A positive correlation is a relationship between two variables such that their values increase or decrease together. Show Correlation is expressed on a range from +1 to -1, known as the correlation coefficent. In a perfect positive correlation, expressed as +1, an increase or decrease in one variable always predicts the same directional change for the second variable. If two variables sometimes but not always change in tandem, the correlation is expressed as greater than zero but less than +1. Values below zero express negative correlation: As the value of one variable increases, the other decreases. Zero indicates a lack of correlation: There is no tendency for the variables to fluctuate in tandem either positively or negatively. Examples of positively correlated variables include:
There’s a common tendency to think that correlation between variables means that one causes or influences the change in the other one. However, correlation does not imply causation. There may be an unknown factor that influences both variables similarly. This was last updated in February 2013 VARIABLES AND HYPOTHESES Begin with stating the research question, the purpose of the research, the resources needed, and a plan for the research, including a model of the phenomenon under study. Where do research ideas come from? Curiosity; experience; need for deciding or acting; job; school; building on or contesting existing theory; available funding; etc. A model shows how different elements are linked by relationships. The elements for a model can be drawn from personal experience, consulting with key players, published literature, asking experts, existing data sets, and pilot studies. Generally a model is fixed at the beginning of the research; it may be altered as a result of the data analysis. A model is a visual representation of how something works; it both describes and explains some phenomenon. The advantages and drawbacks of models are:
A hypothesis states a presumed relationship between two variables in a way that can be tested with empirical data. It may take the form of a cause-effect statement, or an "if x,...then y" statement. The cause is called the independent variable; and the effect is called the dependent variable. Relationships can be of several forms: linear, or non-linear. Linear relationships can be either direct (positive) or inverse (negative). In a direct or positive relationship, the values of both variables increase together or decrease together. That is, if one increases in value, so does the other; if one decreases in value, so does the other. In an inverse or negative relationship, the values of the variables change in opposite directions. That is, if the independent variable increases in value, the dependent variable decreases; if the independent variable decreases in value, the dependent variable increases. In a non-linear relationship, there is no easy way to describe how the values of the dependent variable are affected by changes in the values of the independent variable. If there is no discernable relationship between two variables, they are said to
be unrelated, or to have a null relationship. Changes in the values of the variables are due to random events, not the influence of one upon the other. To establish that your causal (independent) variable is the sole cause of the observed effect in the dependent variable, you must introduce rival or control variables. If the introduction of the control variable does not change the original relationship between the cause and effect variables, then the claim of non-spuriousness is strengthened. Commonly used control variables for research on people include sex, age, race, education, and income. Commonly used control variables for research on organizations include agency size (number of employees), stability, mission, budget, and region of the country where located. For example, consider the placement rates for three training programs. The independent variable is the type of training, and the dependent variable is the placement rate. Vocational education has a placement rate of 30%; on-the-job training has a rate of 40%; and work-skill training has a rate of 35%. It would appear that on-the-job training is the best program, followed by work-skill training, with vocational education last. However, when education is introduced as a control variable, it can be seen that the effect of the independent variable (type of training) on the dependent variable (placement rate) is quite different for people with different levels of education.
(Note that there are different numbers of people in each educational category, and different numbers of people in each training program, so the overall rate is not simply the average of the rates for each educational category within each training program). What is relationship between two variables if one variable increases the other variable also increases?In general, direct variation suggests that two variables change in the same direction. As one variable increases, the other also increases, and as one decreases, the other also decreases. In contrast, inverse variation suggests that variables change in opposite directions.
What is it called when there is a relationship between two variables?What is Correlation? Correlation is a statistical technique that is used to measure and describe a relationship between two variables. Usually the two variables are simply observed, not manipulated. The correlation requires two scores from the same individuals.
Is a relationship between two variables where if one variable increases the other one also increases * 1 point?Solution : Correlation between two variables is said to be perfect, if one variable increases, the other also increases proportionally.
|
