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Psychology A level- Final > Paper 2 - Research Methods > Flashcards

Paper 2 - Research Methods Flashcards

1
Q

Describe a lab experiment

A

A lab experiment is conducted in a controlled setting where the researcher manipulates the independent variable (IV) and measures its effect on the dependent variable (DV).

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2
Q

Describe a field experiment

A

A field experiment is conducted in a natural environment, where the researcher manipulates the IV and observes its effect on the DV in real-world conditions

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3
Q

Describe a natural experiment

A

A natural experiment occurs when the researcher takes advantage of a naturally occurring event to observe its effect on the DV. The IV is not manipulated but happens naturally

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4
Q

Describe a quasi-experiment

A

Quasi-experiment involves studying the effect of the IV on the DV in a setting where random assignment is not possible. The IV is often a pre-existing variable (e.g., age or gender).

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5
Q

Explain strengths and limitations of lab experiments

A

Strengths: High control over variables, easy replication, high internal validity.

Limitations: Low ecological validity, potential for demand characteristics, unnatural setting may influence participant behaviour.

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6
Q

Explain strengths and limitations of field experiments

A

Strengths: High ecological validity, participants behave more naturally.

Limitations: Less control over extraneous variables, harder to replicate, ethical concerns regarding consent

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7
Q

Explain strengths and limitations of natural experiments

A

Strengths: High ecological validity, real-world conditions.

Limitations: Lack of control over variables, difficulty in establishing cause-and-effect relationships, low internal validity

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8
Q

Explain strengths and limitations of quasi-experiments

A

Strengths: Real-world settings, useful when random assignment is not possible.

Limitations: Lack of control over IV, harder to establish causality, potential confounding variables.

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9
Q

Explain strengths and limitations of correlations

A

Strengths: Can identify relationships between variables, useful for exploring patterns in large datasets.

Limitations: Does not imply causation, may be influenced by third variables.

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10
Q

Name and explain the 4 different types of observation

A

Naturalistic Observation: Observing behavior in its natural environment without interference.

Controlled Observation: Observing behavior in a controlled setting with some manipulation of variables.

Participant Observation: The researcher becomes part of the group being studied.

Non-Participant Observation: The researcher observes without becoming involved

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11
Q

Explain strengths and limitations of naturalistic observation

A

Strengths: High ecological validity, participants behave naturally.

Limitations: Lack of control, observer bias, ethical concerns about consent

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12
Q

Explain strengths and limitations of controlled observation

A

Strengths: Greater control over variables, easier to replicate.

Limitations: Lower ecological validity, artificial setting may influence behaviour

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13
Q

Explain strengths and limitations of participant observation

A

Strengths: Rich, detailed data, high ecological validity.

Limitations: Observer bias, ethical concerns about involvement, loss of objectivity

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14
Q

Explain strengths and limitations of non-participant observation

A

Strengths: Objective data, easier to maintain researcher distance.

Limitations: Limited insight into group dynamics, may affect the behaviour of those being observed

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15
Q

Describe how to carry out an observation (5 steps)

A

Select a research setting

Choose a type of observation (naturalistic, controlled, etc.)

Define the behaviour to be observed,

Decide on a recording method (e.g., time sampling, event sampling).

Ensure ethical guidelines (informed consent, confidentiality) are followed

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16
Q

Define content analysis

A

Content analysis is a method used to analyse the content of text, audio, or visual materials by categorizing and quantifying specific themes or patterns

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17
Q

Outline how to conduct a content analysis

A

Define the research question, select material to analyse, create coding categories based on themes, analyse the frequency of each category, and draw conclusions based on the data

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18
Q

Explain strengths and limitations of content analysis

A

Strengths: Allows for analysis of large amounts of material, objective, and reliable.

Limitations: May miss deeper meanings, relies on subjective interpretation during coding

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19
Q

Define self-report techniques

A

Self-report techniques involve collecting data directly from participants by asking them to report their feelings, thoughts, or behaviours (e.g., through interviews or questionnaires).

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20
Q

Describe the 3 different types of interviews

A

Structured: Pre-set questions, high reliability, low flexibility.

Unstructured: Open-ended questions, flexible, allows in-depth responses.

Semi-structured: Combines both, with some pre-set questions and room for flexibility

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21
Q

Explain strengths and limitations of structured interviews

A

Strengths: Easy to replicate, consistent data across participants.

Limitations: Lacks flexibility, may miss important information

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22
Q

Explain strengths and limitations of unstructured interviews

A

Strengths: Rich, detailed data, flexible to explore issues further.

Limitations: Time-consuming, hard to replicate, interviewer bias

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23
Q

Explain strengths and limitations of semi-structured interviews.

A

Strengths: Balanced flexibility and structure, allows for deep insights.

Limitations: May still be influenced by interviewer bias, less replicable than structured interviews.

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24
Q

What is a correlation?

A

A correlation is a statistical technique used to determine the relationship between two variables. It shows how closely related the variables are, but it does not imply causation.

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25
What are the different types of correlation?
Positive Correlation: As one variable increases, the other also increases. Negative Correlation: As one variable increases, the other decreases. Zero Correlation: No relationship between the variables
26
How are correlations measured?
Correlations are measured using a correlation coefficient (e.g., Pearson’s r), which ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation). A value of 0 indicates no correlation.
27
Name three ways a correlation is different from an experiment.
Correlations do not establish causality, experiments do. Correlations measure relationships, experiments manipulate variables. Correlations use natural data, experiments involve control and manipulation of variables.
28
Describe the 2 different types of questionnaires
Closed-ended: Predefined response options, easy to analyze. Open-ended: No predefined responses, allows for more detailed answers.
29
Explain strengths and limitations of open and closed questions
Open: Strengths: In-depth answers, rich data. Limitations: Difficult to analyse, time-consuming. Closed: Strengths: Easy to analyse, quantitative data. Limitations: Limited responses, may not capture full range of views.
30
Define case studies
A case study is an in-depth, detailed examination of an individual or group, often used for rare or unusual phenomena
31
Explain strengths and limitations of case studies
Strengths: Provides deep insights, useful for rare cases. Limitations: Low generalizability, potential for researcher bias
32
Define variables: IV, DV, EV.
IV (Independent Variable): The variable manipulated by the researcher. DV (Dependent Variable): The variable measured in response to the IV. EV (Extraneous Variable): Variables that are not of interest but may affect the DV if not controlled
33
What’s the difference between aims and hypotheses?
Aim: A general statement of the purpose of the research. Hypothesis: A specific, testable prediction about the relationship between variables.
34
What’s the difference between directional and non-directional hypothesis?
Directional Hypothesis: Predicts the direction of the relationship between variables (e.g., “increases” or “decreases”). Non-directional Hypothesis: Does not predict the direction, only the presence of a relationship.
35
Write a directional and non-directional hypothesis
Directional: "There will be a positive correlation between hours spent studying and exam performance." Non-directional: "There will be a correlation between hours spent studying and exam performance."
36
How do you decide when to use a directional or non-directional hypothesis?
Use a directional hypothesis when prior research or theory suggests a specific relationship. Use a non-directional hypothesis when there is no clear expectation about the direction of the relationship
37
Describe each of the 3 experimental designs:
Independent Measures: Different participants are assigned to each condition. Repeated Measures: The same participants take part in all conditions. Matched Pairs: Participants are paired based on similar characteristics, with each pair assigned to different conditions.
38
Explain a strength and limitation of each of the experimental designs.
Independent Measures: Strength – no order effects. Limitation – participant variables. Repeated Measures: Strength – fewer participants needed. Limitation – order effects. Matched Pairs: Strength – controls participant variables. Limitation – difficult to match perfectly.
39
What is a pilot study? Why is it important?
A pilot study is a small-scale trial run of the research to identify potential problems before the full study. It ensures the study is feasible and helps refine the methodology.
40
Define demand characteristics & investigator effects.
Demand Characteristics: Cues in an experiment that may influence participants to behave in a certain way. Investigator Effects: The influence of the researcher’s behavior or expectations on the participants.
41
Controlling variables: How to do random allocation? When/why?
Random allocation involves randomly assigning participants to experimental conditions to control for participant variables. It ensures fairness and minimizes bias.
42
Controlling variables: How to do counterbalancing? When/why?
Counterbalancing involves varying the order of conditions to control for order effects. It ensures that the order in which conditions are experienced does not affect the results.
43
Controlling variables: Randomisation – what does this mean?
Randomisation refers to the random selection or assignment of participants or stimuli to control for bias and ensure that extraneous variables do not systematically affect the results.
44
Controlling variables: Standardisation – what does this mean?
Standardisation refers to keeping all procedures, instructions, and conditions the same for all participants to ensure consistency and fairness.
45
Evaluate and explain how to conduct random Sampling
Every participant has an equal chance of being selected. Strength: unbiased. Limitation: may not be representative.
46
Evaluate and explain how to conduct Systematic Sampling
Selecting every nth participant. Strength: more structured. Limitation: may still be biased.
47
Evaluate and explain how to conduct Stratified Sampling
Participants are divided into subgroups and randomly sampled from each. Strength: more representative. Limitation: difficult to achieve.
48
Identify and describe 5 Ethical Issues & how to deal with them.
Informed Consent: Ensure participants are fully aware of the study and consent voluntarily. Deception: Avoid misleading participants; if necessary, provide a debrief. Confidentiality: Keep participant data private. Protection from Harm: Minimise stress and harm to participants. Right to Withdraw: Ensure participants can leave the study at any time without consequence.
49
Describe the purpose, process, and problems of Peer review
Purpose: To assess the quality, validity, and reliability of research before publication. Process: Researchers submit their work to journals where experts in the field review it, providing feedback or suggesting revisions. Problems: Potential bias, rejection of innovative ideas, and the risk of reviewer competition leading to conflicts of interest.
50
Explain face, concurrent, ecological, and temporal validity.
Face Validity: The extent to which a test appears to measure what it claims to measure. Concurrent Validity: The degree to which the results of a test correspond with those of an established test measuring the same thing. Ecological Validity: The extent to which research findings generalize to real-life settings. Temporal Validity: The degree to which research findings remain valid over time.
51
Explain test-retest and inter-observer reliability.
Test-retest Reliability: The consistency of a test’s results over time when administered to the same participants. Inter-observer Reliability: The degree to which different observers produce consistent results when observing the same phenomenon.
52
How would you improve reliability if your assessment showed it wasn’t reliable?
Increase consistency by improving the research design (e.g., using standardized procedures), increasing sample size, or training researchers and observers to ensure uniformity in the process.
53
Give examples of the Implications of Psychological research for the economy.
Workplace Productivity: Understanding motivation and employee behavior can lead to strategies that improve work efficiency. Mental Health Services: Research into psychological disorders can help improve treatment, reduce absenteeism, and improve overall economic productivity. Consumer Behaviour: Psychological research can influence marketing strategies, improving sales and market outcomes.
54
Name and describe all the parts of Reporting Psychological Investigations.
Abstract: A brief summary of the study, including its purpose, methods, and key findings. Introduction: Explains the background, research question, and hypotheses. Method: Describes the research design, participants, materials, and procedures used. Results: Presents the findings, including statistical analyses and figures. Discussion: Interprets the results, considering their implications, limitations, and suggestions for future research. References: Lists the sources cited in the study.
55
How would you improve validity if your assessment showed it wasn’t valid?
Improve the research design (e.g., using better operational definitions), use more precise measurement tools, or control extraneous variables that may affect the validity of the results.
56
Define validity and reliability
Validity: The extent to which a test or research accurately measures what it is supposed to measure. Reliability: The consistency of a test or research results over time.
57
Define Qualitative & Quantitative data. Explain strengths & limitations of each
Qualitative Data: Descriptive data that involves non-numerical insights (e.g., interviews, observations). Strengths: Provides rich, in-depth information. Limitations: Subjective, harder to analyze systematically. Quantitative Data: Numerical data that can be measured and analyzed statistically. Strengths: Easier to analyze and compare. Limitations: May lack depth and context.
58
Define Meta-analysis, with an example. Explain the strengths & limitations.
Meta-analysis: A statistical technique that combines the results of multiple studies on a specific topic to identify patterns or effects. Example: Meta-analysis of therapy effectiveness across various psychological disorders. Strengths: Increases statistical power, provides a more generalizable conclusion. Limitations: Quality of included studies may vary, publication bias.
59
What are the two types of descriptive statistics?
Measures of Central Tendency: Mean, median, mode. Measures of Dispersion: Range, variance, standard deviation.
60
Define Primary & Secondary Data. Explain strengths & limitations of each.
Primary Data: Data collected directly by the researcher for their specific study. Strengths: Directly relevant to the research question. Limitations: Time-consuming and expensive to collect. Secondary Data: Data collected by someone else for a different purpose. Strengths: Saves time and resources. Limitations: May not perfectly fit the research question, quality may vary.
61
Define measures of dispersion. Describe each type.
Range: The difference between the highest and lowest scores. Variance: The average of the squared differences from the mean. Standard Deviation: The square root of the variance, showing how spread out the scores are.
62
Explain a strength & limitation of each measure of dispersion.
Range: Strength: Easy to calculate. Limitation: Can be affected by outliers. Variance: Strength: Provides a measure of the spread of all data points. Limitation: Not as easily interpretable as standard deviation. Standard Deviation: Strength: More informative and interpretable than variance. Limitation: Still affected by extreme values.
63
Explain a strength & limitation of each type of central tendency.
Mean: Strength: Takes all data into account. Limitation: Sensitive to outliers. Median: Strength: Not affected by outliers. Limitation: Does not consider the distribution of all data. Mode: Strength: Useful for categorical data. Limitation: Can be unrepresentative if the data set has multiple modes.
64
Name three graphs. When would you use each one? What would they look like?
Bar Graph: Used for comparing categories. Bars do not touch. Scatter Graph - To plot correlations Pie Chart: Used for showing proportions or percentages of a whole
65
Describe a normal distribution.
A normal distribution is a bell-shaped curve where most of the data points cluster around the mean, with fewer points at the extremes. It is symmetric and follows the empirical rule (68%-95%-99.7%).
66
Explain each Level of measurement, and give an example. Which type of central tendency relates to each Level of Measurement?
Nominal: Categories without a meaningful order (e.g., gender). Central tendency: Mode. Ordinal: Ordered categories without equal intervals (e.g., rankings). Central tendency: Median. Interval: Ordered data with equal intervals, but no true zero (e.g., temperature). Central tendency: Mean.
67
Describe the difference between a negatively & positively skewed distribution
Negatively Skewed: Tail is on the left side, with a few lower values pulling the mean down. Positively Skewed: Tail is on the right side, with a few higher values pulling the mean up
68
Describe each type of central tendency
Mean: The arithmetic average of all data points. Median: The middle value when the data is ordered. Mode: The most frequent value in the data set.
69
What level of significance do psychologists use? Why?
Psychologists commonly use a significance level of 0.05 (5%). It represents a balance between minimizing the risk of Type I errors (false positives) and being reasonably confident in the results
70
What’s the difference between a Type I and II errors?
Type I Error: False positive—rejecting the null hypothesis when it is actually true. Type II Error: False negative—failing to reject the null hypothesis when it is actually false.
71
Explain how to know when a statistical test is significant
If the p-value is less than the significance level (usually 0.05), the result is significant. This means the observed effect is unlikely to have occurred by chance.
72
What three pieces of information do you need to find the critical value?
Significance level (e.g., 0.05). Degrees of freedom (based on the sample size). Type of test being used (e.g., one-tailed or two-tailed).
73
Explain how to calculate the sign test.
The sign test is used to determine if there is a significant difference between two related groups. Calculate the difference between each pair of scores, count the number of positive and negative differences, and use these counts to determine significance.

Psychology A level- Final (8 decks)

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