morta

lity

E and

morta

lity

N and

morta

lity

Ca in

take

and b

one mas

s

BP med

s an

d st

roke

Aspiri

n an

d MI d

eath Equals 85 fewer deaths

among patients of 10,845 study physicians (OR 5,408 deaths among all 690,000 US physicians)

Could we review the differences between reactive interactions, evocative interactions and proactive interactions and their types of correlations along with the corresponsive principle?

People and their environments: 3 kinds of interaction

People react differently to their environments“reactive” interactionsame as statistical interaction

Environments react differently to people“evocative” interactionsame as statistical interaction

People select their environments“proactive” interaction

This last kind of interaction is not the same as statistical interaction; creates a correlation between personality and the environment (main effect).

Do we need to know the 16 factors that are associated with the big 5?

NO

What was the purpose for Hans Eysenck to create the "3rd Dimension" Psychoticism?

Observation + factor analysis + a physical substrate (testosterone and MAO)