Psychology Final (Test 4) Flashcards

1
Q

Factorial ANOVA

A
  • at least two independent variables/predictors/factors
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2
Q

Main-Effects

A
  • looking if there is a difference between the levels of the ONE variable
  • “is there a difference with the orientation of psychology used in therapy?”
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3
Q

Interactions

A
  • looking to see how the variables interact/depend on each other
  • “does life satisfaction DEPEND on how many clubs you belong to”
  • “does attention DEPEND on the relationship and emotion”
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4
Q

Interpretation

A
  • estimated by looking at the means

- dependent variable on the Y-axis

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5
Q

Terminology

A
  • number of variables > at least two [orientation, time practiced]
  • levels for each > orientation for psychology has 3 LEVELS [cognitive, psychodynamic, gestalt]
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6
Q

Correlation

A
  • relationship between two continuous variables
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7
Q

scatterplot

A
  • displays scores of individuals simultaneously
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8
Q

positive relationship

A
  • direct

- line points to top right

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9
Q

negative relationship

A
  • non-direct

- line points to the bottom right

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10
Q

non-realtionship

A
  • no relationship

- no directionality

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11
Q

strength

A
  • range rom 1 to -1
  • the closer to 1/-1, the stronger the relationship
  • coefficient = r
  • sign = positive/negative
  • value = strength of relationship
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12
Q

effect size

A
large = .50
medium = .30
small = .10
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13
Q

correlation formula

A

r = ∑(X - Mx)(Y - My)/√(SSx)(SSy)

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14
Q

correlation df

A

df = N - 2

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15
Q

Chi-Square

A
  • examine multiple categorical/nominal variables

- no means

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16
Q

goodness of fit

A
  • does the data fit expectations

- null = test equality OR test pattern

17
Q

Independence

A
  • are the variables independent of each other > two variables
  • look at relation > chi-sq
18
Q

chi-square formula [both]

A

∑(O-E)²) / E

19
Q

contingency table

A
  • displays the frequency for each level of one nominal/categorical variable crossed with each level of the other nominal/categorical variable
20
Q

independence chi-sq df

A

df = (Ncolumns - 1) x (Nrows - 1)