Key Science Skills

research question structure

"What is the effect of [IV] on the [DV] in [Population]?"

Aim structure

"To investigate the effect of [IV] on [DV] in [Population]."

Hypothesis structure and example

"It is hypothesised that [Population] who [IV - Group 1] will [Direction] in [DV] compared to [Population] who [IV - Group 2/Control]."

IPAD

• Independent variable and dependent variables

• Population

• and

• Direction

It was hypothesised 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.

Independent variable

The variable that is manipulated, changed, or controlled by the researcher.

• It is assumed to have a direct effect on the dependent variable.

Dependent Variable

• The variable that the researcher measures.

• It is observed to see how it changes in response to the IV.

Placebo

an inactive substance or treatment

placebo effect

when participants respond to an inactive substance/treatment as a result of their expectations + beliefs

controlled variables

variables other than the IV that a researcher holds constant in an investigation, to ensure that changes in the DV are solely due to changes in the IV

Controlled Experiment

A type of investigation used to test a causal relationship between an IV and DV in a controlled environment

Strengths:

• Allows researchers to conclude that the IV directly caused the change in the DV

Limitations:

• conducted in highly controlled setting → doesn't reflect "real life."

• may cause participants to act unnaturally

Between-Subjects Design

Different participants are randomly allocated to either the control or experimental group

• Strength: No order effects

• Limitations:

  • Requires more participants

  • participant differences can affect results

Within-Subjects Design

Each participant is in both the control and experimental groups.

• Strength: Eliminates participant-related variables (everyone acts as their own control); requires fewer people.

• Limitation: Prone to order effects (boredom or practice).

Mixed Design

Combines elements of both within and between-subjects designs (e.g., testing two different groups over time).

• Strength: Allows researchers to see both differences between groups and changes over time.

• Limitation: Can be complex and costly to implement.

Case Study

An in-depth investigation of an individual, group, or event.

• Strength: Provides rich, highly detailed data; useful for rare phenomena

• Limitation: Results cannot be generalised to the wider population

Correlational Study

Observes the relationship between two variables without manipulating them

• Strength:

  • Can be used when experiments are unethical

  • identifies trends and relationships.

• Limitation:

  • Correlation does not equal causation

Fieldwork

Gathering primary data in a natural setting

• Strength: High ecological validity (behaviour is natural).

• Limitation:

  • Difficult to control extraneous variables

  • can be time-consuming.

Literature Review

Collating and analysing secondary data from existing research.

• Strength: Provides background for new research

• Limitation: can be time-consuming to find relevant sources.

Modelling & Simulation

Creating physical or conceptual representations to predict reality.

• Strength: Allows for testing of theoretical scenarios safely.

• Limitation: can never fully capture the complexity of the real-world

Product, Process, or System Development

Designing something to meet a human need

• e.g. a new mental health app

• Strength: Directly applies psychological theory to solve real-world problems.

• Limitations:

  • doesn’t necessarily test a broad psychological theory

  • doesn’t establish cause-and-effect.

Population vs. Sample

• Population: The entire group of interest from which the sample is drawn (e.g., all VCE students).

• Sample: A smaller subset or group that is actually chosen from the population to participate in the study.

What makes a sample Representative versus Biased?

• Representative: accurately reflects the relevant characteristics of the population

  • e.g. same ratio of ages/genders

    
    

• Biased: does not accurately represent the population

  • e.g. only testing girls when the population is all students

Convenience Sampling

Selecting participants based on their availability and readiness to participate

• Strengths:

  • Quick

  • easy

  • Inexpensive to perform.

• Limitations:

  • Likely to produce a biased sample

  • results cannot be easily generalised

Random Sampling

Every member of the population has an equal chance of being selected

• Strength: Reduces bias- more likely to be representative than convenience sampling.

• Limitation:

  • Can be time-consuming

  • May accidentally miss small sub-groups by chance.

Stratified Sampling

Definition: Dividing the population into distinct sub-groups (strata) based on shared characteristics, then selecting a sample from each stratum in the same proportion as they exist in the population.

• Strength: The most representative method; ensures all sub-groups are accurately represented.

• Limitation: Very time-consuming and expensive to identify and calculate sub-groups.

Differentiate between Sampling and Allocation.

• Sampling: The process of choosing people from the population to be in the study. (Population →Sample).

• Allocation: The process of assigning the chosen participants into groups within the experiment (e.g., Experimental group vs. Control group)

Random Allocation

when `each person has an equal chance of being in the experimental or control group

Experimental Group

Exposed to the Independent Variable (the "treatment" or "intervention").

Control Group

Not exposed to the IV; provides a baseline to compare the experimental results against

Identification Example: In a study on caffeine and memory:

• Experimental: Group drinking 2 cups of coffee (IV).

• Control: Group drinking plain water (No IV).

Extraneous Variable

Any variable other than the IV that may cause an unwanted effect on the DV.

They should be controlled or monitored.

Confounding Variable

A variable other than the IV that has directly and systematically affected the DV.

• can only be identified at the end of an experiment

Participant Variables

• Personal characteristics of the participants (e.g., age, intelligence, prior experience, mood).

• Example: In a memory test, one participant naturally has a better memory than another.

Situational Variables

• Factors in the environment that can affect the results

• (e.g., background noise, temperature, time of day).

Systematic Errors

Errors that differ from the true value by a consistent/predictable amount (e.g., a scale always being 100g light).

• Affects: Accuracy

Random Errors

Unsystematic errors that occur due to chance and vary unpredictably

• e.g. a participant being momentarily distracted

• Affects precision

How can extraneous variables be reduced, prevented, or accounted for?

• Random Allocation

• Single-Blind Procedure

• Double-Blind Procedure

Uncertainty

The lack of exact knowledge of the "true" value of the quantity being measured.

• All data has some uncertainty due to errors.

Outliers

Data points that differ significantly

• can distort the mean and increase uncertainty.

Repeatability

the closeness of results when the same researcher repeats the experiment under the same conditions.

Reproducibility

the closeness of results when different researchers conduct the experiment using different equipment or settings to test the same hypothesis.

Internal Validity

Does the study actually measure what it claims to measure? - was the change in the DV only caused by the IV

External Validity

Can the results be generalised to the wider population or other settings?

Robust

having valid and reliable data despite small changes in the conditions or errors.

Improvements to reduce uncertainty in data and avoid errors

• Increase Sample Size

• Standardise Procedures

• Refine Measurement Tools

Informed consent

Participants should be informed of the nature, purpose and risks involved with a study prior to giving their consent to participate

• Under 18- must be provided by legal guardian

• usually provided in writing.

Beneficence

The consideration of the benefits or gains from research in relation to the risks (MAXIMISE benefits and MINIMISE risks/harm)

Non-maleficence (also known as the no-harm principle)

the idea that any potential harm should be avoided and minimised as much as possible

Integrity

the commitment to search for knowledge, and then honestly report information and findings.

Justice

The distribution of fair access to the benefits of research to everyone,

ensuring that there is no burden on one group,

and ensuring that any opposing claims are considered.

Respect

the belief that everyone has value in regards to their welfare and beliefs,

and has a right to make their own decisions.

Withdrawal rights

Participants have the right to:

• withdraw from a study at any time

• elect to have their results withdrawn from a study.

Voluntary participation

• Participants must not be coerced or forced into participating, and must do so freely.

• There must not be adverse consequences for choosing not to participate.

Confidentiality

Participants names or identifying details must not be revealed.

*Deception

When the true nature or purpose of a study is not revealed to participants, it must be because doing so would undermine the results

• If deception is used, debriefing MUST occur to explain the true nature/purpose of the study, and why the deception was necessary.

Debriefing

• participants should be told of the results of a study after its completion

• participants may be offered counselling

• participants are allowed to have their results removed

Primary data

Data that is sourced first-hand.

Secondary data

Data that is sourced through someone else’s research.

Primary data strengths

• specific + detailed

• Inform future research

• current

Primary data weaknesses

• greater cost involved

• greater sample size restrictions

Secondary data strengths

• Cheap, easy to obtain

• Large amounts available

Secondary data weaknesses

• Might not be up to date

• May not be specific to research

Quantitative data

Data that are numerical and categorical.

Quantitative data strengths

Easy to compare values between participants or groups

Quantitative data weaknesses

Some psychological data can be difficult to quantify.

Qualitative data

Data that are descriptive.

Qualitative data strengths

Can be a rich source of data on people’s thoughts, feelings and observations on behaviour

Qualitative data weaknesses

can be difficult to compare.

Subjective data

Data that relies on assumptions or personal experience.

Subjective data strengths

offer nuanced, qualitative understanding

Subjective data weaknesses

high variability, cognitive biases

Objective data

Data that can be directly observed or measured.

Objective data strengths

enhances the consistency and validity of research

Objective data weaknesses

can lack depth and nuance

failure to capture the subjective human experience

Mean

The ‘average’ score

• Add all scores together and divide by the number of pieces of data.

Mean usefulness

Large amounts of data, even distribution of data around the centre

Mean disadvantages

when data values are widely distributed, the data set is likely to be influenced by outliers.

Median

The ‘middle’ score: Place all scores in order and find the centre

Median usefulness

When there are outliers

Median disadvantages

May not be representative in datasets with large gaps or uneven distributions.

Mode

The most ‘frequent’ score:

Tally the number of times each score appears.

Mode usefulness

Identifying patterns in categorical/numerical data

Mode disadvantages

may not exist, may be multiple

standard deviation

The variability of a set of values within a group, indicating how narrowly or broadly they deviate from the mean

• High standard deviation→ greater the range of values

• Low standard deviation → scores are clustered around the mean

how to label data tables

• IV goes in the first/left hand column

• DV goes in the second/right hand column

Why?

because the IV is what you’re changing or categorising, and the DV is what you’re measuring as a result

Bar charts

a good way to show comparisons between groups.

They require:

• Title

• Labelled axes

• Accuracy

IV= x axis

DV= y axis

Line graph

a good way to show trends over time.

They require:

• Title

• Labelled axes

• Accuracy

IV= x axis (time)

DV= y axis

Percentage change

Operationalised IV

Specific details of the IV

• include a comparison statement to describe all conditions being compared.

Counterbalancing

A procedure used to control for order effects in within-participants designs

Single blind procedure

When participants are unaware if they are taking the placebo or active medication

Double blind procedure

When both the participants and the experimenter are unaware of which participants are receiving the placebo treatment

Conclusion

Include:

• whether the hypothesis was supported or rejected

• results

• what the study suggests(relationship b/w variables)

• whether further evidence is required

Reliability

the extent to which a study produces consistent results. 

• replicating studies tests reliability

Anecdote

a story based on personal experience

Classification

the arrangement of phenomena, objects, or events into manageable sets

Identification

a process of recognition of phenomena as belonging to particular sets or possibly being part of a new or unique set

Classification and identification strengths & limitations

Strengths

• Provides common language to communicate about scientific phenomena.

• Helps to simplify, explain and describe complex phenomena.

Limitations

• Can over-simplify reality.

• Labels can be inaccurate + create bias.

True value

the value, or range of values, that would be found if the quantity could be measured

perfectly