Feb Mocks

Question 1

4 experimental methods and explain

Answer 1

Lab - takes place in a specific environment, whereby different variables can be carefully controlled
Field - more natural environment, not in a lab but with variable still being well controlled
Natural - the IV it’s not brought about by the researcher, hence would’ve happened even if the researcher had not been there
Quasi - the IV has not been determined by the researcher, instead, it naturally exists eg: gender and age. No rando, allocation can occur

Question 2

The for non-experimental methods

Answer 2

Self report (questionnaires and interviews)
Observations
Case studies
Correlational studies

Question 3

What does the experimental method concern

Answer 3

The manipulation (changing) of the IV to have an effect on the DV which is measured and stated in results

Question 4

Define extraneous variables and confounding variables

Answer 4

Extraneous- any other variable (which is not the IV) that affects the DV and does not very systematically with the IV. aka nuisance variables
EV could affect the results (DV) but a confounding variable has affected the results
(DV)
Eg: age and gender of p’s and lighting of lab
Confounding - a variable other than the IV which has an affect on the DV but also does change systematically with the impact of the DV as the confounding variable could have been the cause
Confounding variable is a type of EV that hasn’t been controlled

Question 5

Control of variables
Randomisation and standardisation

Answer 5

A way to minimise the effects of extraneous or confounding variables
Randomisation is the use of chance to reduce the effects of bias from investigator effects
Standardisation: using the exact same formalised procedures and instructions for every single participant involved in the research process
This allows there to eliminate nonstandardised instructions as being possible extraneous variables

Question 6

Strengths and weaknesses of the labatory experiment

Answer 6

-High control over extraneous variables
Experiments is controlled all the variables and Iv has been precisely replicated between conditions so has high internal validity
-Replication - researchers can repeat experiments and check reliability of results due to standardisation

-Experimenters bias
-Low ecological validity - high degree of control and environment makes the situation artificial so has low mundane realism
-pts know they’re being tested so increases demand charectaristics so lowers internal validity

Question 7

Strengths and weaknesses of field experiments

Answer 7

-Naturalistic environment - natural behaviours therefore high ecological validity whilst still having a Controlled IV
-ppts COMT know they’re in an experiment so reduces demand charectarisits
• Ethical considerations - invasion of privacy and no informed consent
-Loss of control over extraneous variables so precise replication Isnt possible and harder to establish cause and effect so lower internal validity

Question 8

Strengths and weaknesses of a quasi experiment

Answer 8

Controlled conditions - replicable so can check for reliable results and have a high internal validity

Cannot randomly allocate participants to conditions- so there may be participant confounding variables, lowers internal validity

Question 9

Strengths and weaknesses of natural experiment

Answer 9

-Provides opportunities: for research that might not otherwise be undertaken for practical or ethical reasons. They offer unique insights.
-High ecological validity as you’re dealing with real life situations

-diffcuilt to establish causality due to lack of controls over variables
-ppts may not be randomly allocated to conditions so increases participant confounding variables so lowers internal validity
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Question 10

Scientific processes:
Whats a pilot study

Answer 10

A Small scale versions of an investigation that takes place before the real investigation to examine the feasibility of the methodology before carrying out a larger scale study.
Allows researcher to identify problems and procedure to be changed to deal with these.
Allowing money and time to be saved in the long run
Checks the clarity of the study

Question 11

2 types of procedures

Answer 11

Single-blind procedure: a research method where the researchers don’t tell the participants if they’re being given a test or control treatment. Ensures less bias in the results and avoids demand charectaristics

Double-blind procedures: neither p’s nor the experiment knows who is receiving a particular treatment. Prevents bias in research results due to demand characteristics or the placebo effect. Reduces investigator effects so can’t give unconscious
Neither participants nor the experiment

Question 12

Control group / condition define

Answer 12

Set a baseline whereby results from the experimental condition can be compared to results from this one.
Ifthere’s a great change in the experimental group compared to control then they can conclude the cause of effect was the IV

Question 13

Naturalistic and controlled observational techniques
Strengths and weaknesses

Answer 13

Naturalistic:
Strengths
-high external validity bc it’s in a natural environment
Limitations
-had to replicate
-EV’s are high bc it’s in a natural environment

Controlled:
Strengths
-more control over EV
-easy to replicate
Limits
-unnatural behaviour
- low mundane realism so low ecological validity
-demand charectaristics

Question 14

Overt and covert observational techniques
Strengths and weaknesses

Answer 14

Overt:
Strengths
-ethically acceptable bc informed consent is given
Limits
-more likely unnatural participant behaviour as they know they’re being watched
Demand characteristics - reduces validity
Covert:
Strengths
-natural behaviour recorded - high internal validity
-removes participant reactivity
Limits
-Ethical issues presented (no informed consent given)

Question 15

Participant and nonparticipant observational techniques
Strengths and weaknesses

Answer 15

Participant
Strength
-more insightful - increases validity of findings
Limits:
-researcher may lose objectivity as they may identify too strongly with the participants

Nonparticipant
Strength:
-researcher can be more objective
Limit.
-observer bias eg: stereotypes
-researcher may lose some valuable insight

Question 16

What’s a problem with Observerational designs and it’s solution

Answer 16

Observer bias
When an observers reports are biased by what they expect to see

Solution: inter observer reliability
Having 2+ observers to compare reports and calculate a score with:
Total number of agreements / total number of observations X100
If there’s a correlation higher than 0.8 / 80% then their results are reliable

Question 17

Types of obervational designs

Answer 17

Unstructured - continuous recordings where researcher writes everything they see during the observation
Structured - researcher qualifies what they are observing with a predetermined list of behaviours and sampling methods

Question 18

Observational designs (structured and unstructured) strengths and weaknesses

Answer 18

Unstructured:
+richer and more detailed observations recorded
- produces qualitative data which is more difficult to record and analyse
-greater risk of observer bias

Structured:
+ easier, more systemIc
-quantitive data is collected, easier to record and analyse and compare
-ess risk of observer bias
- less depth of richness of infomation, may miss out on valuable info

Question 19

What can be used whilst conducting observations

Answer 19

Behavioural categories
When a target behaviour which is being observed is broken up into more precise components which are observable and measurable and operationalised
Eg: anger - shouting, punching, swearing
It’s important that the behaviours don’t overlap with other behaviours when forming berhavioural category list. Operationalised

Question 20

The 3 Experimental design methods

Answer 20

Independent groups design - p’s only participant in one condition of the IV
Repeated measures - same p’s take part in all conditions
Matched pairs - pairs of p’s are matched on a confounding variable (one that affects the DV) then one member of each pair does one condition and the other does the other

Question 21

Strengths weaknesses and solutions to independent groups design

Answer 21

+ No order effects
+ Minimises demand characteristics

-No control over participant variables, between conditions
-need more participants than other designs, so can be more time consuming and expensive

Random allocation solves participant variables. It insures that each participant has the same chance of being in one condition as another, unbias

Question 22

Strength, limitations and solutions of repeated measures

Answer 22

+Eliminate participant variables
+ fewer participants needed so not as time-consuming

-Order effects presented, e.g. boredom tiredness
So participants may not do as well in the second task/condition

Counterbalancing half of the participants to conditions in one order and the other half do it in an opposite order

Question 23

Strengths and limitations of matched pairs

Answer 23

† no order effects
+ minimises demand characteristics

-Time-consuming, and expensive to match participants
-Large pool of potential participants is needed
-Difficult to know which variables are appropriate to match p’s

Question 24

Define observations

Answer 24

A non experimental method the researcher watches and records spontaneous / natural behaviour of participants whiteout manipulating levels of IV

Question 25

Define controlled observation

Answer 25

Type of obersvatiom where p’s are observed ina lab
Increases control and reliability but decreases ecological validity

Question 26

Define covert observation

Answer 26

Type of observation where observer is hidden so p’s don’t know they’re being observed
Reduces demand characteristics but raises ethical issues around consent

Question 27

Define experiment

Answer 27

Investigation where a hypothesis is tested by manipulating the IV in order to see its effects on the DV

Question 28

Define interviews

Answer 28

Self report technique where p’s are asked questions by an interviewer which allows for flexibility in the info gathered

Question 29

Define observations

Answer 29

Type of data collection where p’s behaviour is observed/ watched

Question 30

Define questionnaires

Answer 30

A self report technique where p’s answer pre-decided questions, in form of paper or electronically. Allows for anonymity

Question 31

Scientific processes:
Define closed questions

Answer 31

Type of question where ppts can only be answer with a limited number of answers usually ‘yes’ or ‘no’ or a scale from 1-10

Question 32

Define nominal data

Answer 32

Type of data that is in form of catagories.
It’s discrete - one item can only appear in one category.
It doesn’t enable sensitive analysis as it doesn’t yield a numerical result for each pot

Question 33

Define ordinal data

Answer 33

Data which is represented in a ranking form eg: 1 = hate maths and 10 = loves
maths.
There’s no equal intervals between each unit
Limit: lacks precision as it’s based on the subjective opinion of people

Question 34

Define interval data

Answer 34

Type of data that’s based on numerical scales which include equal units of precisely defined size.
Most sophisticated form of data as it’s based on objective measures. Needed for the use of a parametric test

Question 35

Define statistical testing

Answer 35

Provides a way of determining whether hypothesis should be rejected or accepted. It can tell us whether differences or relationships between variables have been found during experiments are statistically significant or if they occurred due to chance

Question 36

Inference statistiCS:
When can the sign test be used

Answer 36

If the study:
-looked for a differnce not an assossiation
-used a related experimental design - repeated measures or matched pairs
-collected nominal data

Question 37

Inference statistics:
How to conduct a sign test

Answer 37

1) state the hypothesis: both alternative and null
2) record data and work out the sign, -ve if value has decreased, +ve if increased. If value stays the same it’s ignored and the the N adjusted to exclude it
3) find the calculated value for the sign test, S, which is the no. of times the less frequent sign occurs
4) find the critical value of S - use the calculated value N (total no. of values with the ignored values excluded) and p is less than or equal to 0.05 which means there’s a less than 5% probability that the results occurred by chance
- if S is less than or equal to the critical value - reject the null, significant differnce
- if S is more than or equal to the critical value - accept null, no significant differnce
5) state conclusion, referring to hypothesis mentioning the IV and DV and support your conclusion with the exact values of the - critical value, S, N and p value

Question 38

Inference statistics:
For nominal data:
What test should I use if the test of differnce is unrelated and related

Answer 38

Unrelated (IG) chi-square
Related (MP and RM): sign test

Question 39

Inferential statistics:
For nominal data:
What test should I use if it’s a test of association or correlation

Answer 39

Chi squared

Question 40

Inferential statistics:
For ordinal data:
What test should I use if the test of differnce is unrelated or related

Answer 40

Unrelated: Mann-Whitney
Related: Wilcoxon

Question 41

Inferential statisticS:
For ordinal data:
What test should I use if it’s a test of association/correlation

Answer 41

Spearman’s rank

Question 42

Inferential statistiCS:
For interval data:
What test should I use if the test of differnce is unrelated or related

Answer 42

Unrelated: unrelated t-test
Related: related t-test

Question 43

Inferential statisticS:
For interval data:
What test should I use if it’s a test of association/correlation

Answer 43

Pearson’s Rho

Question 44

Inferential statistics:
What are stats tests used to determine

Answer 44

Whether a significant differnce or correlation exists.
Discovered using the calcaued value and the critical value
Critical value is worked out from a table of probability values and depends on:
1) whether it’s one tailed (directional) or 2 tailed (nondirectional)
2) the p value
3) N value or the degrees of freedom value

Question 45

Inferential statistics:
What’s the rule of R

Answer 45

If there’s an R in the name of the statistical test the calculated value has to be greater or equal to the critical value for the result to be significant.
If so reject the null hypothesis
If there’s no R in the tests name then the calculated value has to be less than or equal to the critical value to be significant

Question 46

Probability and significance:
What’s significance

Answer 46

Stats term about how sure we are about a correlation or differnce existing.
If signicsnt, reject null.
Null: there’s no differnce/correlation between the conditions
Alternative: there’s a differnce/correlation between conditions
Significance level of 0.01:
P value of 0.01 There’s a 1% possibility that the differnce between the conditions is due to chance

Question 47

Probability and significance:
What’s probability

Answer 47

Calculation of how likely it is for an event to happen.
0= statistically impossible
1 = statistically certainty

The level of significance in probability is 0.05
So p value is less than or equal to 0.05
Meaning the findings being due to chance is 5% or less so researchers have a 95% confidence level in their results.

If there’s any risk attached to the research like a ‘human cost’ eg: climcial drug trials the p value is 0.01 (1 %)

Question 48

Probability and significance:
What’s type 1 error

Answer 48

Optimistic error / False positive
Incorrect rejection of a null hypothesis whoch is actually true.
Researchers claim to have found a significant differnce when they actually isn’t any

Remedy:
Using a more stringent p value (more strict p-value of 0.01 compared to 0.05) to allow less results through

Question 49

Probability and significance:
What’s type 2 error

Answer 49

Pessimistic error / false negative
Accept the null hypothesis when you shouldn’t.
Researchers claim that there’s no significant difference when they actually is one

Remedy:
Using a less stringent p value (less strict p-value of 0.1 compared to 0.05) to allow more results through, reducing the risk of missing true results.