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Research Methods Flashcards

1
Q

Ethical issues

A

Considerations that researchers need to consider before ,during and research is conducted

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2
Q

What do ethical issues take into consideration

A

The welfare of the participants the integrity of the research and the use of the data

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3
Q

The British psychological society

A

Code of ethics sets out of a series of guidelines that researchers need

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4
Q

5 main ethical guidelines

A

Deception
Right to withdraw
Informed consent
Privacy and confidentiality
Protection from harm

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5
Q

Deception

A

When information is deliberately withheld from participants or they are knowingly misled. It prevents participants from giving fully informed consent which means that they might be taking part in research that goes against their views or beliefs.

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6
Q

How deception dealt with

A

participants should be fully
and told the
. At this
point the participant should be given the right to withdraw the publication of their results. The participants should be fully
debriefed the and nature of the research
contact details of the
experimenter should be given if
participants have any further
questions or querie

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7
Q

Right to withdraw

A

withdraw (remove themselves or their data from the study) at any stage. This includes after the research has been conducted, in which case the researcher must destroy any data or information collected. Participants who are not given the right to withdraw may feel unnecessary or undue stress and are therefore not protected from harm.

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8
Q

How to deal with deception

A

fully debriefed and told
. At this point the participant
should be given the right to withdraw the publication of their results

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9
Q

confidentiality

A

where a participant’s personal information is protected by law under the Data Protection Act both during and after the experiment.

person’s details or data may be used by other parties against the participant’s wishes.

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10
Q

How to deal with the issue

A

provide with a fake name, number or initials to protect their identity and assure anonymity

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11
Q

Informed consent

A

aims of the
research should be made
before they agree to
participate.

It is possible that the participant may have
felt obliged to take part or even coerced into it, especially if they are not fully informed.

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12
Q

How to deal with informed consent

A

1)presumptive consent
2)prior general consent
3)retrospective consent

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13
Q

Presumptive consent

A

involves taking a random sample of the population and introducing them to the research, including any deception which may result. If they agree to take part in the research it can be presumed that other future participants would do the same so the consent is generalised

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14
Q

Prior general consent

A

Agreeing to take part before hand in numerous psychological investigations which may or may not involve deception

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15
Q

Retrospective consent

A

Participants giving consent for their participation after already taking part

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16
Q

Privacy

A

The right of individuals to decide how informstion about them will be communicated to others a skilled researcher may obtain more info from a participant than they wish to give which could be an invasion of privacy and the participant may layer feel ashamed or embarrassed

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17
Q

How to deal with privacy

A

Informed consent and right to withdraw
Should explain to participants the way in which their info will be protected and kept confidential

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18
Q

Protection from harm

A

Psychologists have the responsibility to protect their participants from physical or psychological harm, including stress. The risk of harm must be no greater than that which they are exposed to in everyday life.

Participants should leave the research in the same state as they entered it. If
they are harmed, they
may suffer from
long‐term effects that
could impact their lives in future

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19
Q

How to deal with the protection from harm

A

Participants should be debriefed at the end of the experiment and in some instances they
referred to counselling.

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20
Q

What is an experiment

A

The manipulation of an independent variables to see what effect it has on the dependent variable
Control other variables whch might affect the results

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21
Q

Independent variable

A

Is the variable that you manipulate

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22
Q

Dependent variable

A

Variable you measure

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23
Q

Aim

A

General statement that describe the purpose of an investigation

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24
Q

Alternate hypothesis

A

Testable statement which predicts how one variable will affect another
It is a statement which predicts a difference between conditions in an experiment
Correslqtion between two variable
States there will. Be a difference/relationships between the variables to be investigated

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25
Directional hypothesis
A higher number of States the direction of the difference /relariondhip
26
Non directional
Will be a difference Does not state the direction of the difference /relstionship
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Null
Will be no difference Stated there will not be a difference/relationships between the variables
28
Non directional hypothesis example
There will be a difference in the number of words recalled in the morning than the evening
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Sampling techniques
Opportunity sa,plying Volunteer sampling Random sampling Systematic sampling Stratified sampling
31
Opportunity sampling
Selecting anyone who is available and willing t take part in the study at the time Used in psychological research due to its ease of application
32
Example of opportunity sampling
A researcher went t. Supermarket and asked customers if they want to participate in a study
33
Evaluation of opportunity sampling
Convenient Quicker and easier Save money Bias due to specific area not rep of target population Investigator bias select particular individuals or avoid others according to their own subjective preferences
34
Volunteer sampling
self‐selecting to take part in a study by responding Posters in various locations Eg asking a level students to participate First 20 volunteers
35
Evaluation of volunteer
-participants generally approach the researcher -minimal effort %quicker and easier Looking for particular participants Bias-particular type of person putting them self forward More curious or inquisitive It representative so less generalisable
36
Random sampling
 With random sampling, every member of the target population has an equal chance of being selected. This involves identifying everyone in the target population and then selecting the number of participants you need in a way which gives everyone an equal chance of being selected, such as pulling names from a hat, or using a computer software package which generates names/number randomly and without bias
37
Evaulaursrion for random sampling
free from researcher bias. Since the sample is generated by a computer generator or by selecting names from a hat the researcher does not have any input into who is selected. This significantly reduces the possibility of them choosing a biased sample of participants who would serve to support their aims. This means that the sample is likely to be representative so can be generalised to the targetpop
38
Bad evaluation for random
Ensuring that everyone in the target population has an equal chance of being selected is a difficult and time consuming task. It is also a possibility thatindividuals who are picked may be unwilling to take part
39
Systematic sampling
predetermined system is used to select participants. For example, every fifth person is chosen and the same interval is then consistently applied to the whole of the target population such as the 10th, 15th, 20th person
40
Evaluation of systematic
is free from researcher bias. Since the researcher is not selecting participants by choice, but by following a predetermined system, this reduces any potential influence that the investigator may have over obtaining the sample.
41
Stratified sampling
subgroups within a population are identified. Participants are obtained from each stratum (‘layer’ or category) in proportion to their occurrence within the population.
42
Evaluations is startsifies sampling
largely free from researcher bias. In this technique, the sample is generated randomly once the subcategories/strata have been identified. This significantly reduces the possibility of the researcher choosing a biased sample of participants who would serve to support their aims. This means that sample is likely to be representative because each particular subgroup, if selected appropriately, will be represented within the sample. This means that any findings generated from research with a stratified sample can be generalised to the target Don’t want to miss anyone out Represent your populations characteristic
43
Bad Evaluation for stratified sampling
largely free from researcher bias. In this technique, the sample is generated randomly once the subcategories/strata have been identified. This significantly reduces the possibility of the researcher choosing a biased sample of participants who would serve to support their aims. This means that sample is likely to be representative because each particular subgroup, if selected appropriately, will be represented within the sample. This means that any findings generated from research with a stratified sample can be generalised to the targe
44
what is a control used for
make sure the experiment is valid . A control is something that is kept the same for each participant doing the experiment.​ The variables that need to be controlled in research are called extraneous variables.​
45
what is extraneous variable
any variable that you are not investigating that can influence the dependent variable.​
46
what if extraneous variables are not controlled
any variable that you are not investigating that can influence the dependent variable.​
47
what is a confounding variable
type of extraneous variable affect both the independent and dependent variables. They influence the dependent variable directly and either correlate with or causally affect the independent variable.​ If a extraneous variable is not controlled, it will become a confounding variable. ​
48
participant variables
gender age personaity intelligence
49
situational variables
time pf day heat demand characteristics
50
what are demand characteristics
A feature of a procedure that influences a participant to try to guess what a study is about and look for clues as to how to behave.​
50
controls for demand characteristics
deception - distractor questions ,lying about aim single blind - The participant is unaware of which condition they’re in
51
Investigator Effects​
term used to describe subtle cues or signals from an experimenter that affect the performance of participants in studies. ​ The cues may be unconscious nonverbal cues, such as muscular tension or gestures. They may be vocal cues, such as tone of voice.​
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New area
Going to be a difference
54
Independent groups design
Participants only take part in one condition of the experiment
55
Repeated measures design
Participants take part in both conditions of the experiment (1 group)
56
Matched pairs design
Participants are matched on variables relevant to the experiment, e.g. IQ, gender. One participant from each pair are then allocated to a different condition.
57
Repeated measures strengths
Any differences between conditions are likely to be due to changes in the IV and not due to participant variables.  Fewerparticipantsneed to be recruited, as they are used twice.
58
Repeated measure limitations
Order effects may occur (e.g. practice, fatigue, boredom) as participants take part in all conditions.  May see more demand characteristics as participants are more likely to work the aim if they take part in both conditions
59
Independent groups strengths
No order effects (practice, fatigue, boredom) as participants only take part in one condition.  Less chance of demand characteristics.
60
Limitation of independence groups
Anydifferencesbetween conditions could be due to participant variables, e.g. one group could do better because they are more intelligent – control by randomly allocating to groups.  Lesseconomicalastwiceas many participants are needed in comparison to repeated measures
61
Matched pairs strength
There are no order effects as participants only take part in one condition.  Participant variables between conditions are reduced as participants have been matched.
62
Matched pairs limitstion
It is time consuming and expensive to match up participants. Participants can never be matched exactl
63
Order effects
Carrying out a task repeatedly leads to changes in performance  Boredom Effect: Deterioration of performance across conditions as PPs become tired or bored.  Practice Effect: Improvement across conditions through familiarity of the task or environment.  This is a problem with repeated measures design  Leave a long gap between conditions.  Use independent groups or matched pairs.  Counterbalanced design
64
Counterbalancing
Important control when using repeated measures.  It reduces order effects as half participants do condition A then B and the other half do condition B then A. Participant 1 – Condition A then Condition B Participant 2 – Condition B then Condition A Participant 3 – Condition A then Condition B
65
Primary data Strengths and limitstion
it is data collected first hand by the researcher. It is collected specifically to match the aims of the investigation. It has high validity because it is collected by the researcher with his/her aims in mind so it will be more useful to get valid conclusions. it is time consuming and more costly because the researcher has to find participants and carried out the research to collect data.
66
Secondary data
is data that it is already published by other researchers so it is not specifically collected to match the researcher’s aims. Strength: it is less time consuming so it is cheaper to collect because the data is already available to the researcher to be analysed Strength: it is also useful when research cannot be carried out because of ethical issues. An example of this type of data is meta-analysis and content analysis where the data is published in reports, newspaper, books, academic journals etc Limitation: it may not be valid because it is not specifically collected having in mind the aims of the research
67
Quantative
Definition: it is numerical data which means that it is objective. For example, scores from a test or rating in a questionnaire.  Strengths: As it is objective data because it is numerical, it allows the researcher to make objective comparisons without the need to make biased interpretations of the data collected. This increases reliability.  Limitation: As it is numerical data , it does not tell the researcher how and why the behaviour occurred. It only shows what behaviour occurred and how often. This decreases validity.
68
Qualitative data
Definition: it is data collected in a narrative form it provides in-depth information about the behaviour being investigated.  Strength: As the participants can expand on their answers and give reasons for their behaviour, the researcher gains a better understanding of how and why the behaviour happened. This increases the validity of the findings.  Limitation: As the researcher makes annotations of what is being said, the analysis of the data can be more difficult to compare and the interpretation can lead to bias conclusions. This decreases the reliability of the findings as it will be difficult to replicate to find similar findings
69
Level of measurements -nominal data
 Nominal data: Collection of information that is divided into groups. For example, age, size, race, gender.  Strength : it is easy to analyse and reliable.  Limitations: it is subjective data because it does not have standardised intervals so numerical operations cannot be performed. The only central tendency that can be used with this data is the mode. Therefore, this data is not very useful.
70
Levels of measurement -ordinal data
it is quantitative data and categorical data with a set of order or scale to it. For example, scores in a test or ratings in a questionnaire.  Strength: it is easy to collect and categorise because it is numerical data. It is more informative than nominal data because it indicates relative values on a linear scale. Median can be calculated to make comparisons.  Limitation: this type of data is subjective because the gaps between values are not equally measured. For example, in a rating scale on happiness form 1-5 the gaps between each scale are not equal. The mean cannot be used to assess central tendency.
71
Levels of measurements -interval data
Interval data: it is quantitative data which is objective. For example, time taken to complete a puzzle, heart beat, blood pressure, temperature.  Strength: it is easy to collect and categorise because it is numerical data. It is objective data as researchers do not need to interpret this data as the gaps between values are equally measured.
72
Pilot study
small-scale investigation. test out their methodology and make minor changes check if the questions of a questionnaire are valid for the research they are carrying out.
73
Formation of pilot study
involves running the planned methodology but with a much smaller number of participants. The participants will often be a small opportunity sample such as classmates or colleagues.
74
Function of pilot study
allows for modification of methodology where necessary. For example, researchers may decide that an experimental memory task is too easy, and needs to be made more difficult to avoid a ceiling effect (where everyone gets a high score, skewing the data). Any part of the study could be tested, for instance the validity of measure (e.g. does the questionnaire measure what it is supposed to?) or whether a procedure is effective (e.g. does it take too long, are the instructions too complicated for participants to understand, or have any vital steps been left out
75
Advantages of pilot study
A pilot study allows a researcher to decide whether or not it will be worthwhile to conduct a planned study on a larger scale A pilot study also provides the researchers with practice of running the study before the full data gathering begins, allowing all aspects of the study to go more smoothly.
76
Measures of central tendency
Mean average Median average Mode average
77
Mean average
It is the arithmetic average of a set of data. It is calculated by adding up all the scores in the data set and then dividing by the number of scores.
78
Mean strength
Most representative of all the measures of central tendency because it is comprised of the whole data set.
79
Mean limitstion
Most sensitive measure as outliers can distort the mean. It can be very misrepresentative of the data set if there are extreme scores present. Therefore, it can only be used with ordinal and interval data.
80
Median score definition
Calculated by putting all scores in rank order from smallest to largest then selecting the middle number from the data set.
81
Mean strength
Not distorted by extreme scores. Therefore, when there are extreme values in a data set, the median is used rather than the mean, so the data is more representative, increasing validity.
82
Median limitstion
Does not reflect all scores in the data set. Therefore, it is not the most representative of the data set and it should only be used when there are outliers.
83
Mean limitation
Most sensitive measure as outliers can distort the mean. It can be very misrepresentative of the data set if there are extreme scores present. Therefore, it can only be used with ordinal and interval data.
84
Median definition
Calculated by putting all scores in rank order from smallest to largest then selecting the middle number from the data set.
85
Median strength
Not distorted by extreme scores. Therefore, when there are extreme values in a data set, the median is used rather than the mean, so the data is more representative, increasing validity.
86
Mode definition
Calculated by identifying the most frequently occurring score within the data set
87
Moore strength
Not distorted by extreme scores. The only method which can be used with nominal data.

Pysch (21 decks)

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