Ch 3: Differences, Consistency, Test Scores

Question 1

Variance - Definition

Answer 1

• 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

Question 2

Interindividual Variability - Definition

Answer 2

Differences that exist between people

Question 3

Intraindividual Variability - Definition

Answer 3

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

Question 4

What factors determine the size of the variance?

Answer 4

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

Question 5

Variance - Formula

Answer 5

 N  - sum of squares / nr of scores

Question 6

Variance (Binary) - Formula

Answer 6

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

Question 7

Can the variance/SD be 0?

Answer 7

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

Question 8

Covariance - Definition

Answer 8

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

Question 9

Covariance - Formula

Answer 9

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

Question 10

What information can be discerned from the covariance?

Answer 10

The direction of the association (not the magnitude though)

Question 11

Correlation - Definition

Answer 11

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

Question 12

Correlation - Formula

Answer 12

    (SDx)(SDy)

Question 13

What information can be discerned from the correlation?

Answer 13

• direction of association
• magnitude of association

Question 14

Central Tendency - Definition

Answer 14

The score that is the most representative of the entire distribution

Question 15

What is the most commonly used reflection of central tendency?

Answer 15

The mean

Question 16

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

Answer 16

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

Question 17

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

Answer 17

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

Question 18

Skew - Formula

Answer 18

(N)(SD³)

Question 19

Raw Score - Definition

Answer 19

Scores obtained most directly from the responses to test items

Question 20

Percentile Rank - Definition

Answer 20

Percentage of scores that are below a specific test score

Question 21

How do you calculate the percentile rank using raw scores?

Answer 21

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

Question 22

How do you calculate the percentile rank using z scores?

Answer 22

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

Question 23

Z score - Definition

Answer 23

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

Question 24

Z score - Formula

Answer 24

SD

Question 25

Z score - Statistical Properties

Answer 25

• mean = 0
• SD = 1

Question 26

What are the benefits of using z scores?

Answer 26

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

Question 27

What are the limitations of z scores?

Answer 27

Z scores are not intuitively understandable for many people

Question 28

Standardised Scores - Definition

Answer 28

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

Question 29

Standardised Scores - Formula

Answer 29

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

Question 30

When do you conduct a normalisation transformation?

Answer 30

When the data is not normally distributed

Question 31

What are the assumptions underlying a normalisation transformation?

Answer 31

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

Question 32

Normalisation Transformation - Steps

Answer 32

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)

Question 33

Test Norms - Definition

Answer 33

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