Ch 3: Differences, Consistency, Test Scores Flashcards

1
Q

Variance - Definition

A
  • Statistical way of quantifying variability/individual differences in a distribution
  • Tells you how far each data point is from the mean & every other data point
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2
Q

Interindividual Variability - Definition

A

Differences that exist between people

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3
Q

Intraindividual Variability - Definition

A

Differences that emerge in one person over time or under different circumstances

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4
Q

What factors determine the size of the variance?

A

1) How much the scores in a distribution differ from each other (duh)
2) Metric of the scores in the distribution

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5
Q

Variance - Formula

A
 N  - sum of squares / nr of scores
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6
Q

Variance (Binary) - Formula

A

p(1-p)
- p = proportion of Yes answers

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7
Q

Can the variance/SD be 0?

A

No, because this would mean that there are no differences in the data points at all, meaning that everyone would essentially have the same score

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8
Q

Covariance - Definition

A

Degree of association between the variability in the two distributions of scores

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9
Q

Covariance - Formula

A
     N  - sum of the cross product of deviation scores X & Y divided by the total number of observations
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10
Q

What information can be discerned from the covariance?

A

The direction of the association (not the magnitude though)

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11
Q

Correlation - Definition

A

Index of linear association between two variable, expressed as a value between -1 and +1

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12
Q

Correlation - Formula

A
    (SDx)(SDy)
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13
Q

What information can be discerned from the correlation?

A
  • direction of association
  • magnitude of association
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14
Q

Central Tendency - Definition

A

The score that is the most representative of the entire distribution

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15
Q

What is the most commonly used reflection of central tendency?

A

The mean

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16
Q

What is the distribution of data when there is a positive skew?

A
  • There are few data points above the mean
  • Distribution is skewed to the left (most values are below the mean)
17
Q

What is the distribution of data when there is a negative skew?

A
  • There are few data points below the mean
  • Distribution is skewed to the right (most values are above the mean)
18
Q

Skew - Formula

A

(N)(SD³)

19
Q

Raw Score - Definition

A

Scores obtained most directly from the responses to test items

20
Q

Percentile Rank - Definition

A

Percentage of scores that are below a specific test score

21
Q

How do you calculate the percentile rank using raw scores?

A

(nr of ppl w lower score÷total nr of scores)x100

22
Q

How do you calculate the percentile rank using z scores?

A

Note: use a normal distribution table for this

if Z score is positive:
(Area) + 0.5 x 100

if Z score is negative:
0.5 - (Area) x 100

23
Q

Z score - Definition

A

Number of standard deviations by which the raw score is above or below the mean

24
Q

Z score - Formula

A

SD

25
Q

Z score - Statistical Properties

A
  • mean = 0
  • SD = 1
26
Q

What are the benefits of using z scores?

A
  • it is not ambigious
  • it allows for the comparison of scores that have differing unit scales
  • expresses score in relative terms
27
Q

What are the limitations of z scores?

A

Z scores are not intuitively understandable for many people

28
Q

Standardised Scores - Definition

A
  • Z scores that have been converted into values that people find easier to understand.
  • scores are rescaled to have a different mean and SD
29
Q

Standardised Scores - Formula

A

Ti = (ZSCORE)(SDnew) + (MEANnew)

30
Q

When do you conduct a normalisation transformation?

A

When the data is not normally distributed

31
Q

What are the assumptions underlying a normalisation transformation?

A

1) The construct being measured is actually normally distributed
2) The collected data is an imperfect reflection of the actual (normal) distribution

32
Q

Normalisation Transformation - Steps

A

1) Convert Raw Scores into Percentile Ranks
(nr of ppl below/total) x 100
2) Convert Percentile Ranks into Z Scores
(p.rank/100) - 0.50
3) Convert Z score into Standard Score
(zscore)(newSD) + (newMean)

33
Q

Test Norms - Definition

A

The scores of a large group of people is used as the reference sample