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Pyschology > Research methods > Flashcards

Research methods Flashcards

1
Q

4 experimental methods and explain

A

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

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2
Q

The for non-experimental methods

A

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

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3
Q

Aim define

A

General statement made by researcher
Tells us what they plan on investigating
The purpose of their study
Aim is developed from theories and develop from reading about other similar research

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4
Q

Hypothesis define

A

Precise, testable statement of what the researchers predict will be the outcome of the study which clearly states the relationship between the variables being investigated (IV and DV)

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5
Q

What does the experimental method concern

A

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

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6
Q

Types of hypothesis and types of experimental/alternative hypothesis and what they are

A

Null hypothesis - predicts no differnce or relationship between the 2 conditions
Alternative hypothesis / experimental hypothesis: predicts a differnce or a relationship between groups/conditions

Non directional (two tailed) - Predicts a difference or a relationship between groups/conditions but does not state the direction of the difference/relationship

Directional (one tailed) - Predicts a difference or a relationship between conditions and states the direction of the difference/relationship. Used when there’s already pre-existing similar research

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7
Q

Independent and dependant variables

A

IV - been manipulated (changed) by the researcher or simply changes naturally to have an effect on the DV
To test the effect of IV we need different conditions: the experimental and control condition

DV - measured by the researcher and has been caused by a change to the IV

All other variables affecting the DV should be controlled (extraneous variables) so the researcher can conclude that the effect on the DV was caused by just the IV

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8
Q

Operationalisation of variables define

A

Variables should be defined and measured
make the hypothesis testable and measurable

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9
Q

Control of variables
Extraneous and confounding variables define

A

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

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10
Q

Control of variables
Demand characteristics and investigator effects

A

Demand characteristics - any cue the researcher or the research situation may give which makes the participant guess the aim of the investigation and change their behaviour due to participant reactivity and affect the validity of the results
Please you effect - act in a way they think the researcher wants them to
Screw you effect -intentionally and performed to sabotage the studies results

Participant reactivity can lead to investigator effects
Investigator effects:any unwanted influence from the researchers behaviour (conscious or unconscious) on the DV measured (the results) for example facial expressions or over explaining the task to the participants
Includes a variety of factors, such as the design of the study, the selection of participants and the interaction with each participant during the research investigation

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11
Q

Control of variables
Randomisation and standardisation

A

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

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12
Q

Strengths and weaknesses of the labatory experiment

A

-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
-ppts know they’re being tested so increases demand charectaristics so lowers internal validity

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13
Q

Strengths and weaknesses of field experiments

A

-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 charectarisitcs

  • 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
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14
Q

Strengths and weaknesses of a quasi experiment

A

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

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15
Q

Strengths and weaknesses of natural experiment

A

-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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16
Q

Define sampling

A

The researchers need to decide how they select participants to take part in the investigation
The population is a group of people from whom This sample is drawn.

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17
Q

Define and describe the 5 sampling methods

A

Opportunity sampling:
Participants happen to be available at the time which the study is carried out so recruited conveniently

Random sampling
Target population has Equal chances of being the one that is selected for the sample
Each member population is assigned a number, then either a random number table or A random number generator or the lottery method is used to randomly choose a partner

Systematic sampling
Participants are chosen from a list of the target population.
Every Nth participant is chosen to form the sample

Stratified sampling

By selecting from within strata, The characteristics of participants within the sample are in the same proportion as found within the target population.

You identify strat then calculate the required proportion needed for each stratum based on target population. Then select sample at random from each stratum using a random selection method

Volunteer sampling
Involve self selection where ppt offers to take part in response to an advert or when asked to

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18
Q

What must studies be in order to trust the results

A

Reliable and valid

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19
Q

Why is reliable studies important

A

Methodology – design measure and procedures
Effects – the patterns of results
Reliable methodologies – produced the same/similar results every time they’re used with a particular sample of individuals
Reliable effects – replicated across a number of different studies and individuals

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20
Q

Measures of external reliability:

A

Test-retest – measures test consistency and reliability over time
Same test on the same person on differnt occasions.
If results achieve a correlation co-efficient of 0.8 or above then we assume it’s reliable
Inter rater / observer – degree of agreement among raters to reduce bias,if there’s a high positive correlation (0.8+) between the observers/raters the measure is reliable
If you have a correlation of 1 it’s 100% prediction of A and B

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21
Q

4 ways of improving reliability

A

observations – improve training given to raters to increase accuracy, use pilot studies to identify procedural weaknesses
- interviews – structured interviews are more reliable than unstructured
-questionnaires – use closed rather than open questions
-experiments – use standardised procedures use established tests rather than new ones

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22
Q

Validity define

A

the extent to which something is measuring what it is claiming to measure

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23
Q

Internal validity define and how it’s measured

A

Internal: extent to which a study establishes a cause and effect relationship between IV and DV

split half method: split test into 2 parts, participants complete both parts, test the strength of correlation,
Correlations shown on a scatter graph
Large positive correction – high + strong correlation
Small negative correlation

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24
Q

Types of extraneous variables

A

Participant variables – differences between participants
Situational variables – features of the experimental situation
Other EV’s - eg: researcher bias, demand charectaristics (please you or screw you) and order effect (practise or fatigue

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25
Q

External validity define

A

generalisability – the extent the results can be generalised to other settings like real life

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26
Q

3 Types of external validity

A
  • Ecological (setting) -> whether results is generalisable to the real world, lack mundane realism (task is not realistic of everyday behaviour)
  • Populational (people) -> describes how well the sample used can be generalised to the population as a whole
  • Temporal (time) -> whether the findings are still valid today. It’s high when research findings successfully apply across time
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27
Q

Types of test validity

A

Construct – assessment to see the degree to which a test measures what it claims to
Concurrent – whether a measure is in agreement with a pre-existing measure that’s validated to test for the same concept – if your measure agrees with other measures
Predictive – degree to which a test accurately predicts a criterion that’ll occur in the future
Face (logical) – a superficial and subjective assessment of whether your study or test looks like it’s measures what it claims to

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28
Q

Types of demand characteristics

A
  • please-U —> acts the way the researchers wants them to
  • screw-U —> intentionally underperform to sabotage the study’s results

These effect validity

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29
Q

What is a pilot study

A

A small scale version of an investigation which is done belfre the real investigation
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

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30
Q

2 types of procedures

A

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

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31
Q

Control group / condition define

A

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

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32
Q

Who researched temporal validity

A

Perrin and Spencer

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33
Q

Types of observational techniques

A

Naturalistic - observing behaviour in the setting it would usually take place
Controlled - in a structured environment eg: lab

Overt - p’s know they’re being watched
Covert - p’s are unaware they’re being watched and recorded

Participant - observer is part of the observed group
Non-participant -observer does it from a distance

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34
Q

Naturalistic and controlled observational techniques
Strengths and weaknesses

A

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

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35
Q

Overt and covert observational techniques
Strengths and weaknesses

A

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)

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36
Q

Participant reactivity define

Differnce between it and demand charectaristics

A

Participants try to make sense of the situation they’re in which makes them more likely to guess the aim of the study

Demand charectaristics are cues made not by the participant but by the researcher / research process

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37
Q

Participant and nonparticipant observational techniques
Strengths and weaknesses

A

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

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38
Q

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

A

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

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39
Q

Types of obervational designs

A

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

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40
Q

Observational designs (structured and unstructured) strengths and weaknesses

A

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
-less risk of observer bias
- less depth of richness of infomation, may miss out on valuable info

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41
Q

What can be used whilst conducting observations

A

Behavioural categories
When a target behaviour which is being observed is broken up into more precise components which are observable and measureable 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

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42
Q

Sampling methods used during structured interviews

A

Time sampling - recording behaviour within a pre-established timeframe before the observational study
+ reduces no. of observations to be made so less time consuming
-can be unrepresentative of the observation as a whole

Event sampling
Counting of the number of times a particular behaviour is carried out
+ good for infrequent behaviours, less likely to miss behaviours
-important details of behaviour may be overlooked by observer
- hard to judge beginning and ending of a behaviour

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43
Q

The 3 Experimental design methods

A

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

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44
Q

Strengths weaknesses and solutions to independent groups design

A

+ 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

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45
Q

Strength, limitations and solutions of repeated measures

A

+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

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46
Q

Strengths and limitations of matched pairs

A

+ 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

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47
Q

Define mundane realism

A

Degree to which experimental study, resembles real life situations and experiences

No mundane realism = lacks external validity

Introduced in 1968 by Aronson and Carlsmith

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48
Q

Define demand characteristics

A

Hints/cues made by the researcher or research method, which made it participants to guess the research is hypothesis or aim, and therefore potentially influencing their behaviour

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49
Q

What type of bias is it called if only males are studied?

A

Androcentric bias

Eg: Asch line judgement experiment

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50
Q

What type of bias is it called if only females are studied?

A

Gynocentric bias

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51
Q

What type of bias is it called if only one country is studied?

A

Ethnocentric / cultural bias

Results can’t be a reliable and valid representative of all the countries

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52
Q

What did bond and Smith propose in 1996?

A

Collectivist and independent countries

Collectivist - families in these countries are family orientated - conformity rate is higher
Independent - members of the family in these countries are more orientated on themselves and their careers

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53
Q

Relationship between variables with correlation

A

Positive correlation – as one variable increases so does the other
Negative – as one variable increases the other variable decreases

Strong correlation – closest to line of best fit, more likely to predict other results
Weak – less accurate prediction

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54
Q

Difference between sample and population

A

Sample - a small group of people who represent the target population and take part in the study

Population - the group of people who the sample of drawn from not all of them take part in the study

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55
Q

Define sampling frame

A

A complete list of all the members of the target population

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56
Q

Implications of bias and generalisation in sampling

A

WEIRD

W – western
E – educated
I – industrialised
R – rich
D – democratic

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57
Q

Define a case study

A

In-depth study of a particular individual or small group or event, usually yielding a large amount of information. Done Over time. Carried out in real world. Idiographic

Gives us insight on or opposes a whole psychological theory

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58
Q

How are case studies used in clinical psychology?

A

Clinical: Brain damage can cause a change in behaviour

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59
Q

What case study changed theory of critical periods and language development?

A

Genie Wille

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60
Q

Strengths and weaknesses of case studies

A

longitudinal-> patient / individual / group can be followed over an extended period of time
-> high level of validity as they go into depth and give a rich insight
- allow multiple methods to be used (triangulation) = increasing validity
- allows researchers to study events or complex psychological areas they could not practically or ethically manipulate
-efficient because it only takes 1 case study to refute (reject) a theory
- enables study of unusual behaviour found in rare cases

researcher bias: researchers become too involved and lose their objectivity – misinterpreting or inflicting outcomes
- lack of controls due to lots of confounding variables that can effect DV
-lack scientific rigour as they’re unique they can be difficult to replicate

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61
Q

Define observations

A

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

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62
Q

Define participant reactivity

A

Hawthorne effect

Behaviour changes of the participant USA,y due to demand charectaristics or investigator effects

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63
Q

Define controlled observation

A

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

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64
Q

Define correlation

A

Mathematical technique used to measure the Extent to which 2 covariables are assossiated with eachother

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65
Q

Define covert observation

A

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

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66
Q

Define experiment

A

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

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67
Q

Define interviews

A

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

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68
Q

Define observation

A

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

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69
Q

Define questionnaires

A

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

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70
Q

Scientific processes:

Define abstract

A

A part of a scientific report that aims to summarise the report

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71
Q

Scientific processes:

Define bias

A

Inclination to a certain position or thought.

72
Q

Scientific processes:

Define closed questions

A

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

73
Q

Scientific processes:

Define concurrent validity

A

Occurs if a test is similar to an older test that’s already has well-established validity

74
Q

Scientific processes:

Define counterbalancing

A

Make half of the participant sample experience the differnt conditions of the experiment in one order and the other half of the participants complete it in the opposite order

75
Q

Scientific processes

Define falsifiability

A

The quality of being able to be proven wrong

76
Q

Scientific processes

Define generalisation

A

To Attribute info from a sample to the rest of the population

77
Q

Scientific processes

Define investigator effects

A

Unconscious changes in the investigators behaviour to comply with the hypothesis of the investigation

78
Q

Scientific processes

Define objectivity

A

Empirical; something that’s not influenced by personal feelings

79
Q

Scientific processes

Define paradigm

A

A basic concept; a well accepted core belief

80
Q

Scientific processes

Define paradigm shift

A

When previously accepted core concepts in a science are changed, usually due to the emergence of new evidence

81
Q

Scientific processes

Define peer review

A

Assessment of work by other people with similar levels of expertise in that field, provide unbiased expert opinion of the quality of said work

82
Q

Scientific processes

Define population

A

The group represented by a sample

83
Q

Scientific processes

Define random allocation

A

Allocate p’s to separate conditions using some sort of randomisation technique

84
Q

Scientific processes:

Define reliability

A

Essentially repeatability and consistency; extent to which the test can be repeated and gather similar results

85
Q

Scientific processes

Define test-retest reliability

A

Completing a test multiple times and comparing the scores for similarity

86
Q

Scientific processes

Define validity

A

Essentially truthfulness; extent to which a test measures what it aims to measure

87
Q

Data handling and analysis:

Define bar charts

A

A graphical representation of categorical data with numerical values

88
Q

Data handling and analysis:

Define coding

A

Type of analysis wher huge texts are simplified to certain key words that are aligned with certain themes

89
Q

Data handling and analysis:

Define correlation coefficient

A

A value between -1 and 1 that indicates the relationship (correlation) between 2 data sets

90
Q

What are the four types of data

A

Qualitative
Quantitive
Secondary
Primary

91
Q

What is qualitative data?
What are the strengths and weaknesses?

A

Data which is displayed in words, is non-numerical

More subjectivity

+more richness of depth of detail
+allows ppts to further develop their opinions hence has greater external validity.
+more meaningful insight into the ppts views is achieved

-difficult to analyse
-difficult to make make comparisons with other data
-researcher bias as conclusions rely on the subjective interpretations of the researcher (interpretive bias)

92
Q

What is quantitive data?
What are the strengths and weaknesses?

A

Data disyplayed numerically, not in words

More objectivity

+ can be analysed statistically so converted to graphs or charts
+ this makes it easier to compare with other data

-lack of depth in detail – reductionist (complex ideas are reduced into numbers)
-no meaningful insight into ppts views
-as ppts aren’t able to develop their opinions the results have low external validity

93
Q

What’s primary data?

A

Info is obtained first hand by the researcher for the purpose of their own investigation. It is of direct relevance to their aim and hypothesis

94
Q

Strengths and weaknesses of primary data

A

targets the exact info which the researcher needs, so the data fits their aims and objectives
-replicable (check for reliability of results)
-taken directly from the population (generalisability)

-requires time and effort as you run the study to get the data
-can be expensive as you need a big sample for generalisability
- researcher bias bc you designed the study and collected the data

95
Q

What’s secondary data with examples

A

Data that pre-exists and was collected for another prupsoe by the researcher for their own investigation. Aka ‘Desk research’

Eg: government statistics
Newspapers
Websites
Consortia / consortium (singular)

96
Q

Strengths and weaknesses of secondary data

A

data is accessible so requires minimal effort to collect – not time consuming
-large samples may exist eg: gov data

-data may not fit what the researcher wants to find out
-may be of poor quality – outdated or incomplete
-data may not be reliable – the researcher was not there when the study was conducted so is likely to be unsure of the validity of the results

97
Q

What are the 4 Levels of measurement of quantitive data
From highest level to lowest level

A

Ratio - has name, order, equal distance between each measurement (equal intervals) and measured from zero
Interval - has name, order, interval
Ordinal - has name and order
Nominal - has name

98
Q

Data types
What is meta-analysis

A

when a researcher combines results from many different studies on a particular topic and uses all the data to form an overall view of the subject they’re investigating

Quantitive review of results proving an effect size

99
Q

Describe strengths and weaknesses of meta analysis

A

Strengths:
-More generalisability as a larger amount of data is studied
-researcher is able to view the evidence with more confidence as there’s a lot of it

Limit:
-publication bias eg: file drawer problem may be presented. This is when researcher intentionally does not publish all the data from the relevant studies but chooses to leave out negative results. Gives false representation of what the researcher was investigating

100
Q

What are the 2 types of statistics

A

Descriptive statistics:
Measure of central tendency, dispersion, graphs and charts

Inference statistics: stats tests

101
Q

Define descriptive statistics

A

A way of how researchers identify trends and analyse the raw data of their study to accept or reject their hypothesis, using tables, graphs, summary statistics

you need central tendency and dispersion and graphs

102
Q

Define inference statistics

A

A types of data to conclude the research of a study
Including Statistival tests which allow the research to accept or reject their hypothesis

103
Q

What is meant by a measure of central tendency?

Why do psychologists find them useful?

A

Summary statistics that represent the centre point or typical value of a dataset

Quantitive data so can compare between with other studies or between 2 groups on the study

104
Q

Define the mean and it’s strengths and limitations and which type of data is it used with

A

Mathematical average of a set of scores

Interval and ratio because it needs equal distances between each interval to create a scale and an absolute zero

  • The most sensitive type as it takes into account all the data = more representative of the scores of all ppts
  • Easily distorted by anomalous data
105
Q

Define the median and it’s strengths and limitations and which type of data is it used with

A

The middle score in the data set

Arrange scores from lowest to highest and find middle value

Ordinal data - data has an order

  • Less affected by extreme scores
  • Doesn’t take into account of all ppts scores and so not representative
106
Q

Define the mode and it’s strengths and limitations and which type of data is it used with
What’s the bimodal?

And how to calculate it

A

Value that occurs the most frequently in a data set

Used with nominal data

Bimodal - When there’s 2 values that are most common

Identify the value that’s most common
If there’s 2 values that are most common this is the bimodal

  • Only average to use when the data is normal
  • Easy to calculate
  • Very basic method and isn’t always representative of the data
  • When the data is bimodal or there’s no mode it has limited usefulness
107
Q

Descriptive stats:

What is meant by measures of dispersion?

And 2 ways it’s measured

A

the spread of data around a central value

Range and standard deviation

108
Q

Measures of dispersion:

What’s the range?
How to calculate it and it’s strengths and limitations

A

Differnce between your highest and lowest values

Take lowest value from highest value and adding 1
Adding 1 enables the data to be rounding up or down to account for any margin error

  • Quick and easy to calculate
  • Doesn’t take the central values of a data set into account, and so it can be skewed by extremely high or low values
109
Q

Measures of dispersion:

What’s the standard deviation?
How to calculate it and it’s strengths and limitations

A

A single value that tells us how far scores deviate (move away) from the mean

The larger the SD = the greater the dispersion (spread of data within a data set) = not all ppts were affected by the IV

Low SD is better as it means the data are tightly clustered around the mean suggesting ppts responded in a similar way and the results are more reliable

Strengths: More precise measure than the range as it include all values As it takes central values into account by using the mean

-limit: difficult to calculate and affected by extreme values

110
Q

Template structure for a null hypothesis

A

There will be no difference in [operationalised DV] between [comdition 1] compared to [condition 2]

111
Q

Template structure for Non-directional hypothesis

A

There will be a difference in [operationalised DV] between [comdition 1] compared to [condition 2]

112
Q

Template structure for Directional hypothesis

A

Participants who [condition 1] will get higher/lower [operationalalised DV] compared to participants who [condition 2]

113
Q

When do researchers use a directional hypothesis

A

When previous published scientific research allows them to predict the direction of the results

Opinions, newspaper articles and inconsistent research aren’t enough so use non-directional then

114
Q

Descriptive statistics: Distribution curves

What is meant by normal distribution?
Where’s the mode, median and mean

A

Symmetrical pattern of frequency data that forms a bell-shaped curve.
It is the predicted distribution when considering an equally likely set of results.

All in the exact point (in the middle)

The dispersion of scores either side of the mid-point is experessed in SD’s
In one standard deviation of the mean – 68%
How uncommon your data is the more standard deviations you have

115
Q

Descriptive statistics: Distribution curves

What is meant by a positive skew? (Right skewed)

Where’s the mode, median and mean

A

where most of the distribution of data is concentrated towards the left of the graph resulting in a long tail on the right.

When you only have the data the rule is the median and mean are higher than the mode

Eg: test scores average is more lower values than higher values

Mean value is higher than mode value

116
Q

Descriptive statistics: Distribution curves

What is meant by a negative skew?
Aka left skewed

Where is the mode, mean and median?

A

Most of the data is concentrated on the right, resulting in the long tail of anomalous scores on the left.

mean is pulled to the left this time (due to the lower scorers who are in the minority), with the mode dissecting the highest peak and the median in the middle.

When you only have the data the rule is the median and mean is lower than the mode

117
Q

Descriptive statistics: Distribution curves

What is a skewed distribution

What is the order of the mode median and mean for both skewed distributions

A

A spread of distribution that’s not symmetrical, instead the data all clusters to one end

mode - always the highest point,
mean - always the furthest average from the mode
median - always the middle

118
Q

Define Ethics
And when they arise in research

A

Ethics are concerned with what is right and wrong.

Ethical issues arise in research if there’s conflicting values between the researcher and the participants.

119
Q

Ethics:

How governs all psychological research

A

1)All research starts with a research proposal which is submitted to the Ethics Panel at a University.
2) They decide whether or not to grant permission for research to take place.
3) They have to use the guidelines from the BPS (British Psychological Society) in the UK and APA (American Psychological Association) in the USA.
4) They will do a cost v benefit analysis.

120
Q

What are the ethical guidelines and the pneumonic

A

Can Do Cant Do With Participants

Informed:
- Consent
- Deception
- Confidentiality
- Debrief
Right to:
- Withdraw
- Protection from harm

121
Q

Ethics:

What is informed consent? How should it be dealt with?

A

Participants awareness of what they’re needed to do as part of the study to give valid consent.this means revealing the true aims of the study when appropriate.

Written consent - ppts sign a consent form after reading an info sheet containing all the info they need to make an informed condition.

Parental consent - needed if study involves children 16 years old or younger.
Next of kin will need to give consent for people without mental capacity.

Presumptive consent – used if consent is difficult to obtain, asl a group of people from the same target population as the sample would agree to take part, if they say yes then researcher can presume the sample would.

Retrospective (differed) consent – if consent isn’t gained before the study. Ppts give consent for their data to be used in the research after they’ve taken part and have been debriefed on the true nature of the research

122
Q

Ethics:

What is the right to withdraw? How should it be dealt with?

A

Even if given consent, ppts have right to leave the experiment at any time. Ppts must be made aware of this when signing the consent form.
When questionnaires are anonymous, ppts can only withdraw until they submit their answers

123
Q

Ethics:
What is deception? How should it be dealt with?

A

Deception – deliberately misleading or withholding info

Deceiveing ppts must be kept to a minimum. It’s only allowed if telling the truth will effect the DV (results)
Using mild deception reduces demand characteristics and increases interval validity.
Eg of deception: confederates
Deliberately providing false information is not acceptable.
ppts must be informed in the debrief at the end of the study

124
Q

Ethics:
What is a debrief? How should it be dealt with?

A

-telling ppt about the experiment
-giving them the option of withdrawing their information if they wish during the debrief.

-aims to provide information and help the participant leave the experimental situation in a similar frame of mind as when ppts entered it.

-If consent cannot be obtained (eg: field experiment) participants must be fully debriefed afterwards
-Sometimes it is impractical to debrief all ppts but in this case all data should be kept anonymous.

125
Q

Ethics:
What is the right to confidentiality ? How should it be dealt with?

A

Researchers need to be sure info they publish won’t allow their ppts to be identified so they use pseudonyms to keep them anonymous eg: Patient HM.

Communication of personal info from one person to another and the trust this will be data protected

126
Q

Ethics:

What is the right to privacy ? How should it be dealt with?

A

Privacy is about persons right to control flow of info about themselves. We expect privacy in certain situations.
if the research is in a public space the research will be granted permission as long as it does not invade privacy

127
Q

Ethics:
What’s the difference between confidentiality and privacy?

A

Participants have a right to privacy, if this is broken then confidentiality must be maintained.

128
Q

Ethics:
What is the protection from harm? How should it be dealt with?

A

Ppts should leave the study in the same condition they entered it in.
Risk is considered acceptable if it is no greater than what would be experienced in everyday life.
If harms caused, researcher should check ppts are in a positive state of health. They should also signpost to relevant services eg: councilling
Researcher must also ensure that id vulnerable groups are to be used (children, elderly and disabled). They must receive special care.

129
Q

Define bar charts

A

Allows for differences in data to be seen more clearly.
Used for discrete data, which describes data that’s been divided into catagories.
Bars don’t touch eachother which shows that we’re dealing with separate conditions.

X axis: condition A and B
Y axis: amount of frequency for each category

130
Q

Define histograms

A

Bars touch eachother unlike in bar charts
Used for continuous data.
X axis has equal sized intervals of one category
Y axis represents frequency

131
Q

Define line graphs

A

Represents continuous data
Points are connected by lines to show the change in values
X axis: IV
Y axis: DV

132
Q

Define scattergrams / graphs

A

Show assossiatioms between co-variables
For correlations
X axis: either of the co-variables
Y axis: either of the covariables
Each point displayed on the graph coincides with the X and Y position of the co-variables

133
Q

Define experimental design

A

The differnt ways in which the testing of ppts can be organised in conditions

134
Q

Strengths and weaknesses of opportunity sampling

A

+ easy method of recruitment so time saving and less costly
-not representative of the whole population so lacks external validity
-researcher bias as they control who they select

135
Q

Strengths and weaknesses of random sampling

A

+ no researcher bias: researcher has no influence over who is picked
-time consuming: need to habe a list of members of the population (sampling frame) and then contact those chosen
-volunteer bias: ppts can refuse to take past so can end up with an unrepresentative sample

136
Q

Strengths and weaknesses of systematic sampling

A

+ avoids researcher bias and usually representative of population
-not truely unbiased unless you use a random number generator and then start the systematic sample

137
Q

Strengths and weaknesses of stratified sampling

A

+no researcher bias: random selection in each statum
+ produces representative data due to proportion starta so can generalise results

  • time consuming to identify strata and contact people from each
  • complete representation of the target population is not possible as the identified strata can’t reflect all the differences between people of the wider population
138
Q

Strengths and weaknesses of volentter sampling

A

+ quick access to willing ppts makes it easy and not time consuming
+ ppts are willing to take part of more likely to cooperate with the study

-volentter bias: their study may attract a particular profile of a person. So generalise with caution
-motivation like money could be driving ppts so ppts may not take study seriously, influencing the results

139
Q

What’s falsifiability

A

the logical possibility that an assertion, hypothesis, or theory can be shown to be false by an observation or experiment.

140
Q

What measure of central tendency is appropriate measures for nominal data

A

Mode

141
Q

What measure of central tendency and measures of dispersion are appropriate measures for ordinal data

A

Median
Range

142
Q

What measure of central tendency and measures of dispersion are appropriate measures for interval data

A

Mean
Standard deviation

143
Q

Define nominal data

A

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 ppt

144
Q

Define ordinal data

A

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 subjectiveopinion of people

145
Q

Define interval data

A

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

146
Q

Correlations:
Define curvilinear relationships

A

As one variable increases so does the other until a certain point one increases and the other decreases.
Eg: Inverted u shape eg: Yerkes Dodson Law showing how anxiety effects EWT

147
Q

Strengths and limitations of correlations

A

Strengths:
Can be used as starting points to assess patterns between co-variables before committing to conduct experiments
Quick and economical to carry out
Secondary data can be used making it less time consuming

Limits:
Difficult to establish a cause and effect relationship, only assosaitoms are found between co-variables
Third variable problem can be represented - a third variable whoch the researcher is unaware of can be resonposnle for the relationship between co-variables

148
Q

Can you still have a hypothesis for correlational studies

A

Yes
Directional - shows whether there’s a positive or negitive correlation between co-variables
Non directional - only states there will be a correlation

149
Q

How to conduct systematic sampling

A

1) The researcher needs a full list of the entire target population.
2) The researcher reads down the list selecting every Nth participant to form the sample (this can be any number for example every 5th, or 10th or 100th name)
3) The process continues until the sample required is chosen.

150
Q

How to conduct opportunity sampling

A

1) Researcher directly asks any members from within the target population (that they have access to) to take part in the research.
2) Any individuals who agree to take part are added to the sample until the number of participants required is met.

151
Q

How to conduct stratified sampling

A

1) Strata/ subgroups are identified along with their proportion in the target population (e.g. gender, ethnicity, education level).
2) Random sampling is then used to select the number of participants required from within each stratum.

152
Q

What are the 6 sections of a scientific report

A

Abstract
Introduction
Method
Results
Discussion
Referencing

153
Q

Inference statistics:

Define statistical testing

A

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

154
Q

Inference statistics:

When can the sign test be used

A

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

155
Q

Inference statistics:

How to conduct a sign test

A

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

156
Q

Inference statistics:

For nominal data:

What test should I use if the test of differnce is unrelated and related

A

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

157
Q

Inferential statistics:

For nominal data:

What test should I use if it’s a test of association or correlation

A

Chi squared

158
Q

Inferential statistics:

For ordinal data:

What test should I use if the test of differnce is unrelated or related

A

Unrelated: Mann-Whitney
Related: Wilcoxon

159
Q

Inferential statistics:

For ordinal data:

What test should I use if it’s a test of association/correlation

A

Spearman’s rank

160
Q

Inferential statistics:

For interval data:

What test should I use if the test of differnce is unrelated or related

A

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

161
Q

Inferential statistics:

For interval data:

What test should I use if it’s a test of association/correlation

A

Pearson’s Rho

162
Q

Inferential statistics:

What are stats tests used to determine

A

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

163
Q

Inferential statistics:

What’s the rule of R

A

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 cortical value to be significant

164
Q

Inferential statistics:

A
165
Q

Inferential statistics:

A
166
Q

Inferential statistics:

A
167
Q

Inferential statistics:

A
168
Q

Probability and significance:

What’s significance

A

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

169
Q

Probability and significance:

What’s probability

A

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%)

170
Q

Probability and significance:

What’s type 1 error

A

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

171
Q

Probability and significance:

What’s type 2 error

A

Pessimistic error
Failure to reject the null hypothesis that is false.
Researchers claim that there’s no significant difference when they actually is one (a false negative)

172
Q

Implications of Psychology for the Economy:

Define it

A

How what we learn from psychological research influences our country’s economic prosperity. The economy is the state of the regions activities of producing or consuming good and services.

173
Q

Implications of Psychology for the Economy:

How does the topic of Psychopathology link to specific areas and therefore the economy

A

Treatments - cognitive behavioural therapy and rational emotive behavioural therapy for depression, drug therapy for OCD
-workers able to return to work

174
Q

Implications of Psychology for the Economy:

How does the topic of Attachment link to specific areas and therefore the economy

A

Role of the father - Tiffany Field found fathers can take on the role of being a primary caregiver
- mothers can return to work
- more flexible working arrangements within families
- can maximise their income and effectively contribute to the economy

175
Q

Implications of Psychology for the Economy:

How does the topic of social influence link to specific areas and therefore the economy

A

Social influence leading to social change: minority influence, appealing to NSI, disobedient models
- health campaigns
- unions strike - make working conditions better
- environmental campaigns - like getting companies to reduce their waste and use of non-renewable energy

176
Q

Implications of Psychology for the Economy:

How does the topic of memory link to specific areas and therefore the economy

A

Eyewitness testimony - how leading questions or post event discussion can affect eyewitness testimony.
- led to police using the cognitive interview which reduces wrongful convictions hence reduces waste of money and space in jail

Pyschology (11 decks)

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