All of it Flashcards
(207 cards)
1
Q
What is an independent variable?
A
The variable a researcher manipulates.
2
Q
What is a dependent variable?
A
The variable a researcher measures.
3
Q
Define ‘operationalisation’
A
When variables are clearly specified i.e. made precise.
4
Q
Explain why operationalisation is important
A
Increases objectivity and replicability.
5
Q
What is an extraneous variable?
A
A random variable, other than the IV, that could affect the results.
6
Q
Why is it important to control extraneous variables?
A
To prevent them from affecting the results, increasing the validity of the findings.
7
Q
What is a confounding variable?
A
A variable that varies with the IV that has affected the results.
8
Q
What is the purpose of counterbalancing?
A
To equally distribute order effects across conditions in a repeated measures design.
9
Q
Outline the procedures of counterbalancing
A
Participants are allocated into one of two groups. One group completes conditions A then B. The other group completes conditions B then A.
10
Q
What is the purpose of random allocation?
A
To remove researcher bias when allocating participants to groups.
11
Q
Outline the procedures of random allocation
A
Participants’ names are written on pieces of paper, placed into a hat and shuffled. The researcher blindly pulls out names to allocate groups.
12
Q
What is the purpose of randomisation?
A
To randomly present the order of stimuli.
13
Q
Outline the procedures of randomisation
A
All stimuli are printed on pieces of paper, placed into a hat, shuffled, and then drawn blindly for presentation order.
14
Q
What is the purpose of standardisation?
A
Ensures all participants have the same experience.
15
Q
Identify three ways in which you could standardise a study
A
E.g. use the same instructions, same researcher, keep the location/level of distractions the same.
16
Q
Outline the experimental method
A
Involves manipulating an independent variable to result in at least two conditions.
17
Q
Identify the experimental designs
A
Independent groups, repeated measures and matched pairs.
18
Q
Explain what is meant by an independent groups design
A
When different participants take part in each condition.
19
Q
Explain what is meant by a repeated measures design
A
When the same participants take part in all conditions.
20
Q
Explain what is meant by a matched pairs design
A
When different participants take part in each condition but are matched on key variables.
21
Q
Describe the procedures of a matched pairs design
A
Match participants on key variables and randomly allocate each member to conditions.
22
Q
Give two strengths of an independent groups design
A
No risk of order effects and low risk of demand characteristics.
23
Q
Give two limitations of an independent groups design
A
High risk of participant variables and more time consuming.
24
Q
Give two strengths of a repeated measures design
A
No risk of participant variables and less time consuming.
25
Give two limitations of a repeated measures design
High risk of order effects and high risk of demand characteristics.
26
Give two strengths of a matched pairs design
No risk of order effects and low risk of demand characteristics.
27
Give two limitations of a matched pairs design
Participant variables may remain and the matching process is time consuming.
28
Identify the types of experiments
Laboratory experiment, field experiment, natural experiment and quasi experiment.
29
Explain what is meant by a laboratory experiment
When the researcher manipulates the IV and measures the DV in a controlled environment.
30
Explain what is meant by a field experiment
When the researcher manipulates the IV and measures the DV in a natural environment.
31
Explain what is meant by a natural experiment
The IV has not been manipulated by the researcher and was a natural occurring event.
32
Explain what is meant by a quasi experiment
The IV has not been manipulated and is a pre-existing difference.
33
Give two strengths of a lab experiment
Easy to replicate and more ethical.
34
Give two limitations of a lab experiment
Lacks ecological validity and increased risk of demand characteristics.
35
Give two strengths of a field experiment
High in ecological validity and reduced risk of demand characteristics.
36
Give two limitations of a field experiment
Difficult to replicate and may be less ethical.
37
Give two strengths of quasi and natural experiments
Allow for research where manipulation is impractical and less time consuming.
38
Give two limitations of quasi and natural experiments
Can be difficult to replicate and may be at risk of participant variables as cannot randomly allocate.
39
Outline the correlational method
Investigates whether there is a relationship between two continuous co-variables. Data is typically gathered through observations/questionnaires and plotted on a scattergram to show the direction and strength of the relationship.
40
Explain what is meant by a positive correlation
As one co-variable increases, so too does the other co-variable. This leads to an upward trend in the scattergram.
41
Explain what is meant by a negative correlation
As one co-variable increases, the other co-variable decreases. This leads to a downward trend in the scattergram.
42
Explain what is meant by a zero correlation
Refers to when there is no relationship between the two co-variables and so there is no clear trend in the scattergram.
43
Explain how you would assess the direction and strength of a correlation presented in a scattergram
Identify the direction by checking for an upward (positive), downward (negative), or no trend (zero correlation). Assess strength by imagining a line of best fit and seeing how closely data points cluster around it.
44
What things should you refer to when drawing conclusions about a scattergram?
Identify the direction of the correlation. Explain what this means with reference to the co-variables. Mention any plateau or outliers and their effect on strength. Comment on sample size and its impact on generalisability.
45
What is a correlation coefficient?
A number from -1 to 1 that tells the strength and direction of a correlation. A minus figure = negative correlation. A positive figure = positive correlation. 0 = zero correlation. The closer to -1 or 1, the stronger the correlation.
46
Compare experiments to correlations
Experiments test for differences, correlations test for relationships. Experiments manipulate variables, correlations do not. Experiments can establish cause and effect, correlations cannot. Experiments use categorical data, correlations use continuous data. Experiments are shown in bar charts, correlations in scattergrams.
47
Give one strength of correlations
Allows research where it would be unethical or impractical to manipulate a variable.
48
Give one limitation of correlations
Cannot establish cause and effect as no variable is manipulated.
49
Outline the self-report method
Involves participants responding to questions about their thoughts/feelings/behaviour. Two types include questionnaires and interviews.
50
Outline what is involved in an interview
A verbal Q&A between interviewer and interviewee, usually face to face. Can be audio/video recorded. Can be structured or unstructured.
51
Distinguish between a structured and unstructured interview
Structured = fixed set of questions in a set order. Unstructured = flexible, with spontaneous follow-ups depending on answers.
52
Give two strengths of a structured interview over an unstructured interview
More focused on research aims, so gathers more relevant data. Easier to analyse and compare, as all participants are asked the same questions.
53
Give two strengths of an unstructured interview over a structured interview
Allows follow-up on unexpected responses for more depth. Feels more natural and conversational, so participants may open up more.
54
Outline what is involved in a questionnaire
A list of standardised written questions that participants complete. Can be administered to large groups. Includes open and closed questions.
55
Distinguish between open and closed questions
Open: participants respond in their own words. Closed: participants select from fixed options (e.g. yes/no, rating scales).
56
Give two strengths of open questions over closed questions
Reveal unexpected trends as answers aren't restricted. Avoids frustration by letting participants express themselves fully.
57
Give two strengths of closed questions over open questions
Easier to analyse and compare due to fixed responses. Ensures participants provide the specific information needed.
58
Give two strengths of using questionnaires over interviews
Less time-consuming — large samples can complete them without the researcher. Reduces investigator effects as researcher doesn’t need to be present.
59
Give two strengths of using interviews over questionnaires
Questions can be clarified to ensure relevant responses. Verbal format encourages more detailed and rich responses.
60
Outline the observational method
Involves a researcher watching and recording the behaviour of participants. Behavioural categories are formed and tallied using event or time sampling. The observation can be carried out in the moment or video recorded for later analysis.
61
Define behavioural categories
Operationalised/specific behaviours that are recorded as part of an observation (e.g. punching) that are supposed to represent the general behaviour being studied (e.g. aggression).
62
Explain why behavioural categories are important in observational research
Provides clear focus for the observer so that they can collect relevant data.
Removes the need for subjective interpretation to allow for more objective recording.
Increases inter-observer reliability as all observers will be clear on the behaviours they are supposed to be recording.
Allows observers to quickly tally their observations in a tally chart.
63
Distinguish between event sampling and time sampling
Event sampling: observers record behavioural categories every time they happen.
Time sampling: observers record behavioural categories at specific time intervals (e.g. every 5 minutes).
64
What is the formula for working out the time sampling method used by an observer?
Total observation time (in minutes) / Number of observations made
65
Give one strength of event sampling
Researchers are unlikely to miss behaviours, resulting in more data and more valid conclusions.
66
Give one limitation of event sampling
Less appropriate in busy observations as it could cause the observers to become overwhelmed.
67
Give one strength of time sampling
More appropriate in busy observations as observers can take breaks between the observation times and so are less likely to become overwhelmed.
68
Give one limitation of time sampling
Important behaviours may occur in the time periods when the observer is not recording, resulting in less data and less valid conclusions.
69
Distinguish between a naturalistic and controlled observation
Naturalistic: behaviour observed in its natural environment.
Controlled: behaviour observed in an artificial environment, like a lab.
70
Give one strength of naturalistic observation
Produces more natural behaviour and therefore has higher ecological validity.
71
Give one strength of controlled observation
Higher level of control over the environment makes it easier to replicate to assess reliability.
72
Distinguish between overt and covert observation
Overt: observer is visible, participants are aware.
Covert: observer is hidden, participants are unaware.
73
Give one strength of overt observation
More ethical — participants are aware and can provide consent.
74
Give one strength of covert observation
Reduces social desirability bias as participants are unaware they are being observed.
75
Distinguish between participant and non-participant observation
Participant: observer becomes a member of the group.
Non-participant: observer watches from a distance without interacting.
76
Give one strength of participant observation
Allows greater insight into observed behaviours for more valid conclusions.
77
Give one strength of non-participant observation
Helps prevent subjectivity as observer remains detached.
78
Outline the case study method
Involves an in-depth study of one person/small group. Conducted in the real world over time (longitudinal) using multiple methods such as observations, interviews, and psychological testing.
79
Give two strengths of case studies
High ecological validity due to real-world setting.
Rich, detailed data increases validity of conclusions.
Use of multiple methods allows for cross-checking of data.
80
Give two limitations of case studies
Small sample size limits generalisability.
Hard to replicate due to their longitudinal and unique nature.
Risk of researcher bias from single-researcher interpretation.
81
Define reliability
The extent to which the results are consistent.
82
Identify and define two ways of assessing reliability
Inter-observer reliability: consistency between different observers' recordings.
Test-retest reliability: consistency of results when the same test is repeated.
83
Outline inter-observer reliability as a way of assessing reliability
Two observers independently record the same behaviour. Results are correlated; a coefficient of 0.8 indicates strong agreement. Statistical tests like Spearman’s rho or Pearson’s r assess significance.
84
Explain why it’s important to assess inter-observer reliability
Prevents subjective bias and ensures consistency across different researchers, increasing confidence in findings.
85
Identify ways to improve inter-observer reliability
Use clear behavioural categories.
Train observers with examples.
Video record sessions for review.
86
Outline test-retest reliability
Same participants take the test at two different times. Results are correlated to assess consistency. Coefficient of 0.8 indicates strong correlation. Use of statistical tests determines significance.
87
Explain why it’s important to assess test-retest reliability
Ensures results are not due to extraneous variables and can be replicated over time, increasing confidence in findings.
88
Identify ways to improve test-retest reliability
Experiments: use lab settings and standardisation.
Observations: use controlled settings and trained observers.
Questionnaires: use closed questions and offer anonymity.
Interviews: use structured format and trained interviewers.
89
Define validity
Refers to how well a study measures what it intends to measure.
90
Define ecological validity
Whether the setting is natural and results in natural behaviour.
91
Define temporal validity
Whether past research findings still apply to the current time period.
92
Identify factors affecting validity
Extraneous variables: order effects, participant, situational, and task variables.
Investigator effects, demand characteristics, social desirability bias.
93
Identify ways to control extraneous variables
Order effects: counterbalancing or matched pairs.
Participant variables: random allocation or matched pairs.
Situational variables: lab settings or standardisation.
Task variables: randomisation or standardisation.
94
Explain investigator effects
When the investigator’s characteristics or behaviour unintentionally affect participant responses.
95
Identify ways of reducing investigator effects
Use double-blind techniques.
Standardise instructions.
Use same/similar investigators.
Avoid leading questions.
Use covert observation or questionnaires.
96
Explain demand characteristics
When participants try to guess the aims of the study and change their behaviour accordingly.
97
Identify ways of reducing demand characteristics
Use matched pairs.
Avoid volunteers.
Use covert observation.
Include filler questions.
98
Explain social desirability bias
When participants change their behaviour to appear more favourable because they know they are being studied.
99
Identify ways of reducing social desirability bias
Offer anonymity or confidentiality.
Use covert observation.
Use questionnaires instead of interviews.
100
Identify and define two ways of assessing validity
Face validity: whether the measure appears valid to other researchers.
Concurrent validity: whether results correlate highly with an established test measuring the same thing.
101
Explain how face validity is checked
Other researchers are asked whether the behavioural categories or DV are good measures of the construct being studied.
102
Explain how concurrent validity is checked
Participants complete both the new and established test. Scores are correlated; a coefficient of 0.8 or higher and significant result indicates high validity.
103
Identify ways to improve concurrent validity
Use or adapt questions from the existing validated test.
104
What is meant by an ‘aim’?
A statement about the purpose of the study — what it is aiming to investigate.
105
What is meant by a ‘hypothesis’?
A precise, testable prediction about the expected outcome of a study.
106
Define a directional hypothesis and when it is used
Predicts a specific direction of difference/relationship. Used when prior research allows for a directional prediction.
107
Define a non-directional hypothesis and when it is used
Predicts a difference/relationship without stating the direction. Used when there is no prior or consistent research.
108
Difference between population and sample
- Population: All people the researcher is interested in.
- Sample: Subset of the population who participate. Should be representative to allow generalisation.
109
Stratified sampling procedure
- Identify key subgroups (strata).
- Calculate their percentage in the population.
- Use same proportions in the sample.
- Randomly select from each group (e.g., using the hat method).
110
Evaluate stratified sampling
✅ No researcher bias.
✅ Representative — improves generalisability.
❌ Time-consuming and costly to identify and categorise the population.
111
Systematic sampling procedure
- Randomly order the population list.
- Calculate nth person (e.g., every 10th).
- Select every nth person.
112
Evaluate systematic sampling
✅ No researcher bias — objective method.
❌ May create unrepresentative sample if characteristics repeat every nth term.
❌ Time-consuming to compile population list.
113
Random sampling procedure
- Gather full list of population.
- Place names in a hat.
- Randomly draw the required number.
114
Evaluate random sampling
✅ No researcher bias.
❌ May lead to unrepresentative sample (e.g., all male).
❌ Time-consuming to collect and manage population data.
115
Opportunity sampling procedure
- Go to a location with target population.
- Ask those nearby to participate.
116
Evaluate opportunity sampling
✅ Quick and easy — lowers cost.
❌ Researcher bias — they choose who to ask.
❌ Unrepresentative — only includes available people.
117
Volunteer sampling procedure
- Advertise the study.
- Participants contact researcher to join.
118
Evaluate volunteer sampling
✅ No researcher bias — self-selected.
✅ Low drop-out (participants are motivated).
❌ Unrepresentative — volunteers may share traits.
❌ Higher chance of demand characteristics.
119
Role of BPS Code of Ethics
Ensures researchers follow ethical principles: informed consent, no deception, confidentiality, right to withdraw, and protection from harm. Research proposals must be reviewed by an ethics committee.
120
Informed consent
Participants must be fully informed and agree to take part. Under-18s require parental consent.
121
When is informed consent an issue?
In covert observations or field experiments where participants don’t know they’re being studied.
122
Solutions for informed consent issues
- Retrospective consent after study: This is where the participant gives consent for their data to be used in the research once they've taken part and have been debriefed.
- Presumptive consent from similar people: Tell a group of people similar to the participants about the study and ask if they would consent to taking part. If they say yes, you can presume that the participants would also consent.
123
Deception
Misleading participants is unethical but sometimes necessary to prevent demand characteristics.
124
Dealing with deception
Fully debrief participants afterwards — explain the true aims and reasons for deception.
125
Confidentiality
Keep personal details private. Can be an issue in case studies where participants are identifiable.
126
Solution for confidentiality issues
Use pseudonyms, initials or participant numbers.
127
Debriefing
After the study: explain aims, ethical rights, and offer support services.
128
Right to withdraw
Participants can leave the study at any time and remove their data.
129
When is right to withdraw an issue?
Covert studies — participants unaware they're in a study.
130
Solution for right to withdraw issues
Debriefing at the end; reiterate right to withdraw data.
131
Protection from harm
Must not cause physical or psychological harm beyond everyday experiences.
132
Solution for protection from harm issues
Debrief and offer third-party psychological support (e.g., Mind.org.uk).
133
Consent form
Given before study. Includes aims, procedures, confidentiality, right to withdraw. Signed agreement to participate.
134
Debrief form
Given after study. Includes aims, full disclosure, withdrawal rights, support contacts, and researcher details.
135
Define a pilot study
A small-scale version of the full study to check for problems.
136
Purpose of a pilot study
To identify and fix design flaws before full-scale research, saving time and money.
137
What can a pilot study check?
- Experiments: clarity of instructions, stimuli, extraneous variables.
- Observations: suitability of behavioural categories and sampling methods.
- Self-reports: clarity and relevance of questions, format (open/closed).
138
Four types of data
Quantitative, Qualitative, Primary, Secondary.
139
Quantitative vs Qualitative data
- Quantitative: Numbers, statistical analysis.
- Qualitative: Descriptions, rich detail.
140
Strength of quantitative data
More objective — less researcher bias in analysis.
141
Strength of qualitative data
More in-depth — allows elaboration for meaningful insights.
142
Primary vs Secondary data
- Primary: Collected first-hand for the study.
- Secondary: Pre-existing data used for a new purpose.
143
Strength of primary data
More insightful — tailored to current research aims.
144
Strength of secondary data
Quicker and cheaper — no need to collect new data.
145
What is a meta-analysis?
Combines findings from multiple studies to produce an overall conclusion.
146
Evaluation of meta-analysis
✅ Large samples — more generalisable.
❌ May include flawed or inconsistent methodologies.
147
Levels of Measurement: Nominal
Categories only. Example: Introvert vs Extrovert. Least informative.
148
Levels of Measurement: Ordinal
Ranked data on non-standard scale. Example: Happiness scale 1–10. More informative but subjective.
149
Levels of Measurement: Interval
Standardised scale with equal units. Example: Temperature, time. Most informative and objective.
150
Measures of Central Tendency: Mean
Use: Interval data. Strength: Most representative. Weakness: Affected by extreme scores.
151
Measures of Central Tendency: Median
Use: Ordinal data. Strength: Not affected by outliers. Weakness: Ignores rest of the data.
152
Measures of Central Tendency: Mode
Use: Nominal data. Strength: Easy to identify. Weakness: May be multiple/no modes.
153
Measures of Dispersion: Range
Use: Quick look at spread. Strength: Easy to calculate. Weakness: Affected by outliers.
154
Measures of Dispersion: Standard Deviation
Use: Spread around the mean. Strength: Uses all data, less sensitive to extremes. Weakness: Time-consuming to calculate.
155
What is the purpose of a distribution graph?
To inform the researcher about the average and spread of scores in their data set.
156
Identify the three types of distributions.
Normal distribution, positively skewed distribution, and negatively skewed distribution.
157
Explain what is meant by a normal distribution.
The data is symmetrically spread, with 50% of scores above and 50% below the average. Most scores fall in the middle, creating a bell-shaped curve, and the mean, median, and mode are either the same or very close.
158
Explain what is meant by a positively skewed distribution.
Data skews to the right, with the majority of scores at the lower end. The mean is the highest value (Mean > Median > Mode).
159
Explain what is meant by a negatively skewed distribution.
Data skews to the left, with the majority of scores at the higher end. The mean is the lowest value (Mode > Median > Mean).
160
Explain when it is appropriate to use a table.
For presenting raw data, totals, and descriptive statistics.
161
Explain when it is appropriate to use a scattergram.
For correlational research examining relationships between two continuous co-variables.
162
Explain when it is appropriate to use a bar chart.
When comparing discrete or categorical data between groups.
163
Explain when it is appropriate to use a histogram.
When examining the frequency of scores in a continuous data set.
164
Explain the difference between bar charts and histograms.
Bar charts are for discrete/categorical data with gaps between bars, while histograms are for continuous data and bars are contiguous. Bar charts use averages, whereas histograms require individual scores.
165
Why is statistical testing used in psychological research?
To assess the likelihood that a difference or relationship occurred due to chance.
166
Explain what is meant by a p-value.
The probability that the observed result occurred by chance. A p-value of 0.05 means there’s a 5% chance the result happened due to chance, so 95% confidence in a significant result.
167
Explain why researchers typically use p=0.05.
It balances the risks of Type I and Type II errors.
168
Distinguish between a Type I and Type II error.
Type I: Rejecting a true null hypothesis (false positive).
Type II: Failing to reject a false null hypothesis (false negative).
169
Explain how you can assess the likelihood of a Type I error.
By examining the p-value. A smaller p-value (e.g., 0.01) reduces the chance of a Type I error.
170
Explain when it is appropriate to use each statistical test.
Different tests apply based on design (independent or repeated measures) and level of measurement (nominal, ordinal, or interval).
171
Explain how you would use a statistical test to establish concurrent validity.
By correlating the new test results with results from an established, valid test using a correlation test (e.g., Pearson’s r or Spearman’s rho).
172
Explain how you would use a statistical test to establish inter-observer reliability.
By correlating independent recordings of two observers using a correlation test (Pearson’s r or Spearman’s rho).
173
Explain how you would use a statistical test to establish test-retest reliability.
By correlating participants’ results from a retest using Pearson’s r or Spearman’s rho.
174
Explain how you would assess whether a result is significant using a critical table.
Identify the calculated value in the scenario or calculate it as part of a sign test when calculating the value of S.
Identify whether it was a one-tailed test (directional hypothesis) or two-tailed test (non-directional hypothesis).
Identify an appropriate p-value. If the scenario/question does not provide one, always use 0.05.
Identify the df (degrees of freedom) or n (number of participants) in the scenario. Remember, if it is a sign test, the number of participants has to be the total number of participants minus any who has a sign of 0/produced the same result.
Based on the identification of the above, you should now be able to find the critical value in the table.
Look underneath the table at the sentence to assess whether the result is significant. This will involve comparing
the critical value to the calculated value.
Identify calculated value, determine if it's a one-tailed or two-tailed test, and use the appropriate degrees of freedom (df) or number of participants to find the critical value in a table. Compare the calculated and critical values to assess significance.
175
What is the value of S and how do you calculate it?
S is the frequency of the least occurring sign (+ or -) in a sign test. It is calculated by counting the least frequent sign among participants.
176
When is it appropriate to use a content analysis?
When analyzing qualitative data from media forms such as films, advertisements, or diaries.
177
Explain the procedures of a content analysis.
Identify categories in the media, create a tally chart, and count the frequency of each category’s appearance.
178
When is it appropriate to use a thematic analysis?
When analyzing qualitative data from transcriptions (e.g., interviews).
179
Explain the procedures of a thematic analysis.
Read transcripts, generate initial codes for repeated patterns, review codes to form themes, and write a detailed analysis supported by quotes.
180
Explain how the reliability of a content or thematic analysis could be improved.
Have multiple researchers analyze the data, check for inter-observer reliability, and train researchers using clear examples.
181
Identify the sections of a scientific report in chronological order.
Abstract, introduction, methodology, results, discussion, and references.
182
Outline the purpose and structure of the abstract section.
Purpose: Aims to give the reader a general idea of the research without them having to read the full paper.
Structure: Typically one paragraph that follows the structure of the report. It starts with a brief introduction, followed by an overview of methods and results, and ends with a brief discussion of the findings.
183
Outline the purpose and structure of the introduction section.
Purpose: Aims to highlight existing research within the field and offer reasoning for conducting the current study.
Structure: Typically a few thousand words, starting with general ideas about the topic, narrowing to more relevant research, and providing a clear rationale for the study. This section includes identifying the aim of the study and providing hypotheses.
184
Outline the purpose and structure of the methodology section.
Purpose: To provide enough information about how the study was conducted, allowing others to assess its credibility or replicate the research to check reliability.
Structure: Divided into sections:
Design: Which research design was used.
Participants: Sampling technique, number of participants, and their demographic details.
Materials: Details of materials (e.g., forms, stimuli).
Procedures: A step-by-step account of how the study was conducted.
185
Outline the purpose and structure of the results section.
Purpose: To give a detailed breakdown of the findings.
Structure: Includes quantitative research (descriptive statistics like totals, averages, measures of dispersion) and inferential statistics (used to check for significant results). Qualitative research will present findings from content or thematic analysis.
186
Outline the purpose and structure of the discussion section.
Purpose: To draw conclusions from the findings and compare these to previous research. It also addresses criticisms and applications of the research and recommends future research directions.
Structure: Conclusion, comparison with previous research, criticisms of the current research, its applications, and suggestions for future research.
187
Outline the purpose and structure of references.
Purpose: To prevent plagiarism and allow readers to trace and check the cited research.
Structure: References are formatted as: Author surname, first initial. (Date) Title of book. Place of publication. Publisher.
188
Outline the process of peer review.
The report is reviewed independently by experts in a similar field. They assess the appropriateness of methods, the validity of findings, errors, originality, and relevance of the study. Based on their review, they can accept the paper, suggest minor revisions, ask for more substantial revisions, or reject it. The journal editor makes the final decision on publication.
189
Explain the purpose/benefits of peer review.
Ensures findings are accurate, helps prevent mistakes or misinterpretations, guards against plagiarism, and checks the research quality (methods, analysis). It also maintains high standards for publishing research.
190
Explain the limitations of peer review.
Subjectivity and Bias: Experts may have conflicts of interest, affecting their decision.
Publication Bias: Studies with significant results are more likely to be published, while those with null results may be overlooked, hindering scientific progress.
Difficulty in New Areas: Finding experts in emerging fields can be challenging, affecting the quality of the review.
191
Explain how psychological research can impact the economy.
NHS Costs: Research into psychological disorders has led to better treatments, which could reduce long-term financial burdens. However, better treatments may increase costs for the NHS initially.
Policing Costs: Research into eyewitness testimony and methods like the cognitive interview can improve crime-solving efficiency, saving police time and costs. However, the costs of training police officers could increase.
Workplace Productivity: Bowlby’s attachment theory impacted female employment rates negatively. However, research into the role of fathers allowed more mothers to return to work, increasing productivity in female-dominated fields and reducing the gender pay gap.
192
Identify the features of science.
Theory construction, hypothesis testing, the empirical method, paradigms and paradigm shifts, replicability, objectivity, and falsifiability.
193
Explain what is meant by theory construction and explain why it is an important feature of science.
Definition: Theory construction is the development of general laws that explain behavior.
Importance: Theories help to understand, predict, and control behavior.
194
Give two examples of theory construction in Psychology.
Social Learning Theory: Bandura's theory suggests that human behavior is learned through observation and imitation, which helps explain behaviors like criminality.
Bowlby’s Maternal Deprivation Theory: Proposes that losing a primary attachment figure severely impacts child development, predicting outcomes like lower IQ in children without a primary caregiver.
195
Explain what is meant by hypothesis testing and why it is an important feature of science.
Definition: Hypothesis testing involves conducting studies to test predictions about the outcome of a research study.
Importance: It helps validate or disprove theories, advancing knowledge.
196
Give an example of hypothesis testing in Psychology.
Bandura tested the hypothesis that children who observed aggressive role models would display more aggression than those who observed non-aggressive role models.
197
Explain what is meant by the empirical method and why it is important.
Definition: The empirical method emphasizes gathering data through direct observation.
Importance: It ensures that research is objective, reducing subjectivity and bias in the data collection process.
198
Briefly discuss the extent to which psychological research is based on the empirical method.
Empirical: Behaviorists like Pavlov and Skinner used direct observation of behavior, making their research empirical.
Not always empirical: Cognitive psychologists cannot directly observe mental processes but infer what happens in the mind, which isn’t strictly empirical.
199
Explain what is meant by a paradigm in psychology.
A paradigm is a set of shared beliefs about how to explain and study behavior.
200
Explain what is meant by a paradigm shift and why it is important.
A paradigm shift occurs when a scientific community changes its way of explaining/studying behavior due to new evidence, leading to a scientific revolution.
201
Give an example of a paradigm and a paradigm shift in Psychology.
Paradigm: Social learning theorists agreed that behavior should be explained through observation.
Paradigm Shift: The emergence of biological psychology, which shifted focus from social learning to biological factors like hormones and brain structure.
202
Explain what is meant by replicability and why it is important.
Definition: Replicability refers to the ability to repeat a study and obtain consistent results.
Importance: It ensures reliability and checks for consistency in the findings.
203
Briefly discuss the extent to which psychological research is replicable.
Replicable research: Ainsworth’s Strange Situation is easy to replicate due to its standardized procedure.
Not always replicable: Freud’s Little Hans study is difficult to replicate due to its use of diverse methods, dream analysis, observations and interviews, and lack of control since at home.
204
Explain what is meant by objectivity and why it is important.
Definition: Objectivity means that research is not influenced by personal biases or opinions.
Importance: It ensures that conclusions are valid and based on data rather than subjective interpretations.
205
Briefly discuss the extent to which psychological research is objective.
Objective research: Skinner’s operant conditioning research was objective, as it involved counting behaviors.
Not always objective: Freud’s interpretations in his Little Hans case study were influenced by his personal views.
206
Explain what is meant by falsifiability and why it is important.
Definition: Falsifiability refers to the ability to prove a theory wrong through contradictory evidence.
Importance: It allows theories to be tested and, if necessary, adapted or discarded to improve scientific understanding.
207
Briefly discuss the extent to which psychological research is falsifiable.
Falsifiable research: Bowlby’s theory could be falsified if research showed that losing a primary attachment figure does not lead to negative effects.
Not falsifiable: Freud’s theory of the unconscious is unfalsifiable, as it cannot be tested through empirical evidence.
