Scaling, Validity, and Dependability in Measurement

With the use of numbers, we may test our generated hypotheses and do statistical analysis on the data that is produced (deductive research). They also make it easier to communicate the findings of our study.

QUADRATY OF SCALES

To measure anything is to collect numerical data. We need a scale in order to be able to provide numbers to the characteristics of objects. A tool or method used to differentiate people based on how they differ from one another on the factors relevant to our study is called a scale. The process of scaling entails locating our things on a continuum.

Let's say we wish to gauge consumer opinions regarding the consumption of soft drinks. The next phase in the measuring process is to choose a scale that enables us to put numbers on the attribute (attitude toward soft drink intake) of our objects (consumers), once we have generated one or more scale items or questions.

This makes it possible for us to categorize our objects, or consumers, according to how inclined they are to drink soft drinks or not. A Likert scale is one of the many tools at our disposal for categorizing customers. The Likert scale is a five-point rating system with the following anchors to determine how strongly respondents agree with a statement (like "I enjoy having a soft drink"). 1 indicates strongly disagree, 2 disagree, 3 neither agrees nor disagrees, 4 indicates agree, and 5 indicates strongly agree. Because each respondent is given a number representing a more or less favorable, neutral, or more or less negative opinion toward soft drinks, the Likert scale enables us to identify customers based on how they differ from one another in this regard.

What the meaning of the numerals 1, 2, 3, 4, and 5 is is the key question. Is it possible for us to rank our things, for example, using the scale we've used (2 is more than 1)? Is the difference between 1 and 2 equal to the difference between 2 and 3? Does it allow us to compare differences between objects? Does it also let us compute statistics like the mean, or average, and the standard deviation? It depends, is the response. It is dependent upon the scale type (i.e., fundamental scale type) that we have employed.

Scales can be classified into four categories: nominal, ordinal, interval, and ratio. As we go from the nominal to the ratio scale, the level of scale refinement becomes increasingly complex. For this reason, when we use an interval or ratio scale instead of the other two, more detailed information about the variables can be gathered. The scale's potency grows with the intricacy of its calibration or fine-tuning. More potent scales enable more complex data analysis to be carried out, which leads to the discovery of more significant responses to our research questions. Still, certain variables are easier to scale more effectively than others. Now let's look at each of these four scales.

NOMINAL SCALE

A nominal scale enables the researcher to place participants into groups or categories. For instance, respondents can be divided into two groups based on their gender: male and female. Codes 1 and 2 can be allocated to these two groupings. Other than classifying responders into one of two nonoverlapping or mutually exclusive categories, these numbers function as straightforward and practical category labels with no inherent significance. Keep in mind that all of the categories together are thorough. Put otherwise, there isn't a third group that responders would typically fit into. Nominal scales, then, divide people or things into groups that are mutually exclusive and collectively exhaustive.

Finding the proportion (or frequency) of men and women in our sample of respondents is one of the data sets that nominal scaling can produce. Computer examination of the data at the end of the survey may reveal that 98 of the respondents are men and 102 are women, for instance, if we had interviewed 200 people and assigned code number 1 to all male respondents and number 2 to all female respondents. We can infer from this frequency distribution that 51% of respondents to the survey are women and 49% are men. Such scaling provides no further information about the two groups beyond this marginal data. As a result, the nominal scale provides some fundamental, gross, category information.

ORDINAL SCALE

An ordinal scale rank-orders the categories in a meaningful way in addition to classifying the variables in a way that indicates distinctions between the various categories. The ordinal scale would be applied to any variable for which the categories are to be arranged in accordance with a preference. The preference would be assigned a number, 1, 2, and so on, and ranked (for example, from best to worst; first to last). For instance, respondents may be asked to rank the significance of five different characteristics in a profession that the researcher might be interested in examining in order to express their preferences. This kind of question could look like the one in the example below.

The ordinal scale aids the researcher in ascertaining the proportion of participants who rank utilizing a variety of talents as most important, interacting with others as most vital, and so forth.

This information could be useful in creating positions that most employees feel are enriching. It is evident at this point that the ordinal scale offers greater information than the nominal scale. By ranking the categories, the ordinal scale provides information on how respondents discern between them in addition to just separating the categories. But take note that the ordinal scale provides no indication of the size of the disparities between the ranks.

In the example of job qualities, for example, the first-ranked feature may be preferred over the second-ranked feature by a very little margin, whereas the third-ranked feature may be preferred over the fourth-ranked feature by a significant margin. As a result, with ordinal scaling, we are unsure of the extent of disparities in the ranking of the things, people, or events under investigation, even when they are obviously known. Interval scaling fills in this shortfall.

INTERVAL SCALE

Numerically equivalent lengths on the scale indicate equal values in the attributes being assessed in an interval scale, also known as an equal interval scale. The interval scale enables us to compare differences between things, whereas the nominal scale simply permits us to qualitatively separate groups by classifying them into mutually exclusive and collectively exhaustive sets, and the ordinal scale to rank-order the preferences. Any two values on the scale that differ from each other are the same as any other two nearby values on the scale.

An excellent illustration of an interval-scaled device is the clinical thermometer, which has an arbitrary origin and a difference between 104 and 105 degrees that is equal to the difference between 98.6 and 99.6 degrees, which is thought to be the typical body temperature. But take note that while a temperature increase from 98.6 to 99.6 may not cause for alarm, a temperature increase from 104 to 105 degrees most definitely will! The variances, the sequence, and the equality of the magnitude of the variances in the variable are therefore captured by the interval scale. Because of this, it is a more potent scale than the nominal and ordinal scales, and the arithmetic mean serves as its central tendency measure. The variance, standard deviation, and range are its measurements of dispersion.

RATIO SCALE

Because the ratio scale has an absolute zero point as opposed to an arbitrary one, which is a valid measurement point, it overcomes the interval scale's arbitrary origin point drawback. As a result, the ratio scale captures the proportions in the differences as well as the size of the disparities between points on the scale. Because it has a distinct zero origin (rather than an arbitrary one) and has all the characteristics of the other three scales, it is the most potent of the four. One useful illustration of a ratio scale is the weighing balance. It is calibrated with an absolute (as opposed to arbitrary) zero rigin, which enables us to determine the ratio of the weights of two individuals.

A person weighing 250 pounds, for example, is twice as heavy as a person weighing 125 pounds. It should be noted that the 2:1 ratio will remain intact when multiplying or dividing both of these integers (250 and 125) by any given number. The geometric or arithmetic mean can be used to determine the ratio scale's central tendency, while the standard deviation, variance, or coefficient of variation can be used to determine the dispersion. Ratio scales can be found, for instance, in those that record an individual's real age, income, and number of employers.

By now, you must have guessed that some variables, like gender, can only be measured on the nominal scale, while others, like temperature, can be measured using an interval scale with a thermometer or an ordinal scale (hot, medium, and low). It is sage to use a more potent scale wherever it is feasible.

ORDINAL OR INTERVAL

Likert scales are a widely used tool for gauging attitudes and opinions; they are covered later in this chapter. They often range from 1 (strongly disagree) to 5 (strongly agree), with a neutral point in the middle (e.g. neither agree nor disagree) to indicate how much participants agree or disagree with a certain proposition.

It's debatable whether this scale is ordinal or interval in nature; some contend that a Likert scale is ordinal in nature, pointing out that it's not possible to assume that every pair of adjacent levels is equidistant (of the same distance). Nevertheless, Likert scales, along with a few other scales, such as the numerical scale and the semantic differential scale, which are also covered later in this chapter, are typically treated as interval scales because this makes it possible for researchers to compute averages and standard deviations as well as to use other, more sophisticated statistical techniques (like testing hypotheses).

The nominal, ordinal, interval, and ratio scales are the four that can be used to measure variables. The nominal scale gives the least amount of information on the variable and emphasizes the contrasts by grouping things or people. By ranking the categories of the nominal scale, the ordinal scale adds some more information. The interval scale gives us information about the size of the variations in the variable in addition to ranking it. The ratio scale shows the proportion of the differences as well as their magnitude. These ratios would remain intact via division or multiplication.

The precision with which we can characterize the data increases as we go from the nominal to the ratio scale, and we have more options when it comes to applying stronger statistical tests. Therefore, it is best to measure the variables of interest using a more powerful scale rather to a less strong one wherever feasible and appropriate.

Two types of scaling approaches are frequently employed in business research: rating scales and ranking scales. Each item on a rating scale is rated separately from the other items being studied. Comparing items side by side and extracting the preferred options and ranking among them is what ranking scales do, however.

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