Psychology - Approaches to Research

Evidence collected in a casual or informal manner that relies heavily on entirely on personal experience.

Anecdotes are generally not considered reliable, as they draw from personal experience and not controlled and measured scientific studies. They are not applicable to most of the population.

Anecdotal evidence helps us make rapid and practical judgements in our daily lives. For trivial matters, going off of anecdotes or teachings is much faster and easier than pursuing scientific studies into the matter.

When the scientist goes into the field to collect data themself through experiments or other tools.

When the scientist reviews or analyses pre-existing content, consisting of images, texts, videos, etc.

• Predict

• Explain

• Applicability

• Research Evidence

• Testable

• Unbiased

• Gaps/Grey Areas

A theory should not be limited to exploring the past. A strong theory can make predictions about the future and these predictions should be testable and have predictive validity.

A strong theory provides a convincing, logical and reasonable explanation for a certain behaviour.

A good theory must be useful. It should be able to help people in some way, or solve a problem in the world.

Research can be carried out by testing predictions made by the theory. When multiple scientific studies repeatedly confirm the predictions, a theory gains greater acceptance. Repeated testing can also generate new information or new leads.

A good theory must be falsifiable. If a theory cannot be tested and proved wrong, there may be something wrong with the assumptions made by the theory or it may be based on flawed concepts.

To have general applicability, a theory must not be biased towards a particular demographic. Androcentrism, Ethnocentrism/Eurocentrism, YARVIS bias, self-selection bias, etc. should be avoided.

Gaps/Grey Areas are the questions that the theory leaves unanswered. Does it leave you with any further questions that you think need investigation?

Qualitative research looks at information that is not in numerical terms.

It is descriptive in nature.

It tends to be more specific and focuses on a particular group, and provides a nuanced and detailed look into phenomena.

• Observation

• Interviews

• Case studies

Quantitative research looks at patterns in numeric data.

Data is gathered in units of measurement or categories.

It can be used to construct graphs and tables.

• Experiments

• Correlational research

• Surveys/Questionnaires - they’re actually tools but ok

A theoretically defined variable. Must be operationalised to be tested.

Any factor or characteristic that can be measured and which varies quantitatively. It needs to be specific.

Tentative prediction that assumes a causal or correlational relationship between two variables that are to be tested in a study.

Prediction of how the IV affects the DV.

Variable that is manipulated or changed by the experimenter to identify its effects on the behaviour being studied.

The outcome or result that is measured.

Any variables that may affect the result, that weren’t accounted for. Could be any variable you’re not investigating.

A type of Extraneous Variables, that affect not only the DV but also the IV.

The group of participants to whom the IV is not administered.

Defining or expressing a variable in definite, measurable terms.

• To replicate the experiment

• To clarify what is being studied

• To strengthen the whole process

• To convert something conceptual and broad into a specific measurable unit.

A hypothesis that states that the IV will have no effect on the DV and any change in DV can be attributed to chance/extraneous variables.

Experimental hypothesis - states that there is a relation between two variables.

Predicts the nature of effect of IV on DV.

A characteristic or condition that does not vary, but stays the same for every individual.

Procedures must be written in enough detail that they can be easily replicated by another researcher.

The way groups and conditions are organised in an experiment.

The same set of participants receive all conditions of an experiment. Focus is on induced difference by IV, initial differences are eliminated.

• participant variables are controlled. Less confounding variables.

• Fewer participants are needed

• Can keep larger sample size, don’t need to eliminate or balance as much.

• Order Effects

• Demand Characteristics

• Same materials cannot be used for both/all conditions.

When asked to take part in multiple conditions, participants may experience boredom, fatigue, practice effect, etc.

One group starts with condition A and moves to condition B. The other can start with B and move to A. This makes sure the order of conditions doesn’t affect the results of the study.

Participants form an interpretation of the experiment’s purpose and subconsciously/consciously change their behaviour to fit that interpretation. Types:

• Expectancy Effect

• Screw-You Effect

• Social Desirability Effect

A demand characteristic where participants do what they think the researchers want them to do.

Participants may purposefully try to disprove a hypothesis.

Participants may heighten or deliberately show socially desirable traits when being studied.

Members of the sample are randomly allocated to one condition.

• Order effects are controlled for.

• Demand characteristics are lowered.

• Same materials can be used for all conditions.

• Data collection will be less time consuming if all conditions can be conducted simultaneously.

• Participant variability may greatly influence results. Groups must be balanced from the start.

• Different participants for each condition may be difficult to gather, and can be expensive.

IMD, but without random allocation.

Participants are pre-tested with regard to the variable and allocated to groups in such a way as to make all groups equivalent.

Useful when sample sizes are too small where random allocation may not ensure equivalence.

Anyone who fits the inclusion criteria for the study. The group of people the scientist want to study, describe or understand.

It is impractical to study every member of a target population. A group of participants are instead taken who are representative of the larger target population.

Our goal is to generalise results to the target population. In order to do that, the sample needs to have all the characteristics of the target population. Non-representative samples can elicit biases.

Every member of a population has an equal chance of being selected. For very large samples, this provides the best chance of an unbiased representative sample. However for large populations it is time consuming and tedious to create a list of every individual.

The researcher identifies the different strata of people that make up the target population and works out the proportions needed for the sample to be representative. Highly representative and easier to generalise, and suitable for smaller sample sizes, but time consuming and only applicable when there are distinct strata.

Self-selecting; individuals choose to be involved in a study. Relatively convenient and ethical, but highly unrepresentative and leads to self-selection bias.

Convenience sampling; simply selecting people who are available at a time or place. Quick, common and economical. Can be highly unrepresentative for certan stratified populations and lead to researcher bias as researchers are likely to choose those who are helpful.