Quantitative data are measures of values or counts and are expressed as numbers (www.abs.gov.au). In other words, quantitative data are data about numeric variables (www.abs.gov.au). Four types of quantitative data are interval, nominal, ordinal and ratio. Firstly, interval scales are numeric scales in which we know not only the order, but also the exact differences between the values (www.mymarketresearchmethods.com). Other than that, interval data also sometimes called integer is measured along a scale in which each position is equidistant from one another (www.changingminds.org). For example agree, neutral and disagree. Next, nominal data is categories of an object that the researchers are measuring (Hazman, n.d.). The scale categories are merely labels and can only give the frequency. Nominal data are items which are differentiated by a simple naming system and the only thing a nominal scale does is to say that items being measured have something in common, although this may not be described (www.changingminds.org). Nominal items also may have numbers assigned to them. For example, male/female labels are categories to assign people (Hazman, n.d.). After that, ordinal data not only categorizes variables in such a way as to denote differences among various categories, it also rank-orders categories in some meaningful way (Yarina, n.d.). It ranks order the categories from highest to lowest (Hazman, n.d.). For example, in the case of educational qualification, we can arrange from hight to low which is from Ph.D, masters, bachelors, diploma, and certificate (Hazman, n.d.). Lastly, ratio is the interval between values is not interpretable in an ordinal measure. In interval measurement the distance between attributes does have meaning (www.socialresearchmethods.net). It is overcome the disadvantages of the arbitrary origin point of the interval scale, in that, it has absolute zero point which is a meaningful measurement point (Yarina, n.d.). For...
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