Tag Archives: MBA

Measurement Scales and Data Types

3 Feb

Introduction to Measurement Scales and Data Types

This tutorial discusses a classification system that is often used to describe the measurement of concepts or variables that are used in social sciences and behavioral research. This classification system categorizes the variables as being measured on either a nominal, ordinal, interval, or ratio scale. After introducing the classification system and providing examples of variables which are typically measured on each type of scale, we note the implications of these measurement scales for the analysis of data. Specifically, we discuss the statistical tests which are most appropriate for data measured on each type of scale. Finally, we will briefly consider some of the limits and criticisms of this classification system.


I. Nominal, Ordinal, Interval, and Ratio measurement scales

In the social and behavioral sciences, as in many other areas of science, we typically assign numbers to various attributes of people, objects, or concepts. This process is known as measurement. For example, we can measure the height of a person by assigning the person a number based on the number of inches tall that person is. Or, we can measure the size of a city by assigning the city a number which is equal to the number of residents in that city.

Sometimes the assignment of numbers to concepts we are studying is rather crude, such as when we assign a number to reflect a person’s gender (i.e., Male = 0 and Female = 1). This type of measurement is known as a Nominal measurement scale. A Nominal measurement scale is used for variables in which each participant or observation in the study must be placed into one mutually exclusive and exhaustive category. For example, categorizing study participants into “male” and “female” categories demonstrates that ‘sex’ is measured on a nominal scale. Every observation in the study falls into one, and only one, Nominal category.

With a nominal measurement scale, there is no relative ordering of the categories — the assignment of numeric scores to each category (Male, Female) is purely arbitrary. The next level of measurement, Ordinal measurement scales, do indicate something about the rank-ordering of study participants. For example, if you think of some type of competition or race (swimming, running), it is possible to rank order the finishers from first place to last place. If someone tells you they finished 2nd, you know that one person finished ahead of them, and all other participants finished behind them.

Although ordinal variables provide information concerning the relative position of participants or observations in our research study, ordinal variables do not tell us anything about the absolute magnitude of the difference between 1st and 2nd or between 2nd and 3rd. That is, we know 1st was before 2nd, and 2nd was before 3rd, but we do not know how close 3rd was to 2nd or how close 2nd was to 1st. The 1st place finisher could have been a great deal ahead of the 2nd place finisher, who finished a great deal ahead of the 3rd place finisher; or, the 1st, 2nd, and 3rd place finishers may have all finished very close together. The image below illustrates the ordinal ranking of individuals in a competition. The tick mark to the far right illustrates the person who finished in first place, while the tick mark to the far left represents the person who finished sixth out of six.

The limits of ordinal data are most apparent when one looks at the distance between the third and the fourth place finishers. Although the absolute distance between third and fourth was not that large, the measurement of ordinal data does not indicate this detail.

The next level of measurement, Interval scales, provide us with still more quantitative information. When a variable is measured on an interval scale, the distance between numbers or units on the scale is equal over all levels of the scale. An example of an Interval scale is the Farenheit scale of temperature. In the Farenheit temperature scale, the distance between 20 degrees and 40 degrees is the same as the distance between 75 degrees and 95 degrees.

With Interval scales, there is no absolute zero point. For this reason, it is inappropriate to express Interval level measurements as ratios; it would not be appropriate to say that 60 degrees is twice as hot as 30 degrees. Our final type of measurement scales, Ratio scales, do have a fixed zero point. Not only are numbers or units on the scale equal over all levels of the scale, but there is also a meaningful zero point which allows for the interpretation of ratio comparisons. Time is an example of a ratio measurement scale. Not only can we say that difference between three hours and five hours is the same as the difference between eight hours and ten hours (equal intervals), but we can also say that ten hours is twice as long as five hours (a ratio comparison)

II. Measurement Scales and Statistical Tests

One of the primary purposes of classifying variables according to their level or scale of measurement is to facilitate the choice of a statistical test used to analyze the data. There are certain statistical analyses which are only meaningful for data which are measured at certain measurement scales. For example, it is generally inappropriate to compute the mean for Nominal variables. Suppose you had 20 subjects, 12 of which were male, and 8 of which were female. If you assigned males a value of ‘1’ and females a value of ‘2’, could you compute the mean sex of subjects in your sample? It is possible to compute a mean value, but how meaningful would that be? How would you interpret a mean sex of 1.4? When you are examining a Nominal variable such as sex, it is more appropriate to compute a statistic such as a percentage (60% of the sample was male).

When a research wishes to examine the relationship or association between two variables, there are also guidelines concerning which statistical tests are appropriate. For example, let’s say a University administrator was interested in the relationship between student gender (a Nominal variable) and major field of study (another Nominal variable). In this case, the most appropriate measure of association between gender and major would be a Chi-Square test. Let’s say our University administrator was interested in the relationship between undergraduate major and starting salary of students’ first job after graduation. In this case, salary is not a Nominal variable; it is a ratio level variable. The appropriate test of association between undergraduate major and salary would be a one-way Analysis of Variance(ANOVA), to see if the mean starting salary is related to undergraduate major.

Finally, suppose we were interested in the relationship between undergraduate grade point average and starting salary. In this case, both grade point average and starting salary are ratio level variables. Now, neither Chi-square nor ANOVA would be appropriate; instead, we would look at the relationship between these two variables using the Pearson correlation coefficient.


Marketing Managment 12th edition by (PHILIP KOTLER Northwestern University KEVIN LANE KELLER Dartmouth College)

15 Mar

Marketing Management is the leading marketing text because its content and organization consistently reflect changes in marketing theory and practice. The very first edition of Marketing Management, published in 1967, introduced the concept that companies must be customer-and-market driven. But there was little mention of what have now become fundamental topics such as segmentation, targeting, and positioning. Concepts such as brand equity, customer value analysis, database marketing, e-commerce, value networks, hybrid channels, supply chain management, and integrated marketing communications were not even part of the marketing vocabulary then. Firms now sell goods and services through a variety of direct and indirect channels. Mass advertising is not nearly as effective as it was. Companies are exploring new forms of communication, such as experiential, entertainment, and viral marketing. Customers are increasingly telling companies what types of product or services they want and when, where, and how they want to buy them.

In response, companies have shifted gears from managing product portfolios to managing customer portfolios, compiling databases on individual customers so they can understand them better, and construct individualized offerings and messages. They are doing less product and service standardization and more niching and customization. They are replacing monologues with customer dialogues. They are improving their methods of measuring customer profitability and customer lifetime value. They are intent on measuring the return on their marketing investment and its impact on shareholder value. They are also concerned with the ethical and social implications of their marketing decisions.

As companies change, so does their marketing organization. Marketing is no longer a company department charged with a limited number of tasks—it is a company-wide undertaking. It drives the company’s vision, mission, and strategic planning. Marketing includes decisions like who the company wants as its customers; which needs to satisfy; what products and services to offer; what prices to set; what communications to send and receive; what channels of distribution to use; and what partnerships to develop. Marketing succeeds only when all departments work together to achieve goals: when engineering designs the right products, finance furnishes the required funds, purchasing buys quality materials, production makes quality products on time, and accounting measures the profitability of different customers, products, and areas.

And as marketing techniques and organization have changed, so has this text. The biggest change is the addition of a co-author. Kevin Lane Keller is one of the top marketing academics of his generation. He has conducted ground-breaking research and written a highly successful text, Strategic Brand Management. He has also worked with marketing executives from companies around the globe to help them become better marketers. He brings fresh thinking and new perspectives to Marketing Management.

The twelfth edition reflects a collaborative effort between the two authors with a goal of creating the best edition of Marketing Management ever. Extensive focus groups were conducted to fully understand the course and classroom needs of the instructor. Based on this input, the twelfth edition is designed to preserve the strengths of previous editions while introducing new material and organization to further enhance learning. It is dedicated to helping companies, groups, and individuals adapt their marketing strategies and management to the marketplace realities of the twenty-first century.

free download  E-BOOK from  http://rs222.rapidshare.com/files/132978474/USM.ADW618.MARKETING_MANAGEMENT.12th.edition.-.Philip.Kotler.and.Kevin.Lane.Keller.zip