Stats Flashcards

1
Q

Give 2 conditions for using factorial designs.

A

More than 1 IV is thought to effect a DV and/or ignoring an IV detracts from the explanatory power of our experiments.

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2
Q

Give 2 limits of between-subjects designs.

A

Participant variables and number of participants required.

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3
Q

Give 2 limits of within-subjects designs.

A

Practice effects and long testing sessions.

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4
Q

What are the assumptions in mixed factorial ANOVA?

A

Interval/ratio data, normal distribution, homogeneity of variance (between-subjects) and sphericity of covariance (within-subjects).

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5
Q

What should you do if the assumptions of mixed factorial ANOVA are violated?

A

Process with caution, report the violation, and use corrected results if possible.

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6
Q

If there are interactions in a mixed factorial ANOVA, which formulae do you use?

A

Within-subjects formulae.

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7
Q

What are the assumptions for Pearson’s correlation?

A

Linear variable relationship, interval/ratio data and normal distraction free of outliers.

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8
Q

What are the assumptions for Spearman’s correlation?

A

Monotomic variable relationship and ordinal/interval/ratio data.

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9
Q

Why do distribution and outliers not influence Spearman’s correlation?

A

They use ranks, not means and SDs.

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10
Q

How are degrees of freedom calculated in a Pearson’s correlation?

A

df - n*pairs - 2

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11
Q

Give 2 factors that affect the significance threshold of a correlation coefficient.

A

The magnitude of the correlation and the number of observations.

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12
Q

When are tests of regression used?

A

When causal relationships between variables are likely.

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13
Q

What type of tests are regression?

A

Inferential statistical tests of association.

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14
Q

What are the assumptions of regression?

A

Linearity, interval/ratio data, normal distribution free of outliers and homoscedasticity.

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15
Q

Describe residuals.

A

The distance between the actual outcome and predicted outcome.

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16
Q

What do you do if there is heteroscedasticity in a regression?

A

Proceed with caution and inform the reader.

17
Q

What is multicollinearity and how do you test for it?

A

When predicted variables are highly correlated, tested by running simple correlations.

18
Q

How much should variables correlate to be considered to have multicollinearity?

A

(+/-) 0.8

19
Q

What do mixed factorial designs use very efficiently?

A

Participant numbers and participant time.

20
Q

What is homoscedasticity?

A

When residuals have the same degree of variation across all predictor variable scores.

21
Q

What is multiple regression?

A

Predicting one outcome variable from more than one predictor variable.

22
Q

Describe simultaneous regression.

A

All predictors are entered at the same time - used for explanatory analysis.

23
Q

Describe hierarchical regression.

A

Predictors are entered in a pre-defined order - used when informed by theory.

24
Q

Describe stepwise regression.

A

Predictors are entered in an order driven by how well they correlate with the outcome.