VCE Psychology Units 3 & 4: KSS

Psychology

The scientific study of human thoughts, feelings, and behaviour.

Empirical Evidence

Data that is collected directly through observation or experimentation using the scientific method.

Scientific Method

A systematic approach for planning, investigating, conducting, and reporting research to generate models and theories.

Theory

An evidence-based explanation of a psychological process (e.g., Social learning theory).

Model

A representation of how a psychological process works (e.g., Multi-store model of memory).

Pseudoscience

Beliefs, theories, and practices that claim to be scientific but do not use the methods of science, typically formed on the basis of anecdote and personal opinion.

Independent Variable (IV)

The variable manipulated or changed by the experimenter ('the cause') to determine its effect on the dependent variable.

Dependent Variable (DV)

The variable observed and measured ('the effect') to determine the effect of the independent variable.

Investigation Aim

To investigate the impact of the [IV] on the [DV].

Hypothesis (between subject’s design)

It was hypothesised that [population/participants] who were [IV] would have [prediction] [DV] compared to those who were not [IV].

Hypothesis (Within subject’s design)

It was hypothesised that [population/participants] who were [IV] would have [prediction] [DV] compared to when they were not [IV].

Extraneous Variable (EV)

variable other than IV that can cause an unwanted change in the results [DV]

Controlled Variable

variables that are held constant to ensure that only the IV influences the DV

Confounding Variable (CV)

variable other than the IV that has a systematic effect on the result DV

Order Effects

occurs due to the sequence (order) in which conditions are completed in a within-subjects design, specifically practice effects or boredom/fatigue.

Placebo Effect

change in participant's response due to their belief they are receiving a therapeutic drug.

Demand Characteristics

Cues in an investigation that lead participants to guess the true aim or hypothesis and alter their behaviour to conform to expectations.

Experimenter Effect

Unintentional influence by the researcher on participant behaviour or data collection/interpretation that biases results toward the hypothesis.

Single-Blind Procedure

control procedure where participants are unaware of whether they have been allocated to the experimental or control group.

Double-Blind Procedure

control procedure where neither the participants nor the experimenters know who is allocated to the experimental or control condition.

Population

entire group of research interest from which sample has been drawn.

Sample

smaller group selected from population

Non-Random Sampling

Selecting participants who are easily accessible at the time of the study (first 10 students who enter library)
+ Quick and easy, as participants are readily available, no forward planning
- Unlikely to generate a representative sample, as not every member of the population has an equal chance of being selected

Random Sampling

Every member of the population has an equal chance of being selected for the sample (names out of hat)
+ Unbiased as representative of population, compared to non-random
- Requires forward planning, time-consuming

Stratified Sampling

Dividing the population into groups based on specific characteristics deemed important to study & randomly selecting participants from each strata in same proportions as they occur in population.
+ Representative of population, each strata in population represented in same proportion
- time-consuming than non-random cause the need to divide members of population into strata.

Control condition

not exposed to IV, act as baseline of comparison

Experimental condition

exposed to IV

Random Allocation

Participants in the sample are given an equal chance of partaking in either the control or the experimental group (names in hat)

Non-random Allocation

Not given equal chance (boys v girls)

Between-Subjects Design

Each participant is randomly (or non-randomly) allocated to control or experimental group.
+ less time-consuming, no order effects
- participants variables are not controlled

Within-Subjects Design

Each participant is involved in both the experimental and control conditions
+ Participant variables are controlled
- Order effects can occur without counterbalancing

Mixed Design

Combination of elements of between and within→ participants are allocated to either control or exp. group, participants are tested at multiple time periods
+ allows experimenters to compare results across both exp. conditions & across individuals
- higher rate of withdrawal, increases order effects

Controlled experiment

Investigates a cause-and-effect relationship by manipulating the IV and measuring the DV, whilst controlling all other variables.
+ Reduces EVs/CVs which may increase internal validity, ensure repeatability, and reproducibility
- Usually conducted in lab or highly controlled settings reducing external validity

Correlational Study

Planned observation and recording of events and behaviours that have not been actively manipulated, used to measure the relationship between variables.
+ allows the direction and strength of relationship between variables to be determined
- correlation does not equal causation

Fieldwork

Observing and interacting with a selected environment in a naturalistic or real-world setting.
+natural settings → more realistic behaviours
-Observed behaviours may be subjective and prone to bias.

Case Study

An in-depth, detailed investigation of an individual or small group of people on a particular phenomenon.
+ Useful when it would be unethical or impossible to conduct controlled exp. ,gathers highly detailed info
- small sample size → findings can't be generalised to population, time-consuming & difficult to replicate

Product, Process or System Development

The design or evaluation of an artefact to meet a human need, often involving technological applications such as mindfulness apps.
+ creates new products/systems that can improve efficiency in collecting data
- time-consuming and expensive

Classification and Identification

Organising human behaviour and mental processes into common groupings with similar features (classification) and assigning labels to them (identification).
+ can allow predictions to be made about certain behaviours
- classifications may be based on subjective criteria

Literature Review

The collation and analysis of secondary data related to other people’s scientific findings to answer a question or provide background information.
+ Discover gaps in previous research, enables replication to improve robustness
- time-consuming to find suitable secondary resources, previous studies may have low validity

Modelling

The construction and/or manipulation of a physical or conceptual model.
+assist in understandings
-may oversimplify

Simulation

The process of using a model to study the behaviour of a real or theoretical system by creating artificial situations that mimic reality.
+ Collect data without risking the health and safety of participants
- usually involves artificial environments which may change participants’ behaviours and reduce external validity, time-consuming and expensive to develop

Self-Reports

Data collection methods involving statements and answers made by participants concerning their thoughts, feelings, and behaviours.
+ elaborate of responses
- Qualitative data can be difficult to summarise and people may give socially desirable and inaccurate responses

Likert Scale

A rating scale (quantitative+subjective)

Yarning Circles

Use to explore topics with Indigenous participants involving reciprocal dialogue, storytelling, and informal conversation.

Qualitative Data

Data that describes changes in the quality of behaviour, often expressed in words (questionnaires)
+ detailed insight,elaborate of responses
- difficult to statistically analyse, open to bias

Quantitative Data

Numerical data collected via systematic and controlled procedures.
+ easy to statistically analyse
- not able to elaborate on response →less detail

Primary Data

Collected directly by the researcher
+tailored information
- time-consuming

Secondary Data

collected by someone other than the original person
+ readily available, less time
- validity of og study may not be known, time-consuming

Objective Data

Collected through direct observation or measurement should obtain identical results regardless of who measures it.
+doesnt’t rely on experimenter interpretation, valid
- not allow researchers to understand perspectives of participants

Subjective Data

Data based on personal experience or perceptions.
+ Insight into unobservable phenomena
- response bias, difficult to statistically analyse, low validity/reproducibility/replicability

ETHICAL CONCEPTS

BIRJN (bert is racing jimmy neutron)

Beneficence

The commitment to maximising benefits and minimising physical or psychological harm.

Non-Maleficence

avoid causing physical or psychological harm

Justice

no unfair burden on a particular group, and fair distribution and access to the benefits.

Integrity

commitment to searching for knowledge and understanding and the honest reporting of all sources of information and results, whether favourable or unfavourable

Respect

Recognising the different beliefs of participants and empowering participants to make their own decisions.

ETHICAL GUIDELINES

CVIWDD (Crazy Vikings Invent Wacky Disco Dances)

Confidentiality

All personal details/information gathered from participants should be kept private.

Voluntary participation

Participants must not be forced to be involved in the study.
cannot be bribed with incentive or experience negative consequence

Informed Consent

Participants must be told about nature, rights, and risks of the study and sign a permission form (if under age of 18 or experience cognitive impairment, a parent/guardian signs as well)

Withdrawal rights

Participants are allowed to stop participating in the experiment at any time. They can also ask to have their results removed after the conclusion of the study.

Use of Deception

Deception refers to deliberately withholding the true aim of the study from participants, as knowledge of the true aim may result in response bias from participants and impact the accuracy/validity of results. Deception can occur if non maleficence is upheld and they are debriefed at the conclusion of the study.

Debriefing

Participants must always be provided with information about the true nature, results and conclusions at the end of the study and offered psychological support.

Percentage change

Percentage change is a calculation for any quantity that has been measured over time to determine how much it has changed.

Mean

(average) measure of central tendency desribing numerical average
*best used when set of scores are evenly distributed

Median

mid-point
*best used when there are extreme scores in set of data

Mode

most commonly occurring score
*best used to show what happens most often for that group

Range

a measure of variability that measures the difference between the highest and lowest scores in a distribution.

Standard Deviation

a measure of variability that measures the spread of scores (variability) around the mean.

LOW standard deviation

low variability, and high precision

HIGH standard deviation

high variability, and low precision

Line graph

used to indicate the relationship between two variables, where the data is numerical and continuous

Bar graph

relationship between at least two variables (often one categorial) by using discrete/separate bars

True Value

value that would be found if the quantity could be measured perfectly, without any errors.

Accuracy

how close a measurement is to the true value of the quantity being measured.
→ measured value is accurate if determined close to true value, and the level of accuracy can be judged by repeating the experiment.

Precision

how close/consistent a set of measurement values are to each other that were measured under the same conditions
→Determined by repeatability and/or the reproducibility of measurements

Repeatability

same results = identical conditions

Reproducibility

same results = different conditions

Internal Validity

extent to which the results of a controlled experiment are caused by the manipulation of the independent variable (IV), rather than by extraneous or confounding variables.
*high validity
-Measurement tools and procedures: Do they test what they claim to?
-Experimental design: Did it minimise extraneous variables?
-Sampling and allocation procedures: Was the sample and its allocation representative and unbiased? Whether the independent variable truly affected the dependent variable: Were there confounding or extraneous variables?

External Validity

The extent to which the results of a study can be applied to similar individuals in different settings (i.e, generalised)
*High validity
-Using appropriate sampling procedures (random or stratified sampling) and broad inclusion criteria to create a more representative sample

-Using a larger sample size

-Conducting studies in more naturalistic settings (i.e, so it reflects the ‘real world’)

Uncertainty and robustness

Uncertainty refers to a lack of exact knowledge of the value being measured due to potential sources of error.
Robust findings are ones that remain valid across different conditions when the study is replicated

Personal Errors

mistakes, miscalculations and observer errors made when conducting research.
→To remove personal errors, the investigation should be repeated correctly

Measurement error

difference between the measured value and the true value of what is being measured

Systematic Errors

errors in data that differ from the true value by a consistent amount, or the same proportion, each time a measurement is made, hence reducing the accuracy (and validity)

Random Errors

errors in data that are unsystematic and occur due to chance, creating unpredictable/inconsistent variations, hence reducing precision
*Extraneous variables cause random errors
→may be reduced by repeating and conducting more measurements, increasing the sample size, and calculating a new mean.

Conclusion

1.The results demonstrated that______________ (outline any trends). Hence, the hypothesis was accepted/rejected and it can/cannot be concluded that _____________ (the IV caused a change in the DV).

2.Although the results indicated _____________ (outline any trends) and supported the hypothesis, it can’t be said that the IV (___________) caused the change in the DV (___________). This is due to the presence of CVs/EVs (identify and explain them). Therefore, no conclusion can be made.

Generalisation

A decision about how widely any findings from an experiment can be applied to the wider population of interest

→For example: Results cannot be generalised to the population of interest (_______), this is due to the use of non-random sampling and small sample size, therefore resulting in an unrepresentative sample, hence results have low external validity and are unlikely to reflect the population of interest

Implications

An implication is a statement about the real-life applications of the findings.
→WEIRD- Western, Education, Industrialised, Rich, Democratic