Research Methods Flashcards
(128 cards)
1
Q
define aim
A
a statement of what the researcher intends to find out in a research study
2
Q
define hypothesis
A
a precise testable statement about the relationship between two variables (IV and DV).
3
Q
define operationalise
A
ensuring all the variables are measureable.
4
Q
define independent variable
A
something that is manipulated by the experimenter
5
Q
define dependent variable
A
what the IV affects, what is measured by the experimenter.
6
Q
define experiment
A
a research method where the IV is deliberately manipulated to observe the effect on the DV.
7
Q
define standardised procedure
A
a set of procedures that are the same for all participants so the study can be repeated e.g. standardised instructions.
8
Q
define extraneous variables
A
variables that make it difficult to detect a significant effect, that may affect the DV but are not part of what is being manipulated or measured.
9
Q
define directional hypothesis
A
states the direction of the predicted difference between two conditions/groups. (predicts an outcome - 1 tailed). Used when previous research suggests the findings will produce a particular outcome.
10
Q
define non-directional hypothesis
A
predicts there is a difference between two conditions/groups but doesn’t state the direction of the difference. (2 tailed)
11
Q
define null hypothesis
A
a prediction of what may not happen in the experiment. e.eg there will be no difference in —- and —–.
12
Q
Hypothesis rules
A
- must contain variables that are operationalised. (measurable)
- a directional hypothesis is used due to previous research demonstrating precise findings in an area of research.
- a null hypothesis will state ‘there will be no difference’
- if the study describes a ‘relationship’ it will be correlational and so the hypothesis must include the term ‘relationship’ or correlational.
- a directional hypothesis for a correlational study will include the phrase ‘positive/negative relationship’.
13
Q
define experimental design
A
procedures used to control the influence of factors such as participant variables in an experiment.
14
Q
types of experimental design:
repeated measures design
A
each participant takes part in each condition.
15
Q
types of experimental design:
independent group design
A
different participants are in different groups. They are usually randomly allocated.
16
Q
types of experimental design:
matched participants design
A
pairs of participants are matched in terms of key variables e.g. age, IQ. One member is allocated to one condition and the other member is allocated to the other condition.
17
Q
what are the limitations of repeated measures
A
- order effects: participants may do better on the second task due to practice or worse due to fatigue e.g. boredom/hunger.
- when in the second condition, participants may guess the purpose of the experiment which may purposely affect their behaviour.
18
Q
How do we deal with the limitations of repeated measures design?
A
AB or BA
- divide the participants into 2 groups.
- group 1: each participant does condition A then condition B
- group 2: each participant does condition B then condition A.
ABBA:
All participants take part in each condition twice.
Trial 1: Condition A (morning)
Trial 2: Condition B (afternoon)
Trial 3: condition B (afternoon)
Trial 4: condition A (morning).
19
Q
Independent groups: Limitations
A
Individual differences: participants in condition 1 may be naturally better at the task e.g. remembering
More participants are required than for a repeated measures design to have the same amount of data.
20
Q
How do we control individual differences: Independent group design limitations
A
Randomly allocate participants to conditions that should in theory distribute participants variables equally. This can be done by putting participants’ names in a hat and drawing out names so every other person goes into group 1.
21
Q
Matched participants design: limitations
A
- time-consuming and difficult to match participants on key variables.
- it is not possible to control all participants’ variables e.g. in a memory experiment you can match on memory ability but later find that some participants know memory boosting techniques which others didn’t.
22
Q
Matched pair design: Limitations
How can these limitations be controlled?
A
- Restrict the number of variables to match to make it easier
- Conduct a pilot study (a small-scale trial run of the study to test the design of the study) to help identify the key variables worth studying.
23
Q
Strengths of each experimental design
A
repeated measures: control participants variables, need fewer participants.
independent groups: less time-consuming than matched participants design, doesn’t suffer order effects as participants are in separate conditions.
matched participants: it tries to match variables so equals fewer order effects and more chance of having more varied participants in each group.
24
Q
What is mundane realism
A
how the study mirrors the real world. is the research environment realistic to real life experiences.
25
what is a confounding variable?
a variable that changes the DV rather than the IV affecting it. therefore the results may be meaningless.
26
what is an extraneous variable?
'extra variables' that can have an effect upon the DV, making it more difficult to detect a significant effect.
27
what is internal validity?
the degree to which an observed effect was due to experimental manipulation rather than the other variables.
does the research measure what it intends to measure?
28
what is external validity?
the degree to which findings can be generalised.
29
what is ecological validity?
the ability to generalise a research effect beyond a particular setting.
30
what is population validity?
the extent to which the findings can be generalised to other groups besides those who took part in the study.
31
what is historical validity?
the extent to which the findings from one time period can be applied to another. e.g. Asch '50s.
32
what are the 4 types of experiments?
laboratory
field
natural
quasi
33
Lab Experiments
conducted in a special environment
variables are carefully controlled
PPs are aware they are taking part, but unlikely to know the aim of the study.
34
Lab Experiments
strengths and weaknesses
strengths:
control over variables leads to high internal validity, confident that the IV is affecting the DV.
weaknesses:
low ecological validity due to PPs knowing they are being watched - may show social desirability.
low mundane realism means people don't behave as they usually do.
35
Field Experiments
conducted in a natural 'ordinary' environment. PPs are usually unaware of their participation in an experiment, so behaviour will be more natural.
36
Field Experiments
strengths and weaknesses
strengths:
great mundane realism= high external validity
less likely to respond to cues from the experimenter so behaviour is natural
weaknesses:
IV may lack realism so not like everyday occurences.
low internal validity due to difficulty in controlling extraneous and confounding variables.
37
Natural experiments
this is a naturally occuring event that couldn't be researched in a laboratory due to practical and ethical reasons. therefore IV occurs naturally but DV may still be tested.
38
Natural experiments
strengths and weaknesses
strengths:
high ecological validity and mundane realism due to studying the effects of real issues.
limitations:
IV is not directly manipulated, so can't establish cause and effect.
Internal validity is questioned as PPs aren't randomly allocated so there could be confounding variables.
39
Quasi-Experiments
studies that are almost experiments. IV is naturally occurring and the DV is measured in a laboratory. IV is based on differences that naturally occur between people e.g. gender and age.
40
Quasi-Experiments
strenths and weaknesses
strengths:
allows comparisons to be made between different types of people
often carried out under controlled conditions and so have the same strengths as a laboratory experiment.
weaknesses:
cannot randomly allocate PPs to conditions so may suffer confounding variables.
low internal validity due to PP knowing they are being studied.
low ecological validity as the DV is likely to be an artificial task.
41
More problems with experiments
the 'helping hand' of PPs or the 'screw you' effect. PPs can be overly helpful in experiments or purposely spoil them.
42
Clever Hans
Horse who was asked what 7 x 4 is. The crowd would count out loud, once they got to 28, he would stamp his hooves. The horse could not count, he just responded to subtle unconscious cues from the owner. Hans did what was expected of him due to cues, he acted on demand characteristics.
43
Indirect investigator effects
the investigator may operationalise variables in a way to favour a particular result or they may not give clear standardised instructions so their instructions to PPs may influence the PP's behaviour.
44
Dealing with the problems of experiments
Single Blind Design: the PP isn't aware of the aims or which condition of the experiment they will receive. This prevents them from seeking cues about the aim and reacting to them.
Double Blind Design: The PP and investigator are blind to the aims and hypotheses, consequently the investigator is less likely to produce cues for the PP to act on.
Experimental realism: if the experimenter makes the task engaging, the PP will focus on the task and not the fact they are being observed and so their behaviour is more realistic.
45
what are the types of sampling?
opportunity
volunteer
random
systematic
stratified
46
Opportunity sampling
recruit people who are available/most convenient
strengths:
easiest method
not time-consuming or expensive
weaknesses:
biased sample
cannot control individual differences
47
Volunteer Sampling
advertise for PPs e.g. newspaper
strengths: representative sample: get who you advertise for
less biased sample - range of certain professionals.
weaknesses:
can be biased as the PPs are more likely to be highly motivated, and eager.
limited control over individual differences.
48
Random Sampling
every PP in the population has an equal chance to be selected e.g. the lottery method, random number table, random number generator
strengths:
less bias as all members of population have euqal chance of being selected
can be quick and easy
weaknesses:
contacting the PPs can be time consuming with a large sample
bias can randomly occur so lack control over individual differences
49
Stratified sampling
you select a sub-group (strata) from an identified population. then obtain the PPs in proportion to their occurrence in the population. select them using a random sampling method.
strengths:
more representative due to proportionality and random sampling
avoids researcher bias as it is out of their hands who is selected.
weaknesses:
very time-consuming to identify and contact all the potential PPs.
May not provide a completed representation of the target population you could still have a biased sample.
50
systematic sampling
list of names from the target population and select every 6th person. the numberical interval is applied consistently.
strengths:
lacks bias as every PP is selected using an objective system
lacks any biased input from the researcher
weaknesses:
to ensure a true lack of bias, you need to select a number using a random method, start with this and then select every nth person.
51
Naturalistic Observations
behaviour is studied in a natural environment, where things are left as they normally would be. the researcher doesn't interfere with what's going on.
strengths:
high ecological validity
low investigator effects and demand characteristics
weaknesses:
little control of variables so other things unknown to the observer may effect their behaviour, so lacks validity.
52
controlled observations
some variables are controlled by the researcher. PPs are likely to know they are being observed. conducted in a laboratory. allow the researcher to investigate the effects of certain things on behaviour.
strengths:
variables are controlled which could affect PPs behaviour.
weaknesses:
environment may feel unnatural to the PPs and so may affect their behaviour, lacks validity.
53
Overt Observations
occurs in natural and controlled observations. observers try to be as unobtrusive as possible. PPs know they are being observed.
strengths:
control of variables, so establishes a cause and effect relationship.
weaknesses:
PPs may suffer social desirability.
54
Covert observations
PPs have no knowledge of the observation before or during the study.
they will be informed afterwards.
strengths:
unaware of observation so behaviour is more natural.
weaknesses:
ethical issues of being observed without consent - okay if in public places, as long as behaviour is appropriate.
55
Non-Patricipant Observation
they observe from a distance, watch and listen to others, and don't interact with the people being observed.
strengths:
more likely to be objective as they aren't part of a group being observed.
weaknesses:
ethical issues
observer bias
56
Participant Observation
the observer is part of the group being observed. they may be a PP who unbeknown to the rest of the group is an observer.
strengths:
provides a special insight to what is going on that may be missed.
weaknesses:
ethical issues
observer bias
57
Event Sampling
count how many times a certain behaviour or event occurs in a target individual(s).
strengths:
good when behaviour infrequently occurs
weaknesses:
could miss out important details
58
Time Sampling
record behaviours in a given, pre-established time frame. e.g. may note what the target individual is doing every 20 seconds.
strengths:
reduces the number of observations to be made.
weaknesses:
may not be representative of behaviour overall
59
Questionnaires
set of written questions designed to collect information about a topic. questions allow the researcher to discover what people think/feel. always pre-determined
strengths:
gains a lot of emotions, and thoughts from people which can be generalised to the issue being studied.
weaknesses:
PPs may not be truthful - social desirability.
60
Questionnaire construction
clarity: questions should be clear
bias: leading questions should be avoided
analysis: are the questions easy to analyse?
likert scale: strongly agree / agree / not sure / disagree
filler questions: reduce demand characteristics by including irrelevant questions to distract from main aim of the study.
sequence of questions: start with easy ones first to relax PP.
61
Interviews - structured
pre-determined questions
don't deviate from the original questions
the interviewer asks questions, interviewee answers.
strengths:
easily repeated due to standarised questions, answers can be easily compared and analysed.
weaknesses:
interviewer bias
comparability may be a problem if the interviewer behaves differently with each PP.
62
Interviews - unstructured
not all pre-determined questions
extra questions may be asked based on what response the interviewee gives.
strengths:
more information is gained as more questions may be asked based on what response is given
weaknesses:
questions may lack objectivity compared to predetermined ones: interviewer has no time to reflect on what they say.
63
Displays of quantitative data: rules for tables and graphs
titles should include the variables being tested.
axis on graphs should be labelled
64
what is quantitative data?
deals with measurable numbers
psychologists develop measures of psychological variables: behavioural categories
look at averages and differences between groups.
+ Quantitivate data are easy to analyse using descriptive statistics and statistical tests, this means conclusions are easily drawn.
-such data may oversimplify reality. for example, using closed-ended questionnaires may force people to tick answers that don't truly represent their feelings, meaning the conclusions drawn may be useless.
65
what is qualitative data?
deals with descriptions
data that is observed but not measured
observing people through the messages they produce and the way they act
concerned with attitudes, beliefs, fears and emotions
it can be turned into quantitative data
+ detailed information provided, giving insight into thought and behaviour. therefore, it has greater external validity
- the complexity makes it more difficult to analyse and draw conclusions from
66
what is primary data?
observed or collected first hand
you design the research, implement it and gather the data, draw conclusions
could be gathered via an experiment, interview, observation, questionnaire
evaluation:
researcher has control over the data
data collection can be designed so it fits the aims and hypotheses of the study
lengthy and expensive process
67
what is secondary data?
the data already exists
it may have been subject to statistical testing and the significance is already known
sources: journals, articles, books, websites, population records, employee absence records etc
evaluation:
simpler to access someone else's data
cheaper, less time-consuming
if it has been statistically tested, it is known whether it is significant or not
data could be inaccurate
68
what is a correlation?
a correlation is a method used to analyse data, more specifically, the association between 2 variables.
Positive correlation: two variables increase together
negative correlation: as one variable increases, the other decreases.
Zero correlation: no relationship between the variables.
The procedures in a correlation can be easily repeated, meaning the findings can be confirmed
- in a correlation, the variables are only measured, and no deliberate change is made. therefore, no conclusion can be made about one co-variable causing another.
69
meta-anaylsis
this is a method of analyzing secondary data
a systematic review involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses from a database
you select certain search criteria
you then analyze the data using meta-analysis
this produces an effect size (measure of strength between 2 variables)
evaluation:
increased validity from reviewing the results from more than one study due to for example an increase in sample size
it has a statistic to represent different studies and so allows us to reach an overall conclusion that isn't contradictory
however, putting studies together to calculate an effect size may not be appropriate as they may differ greatly in their design.
70
descriptive statistics: measures of central tendency
mean, mode, median. these inform us of the middle values of a data set
71
descriptive statistics: measures of dispersion
range, standard deviation, calculation of percentages, postive, negative and zero correlations. these inform us how dispersed a data set is
72
measures of central tendency: mean
add up all data, divide by the number of data items
it can only be used with ratio and interval data
evaluation:
most sensitive measure of central tendency as it takes into account the exact distance between all the values of all the data
due to it being so sensitive, it can easily be distorted by extreme values and this can then misinterpret the data
cant be used with nominal data
doesnt make sense with discrete data e.g. average number of legs
73
measures of central tendency: median
middle value in an ordered list, arrange all numbers in order, the central value is the median
if there is an even number of data items there will be 2 central values
to calculate the median, add the 2 numbers up and divide by 2.
can be used with ratio, interval, and ordinal data
evaluation:
not affected by extreme scores
appropriate for ordinal data
can be easier to calculate
not as sensitive as the mean because the exact values aren't represented in the median.
74
measures of central tendency: mode
the most common data item
nominal data: it is the category that has the highest frequency count
interval and ordinal data: it is the data item that occurs most frequently
to identify the mode, data items need to be put in order
if 2 categories or data items have the same frequency, the data has 2 modes: bi-model
evaluation:
unaffected by extreme values
can be used with nominal data
useful with discrete data
not useful when describing data that has several modes
75
measures of dispersion: range
the arithmetic distance between tthe op and bottom values in a data set
evaluation:
easy to calculate
affected by extreme values
fails to account for the distribution of the numbers e.g. are they closely grouped around the mean or evenly spread out?
76
measures of dispersion: standard deviation
the measure of the average distances between each data item above and below the mean
+ it is the most precise measure of dispersion as it takes all of the exact values into account
+ relatively easy to calculate
- it may hide some characteristics of the data e.g. extreme scores
77
behavioural categories
operationalise the variables
they are used to help measure behaviour, not ambigous, and each researcher has a clear behaviour to observe
78
what are descriptive statistics?
measures of central tendency: mean, mode, median. measures of dispersion: range, standard deviation, graphs, bar charts, scatter graphs
79
what are inferential statistics?
sign test, Spearmans Rho, Mann-Whitney, Wilcoxon, Chi Squared, Pearons R, related and unrelated t test
80
inferential testing
these are designed by statisticians who work out the probability of certain results o they know whether to accept/reject the null hypothesis.
they are called inferential because statisticians make an inference (deduction) about whole populations based on smaller samples.
they used these tests to work out whether the results arweree statistically significant or not
non-parametric: the sign test, Wilcoxon, Mann-Whitney, Spearman's Rho, Chi-Squared
parametric: related and unrelated t test, Pearsons R
81
The Sign Test
when to use the sign test: this is a test used when looking at paired or related data. the two related pieces of data could come from repeated measures design. the sign test can also be used with a matched pairs design because the PPs are paired and therefore count, for statistics, as one person tested twice.
How to do the sign test?
step 1: state the hypothesis
step 2: record the data and work out the sign
step 3: find calculated value
step 4: fin critical value
82
Chi-Squared test
reasons for the choice of test: the hypothesis states a difference/association between 2 sets of data, the data in each cell is independent, and the data is nominal because each person belongs to one of 4 categories.
step 1: state the hypothesis
step 2: place raw data in the contingency table
step 3: find the observed value of x2.
step 4: find critical value of x2.
83
Related t-test
reasons for choice of test: the hypothesis states a difference between data sets, the sets of data are pairs of scores from one person = related
the data is interval because there are equal intervals when counting frequency, the data fit the criteria for a parametric test: the data is interval, the population is assumed to have a normal distribution, and the variances of the samples are the same because they come from the same PP.
step 1: state the hypothesis
step 2: place raw data in the table
step 3: find calculated value of t
step 4: find critical value of t
84
Unrelated t test
reasons for choice of test: the hypothesis states a difference between data sets, the 2 sets of data are pairs of scores from seperate groups of PPs = unrelated
the data is interval because there are equal intervals when counting frequency, the data fit the criteria for a parametric test: the data is interval, the populations are assumed to have a normal distribution and the variances of of the samples are assumed to be the same as the PPs were randomly assigned to conditions
step 1: state the hypothesis
step 2: place raw data in the table
step 3: find calculated value of t
step 4: find critical value of t
85
Wilcoxon
Reason for choice of test: the hypothesis states a difference between 2 sets of data, the 2 sets of data are pairs of scores from one person = related, and the data is ordinal because there are not equal intervals between ratings.
step 1: state the hypothesis
step 2: place raw data in table
step 3: find the differences and rank
step 4: find calculated value of T
step 5: find critical value of T
86
Mann-Whitney
reason for choice of test: the hypothesis states a difference between 2 sets of data, the 2 sets of data are separate groups of PPs = unrelated, and the data is ordinal because there are not equal intervals between ratings
step 1: state the hypothesis
step 2: place raw data in table
step 3: rank each data set
step 4: add each set of ranks
step 5: find calculated value of U
step 6: find critical value of U
87
Spearman's Rho
reasons for choice of test: the hypothesis states a correlation between 2 sets of data, the 2 data sets are pairs of scores from 1 person = related, and the dtdataaat is ordinal because numeracy skills are measured using a test and may not have equal intervals between scores
step 1: state the hypothesis
step 2: place raw data in table
step 3: find calculated value of rho
step 4: find critical value of rho
88
Pearson's R
reasons for choice of test: the hypothesis states a correlation between 2 sets of data, the 2 sets of data are pairs of scores that are related, the data is interval because they are counting the number of observations, and the data fit the criteria for a parametric test because the data is interval, and the populations are assumed to have a normal distribution and the variances of samples are assumed to be the same as the PPs are related
step 1: state the hypothesis
step 2: place raw data in table
step 3: find calculated value of R
step 4: find critical value of R
89
the implications of psychological research for the economy: irrational thinking - availability heuristic
heuristic means rule. the availability heuristic is the rule that the likelihood of selecting something is related to its availability. for example, people will tend to overestimate the likelihood of dying in a plane crash. this irrational thinking comes from us often reading and watching programmes about such incidents, so they are more available when making a probability judgement about the likelihood of being in such an accident.
90
the implications of psychological research for the economy: irrational thinking - the framing effect
irrational thinking is also linked to peoples' decisions differing depending on whether a choice is presented as a gain or a loss. tversky and Kahneman asked participants to choose between two treatments that would be used with 600 people suffering from a deadly disease. 2 groups of PPs were given the same facts about the success and failure rate of the treatment but the facts were 'framed' differently.
group 1 were told that Treatment A would result in 400 deaths, whereas Treatment B would have a 33% chance that no one would die and a 66% chance that all would die.
group 2 were told that Treatment A would save 200 lives whereas Treatment B had a 33% chance of saving everyone and a 66% of saving no-one.
when pps are given the description positively framed, 72% selected that treatment.
91
Ethical issues: what is informed consent?
PPs must be given enough information about the study so they can make an informed decision about whether or not to take part. if PPs are under 16, then consent must be obtained from their parent or guardian. true informed consent may mean revealing the aims of the study, or at least telling the PPs what is going to happen.
92
Ethical issues: how to deal with informed consent?
PPs sign a document containing comprehensive information about the study. Presumptive consent can also be gained: this is where a group of people similar to the PPs are asked if they would agree to take part, if they agree it is presumed the actual PPs would have. researchers must also inform PPs of their right to withdraw.
93
Ethical issues: limitations of informed consent
giving full information about the study can invalidate its purpose, PPs may guess what it is about and what they should be doing.
full consent doesn't mean the PPs will be fully aware of what they are taking part in
Presumptive consent is no guarantee that when PPs are actually in the study that they will be agreeable to what they're experiencing.
94
Ethical issues: what is deception?
this sometimes has to occur otherwise the research would be meaningless e.g. Milgram. However,the researchers should be aware of what is reasonably acceptable. the PPs deception shouldn't occur without good cause as it prevents informed consent. It can lead to people suffering harm and being untrusting of psychologists.
95
Ethical issues: how to deal with deception
an ethics committee need to approve the deception planned by weighing up the benefits of the study and the costs against the PPs. the PPs should also be fully debriefed after the study and offered any form of help if required. they should also be able to withdraw their data.
96
Ethical issues: limitations of deception
cost/benefit decisions are not always apparent until after the study and they are subjective judgements
debriefing cannot change the fact you have been part of a study and may have suffered due to it.
97
Ethical issues: What is the right to withdraw
PPs should be fully aware that they can leave the study at any point and also that they can refuse for their data to be used. they should be informed of this at the beginning of the study and should be made aware that this still stands even if they have received payment for partaking in the study. this can be problematic for the researcher as if PPs start to leave this can lead to a biased sample.
98
Ethical issues: how to deal with the right to withdraw
informed at the start of the study
99
Ethical issues: limitations of how to deal with the right to withdraw
due to payment, they may feel they are unable to leave
may feel they cant leave due to feeling pressures that they may spoil the study
100
Ethical issues: what is protection from physical and psychological harm
PPs shouldn't experience these things unless they have given true informed consent to do so, physical harm could include being asked to smoke, and psychological harm could be being made to feel anxious. it is deemed acceptable if the risk of harm for the PPs is no greater than what they encounter in everyday life. it is difficult for researchers to prevent this as they may not expect some outcomes in certain pieces of research and some degree of stress may be required for the research.
101
Ethical issues: how to deal with protection from physical and psychological harm
avoid risk greater than experiences in everyday life and stop the study straight away if harm is suspected.m
102
Ethical issues: limitations of protection from physical and psychological harm
harm may not be apparent at the time of the study but only afterwards when the PPs reflect on the experience
103
Ethical issues: what is confedentiality
the Data Protection Act makes confidentiality a legal right. personal data should only be recorded and made available in a form that doesn't identify the PPs. Confidentiality can be difficult for the researcher because they publish findings and anonymity is difficult as if people are known to have taken part in the study they can be traced.
104
Ethical issues: how to deal with confidentiality
Don't record the names of any PPs - use numbers or pseudonyms.
105
Ethical issues: limitations of confidentiality
in practice, confidentiality can be difficult as it can be possible to trace PPs from other data.
106
Ethical issues: what is privacy
this can be difficult to avoid if people are being studied without them being aware of it. for the PP and the researcher, it has to be considered whether people are in situations where they would expect total privacy.
107
Ethical issues: how to deal with privacy
dont study anyone without infromed consent unless it is in a public space however this would still exclude people in public places being intimate e.g. couple kissing whilst walking down the street.
108
Ethical issues: limitations of privacy
what constitutes as a public space can vary as to can the behaviour is deemed intimate or not.
109
What is the main goal of scientific research?
To collect facts and gain empirical evidence
## Footnote
Empirical evidence is crucial for confirming the truth of scientific claims.
110
Define empirical methods in science.
Information gained through direct observation or experiment
## Footnote
Empirical methods are essential for validating scientific claims.
111
What does objectivity in scientific research aim to achieve?
To ensure that expectations do not affect observations and measurements
## Footnote
Objectivity is vital for the systematic collection of measurable data.
112
Why is controlled experimentation important in scientific research?
It allows for careful control of conditions to ensure objective results
## Footnote
Controlled environments are used for both observational studies and experiments.
113
What does replicability in scientific research refer to?
The ability to repeat an observation or experiment and achieve the same outcome
## Footnote
Replicability affirms the validity of research findings.
114
How does theory construction relate to scientific facts?
Facts are made meaningful through the construction of theories and explanations
## Footnote
Theories help us understand observations and predict future events.
115
What is hypothesis testing in the context of scientific theories?
A method to test the validity of a theory by generating testable hypotheses
## Footnote
If hypotheses are not supported, the theory may require modification.
116
Fill in the blank: A good theory should be able to generate _______.
[testable hypotheses]
117
True or False: The process of scientific research is static and does not evolve.
False
## Footnote
The process of science is constantly evolving and improving.
118
What is the difference between reliability and replicability in scientific research?
Reliability tests the same people in the same way; replicability tests different groups
## Footnote
Replicability often uses slightly different tasks to observe similar behaviors.
119
features of science: what is the inductive model?
observations - testable hypothesis - conduct study to test the hypothesis - draw conclusions - propose the theory
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features of science: what is the deductive model?
observations - propose model - testable hypothesis - conduct a study to test the hypothesis - draw conclusions
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the role of peer review: what is it?
Peer review plays a key part in the verification of research
It helps to determine if the research can be deemed scientifically acceptable
Peer review is an independent assessment carried out before the research is published by other experts in the field
It is completed independently and usually anonymously
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the role of peer review: what are the aims of peer review?
- assess the appropriateness of the research to the research topic/aim
- check the validity of the findings
- judge the significance of the research
- check that the research is original and has not been plagiarised
- suggest or provide recommendations/amendments
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the role of peer review: what are the 4 outcomes that can be reached from peer review?
1. Accept the work unconditionally
2. Accept the work as long as the researcher makes specific improvements/amendments
3. Reject the work, but suggest amendments for re-submission of the work
4. Reject the work outright
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the role of peer review: what are the types of peer review?
Open Review: The researchers and reviewers are known to each other. This type of review is believed to reduce plagiarism
Single-blind Review: This is the most common form of peer review. The researcher's name is not revealed to the reviewers
Double-blind Review: The researcher and the reviewers are anonymous to each other
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content analysis
Content analysis is a method used to analyse qualitative data (non-numerical data). In its most common form it is a technique that allows a researcher to take qualitative data and to transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.
+ It is a reliable way to analyse qualitative data as the coding units are not open to interpretation, and so are applied in the same way over time and with different researchers.
+ It is an easy technique to use and is not too time-consuming
+ It allows a statistical analysis to be conducted if required, as there is usually quantitative data as a result of the procedure
- Causality cannot be established as it merely describes the data
- As it only describes the data, it cannot extract any deeper meaning or explanation for the data patterns arising.
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pilot studies
Pilot studies are small, trial versions of proposed studies to test their effectiveness and make improvements. They help identify potential issues early, which can then be rectified before committing to the length and expense of a full investigation.
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).
The pilot study is an important part of the experimental process and is a good practice which is widely used.
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why is the 5% level normally used in research?
Psychologists normally use a 5% significance level as this is an acceptable level (within the Social Sciences) to claim that the results of an experiment are significant. This means that psychologists are 95% confident that their results are due to the independent variable affecting the dependent variable and not other factors.
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what is a type II error?
A Type II error is otherwise known as a false negative, and it’s when a researcher accepts the null hypothesis, despite the fact their results were significant.
