Yes it depends on what you are measuring in your study. some examples of variable include age, sex, marital status among others
The independent variable in ANOVA must be categorical (either nominal or ordinal). The dependent variable must be scale (either interval or ratio). However, it is possible to recode scale variables to categorical and vice versa in order to perform ANOVA. While this is a common practice in many social sciences, it is controversial. I have also seen studies where ordinal data is treated as scale in ANOVA. Personally, I do not endorse either practice as they are tailoring the data to fit the test instead of the proper method of selecting a test that fits the data.
A positive correlation between two variables, say X and Y, means that if one increases, the other will too. No correlation means that they are not related. A negative correlation means that as one increases, the other decreases. Normally you will see this in studies as "Recent studies demonstrated a positive correlation between eating too much and obesity." Or, "recent studies demonstrate a negative correlation between a healthy, balanced diet and obesity".
Acountance
A statistician
A person who studies Fleas.
The answer depends on the context. A variable can be independent in some studies but dependent in others. Time can be an independent variable in distance-time or speed-time studies but the time (to failure of a component) is a dependent variable. Perhaps confusingly, the same two variables can swap places depending upon the context. Suppose I believe that healthier people are taller (their growth is less likely to be stunted by illnesses) then my independent variable is some measure of their health and the dependent variable is their height. If instead, I believe that taller people are healthier (their parents must have had good genes) then the independent variable is height and the dependent is health.
Because a t-test is designed to measure the difference between means on variables that can be measured (interval data). For example, comparing the difference of height between males and females in centimetres. Qualitative studies are not interval data, but qualitative information is coded and analysed by frequencies - you are not comparing two normally distributed variables that can be measured on a continuous spectrum of measurement.
Variables are the basis of a scientific experiment. When the scientist is carrying out his procedure, he is actually altering a variable, called the independent variable. Unless he just likes playing with chemicals, he is likely attempting to get a result, and he does this by using measurements of the dependent variable, which has changed because of the manipulation of the independent variable. Identifying the variables becomes very important when formulating more complicated studies, and becomes the bread and butter of psychological experiments.
quantitative studies are for bulk while qualitative studies are more focused on the quality of the work. "quality" and "quantity" can be found within each of the words.
Independent variables are values that can be changed in a given model or equation. They provide the "input" which is modified by the model to change the "output."A scientist studies how many days people can eat soup until they get sick. The independent variable is the number of days of consuming soup. The dependent variable is the onset of illness.The independent variable is what causes the results.
Qualitative studies
Isn't it both? It studies the Earth's atmosphere (it's structure and composition) and all meteorological (weather) phenomena that occur in our atmosphere. It deals with stuff like heating, gravity (air pressure), motion (wind), Corioli effect, evaporation, saturation, etc.
Centre for Independent Studies was created in 1976.
Independent variables. If the treatment has no effect , the dependent variables for both the control and experimental group may be the same. cw: In some studies, there is no specific control group. For instance, in a drug study where subjects are given a random amount of the drug (from 0 up to some presumed safe level) then you cannot easily tell what the "experimental" group is -- you can't compare everyone else to the two subjects who got a placebo (0 mg/kg). You can tell whether the treatment is having a linear effect, etc.
It is a study where two variables, each on a continuous scale, are measured against each other without accounting for which variable is acting as the independent or dependent variable. It just measures whether an increase or decrease in one variable has a significant effect on the other. (Example: Measuring extraversion to levels of happiness. If a person is more extroverted, studies have shown that they are also more happy and vice versa.)
Quantitative data may be used to make a graph or table. Qualitative is easier to explain with numbers or a written description.
It is a research design part of qualitative method that allow the researcher to describe a phenomenon by presenting the facts in rich detail without attempting to interpret them. Gervais D