If you are reading this as you get stuck somewhere in your PhD thesis research, then you are at the right place. PhD research is the hardest nut to crack, especially in this era when international writing standards are so high. Problems encounter you at every second step, but being consistent, persistent, and enthusiastic is the only thing that gives you the courage to face every hardship.
How fast you get out of a specific research problem depends on your expertise and abilities to make good decisions. With that in mind, getting assistance from one that has already passed through this phase of life is an important first step in solving every doctorate research problem. Thus, you will be glad to know that the topic of our today’s discussion will guide you about the measurement scales used in PhD thesis, that otherwise become a nightmare for students. Let’s get started with our discussion without taking too much time:
Measurement scales- A brief introduction:
Basically, in statistics, variables and numbers are categorised and explained by means of different measurement scales. Different levels of measurement scales hint at some specific characteristics that are extremely helpful in conducting different types of statistical analysis. The word measurement actually refers to the act of measuring something, while the scale refers to the device used to measure the characteristics of a certain object and any event. All in all, the measurement scale aims to quantify the data variables in research statistics that further help in knowing which data analysis method will be most useful to extract the information.
There are various types of measurement scales that measure the different types of data to provide you with the best possible results. Some of them, along with a brief description, are given below:
Different Levels of Measurement:
The level of measurement refers to a particular dataset that determines the relationship between different values given to the attributes of the data variables. By getting an idea of the different measurement levels, researchers easily choose the best statistical method of analysis. These levels include; nominal, interval, ratio scale, or ordinal.
Nominal Scales:
It is one of the measurement scales that help in the identification process. It is the oldest and less commonly used level of data measurement and is also known as a categorical scale. It usually does not assign numbers to the different attributes of variables. Instead, it gives them labels that are qualitative in nature. So not every data analysis technique can be applied to the nominal scales; only frequency count or percentage can be used. Furthermore, results are interpreted by only using pie or bar charts.
Ordinal scale:
It refers to the second level of data measurement that involves ranking the attributes of the variables. How many items one must add to a scale depends on how many times it occurs in the question being asked. Usually, ascending or descending orders are used to arrange the point in the ordinal scale. Medians and modes are the most commonly applied statistical analysis techniques on an ordinal scale.
Interval Scale:
It is the third level of data measurement or, in simple words, a measurement scale which is also known as a metric scale that aims to collect reflective quantitative data. Simply, it collects and measures data where the distance between different points must be equal. It is known by market researchers and surveyors as it gives different responses in the form of different ranks and orders. It gives the zero an arbitrary presence that simply means that the zero has no real meaning in the interval scale.
Ratio scale:
It includes the properties of all the above-mentioned measurement scales. It makes it possible by taking nominal data, defining its attributes, selecting some specific or equal interval and lastly, breaking it down into exact values. However, zero is not given an arbitrary presence in the ratio scales. Rather, zero simply means that the data has no value point.
In addition to this, the researcher can use a few more specific measurement scales in PhD thesis. Such as:
10 Point Likert Scale:
It is a type of psychometric scale that is commonly used in PhD thesis that collects data by designing a questionnaire. It usually aims to collect quantitative data by giving respondents a list of options to limit their responses. Furthermore, it is named Likert on behalf of its inventor, named Psychologist Rensis Likert. Basically, in most of the questionnaires, as an answer to the given question, the respondents are given certain options, and after specifying their level of agreement or disagreement, they have to select one. Thus, in this way, the Likert scale records the intensity of feeling about a specific item. With a few exceptions, the Likert scale can be 5 points, 7 points or even 10 points based on the range selected by the researcher. Social sciences, business, management, statistics, and psychology use this measurement scale for completing their PhD thesis research.
Comparative Scale:
It is an example of the ordinal measurement scale that is commonly referred to as a non-metric scale. It is the simplest measuring scale as here the researcher only collects the essential information about two or more things and directly compares them in the measuring process.
Constant Sum Scale:
It is another type of scale often used in market research or other related surveys where the respondents are asked to categorise the given number of points and per cent as a part of the total sum. This division or categorisation is later used to find the variance.
Itemized Scale:
It is the type of ordinal scale that measures consumer attitudes such as product preference, purchase intention, and customer satisfaction.
Final thoughts:
In a nutshell, measurement scales are used for the quantification of different variables used in research to conduct data analysis. They are important aspects of research as the level of data measurement determines which analysis technique must be used. Thus, the article has provided a brief description of the same measurement scales or level of data measurement along with the most suitable technique to analyse it.
