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 Bias
In the context of sampling, when certain groups are over-or under-represented within the sample selected. For instance, there may be too many younger people or too many people of one ethnic origin in the sample. This limits the extent to which generalisations can be made to the target population.
Sampling: Define Generalisation
The extent to which findings and conclusions from a particular investigation can be broadly applied to the population. This is possible if the sample of participants is representative of the target population.
Sampling: Define Random sampling
Random sampling is a technique in which every individual in a population has an equal chance of being selected for a study. This method aims to create a sample that is representative of the larger population, minimizing bias in the selection process.
How is it carried out?
The first step is to obtain a complete list of all members of the target population
Second, all of the names on the list are assigned a number.
Finally, the actual sample is selected through the use of some lottery method.
Strengths:
Reduces Bias: Random sampling ensures that each individual in the population has an equal chance of being chosen. This minimizes selection bias, meaning the sample is less likely to be influenced by the researcher’s preferences or external factors. As a result, the sample is more likely to represent the diversity and characteristics of the entire population.
Generalizability: Because random sampling creates a more representative sample, the results from the study can be more confidently generalized to the larger population. This enhances the external validity of the study, making the findings more applicable in real-world settings.
Weaknesses:
Practical Challenges: While the concept of random sampling is straightforward, it can be difficult to implement, especially in large or hard-to-reach populations. For instance, obtaining a complete list of individuals in a population may not always be feasible, and tracking down people in geographically dispersed areas can be resource-intensive. In some cases, logistical issues may prevent random sampling from being conducted effectively.
Sampling Error: Even though random sampling reduces bias, there is still the possibility of sampling error. This occurs because the sample selected may still not perfectly reflect the population, even though the selection process was random. This discrepancy can arise purely by chance and may affect the accuracy of the study’s results, especially in smaller samples. Additionally, certain subgroups may be underrepresented or overrepresented in the sample.
Sampling: Define Systematic sampling
Systematic sampling is a method where you select participants from a larger population at regular intervals, based on a fixed starting point. This approach is simpler than random sampling and involves choosing every “kth” individual from a list.
How it is carried out?
A sampling frame is produced, which is a list of people in the target population organised into. A sampling system is nominated. May begin from a randomly determined start to reduce bias. The researcher then works through the sampling frame until the sample is complete.
Strengths:
Simplicity and Efficiency: Systematic sampling is straightforward and easy to implement, especially when working with large populations. It is less time-consuming than random sampling because it does not require creating a completely random list, just selecting every kth individual.
More Structured and Organized: This method ensures that the sample is spread evenly across the entire population. The regular intervals create a more organized and predictable sampling process compared to random sampling, reducing the likelihood of clustering or gaps in the sample.
Weaknesses:
Risk of Periodicity or Bias: If the population has a hidden periodic pattern (e.g., every 10th person shares a similar characteristic), systematic sampling may inadvertently produce biased results. This is because the regular interval could match a natural cycle within the population, leading to an unrepresentative sample.
Not Truly Random: While systematic sampling is more organized, it is not fully random. If the list is ordered in some way that correlates with the characteristic being studied, the sample may not accurately represent the population. The lack of true randomness can reduce the generalizability and internal validity of the results.
Sampling: Define Stratified sampling
Stratified sampling is a method where the population is divided into distinct subgroups, or strata, based on a shared characteristic (e.g., age, gender, income). A random sample is then taken from each stratum, ensuring that the sample includes representation from all relevant subgroups.
How is it carried out?
The researcher first identifies the different strata that make up the population. Then, the proportions needed for the sample to be representative are worked out. Finally, the participants that make up each stratum are selected using random sampling.
Strengths:
Increased Precision and Representativeness: Stratified sampling ensures that all key subgroups of the population are adequately represented. By ensuring that each stratum is properly sampled, the results are more precise and reflective of the entire population. This leads to more reliable and accurate conclusions, particularly in heterogeneous populations.
Improved Comparisons Between Subgroups: Since each subgroup is intentionally represented, stratified sampling makes it easier to compare results across different strata. This can be particularly useful when the research focuses on differences between groups (e.g., comparing outcomes across different age groups or income levels).
Weaknesses:
Complexity in Implementation: Stratified sampling requires identifying and dividing the population into strata, which can be time-consuming and complex, particularly if there are multiple strata or if detailed information about the population is needed. This process can be more labour-intensive than simpler methods like random sampling.
Potential for Mis-stratification: The accuracy of the results depends on how well the population is divided into strata. If the strata are poorly defined or do not capture the relevant differences within the population, the sample may not be as representative as intended. Additionally, if the strata are too small, it may lead to insufficient sample sizes within certain groups.
Sampling: Define Opportunity sampling
Opportunity sampling (also called convenience sampling) is a non-probability sampling method where participants are selected based on their availability and willingness to take part in the study. It involves choosing people who are easiest to access, often at the time and place where the researcher is conducting the study.
How is it carried out?
The researcher simply takes the chance to ask whoever is around at the time of their study e.g the street.
Strengths:
Ease of Access and Convenience: Opportunity sampling is quick and simple to implement because it focuses on selecting participants who are easiest to access. It requires less time, effort, and resources than other methods, making it ideal for pilot studies or research with limited time.
Cost-Effective: Since participants are easily accessible and no complex sampling techniques are required, opportunity sampling is often less expensive compared to methods that require comprehensive planning, like random or stratified sampling.
Weaknesses:
Lack of Representativeness: Because participants are chosen based on availability, the sample is unlikely to be representative of the larger population. This can lead to biased results and limits the generalizability of the study’s findings.
High Risk of Bias: Opportunity sampling is prone to researcher bias, as the researcher may unintentionally select participants who share certain characteristics. For example, only people available during certain hours or in specific locations may be chosen, leading to skewed data that doesn’t reflect the diversity of the population.
Sampling: Define Volunteer sampling
Volunteer sampling is a non-probability sampling method where participants self-select to be part of a study. Individuals choose to participate because they are interested in the research, often responding to advertisements or invitations.
How is it carried out?
To select a volunteer sample, a researcher may place an advert in a newspaper or on a common room noticeboard. Alternatively, willing participants may simply raise their hand when the researcher asks.
Strengths:
Easy to Implement: Volunteer sampling is simple and straightforward to carry out. Since participants self-select, the researcher does not need to invest much time or effort in recruiting individuals, making it a time-efficient sampling method.
Ethical and Non-Coercive: Since participants volunteer for the study, they are willingly taking part, which makes the method ethical. It avoids any coercion or pressure on participants to join the study, respecting their autonomy.
Weaknesses:
Volunteer Bias: Participants who choose to volunteer are likely to differ from those who do not, as they may have specific characteristics or motivations (e.g., they might be more motivated, interested in the topic, or have more free time). This can lead to a biased sample that does not represent the broader population.
Limited Generalizability: Because volunteer samples tend to be unrepresentative and self-selected, the findings may not be generalizable to the entire population. The sample may overrepresent certain groups (e.g., people with a strong interest in the research topic) and underrepresent others.
Ethical issues and ways of dealing with them: Define Ethical issues
These arise when a conflict exists between the rights of participants in research studies and the goals of research to produce authentic, valid and worthwhile data.
Ethical issues and ways of dealing with them: Define BPS code of ethics
A quasi-legal document produced by the British Psychological Society (BPS) that instructs psychologists in the UK about what behaviour is and is not acceptable when dealing with participants. This code is built around 4 major principles: respect, competence, responsibility and integrity.
Ethical issues and ways of dealing with them: Informed consent
Informed consent involves making participants aware of the aims of the research, the procedures, their rights , and also what their data will be used for. Participants should then make an informed judgement whether or not to take part without feeling obliged.
From a researcher’s POV, asking for informed consent may make the study meaningless because participants’ behaviour will not be ‘natural’ as they know the aims of the study.
How can it be dealt with?
Participants should be issued with a consent letter or form detailing all relevant information that might effect their decision to participate. Assuming the participant agrees, this is then signed. For investigations involving children under 16, a signature of parent consent is required. There are other ways to obtain consent.
Ethical issues and ways of dealing with them: Deception
Deception means deliberately misleading or withholding information from participants at any stage of the investigation. Participants who have not received adequate information when they agreed to take part cannot be said to have given informed consent.
Despite that, there are occasions when deception can be justified if it does not cause the participant undue distress.
How is it dealt with?
At the end of the study, participants should be given full debrief. Within this, participants should be aware of the true aims of the investigation. and any details they were not supplied with during the study, such as the existence of other groups or experimental conditions.
Ethical issues and ways of dealing with them: Protection from harm
As a result of their involvement, participants should not be placed at any more risk than they would be in their daily lives, and should be protected from physical and psychological harm. The latter includes being made to feel embarrassed, inadequate or being placed under undue stress or pressure. An important feature of protection from harm. Participants are reminded of the fact that they have the right to withdraw from the investigation at any point.
How is it dealt with?
Participants should also be told what their data will be used for and must be given the right to withdraw during the study and the right to withhold data if they wish. This is particularly important if retrospective consent is a feature of the study.
Participants may have natural concerns related to their performance within the investigation, and so should be reassured that their behaviour was typical or normal. In extreme cases, if participants have been subject to stress or embarrassment, they may require counselling, which the researcher should provide.
Ethical issues and ways of dealing with them: Privacy and confidentiality
Participants have the right to control information about themselves. This is the right of privacy. If this is invaded then confidentiality should be protected. Confidentiality refers to our right, enshrined in law under the Data Protection Act, to have any personal data protected.
The right to privacy extends to the area where the study took place such as institutions or geographical locations are not named.
How is it dealt with?
If personal details, are held these must be protected. However, it is more usual to simply record no personal details, i.e. maintain anonymity. Researchers usually refer to participants using numbers or initials when writing up the investigation. In a case study, psychologists often use initials when describing the individual or individuals involved.
Finally, it is standard practice that during briefing and debriefing, participants are reminded that their data will be protected throughout the process and told that the data will not be shared with other researchers.
Pilot studies (and more): Define Pilot study
A small-scale version of an investigation that takes place before the real investigation is conducted. The aim is to check the procedures, materials, measuring scales, etc. The aim is also to allow the researcher to make changes or modifications in necessary.
Strengths:
Identifying Issues Early: Pilot studies help identify problems with the research design, procedures, or tools (e.g., survey questions or equipment) before the full study is conducted. This allows researchers to make adjustments to improve the overall study.
Testing Feasibility: They assess whether the study is realistic and practical, checking for issues like participant recruitment, data collection methods, and time constraints. This helps ensure that the main study can be carried out smoothly.
Weaknesses:
Limited Generalizability: Since pilot studies involve a small sample size, their results may not be generalizable to the larger population. They are primarily intended for testing methods, not for drawing definitive conclusions.
Resource and Time Intensive: Although pilot studies are smaller in scale, they still require time, effort, and resources to implement. This can be seen as a limitation if the results don’t lead to significant improvements in the full study.
Pilot studies (and more): The aims of piloting
A pilot study is a small-scale trial run of the actual investigation.
A pilot study involves a handful of participants, rather than the total number, in order to ‘road-test’ the procedure and check the investigation runs smoothly. It is also important to recognise that pilot studies are not just restricted to experimental studies. When using self-report methods, such as questionnaires or interviews, it is helpful to try out questions in advance and remove or reword those that are ambiguous or confusing.
In observational studies, a pilot study provides a way of checking coding systems before the real investigation is undertaken. This may not be an important part of training observers.
In short then, a pilot study allow a the researcher to identify any potential issues and to modify the design or procedure, saving time and money in the long run.
Pilot studies (and more): Single-blind procedure
A single-blind procedure is an experimental design in which the participants are unaware of which group they are in (e.g., control or experimental group), but the researchers know. This helps prevent participant bias by ensuring that the participants’ expectations or knowledge do not influence their behaviour or the results of the study.
Strengths:
Reduces Participant Bias: Since participants do not know which group they are in, they are less likely to alter their behavior to conform to expectations, thus reducing bias caused by prior knowledge or assumptions about the study.
Increases Objectivity: By keeping participants unaware of their group assignment, researchers can obtain more unbiased responses and behaviour, which helps maintain the integrity of the data and improves the validity of the results.
Weaknesses:
Researcher Bias: Since the researchers know which participants are in which group, there is a risk of bias in how they collect data or interact with participants. This could influence the outcome of the study unintentionally.
Limited Control of Expectations: While the participants are unaware of their group, the researchers’ knowledge could still influence the interpretation of the results. Some subtle cues or expectations from the experimenter may still affect the participant’s responses or behaviour, reducing the objectivity of the findings.
Pilot studies (and more): Double-blind procedure
A double-blind procedure is an experimental design in which both the participants and the researchers are unaware of critical aspects of the study, such as who is in the experimental group or control group. This is often used in drug trials or clinical studies to prevent both participant bias and researcher bias, ensuring that neither the participants’ expectations nor the researchers’ expectations influence the results.
Strengths:
Reduces Researcher and Participant Bias:
Participant Bias: Since participants are unaware of their group assignment (e.g., experimental or control), they are less likely to alter their behaviour based on expectations or prior knowledge.
Researcher Bias: Researchers, not knowing which group participants are in, are less likely to unintentionally influence participants’ behaviour or responses through subtle cues or expectations.
Increases Validity: The reduction of both participant and researcher biases leads to more objective and valid data. This ensures that the results are more likely to reflect the true effect of the independent variable rather than the influence of extraneous factors like expectations.
Weaknesses:
Practical Challenges: Double-blind procedures can be difficult and resource-intensive to implement. In some situations, it may be challenging for both the researchers and participants to remain unaware of key aspects of the study (e.g., if the treatment has visible effects or requires specialized procedures).
Ethical Concerns: In some cases, keeping both the participants and researchers unaware of certain aspects of the study might lead to ethical concerns. For instance, withholding certain information from participants might be seen as a violation of informed consent or could cause distress if participants later learn that they were unaware of the true nature of the study.
Pilot studies (and more): Control groups and conditions
The group that receives the real drug is the experimental group and the group that receives the placebo is the control group.
We use the word ‘control’ in research to refer to the control of variables but we use it here to refer to setting a baseline. Control is used in many experimental studies for the purpose of comparison. If the change in behaviour of the experimental group is significantly greater than that of the control group, then the researcher can conclude that the cause of this effect was the independent variable.
Having 2 groups in an experiment is an independent groups design, but we can also have control conditions in a repeated measures design. Each participant takes part twice- once in the experimental condition and then in the control condition.
Observational techniques: Define Naturalistic observation
Watching and recording behaviour in the setting within which it would normally occur.
Strengths:
High Ecological Validity:
Since the observation takes place in a natural setting (e.g., a classroom, park), the behavior observed is likely to be more authentic and representative of real-life situations. This makes the findings more applicable to real-world scenarios.
No Artificial Influence:
As the researcher does not interfere or manipulate the environment, the behaviour of the subjects is not influenced by the experimenter, which helps in studying natural behaviour without demand characteristics or experimenter bias.
Weaknesses:
Lack of Control:
Since the researcher does not control the environment, there are many uncontrolled variables that could influence the behaviour of the subjects. This makes it difficult to establish cause-and-effect relationships and can threaten the internal validity of the findings.
Ethical Issues:
Observing people without their knowledge or consent can raise ethical concerns, especially regarding privacy and informed consent. Participants may not be aware they are being studied, which could violate ethical guidelines like protection from harm and confidentiality.
Observational techniques: Define Controlled observation
Watching and recording behaviour within a structured environment, i.e. one where some variables are managed.
Strengths:
High Control Over Variables:
In a controlled environment, researchers can control extraneous variables, such as noise, lighting, or other distractions, which allows them to focus on the specific behaviour being studied. This increases the internal validity of the study because it reduces the likelihood of external factors influencing the behaviour.
Replicability:
The controlled conditions make it easier to replicate the study. Since the variables are controlled and the setup is consistent, other researchers can follow the same procedure to verify the findings, enhancing the reliability of the results.
Weaknesses:
Low Ecological Validity:
Because the observation takes place in an artificial environment (e.g., a laboratory), the behaviour may not reflect real-life situations. The participants may behave differently because they are aware they are being observed, leading to demand characteristics and reducing the generalizability of the findings.
Hawthorne Effect:
Participants may alter their behaviour because they know they are being observed (this is known as the Hawthorne effect). This can affect the naturalness of the behaviour being studied, leading to unrealistic or biased results that do not accurately reflect typical behaviour in real-world settings.
Observational techniques: Define covert observation
Participants’ behaviour is watched and recorded without their knowledge or consent.
Strengths:
Natural Behavior:
Since participants are unaware they are being observed, they are more likely to act naturally and exhibit authentic behaviors. This increases the ecological validity of the study and ensures that the data collected reflects real-world behavior.
Reduces Demand Characteristics:
Participants are not aware that they are part of an experiment, so they are less likely to alter their behaviour in response to perceived expectations. This reduces the risk of demand characteristics (when participants change their behaviour to align with what they think the researcher expects).
Weaknesses:
Ethical Concerns:
Covert observations raise significant ethical issues, particularly around informed consent. Since participants are unaware they are being observed, they cannot give consent to take part in the study. This raises concerns about privacy, autonomy, and deception.
Lack of Control Over Data Collection:
Because the observation is covert, researchers may not have the ability to ask for clarification or follow up on ambiguous behaviours. The lack of control over the data collection process can lead to misinterpretation or incomplete data, impacting the study’s reliability.
Observational techniques: Define Overt observation
Participant’s behaviour is watched and recorded with their knowledge and consent.
Strengths:
Ethical Transparency:
Since participants are aware they are being observed, there is informed consent, which is an essential ethical guideline in research. This ensures that the study follows ethical standards, with participants fully aware of their role in the research.
Opportunity for Clarification:
The researcher can interact with the participants if needed, ask for clarifications, or gather more information. This allows for a more accurate and comprehensive collection of data, especially if the behaviour being studied is complex.
Weaknesses:
Demand Characteristics:
Participants may alter their behaviour because they know they are being observed (this is known as the Hawthorne effect). This can lead to unnatural behaviour or biases, reducing the ecological validity of the findings.
Limited Control Over the Environment:
While participants are aware of the observation, they may still change their behaviour because they feel self-conscious. This lack of complete naturalness can still impact how accurately the observed behaviours represent real-world actions.
Observational techniques: Define Participant observation
The researcher becomes a member of the group whose behaviour he/she is watching and recording.
Strengths:
In-Depth Insight:
As the researcher participates in the group, they can gain a deeper understanding of the social dynamics and context. This provides rich, qualitative data and allows the researcher to experience the situation first-hand, offering a more immersive perspective.
Increased Authenticity:
By being part of the group, the researcher may be able to observe more natural behaviour because the participants may not be as aware of being studied. This can lead to more genuine insights into how individuals behave in their natural environment.
Weaknesses:
Loss of Objectivity:
The researcher’s involvement in the group may lead to a bias in their observations. The researcher might become too emotionally invested or influenced by group dynamics, which can compromise their ability to remain objective and impartial. This can affect the validity of the findings.
Ethical Issues:
There are potential ethical concerns in participant observation, especially when the researcher is involved in the group’s activities without the participants’ full awareness of the research. Issues like informed consent and deception may arise, especially if the researcher does not disclose their role in the study.
Observational techniques: Define Non-participant observation
The researcher remains outside of the group whose behaviour he/she is watching and recording.
Strengths:
Objectivity:
Since the researcher is not involved in the group, they can maintain a higher level of objectivity. They are less likely to be influenced by the group’s dynamics or become emotionally involved, which helps maintain the validity of the findings and reduces researcher bias.
Minimized Ethical Issues:
Ethical concerns such as informed consent and deception can be easier to manage in non-participant observation. The researcher does not become part of the group, which may reduce ethical complications related to participation. If done openly, participants can be informed about the study and give consent.
Weaknesses:
Limited Insight:
Since the researcher is not involved in the group, they may miss out on the context and deeper understanding of the behaviour being studied. They may have a more surface-level view and lack the experiential insight that a participant observer might gain.
Hawthorne Effect:
Although the researcher is not participating, the participants might still alter their behaviour because they are aware they are being observed (known as the Hawthorne effect). This can reduce the naturalness of the behaviour and lower the ecological validity of the study.
Observational design: Define Behavioural categories
When a target behaviour is broken up into components that are observable and measurable (operationalisation).
In order to produce a structured record of what a researcher sees (or hears), it is first necessary to break the target behaviour up into a set of behavioural categories (sometimes referred to as a behaviour checklist). This is very similar to the idea of operationalisation. Target behaviours to be studied should be precisely defined and made observable and measurable.
For instance, the target behaviour ‘affection’ can be broken down into observational categories such as hugging, kissing, smiling, holding hands etc. Each of these behaviours must be observable- there should not be any inferences to be made, such as ‘being loving’. Two observers might interpret this differently and thus it would not be a reliable category.
Before the observation begins, the researcher should ensure that they have included all the ways in which the target behaviour may occur within their behavioural checklist.
Observational design: Define Event Sampling
A target behaviour or event is first established then the researcher records this event every time it occurs.
Observational design: Define Time sampling
A target individual or group is first established then the researcher records their behaviour in a fixed time frame, say, every 60 seconds.
Observational design: Sampling methods
Continuous recording of behaviour is a key feature of unstructured observations in which all instances of a target behaviour are recorded. For very complex behaviours, however, this method may not be practical or feasible. As such, in structured observations, the researcher must use a systematic way of sampling their observations.
Observational design: Ways of recording data
One of the key influences on the design of any observation is how the researcher intends to record their data. The researcher may simply want to write down everything they see. This is referred to as an unstructured observation and tends to produce accounts of behaviour that are rich in detail. This method may be appropriate when observations are small in scale and involve few participants. For example, observing interaction between a couple and a therapist within a relationship support counselling session.
Often, however, there may be too much going on in a single observation for the research to record it all. Therefore, it is necessary to simplify the target behaviours that will become the main focus of the investigation using behavioural categories. This then becomes a structured observation.
Observational design: Inter-observer reliability
It is recommended that researchers do not conduct observational studies alone. Single observers may miss important details or may only notice events that confirm their opinions or hypothesis. This introduces bias into the research process.
To make data recording more objective and unbiased, observations should be carried out by at least two researchers. Then data from different observers is compared to check for consistency. i.e. reliability, and this is called inter-observer reliability. To do this:
-Observers should familiarise themselves with the behavioural categories to be used.
- They can observe the same behaviour at the same time, perhaps as part of a small-scale pilot study.
-Observers should compare the data they have recorded and discuss any differences in interpretations.
-Finally, observers should analyse the data from the study. Inter-observer reliability is calculated by correlating each pair of observations made and an overall figure is produced.
Observational design: (Evaluation) Structured versus unstructured
Structured:
Strengths:
Increased Reliability:
Because the observation follows a predetermined system (e.g., using a checklist or coding scheme), it is more likely to produce consistent results. This makes it easier to replicate the study and ensures that the data is comparable across different researchers.
Focus and Clarity:
The use of predefined categories ensures that the researcher is focused on specific behaviours, making it easier to analyse and interpret the data. This helps in drawing clear, focused conclusions relevant to the research question.
Weaknesses:
Reduced Flexibility:
The structured approach can limit the researcher’s ability to notice other unexpected behaviours. If new or relevant behaviours emerge that were not part of the predefined categories, they may be missed.
Lack of Depth:
Because the observation is focused on specific behaviours, it may fail to capture the full context of the situation, potentially missing out on the underlying reasons or broader dynamics influencing the behaviour.
Unstructured:
Strengths:
Rich, Detailed Data:
Since the researcher observes all behaviour without predefined categories, the data collected is richer and more comprehensive. This provides a more holistic view of the participants’ actions and interactions.
Flexibility:
The researcher can capture any behaviours that emerge during the study, which allows them to adapt and observe behaviours that may not have been anticipated, providing a broader understanding of the situation.
Weakness:
Low Reliability:
Because the observation lacks a structured framework, different researchers may interpret and record data differently, leading to inconsistencies and making it difficult to replicate the study reliably.
Researcher Bias:
Without predefined categories, there is a higher likelihood that the researcher’s personal subjectivity or biases may influence what they choose to observe and record, potentially compromising the validity of the data.
Observational design: (Evaluation) Behavioural categories
Although the use of behavioural categories can make data collection more structured and objective, it is important that such categories are as clear and unambiguous as possible. They must be observable, measurable and self-evident. In other words, they should not require further interpretation.
Researchers should also ensure that all possible forms of the target behaviour are included in the checklist. There should not be a ‘dustbin category’ in which many different behaviours are deposited.
Finally, categories should be exclusive and not overlap. For instance, the difference between ‘smiling’ and ‘grinning’ would be very difficult to discern.
Observational design: (Evaluation) Event sampling
Strengths:
Focus on Specific Behaviours:
Event sampling allows researchers to concentrate on particular behaviours or events that are relevant to the research question. This ensures that the data collected is directly linked to the target behaviour, helping to maintain focus on what is most important in the study. It also ensures that significant occurrences are captured in detail.
Useful for Rare or Unpredictable Behaviours:
Event sampling is especially helpful when studying rare or infrequent behaviours that may not occur regularly. By focusing on these specific events, the researcher ensures that the behaviour is recorded when it does happen, making it a good method for capturing unusual but important occurrences.
Weaknesses:
Potential for Missing Other Important Behaviours:
Since event sampling focuses solely on specific events or behaviours, there is a risk that other relevant behaviours occurring at the same time may be overlooked. If the event of interest doesn’t occur frequently, the researcher might miss out on capturing other aspects of the participant’s behaviour that could have been relevant to the study.
Data May Lack Context:
When only specific behaviours or events are recorded, the researcher might miss the context in which these events occur. The surrounding interactions or environmental factors that influence the behaviour are not always captured, which could limit the ability to understand the full picture of the participants’ actions.
