04. Ways of Collecting Data

After we have paid very careful attention to the way we structure our experiments it is time to collect our data (or run our experiments)

Remember, that up to this point, every step that we have taken is to ensure that the data will demonstrate and difference in the DV as a result of the presence of the IV and not due to external factors such as poor sampling procedures, poor experimental design, poor methodology etc.

The data we are collecting should be directly related to our hypothesis and is expressed in our DV.

Data For example, we might think that a contrived (or specifically setup) environment that is free of any extraneous variables may always be the best means of collecting data, but what happens if that environment itself creates an extraneous variable. Maybe participants won't behave in their typical manner in such a set-up environment and yet maybe it's a lot harder for us to collect data in a completely natural environment. 

Whichever way it goes, we need to make sure that we have chosen the most appropriate means of collecting data for our experiment.

TYPES OF EXPERIMENTS:

Laboratory Experiment:

Definition: A laboratory experiment is a type of experimental design where researchers manipulate one or more independent variables in a controlled environment while measuring the effect of those variables on a dependent variable.

Advantage: Laboratory experiments allow for precise control over the experimental conditions, making it easier to establish a cause-and-effect relationship between the independent and dependent variables.

Disadvantage: Laboratory experiments can lack ecological validity, meaning that the results may not generalize to real-world situations.

Field Experiment:

Definition: A field experiment is a type of experimental design where researchers manipulate one or more independent variables in a natural or real-life setting while measuring the effect of those variables on a dependent variable.

Advantage: Field experiments allow researchers to study phenomena in a real-world setting, increasing the external validity of the results.

Disadvantage: Field experiments are often more difficult to control than laboratory experiments, making it more challenging to establish causality.

Correlational studies:

Definition: Correlational studies are a type of research design where researchers observe and measure the relationship between two or more variables without manipulating any of them.

Advantage: Correlational studies are useful for identifying relationships between variables that cannot be manipulated, providing important insights into the nature of those relationships.

Disadvantage: Correlational studies cannot establish causality, as the relationship between the variables may be bidirectional or there may be a third variable influencing both.

Self-reports:

Definition: Self-reports are a type of research method where participants provide information about themselves through answering questions about their beliefs, attitudes, or experiences. There are two types of self-reports: free-response questions, which allow participants to provide open-ended answers, and fixed-response questions, which provide a list of options for participants to choose from.

Advantage: Self-reports are a cost-effective and convenient way to gather information about people's experiences and opinions, providing insights into their attitudes, behaviors, and beliefs.

Disadvantage: Self-reports can be subject to response bias, where participants may not provide truthful or accurate information, or may be influenced by social desirability biases.

Case studies:

Definition: Case studies are a research method that involves an in-depth analysis of a single individual, group, or event.

Advantage: Case studies allow for in-depth exploration of a particular phenomenon, providing rich qualitative data and insights into complex and unique situations.

Disadvantage: Case studies may suffer from a lack of generalizability, as the findings may not be applicable to other contexts or populations.

Simulation studies:

Definition: Simulation studies involve creating a computerized or mathematical model of a real-world process, system, or phenomenon.

Advantage: Simulation studies allow for the manipulation of complex systems and processes, providing insights into how they might behave under different conditions without the need for costly or time-consuming experiments.

Disadvantage: Simulation studies are only as good as the assumptions that underlie the model, which may not accurately represent real-world situations. Additionally, simulation studies may not account for unforeseen interactions or events that could affect the results.