Mathematics
Quantitative Variables
Quantitative variables are numerical in nature and represent measurable quantities. They can take on a range of values and are often used in mathematical calculations and statistical analysis. Examples of quantitative variables include age, weight, height, and temperature.
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5 Key excerpts on "Quantitative Variables"
eBook - PDFLet Your Data Speak
Quantitative and Qualitative Methods
- Moscarola, Jean(Authors)
- 2022(Publication Date)
- Editions EMS(Publisher)
2. VARIABLES From a semantic point of view, a variable refers to an idea or a concept that gives meaning to the information contained in the column. This meaning is defined in theory (What is overall satisfaction?) and in practice (formulation of the question leading to the answer and thus to the data) (Figure 12). 1. Or several tables if the sources are of different kinds. Measuring and Analyzing: Quantitative Methods 84 When statistical methods originated, the only types of variables considered corresponded to numbers measuring phenomena that vary continuously, such as age or weight, or discretely, such as gender. In this case, the value 1 corresponds to the presence of a characteristic (e.g., male or female), and the value 0 to its absence. We then find two columns: one for each gender. When one of them is 1, the other is 0, if we consider genders to be exclusive. The further development of the domains of application of statistics, 2 computerized techniques and data processing techniques has led to the differentiation of four kinds of variables that are frequently presented in publications (Ganassali, 2014): numerical, scale, nominal and textual. 2.1. Numerical variables Numerical variables have the arithmetical properties of numbers. They come from precise measurements. We can add, subtract, divide, or multiply them, obtaining a result of the same kind. They are appropriate for a wide variety of calculations, which makes them valuable for statistics. 2.2. Scale variables These variables correspond to discontinuous orders of magnitude (age groups, degrees of agreement, satisfaction levels, etc.) and are used when a precise measurement is inapplicable. Orders of magnitude can be compared among themselves, but to use them in calculations, it is important to ensure that they respect the properties of numbers. 3 These variables are also called ordinal variables, or Likert scales.
eBook - PDF- Melissa A Hardy, Alan Bryman, Melissa A Hardy, Alan Bryman(Authors)
- 2009(Publication Date)
- SAGE Publications Ltd(Publisher)
2 Constructing Variables ALAN BRYMAN AND DUNCAN CRAMER The process of quantitative research is frequently depicted as one in which theory is employed in order to deduce hypotheses which are then submitted to empirical scrutiny.Within the hypothesis will be two or more concepts that will require translation into empirical indicators. These indicators are frequently referred to as variables and repre-sent the fundamental focus of all quantitative research. While some writers might question the degree to which quantitative research necessarily follows such a linear progression and indeed how far it is driven by hypotheses (as against simply research questions), there is no doubt that the variable represents a major focus (Bryman, 2001). It constitutes a crucial bridge between conceptualization and findings. Essentially, the quantitative researcher is concerned to explore variation in observed values among units of analysis and the corre-lates and causes of variation. All techniques of quantitative data analysis – from the most basic methods to the most advanced – are concerned with capturing variation and with helping us to understand that variation. The variable is crucial because it is the axis along which variation is measured and thereby expressed. Indeed, so central is the variable to the discourse of quantitative research that it has to all intents and purposes become synonymous with the notion of a concept. Variables are, after all, supposed to be mea-sures or indicators that are designed to quan-tify concepts, but frequently writers of research papers and methodology texts refer to the process of measuring variables. In the process, concepts and variables become almost indistinguishable. The variable is also frequently the focus of attention for critics of quantitative research (e.g., Blumer, 1956), in large part because it is emblematic of the research strategy. The variable can be usefully contrasted with the idea of a constant .
- Richa Tiwari(Author)
- 2023(Publication Date)
- Society Publishing(Publisher)
It is used in modern product of mathematical representation of the world in the form of noun through scientific modeling. The Oxford English Dictionary (OED) defines a variable in math and physics as; A quantity or a force, which throughout a mathematical calculation or investigation is assumed to vary or be capable of varying in value. The OED dates the first use to the early 19 th century. To describe anything that varies this specifically scientific usages seems to predate the more general application of ideas. Its OED definition actually contains two very different conceptions of the nature of variable and two different conceptions of the processes in which these variables occur. This variable as a quantity implies measurement can and has been made as a force it implies something real exists and can be measured. Mathematical calculation process for variables is abstract. The process of investigation involves empirical engagement with the world. The Encyclopaedia of Statistical Sciences (1999) describes variables as ‘elements’ even though it does not specify what are the variables. It then differentiates between ‘hidden’ or ‘latent’ changes on the one hand and ‘observed’ or ‘manifest’ on opposite. This distinction derives from the method of measurement, with ‘observed’ or ‘manifest’ variables being directly measurable and ‘hidden’ or ‘latent’ variables being divided into two further classes of these that would be measured, were good enough instruments available, and people that are ‘idealized constructs at best only indirectly measurable’ (1999, p. 772). This is often basically an argument about the ‘reality of variables, with the degree of commitment to the fact of any given variable being a function of the convenience of its measurement.
eBook - PDFStatistics with JMP
Graphs, Descriptive Statistics and Probability
- Peter Goos, David Meintrup(Authors)
- 2015(Publication Date)
- Wiley(Publisher)
It is obvious that it is also not very useful to perform arithmetic operations with ordinal variables. 2.1.2 Quantitative Variables A variable that is measured on a quantitative scale can be expressed as a fixed number of measurement units. Examples are length, area, volume, weight, duration, number of bits per unit of time, price, income, waiting time, number of ordered goods, and so on. For Quantitative Variables, almost all arithmetic operations make sense. This is due to the fact that the difference between two levels of a quantitative variable can be expressed as a number of units in contrast to differences between two levels of an ordinal variable. Within the class of Quantitative Variables, a distinction is made between variables that are measured on an interval scale and variables measured on a ratio scale. 2.1.2.1 Interval scale An interval scale has no natural zero point, that is, no natural lower limit. For variables measured on an interval scale, calculating ratios is not meaningful. Well-known examples of interval variables are the time read on a clock or the temperature expressed in degrees Celsius or Fahrenheit. The difference between 10 STATISTICS WITH JMP 2 o’clock and 4 o’clock is the same as the difference between 21:00 and 23:00, but it’s not like 4 o’clock is twice as late as 2 o’clock. This is due to the fact that time read on a clock has no absolute zero. The same applies to the temperature measured in degrees Celsius: 20 ∘ C is not four times as hot as 5 ∘ C. 2.1.2.2 Ratio scale A ratio scale does have an absolute zero. Therefore, for variables measured on a ratio scale, ratios can be calculated. A length of 6 cm is twice as much as a length of 3 cm, as the length scale has an absolute zero point. Analogously, an order of six products is twice as large as an order of three products. The temperature measured in Kelvin does have an absolute minimum, so that temperature is sometimes measured on a ratio scale.
Available until 30 Nov |Learn more- Samprit Chatterjee, Ali S. Hadi(Authors)
- 2015(Publication Date)
- Wiley(Publisher)
CHAPTER 5 QUALITATIVE VARIABLES AS PREDICTORS 5.1 INTRODUCTION Qualitative or categorical variables can be very useful as predictor variables in regression analysis. Qualitative variables such as gender, marital status, or political affiliation can be represented by indicator or dummy variables. These variables take on only two values, usually 0 and 1. The two values signify that the observation belongs to one of two possible categories. The numerical values of indicator variables are not intended to reflect a quantitative ordering of the categories, but only serve to identify category or class membership. For example, an analysis of salaries earned by computer programmers may include variables such as education, years of experience, and gender as predictor variables. The gender variable could be quantified, say, as 1 for female and 0 for male. Indicator variables can also be used in a regression equation to distinguish among three or more groups as well as among classifications across various types of groups. For example, the regression described above may also include an indicator variable to distinguish whether the observation was for a systems or applications programmer. The four conditions determined by gender and type of programming can be represented by combining the two variables, as we shall see in this chapter. Regression Analysis by Example, Fifth Edition. By Samprit Chatterjee and Ali S. Hadi Copyright © 2012 John Wiley & Sons, Inc. 129
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