This document discusses different types of measurement scales used in research. It defines measurement as assigning numbers or symbols to characteristics according to rules, while scaling involves placing measured objects on a continuum. The primary scales of measurement are nominal, ordinal, interval, and ratio. Nominal scales use numbers as labels, ordinal scales reflect ranking, interval scales have equal differences, and ratio scales have a true zero point. Examples of scales discussed include Likert scales, semantic differentials, and constant sum scales for measuring attitudes and importance of attributes.
Here are some key considerations when developing response scales for surveys:
1. Number of scale categories - Generally, 5-7 point scales are most commonly used. More categories provide more discrimination but also more complexity.
2. Type of anchors - Semantic differential scales use bipolar adjectives (e.g. good-bad). Likert scales use agreement statements.
3. Strength of anchors - Use clear, unambiguous labels that respondents can easily interpret (e.g. strongly agree vs agree).
4. Balance of scale - Scales should have equal numbers of positive and negative categories to avoid bias.
5. Forced vs non-forced choice - Forced scales require a response while non-
Concept of Measurements in Business ResearchCS PRADHAN
Measurement is a fundamental concept in business research used to quantify variables and enable comparison. It requires defining what is to be measured and how through operational definitions. There are four levels of measurement - nominal, ordinal, interval, and ratio - determined by the characteristics of order, distance, and origin represented. Validity and reliability are important criteria for any measurement and various techniques like rating, ranking, and sorting are used depending on whether the concept is simple or complex.
Comparative scaling techniques in business researchSatyam Anand
This document discusses different types of comparative scaling techniques used to measure preferences and rankings. Comparative scaling involves directly comparing two or more objects and can be used to rank items like MP3 players. Rank order scales ask respondents to order a set of items by preference, importance, or other criteria. Spearman and Kendall rank correlation coefficients are statistical methods used to measure correspondence between sets of rankings. Paired comparisons involve presenting two objects at a time and selecting a preferred one, with the total number of possible comparisons calculated as n(n-1)/2 for n items.
This document is an assignment submission for a Business Statistics course taught by Ms. Samiha Sanjana at North East University Bangladesh. It was submitted by student Hasan Mohammed Abdullah Faroqui. The assignment discusses the application of statistics in business. It defines business statistics and explains how statistics are used in various business functions like marketing, finance, insurance, purchasing and inventory, economics, operations, human resource management, research and development, information systems, and controlling.
SPSS is a popular statistical software package that allows users to perform complex data analysis with simple instructions. It requires variables, data, measurement scales, and a code book to be defined. The document then describes different variable types (independent, dependent), measurement scales (nominal, ordinal, interval, ratio), how to start and use SPSS, and basic functions for data entry, analysis including frequencies, descriptives, correlation, and reliability which can be measured using Cronbach's alpha.
This document discusses inflation accounting and its need. Traditional historical cost accounting fails to accurately match current revenues and costs during inflation as historical costs are much lower than current costs. It also fails to realistically report profits. The document describes two methods of inflation accounting: current purchasing power method and current value systems method. Both methods aim to express financial statement figures in terms of their current purchasing power to provide a more accurate picture during inflationary periods.
The document discusses business statistics and its importance. It defines statistics as the study of collecting, organizing, analyzing, and interpreting numerical data. There are five stages to statistical investigation: data collection, organization, presentation, analysis, and interpretation of results. Statistics helps simplify complex data, facilitate comparison between data sets, test hypotheses, formulate policies, and derive valid inferences. However, statistics has limitations as it does not study individuals, statistical laws are approximations rather than exact, and it only analyzes aggregated data rather than individual observations.
Product differentiation in indian marketArbaaz khan
This document discusses product differentiation in the Indian market. It defines product differentiation as creating differences between a firm's products and competitors' products. These differences can provide competitive advantage if customers value them. The document then lists and describes various bases for product differentiation, including form, features, customization, performance quality, conformance quality, durability, reliability, reparability, and style. It concludes that durability, features, performance quality, and style are common bases for differentiation in India given the large middle class population and need for affordable yet high-quality products.
Here are some key considerations when developing response scales for surveys:
1. Number of scale categories - Generally, 5-7 point scales are most commonly used. More categories provide more discrimination but also more complexity.
2. Type of anchors - Semantic differential scales use bipolar adjectives (e.g. good-bad). Likert scales use agreement statements.
3. Strength of anchors - Use clear, unambiguous labels that respondents can easily interpret (e.g. strongly agree vs agree).
4. Balance of scale - Scales should have equal numbers of positive and negative categories to avoid bias.
5. Forced vs non-forced choice - Forced scales require a response while non-
Concept of Measurements in Business ResearchCS PRADHAN
Measurement is a fundamental concept in business research used to quantify variables and enable comparison. It requires defining what is to be measured and how through operational definitions. There are four levels of measurement - nominal, ordinal, interval, and ratio - determined by the characteristics of order, distance, and origin represented. Validity and reliability are important criteria for any measurement and various techniques like rating, ranking, and sorting are used depending on whether the concept is simple or complex.
Comparative scaling techniques in business researchSatyam Anand
This document discusses different types of comparative scaling techniques used to measure preferences and rankings. Comparative scaling involves directly comparing two or more objects and can be used to rank items like MP3 players. Rank order scales ask respondents to order a set of items by preference, importance, or other criteria. Spearman and Kendall rank correlation coefficients are statistical methods used to measure correspondence between sets of rankings. Paired comparisons involve presenting two objects at a time and selecting a preferred one, with the total number of possible comparisons calculated as n(n-1)/2 for n items.
This document is an assignment submission for a Business Statistics course taught by Ms. Samiha Sanjana at North East University Bangladesh. It was submitted by student Hasan Mohammed Abdullah Faroqui. The assignment discusses the application of statistics in business. It defines business statistics and explains how statistics are used in various business functions like marketing, finance, insurance, purchasing and inventory, economics, operations, human resource management, research and development, information systems, and controlling.
SPSS is a popular statistical software package that allows users to perform complex data analysis with simple instructions. It requires variables, data, measurement scales, and a code book to be defined. The document then describes different variable types (independent, dependent), measurement scales (nominal, ordinal, interval, ratio), how to start and use SPSS, and basic functions for data entry, analysis including frequencies, descriptives, correlation, and reliability which can be measured using Cronbach's alpha.
This document discusses inflation accounting and its need. Traditional historical cost accounting fails to accurately match current revenues and costs during inflation as historical costs are much lower than current costs. It also fails to realistically report profits. The document describes two methods of inflation accounting: current purchasing power method and current value systems method. Both methods aim to express financial statement figures in terms of their current purchasing power to provide a more accurate picture during inflationary periods.
The document discusses business statistics and its importance. It defines statistics as the study of collecting, organizing, analyzing, and interpreting numerical data. There are five stages to statistical investigation: data collection, organization, presentation, analysis, and interpretation of results. Statistics helps simplify complex data, facilitate comparison between data sets, test hypotheses, formulate policies, and derive valid inferences. However, statistics has limitations as it does not study individuals, statistical laws are approximations rather than exact, and it only analyzes aggregated data rather than individual observations.
Product differentiation in indian marketArbaaz khan
This document discusses product differentiation in the Indian market. It defines product differentiation as creating differences between a firm's products and competitors' products. These differences can provide competitive advantage if customers value them. The document then lists and describes various bases for product differentiation, including form, features, customization, performance quality, conformance quality, durability, reliability, reparability, and style. It concludes that durability, features, performance quality, and style are common bases for differentiation in India given the large middle class population and need for affordable yet high-quality products.
Standard costing is a technique that involves setting predetermined standards for costs and comparing them to actual costs. Standards are set for materials, labor, overhead and sales prices/margins. Variances between standards and actuals are analyzed to identify reasons for differences and take corrective actions. It helps management evaluate performance, control costs, set budgets and motivate staff. Some key advantages include cost control, delegation, efficiency improvements, and anticipating future costs and profits. Limitations include requiring technical skills and difficulty separating controllable vs. uncontrollable variances.
This document discusses the use of statistics in real life, business, and good governance. It notes that statistics are used at every stage of human life and activities, and are important for employment opportunities, minimizing uncertainty, and running businesses successfully by understanding consumer behavior, product design, quality control, and market research. The document also emphasizes that good governance relies on evidence-based policymaking, transparency, and using statistics and data to ensure effectiveness, accountability, and access to information. It outlines India's current statistical system and quality measures needed to make statistics useful, as well as recent developments like the National Statistical Commission and Result Framework Documents.
- Index numbers measure relative changes in variables like prices, quantities, values over time from a base period. They are used to frame policies, reveal trends, and for deflating purposes.
- There are different methods for constructing index numbers, including simple aggregate methods, simple average of relatives methods, and weighted index numbers that assign weights.
- Common weighted indexes include the Laspeyres method which uses base period weights, the Paasche method which uses current period weights, and the Fisher Ideal Index which takes the geometric mean of the Laspeyres and Paasche.
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
This document describes a solar bottle that generates solar energy to charge mobile devices. It has two solar panels that convert sunlight into electricity, which is stored in a battery. An inverter converts the DC current from the battery to AC current that can charge phones. The target market is people in remote areas or defense personnel who lack electricity access. It discusses the product components, positioning in the market, and marketing strategies like endorsements and influencer groups to promote the solar bottle.
This document discusses the scope and uses of statistics across various fields such as planning, economics, business, industry, mathematics, science, psychology, education, war, banking, government, sociology, and more. It outlines functions of statistics like presenting facts, testing hypotheses, forecasting, policymaking, enlarging knowledge, measuring uncertainty, simplifying data, deriving valid inferences, and drawing rational conclusions. It also covers characteristics, advantages, and limitations of statistics.
The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.
Capital goods are goods that are used to make consumer goods and services and include fixed plant and machinery, hardware, software, and new factories and buildings. Consumer goods and services directly satisfy needs and wants and are divided into durables, which provide utility over time like washing machines; non-durables, which are used up immediately like coffee; and services like haircuts. Capital goods help provide the infrastructure needed to produce consumer goods and services.
Scaling involves assigning quantitative values or symbols to subjective concepts or attributes. There are four main types of scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers as labels while ordinal scales indicate ranking or order. Interval scales show equal distances between scale points and ratio scales have an absolute zero point. Common scaling techniques include rating scales, which assign qualitative ratings, and ranking scales, which compare items. Paired comparisons and rank ordering are two approaches used in ranking scales.
The document discusses concepts related to managerial economics including demand forecasting. It begins with an introduction to Kiran Shetty, an assistant professor, and outlines the topics to be covered in the course including the nature and scope of managerial economics. The key concepts of demand analysis, elasticity, and forecasting techniques are then explained in detail with examples. Determinants of demand and exceptions to the law of demand are also reviewed.
A power point presentation on statisticsKriace Ward
Statistics originated from Latin, Italian, and German words referring to organized states. Gottfried Achenwall is considered the "father of statistics" for coining the term to describe a specialized branch of knowledge. Modern statistics is defined as the science of judging collective phenomena through analysis and enumeration. While statistics can be an art and a science, its successful application depends on the skill of the statistician and their knowledge of the field being studied. Statistics are important across many domains from business, economics, and planning to the sciences. However, statistics also have limitations such as only studying aggregates, not individuals, and results being valid only on average and in the long run.
Product concept, level , classification, mix and product life cycle and its m...PROF.JITENDRA PATEL
This Presentation Discuss the first P of Marketing i.e Product. The topic covered are Product concept, level , classification, mix and product life cycle and its management.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
Marketing is the process of planning and executing the conception, pricing, promotion, and distribution of ideas, goods, and services to create exchanges that satisfy individual and organizational goals. The marketing mix, also known as the 4Ps, refers to product, price, place, and promotion - the key elements of a company's marketing strategy that are under their control. An effective marketing strategy balances these elements to meet the needs of the target market and achieve the company's objectives.
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
The document discusses measurement and scaling in marketing research. It defines key concepts like measurement, scales, and reliability and validity. It explains the four basic levels of measurement scales - nominal, ordinal, interval, and ratio scales. It also describes different scaling techniques like Likert scales, semantic differential scales, and behavioral intention scales. Scale development and evaluation of reliability and validity are important aspects of gathering primary data.
1. Measurement involves assigning numbers to objects or observations based on established rules. There are different scales of measurement that determine what statistical analyses can be used.
2. The scales of measurement from least to most powerful are nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize data while ratio scales have a true zero point and allow comparisons of ratios.
3. Each scale of measurement is associated with different statistical analyses that can appropriately be used. For example, only nominal data allows the use of the mode as a measure of central tendency while more powerful scales like interval and ratio allow the use of more sophisticated tests.
This document discusses non-parametric statistical tests, which make few assumptions about the distribution of the underlying population. It provides examples of non-parametric tests like the sign test, Wilcoxon rank sum test, and Kruskal-Wallis test. These tests involve ranking all observations from different groups together and applying statistical tests to the ranks rather than the original values. Non-parametric tests are useful when assumptions of parametric tests may not hold but lack power with small samples.
This document discusses various types of index numbers used to measure changes in economic variables over time. It defines an index number as a quantitative measure of the growth of prices, production, or other quantities of economic interest from one period to another. The document then describes different characteristics, uses, problems, classifications, and methods for constructing index numbers, including simple aggregative methods, weighted aggregative methods like Laspeyres and Paasche, and chain index numbers. It provides examples to illustrate how to calculate different index numbers.
Measurement involves assigning numbers or symbols to characteristics according to prespecified rules with a one-to-one correspondence between the numbers and characteristics. Scaling creates a continuum to locate measured objects. There are four primary scales of measurement - nominal, ordinal, interval, and ratio - which differ in the types of statistical analyses permitted and operations allowed on the assigned numbers.
This document discusses different methods of measurement and scaling used in research. It describes four primary scales of measurement: nominal, ordinal, interval, and ratio scales. It also explains several types of attitude measurement scales used including Likert scales, semantic differential scales, Thurstone scales, paired comparison scales, and Stapel scales. These scales allow researchers to measure and analyze characteristics, rank and compare objects, and assess attitudes in quantitative studies.
Standard costing is a technique that involves setting predetermined standards for costs and comparing them to actual costs. Standards are set for materials, labor, overhead and sales prices/margins. Variances between standards and actuals are analyzed to identify reasons for differences and take corrective actions. It helps management evaluate performance, control costs, set budgets and motivate staff. Some key advantages include cost control, delegation, efficiency improvements, and anticipating future costs and profits. Limitations include requiring technical skills and difficulty separating controllable vs. uncontrollable variances.
This document discusses the use of statistics in real life, business, and good governance. It notes that statistics are used at every stage of human life and activities, and are important for employment opportunities, minimizing uncertainty, and running businesses successfully by understanding consumer behavior, product design, quality control, and market research. The document also emphasizes that good governance relies on evidence-based policymaking, transparency, and using statistics and data to ensure effectiveness, accountability, and access to information. It outlines India's current statistical system and quality measures needed to make statistics useful, as well as recent developments like the National Statistical Commission and Result Framework Documents.
- Index numbers measure relative changes in variables like prices, quantities, values over time from a base period. They are used to frame policies, reveal trends, and for deflating purposes.
- There are different methods for constructing index numbers, including simple aggregate methods, simple average of relatives methods, and weighted index numbers that assign weights.
- Common weighted indexes include the Laspeyres method which uses base period weights, the Paasche method which uses current period weights, and the Fisher Ideal Index which takes the geometric mean of the Laspeyres and Paasche.
SPSS is a statistical software package used for data analysis in business research that was originally developed for social science applications. It allows users to import, organize, and analyze data using a variety of statistical procedures to generate reports and visualizations. SPSS has evolved over time from mainframe usage to its current version as a product of IBM after being acquired from SPSS Inc. in 2009.
This document describes a solar bottle that generates solar energy to charge mobile devices. It has two solar panels that convert sunlight into electricity, which is stored in a battery. An inverter converts the DC current from the battery to AC current that can charge phones. The target market is people in remote areas or defense personnel who lack electricity access. It discusses the product components, positioning in the market, and marketing strategies like endorsements and influencer groups to promote the solar bottle.
This document discusses the scope and uses of statistics across various fields such as planning, economics, business, industry, mathematics, science, psychology, education, war, banking, government, sociology, and more. It outlines functions of statistics like presenting facts, testing hypotheses, forecasting, policymaking, enlarging knowledge, measuring uncertainty, simplifying data, deriving valid inferences, and drawing rational conclusions. It also covers characteristics, advantages, and limitations of statistics.
The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.
Capital goods are goods that are used to make consumer goods and services and include fixed plant and machinery, hardware, software, and new factories and buildings. Consumer goods and services directly satisfy needs and wants and are divided into durables, which provide utility over time like washing machines; non-durables, which are used up immediately like coffee; and services like haircuts. Capital goods help provide the infrastructure needed to produce consumer goods and services.
Scaling involves assigning quantitative values or symbols to subjective concepts or attributes. There are four main types of scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers as labels while ordinal scales indicate ranking or order. Interval scales show equal distances between scale points and ratio scales have an absolute zero point. Common scaling techniques include rating scales, which assign qualitative ratings, and ranking scales, which compare items. Paired comparisons and rank ordering are two approaches used in ranking scales.
The document discusses concepts related to managerial economics including demand forecasting. It begins with an introduction to Kiran Shetty, an assistant professor, and outlines the topics to be covered in the course including the nature and scope of managerial economics. The key concepts of demand analysis, elasticity, and forecasting techniques are then explained in detail with examples. Determinants of demand and exceptions to the law of demand are also reviewed.
A power point presentation on statisticsKriace Ward
Statistics originated from Latin, Italian, and German words referring to organized states. Gottfried Achenwall is considered the "father of statistics" for coining the term to describe a specialized branch of knowledge. Modern statistics is defined as the science of judging collective phenomena through analysis and enumeration. While statistics can be an art and a science, its successful application depends on the skill of the statistician and their knowledge of the field being studied. Statistics are important across many domains from business, economics, and planning to the sciences. However, statistics also have limitations such as only studying aggregates, not individuals, and results being valid only on average and in the long run.
Product concept, level , classification, mix and product life cycle and its m...PROF.JITENDRA PATEL
This Presentation Discuss the first P of Marketing i.e Product. The topic covered are Product concept, level , classification, mix and product life cycle and its management.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
Marketing is the process of planning and executing the conception, pricing, promotion, and distribution of ideas, goods, and services to create exchanges that satisfy individual and organizational goals. The marketing mix, also known as the 4Ps, refers to product, price, place, and promotion - the key elements of a company's marketing strategy that are under their control. An effective marketing strategy balances these elements to meet the needs of the target market and achieve the company's objectives.
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
The document discusses measurement and scaling in marketing research. It defines key concepts like measurement, scales, and reliability and validity. It explains the four basic levels of measurement scales - nominal, ordinal, interval, and ratio scales. It also describes different scaling techniques like Likert scales, semantic differential scales, and behavioral intention scales. Scale development and evaluation of reliability and validity are important aspects of gathering primary data.
1. Measurement involves assigning numbers to objects or observations based on established rules. There are different scales of measurement that determine what statistical analyses can be used.
2. The scales of measurement from least to most powerful are nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize data while ratio scales have a true zero point and allow comparisons of ratios.
3. Each scale of measurement is associated with different statistical analyses that can appropriately be used. For example, only nominal data allows the use of the mode as a measure of central tendency while more powerful scales like interval and ratio allow the use of more sophisticated tests.
This document discusses non-parametric statistical tests, which make few assumptions about the distribution of the underlying population. It provides examples of non-parametric tests like the sign test, Wilcoxon rank sum test, and Kruskal-Wallis test. These tests involve ranking all observations from different groups together and applying statistical tests to the ranks rather than the original values. Non-parametric tests are useful when assumptions of parametric tests may not hold but lack power with small samples.
This document discusses various types of index numbers used to measure changes in economic variables over time. It defines an index number as a quantitative measure of the growth of prices, production, or other quantities of economic interest from one period to another. The document then describes different characteristics, uses, problems, classifications, and methods for constructing index numbers, including simple aggregative methods, weighted aggregative methods like Laspeyres and Paasche, and chain index numbers. It provides examples to illustrate how to calculate different index numbers.
Measurement involves assigning numbers or symbols to characteristics according to prespecified rules with a one-to-one correspondence between the numbers and characteristics. Scaling creates a continuum to locate measured objects. There are four primary scales of measurement - nominal, ordinal, interval, and ratio - which differ in the types of statistical analyses permitted and operations allowed on the assigned numbers.
This document discusses different methods of measurement and scaling used in research. It describes four primary scales of measurement: nominal, ordinal, interval, and ratio scales. It also explains several types of attitude measurement scales used including Likert scales, semantic differential scales, Thurstone scales, paired comparison scales, and Stapel scales. These scales allow researchers to measure and analyze characteristics, rank and compare objects, and assess attitudes in quantitative studies.
1. The document discusses various methods of measurement and scaling used in marketing research, including nominal, ordinal, interval, and ratio scales.
2. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum are described and compared to noncomparative rating scales.
3. Factors like sample characteristics, research objectives, and cross-cultural contexts are important considerations in selecting the appropriate scale of measurement. Ethical practices around informed consent and data anonymity are also discussed.
The document discusses various methods of measurement and scaling, including assigning numbers or symbols to characteristics according to rules, and placing objects on a continuum to indicate their relative positions. It describes different types of scales such as nominal, ordinal, interval, and ratio scales, and compares comparative scaling techniques like paired comparisons that involve direct object comparisons to noncomparative techniques like continuous and itemized rating scales that evaluate objects individually.
This presentation emphasizes on assessing science based on learning competencies, selecting appropriate forms of assessment and developing written and performance based tasks on science.
Mba2216 week 07 08 measurement and data collection formsStephen Ong
This document discusses research design and measurement concepts related to data collection forms. It begins with learning outcomes, which focus on measurement scales, concepts, attitudes, and questionnaire design. It then covers determining what to measure based on research questions, operationalizing concepts, and different levels of measurement scales including nominal, ordinal, interval, and ratio. The document also discusses techniques for measuring attitudes, such as ranking, rating, sorting, and choice. Specific scales are described like Likert scales, semantic differentials, and category scales. Guidelines are provided for selecting a measurement scale based on objectives and properties of the data.
3. Principles of Marketing - SS2014 - University of Siegen - Paul Marx: Chapt...Paul Marx
This document provides an overview of market research from Jun.-Prof. Dr. Paul Marx at Universität Siegen. It discusses the role of market research, the market research process, types of market research by objectives, data sources and methodology. Market research involves planning, collecting and analyzing relevant data to inform marketing decisions and communicating results to management. The key types are exploratory, descriptive and causal research using primary or secondary qualitative and quantitative methods. Market research is important to prevent costly marketing mistakes and ensure efficient decision making.
This document discusses various techniques for scaling and measurement in research, including:
1. Primary scales of measurement like nominal, ordinal, interval, and ratio scales.
2. Comparative scaling techniques like paired comparison, rank order, and constant sum scales that involve comparing objects.
3. Noncomparative or monadic scaling techniques like continuous and itemized rating scales that involve rating single objects like Likert, semantic differential, and Stapel scales.
4. Factors that influence measurement accuracy like true score, systematic error, and random error in the true score model.
The document discusses different types of scales used in measurement and data collection, including nominal, ordinal, interval, and ratio scales. It provides examples of each scale type and explains their properties and appropriate uses. The document also covers different types of rating scales commonly used in surveys, such as Likert scales, semantic differential scales, and Stapel scales.
This document discusses measurement and scaling techniques. It defines measurement as associating numbers to observations in a study. Variables are classified as quantitative/numeric or qualitative/categorical. Qualitative variables can only be observed, not measured directly. Characteristics of both humans and businesses need to be measured for meaningful assessment. This is done by assigning numbers and forming scales. There are four types of measurement scales: nominal, ordinal, interval, and ratio scales. Nominal scales classify into categories. Ordinal scales have order or ranking. Interval scales have equal distance between categories. Ratio scales have a fixed zero point, indicating absence of a characteristic. The document provides examples to explain the properties and uses of each scale type.
There are four primary types of measurement scales: nominal, ordinal, interval, and ratio. Nominal scales assign numbers or symbols to objects for identification purposes only, while ordinal scales indicate relative position or rank. Interval scales represent equal distances between scale values, and ratio scales allow for meaningful comparisons using ratios. Common scaling techniques include paired comparisons, rank ordering, constant sum, and various rating scales like Likert scales that assign values along a range of agreement levels. Non-comparative techniques involve direct ratings on continuous or itemized scales to measure attributes or characteristics of objects.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
This document discusses the concept of measurement and defines it as a process of mapping aspects of a domain onto aspects of a range according to some rule of correspondence. It provides examples of how measurement can be used to determine ratios like male to female attendance at an event by tabulating people according to sex. Measurement is also described as more complex and demanding when dealing with qualitative or abstract concepts rather than physical properties. The document later discusses ordinal scales and states that with ordinal data, the appropriate measures of central tendency are the median and percentiles or quartiles for measuring dispersion.
The document discusses various measurement scales and scaling techniques. It describes nominal, ordinal, interval, and ratio scales, with ratio scales being the most precise. It also outlines sources of error in measurement, such as respondents, situations, measurers, and instruments. Scaling techniques covered include rating scales, rank ordering, arbitrary scales, summated scales, factor scales, multidimensional scaling, and cumulative scales. Cumulative scales follow a cumulative response pattern where agreement with one item implies agreement with previous items.
This document discusses different methods of measurement and scaling used in marketing research. It begins with an overview of measurement and scaling, describing measurement as assigning numbers to characteristics according to rules. Scaling places measured objects on a continuum. There are four primary scales discussed: nominal, ordinal, interval, and ratio scales. The document then examines various comparative and noncomparative scaling techniques, such as paired comparisons, rank ordering, and constant sum scaling. It provides examples of how each technique is used to measure preferences. Finally, there is a comparison of scaling techniques and their resulting data types.
This document discusses measurement and scaling. It defines measurement as assigning numbers and scaling as placing respondents on a continuum. There are four types of measurement scales: nominal, ordinal, interval, and ratio scales. Nominal scales use numbers for identification while ordinal scales show more or less of a characteristic. Interval and ratio scales can be added and subtracted. The document also covers scaling techniques like Likert scales, semantic differentiation, and paired comparisons. It concludes with discussing criteria for good measurement including validity, reliability, and sensitivity.
200 chapter 7 measurement :scaling by uma sekaran Irfan Sheikh
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. It also discusses developing rating scales and ranking scales to measure attitudes. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the constructs they are intended to measure.
The document discusses various techniques for measurement and scaling in research. It begins by defining measurement as assigning numbers or symbols to object characteristics according to rules, while scaling creates a continuum to locate measured objects. There are four primary scales of measurement: nominal, ordinal, interval, and ratio. Nominal involves labels, ordinal involves ranking, interval involves equal distances between numbers, and ratio has a true zero point. Comparative techniques like paired comparisons and rank ordering involve direct object comparisons, while noncomparative techniques scale objects independently. Constant sum and Likert scaling are provided as examples.
The document discusses various techniques for measuring and scaling objects, characteristics, and attitudes. It begins by defining measurement as assigning numbers or symbols to objects according to rules, while scaling creates a continuum to locate measured objects. It then covers primary scales of measurement (nominal, ordinal, interval, ratio) and provides examples. Several comparative and non-comparative scaling techniques are described in detail, including paired comparison, rank ordering, constant sum, Likert scales, semantic differentials, and continuous rating scales. Advantages and disadvantages of different methods are also reviewed.
The document discusses various techniques for measuring and scaling objects, characteristics, and attitudes. It begins by defining measurement as assigning numbers or symbols to objects according to rules, while scaling creates a continuum to locate measured objects. It then covers primary scales of measurement (nominal, ordinal, interval, ratio) and provides examples. Several comparative and non-comparative scaling techniques are described in detail, including paired comparison, rank ordering, constant sum, Likert scales, semantic differentials, and continuous rating scales. Advantages and disadvantages of different methods are also reviewed.
This document discusses measurement and scaling techniques used in marketing research. It defines measurement as assigning numbers to characteristics according to rules, while scaling creates a continuum to locate measured objects. There are four primary scales of measurement - nominal, ordinal, interval, and ratio - which differ in the types of mathematical operations and statistics permitted. Comparative scaling techniques like paired comparisons and rank ordering require direct object comparisons, while noncomparative techniques scale objects independently. The appropriate scale must match the research problem and inform questionnaire design and data analysis.
This document discusses various methods of measurement and scaling used in research. It defines measurement as assigning numbers or symbols to characteristics according to rules. Scaling extends measurement by locating objects on a continuum. The document then covers different types of scaling techniques including nominal, ordinal, interval and ratio scales. It distinguishes between single item and multiple item scales. Finally, it describes different scaling methods such as Likert scales, semantic differential scales, paired comparisons, ranking, and rating scales.
Measurement involves assigning numbers or symbols to characteristics of objects to provide an accurate description. Scaling extends measurement by creating a continuum on which measurements are located. The level of measurement is important as it helps decide how to interpret and analyze the data. There are four primary levels of measurement: nominal, ordinal, interval, and ratio scales. Nominal scales use numbers for identification only, while ordinal scales show order but not absolute differences. Interval and ratio scales indicate distances between values and ratio scales also have a true zero point. Choosing the appropriate scale depends on the attitude component being measured such as knowledge, beliefs, or preferences. Good measurement scales are reliable, valid, and sensitive.
Research Methodology: Questionnaire, Sampling, Data Preparationamitsethi21985
As per PTU's MBA Syllabus, Unit No. 2: Sources Of Data: Primary And Secondary; Data Collection Methods; Questionnaire Designing: Construction, Types And Developing A Good Questionnaire. Sampling Design and Techniques, Scaling Techniques, Meaning, Types, Data Processing Operations, Editing, Coding, Classification, Tabulation. Research Proposal/Synopsis Writing. Practical Framework
This document discusses key concepts in measurement and scaling, including different types of scales and scaling techniques. It defines measurement as assigning numbers or symbols to characteristics according to rules. Scales are quantifying measures that arrange items progressively by value. There are four primary scales of measurement: nominal, ordinal, interval, and ratio scales. Scaling techniques can be comparative, involving direct comparisons, or noncomparative where objects are scaled independently. Examples of scaling techniques provided include paired comparisons, rank ordering, constant sum, rating scales like Likert and semantic differential, and graphic rating scales. The document also discusses evaluating scales based on reliability, which measures consistency, and validity, which measures accuracy. Reliability is a prerequisite for validity.
Measurement and scaling fundamentals and comparative scalingRohit Kumar
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
This chapter discusses different methods of measurement and scaling used in marketing research. It describes four primary scales of measurement - nominal, ordinal, interval, and ratio scales - and explains their characteristics. Comparative scaling techniques like paired comparisons, rank ordering, and constant sum scaling are presented, which involve direct comparisons between objects. Noncomparative scales that measure objects independently are also covered. The chapter provides examples to illustrate different scaling methods and their applications in marketing research.
The document discusses various methods for scaling in marketing research, including nominal, ordinal, interval, and ratio scales. It then compares comparative scaling techniques like paired comparisons and rank ordering, which involve direct comparisons between items, to noncomparative techniques like Likert scales that measure items independently. Finally, it provides examples of using paired comparisons, rank ordering, and constant sum scaling to measure preferences.
This document provides information about obtaining fully solved assignments. It lists an email address and phone number to contact for assignment help. It also includes 5 sample marketing research assignment questions with detailed multi-paragraph answers covering topics such as types of consumer and B2B market research, scales of measurement, sampling techniques, non-comparative scaling, and types of online marketing research. Students are encouraged to send their semester and specialization to the email or call the phone number provided to receive assistance with their assignments.
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Measurement involves assigning numbers or symbols to object characteristics according to rules. Scaling creates a continuum to locate measured objects. There are several types of scaling techniques used in research. Nominal scaling uses numbers as labels for identification purposes only, while ordinal scaling ranks attributes in order. Interval and ratio scaling measure distances between attributes on a scale with consistent intervals or a true zero point.
The document discusses different types of scales used to measure variables in marketing research, including nominal, ordinal, interval, and ratio scales. It explains what each scale measures and provides examples. Various scaling techniques are also covered, such as paired comparison scales, rank order scales, and constant sum scales that can be used to measure attitudes, preferences, and opinions.
This document discusses various techniques for measuring attitudes, including rating scales, ranking, sorting, and choice. It describes several types of rating scales such as Likert scales, semantic differentials, and constant-sum scales. It also covers techniques like ranking, sorting, paired comparisons, and Q-sorting for assessing attitudes. The key aspects of selecting an appropriate measurement scale are also summarized.
3. Secondary Data, Online Information Databases, and Measurement.docxtamicawaysmith
3. Secondary Data, Online Information Databases, and Measurement Scaling
1
Primary Scales of Measurement
7
3
8
Scale
Nominal Numbers
Assigned
to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to Finish, in
Seconds
Third
place
Second
place
First
place
Finish
Finish
8.2
9.1
9.6
15.2
14.1
13.4
Primary Scales of Measurement
Nominal Scale: The numbers serve only as labels or tags for identifying and classifying objects.
Ordinal Scale: A ranking scale
Interval Scale: Numerically equal distances on the scale represent equal values in the characteristic being measured.
Ratio Scale: Possesses all the properties of the nominal, ordinal, and interval scales. It has an absolute zero point.
Illustration of Scales of Measurement
Nominal Ordinal Ratio
Scale Scale Scale
Preference $ spent last No. Store Rankings 3 months
1. Parisian
2. Macy’s
3. Kmart
4. Kohl’s
5. J.C. Penney
6. Neiman Marcus
7. Marshalls
8. Saks Fifth Avenue
9. Sears
10.Wal-Mart
Interval
Scale
Preference Ratings
1-7
A Classification of Scaling Techniques
Comparative Scaling Techniques
Paired Comparison Scaling
A respondent is presented with two objects and asked to select one according to some criterion.
The data obtained are ordinal in nature.
Paired comparison scaling is the most widely-used comparative scaling technique.
With n brands, [n(n - 1) /2] paired comparisons are required.
Under the assumption of transitivity, it is possible to convert paired comparison data to a rank order.
Obtaining Shampoo Preferences
Using Paired Comparisons
Instructions: We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of shampoo you would prefer for personal use.
Recording Form:
aA 1 in a particular box means that the brand in that column was preferred over the brand in the corresponding row. A 0 means that the row brand was preferred over the column brand. bThe number of times a brand was preferred is obtained by summing the 1s in each column.
Paired Comparison Selling
The most common method of taste testing is paired comparison. The consumer is asked to sample two different products and select the one with the most appealing taste. The test is done in private and a minimum of 1,000 responses is considered an adequate sample. A blind taste test for a soft drink, where imagery, self-perception and brand reputation are very important factors in the consumer’s purchasing decision, may n ...
The document discusses various scaling techniques used in market research, including four primary techniques (nominal, ordinal, interval, and ratio scales) and other comparative and non-comparative techniques. It provides examples and definitions of each technique. The primary techniques are based on order, description, distance, and origin and differ in their ability to measure variables numerically. Other techniques include paired comparison, rank order, constant sum, Q-sort, Likert scales, semantic differential, and Stapel scales. Proper use of scaling allows researchers to analyze consumer behavior and product performance.
This chapter discusses measurement and scaling techniques. It defines measurement as assigning numbers or symbols to object characteristics according to standardized rules. Scaling is measuring quantitative attributes. There are four primary scales of measurement: nominal (labels), ordinal (rank order), interval (equal distances), and ratio (absolute zero). Comparative scaling involves directly comparing objects and yields only ordinal data, while non-comparative scaling scales objects independently and can produce interval or ratio data. Comparative techniques include paired comparisons (preference between pairs), rank ordering (simultaneously ranking all objects), and constant sum scaling (allocating a fixed number of points among attributes).
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
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In the face of the news of Google beginning to remove cookies from Chrome (30m users at the time of writing), there’s no longer time for marketers to throw their hands up and say “I didn’t know” or “They won’t go through with it”. Reality check - it has already begun - the time to take action is now. The good news is that there are solutions available and ready for adoption… but for many the race to catch up to the modern internet risks being a messy, confusing scramble to get back to "normal"
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
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From Hope to Despair: The Top 10 Reasons Businesses Ditch SEO Tactics
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1. Measurement and Scaling
Measurement means assigning numbers or other symbols to
characteristics of objects according to certain prespecified rules.
– One-to-one correspondence between the numbers and the
characteristics being measured.
– The rules for assigning numbers should be standardized and
applied uniformly.
– Rules must not change over objects or time.
2. Measurement and Scaling
Scaling involves creating a continuum upon which measured
objects are located.
Consider an attitude scale from 1 to 100. Each respondent is
assigned a number from 1 to 100, with 1 = Extremely
Unfavorable, and 100 = Extremely Favorable. Measurement is
the actual assignment of a number from 1 to 100 to each
respondent. Scaling is the process of placing the respondents
on a continuum with respect to their attitude toward
department stores.
3. Primary Scales of Measurement
Nominal Scale
• The numbers serve only as labels or tags for identifying and
classifying objects.
• When used for identification, there is a strict one-to-one
correspondence between the numbers and the objects.
• The numbers do not reflect the amount of the characteristic
possessed by the objects.
• The only permissible operation on the numbers in a nominal
scale is counting.
• Only a limited number of statistics, all of which are based on
frequency counts, are permissible, e.g., percentages, and
mode.
4. Primary Scales of Measurement
Nominal Scale
• The numbers serve only as labels or tags for identifying and
classifying objects.
• When used for identification, there is a strict one-to-one
correspondence between the numbers and the objects.
• The numbers do not reflect the amount of the characteristic
possessed by the objects.
• The only permissible operation on the numbers in a nominal
scale is counting.
• Only a limited number of statistics, all of which are based on
frequency counts, are permissible, e.g., percentages, and
mode.
5. Primary Scales of Measurement
Ordinal Scale
• A ranking scale in which numbers are assigned to objects to
indicate the relative extent to which the objects possess some
characteristic.
• Can determine whether an object has more or less of a
characteristic than some other object, but not how much
more or less.
• Any series of numbers can be assigned that preserves the
ordered relationships between the objects.
• In addition to the counting operation allowable for nominal
scale data, ordinal scales permit the use of statistics based on
centiles, e.g., percentile, quartile, median.
6. Primary Scales of Measurement
Ratio Scale
• Possesses all the properties of the nominal, ordinal, and
interval scales.
• It has an absolute zero point.
• It is meaningful to compute ratios of scale values.
• Only proportionate transformations of the form y = bx, where
b is a positive constant, are allowed.
• All statistical techniques can be applied to ratio data.
7. Primary Scales of Measurement
Interval Scale
• Numerically equal distances on the scale represent equal
values in the characteristic being measured.
• t permits comparison of the differences between objects.
• The location of the zero point is not fixed. Both the zero point
and the units of measurement are arbitrary.
• Statistical techniques that may be used include all of those
that can be applied to nominal and ordinal data, and in
addition the arithmetic mean, standard deviation, and other
statistics commonly used in marketing research.
8. 7 38
Primary Scales of Measurement
Scale
Nominal Numbers
Assigned
to Runners
OrdinalRank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to
Finish, in
Seconds
Third
place
Second
place
First
place
Finish
Finish
8.2 9.1 9.6
15.2 14.1 13.4
10. Primary Scales of MeasurementTable 8.1
Scale Basic
Characteristics
Common
Examples
Marketing
Examples
Nominal Numbers identify
& classify objects
Social Security
nos., numbering
of football players
Brand nos., store
types
Percentages,
mode
Chi-square,
binomial test
Ordinal Nos. indicate the
relative positions
of objects but not
the magnitude of
differences
between them
Quality rankings,
rankings of teams
in a tournament
Preference
rankings, market
position, social
class
Percentile,
median
Rank-order
correlation,
Friedman
ANOVA
Ratio Zero point is fixed,
ratios of scale
values can be
compared
Length, weight Age, sales,
income, costs
Geometric
mean, harmonic
mean
Coefficient of
variation
Permissible Statistics
Descriptive Inferential
Interval Differences
between objects
Temperature
(Fahrenheit)
Attitudes,
opinions, index
Range, mean,
standard
Product-
moment
11. A Classification of Scaling Techniques
Likert
Semantic
Differential
Stapel
Figure 8.2
Scaling Techniques
Noncomparative
Scales
Comparative
Scales
Paired
Comparison
Rank
Order
Constant
Sum
Q-Sort and
Other
Procedures
Continuous
Rating Scales
Itemized
Rating Scales
12. A Comparison of Scaling Techniques
• Comparative scales involve the direct comparison of stimulus
objects. Comparative scale data must be interpreted in
relative terms and have only ordinal or rank order properties.
• In Non Comparative scales, each object is scaled
independently of the others in the stimulus set. The resulting
data are generally assumed to be interval or ratio scaled.
13. Relative Advantages of Comparative Scales
• Small differences between stimulus objects can be detected.
• Same known reference points for all respondents.
• Easily understood and can be applied.
• Involve fewer theoretical assumptions.
• Tend to reduce halo or carryover effects from one judgment
to another.
14. Relative Disadvantages of Comparative Scales
• Ordinal nature of the data
• Inability to generalize beyond the stimulus objects scaled.
15. Comparative Scaling Techniques
Paired Comparison Scaling
• A respondent is presented with two objects and asked to select one
according to some criterion.
• The data obtained are ordinal in nature.
• Paired comparison scaling is the most widely used comparative
scaling technique.
• With n brands, [n(n - 1) /2] paired comparisons are required
• Under the assumption of transitivity, it is possible to convert paired
comparison data to a rank order.
16. Paired Comparison Selling
The most common method of taste testing is paired comparison. The
consumer is asked to sample two different products and select the one with
the most appealing taste. The test is done in private and a minimum of 1,000
responses is considered an adequate sample. A blind taste test for a soft drink,
where imagery, self-perception and brand reputation are very important
factors in the consumer’s purchasing decision, may not be a good indicator of
performance in the marketplace. The introduction of New Coke illustrates this
point. New Coke was heavily favored in blind paired comparison taste tests,
but its introduction was less than successful, because image plays a major role
in the purchase of Coke.
A paired comparison taste test
17. Comparative Scaling Techniques
Rank Order Scaling
• Respondents are presented with several objects
simultaneously and asked to order or rank them according to
some criterion.
•
• It is possible that the respondent may dislike the brand ranked
1 in an absolute sense.
• Furthermore, rank order scaling also results in ordinal data.
• Only (n - 1) scaling decisions need be made in rank order
scaling.
18. Preference for Cold Drink Brands
Using Rank Order ScalingFigure 8.4
Instructions: Rank the various brands of Cold Drinks in order of
preference. Begin by picking out the one brand that you like most
and assign it a number 1. Then find the second most preferred
brand and assign it a number 2. Continue this procedure until you
have ranked all the brands of toothpaste in order of preference. The
least preferred brand should be assigned a rank of 10.
No two brands should receive the same rank number.
The criterion of preference is entirely up to you. There is no right or
wrong answer. Just try to be consistent.
19. Brand Rank Order
1. Pepsi _________
2. Coco Cola _________
3. Thums Up _________
4. Fanta _________
5. Maaza _________
6. Frooti _________
7. Mirinda _________
8. 7 Up _________
9. Sprite _________
10. Mountain Dew _________
Preference for Toothpaste Brands
Using Rank Order ScalingFigure 8.4 cont.
Form
20. Comparative Scaling Techniques
Constant Sum Scaling
• Respondents allocate a constant sum of units, such as 100
points to attributes of a product to reflect their importance.
• If an attribute is unimportant, the respondent assigns it zero
points.
• If an attribute is twice as important as some other attribute, it
receives twice as many points.
• The sum of all the points is 100. Hence, the name of the
scale.
21. Importance of Bathing Soap Attributes
Using a Constant Sum ScaleFigure 8.5
Instructions
On the next slide, there are eight attributes of bathing
soaps. Please allocate 100 points among the attributes
so that your allocation reflects the relative importance
you attach to each attribute. The more points an
attribute receives, the more important the attribute is.
If an attribute is not at all important, assign it zero
points. If an attribute is twice as important as some
other attribute, it should receive twice as many points.
22. Figure 8.5 cont.
Form
Average Responses of Three Segments
Attribute Segment I Segment II Segment III
1. Mildness
2. Lather
3. Shrinkage
4. Price
5. Fragrance
6. Packaging
7. Moisturizing
8. Cleaning Power
Sum
8 2 4
2 4 17
3 9 7
53 17 9
9 0 19
7 5 9
5 3 20
13 60 15
100 100 100
Importance of Bathing Soap Attributes
Using a Constant Sum Scale
23. Noncomparative Scaling Techniques
• Respondents evaluate only one object at a time, and for this
reason non comparative scales are often referred to as
monadic scales.
• Non comparative techniques consist of continuous and
itemized rating scales.
24. Continuous Rating Scale
Respondents rate the objects by placing a mark at the appropriate position
on a line that runs from one extreme of the criterion variable to the other.
The form of the continuous scale may vary considerably.
How would you rate Sears as a department store?
Version 1
Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Probably the best
Version 2
Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - Probably the best
0 10 20 30 40 50 60 70 80 90 100
Version 3
Very bad Neither good Very good
nor bad
Probably the worst - - - - - - -I - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - -Probably the best
0 10 20 30 40 50 60 70 80 90 100
25. Itemized Rating Scales
• The respondents are provided with a scale that has a number
or brief description associated with each category.
• The categories are ordered in terms of scale position, and the
respondents are required to select the specified category that
best describes the object being rated.
• The commonly used itemized rating scales are the Likert,
semantic differential, and Stapel scales.
26. Likert Scale
Named after its inventor Renis Likert
The Likert scale requires the respondents to indicate a degree of agreement or
disagreement with each of a series of statements about the stimulus objects.
Strongly Disagree Neither Agree Strongly
disagree agree nor agree
disagree
1. Sears sells high quality merchandise. 1 2X 3 4 5
2. Sears has poor in-store service. 1 2X 3 4 5
3. I like to shop at Sears. 1 2 3X 4 5
• The analysis can be conducted on an item-by-item basis (profile analysis), or a total
(summated) score can be calculated.
• When arriving at a total score, the categories assigned to the negative statements by
the respondents should be scored by reversing the scale.
27. Semantic Differential Scale
The semantic differential is a seven-point rating scale with end
points associated with bipolar labels that have semantic meaning.
SEARS IS:
Powerful --:--:--:--:-X-:--:--: Weak
Unreliable --:--:--:--:--:-X-:--: Reliable
Modern --:--:--:--:--:--:-X-: Old-fashioned
• The negative adjective or phrase sometimes appears at the left side of the
scale and sometimes at the right.
• This controls the tendency of some respondents, particularly those with
very positive or very negative attitudes, to mark the right- or left-hand
sides without reading the labels.
• Individual items on a semantic differential scale may be scored on either a
-3 to +3 or a 1 to 7 scale.
29. Stapel Scale
The Stapel scale is a unipolar rating scale with ten categories
numbered from -5 to +5, without a neutral point (zero). This scale
is usually presented vertically.
SEARS
+5 +5
+4 +4
+3 +3
+2 +2X
+1 +1
HIGH QUALITY POOR SERVICE
-1 -1
-2 -2
-3 -3
-4X -4
-5 -5
The data obtained by using a Stapel scale can be analyzed in the
same way as semantic differential data.
30. Scale Basic
Characteristics
Examples Advantages Disadvantages
Continuous
Rating
Scale
Place a mark on a
continuous line
Reaction to
TV
commercials
Easy to construct Scoring can be
cumbersome
unless
computerized
Itemized Rating
Scales
Likert Scale Degrees of
agreement on a 1
(strongly disagree)
to 5 (strongly agree)
scale
Measurement
of attitudes
Easy to construct,
administer, and
understand
More
time - consuming
Semantic
Differential
Seven - point scale
with bipolar labels
Brand,
product, and
company
images
Versatile Controversy as
to whether the
data are interval
Stapel
Scale
Unipolar ten - point
scale, - 5 to +5,
witho ut a neutral
point (zero)
Measurement
of attitudes
and images
Easy to construct,
administer over
telephone
Confusing and
difficult to apply
Table 9.1
Basic Noncomparative Scales
31. Summary of Itemized Scale Decisions
1) Number of categories Although there is no single, optimal number,
traditional guidelines suggest that there
should be between five and nine categories
2) Balanced vs. unbalanced In general, the scale should be balanced to
obtain objective data
3) Odd/even no. of categories If a neutral or indifferent scale response is
possible from at least some of the respondents,
an odd number of categories should be used
4) Forced vs. non-forced In situations where the respondents are
expected to have no opinion, the accuracy of
the data may be improved by a non-forced scale
5) Verbal description An argument can be made for labeling all or
many scale categories. The category
descriptions should be located as close to the
response categories as possible
6) Physical form A number of options should be tried and the
best selected
Table 9.2
32. Jovan Musk for Men is Jovan Musk for Men is
Extremely good Extremely good
Very good Very good
Good Good
Bad Somewhat good
Very bad Bad Extremely bad
Very bad
Figure 9.1
Balanced and Unbalanced Scales
33. A variety of scale configurations may be employed to measure the
gentleness of Cheer detergent. Some examples include:
Cheer detergent is:
1) Very harsh --- --- --- --- --- --- --- Very gentle
2) Very harsh 1 2 3 4 5 6 7 Very gentle
3) . Very harsh
.
.
. Neither harsh nor gentle
.
.
. Very gentle
4) ____ ____ ____ ____ ____ ____ ____
Very Harsh Somewhat Neither harsh Somewhat Gentle Very
harsh Harsh nor gentle gentle gentle
5)
Very Neither harsh Very
harsh nor gentle gentle
Rating Scale Configurations Figure 9.2
-3 -1 0 +1 +2-2 +3
Cheer
34. Thermometer Scale
Instructions: Please indicate how much you like McDonald’s hamburgers by coloring in
the thermometer. Start at the bottom and color up to the temperature level that best
indicates how strong your preference is.
Form:
Smiling Face Scale
Instructions: Please point to the face that shows how much you like the Barbie Doll. If
you do not like the Barbie Doll at all, you would point to Face 1. If you liked it very much,
you would point to Face 5.
Form:
1 2 3 4 5
Figure 9.3
Like very
much
Dislike
very much
100
75
50
25
0
Some Unique Rating Scale Configurations
35. Development of a Multi-item Scale
Develop Theory
Generate Initial Pool of Items: Theory, Secondary Data, and
Qualitative Research
Collect Data from a Large Pretest Sample
Statistical Analysis
Develop Purified Scale
Collect More Data from a Different Sample
Final Scale
Figure 9.4
Select a Reduced Set of Items Based on Qualitative Judgement
Evaluate Scale Reliability, Validity, and Generalizability