Variables, Design and Hypothesis

Question 1

What is an experimental scientific method?

Answer 1

Vary/manipulate IV whilst holding everything else constant

Measure changes in a chosen DV

Changes in IV should cause changes in DV - can infer causality

Question 2

What is a quasi-experimental scientific method?

Answer 2

Similar to experimental but IV cannot be manipulated

Can be trickier to eliminate all confounding variables

Question 3

What designs can a quasi-experimental method be used with?

Answer 3

Non-equivalent groups

Pretest-post-test design

Question 4

What is a correlational scientific method?

Answer 4

No manipulations made

Measure two or more variables and determine extent to which they are related to each other (co-related)

Cannot infer causality

Question 5

How many IVs can an experiment have?

Answer 5

One or more

Question 6

How many levels should an IV have?

Answer 6

Two or more

Question 7

Why does an IV need levels?

Answer 7

To have comparison to see efficiency

Question 8

How many DVs should an experiment have?

Answer 8

One or more

Question 9

What is the operationalisation of DV?

Answer 9

Specifying how we should measure it as precisely as possible

Question 10

What are the four measurement scales for DVs?

Answer 10

Nominal

Ordinal

Interval

Ratio

Question 11

What is nominal data?

Answer 11

Non-numerical categories

Very distinct

Question 12

What are examples of nominal data?

Answer 12

Preferred travel method (car, bus, train, air)

Favourite colour

Question 13

What is ordinal data?

Answer 13

Discrete numbers in a certain order

Question 14

What are examples of ordinal data?

Answer 14

Socioeconomic status

Education level

Happiness levels

Question 15

What is interval data?

Answer 15

Values that have a meaningful difference between them

Continuous data

Question 16

What are examples of interval data?

Answer 16

Temperature

Year

IQ

Question 17

What is ratio data?

Answer 17

Values that have an absolute zero

Question 18

What are examples of ratio data?

Answer 18

Height

Weight

Income

Question 19

What are confounding variables?

Answer 19

Things that can interfere with results that we’re not controlling

Not manipulated but could have influence on results of an experiment

Want to eliminate/minimise these as much as possible

Question 20

When can confounding variables occur?

Answer 20

When some aspect of experimental situation varies systematically with IV

Question 21

What is a between-subjects design?

Answer 21

Participants only take part in one level of IV

Question 22

How can individual differences be accounted for in a between-subjects design?

Answer 22

Randomly assign participants to groups

Question 23

How powerful is a between-subjects design?

Answer 23

Less powerful as need more participants to detect a genuine effect

Question 24

What is a within-subjects design?

Answer 24

Same participant performs all levels of IV

Also repeated measures design

Question 25

How powerful is a within-subjects design?

Answer 25

More powerful as fewer participants needed to detect a genuine effect

Question 26

What are order effects?

Answer 26

When order of conditions are same could argue results due to order Due to practice or boredom

Question 27

What is best to use in a within-subjects design?

Answer 27

Randomisation of trials Counterbalancing

Question 28

What is a matched subjects design?

Answer 28

Participants matched with someone else with regards to demographic characteristics This "pair" tested as one individual over two levels of IV

Question 29

What is a correlational design?

Answer 29

Not manipulating variables Look at variables that already exist and see to what extent they co-vary Doesn't imply causation

Question 30

What are hypotheses?

Answer 30

Theory-driven idea as to why narrow set of phenomenon occur

Question 31

What are the two types of hypotheses?

Answer 31

Experimental Statistical

Question 32

What is an experimental/research hypothesis?

Answer 32

Conceptual idea that tries to explain an observation Based on our original theories

Question 33

What are statistical hypotheses?

Answer 33

Specific statement that we can use to collect data and test our hypothesis with Prediction

Question 34

What are the two types of statistical hypothesis?

Answer 34

Null (H0) Alternative (H1)

Question 35

What is a null hypothesis?

Answer 35

Our observations from our samples imply they come from same population

Question 36

For parametric statistics, what does a null hypothesis say?

Answer 36

All means equal

Question 37

For non-parametric statistics, what does a null hypothesis state?

Answer 37

All distributions equal

Question 38

What is an alternative hypothesis?

Answer 38

Logical alternative to null hypothesis Predict significant different/relationship between variables

Question 39

What are the two types of alternative hypothesis?

Answer 39

Directional Non-directional

Question 40

What is a directional alternative hypothesis?

Answer 40

Will be a higher/lower difference

Question 41

What is a non-directional alternative hypothesis?

Answer 41

There will be a difference

Question 42

What are the properties of H0 and H1?

Answer 42

Mutually exclusive - only 1 statement true Exhaustive - cover all possible outcomes in experiment

Question 43

How do you conduct null hypothesis significance testing?

Answer 43

Only reject H0 when probability of it being true (p) lower than specific criterion (alpha) Using inferential testing

Question 44

How is an inferential test conducted?

Answer 44

Generate test statistic Set specific alpha criterion (usually 0.05) Using these values to help determine our probability value (p-value)

Question 45

What does p < 0.05 mean?

Answer 45

Less than 5% probability results happened by chance Suggest something unique happening between population Significant result Reject H0 and support H1

Question 46

What does it mean if p > 0.05?

Answer 46

5% or more probability events happened by chance Suggest nothing unique happening between populations Non-significant result Failed to reject H0 and only have support for H0