Key Science Skills (Psychology Units 3 & 4)

Key Science Skills Part 1 & 2 (Psychology Units 3 & 4)

Section One: Develop Aims, Questions, Formulate Hypotheses and Make Predictions

• Key Science Skills (KSS)
  • Edrolo Reference: Chapter 1 and ‘UNIT 4 AOS 3’ Chapter.
  • Reminder: All Key Knowledge relating to KSS were provided in Early Commencement (resource in Early Commencement folder on COMPASS).

KKDPs:

• Identify, research and construct aims and questions for investigations.
• Identify independent, dependent and controlled variables in controlled experiments.
• Formulate hypotheses to focus investigations.
• Predict possible outcomes of investigations.

IDENTIFY, RESEARCH AND CONSTRUCT AIMS AND QUESTIONS FOR INVESTIGATIONS

• Can you find/recognise the aim and/or research question in a scenario provided?
• Can you explore and find information relating to a particular aim/question?
• Can you write an aim and/or research question?

AIM:

• Purpose for the experiment.
• Format: To investigate, To find out, To explore, To compare….
• Written at the start of investigation.

RESEARCH QUESTION

• The question that researchers are trying to answer.
• It is very similar to the aim however written in question format
  • Example:
    • Aim: To investigate if sleep deprivation impacts memory ability
    • Research Question: Does sleep deprivation impact memory ability?

IDENTIFY INDEPENDENT, DEPENDENT AND CONTROLLED VARIABLES IN CONTROLLED EXPERIMENTS

• IV: variable being changed by researchers
• DV: variable being measured by researcher (it is the DATA being collected)
• Controlled: variables that remain the same across all conditions (control and experimental groups have same conditions in relation to these variables)

FORMULATE HYPOTHESES TO FOCUS ON INVESTIGATIONS

• Hypotheses (predictions) need to have the following components:
  • Population (the group of interest)
  • Independent variable
  • Dependent variable
  • Direction (HOW the IV will affect the DV)

PREDICT POSSIBLE OUTCOMES OF INVESTIGATIONS

• As a hypothesis is a prediction, if you can create a hypothesis (with a direction), then you can predict the possible outcome of an investigation.
• Additionally, you should be able to identify patterns in data and as a result predict the possible conclusion for that experiment.

Section Two: Plan and Conduct Investigations

KKDPs:

• Determine appropriate investigation methodology: case study; classification and identification; controlled experiment (within subjects, between subjects, mixed design); correlational study; fieldwork; literature review; modelling; product, process or system development; simulation
• Design and conduct investigations; select and use methods appropriate to the investigation, including consideration of sampling technique (random and stratified) and size to achieve representativeness, and consideration of equipment and procedures, taking into account potential sources of error and uncertainty; determine the type and amount of qualitative and/or quantitative data to be generated or collated
• Work independently and collaboratively as appropriate and within identified research constraints, adapting or extending processes as required and recording such modifications

DETERMINE APPROPRIATE INVESTIGATION METHODOLOGY

• Case study; classification and identification; controlled experiment (within subjects, between subjects, mixed design); correlational study; fieldwork; literature review; modelling; product, process or system development; simulation

RESEARCH METHODOLOGIES:

• There are many different investigation methodologies researchers can use to learn about psychological phenomena. Knowing which methodology to use for a given topic is an important research skill, so you will also learn how to evaluate them
• You need to be familiar with the following:
  • Case study
  • Classification and identification
  • Controlled experiments
  • Correlational study
  • Fieldwork
  • Literature review
  • Modelling
  • Product, process or system development
  • Simulation

CONTROLLED EXPERIMENTS

• Measures causal relationship between one or more independent variables and a dependent variable, whilst controlling for all other variables.
• There are three designs you need to familiar with

BETWEEN-SUBJECTS DESIGN

• Individuals are divided into different groups and complete only one experimental condition

Advantages

• May be less time-consuming than within-subjects design as different participants can complete the different conditions simultaneously and procedures do not need to be repeated

Disadvantages

• May require more participants than a within-subjects design

WITHIN-SUBJECTS DESIGN

• Participants complete every experimental condition (and control group)

Advantages

• Ensures that the results of the experiment are more likely due to the manipulation of the IV rather than individual participant difference that would occur if they were in separate groups

Disadvantages

• It can produce order effects; completing one experimental condition first and then the other/s may influence how participants perform in the second condition

MIXED DESIGN

• Combines elements of between and within-subjects design
• Allows experimenters to note differences that occur within each experimental group over time, and compare differences across experimental groups
• For example, in an experiment about the role of smell in anxiety, a researcher may have two experimental conditions: the presence of an unpleasant smell and the presence of a pleasant smell. Participants may be divided into one of two experimental conditions and complete a task with either of the two smells present. This reflects a between-subjects design. However, the experimenter may also measure participants’ anxiety in both groups before (to provide a baseline for comparison) and after the completion of a task with a smell present. This latter element reflects a within-subjects design. This allows participants’ own results to be compared over time.

Advantages

• Allows experimenters to compare results both across experimental conditions and across participants groups over time

Disadvantages

• Can be more costly and time consuming to plan, conduct and analyse results

CASE STUDIES

• A case study is an in-depth investigation, group, or particular phenomenon that contains a real or hypothetical situation that contain complexities that would be encountered in the real world.
• Used when information is needed about a specific phenomenon that is rare or hard to study repeatedly with a larger group of people

CLASSIFICATION AND IDENTIFICATION

• Classification is the arrangement of phenomena, objects, or events into manageable sets. Classification is used by psychologists to create labels or groups for phenomena that may help to provide some functional or theoretical benefit.
  • For example:
    • In clinical psychology, psychologists have created groupings of symptoms, behaviours, and other characteristics into different mental disorders. Major depressive disorder is one example. There are both functional and theoretical arguments in favour of such a classification system.
    • In psychology, researchers may wish to classify different affects (emotions). For example, classifying a human affect as ‘disgust’ may involve understanding the set of characteristics (emotions, reactions, physiological responses and so on) that occur when ‘disgust’ is felt. This may have theoretical benefits, such as being able to research when or what causes disgust in humans.
• Identification is a process of recognition of phenomena as belonging to particular sets or possibly being part of a new or unique set. Identification is used by psychologists to then ascribe phenomena to a particular classification; in other words, to assign certain things to their respective label or group.
  • For example:
    • Clinical psychologists may diagnose a patient with a particular mental disorder based on matching what they observe to the ‘set’ of symptoms in a classification system. This may allow them to provide an explanation for the patient’s symptoms and possibly more targeted treatment.
    • In a study on people’s reactions to breaching moral codes or norms, such as violence, it may be helpful to identify different reactions, like disgust, in order to understand patterns of human behaviour and mental states.

CORRELATIONAL STUDY

• Type of non-experimental study in which researchers observe and measure the relationship between two or more variables without any active control or manipulation
• Correlation refers to the strength of the relationship between variable, or in other words, how likely they are to occur together
• Can be positive, negative or zero correlation
• This research can be conducted using other formats such as fieldwork.
• Example:
  • There is a positive correlation between the number of ice creams sold and the number of arrests for physical arrests on the Gold Coast during schoolies week. It cannot be assumed, however, that ice cream sales or eating ice cream causes assault. The correlation is strong because a third variable such as the presence of hot weather may account for both ice cream sales and number of aggravated assaults.

FIELDWORK

• Any research involving observation and interaction with people and environments in real-world settings, conducted beyond the laboratory
• Used when researchers wish to investigate correlation rather than causation (correlational study merits apply here)
• Used when it is important to the research that data is collected in a real-world, authentic setting. Eg. the effects of hospital ward appearance, on patient recovery times needs to occur in the field
• Example:
  • Observational study example: In a study on the development of social behaviour, a researcher might observe children at play in a preschool outside area at lunchtime. They would do so so that the children are not aware that they are being observed to help ensure they behaviour naturally. They may observe that younger children play next to each other, rather than with each other and older children play with each other.

LITERATURE REVIEW

• Process of collating and analysing secondary data related to other people’s scientific findings and/or viewpoints to answer a question or provide background information
• Helps researchers gain current understanding of concepts
• Often used before conducting a new study and/or collecting primary data, or when someone begins researching new topic

MODELLING

• Construction and/or manipulation of either a physical model such as a small-or-large scale representation of an object or a conceptual model that represents a system.
• Models can be physical (a plastic brain) or conceptual (GAS model to explain stress)
• Examples:
  • Physical model: A plastic brain. This can be used to help explain brain processes or regions to patients. It would be impractical to use the real thing
  • Conceptual model: Multi-store model of memory Used to simplify, explain and demonstrate the complex phenomena of memory. It removes the complexity of biological processes, explains in lay-person terms

PRODUCT, PROCESS OR SYSTEM DEVELOPMENT

• Design and evaluation of an artefact, process, or system to meet a human need. This may involve technological applications, in addition to scientific knowledge and procedures.
• Used when psychologists, developers, or researchers have identified a human need that can be served by technology/scientific knowledge/procedure
• Example:
  • Meditation apps were created to meet the human need of wanting a convenient way to practice mindfulness. Quality meditation apps may be informed by scientific research and were created based on product development.

SIMULATION

• Using a model to study the behaviour of a real or theoretical system
• Useful when it would be too complex, impractical or dangerous to test the relationships between variables in reality
  • Eg. Computer program could show what happens in neurons at a micro level when learning. This gives visual access to inaccessible phenomena

DESIGN AND CONDUCT INVESTIGATIONS; SELECT AND USE METHODS APPROPRIATE TO THE INVESTIGATION, INCLUDING CONSIDERATION OF SAMPLING TECHNIQUE (RANDOM AND STRATIFIED) AND SIZE TO ACHIEVE REPRESENTATIVENESS, AND CONSIDERATION OF EQUIPMENT AND PROCEDURES, TAKING INTO ACCOUNT POTENTIAL SOURCES OF ERROR AND UNCERTAINTY; DETERMINE THE TYPE AND AMOUNT OF QUALITATIVE AND/OR QUANTITATIVE DATA TO BE GENERATED OR COLLATED

• Previous slides

SAMPLING TECHNIQUES

• There are different ways we can select a sample (participants in study) from a population (research group of interest)
• Each has advantages and limitations
• Random and stratified sampling are listed in the study design (convenience is not explicitly mentioned)

RANDOM SAMPLING

• Every person in population has an equal chance of being selected Eg. Using random name generator, pulling names out of a hat Cannot say: “Randomly ask 10 people”. This is convenience sample.

STRATIFIED SAMPLING

• Involves selecting a group of people from the population in a way that ensures that its strata (subgroups) are proportionally represented
• Dividing the research population into different strata based on characteristics relevant to the study
• Selecting participants from each stratum in proportion to how they appear in the population

SAMPLING: SIZE

• Achieving a representative sample is not only dependent on the sampling technique but also the size
• The bigger the sample, the more likely it is to be representative of the population
• Eg. If we used two Year 12 students to represent the Year 12 cohort vs 100 Year 12 students.

SOURCES OF ERROR AND UNCERTAINTY

• To ensure that a clear conclusion can be drawn, researchers must exclude other possible explanations for their results. This involves accounting for extraneous and confounding variables.
  • Extraneous: any variable that is not the IV but may cause an unwanted effect on the DV
  • Confounding: variable that has directly and systematically affected the DV, apart from the IV
    • Confounding variables may have been an extraneous variable that has not been controlled for, or simply cannot be controlled for

TYPES OF EXTRANEOUS AND CONFOUNDING VARIABLES

• In order to improve your research, it helps to be aware of some common sources of error. You should be familiar with the following variables:
  • Participant-related variables/individual participant differences
  • Order effects
  • Placebo effects
  • Experimenter effects
  • Situational variables
  • Non-standardised instructions and procedures
  • Demand characteristics

MINIMISING EV/CV

• Also relates to KKDP (later): Evaluate investigation methods and possible sources of error or uncertainty, and suggest improvements to increase validity and to reduce uncertainty Ways to prevent EV and CVs.

• Sampling size and procedures - Participant related variables

• Experimental design choice - Order effects (if within is not used) Participant related variables (if within is used)

• Counterbalancing - Order effects

• Placebo - Placebo effects

• Single-blind procedure - Participant related variables, demand characteristics, placebo effect

• Double-blind procedure - Experimenter effects, participant expectations, demand characteristics

• Standardised testing and procedures - Situational variables, non-standardised testing conditions/procedures

• Controlled variables - Most extraneous variables that the ability to be controlled

MINIMISING EV/CV

• Counterbalancing
  • Only relevant for within-subjects design
• Placebo
  • Inactive substance or treatment If there is no difference between groups- researchers would not be able to conclude that eh effect of an intervention is stronger than a placebo effect in response to a placebo
• Single-blind procedure
  • Participants are unaware of the group they have been allocated to
• Double-blind procedure
  • Both participants and the experimenter do not know which groups participants are allocated to
  • Note: someone is aware of grouping however they are not working directly with participants

TYPES OF DATA

Section Three: Comply with Safety and Ethical Guidelines

KKDP:

• Demonstrate ethical conduct and apply ethical guidelines when undertaking and reporting investigations.
• Demonstrate safe laboratory practices when planning and conducting investigations by using risk assessments that are informed by safety data sheets (SDS), and accounting for risks.
• Apply relevant occupational health and safety guidelines while undertaking practical investigations.

ETHICAL CONCEPTS

• Refer to broad, moral guiding principles that people should consider when conducting research, practising psychology, or when analysing psychological issue or debate.
• They help guide people in a more morally-conscious way

• Beneficence: Maximising benefits and minimising harms/risks Does research design minimise harm? Is harm outweighed by the merits? Is participant welfare upheld?
• Integrity: Searching for knowledge and understanding, honest reporting of all sources of information and results. Objective and open reporting of results, processes of peer review, thoroughness of any review Note: This is a summarised version. You should read pages 70-71 to provide more context
• Justice: Moral obligation to ensure that there is fairness, no unfair burden on one group, fair distribution of benefits. Not discriminatory, equity in access to services and findings
• Non-maleficence: Avoiding causing harm. Harm should not be disproportionate to the benefits.
• Respect: Consideration of the extent to which living things have an intrinsic value. Respect for personal beliefs and cultures Note: This is a summarised version. You should read pages 70-71 to provide more context

ETHICAL GUIDELINES

• Ethical guidelines are the procedures and principles used to ensure that participants’ rights are safe and respected.
  • Informed consent
  • Voluntary participation
  • Withdrawal rights
  • Deception
  • Debriefing
  • Confidentiality

INFORMED CONSENT

• Informed consent procedures: 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 (VCAA).
• A written consent form is required
• Participants under 18 must have parent/guardian consent in addition to their own (where possible)
• Participants who cannot give their own consent must have someone give consent on their behalf

VOLUNTARY PARTICIPATION

• Voluntary participation: a principle that ensures there is no coercion or pressure put on the participant to partake in an experiment, and they freely choose to be involved (VCAA).
• Cannot exist without informed consent
• Participants cannot be coerced
• Rewards can be offered to encourage participation, but no negative consequences can be incurred if a person chooses not to participate

WITHDRAWAL RIGHTS

• Withdrawal rights: the right of participants to be able to discontinue their involvement in an experiment at any time during, or after the conclusion of, an experiment without penalty (VCAA).
• Cannot be coerced to remain in the study if they want to leave, even if the experiment has concluded
• Includes removing their results from the study
• Any compensation offered for participation must still be offered to those who withdraw

DECEPTION

• Deception: the act of intentionally misleading participants about the true nature of a study or procedure.
• Deception is permitted only when:
  • Participant knowledge of the truth about the experiment would affect the validity of the experiment
  • Outlined that it may be used in consent form
  • Deception is corrected during debriefing

DEBRIEFING

• Debriefing: a procedure that ensures that, at the end of the experiment, the participant leaves understanding the experimental aim, results and conclusions (VCAA).
• Debriefing must be conducted at the end of every study – (not just if deception was used).
• Any deception used must be corrected.
• Any questions participants have should be answered here.
• Support should be offered to participants to address any harm caused by the study.

CONFIDENTIALITY

• Confidentiality: 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 (VCAA).
• This means:
  • Having safe and secure data storing purposes.
  • Ensuring participants cannot be identified when published.

DEMONSTRATE SAFE LABORATORY PRACTICES WHEN PLANNING AND CONDUCTING INVESTIGATIONS BY USING RISK ASSESSMENTS THAT ARE INFORMED BY SAFETY DATA SHEETS (SDS), AND ACCOUNTING FOR RISKS

• Demonstrated during practicals

APPLY RELEVANT OCCUPATIONAL HEALTH AND SAFETY GUIDELINES WHILE UNDERTAKING PRACTICAL INVESTIGATIONS

• This is demonstrated when we conduct practicals in class

Section Four: Generate, Collate and Record Data

KKDPs:

• Systematically generate and record primary data, and collate secondary data, appropriate to the investigation
• Record and summarise both qualitative and quantitative data, including use of a logbook as an authentication of generated or collated data
• Organise and present data in useful and meaningful ways, including tables, bar charts and line graphs
• Process quantitative data using appropriate mathematical relationships and units, including calculations of percentages, percentage change and measures of central tendencies (mean, median, mode), and demonstrate an understanding of standard deviation as a measure of variability
• Identify and analyse experimental data qualitatively, applying where appropriate concepts of: accuracy, precision, repeatability, reproducibility and validity; errors; and certainty in data, including effects of sample size on the quality of data obtained
• Identify outliers and contradictory or incomplete data
• Repeat experiments to ensure findings are robust
• Evaluate investigation methods and possible sources of error or uncertainty, and suggest improvements to increase validity and to reduce uncertainty

SYSTEMATICALLY GENERATE AND RECORD PRIMARY DATA, AND COLLATE SECONDARY DATA, APPROPRIATE TO THE INVESTIGATION

• U4 AOS 3 Types of data covered previously Collected by the researcher. Covered previously Collected, processed and published by someone else (not current researcher) Covered previously.

ORGANISE AND PRESENT DATA IN USEFUL AND MEANINGFUL WAYS, INCLUDING TABLES, BAR CHARTS AND LINE GRAPHS

CALCULATING PERCENTAGE AND PERCENTAGE CHANGE

• Percentage formula: $\frac{Given number}{Total number} \times 100$
• Percentage change formula: $\frac{Old number - new number}{Old number} \times 100$

MEASURES OF CENTRAL TENDENCY

• Mean: Numerical average of a data set (total divided by number of data points)
  • Useful: distributed around a centre
  • Less useful: widely distributed, likely to be influenced by extreme values and outliers
• Median: Middle value in a data ordered from lowest to highest
  • Useful: when it is not distributed around centre or there are outliers
• Mode: Most frequently occurring value
  • Used the least Useful: knowing most common and frequently occurring value

STANDARD DEVIATION

• Describes the spread of data around the mean
• The higher this value= the greater the data values differ from the mean (more spread out from mean)
• It allow comparison between groups, as although the mean may be the same, one group may have more spread (extreme values)

IDENTIFY AND ANALYSE EXPERIMENTAL DATA QUALITATIVELY, APPLYING WHERE APPROPRIATE CONCEPTS OF: ACCURACY, PRECISION, REPEATABILITY, REPRODUCIBILITY AND VALIDITY; ERRORS; AND CERTAINTY IN DATA, INCLUDING EFFECTS OF SAMPLE SIZE ON THE QUALITY OF DATA OBTAINED

• Reviewing data and making decisions about all of the following concepts

ACCURACY AND PRECISION

• Accuracy: how close a measurement is to the true value (value if it could be measured perfectly) of the quantity being measured
• Precision: how closely a set of measurement values agree with each other

EXAMPLES OF ACCURACY & PRECISION

• Accuracy
  • Imagine you are trying to measure the true length of a table, which is exactly 2.00 meters.
  • Accurate Measurement: If you measure the length of the table with a ruler and get 1.99 meters, that measurement is accurate because it's very close to the true value (2.00 meters).
  • Inaccurate Measurement: If you measure the same table and get 1.80 meters, this is inaccurate, as it is quite far from the true value.
  • In this case, accuracy reflects how close your measurement is to the actual length of the table.
• Precision
  • Now, let's take several measurements of the same table.
  • Precise Measurements: If you take four measurements and they are 1.99 meters, 2.00 meters, 1.98 meters, and 1.99 meters, your measurements are very precise because they are all very close to each other.
  • Imprecise Measurements: On the other hand, if your measurements are 1.80 meters, 2.10 meters, 1.70 meters, and 2.20 meters, they are not precise because they differ greatly from one another.
  • Here, precision reflects how consistent or repeatable your measurements are, regardless of whether they are accurate.

REPEATABILITY VS REPRODUCIBILITY

VALIDITY

• The extent to which psychological tools and investigations truly support their findings or conclusions
• There are two types of validity: internal and external validity

• WEIRD: western, educated, industralised, rish and democratic

ERRORS

• There are other types of errors that can occur in psychological research when taking measurements. These can be:
  • Systematic Errors: differ by consistent amount. – E.g. the scale may consistently add 100 grams. Affects accuracy.
  • Random Errors: occur due to chance (not consistent manner). – Eg. scale measures the same object and shows different, unpredictable readings each time. Affects precision.

CERTAINTY IN DATA

• When there is uncertainty in data, there is a lack of exact knowledge relating to something being measured due to potential sources of variation in knowledge. We want to prevent this.
• Eg. A study may aim to test ‘positive mood’ and use a range of measures that attempt to assess it. However, given the blurred boundaries of what ‘positive mood’ truly is, researchers would have uncertainty in its assessment.

EFFECT OF SAMPLE SIZE

• We have previously looked at ways in which you can obtain a sample. To achieve a representative sample researchers should consider:
  • The size (bigger a sample, the more likely it is to be representative)
  • The sampling techniques used. How a sample of selected can determine whether it is biased or representative

IDENTIFY OUTLIERS AND CONTRADICTORY OR INCOMPLETE DATA

• Outlier: a value that differs significantly from other values in a data set
• Outliers can be observed in visual representations of data, in which the outlier may lie far away from the rest of the data points in the data set
• More likely to happen in large sample. Can also occur due to measurement and recording errors, and could at times negatively impact validity

REPEAT EXPERIMENTS TO ENSURE FINDINGS ARE ROBUST

EVALUATE INVESTIGATION METHODS AND POSSIBLE SOURCES OF ERROR OR UNCERTAINTY, AND SUGGEST IMPROVEMENTS TO INCREASE VALIDITY AND TO REDUCE UNCERTAINTY

• These general concepts (e.g. validity) have been explained in previous slides
• Additionally, in the notes about extraneous and confounding variables we covered some of the source of error and suggested improvements.
• The skills involved with this KKDP are often used in a discussion/conclusion of a report
• A conclusion is a statement that summarises the findings of a study. The diagram on the next slide outlines what should be considered when making conclusions (evaluation component)

CONSIDERATIONS for Evaluating Psychological Research

• Accuracy and precision of measurement
• Avoiding or awareness of random and systematic errors
• Awareness of any uncertainty in data
• Repeatability and reproducibility
• Validity
  *Internal validity
  *External validity