Artificial Intelligence

Artificial Intelligence. Topic- case, variable, levels of measurement..

CASE AND VARIABLE. A dataset contains information about a sample. A dataset consists of cases. Cases are nothing but the objects in the collection. Each case has one or more attributes or qualities, called variables which are characteristics of cases. A quantity whose value changes across the population and can be measured is called variable..

There are two categories under the term variable, Numeric and Categorical..

LEVELS OF MEASUREMENT. Levels of measurement, also called scales of measurement, tell you how precisely variables are recorded. The way a set of data measured is called the level of measurement. Not all data can be treated equally. Some are quantitative, and some qualitative. Some data sets are continuous and some are discrete. Qualitative data can be nominal or ordinal. And quantitative data can be split into two groups: interval and ratio. Each scale is an incremental level of measurement, meaning, each scale fulfils the function of the previous scale. Nominal scale is naming scale, where variables are simply “named” or labelled, with no specific order. Ordinal scale has all its variables in a specific order, beyond just naming them. Interval scale offers labels, order, as well as, a specific interval between each of its variable options. Ratio scale bears all this characteristics of an interval scale, in addition to that, it can also accommodate the value of ”zero” on any of its variables..

LEVELS OF MEASUREMENT. Absolute zero. Distance is meaningful.

NOMINAL LEVEL. A Nominal Scale is a measurement scale, in which numbers serve as “tags” or “labels” only, to identify or classify an object. This measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value. Nominal variables are like categories such as Mercedes, BMW, or Audi, or like the four seasons – winter, spring, summer, and autumn. They aren’t numbers, and cannot be used in calculations and neither in any order or rank..

EXAMPLE FOR NOMINAL LEVEL. Incremental progress What is your Political preference? Where do you live? M- MALE INDEPENDENT 1- SUBURBS F- FEMALE 2) CONGRESS 2- CITY 3) BJP 3- TOWN.

CHARACTERISTICS OF NOMINAL LEVEL. In nominal scale a variable is divided into two or more categories, for example, agree/disagree, yes or no etc. It’s is a measurement mechanism in which answer to a particular question can fall into either category. Nominal scale is qualitative in nature, which means numbers are used here only to categorize or identify objects. For example, football fans will be really excited, as the football world cup is around the corner! Have you noticed numbers on a jersey of a football player? These numbers have nothing to do with the ability of players, however, they can help identify the player. In nominal scale, numbers don’t define the characteristics related to the object, which means each number is assigned to one object. The only permissible aspect related to numbers in a nominal scale is “counting.”.

ORDINAL LEVEL. Ordinal scale is defined as a variable measurement scale used to simply depict the order of variables and not the difference between each of the variables. These scales are generally used to depict non-mathematical ideas such as frequency, satisfaction, happiness, a degree of pain, etc. It is quite straightforward to remember the implementation of this scale as ‘Ordinal’ sounds similar to ‘Order’, which is exactly the purpose of this scale. Ordinal Scale maintains descriptional qualities along with an intrinsic order but is void of an origin of scale and thus, the distance between variables can’t be calculated. Descriptional qualities indicate tagging properties similar to the nominal scale, in addition to which, the ordinal scale also has a relative position of variables. Origin of this scale is absent due to which there is no fixed start or “true zero”..

EXAMPLE FOR ORDINAL LEVEL. Status at workplace, tournament team rankings, order of product quality, and order of agreement or satisfaction are some of the most common examples of the ordinal Scale. These scales are generally used in market research to gather and evaluate relative feedback about product satisfaction, changing perceptions with product upgrades, etc. For example, a semantic differential scale question such as: How satisfied are you with our services? Very Unsatisfied – 1 Unsatisfied – 2 Neutral – 3 Satisfied – 4 Very Satisfied – 5.

CHARACTERISTICS OF ORDINAL LEVEL. Along with identifying and describing the magnitude, the ordinal scale shows the relative rank of variables. The properties of the interval are not known. Measurement of non-numeric attributes such as frequency, satisfaction, happiness etc. In addition to the information provided by nominal scale , ordinal scale identifies the rank of variables. Using this scale, survey makers can analyze the degree of agreement among respondents with respect to the identified order of the variables..

INTERVAL LEVEL. Interval level is defined as a numerical scale where the order of the variables is known as well as the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. It is easy to remember the primary role of this scale too, ‘Interval’ indicates ‘distance between two entities, which is what Interval scale helps in achieving. These scales are effective as they open doors for the statistical analysis of provided data. Mean, median, or mode can be used to calculate the central tendency in this scale. The only drawback of this scale is that there no pre-decided starting point or a true zero value. Interval scale contains all the properties of the ordinal scale, in addition to which, it offers a calculation of the difference between variables. The main characteristic of this scale is the equidistant difference between objects..

EXAMPLE FOR INTERVAL LEVEL. 80 degrees is always higher than 50 degrees and the difference between these two temperatures is the same as the difference between 70 degrees and 40 degrees. Also, the value of 0 is arbitrary because negative values of temperature do exist – which makes the Celsius/Fahrenheit temperature scale a classic example of an interval scale. Interval scale is often chosen in research cases where the difference between variables is a mandate – which can’t be achieved using a nominal or ordinal scale. The Interval scale quantifies the difference between two variables whereas the other two scales are solely capable of associating qualitative values with variables..

CHARACTERISTICS OF INTERVAL SCALE. The interval scale is preferred to nominal scale or ordinal scale because the latter two are qualitative scales. The interval scale is quantitative in the sense that it can quantify the difference between values. Interval data can be discrete with whole numbers like 8 degrees, 4 years, 2 months, etc., or continuous with fractional numbers like 12.2 degrees, 3.5 weeks or 4.2 miles. You can subtract values between two variables that help understand the difference between two variables. Interval measurement allows you to calculate the mean and median of variables. Interval data is especially useful in business, social, and scientific analysis and strategy because it is straightforward and quantitative..

RATIO LEVEL. Ratio level is defined as a variable measurement scale that not only produces the order of variables but also makes the difference between variables known along with information on the value of true zero. It is calculated by assuming that the variables have an option for zero, the difference between the two variables is the same and there is a specific order between the options. With the option of true zero, varied inferential, and descriptive analysis techniques can be applied to the variables. In addition to the fact that the ratio scale does everything that a nominal, ordinal, and interval scale can do, it can also establish the value of absolute zero. The best examples of ratio scales are weight and height. In market research, a ratio scale is used to calculate market share, annual sales, the price of an upcoming product, the number of consumers, etc..

EXAMPLE FOR RATIO LEVEL. The following questions fall under the Ratio Scale category: What is your daughter’s current height? Less than 5 feet. 5 feet 1 inch – 5 feet 5 inches 5 feet 6 inches- 6 feet More than 6 feet What is your weight in kilograms? Less than 50 kilograms 51- 70 kilograms 71- 90 kilograms 91-110 kilograms More than 110 kilograms.

CHARACTERISTICS OF RATIO LEVEL. Ratio scale, as mentioned earlier has an absolute zero characteristic. It has orders and equally distanced value between units. The zero point characteristic makes it relevant or meaningful to say, “one object has twice the length of the other” or “is twice as long.” Ratio scale doesn’t have a negative number, unlike interval scale because of the absolute zero or zero point characteristic. To measure any object on a this scale, researchers must first see if the object meets all the criteria for interval scale plus has an absolute zero characteristic. Ratio scale provides unique possibilities for statistical analysis. In this scale, variables can be systematically added, subtracted, multiplied and divided (ratio). All statistical analysis including mean, mode, the median can be calculated using it. Also, chi-square can be calculated on this scale variable..

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