Key science skills psychology exam revision

Controlled experiment

A type of investigation that measures the causal relationship between one or more independent variables and a dependent variable, whilst controlling for all other variables. The 3 types of it are between-subjects, within-subjects, and mixed-methods.

Between-subjects design

Participants are only exposed to one condition of the experiment and the groups are compared based on the DV.

Within-subjects design

Participants are exposed to both conditions of the experiment. Their results from each trial are compared.

Mixed-method design

Researchers will use aspects of within-subjects and between-subjects designs to collect data. This design allows researchers to compare between each condition (between groups) but to also measuring change over time within the individual group members.

Different types of scientific investigation

Case studies, observational studies, and correlational studies.

Case studies

an in-depth or detailed study on a particular activity, behaviour, event or problem. It may be historical, hypothetical, or current.

Observational studies or field work

A methodology that involves investigation through observing and interacting with an environment in a naturalistic setting. For example a researcher may decide to observe students in a classroom.

Correlational studies

Investigate the relationship between two variables. The research is only measured variables and not manipulating them. Therefore this type of research does not determine cause-and-effect.

Generalise

Using a sample’s results to make conclusions about the wider research population.

Population

The group of people who are the focus of the study and from which the sample is drawn.

Sample

A subset of the research population who participate in a study. A good sample needs representatives and need to be relatively large .

Representative

that any relative characteristics in the population should be reflected in the sample in the same proportions.

Relatively large

the larger a sample is, the more likely that it is to be representative of the population.

types of sampling

Convenience sampling, Random sampling, and stratified sampling.

Convenience sampling

refers to any sampling technique that involves selecting readily-available members of the population. example of this is Selecting participants who respond to an advertisement. The strength of this is that it is time and cost effective. The limitation of this is that the sample is unlikely to be representative of the population.

Random sampling

when every member of the population has an equal chance of being selected for the sample. An example of this would be randomly selecting data from a government database. I strength for this is that it is free of bais. A limitation would be that the sample is unlikely to

be representative of the population.

stratified sampling

breaks the population into subgroups (strata) and selects participants from each group in the same proportion they appear in the population. for example If a study is being conducted on Australians, a researcher would ensure that their sample has the same percentage of males and females as the population. The strength of this is that it is more representative of the population. The limitation of this is that it is time comsuming.

allocation

After creating a sample during an experiment, the researcher must allocate participants to different conditions of the experiment. This is usually done through random allocation.

random allocation

Every member of the sample has an equal chance of being in the control or experimental group.

Data

any information that is used within an investigation or is collected during an investigation. types of data is Primary data, secondary data, Qualitative data, quantitative data, Objective data, and subjective data.

Primary data

data that the researcher has collected themself. for example Observational research, Interviewing participants, Conducting experiments, and Conducting surveys.

secondary data

data that has been sourced from the research of someone else. for example Data from public records, Literature reviews, and Books/historical artifacts

Qualitative data

data that is expressed using language (as opposed to numerically), and often provides insightful information into what is being studied. for example Written or verbal descriptions made by participants.

quantitative data

data that is expressed numerically. This type of data provides data points that can be statistically compared and analysed. for example Test scores or measurements.

Objective data

data that is unbiased and is not subject to personal opinion or interpretation, is often also quantitative. for example A participant’s resting heart rate. This number is not impacted by the researcher’s opinion and would not vary depending on which researcher was collecting the data.

subjective data

data that relies on a researcher’s personal opinion or interpretation, is sometimes qualitative. for example Self-reports regarding participant’s mental health. The way in which responses are interpreted would likely change depending on the researcher conducting the experiment.

Descriptive statistics

statistics that are used to summarise, organise and describe data. They are how we process and understand quantitative data. Without this, data collected from a study is often not very useful and is difficult to draw conclusions from. and understand quantitative data.

mean

describes the average value within a data set. should be used when data points are distributed evenly around a centre point. It is susceptible to being influenced by extreme values and outliers. To calculate this, you must add up all the data points, and then divide this total by the number of data points.

median

refers to the data point that falls in the ’middle’ of all values when the values are ordered from highest to lowest. This is less likely to be impacted by outliers and uneven data distribution. If there are two central numbers, these numbers are added together and then divided by two.

Mode

the data point that is recorded most frequently. It is useful for knowing the most commonly occurring value.

Measures of variability

statistics that describe the spread and distribution of a data set. They help to indicate how much the responses of participants vary. The two key measures include Range, and Standard deviation.

Range

a measure of variability that that is calculated by subtracting the lowest value in the data set from the highest value. This indicates how distributed the scores are.

Standard deviation

a value that describes how the data is spread around the mean. The higher the value is, the more the values in the data differ from the mean.

Aim

the purpose of the study – it explains what you are intending to investigate. Example of how to write an aim is ‘To test the effect of [one variable] on [another variable]’, and ‘To investigate the impact of [one variable] on [another variable]’.

Variable

A condition or component of an experiment that can be measured or manipulated.

Independent variable (IV)

is the variable which is manipulated by the experimenter. To identify this, ask yourself, ‘what am I testing the effect of?’

Dependent variable (DV)

is the variable that is measured to test the effect of the independent variable. To identify this, ask yourself, ‘what do I have to measure?’

Hypothesis

A testable prediction that identifies the population, the strength and direction of a relationship between two variables. A hypothesis should include identification of the population, identification of conditions of the independent variable, identification of the dependent variable, and a directional prediction. An example of a hypothesis is: ‘It is hypothesised that Monash University students who are sleep deprived for a 24 hour period will take longer to complete simple tasks, compared to those who are not sleep deprived.’

Systematic errors

Errors in data that differ from the true value by a consistent amount.

Random errors

Errors in data that are unsystematic and occur due to chance.

Accuracy

How close a measurement is to the true value of the quantity being measured. Systematic errors effect this. An example of this Is the real weight of an apple consistent with the weight that a scale is showing?

Precision

How closely a set of measurement values agree with each other. Random errors effect this. An example of this is If you measure the same apple twice, does the scale give the same weight? Or does it differ?

Uncertainty

The lack of exact knowledge about something being measured due to potential sources of variation in knowledge. It can also mean a lack of confidence.

Validity

The extent to which a tool measures what it is supposed to measure. For example Does running speed measure overall fitness?

Internal validity

assesses whether the research tools are effectively measuring what they are believed to measure.

External validity

assesses whether the findings of the study can be applied to the wider population.

Repeatability

assesses whether the results of a study are the same when the study is replicated. This includes the same procedure, tools, setting etc, and conducted over a short period of time. for example If we measure fitness one week, and then conduct the experiment again the next week, will we get the same results?

Reproducibility

assesses whether the results of a study are the same when the research is conducted under different conditions. For example, a different method of measurement, observer, measuring instrument, location, and time.