Primary Data: Data collected first-hand by the researcher.
Secondary Data: Data sourced from others' prior research.
Evidence and Information:
Opinion: A view or perspective not necessarily based on evidence.
Anecdote: Stories based on personal experience.
Evidence: Information obtained through direct and systematic observation or experimentation.
Scientific Ideas: Formed with empirical evidence and scientific methods.
Non-Scientific Ideas: Formed without empirical evidence or scientific methods.
Quality of Evidence: Includes uncertainty, validity, and authority of data and sources, and possible errors or bias.
Organizing, Analyzing, and Evaluating Secondary Data: Methods for processing existing data.
Logbook: Use of a logbook to authenticate collated secondary data.
Introduction to Research
Aims to construct evidence-based arguments and draw conclusions.
Helps analyze, evaluate, and communicate scientific ideas.
Develop aims and questions, formulate hypotheses, and make predictions.
Relies on empirical evidence: Information obtained through direct and systematic observation or experimentation.
Non-Scientific Claims
Non-Scientific: Ideas formed without empirical evidence or scientific methods.
Includes pseudoscience.
Cannot be verified through observation or evidence.
Often commits wrong or invalid steps of reasoning.
Asserting conclusions with weak or false premises while ignoring non-supporting, empirical evidence.
Non-Scientific Ideas
May be:
Non-objective
Unempirical
Imprecise or vague
Dogmatic (not open to questioning)
Unverifiable
May be formed on the basis of:
Anecdote (stories based on personal experience)
Opinion (the view or perspective of someone not necessarily based on evidence)
Intuition (something that one feels instinctively as opposed to arrived at through considered reasoning)
Hearsay (rumour or information from others which cannot be supported with evidence).
The Scientific Method
Most commonly used approach across the sciences.
A procedure used to obtain knowledge that involves hypothesis, formulation, testing, and retesting through processes of experimentation, observation, measurement, and recording.
Centered around generating a prediction then testing it to generate evidence that either supports or refutes it.
8 steps
Steps of the Scientific Method
Ask a question
Research the question
Form a hypothesis
Test with an experiment or investigation
Analyse data and results
Results support hypothesis
Communicate results
Reproduce findings
Results do not or only partially support hypothesis
Results obtained form basis for new research. This could be a brand new hypothesis or a reformulated version of the original hypothesis.
Spotting Bad Science
3 ways to spot bad science
Models and Theories
Explain psychological phenomena.
How we can organize and understand observations and concepts related to psychology.
Give a common language that we can use to communication with other scientific thinkers and devise informed solutions to our problems.
Theories
Definition: A proposition or set of principles used to explain something or make predictions about relationships between concepts.
Main Function: Explain and predict.
Informed by: Scientific research or logic.
Example in Psychology: Behaviourism (behaviour is learnt through interaction with the environment).
Models
Definition: A representation of a concept, process, or behaviour, often made to simplify or make something easier to understand.
Main Function: Simplify and represent.
Informed by: Scientific theories and ideas.
Example in Psychology: The multi-store model of memory posits that we have a sensory, short-term, and a long-term memory "store".
Aims and Hypotheses
Created first to help give an aim of the research.
Follows the scientific method.
Narrows the scope of what is to be investigated- forms the research question or problem.
Aim
Is a statement outlining the purpose of the investigation
Written as a straightforward sentence to narrow the investigation
Hypothesis
Is a testable prediction about the outcome of the investigation
Is a core function of the scientific method
Written in a way that makes them testable
Includes:
Variables
Population
Direction of the results
Examples of Aims
"The aim of this investigation is to explore the relationship between partial sleep deprivation and low mood."
"The aim of this study is to investigate the role of high-quality sleep on concentration."
Example of Hypotheses
"It was hypothesized that Australian females aged 12–16 who experienced partial sleep deprivation would be more likely to also experience low mood than those who did not experience partial sleep deprivation."
"It was hypothesized that high school students who had high-quality sleep would perform better on tests of concentration than those who did not have high-quality sleep."
Variables
Independent Variables (IV):The variable for which quantities are manipulated (controlled, selected, or changed) by the researcher, and the variable that is assumed to have a direct effect on the dependent variable.
Dependent Variables (DV): The variable the researcher measures in an experiment for changes it may experience due to the effect of the independent variable.
IV & DV Examples
Hypothesis: "It was hypothesised that Australian females aged 12–16 who experienced partial sleep deprivation were more likely to experience low mood than those who did not experience partial sleep deprivation."
Hypothesis: "It was hypothesised that high school students who had high quality sleep would perform better on tests of concentration than those who did not have high quality sleep."
Operationalising Variables
Refers to how exactly the variables will be manipulated or measured.
Important that the variables are specific enough so that a hypothesis can be clearly supported or refuted.
Example: If the independent variable that is manipulated to influence concentration is ‘quality of sleep’, this may be operationalised as ‘the hours of REM, NREM, and total sleep as measured by EEG recordings’.
Controlled Variables
Are other variables other than the IV that a researcher holds constant to ensure that changes in the DV are solely due to the changes in the IV.
They are not part of the investigation because a controlled variable is not an experimental variable.
Example: "It was hypothesised that Australian females aged 12–16 who experienced partial sleep deprivation were more likely to also experience low mood than those who did not experience partial sleep deprivation."
Scientific Research Methodologies
Different ways in which researchers can use to learn about psychological phenomena.
Refer to the different processes, techniques and/or types of studies researchers use to obtain information about the psychological phenomena.
Controlled Experiments
Definition: Type of investigation in which the casual relationship between two variables is test in an controlled environment; more specifically the effect of the independent variable (IV) on the dependent variable (DV) is tested while aiming to control all other variables.
Control experiments include Experimental and Control groups
Advantages:
Allow researchers to infer casual relationships between and draw conclusions
Provide high level of control over conditions and variables
Follow a strict procedure so it can be repeated.
Disadvantages:
Often conducted in a laboratory or highly controlled setting
May affect participants responses due to environment
Due to human control often open to researcher error or experimenter effect
Time consuming and expensive
Confounding or extraneous variables can occur
Controlled Experiment Groups
Experimental Group: The group of participants in an experiment who are exposed to a manipulated independent variable. Exposed to the IV.
Control Group: Refers to the group of participants in an experiment who receive no experimental treatment or intervention in order to serve as a baseline for comparison. Not exposed to the IV
Controlled Experiment Designs – Within Subjects
Explanation: Experimental design in which participants complete every experimental condition.
Advantages:
Ensures that the results of the experiment are more likely due to the manipulation of the IV than any other variable
Less people are needed as each participant completes each condition
Good for real-world settings and phenomena.
Disadvantages:
Can produce order effects- may influence how participants perform in the latter condition due to fatigue practice, expections
Participant drop out rate is greater and may have a greater impact on the study as researcher will have to lose 2 data points instead of one.
Controlled Experiment Designs – Between Subjects
Explanation: Experimental design in which individuals are divided into different groups and complete only one experimental condition.
Advantages:
May be less time consuming as different participants complete different conditions simultaneously.
Does not create order effects
Disadvantages:
May require more participants than a within subjects design.
Differences across groups can affect results, i.e. participant differences.
Controlled Experiment Designs – Mixed Designs
Explanation: Experimental design which combines elements of within-subjects and between subjects.
Advantages:
Allows experimenters to compare results both acrros experimental conditions and across individuals/participants/ groups over time.
Allows for multiple experimental conditions to be compared to a baseline group.
Disadvantages:
Can be more costly and time consuming to plan, conduct and then analyse results.
Demanding for researchers and assistants to be across multiple methods.
Case Studies
Definition: An in-depth investigation of an individual, group, or particular phenomenon (activity, behaviour, event, or problem) that contains a real or hypothetical situation and includes the complexities that would be encountered in the real world
Historical- analyzing cause and effect
Real situation or role play
Problem solving
Advantages:
Gather highly detailed in- depth information. Often when information is needed about a specific phenomenon that is rare or hard to study repeatedly.
Allow rare phenomena to be examined in depth.
Can incorporate other scientific methodologies to gain data.
Disadvantages:
Results cannot be generalized to a wider population
Subjective to researcher bias and errors
Can be difficult to draw conclusions about cause and effect
Time consuming
Correlational Studies
Definition: Type of non experimental study in which researchers observe and measure the relationship between2 or more variables without any active control or manipulation of them.
Can be positive or negative and can also be zero correlation.
Include: fieldwork, observational studies, examining archival data & surveys
Advantages:
No manipulation of variables required
Provides ideas for future hypotheses
Provides information about the relationships and associations between variables
Can be conducted in naturalistic settings
Disadvantages:
Results cannot draw conclusions about cause and effect
Can be subject to the influence of extraneous variables
Circumstances for Correlational Studies
The relationship between variables is less likely to be causal (i.e. two variables often occur together (correlate) but one does not necessarily cause the other).
There is thought to be a causal relationship between variables, but the variables are too difficult, dangerous, or unethical to actively manipulate.
A new measurement procedure or tool needs to be tested.
It is more valuable or practical to collect data in a real-world setting.
Examples for Correlational Studies
The variable of high test scores on a VCE mathematics exam might correlate with high test scores for mathematics questions on the General Assessment Test, but one does not cause the other.
If a relationship between rainfall and low mood is predicted, the variable of rainfall is impossible to manipulate, so researchers may instead elect to simply measure it and participants self-rated mood scores.
If a research team develops a new emotional intelligence test and wants to see if it is accurate and reliable, they may provide this test alongside other, already validated emotional intelligence tests to see if there is a correlation between all tests' scores.
Parenting styles can be researched using both controlled experiments and correlational studies. To understand the effects of different parenting styles, a researcher may use correlational study methods, such as observing the different effects of these parenting styles within different family homes. This allows the researcher to quickly record multiple associations between parenting styles and children's behaviour. In contrast, a controlled experiment might be used when a researcher wishes to know the effect of one specific parenting style on a specific behaviour of children. The latter would require more careful planning and a more specific object of inquiry.
Correlation vs. Causation
Correlation refers to the strength of relationship between variables
Causation refers to a relationship between variables wherein a change to one variable causes a change in another.
Correlation and causation may occur simultaneously however important to know that correlaton does not always equal causation,
Classification & Identification
Classification: The arrangement of phenomena, objects or events into manageable sets.
Identification: A process of recognition of phenomena as belonging to a particular sets or possibly being part of a new or unique set.
These processes enable psychologists to create theoretical language for which to describe and build upon.
Advantages:
Provides common language to communicate phenomena
Helps simplify, explain and describe complex phenomena
Allows for more targeted solutions to real problems
Allows for researchers to form theories and hypotheses about labelled phenomena.
Disadvantages:
Can over simplify reality
Labels can be inaccurate and create bias.
Fieldwork
Definition: Any research involving observation and interaction with people and environments in real world setting, conducted beyond the laboratory.
Important that data is collected in a real world authentic setting. Can be conducted in naturalistic settings
Advantages:
Researcher collects data first hand
Fieldwork provides rich detailed data
Can use a broad range of different methodologies
Can occur over a longer period and uncover information that may not be immediately obvious to researchers
Disadvantages:
Can be time consuming and expensive
Generally cannot inform conclusions about cause and effect
Difficult to replicate in order to verify results.
difficult to control the environment as researchers do not precisely manipulate variables.
Literature Review
Definition: The process of collating and analysing secondary data related to other peoples scientific findings and or viewpoints in order to answer a question or provide a background information to help explain observed events, or as preparation for an investigation to generate primary data.
Advantages:
Often used before conducting a new study and or collecting primary data
Provides background information on specific phenomena that can inform new studies and hypotheses.
Allows for researchers to understand the current state of play.
May uncover patterns of knowledge or gaps of knowledge
Disadvantages:
May be time consuming
May be difficult to do if little research has been done on a topic
Modelling
Definition: Refers to the construction and or manipulation of either a physical model such as a small or large scale represention of an object or a conceptual model that represents a system involving concepts that help people know, understand or simulate the system.
Models can be: Physical; Conceptual
Advantages:
Provides explanatory tools
Physicall modelling allows for researchers to know and understand and problem solve
Conceptual modeeling can simplify and explain the phenomena.
Disadvantages:
Models are often used to simplify and communicate ideas that may over simplify or inaccurately represent reality.
Product, Process, or System Development
Definition: The design or evaluation of an artefact, process or system to meet a human need which may involved technical applications in addition to scientific knowledge and procedures.
Used when psychologists, developers or researchers have identified a hunman need that can be served by technology or scientific knowledge and procedures.
Advantages:
Creates products, process and system that may meet a human need.
Disadvantages:
Can be time consuming
Can be expensive
Simulation
Definition: A process of using a model to study the behaviour of a real or theoretical system.
May be used for explanation and understanding may be useful for understanding how different variables operae in a system.
Used when it is too complex, impractical or dangerous to test the relationships between variables in reality.
Advantages:
Simulation provides insight into potential circumstances and events
Allows researchers to view micro-phenomena such as neurons
Allows researchers to see events that might otherwise be too time-consuming, dangerous or impractical
Disadvantages:
Time cosuming
Expensive
May be subject to programming and human error so may not always be accurate predicition or a reflection of reality.
Types of Fieldwork
Direct Observation: Researcher watches and listens to the participants of a study. No direct intervention and involvement or manipulation variables.
Qualitative Interviews: Involve researcher asking questions to gather indepth information about, topic, theme or idea. May be structured. Open ended questions that provide rich, qualitative data for analysis.
Questionnaires: Set of questions or prompts that often are open ended or closed. Then analysised by the researcher
Focus Groups: Discussion with small groups of people on a specific topic. Formed on the basis of shared characteristics relevant to the discussion.
Yarning Circles: Aboriginal & Torres Strait island approaches. Group discussion involving talking, exchanging ideas, reflection and deep considered listening without judgement.
Population, Sample & Sampling
Refers to the population of an experiment which is the group of people who are the focus of the research.
Population
The group of people who are the focus of the research and from which the sample is drawn
Sample
A subset of the research population who participate in a study
Would be highly representative of the research population – representing proportions of relevant demographics and other characteristics of the study.
Dependent on the size- larger = more representative (generally)
Sampling technique used. Also considered the studies “research participants”
Often used as it is not possible to test everyone in a given population.
Allows this to be generaliseable to the population.
Generalisable
Also known as generalisability) the ability for a sample’s results to be used to make conclusions about the wider research population
Sampling Techniques
Convenience
Definition: Any sampling technique that involves selecting readily available members of the population, rather than using a random or systematic approach
Examples: Asking the first 200 people who enter to complete a survery. A teacher asking her students to participate in an interview
Advantages: Time effective; Cost effective
Disadvantages: Most likely to produce unrepresentative sample; Harder to be generalized due to sample representation
Random
Definition: Any sampling technique that uses a procedure to ensure every member of the population has the same chance of being selected
Example: Using a random generator to select names for the sample
Advantages: Can be more representative then convenience; Reduces experimenter bias in selecting participants; Can be fairly representative is sample is large,
Disadvantages: Time consuming to ensure every member of population has an equal chance of being selected; may not create a representative sample if sample is small
Stratified
Definition: Any sampling technique that involves selecting people from the population in a way that ensures that its strata (subgroups) are proportionally represented in the sample
Example: Can be random or selected specifically based on the strata (Age, Gender etc.)
Advantages: Most likely to produce a representative sample
Disadvantages: Time consuming; Expensive; Demanding on the researcher to select the most appropriate strata to account for.
Allocation
The process of assigning participants to experimental conditions or groups.
Completed once the sample has been selected
Generally separates sample into equal groups and each person has a equal chance of being allocated into either group.
Preventing Error & Bias
Errors refer to changes to the dependent variable caused by something other than the independent variable.
All other variables that impact on the DV are known as Extraneous Variables.
Variables
Extraneous Variables
Definition: A variable that is not the independent variable but may cause an unwanted effect on the dependent variable.
Are controlled or at least monitored so they do not interfere with the results.
Confounding Variables
Definition: A variable that has directly and systematically affected the dependent variable, apart from the independent variable.
May have been an EV that was not accounted for or cannot be controlled.
participant-related variables
order effects
placebo effects
experimenter effects
situational variables
non-standardised instructions and procedures
demand characteristics.
Variables - Definitions
Participant-Related
Characteristics of a study’s participants that may affect the results
Order Effects
The tendency for the order in which participants complete experimental conditions to have an effect on their behaviour
Practice; Fatigue
Placebo Effect
When participants respond to an inactive substance or treatment as a result of their expectations or beliefs
Experimenter Effect
When the expectations of the researcher affect the results of an experiment Aka: Experimenter bias
Situational Effects
Any environmental factor that may affect the dependent variable
Non-Standarised Instructions and Procedures
When directions and procedures differ across participants or experimental conditions
Demand Characteristics
Cues in an experiment that may signal to a participant the intention of the study and influence their behaviour
Preventing Extraneous and Confounding Variables
Sample Size and Procedures
Having a large sample size that is representative of population more likely to be unbiased. use of unobjective sampling i.e. random or stratified.
Experimental Design Choice
Choosing a design method that avoids EV’s.
Counterbalancing
A method to reduce order effects that involves ordering experimental conditions in a certain way
Placebo
An inactive substance or treatment
Single-Blind Procedures
Participants are unaware of the experimental group or condition they have been allocated to
Double-Blind Procedures
A procedure in which both participants and the experimenter do not know which conditions or groups participants are allocated to
Standardized Instructions and Procedures
Ensuring that each participants receives the same instructions.
Controlled Variables
Holding anything that can impact the experiment controlled. As in the controlled experiment design
Organising and interpretating Data
The importance of data allows for us to understand the influence of the IV on the DV.
Different forms of data:
Primary
Secondary
Qualittive
Quantiative
Objective
Subjective
Types of Data
Primary
Data collected first- hand by a researcher
Secondary
Data sourced from others’ prior research
Assessing prior data (not from the current researcher)
Quantitative
Data that is expressed numerically
Test score measures
Qualitative
Data that is expressed non- numerically I.e. words
Open ended questions
May sometimes be converted into quantitative using systematic methods.
Objective
Factual data that is observed and measured independently of personal opinion
Persons weight in KG. Numerical score on IQ test Do not require personal opinions or interpretation by researcher
Subjective
Data that is informed by personal opinion, perception, or interpretation
Cannot be easily interpretated. Often comes from qualitative data Rich data however often also supported by other data.
Processing Quantitative data
Data collected from scientific investigations are considered to be raw data when first collected
Researcher then needs to process the numerical information to make comparisons and observations and notice patterns.
Descriptive statistics are used to summarise, organize and describe data. This is used to process quantitative data
Processing Quantitative Data
Percentages
Common and useful descriptive statistic. Easier to notice trends in data via percentages.
Formula: totalnumbergivennumber×100
Measures of Central Tendency
Descriptive statistics that summarise a data set by describing the centre of the distribution of the data set with a single value
Give a good picture of common or standard responses. Mean, Median, Mode.
Measures of Variability
Statistics that summarise and describe the spread and distribution of a data set
Can show the spread of responses. Range, Standard deviation.
Quantitative Data - Measures of Central Tendency
Mean
A measure of central tendency that describes the numerical average of a data set, expressed as a single value Can tell what the typical score/response is.
Works best when distributed around the centre (normal distribution) Less helpful when data is widely distributed as it can be influenced by outliers.
Outlier
A value that differs significantly from other values in a data set Might happen due to chance.
Median
A measure of central tendency that is the middle value in a data set ordered from lowest to highest Helps identify a more typical response when the data is not even distributed around the centre or when there is outliers
Mode
A measure of central tendency that is the most frequently occurring value in a data set Most common/most frequent number. Helps when the mean or median cannot be calculated
Quantitative Data - Measures of Variability
Range
Measure of variability that is a value obtained by subtracting the lowest value in a data set from the highest value
Standard Deviation
A measure of variability, expressed as a value that describes the spread of data around the mean
the standard deviation number shows how much data ‘deviates’ from the mean
The standard deviation is useful for researchers as it shows the dispersion of data, which provides more detailed information about the true nature of a data set compared to the range.
standard deviation allows comparisons to be made between different data sets based on their dispersion
Presenting Data
Formats that are used to summerise and communicate their findings in an accessible format.
Tables
A presentation of data arranged into columns and rows Shows the relationship between certain variables.
Bar Chart
A graph displaying the relationship between at least two variables using rectangular bars with heights or lengths proportional to the values they represent May be horizontally or vertically. Important that they are 2of equal width and are separated by space.
Line Graph
A graph displaying the relationship between at least two variables using a straight line to connect data points Often used to show data patterns and changes over time.
Graphs should include: title, x & y axes labelled with their appropriate variable, units of measure on each axis.
Evaluating Research
Researchers must rigorously evaluate their research before conclusions can be drawn.
Use statistical procedures as well as consider if the research was high quality and free from errors.
Accuracy & Precision
Accuracy
How close a measurement is to the true value of the quantity being measured
True value the value, or range of values, that would be found if the quantity could be measured perfectly i.e. measurement of weight would use a scale that perfectly records an objects weight, inaccurate might use poor quality scles that is in grams or kilograms of the true weight. Accuracy is not described numerically, measurement values are simply described as more accurate or less accurate
Precision
How closely a set of measurement values agree with each other. Gives no indication of how close the measurements are to the true value however gives a consistent same measure every time. i.e. scales that display the same measurement each time = precise and scales that show different measurements on the same item multiple times= imprecise.
Measurement Errors
Systematic Errors
Errors in data that differ from the true value by a consistent amount. i.e. scales are consistently reading 100 grams lighter than the true value of an objects weight. Affect the ACCURACY
May occur due to: Environmental factors, Observational/researcher error, Incorrect measurement calibration
Random Errors
Errors in data that are unsystematic and occur due to chance Do not occur in a consistent way. Affect the PRECISION
Occur due to: Poorly controlled or varying measurement procedures, Imperfect or faulty measurement tools, Variation in measurement contexts May be reduced by: Repeating and conducting more measurements, Calibrating tools correctly, Controlling other EV, Increasing sample size
Uncertainty in Data
Refers to the lack of exact knowledge relating to something being measured due to potential sources of variation in knowledge.
i.e. measuring “positive mood”- the understanding of this may be blurred due to the difference in understanding of what is a positive mood.
Allows for suggestions to be made for future research.
Repeatability and Reproducibility
Repeatability
The extent to which successive measurements or studies produce the same results when carried out under identical conditions within a short period of time (e.g. same procedure, observer, instrument, instructions, and setting). Repeatability is the extent to which the same study or measure used under the same conditions will produce the same results
Reproducibility
The extent to which successive measurements or studies produce the same results when repeated under different conditions (e.g. different participants, time, observer, and/or environmental conditions). The extent to which the same study or measure used under different conditions or with different people or procedures will produce the same result.
Validity
Refers to the extent which psychological and investigations truly support their findings or conclusions.
Applied to evaluate a specific measurement tool or the investigation as a whole.
Valid measure is one that measures what it intends to measure.
Internval Validity, External Validity
Internal Validity
Extent to which an investigation truly measures or investigates what it claims to. If internal validity is lacking then results of an investigation may not be trud and a conclusion cannot be drawn. Consider: Adequacy of measurement tools, Adequacy of design, Adequacy of sampling/allocation procedures, Did the IV truly affect the DV
External Validity
Extent to which the investigation can be applied to similar individuals in different settings Different settings may be different time. Improved by: Using sample procedures that create a representative sample, Having broad inclusion criteria, Using a larger sample size
Drawing Conclusions
A statement that summarises the findings of a study, including whether the hypothesis was supported or rejected.
Refers to the final section of a written report that summarises the findings an makes recommendations for future research.
Considerations for Drawing Conclusions
The extent to which the data (evidence) supports or rejects the hypothesis
Whether further evidence is required
Whether there are clear recommendations for further studies
Ethical Concepts
Moral guiding principles that should be followed and considered when doing psychological research, practice or examining a psychological issue or debate.
General principles that can help researchers and students act and think in a more morally conscious way.
Concepts are not prescribed by a specific rulebook, body or organization
Ethical Concepts - Definitions
Beneficence
Definition: The commitment to maximising benefits and minimising the risks and harms involved in taking a particular position or course of action.
Considerations: Does the research minimize harm; Where harm is necessary is it outweighed by the benefits of the study; Participants welfare
Integrity
Definition: The commitment to searching for knowledge and understanding, and the honest reporting of all sources of information and results, whether favourable or unfavourable, in ways that permit scrutiny and contribute to public knowledge and understanding.
Considerations: Objective and open reporting and recording of results; Process of peer review; Throughness of literature review and research procedures.
Respect
Definition: Is the consideration of the extent to which living things have an intrinsic value and/or instrumental value; giving due regard to the welfare, liberty and autonomy, beliefs, perceptions, customs and cultural heritage of both the individual and the collective; consideration of the capacity of living things to make their own decisions; and when living things have diminished capacity to make their own decisions, ensuring that they are empowered where possible and protected as necessary
Considerations: Respect for and consideration of the welfare of human and non human participants; Protection of participants autonomy; Respect for personal beliefs and cultures.
Justice
Definition: The moral obligation to ensure that there is fair consideration of competing claims; that there is no unfair burden on a particular group from an action; and that there is fair distribution and access to the benefits of an action
Considerations: Objective in evaluating results; Ensuring practice does not stereotype or discriminate; Equity in access to services and findings
Non-Maleficence
Definition: The principle of avoiding causing harm. Harm may be involved but the concept implies that harm should not be disproportionate to the benefits
Considerations: Research designed to minimise psychologica l and physical harm. Welfare; Cost-benefit analysis
Ethical Guidelines
The procedures and principles used to ensure that participants are safe and respected
Participants are entitled to these and researchers must ensure these are provided
Must e followed and considered when conducting investigations.
Must be approved by an ethics committee to ensure it meets the standards
Ethical Guidelines - Definitions
Confidentiality
Definition: The privacy, protection and security of a participant’s personal information in terms of personal details and the anonymity of individual results, including the removal of identifying elements
Ways to ensure: Data storage tools and procedure that are safe, Annoymising participants results when sharing
Informed Consent
Definition: Processes that ensure participants understand the nature and purpose of the experiment, including potential risks (both physical and psychological), before agreeing to participate in the study
Ways to ensure: Voluntary written consent; Must be over the age of 18 or have parents consent; Legal consent if cannot be given (i,.e. for those with a disability; Possibility of deception listed in consent form
Deception
Definition: The act of intentionally misleading participants about the true nature of a