Research methods :star: - Coggle Diagram
Research methods :star:
Longitudinal research :
- a type of correlational research in which researchers observe and collect data on a number of variables without trying to influence those variables.
- longitudinal studies repeatedly observe the same participants over a period of time
:check: Longitudinal studies allow researchers to follow their subjects in real time. This means you can better establish the real sequence of events, allowing you insight into cause-and-effect relationships.
:check: Longitudinal studies also allow repeated observations of the same individual over time. This means any changes in the outcome variable cannot be attributed to differences between individuals.
:red_cross: Longitudinal studies are time-consuming and often more expensive than other types of studies, so they require significant commitment and resources to be effective.
:red_cross: Attrition, which occurs when participants drop out of a study, is common in longitudinal studies and may result in invalid conclusions
cross sectional :
- a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.
- Cross-sectional studies aim to describe a variable, not measure it.
:check: data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.
:red_cross: Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods
:check: The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.
:red_cross: Cross-sectional studies rely on surveys and questionnaires which might not result in accurate reporting as there is no way to verify the information presented.
- Correlational research is a nonexperimental research method that measures two variables and assesses the relationship (correlation) between them.
- involves observing two variables that don't change and analyzing whether there is a correlation between the variables
- this method doesn't account for any causal relationships between the variables you study.
- orrelational research can help you identify relationships between two events, traits or behaviors
Naturalistic observation :
- Naturalistic observation is a qualitative research method psychologists apply to study patients' behavior when patients are in their natural environments
- an effective method for studying how people interact and behave while they are in their habitual surroundings
Structured observation :
- a qualitative observational research method that evaluates human behavior in a more structured environment
- Many clinical and medical research studies use structured observation methods to better control variables in experiments and ensure higher accuracy when analyzing results
- Observer is a participant in the behavior being observed.
- Likely to provide special insight into the behavior. The participant has greater familiarity with what is likely to happen.
- More difficult ro record and monitor behavior unobtrusively if the observer is part of the group being observed.
Non- participant :
- Observer is not a participant in the behavior being observed
- Increased objectivity because of a psychological and also possibly a physical distance
- Reduces validity because the observer may misinterpret the communications within the group since they are a outsider.
Types of correlation* :
- Positive = Where one co-variable increases and so does the other
- Negative = Where one co-variable increases and the other decreases
- No correlation= There may be variables that have no relationship ( the dots are scattered everywhere on the graph)
- Directional correlational hypothesis= states whether the relationship will be a positive or a negative
- Non directional correlational hypotheis= states that there will be a correlation
:check: Quite quick and economical to carry out because there is no need for a controlled environment and no manipulations of variables is required.
:red_cross: correlations don't provide a cause and effect relationship therefore we can't conclude that one variable is causing the other to change. This can sometimes lead to correlations being misinterpreted.
:check: Useful tool of research as they provide a strength and direction of a relationship between variables and can be used as a starting point to assess the relationship between variables before committing to an experimental study
:red_cross: It may also be the case that another untested variable is causing th relationship between the 2 co-variables. This is known as the 3rd variable problem.
Statistical testing :
- A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation or association in the variables tested.
- If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.
- If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.
- In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.