Variables in a paradigm are related by being interconnected components that collectively make up the framework or system being studied. Each variable represents a specific aspect to be analyzed, and their relationships with one another help researchers understand the complex interactions within the paradigm. Through identifying and examining these relationships, researchers can gain insights into how variables influence each other and contribute to the overall understanding of the paradigm.
correlation.
Variables are expected to be related to one another based on the assumptions and logical reasoning within a theory. The theory specifies the nature and direction of relationships between variables, guiding the researcher's predictions. These relationships can be tested through empirical research to evaluate the theory's validity.
A direct correlation, it appears as a straight line on a graph and occurs when variables are related as y=xk.
When an experiment shows that two variables are closely related, it suggests that changes in one variable are associated with predictable changes in the other variable. This can indicate a causal relationship between the variables, where one variable directly influences the other. However, it is important to consider other factors and conduct further research to establish a robust understanding of the relationship.
that there is a correlation between the two variables. However, correlation does not imply causation, so it is important to further investigate to determine the nature of the relationship between the variables.
It is related to the two variables that are plotted in the line graph.It is related to the two variables that are plotted in the line graph.It is related to the two variables that are plotted in the line graph.It is related to the two variables that are plotted in the line graph.
variables are all related because they can equal to any number
a diagram that tells how two variables are related
There are infinitely many possible ways in which two variables can be related to one another.
A visual display showing how two variables are related is called a graph.
Humanistic paradigm would be least likely to manipulate independent variables as it focuses on personal growth, self-awareness, and individual experiences. Humanistic approach emphasizes the uniqueness of each individual and does not involve controlling or manipulating external factors in experiments.
if two variables are positively related,it means that the two variables change in the same direction.that is,if the value of one of the variables increases,the value of the other variable too will increase.for example,if employment as an economic variable increases in a country,and price of goods too increases then we can say that these two variables(employment and price) are positively related
The four paradigms of development in psychology are psychoanalytic, cognitive, behavioral, and humanistic. These paradigms offer different perspectives on how individuals develop and grow throughout their lives. Each paradigm emphasizes unique factors and processes that contribute to human development.
nope
If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.
It either enhances and improves a paradigm or it completely obliterates and disproves a paradigm, creating a paradigm shift that results in controversy followed by widespread acceptance
A diagram that shows how two variables are related is called a "scatter plot." It is a visual representation of the relationship between the two variables, often used to identify patterns or trends in the data.