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Research Stats > Week 5 (Correlation, Nonparametric tests, Nonparametric Analysis for Relationships/Associations) > Flashcards

Week 5 (Correlation, Nonparametric tests, Nonparametric Analysis for Relationships/Associations) Flashcards

1
Q

Factors are seen as what in exploratory and observational research?

A

Exposures

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2
Q

Conditions are seen as what in exploratory and observational research?

A

Outcome

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3
Q

Longitudinal studies are studies that are…

A

overtime

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4
Q

What are the 2 types of longitudinal studies?

A

Prospective and retrospective

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5
Q

prospective longitudinal studies

A

into the future

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6
Q

Retrospective longitudinal studies

A

into the past

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7
Q

Cross-sectional studies are studies that are…

A

a “snapshot”

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8
Q

Correlation can be shown using…

A

scatter plots, pairs of scores

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9
Q

How do you know the strength of the correlation?

A

values between -1.0 and 1
- “0” is no relationship
- 1.0 = perfect positive relationship
- -1.0 = perfect negative relationship

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10
Q

what does the ‘sign’ imply on the correlation?

A

sign implies direction of the relationship

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11
Q

Assumptions of correlation

A
  • scores represent the underlying population
  • scores are normally distributed
  • each subject has a score for both X and Y
  • X and Y are independent measures
  • X and Y are observed, not controlled
  • relationship between X and Y is linear
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12
Q

Correlation coefficient: <= .25

A

little or no relationship

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13
Q

Correlation coefficient: .25 to .50

A

low to fair

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14
Q

Correlation coefficient: .50 to .75

A

moderate to good

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15
Q

Correlation coefficient: >= .75

A

strong relationship

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16
Q

limitations of correlations

A
  • relationship between 2 variables only
  • only quantifies linear relationships
  • does not tell us “cause and effect”
  • does not account for agreement
  • range of observations
17
Q

Coefficient of Determination (r^2)

A
  • coefficient of determination
    -“the percent of variance in y that is explained by x”
18
Q

the coefficient is very sensitive to…

A

sample size

19
Q

Small effect size for r

A

r = .10

20
Q

medium effect size for r

A

r = .30

21
Q

large effect size for r

A

r = .50

22
Q

non-parametric statistics are based on:

A
  • comparisons of ranks of scores
  • comparisons of counts (yes/no) or “signs” of scores
23
Q

data can be “collapsed” from Ratio to…

A

ordinal/nominal

24
Q

advantages of nonparametric methods

A
  • appropriate for a wide range of situations
  • can use with categorical data
  • simple computations
  • outliers have LESS effect
25
Q

Disadvantages of Nonparametric Methods

A
  • they waste information
  • less power (65-95%; increase sample size)
  • if outliers are not errors, effects may be underestimated
26
Q

nonparametric test for: 2 independent groups

A
  • Mann-Whitney U (BEST)
  • wilcoxon rank sum test
27
Q

nonparametric test for: >= 3 independent groups

A

Kruskal-Wallis ANOVA by ranks

28
Q

nonparametric test for: two related samples

A
  • Sign test
  • Wilcoxon Signed-ranks test**
29
Q

nonparametric test for: >=3 related samples

A

Friedman two-way ANOVA

30
Q

Non-parametric analog of Pearson r

A
  • 1 continuous, 1 ordinal variable
  • 2 ordinal variables
  • non-normal distribution of ratio/interval data
31
Q

Chi-square (x^2)

A
  • measures association between 2 categorical variables
  • tests the difference between observed frequencies (O) and frequencies expected by chance (E)
32
Q

what are the 2 types of Chi-square (x^2)

A

goodness of fit
tests of independence (association)

33
Q

what is goodness of fit?

A
  • coin flip
  • compare observed frequencies of 1 variable to uniform frequencies of another
  • with enough tests will end up being 50/50
34
Q

what is tests of independence (association)?

A
  • much more common
  • compare observed frequencies from 1 variable to observed frequencies of another variable
  • ex: male or female?
35
Q

assumptions of chi-square

A
  • frequencies represent individual counts
  • categories are exhaustive and mutually exclusive
  • no subject is represented twice
36
Q

Most common application of chi-square is…

A

test of independence

Research Stats (4 decks)

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