Psychology

Scientific Ideas

Are not influenced by personal feelings/opinions, use verifiable evidence, are formed with the scientific method and use predictions, models and theories that can explain reality.

Non-Scientific Ideas

Might be influenced by personal feelings/opinions, unverifiable or vague, not based on observation/experience and not open to questioning or suggestion.

Non-scientific ideas may be formed by:

Anecdote, opinion, intuition or hearsay

Hypothesis

Has a testable prediction, uses the IV against DV, is generated based on scientific knowledge or experience and is used to understand and test ideas.

Used to write a hypothesis:

Prediction, Independent/Dependent Variables, Population, Comparison and Direction

The Scientific Method

A procedure involving a hypothesis formulation and testing through processes of experimentation, observation, measurement and recording. Centred around generating informed hypothesis and then testing it to generate evidence to support/refute it.

Controlled Experiment

Investigation where causal relationship between two variables is tested.

Experimental Group(s)

Group(s) that are exposed to the independent variable

Control Group

Group that is not exposed to the independent variable, acting as a baseline for comparison to see if IV affects DV)

Within-Subjects Design (WSD)

Experimental design where each participant is exposed to both the experimental and control group conditions.

WSD Benefits

The same participants are measured, reducing practice and fatigue effects.

WSD Drawbacks

Causes order effect.

Between-Subjects Design (BSD)

Experimental design where participants in Group 1 are separate to Group 2.

BSD Benefits

Participants have not experienced the experiment before, preventing order effect.

BDS Drawbacks

Difference in participants causing practice and fatigue effects.

Mixed Design (MD)

Experimental design that combines ideas of Within-Subject Design and Between-Subjects Design.

MD Benefits

Helps experimenter compare results across both experimental conditions and individuals/groups over time. Allows multiple experimental conditions to be compared to a baseline control group.

MD Drawbacks

Can be costly, time-consuming, and demanding for researchers and assistants to be across multiple methods.

Direct Observation

Method of fieldwork where researcher watches/listens to participants of study with no intervention/involvement (no manipulation of variables).

Qualitative Interviews

Method of fieldwork where researcher asks questions to gather information about particular topics. Interview is structured but questions are usually open-ended so participants can provide lengthier, more detailed answers. Provides rich data for researchers to analyse.

Questionnaires

Method of fieldwork where questions are given to participants to answer. May be open or close-ended questions. Answers are then analysed by researcher.

Focus Groups

Method of fieldwork where researcher conducts discussion with small group of people (8-12) on specific topic. Groups formed on basis of shared characteristics relevant to discussion. Responses/interactions are recorded, providing rich data for researchers to analyse.

Yarning Circles

Method of fieldwork that takes a traditional approach to group discussion in Aboriginal and Torres Strait Islander cultures. Involves talking, exchanging ideas, reflection and deep listening without judgement. Enables more culturally appropriate approach to research when working with Aboriginal and Torres Strait Islander Peoples. Different to focus groups as emphasis on lack of judgement, letting go of preconceived ideas and use of respect, inclusion and sharing make them unique. Researchers must become an active member of conversation, with a focus on contributing/exchanging ideas to produce new knowledge for all members.

Controlled Experiment Benefits

Allow researchers to infer relationships and draw conclusions between variables, provides researchers control over conditions/variables, can be repeated, is quicker than real-world settings and can prevent extraneous and confounding variables.

Controlled Experiment Drawbacks

May not be reflective of real life if conducted in laboratory, open to researcher error or experimenter bias, time consuming, expensive, and confounding/extraneous variables can still occur.

Case Study Benefits

Highly detailed, can provide new knowledge and allows in-depth research and rare phenomena to be researched and can be incorporated with other methodologies to gain data.

Case Study Drawbacks

Cannot be generalised to the wider population, subject to researcher bias/errors, difficult to draw cause/effect conclusions and time consuming.

Correlation Study Benefits

No manipulation of variables, provide ideas for future hypothesis and basis for theories, show relationship between variables and can be done in natural setting, so findings applicable to real world.

Correlation Study Drawbacks

Cannot draw conclusions between cause and effect and subject to influence of extraneous variables.

Classification and Identification Benefits

Provides common language to communicate about scientific phenomena, helps simplify and explain complex ideas, helps form more targeted solutions and helps with forming theories.

Classification and Identification Drawbacks

Can over-simplify reality and labels/language can create bias/be inaccurate.

Fieldwork Benefits

Can be conducted in natural settings, more applicable to real world, provides rich, detailed data, can use broad range of methodologies and can be conducted over long time, meaning information can be uncovered that wouldn’t be immediately obvious.

Fieldwork Drawbacks

Can be time consuming/expensive, Generally can’t inform conclusions about cause and effect, difficult to replicate and difficult to control environment.

Literature Review Benefits

Provides background information on specific phenomenon, allows researchers to understand current situation of research and may uncover patterns/gaps of knowledge.

Literature Review Drawbacks

Time consuming and difficult if little research done on topic.

Modelling Benefits

Can provide explanatory tools, allows researchers to understand and solve problems and can simplify and explain phenomena.

Modelling Drawbacks

May be over simplified or inaccurately represent reality.

Product, process or system development Benefits

Creates products, processes and systems that may meet human need.

Product, Process or System Development Drawbacks

Can be expensive and time consuming.

Simulation Benefits

Provides view into potential circumstances, allows view of hard-to-see phenomena and allows view of events that might be too dangerous, time consuming or impractical to see in reality.

Simulation Drawbacks

Time consuming and expensive and subject to programming/human error, so may not be accurate or a reflection of reality.

Population

Group of people the research is focused on (e.g. Y11 FGS).

Sample

Subset/portion of population selected to participate in the study (e.g. 60 Y11 FGS students).

Generalisation

Ability to generalise/apply results from research sample to population of interest. (Y11 FGS students)

Allocation

Process of assigning participants to experimental conditions or groups.

Random Allocation

Ensures every sample participant has an equal chance of being allocated to any group within the experiment.

Convenience Sampling

Sampling method involving selecting readily available members of the population, rather than using a random or systematic approach.

An example is asking the first 10 students who walk into class to complete a survey.

Random Sampling

Sampling method that uses a procedure to ensure every member of the population has the same chance of being selected.

An example of this method is putting all members of a population’s names into a computerised random generator to select a set of names for the sample.

Stratified Sampling

Sampling methods that refers to any sampling technique that involves selecting people from the population in a way that ensures that its strata are proportionally represented in the sample.

Extraneous Variables

Any variable that may cause an unwanted effect on the dependent variable. These variables should be controlled or monitored to make sure they don’t interfere with results.

Confounding Variables

Variable that has **directly** and **systematically** affected the dependent variable, that isn’t the independent variable. They provide an alternate explanation for results, meaning researchers cannot confirm whether IV or confounding variables caused changes to DV. Can only be identified at end of experiment, as they must be shown to have consistently and predictably directly affected results.

Participant-Related Variables

Confounding variable where characteristics of a study’s participants may affect the results. This includes characteristics like participants’ age, intelligence, and socioeconomic status.

Order Effect

Confounding variable where the order in which participants have completed experimental conditions has an effect on their behaviour.

Practice Effect

Confounding variable where participants who have done an experiment before could perform better in later conditions as they already know what to do.

Fatigue Effect

Confounding variable where participants may perform worse in later conditions due to being tired or bored from completing prior task.

Placebo Effect

Confounding variable where participants respond to inactive substances or treatments as a result of their expectations of beliefs.

Participants responses are not due to the chemical properties of a substance taken or their allocation to an experiment condition, but rather how they believe it should make them feel or act.

Experimenter Effect

Confounding variable where the expectations of the researcher affect results of an experiment. If experimenters have strong expectations, they may bias the way they collect/record data or how they interact with participants. They pay more attention to what confirms their expectations, leading to inaccurate results.

Situational Variables

Confounding variable where environmental factors may affect dependent variable. E.g. if a room is too hot, it may affect participants’ concentration on test. If test performance is DV, then the temperature would be an extraneous variable.

Non-standardised instructions and procedures

Confounding variable that occurs when directions and procedures differ across participants or experimental conditions, causing unwanted situational variables for either specific participants or entire experimental groups.

Demand Characteristics

Confounding variable where cues in experiment may signal to a participant the intention of the study, influencing their behaviour. E.g. participants may be in a study that first asks them to complete a questionnaire about their ability to concentrate. This may signal to participants that the study is testing concentration in some way, so for the following test procedures, they are highly alert.

Sampling Size and Procedures

Prevents participant-related variables. Having a large sample size can increase representation of the population, meaning application to the population is easier. Findings are often less biased.

Counterbalancing

Reduces order effects by getting participants to participate in both control and experimental groups.

Placebo

Placebos are used to compare results of participants using active treatment against participants using inactive treatment. If those who received active treatment showed significantly different responses compared to the group taking placebo, researchers may make firmer conclusions about effectiveness of treatment. Placebos don’t necessarily stop the placebo effect, but help researchers understand how significantly an active intervention may affect individuals. Prevents placebo effects.

Single-blind procedure

Prevents participant-related variables, demand characteristics and placebo effect. Procedure where participants are unaware of experimental group or condition they have been allocated to. Helps reduce participants’ expectations e.g. whether they are taking a placebo or active treatment.

Double-blind procedure

Prevents participant/experimenter-related variables and demand characteristics. Procedure where both participants and experimenter do not know which conditions or groups participants are allocated to. E.g. Research assistant records allocations, preventing extraneous variables of experimenter and participant expectations.

Standardised instructions and procedures.

Prevents situational variables, non-standardised testing conditions/procedures and demand characteristics.

Ensuring each participant in experiment receives the exact same instructions and follows the same procedures in each condition, allowing researchers to more conclusively infer that results are due to the independent variable. Minimises extraneous variables of non-standardised instructions and procedures.

Controlled variables

Helps control most extraneous variables that are able to be controlled.

Experimenters may hold certain variables constant. This is when they become ‘controlled variables’ so their impact is systematically minimised and accounted for. This can be used for a range of extraneous variables. E.g., in a study on the effect of running on mood, situational. variable of time of day may be held constant for all experimental conditions.

Primary Data

Data collected firsthand by a researcher.

Secondary Data

Data sourced from other’s prior research

Quantitative Data

Data expressed numerically.

Can be manipulated through statistical analysis allowing for comparisons and trends to be seen, and conclusions to be more readily drawn.

Quantitative Data Positives

Can be manipulated through statistical analysis, allowing for comparisons and trends to be seen, and conclusions to be more readily drawn.

Quantitative Data Negatives

Only analysing quantitative data means researchers may not have holistic/detailed understanding of phenomenon they are researching.

Qualitative Data

Data expressed non-numerically

Qualitative Data Positives

Qualitative data can give researchers more thorough/holistic view of context in which quantitative data was collected. Can provide background information or context to the quantitative data.

Qualitative Data Negatives

Qualitative data is difficult to statistically analyse, making it hard to manipulate the data to compare findings across groups/participants.

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It can be more difficult to obtain as it is often more time-consuming.

Objective Data

Factual data observed and measured independently of personal opinion

Objective Data Positives

Objective data does not rely on experimenter interpretation, so there is more chance that the data is valid.

Objective Data Negatives

Using only objective data doesn’t allow researchers to understand perspectives of the participants, which might provide more insight into the research area they are trying to understand.

Subjective Data

Data informed by personal opinion, perception or interpretation.

Subjective Data Positives

Subjective data allows experimenters to better understand perspective of participants. Provides researchers with insight into unobservable phenomena such as motivation, perception, interpretation etc.

Subjective Data Negatives

Subjective data is difficult to validate, can be unreliable. Participants may respond with what they think experimenters want to hear, rather than what they truly believe. It is more difficult to systematically analyse and compare compared to objective data.

Processing Quantitative Data

Researchers need to summarise, organise, and describe their data to form their results before making conclusions. Part of this includes processing their raw quantitative data so they can make meaningful comparisons and observations about their results.

Descriptive statistics

Statistics that summarise, organise, and describe data

Percentages

Very common and useful descriptive statistic. Organising results like this helps researchers to more easily notice patterns and trends, such as the percentage of participants that scored in the high bracket.

Percentage Change

After converting results into percentages, researchers may also wish to know how much total percentages increased or decreased (between experimental conditions or groups, or between different participants over time). This allows for comparison of results. If the result is a positive number, this is a percentage increase. If the result is a negative number, this is a percentage decrease.

Mean

Measure of central tendency that describes the average response of scores eg. 1 4 5 5 5 (20/5=4)

Median

Measure of central tendency that shows the middle response from scores eg. 1 4 (5) 5 5

Mode

Measure of central tendency that shows the largest response from scores. 1 4 5 5 5 (three 5s)

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.

Measures of Variability

Statistics that describe the distribution of a data set.

Range

Measure of variability that is a value obtained by subtracting lowest value from highest value in data set.

Standard Deviation

Measure of variability that describes data around the mean. Shows how much data ‘deviates’ from mean. Higher the standard deviation, the greater the data value in the set differs from the mean.

Accuracy

How close a measurement is to the true value of the quantity being measured.

Precision

How closely a set of measurement values agree with each other (if done multiple times, are the results similar?)

True Value

Value/range of values that would be found if the quantity could be measured perfectly.

Systematic Errors

Errors in data that differ from the true value by a consistent amount. Affects the accuracy of measurement.

Random Errors

Errors in data that occur due to chance. Affects the precision of measurement.

Repeatability

How well measurements/studies provide the same results when carried out under identical conditions. (e.g. same procedure, observer, instrument, instructions, and setting)

Reproducibility

How well measurements/studies provide the same results when carried out under different conditions. (e.g. same procedure, but different participants, observer, instrument, instructions and/or setting)

Validity

How well the experiment measures what it is intended to measure.

eg. If you are doing a test on memory, then the test should measure memory and not intelligence.