Research Methods - Lecture 2: Introduction to research methods in psychology

Question 1

Population

Answer 1

All the scores, events, etc. that we’re interested in

-> NOT the individuals whom measurement/s are coming from

Question 2

Sample

Answer 2

A representative subgroup of the population

Question 3

Why is a sample taken?

Answer 3

We usually can’t measure the entire population we’re interested in, so we take a sample and infer general results about the population from the sample

Question 4

2 ways sampling can go wrong

Answer 4

Sampling error and sampling bias

Question 5

Sampling error (2)

Answer 5

• Results of repeated samples from the same population will always differ -> occurs by chance -> anytime take samples from populations, you are at the mercy of chance probabilities and may end up picking ppl who aren’t representative of the population
• Unavoidable

Question 6

How can you minimise sampling error?

Answer 6

Using bigger samples

Question 7

Sampling bias

Answer 7

Sample misrepresents population in a systematic way -> unrepresentative sample

Question 8

How can you avoid sampling bias?

Answer 8

Random sampling - so every member of the population has an equal chance of being picked for the sample

Question 9

What is the consequence of serious sampling bias?

Answer 9

It invalidates the research

Question 10

What is the problem with random sampling in psychology?

Answer 10

Random sampling is very unlikely because psychological research focuses on behaviour of all humans and it’s very unlikely to get a representative sample therefore samples usually get volunteers - usually uni students (have time, live close by - more convenient for them to partake)
-> have to be cautious about whether you can infer anything from the sample in regards to the behaviour of the population overall

Question 11

Does sampling error or bias relate to validity?

Answer 11

Sampling bias

Question 12

Does sampling error or bias relate to reliability?

Answer 12

Sampling error

Question 13

2 general kinds of research design

Answer 13

Observational and experimental

Question 14

Observational design

Answer 14

• Measure two DVs and look for a relationship between them

* There is no IV, because nothing is manipulated - e.g., is self-esteem related to intelligence?

Question 15

What is another name for observational design and why?

Answer 15

• Sometimes called a correlational design, because a relationship between variables is a correlation

Question 16

When are observational designs used?

Answer 16

When the research/experiment conflicts with ethical or practical reasons

Question 17

Basic weakness of observational design

Answer 17

Just because two DVs are correlated, we can’t conclude that one affects the other
CORRELATION DOES NOT IMPLY CAUSATION
-> Don’t know whether one variable A causes B or vice versa
-> Both variables could be affected by another third variable - Third variable problem

Question 18

Experimental design

Answer 18

Manipulate IV and look to see effect on DV
Can imply causation (because controlling IV) - e.g., is self-esteem related to results of a fake IQ test?
-> Know direction of cause and effect

Question 19

Which designs are more powerful?

Answer 19

Experimental designs are more powerful than observational, so we should use them when it’s ethical and practical to do so

Question 20

When evaluating research that you read about what should you do?

Answer 20

Always consider this issue – has causation really been proved?

Question 21

Fundamental principle of research design

Answer 21

We want to eliminate all explanations of our results except one - That is, if we conduct an experiment, and see a change in behaviour (our DV), we want to be sure that it was caused by our IV

Question 22

What is a confounding variable?

Answer 22

• Alternative explanations of the results are called confounds or confounding variables
• A confounding variable is anything, other than our IV, that might have produced the change in DV that we saw
• They are bad, and we need to eliminate them if want our research to be worthwhile

Question 23

General approaches to eliminating confounds - Standardisation

Answer 23

• Hold the confounding variable constant (standardisation)

* especially good for environmental or external confounds - as can manipulate these

Question 24

General approaches to eliminating confounds - Randomise…

Answer 24

Randomize the confound

• especially good for subject-based or internal confounds - things you can’t change

Question 25

General approaches to eliminating confounds - 3rd variable

Answer 25

• Decide that they’re more interesting and study them instead

Question 26

You should always think about research that you design and that you read in these terms:

Answer 26

• What possible confounds were there?
• Have they been controlled?
• If not, don’t trust the research
-> If you can poke holes in research - repeat the experiment with adjustments to make up for the mistakes

Question 27

Within-subject design (4)

Answer 27

Each subject is exposed to all levels of the IV (all experimental conditions)
• e.g., does alcohol affect short-term memory?
• Each subject tries to learn a list of words in both “sober” and “drunk” conditions
• There is an effect if individual subjects behave differently in the two conditions

Question 28

Advantage of within-subject design

Answer 28

• Good for controlling subject confounds

Question 29

Disadvantages of within-subject design

Answer 29

• But there could be environmental confounds – such as?
-> In alcohol experiment one way to avoid - keep list of words same
• Especially note the risk of an order effect - performance of second condition not due to IV being manipulated but just because it’s individual exposed to it second
-> Taking repeated measures - have to take at different time points - things change with time

Question 30

2 ways of dealing with order effect

Answer 30

Randomise order - e.g. split half of the sample and each half starts off with opposite level of IV
Between-groups design

Question 31

What happens when you measure the same individual twice?

Answer 31

Anytime you measure from the same individual twice, you do it over time and things change

Question 32

Between-group design

Answer 32

• Each subject is only exposed to one level of the IV (one experimental condition)
• e.g., separate groups of subject in “sober” and “drunk” conditions
• Good for controlling environmental confounds

Question 33

How can you tell if there is a difference between the two groups in a between-group design?

Answer 33

• There is a difference if the average behaviour of the two groups is different

Question 34

How do you separate the groups in a between-group design?

Answer 34

Take random sample for each experimental condition

Everything between the two groups is exactly the same except the IV

Question 35

Problem with between-group design and how to fix it

Answer 35

• But could be subject confounds – maybe the people in the “sober” condition just had better memories anyway
• Can partly prevent this by randomly allocating subjects to groups

Question 36

Other names for between-group design

Answer 36

• Between subjects
• Independent samples -> taking many samples
• Independent groups -> each group only exposed to one IV

Question 37

Other name for within-subjects design

Answer 37

• Repeated measures -> taking multiple measures

Question 38

From results what can you not infer from between-group studies?

Answer 38

Can’t infer behaviour of individuals from either group because that study has not been done
e.g. can’t infer behaviour about alcohol group in a sober condition

Question 39

What does within-subject design do that between-group design doesn’t?

Answer 39

Gives built-in test of generality

Question 40

Matched-pairs design

Answer 40

• Give a pretest on memory ability before the experiment
• Find pairs of subjects with similar memory ability
• Randomly allocate one member of each pair to each condition, “sober” and “drunk”

Question 41

How can you tell if there is an effect in a matched-pair design?

Answer 41

• There is an effect if the “sober” member of each pair consistently scores better than their “drunk” partner

Question 42

Benefits of matched-pairs design

Answer 42

• Keeps environmental confounds constant, and (nearly) keeps subject confounds constant too

• > Enviro because each person is only exposed to one IV
• > Confounds in terms of what is being tested due to pairings of similar abilities

Question 43

Con about matched-pairs design

Answer 43

• This is a very good design, but quite labour-intensive, so it isn’t used as often as it should be