"Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables."
A variable in an experiment that is measured to see if it is influenced by the independent variable.
Independent and dependent variables: These are the two main variables in any psychological experiment. The independent variable is the variable that is manipulated by the researcher, while the dependent variable is the variable that is affected by the manipulation.
Operationalization: This refers to the process of defining a variable in concrete, measurable terms so that it can be studied in an experiment. This requires careful consideration of what the variable means and how it can be quantified.
Validity: The validity of an experiment refers to how accurately it measures the variable it is intended to measure. This can include internal validity (the accuracy of the experiment within the context of the study) and external validity (the generalizability of the findings to other contexts).
Reliability: This refers to the consistency and predictability of the results of an experiment. If an experiment is reliable, it will yield consistent results even if it is repeated multiple times.
Confounding variables: These are variables that are not intentionally manipulated by the researcher but can still affect the outcome of the experiment. It is important to account for these in order to ensure that the experiment is measuring what it is intended to measure.
Correlation: This refers to the relationship between two variables. If two variables are correlated, changes in one variable will be associated with changes in the other variable.
Causation: This refers to the relationship between an independent variable and a dependent variable, where the independent variable is causing changes in the dependent variable.
Hypothesis testing: This involves making a prediction about the relationship between variables and then testing that prediction through an experiment.
Statistical analysis: This involves using mathematical tools to analyze the data collected from an experiment in order to draw meaningful conclusions. This can include measures such as means, standard deviations, and correlation coefficients.
Randomization: This refers to the process of randomly assigning participants to different experimental conditions in order to control for individual differences and other factors that could affect the results of the experiment.
Control group: This is a group of participants that is not exposed to the independent variable in an experiment, in order to compare the results with the experimental group.
Experimental group: This is a group of participants that is exposed to the independent variable in an experiment, in order to determine the effects that it has on the dependent variable.
Direct vs. indirect measurement: This refers to the distinction between measuring a variable directly (e.g. by asking a participant to rate their level of anxiety) and indirectly (e.g. by observing changes in physiological markers like heart rate or skin conductance).
Longitudinal vs. cross-sectional studies: Longitudinal studies follow participants over a long period of time, while cross-sectional studies examine a group of people at a single point in time. Both approaches have their own advantages and disadvantages.
Ethical considerations: Researchers must be mindful of the ethical implications of conducting experiments on human subjects, including obtaining informed consent, protecting participants from harm, and ensuring that the benefits of the research outweigh any potential risks.
Continuous Variables: These are variables that can take on any value along a continuum, such as time or weight.
Categorical Variables: These are variables that are divided into categories or groups, such as gender or ethnicity.
Binary Variables: These are categorical variables with only two possible outcomes, such as true/false or present/absent.
Count Variables: These are variables that are based on the number of occurrences of an event, such as the number of times a participant presses a button.
Ordinal Variables: These are variables that have specific order or ranking, such as a Likert scale or a pain rating scale.
Derived Variables: These are variables that are calculated from other variables, such as averages, standard deviations, or ratios.
Discrete Variables: These are variables that can only take on certain values, such as the number of siblings a person has.
Interval Variables: These are variables where the distance between values is meaningful and equal, such as temperature or IQ scores.
Ratio Variables: These are variables where there is a true zero point, such as weight or time.
Qualitative Variables: These are variables that describe qualities or characteristics, such as personality traits or attitudes.
Latent variables: These are variables that are not directly observable but inferred from observable variables. For example, intelligence, happiness, or positive affectivity.
Reaction time measures: These measure the speed of cognitive processing, for example, how long it takes someone to respond to a stimulus.
Physiological measures: These measure bodily responses to stimuli, for example, heart rate or brain activity measured with electroencephalography (EEG).
Behavioral measures: These measure observable behavior, such as the number of times a person smiles or initiates physical contact.
Self-report measures: These measure a participant's subjective experience or perception, such as a Likert scale or a questionnaire.
"Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question."
"Some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable)."
"It is always the dependent variable whose variation is being studied."
"Independent variables are also known as regressors in a statistical context."
"Models and experiments test the effects that the independent variables have on the dependent variables."
"Independent variables may be included for other reasons, such as to account for their potential confounding effect."
"Dependent variables are studied under the supposition or demand that they depend, by some law or rule, on the values of other variables."
"Dependent variables are studied under the supposition or demand that they depend, by some law or rule, on the values of other variables."
"Some common independent variables are time, space, density, mass, fluid flow rate..."
"Any variable that can be attributed a value without attributing a value to any other variable is called an independent variable."
"Models and experiments test the effects that the independent variables have on the dependent variables."
"Previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable)."
"Independent variables may be included for other reasons, such as to account for their potential confounding effect."
"It is always the dependent variable whose variation is being studied."
"Independent variables are also known as regressors in a statistical context."
"Models and experiments test the effects that the independent variables have on the dependent variables."
"Some common independent variables are time, space, density, mass, fluid flow rate..."
"Any variable that can be attributed a value without attributing a value to any other variable is called an independent variable."
"Independent variables may be included for other reasons, such as to account for their potential confounding effect."