Understanding the types of variables you are studying in your doctoral thesis is necessary for all types of quantitative research design, whether you are using an experimental, quasi-experimental, relational or descriptive research concept. When you do your thesis, you may need to measure, manipulate and/or control the variables you are studying. In the research designs section, you`ll find more information about the different types of quantitative design of the research. In this article, we present the different types of variables that you can see in your thesis. First, the main groups of variables are explained: category variables and continuous variables. Second, we explain what dependent and independent variables are. This will allow you to obtain one of the necessary bases to begin a doctoral thesis based on a quantitative conception of research. In scientific research, concepts are abstract ideas or phenomena that are studied (for example. B educational policy performance).
Variables are the characteristics or characteristics of the concept (for example. B performance in school), while indicators are methods of measuring or quantifying variables (for example. B annual reports). Note that many variables can be considered nominal or ordinary depending on the purpose of the analysis. Consider the main knowledge of English, psychology and computer science. This classification can be considered nominal or ordinary depending on whether there is an intrinsic belief that it is “better” to have a primary computer subject than in psychology or English. In general, for a binary variable such as ordinal pass/fail or nominal reflection, it doesn`t matter. To ensure the internal validity of an experiment, you need to change only one independent variable at a time. Continuous data can take any range of values that can only be estimated in a certain degree of accuracy (for example.B.
by increasing accuracy, the value obtained changes). Therefore, the possible number of different values that the data can take is infinite. Examples of continuous data types are the weight, height, volume of milk produced by milk during lactation, and the period of infection of a pathogen. Age can be classified as either discrete (as it is generally measured for whole years) or continuous (the notion of a fraction of a year being plausible) – it is likely that the latter is more appropriate. Of course, age could be categorized and treated as ordinal data. Continuous data can continue to be categorized according to the measures used: if you asked someone if they liked democratic Party politics, and you presented them with the following three categories: Not much, you are OK, or yes, a lot; You have an ordinal variable. What for? Because you have three categories? because not much, you`re fine, and yes, a lot? and you can rank them from the most positive (yes, a lot), to the average response (you`re OK), at least positive (not much). Although we can categorize all three categories, we cannot give them value. For example, we can`t say that the answer, you`re OK, is twice as positive as the answer, not much. To learn more about the different uses of variables, particularly in quantitative research designs (i.e. descriptive, experimental, quasi-experimental and relational designs), see the research designs section. Investigations can contain many types of questions; These questions are often referred to as variables.