Task 4

Question 1

What is an association?

Answer 1

“if the value of one variable tells you something about the value of the other variable, then they are associated”

Question 2

Which 2 types of variables are there?

Answer 2

dependent variables

independent variable

Question 3

What does the dependent (=response) variable measure?

Answer 3

measures outcome of study

Question 4

What does the independent (=explanatory) variable measure?

Answer 4

causes/explains change in dependent variable

Question 5

What does a Scatterplot show?

Answer 5

The relationship between two quantitative variables (e.g. job experience vs. salary)

= Streu/ Punktdiagramm

Question 6

What lays on the X-axis of a scatterplot?

Answer 6

Explanatory / indipendente variable

Question 7

What lays on the Y-axis of a scatterplot?

Answer 7

Response / dependent variable

Question 8

What is the strength of a scatterplot?

Answer 8

How closely the points follow a clear form

Question 9

Which 3 forms can be shown in a scatterplot?

Answer 9

1. overall pattern
2. Striking deviations
3. linear relationship: roughly straight line

Question 10

Which directions can the association in the scatterplot have?

Answer 10

Positive association: increase in one variable means increase in the other

Negative association: increase in one variable means decrease in the other

Question 11

What does the correlation r (Wechselwirkung) measure?

Answer 11

direction and strength of the linear relationship between two quantitative variables

Question 12

What do you know about the properties (Eigenschaften) of a correlation?

In which range do they lay?
How do they deal with outliers?
Which values are they using?

Answer 12

Always between -1 and 1

-1 strongest negative correlation
0 no relation
1 strongest positive correlation

not resistant to outliers

used standardized values; no change in r, when changing units of measurement

Question 13

Name 6 kinds of correlation r

Answer 13

strong positive correlation r=1

moderate positive correlation 01

no correlation r=0

moderate negative correlation 0>r>-1

strong negative correlation r=-1

curvilinear relationship

Question 14

What to use to analyze the relation of two quantitative variables?

Answer 14

Scatterplot

Question 15

What to use to analyze the relation of a quantitative and a categorical variable?
5 options

Answer 15

Boxplot, stem-leaf plot, pie chart, bar chart or histogram

Question 16

What to use to analyze the relation of two categorical variables?

Answer 16

Two-way-table

Question 17

What are the characteristics of a two-way table?
What does it organize?
What does it show?

Answer 17

Organize two categorical variables:

• row variable: across ->
• column variable: down

The table shows counts (= absolute frequencies) or percent (=relative frequencies)

Question 18

Which 3 types of distribution can a two-way-table have?

Answer 18

Joint distribution:

• distribution for each cell
• divide number in cell by total sample size

Marginal distribution:

• distribution for a single variable
• divide total of a variable by total sample size

Conditional distribution:

• relative frequencies
• eg. given that the person is a boy what is the probability his eyes are no green?

Question 19

What happens if you have no association in a two-way table?

Answer 19

• knowledge about x does not give any information about probability of y
• Joint distribution can be calculated by multiplying marginal distribution and dividing the product by the table total
• relative frequency (Prozente) equal in all columns (automatically in all rows as well)

Question 20

What happens if you have a perfect association?

Answer 20

• By knowing x you can perfectly predict y
  -fields have to be 0 for perfect association to occur
  relative frequencies 0% and 100% in one condition, and 100% and 0% in the other

Question 21

What happens if you have an association?

Answer 21

relative frequency distribution different between the columns ( also goes for the rows)

Question 22

Explain the term reliability in the case of interrupter reliability

Answer 22

Reliability: to what extend a repeated measurement yields the same score

Question 23

Explain the special case of interrater reliability with an example

Answer 23

• 2 raters judge a same set of objects independently
• They agree or disagree on different objects and you want to find out how much they agree (=interrater reliability =agreement Kappa)

Question 24

When do you have agreement Kappa k?

Answer 24

If the raters judged the objects randomly, there is still chance of agreement = expected agreement.

Kappa takes out the random factor via subtracting this expected random chance

𝑘 = (𝐴𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 − 𝐴𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 ): (𝑁 − 𝐴𝑒𝑥𝑝𝑒𝑐𝑡𝑒𝑑 )

Question 25

What would happen to k if the teacher would agree in every case?

Answer 25

A-Observed would equal N making the nominator and denominator the same. Kappa would come out exactly as 1.

Question 26

How is the measure of agreement if k<0.4

Answer 26

bad

Question 27

How is the measure of agreement if 0,4

Answer 27

mediocre

Question 28

How is the measure of agreement if k>_0,7

Answer 28

good