You are interested in the career choices of university students. You could ask university students a number of closed questions related to their career choices. For example:
Continuous variables, which are also known as quantitative variables, can be further classified a being either interval or ratio variables. Each of these types of continuous variable (i.e., interval and ratio) has numerical properties. These numerical properties are the values by which continuous variables can be measured, manipulated and/or controlled. We illustrate the two types of continuous variable (i.e., interval and ratio) and some associated values in the sections that follow:
2018-03-07 15:55:59 EST: Today's Date
At the undergraduate and master's dissertation level, you will often focus on just two variables: an independent and a dependent variable; or sometimes, a second or third independent and/or dependent variable [see the article: ]. Only in a minority of cases are you likely to examine a large number of variables at once. However, just because you are only focusing on a small number of variables, this does not mean that these are the only variables that relate to the research you are performing. In this respect, an extraneous variable refers to any variables that you are not intentionally studying (or cannot study, perhaps because of reasons of cost or difficulty). Rather than there being just a few of these extraneous variables, there are likely to be hundreds or even thousands. In other words, it is impossible to avoid extraneous variables.
Some students spend more time revising for their test; and
Imagine that a tutor asks 100 students to complete a maths test. The tutor wants to know why some students perform better than others. Whilst the tutor does not know the answer to this, she thinks that it might be because of two reasons:
Some students are naturally more intelligent than others.
Therefore, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. As such, the dependent and independent variables for the study are:
(General Statistics) Statistics Statistics - CBL
A variable is not only something that you measure, but also something that you can manipulate and control for. An independent variable (sometimes called an experimental or predictor variable) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable). The dependent variable is simply that; a variable that is dependent on an independent variable(s). We discuss these concepts in the example below:
Finding Mean and Standard Deviation with the TI-83
Since you are responsible for setting the measurement scale for a variable, you will need to think carefully about how you characterise a variable. For example, social scientists may be more likely to consider the variable gender to be a nominal variable. This is because they view gender as having a number of categories, including male, female, bisexual and transsexual. By contrast, other researchers may simply view gender as a dichotomous variable, having just two categories: male and female. In such cases, it may be better to refer to the variable gender as sex.
QUOTE: Adolphe Quetelet Quoted in E Mailly, 1874
Sometimes, the measurement scale for data is ordinal, but the variable is treated as though it were continuous. This is more often the case when using Likert scales. When a Likert scale has five values (e.g., strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree), it is treated as an ordinal variable. However, when a Likert scale has seven or more values (e.g., strongly agree, moderately agree, agree, neither agree nor disagree, disagree, moderately disagree, and strongly disagree), the variable is sometimes treated as a continuous variable. Nonetheless, this is a matter of dispute. Some researchers would argue that a Likert scale should never be treated as a continuous variable, even with seven levels/values.