Science Inquiry Skills

SACE Stage 2 Psychology

Biopsychosocial model

An approach that explains psychological health, illness, treatments, and behavior as the result of interactions among biological, psychological, and social factors; no single perspective suffices.

Biological factors

Biology-based influences on behaviour and mental health, including genetics, neurochemistry, brain/body injury, hormones, disease, age, sex, medications, sleep, and immune response.

Genetic factors

Inherited biological influences that affect behaviour and risk for disorders.

Neurochemistry

Brain chemical processes (neurotransmitters, receptors) that influence mood, cognition, and behavior.

Head/body trauma

Injuries to the brain or body that can alter psychological functioning.

Hormones

Endocrine messengers that regulate physiology and behaviour.

Disease

Medical conditions that can impact mental health and functioning.

Age

Developmental stage and aging processes that influence risk and presentation.

Sex

Biological sex affecting risk and manifestations of conditions.

Medication/drugs

Pharmacological substances that can alter mental state and behaviour.

Sleep

Sleep quality and duration affecting cognitive and emotional functioning.

Immune response

Activity of the immune system that can influence mood and cognition.

Psychological factors

Mental processes that influence behaviour, including cognition, learning, emotions, and memory.

Cognition and thinking

Mental processes of acquiring, processing, and using information.

Learning

Acquiring new knowledge or behaviours through experience or instruction.

Emotions

Affective states that influence motivation and actions.

Memory

Ability to encode, store, and retrieve information.

Attitudes

Evaluations or predispositions toward people, objects, or ideas.

Perceptions

How stimuli and events are interpreted and understood.

Beliefs

Convictions about how the world works that guide behaviour.

Cognitive distortions

Biased or irrational patterns of thinking that can contribute to mental illness.

Social factors

External social and cultural influences on behaviour and health.

Cultural values

Shared beliefs about what is important within a culture.

Religion

Systems of faith, beliefs, and practices influencing behaviour.

Family background and social expectations

Family context and societal norms that shape how individuals behave.

Socio-cultural pressures

Cultural expectations that can affect body image, mental health, and behaviour.

Socio-economic status

An individual’s social and economic position, influencing access to resources.

Gender expectations

Societal norms about how different genders should behave.

Social media

Online platforms shaping self-image, norms, and mental health.

Social support

Networks of care and help from others that buffer stress.

Interaction among bio/psycho/social

The way biological, psychological, and social factors influence and reinforce each other.

Scientific method

Systematic, evidence-based approach to planning, conducting, and reporting research.

Empirical evidence

Data obtained through observation or experimentation used to support conclusions.

Independent variable (IV)

The variable deliberately manipulated by the researcher to test its effect.

Dependent variable (DV)

The variable that is measured to assess the effect of the IV.

Operationalised

Defined in observable and measurable terms so it can be tested.

Constant variables

Factors kept the same across all conditions to ensure fair testing.

Extraneous variables

Uncontrolled factors that could influence the DV and bias results.

Participant variables

Individual differences among participants (e.g., gender, intelligence) that may affect outcomes.

Placebo effect

Improvement due to perceived treatment rather than the treatment itself.

Experimenter effects

Researchers’ expectations or behaviour influencing results.

Demand characteristics

Participants’ behaviour changes based on their guesses of the study’s aims.

Situational variables

Environmental factors that influence participants’ responses.

Experimental design

Plans for systematically testing hypotheses by manipulating the IV.

Independent groups design

Participants are randomly allocated to separate groups for each level of the IV.

Control group

Group that receives no treatment or a baseline condition for comparison.

Experimental group

Group that receives the treatment or level of the IV.

Matched participants design

Participants with similar characteristics are paired and split into different groups.

Repeated measures design

The same participants are tested under multiple levels of the IV.

Random assignment

Randomly placing participants into groups to minimize bias.

Single-blind procedure

Participants do not know which group they are in to reduce bias.

Double-blind procedure

Neither participants nor researchers know group assignments to reduce bias.

Experimental Design Characteristics

•IV is manipulated.

•Presence of a control group.

•Random assignment/allocation of participants.

•Hypothesis testing.

•Pre and post testing.

•Replication of the experiment.

•Cause and effect relationship established between IV and DV.

Advantages of Experimental Design

•Maximises control over extraneous variables.

•Can determine cause and effect relationship between IV and DV.

•Easier to replicate.

Disadvantages of Experimental Design

•May be unethical to manipulate certain variables or randomly allocate participants.

•The setting may make it inapplicable to real world (lacks external validity).

•Sample groups may not be representative.

Observational design

Research where the IV is not manipulated, using pre-existing characteristics.

Natural setting

Research conducted outside a laboratory in real-world environments.

Causality

A cause-and-effect relationship between the IV and DV.

Characteristics of Observational Design

•Independent variable is NOT manipulated, it is pre-existing.

•Allows for research in a natural setting.

•Can’t determine causality.

Advantages of Observational Design

•Allows the study of variables that can’t be manipulated.

•Behaviour can be observed in a natural setting. (External validity).

•May allow for bigger sample sizes.

Disadvantages of Observational Design

•Can’t infer cause and effect relationship between variables.

•Hard to replicate the study.

•Can contain observer bias.

Qualitative design

Research designs focused on rich, descriptive data (non-numeric).

Focus groups

Guided group discussions (6–10 participants) to collect qualitative data.

Delphi technique

A method using successive rounds of questionnaires to gather expert opinion.

Interviews

Data collection method using structured, semi-structured, or unstructured questions.

Advantages for Qualitative Design

•More convenient.

•Give significant rich, verbal data.

•Useful to gain start-up knowledge on a topic for further research.

•Information is reliable if using the Delphi Technique.

•Allows opinions to be expressed on complex issues.

Disadvantages for Qualitative Design

•Can’t generalise data.

•Presence of the facilitator can affect what is said.

•Personal bias/extraneous variables can affect data.

Objective quantitative data

Numerical data collected under standardised conditions to minimise bias (e.g., standardised tests, physiological measures, behaviour counts).

Standardised tests

Tests with uniform procedures and scoring for comparable results.

Physiological measures

Biometric data (e.g., heart rate, galvanic skin response) used to quantify responses.

Subjective Quantitative Data

Numerical data that is influenced by personal opinions, perceptions, or interpretations.

Behaviour counts

Frequency counts of specified behaviours within a set period.

Rating scales

Fixed-response scales (e.g., Likert) used to quantify attitudes or perceptions.

Likert scale

A common rating scale (e.g., 1–5) indicating level of agreement or disagreement.

Self-reports

Participants’ own accounts of thoughts, feelings, and behaviours.

Advantages of Quantitative Data

•Usually controlled so more valid.

•Can be directly verified, improving validity.

•Can get substantial data in a short amount of time.

Disadvantages of Quantitative Data

•Doesn’t explain results.

•Subjective quantitative is still bias.

Observations

Systematic watching and recording of behaviour in natural or controlled settings.

Qualitative data

Descriptive data such as interview transcripts, focus group notes, or images.

Advantages of Qualitative Data

•Rich and deep data about a research topic.

•Can get expert opinion using the Delphi technique.

•Useful to kick start a research topic.

Disadvantages of Qualitative Data

•Subjective.

•Unable to be verified.

Data types

Categories of data collected in research (objective quantitative, subjective quantitative, qualitative).

Central tendency

A measure that describes a typical value in a data set (mean, median, mode).

Mean

The arithmetic average of a data set.

Median

The middle value when data are ordered; robust to outliers.

Mode

The most frequently occurring value in a data set.

Line graphs

Graphs that show changes over time or relationships between variables.

Scatterplots

Graphs showing the relationship between two variables by plotting data points.

Column graphs

Bar graphs used for comparing categories; bars do not touch.

Histograms

Bar graphs for continuous data where bars touch to show a distribution.

Normal distribution

Symmetrical bell-shaped distribution with most scores near the center.

Skewed distribution

Distribution with a longer tail on one side; positive or negative skew.

Dispersion

Spread of scores around the central tendency (range, standard deviation).

Range

Difference between the highest and lowest scores.

Standard deviation

Average distance of scores from the mean; a measure of dispersion.

Positive correlation

As one variable increases, the other tends to increase.

Negative correlation

As one variable increases, the other tends to decrease.

No correlation

No clear relationship between the two variables.

Content analysis

A method for analysing qualitative data by coding themes and counting frequencies.

Coding

Labeling data segments to identify patterns or themes.