Research Methods Flashcards
Experimental method: Define Experimental method
Involves the manipulation of an independent variable (IV) to measure the effect on the dependent variable (DV). Experiments may be laboratory, field, natural or quasi.
Experimental method: Define Aim
A general statement of what the researcher intends to investigate, the purpose of the study.
Experimental method: Define Hypothesis
A clear, precise, testable statement that states the relationship between the variables to be investigated. Stated at the outset of any study.
Experimental method: Define Directional hypothesis
States the direction of the difference or relationship.
3 reasons for use:
-Previous research suggests a clear outcome.
-Theoretical predictions point to a specific direction.
-It allows for more precise testing of the relationship between variables.
Experimental method: Define Non-Directional hypothesis
Does not state the direction of the difference or relationship.
Reasons for use:
-Lack of prior evidence on the relationship between variables.
-The researcher is unsure of the direction of the effect.
-It allows for exploring any potential relationship without specifying an outcome.
Experimental method: Define Variables
Any ‘thing’ that can vary or change within an investigation. Variables are generally used in experiments to determine if changes in one thing result in changes to another.
Experimental method: Define Independent variable
Some aspect of the experimental situation that is manipulated by the researcher- or changes- so the effect on the DV can be measured.
Experimental method: Define Dependent variable
The variable that is measured by the researcher. Any effect on the DV should be caused by the change in the IV.
Experimental method: Define Operationalisation
Clearly defining variables in terms of how they can be measured.
Experimental method: Deciding which type of hypothesis to use
Researchers tend to use a directional hypothesis when a theory or the findings of previous research studies suggest a particular outcome.
Research issues: Define Extraneous variable (EV)
Any variable, other than the independent variable (IV), that may affect the dependent variable (DV) if it is not controlled. EVs are essentially nuisance variables that do not vary systematically with the IV.
Research issues: Define confounding variables
A kind of EV but the key feature is that a confounding variable varies systematically with the IV. Therefore we can’t tell if any change in the DV is due to the IV or the confounding variable.
Research issues: Define Randomisation
The use of chance methods to control for the effects of bias when designing materials and deciding the order of experimental conditions.
Example of how it could be used:
In an experiment, participants are randomly assigned to either the treatment or control group to avoid bias.
Strengths:
Eliminates selection bias: Randomization ensures each participant has an equal chance of being assigned to any group, preventing bias in group assignment and ensuring comparable groups from the start of the experiment.
Controls for confounding variables: By randomly assigning participants, randomization helps distribute both known and unknown variables evenly across groups, reducing the risk of confounding factors influencing the results.
Weaknesses:
Unintended imbalances: Despite randomization, there is still a chance that certain characteristics, like age or gender, could be unevenly distributed between groups, which could affect the outcomes.
Not always practical or ethical: In some cases, randomization may be impractical or unethical, especially when participants cannot be randomly assigned to certain treatments or conditions for ethical reasons.
Research issues: Define Demand characteristics
Any cue from the researcher or from the research situation that may be interpreted by participants as revealing the purpose of an investigation. This may lead to a participant changing their behaviour within the research situation.
Research issues: Define Investigator effects
Any effect of the investigator’s behaviour(conscious or unconscious) on the research outcome (DV). This may include everything from the design of the study to the selection of, and interaction with, participants during the research process.
Research issues: Define Standardisation
Using exactly the same formalised procedures and instructions for all participants in a research study.
Example of how it could be used:
In a study on exercise, participants are grouped by age (e.g., young, middle-aged, elderly) before being randomly assigned to ensure each age group is fairly represented.
Strengths:
Consistency across participants: Standardization ensures that all participants are exposed to the same procedures, instructions, and conditions, which minimizes variability in the experiment and allows for fair comparisons between different groups.
Improved reliability: By following a consistent protocol, standardization increases the reliability of the results, making it easier to replicate the study and verify its findings.
Weaknesses:
Reduced ecological validity: The rigid structure of standardized procedures may create an artificial environment that doesn’t reflect real-world conditions, potentially limiting the generalizability of the results.
Inflexibility: Standardization can be too rigid, leaving little room for adjusting the experiment to individual differences or unique circumstances, which may affect how well the study captures real-world complexities.
Experimental designs: Define Experimental design
The different ways in which participants can be organised in relation to the experimental conditions.
Experimental designs: Define Independent groups design
Participants are allocated to different groups where each group represents one experimental condition.
How it can be carried out:
Use random allocation to assign participants to different groups randomly.
Strengths:
No risk of order effects: Since different participants are assigned to different groups, there is no concern about order effects (e.g., learning, fatigue, or practice effects) that can occur in repeated measures designs where the same participants experience all conditions.
Simplicity and clarity: Independent group design is straightforward to set up and analyse, as each group is exposed to only one condition, making the interpretation of results clear and less complex.
Weaknesses:
Individual differences: Since participants in different groups may have varying characteristics, these individual differences could affect the results and create confounding variables that are difficult to control for.
Requires more participants: This design typically requires a larger number of participants than repeated measures designs because each group needs to have a separate set of individuals, which can be resource-intensive.
Experimental designs: Define Repeated measures
All participants take part in all conditions of the experiment.
How it can be carried out:
Use counterbalancing to change the order of conditions for participants to avoid order effects.
Strengths:
Fewer participants required: Since the same participants are used in all conditions, fewer individuals are needed, making the design more efficient and cost-effective compared to independent groups designs.
Control over individual differences: As the same participants experience all conditions, individual differences (e.g., age, gender) are less likely to confound the results, increasing the internal validity of the study.
Weaknesses:
Order effects: Participants may be influenced by previous conditions (e.g., fatigue, practice, or carryover effects), which could impact their performance in later conditions and confound the results.
Demand characteristics: Since participants are exposed to all conditions, they might become aware of the purpose of the study, which could lead to biases in their behaviour or responses, affecting the validity of the results.
Experimental designs: Define Matched pairs design
Pairs of participants are first matched on some variable(s) that may affect the dependent variable. The one member of the par is assigned to Condition A and the other to condition B.
How it can be carried out:
Participants are matched based on similarities and then randomly assigned to different conditions.
Strengths:
Control over individual differences: By pairing participants with similar characteristics (e.g., age, gender, ability), matched pairs design minimizes the impact of individual differences, leading to more accurate comparisons between conditions.
Reduced order effects: Since participants are only exposed to one condition, there are no concerns about order effects (e.g., practice or fatigue) that might arise in repeated measures designs.
Weaknesses:
Difficult to match participants: It can be challenging to find and match participants with similar characteristics, which may limit the feasibility of the design, especially in studies with specific criteria.
Increased complexity: The process of matching participants and analysing the data can be time-consuming and complex, requiring careful planning and sometimes leading to more effort than other experimental designs.
Experimental designs: Define Random allocation
An attempt to control for participant variables in an independent groups design which ensures that each participant has the same chance of being in one condition as any other.
How it can be carried out:
After matching, participants are randomly allocated to different experimental conditions to reduce bias.
Strengths:
Eliminates selection bias: Random allocation ensures that each participant has an equal chance of being assigned to any group, which helps eliminate researcher bias and ensures that the groups are comparable at the start of the experiment.
Controls for confounding variables: By randomly assigning participants, random allocation helps distribute both known and unknown confounding variables evenly across groups, making it easier to isolate the effect of the independent variable on the dependent variable.
Weaknesses:
Unintended imbalances: Despite being random, there is still a chance that certain characteristics (e.g., age, gender) could be unevenly distributed across groups, especially with small sample sizes, which may affect the results.
Not always practical: In certain situations, random allocation may not be feasible or practical, particularly when dealing with specific participant characteristics or when the study involves constraints that limit random assignment.
Experimental designs: Define Counterbalancing
An attempt to control for the effects of order in a repeated measures design: half the participants experience the conditions in one order, and the other half in the opposite order.
How it can be carried out:
In repeated measures, alternate the order of conditions to balance practice or fatigue effects.
Strengths:
Reduces order effects: Counterbalancing helps control for order effects (such as fatigue, practice, or carryover effects) by varying the order in which participants experience different conditions, ensuring that any potential bias caused by the sequence is minimized.
Increases internal validity: By ensuring that each condition appears in every possible order across participants, counterbalancing helps isolate the effects of the independent variable, leading to more reliable and valid results.
Weaknesses:
Complexity in design: Implementing counterbalancing can be complex, especially when there are many conditions, as it requires organizing multiple orders and ensuring that the study remains balanced and manageable.
Doesn’t eliminate all biases: While counterbalancing reduces order effects, it may not fully control for other biases, such as individual differences or other confounding variables that could still influence the results.
Types of experiment: Define Laboratory (Lab) experiment
An experiment that takes place in a controlled environment within which the researcher manipulates the IV and records the effect on the DV, whilst maintaining strict control of extraneous variables.
Strengths:
-Lab experiments have high control over confounding variables and extraneous variables. This means that the researcher can ensure that any effect on the dependent variable is likely to be the result of manipulation of the independent variable. Thus, we can be more certain about demonstrating cause and effect (High internal validity).
-Replication is more possible than in other types of experiment because of the high level of control. This ensures that new extraneous variables are not introduced when repeating an experiment. Replication is vital to check the results of any study to see whether the findings is valid and not just a one-off.
Limitations:
May lack generalisability. The lab experiment may be rather artificial and not like everyday life. In an unfamiliar context participants may behave in unusual ways so their behaviour cannot always be generalised beyond the research setting (low external validity).
-The tasks participants are asked to carry out in a lab experiment may not represent everyday experience, for instance, recalling unconnected lists of words as part of a memory experiment.
Types of experiment: Define Field experiment
An experiment that takes place in a natural setting within which the researcher manipulates the IV and records the effect on the DV.
Strength:
Realistic settings: Field experiments take place in natural environments, providing more ecological validity and generalizability of results.
Natural behaviour observation: Participants behave more authentically in field experiments, as they are unaware of being studied, reducing the risk of demand characteristics.
Limitations:
Lack of control: Field experiments have less control over extraneous variables, making it harder to establish clear cause-and-effect relationships.
Ethical concerns: The natural setting may involve ethical issues, such as lack of informed consent or unintended harm to participants.
Types of experiment: Define Natural experiment
An experiment where the change in the IV is not brought about by the researcher but would have happened even if the researcher had not been there.
The researcher records the effect on a DV they have decided on.
Strengths:
Real-world applicability: Natural experiments occur in real-world settings, offering high ecological validity and ensuring that the findings are relevant to everyday life.
Ethical advantages: These experiments often study variables that cannot be manipulated for ethical reasons, such as the effects of natural disasters or public policy changes.
Limitations:
Lack of control over variables: Since the independent variable in a natural experiment is not manipulated by the researcher, there is limited control over other variables that might influence the outcome, making it harder to establish clear cause-and-effect relationships.
Limited generalizability: The findings from a natural experiment may be specific to a particular event or situation, making it difficult to generalize the results to other contexts or populations.
Types of experiment: Define Quasi-experiment
A study that is almost an experiment but lacks key ingredients. The IV has not been determined by anyone (the researcher or any other person)- the ‘variables’ simply exist, such as being old or young. Strictly speaking this is not an experiment.
Strengths:
Practical and Ethical Feasibility: Quasi-experiments are often more practical and ethical compared to true experiments. For instance, when random assignment isn’t feasible or ethical (e.g., in cases of studying the effects of existing conditions like education systems or natural disasters), quasi-experiments allow researchers to study the effects in real-world settings without manipulating the variables.
Naturalistic Setting: Since quasi-experiments often occur in real-world environments, they provide insights that are more generalizable and applicable to everyday life compared to controlled lab experiments. This can increase the external validity of the findings.
Limitations:
Lack of Random Assignment: Without random assignment, there may be pre-existing differences between groups that could influence the outcome. This threatens internal validity and makes it difficult to confidently claim causality because alternative explanations may account for the observed effects.
Potential for Confounding Variables: Quasi-experiments are more vulnerable to confounding variables because the researcher does not have full control over the variables involved. This can lead to ambiguity in interpreting the results, as other factors could be responsible for the observed effects.
Sampling: Define Population
A group of people who are the focus of the researcher’s interest, from which a smaller sample is drawn.
Sampling: Define sample
A group of people who take part in a research investigation. The sample is drawn from a (target) population and is presumed to be representative of that population, i.e it stands ‘fairly’ for the population being studied.
Sampling: Define Sampling techniques
The method used to select people from the population.
Sampling: Define
Sampling: Define
