Research Methods in Psychology

A critical analysis of existing research on a topic, aiming to define the research problem, understand existing knowledge, and identify gaps or inconsistencies.

The process of placing your research within the context of existing knowledge, demonstrating its relevance and potential contributions.

Identifying new areas of inquiry or unexplored aspects within a research topic, ensuring your work doesn't simply repeat what's already been done.

Connecting your research to relevant academic discussions and debates, showcasing the broader impact of your findings.

Introducing and defining key terms and concepts used in your research, ensuring clear communication and understanding.

Gaining insights into research methods and techniques used by others, providing guidance for your own methodology.

A type of literature review where the goal is to describe the current state of knowledge on a topic and identify potential applications.

A type of literature review where the aim is to provide justifications and arguments for conducting an exploratory study, typically in emerging or under-researched areas.

A variable that influences the relationship between the independent and dependent variable, strengthening or weakening the effect.

A variable that acts as an intermediary between the independent and dependent variable, explaining the mechanism of the effect.

The variable that is being manipulated or changed in an experiment.

The variable that is being measured or observed in an experiment. It's the outcome.

A visual representation of the relationships between variables, showing how they influence each other.

When a variable moves in the same direction as another variable. For example, as one increases, the other also increases.

When a variable moves in the opposite direction of another variable. For example, as one increases, the other decreases.

It adds variance to the dependent variable, modifying the original relationship between the independent and dependent variables.

A process used to determine if the null hypothesis can be rejected, providing evidence to support an alternative hypothesis.

The entire group of individuals, objects, or events that are the focus of the research.

A smaller group selected from the population to represent the whole.

The actual subset of the population that can be included in the sample. It may not represent the whole population.

A sample that accurately reflects the characteristics of the population.

The assumed distribution of data in many statistical analyses.

A statement that there is no relationship or effect between variables.

A statement that there is a relationship or effect between variables.

A design that features a pre-test and post-test for a single group receiving the treatment. The treatment effect is calculated as the difference between the post-test and pre-test scores.

Design that uses a pre-test and post-test for both an experimental group (receives treatment) and a control group (doesn't receive treatment). The treatment effect is calculated by subtracting the difference in scores of the control group from the difference in scores of the experimental group.

A design that involves collecting data on the same variable at regular intervals. This allows for observing trends and patterns over time. However, it's important to consider potential threats such as history, testing, mortality and maturation effects.

A design that aims to study real-world phenomena in a more controlled environment. It focuses on understanding how the system as a whole works rather than specific cause-effect relationships.

A design where the independent variable is not manipulated by the researcher. Instead, participants are grouped based on their pre-existing exposure to the independent variable (e.g., those who received a training program vs. those who didn't).

A design where both the experimenter and the participants are unaware of whether or not the treatment was given. This helps to minimize bias and ensure that the results are truly due to the treatment itself.

Back translation is a method used to check if a questionnaire truly measures the same variable across different languages or cultures. It involves translating the questionnaire into another language, then back into the original, comparing the final version to the original to identify any differences that might arise from translation bias.

Multiple methods of data collection, such as administering questionnaires both in person and via email, can help mitigate potential disadvantages of a single method, but it can also introduce bias if explanations are not consistent across methods.

In research, an experimental design is specifically designed to investigate cause-and-effect relationships between variables.

To establish a cause-and-effect relationship, three conditions must be met. First, the independent and dependent variables must covary, meaning they change together. Second, the independent variable must precede the dependent variable in time. Finally, the researcher must control for extraneous variables, ensuring only the independent variable is responsible for the change in the dependent variable.

Extraneous variables are factors that could also influence the dependent variable, potentially confounding the results of an experiment. These variables need to be controlled to isolate the effect of the independent variable.

Treatment refers to the manipulation of the independent variable in an experiment. It's the specific action or intervention that is being tested.

A testable statement that predicts a relationship between variables.

States that there is NO relationship or effect between variables. It's assumed to be true until proven otherwise.

States that there IS a relationship or effect between variables. This is what you're trying to prove.

The probability of rejecting the null hypothesis when it is actually true. It's like saying 'there's a difference' when there isn't.

The probability of failing to reject the null hypothesis when it is actually false. It's like saying 'there's no difference' when there is.

The level of significance you set to decide whether to reject the null hypothesis. Typically set at 0.05, meaning there's a 5% chance of a Type I error.

The probability of finding a statistically significant result if the alternative hypothesis is true. It's like the power of your test to detect an effect.