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 provides an overview of factor analysis, including:
- Factor analysis is a statistical technique used to reduce a large number of variables into a smaller number of underlying factors or components according to patterns of correlation between variables.
- The two main types are exploratory factor analysis, which is used when the underlying factors are unknown, and confirmatory factor analysis, which is used to test hypotheses about a predetermined factor structure.
- Key steps in factor analysis include determining the appropriateness of the data, extracting factors using various criteria, rotating factors to improve interpretation, and interpreting the results including factor loadings and communalities.
Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. Nominal scales assign categories with no numerical difference between them. Ordinal scales order categories but do not specify numerical distance. Interval scales have equal numerical distance between values but no absolute zero. Ratio scales have all the qualities of the previous scales plus an absolute zero point. Measurement scales are important for categorizing and quantifying variables in research and other applications such as market transactions.
Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Scales are tools used to measure how individuals differ on variables of interest. There are four main types of scales: nominal scales assign subjects to categories, ordinal scales denote differences and rank categories, interval scales allow arithmetic operations on data, and ratio scales measure magnitude and proportions of differences. Examples provided include using Likert scales to rate agreement, ranking apps, and comparing boys and girls in a ratio. Various other scale types were also outlined such as dichotomous, category, semantic differential, numerical, Stapel, graphic rating, and forced choice scales. The presentation concluded with describing measures of central tendency and dispersion that correspond to each scale type, along with some common tests of significance.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
This document discusses different types of measurement scales used in quantitative research. It describes four main levels of measurement scales: nominal, ordinal, interval, and ratio scales. For each scale, it provides the key characteristics and examples to illustrate how variables are classified. The nominal scale simply categorizes variables, while the ordinal scale also ranks the categories. Interval and ratio scales indicate both the order and distances between categories, with the ratio scale uniquely having a true zero point. Other scales mentioned include dichotomous, category, semantic differential, Stapel, graphic rating, and forced choice scales.
This document discusses the Z-test, a statistical test used to compare means and proportions. The Z-test can be used to test if a sample mean differs from a population mean, if two sample means are equal, or if two population proportions are equal. It assumes the population is normally distributed. The steps involve formulating hypotheses, choosing a significance level, calculating the Z-statistic, and comparing it to a critical value to determine if the null hypothesis should be rejected or accepted. The Z-test is useful when sample sizes are large but requires knowing the population standard deviation.
The document provides an overview of factor analysis, including:
- Factor analysis is a statistical technique used to reduce a large number of variables into a smaller number of underlying factors or components according to patterns of correlation between variables.
- The two main types are exploratory factor analysis, which is used when the underlying factors are unknown, and confirmatory factor analysis, which is used to test hypotheses about a predetermined factor structure.
- Key steps in factor analysis include determining the appropriateness of the data, extracting factors using various criteria, rotating factors to improve interpretation, and interpreting the results including factor loadings and communalities.
Here are the steps I would take to analyze this data using exploratory factor analysis:
1. Check assumptions
- Sample size of 300 is adequate
- Most correlations are between .3 and .8
2. Extract initial factors using principal axis factoring
- Kaiser's criterion suggests 4 factors with eigenvalues > 1
3. Rotate factors orthogonally using varimax rotation
- This will make the factor structure more interpretable
4. Interpret the factors based on which items have strong loadings
- Factor 1 relates to anxiety about learning SPSS
- Factor 2 relates to anxiety about using computers
- Factors 3 and 4 may reflect other aspects of statistics anxiety
5. Compute factor scores if desired to use in further
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. Nominal scales assign categories with no numerical difference between them. Ordinal scales order categories but do not specify numerical distance. Interval scales have equal numerical distance between values but no absolute zero. Ratio scales have all the qualities of the previous scales plus an absolute zero point. Measurement scales are important for categorizing and quantifying variables in research and other applications such as market transactions.
Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Scales are tools used to measure how individuals differ on variables of interest. There are four main types of scales: nominal scales assign subjects to categories, ordinal scales denote differences and rank categories, interval scales allow arithmetic operations on data, and ratio scales measure magnitude and proportions of differences. Examples provided include using Likert scales to rate agreement, ranking apps, and comparing boys and girls in a ratio. Various other scale types were also outlined such as dichotomous, category, semantic differential, numerical, Stapel, graphic rating, and forced choice scales. The presentation concluded with describing measures of central tendency and dispersion that correspond to each scale type, along with some common tests of significance.
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
This document discusses different types of measurement scales used in quantitative research. It describes four main levels of measurement scales: nominal, ordinal, interval, and ratio scales. For each scale, it provides the key characteristics and examples to illustrate how variables are classified. The nominal scale simply categorizes variables, while the ordinal scale also ranks the categories. Interval and ratio scales indicate both the order and distances between categories, with the ratio scale uniquely having a true zero point. Other scales mentioned include dichotomous, category, semantic differential, Stapel, graphic rating, and forced choice scales.
This document discusses the Z-test, a statistical test used to compare means and proportions. The Z-test can be used to test if a sample mean differs from a population mean, if two sample means are equal, or if two population proportions are equal. It assumes the population is normally distributed. The steps involve formulating hypotheses, choosing a significance level, calculating the Z-statistic, and comparing it to a critical value to determine if the null hypothesis should be rejected or accepted. The Z-test is useful when sample sizes are large but requires knowing the population standard deviation.
This document discusses various techniques for scaling and measurement 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 and rank ordering are discussed as well as non-comparative techniques. Specific scaling approaches covered include Likert scales, semantic differentials, Stapel scales, and graphic rating scales. The document emphasizes that proper measurement and scaling are important aspects of the overall marketing research process.
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.
The document discusses the four scales of measurement as classified by Steven in 1946: nominal, ordinal, interval, and ratio scales. The nominal scale is the least precise and allows qualitative classification without rank order. The ordinal scale is more precise and allows rank ordering but with unequal units. The interval scale has equal units of measurement but no true zero point. The ratio scale is the most refined with an absolute zero point and is used in maintaining student cumulative records.
The document discusses different scales of measurement proposed by Stanley Smith Stevens, including nominal, ordinal, interval, and ratio scales. It then examines attitude measurement and different response types for measuring attitudes such as rating scales, ranking scales, categorization, and sorting. Key factors that influence selecting an appropriate measurement scale for attitudes include the research objectives, response types, number of dimensions, and whether responses involve forced or unforced choices.
caling is the branch of measurement that involves the construction of an instrument that associates qualitative constructs with quantitative metric units. Scaling evolved out of efforts in psychology and education to measure “unmeasurable” constructs like authoritarianism and self-esteem. In many ways, scaling remains one of the most arcane and misunderstood aspects of social research measurement. And, it attempts to do one of the most difficult of research tasks – measure abstract concepts.
Most people don’t even understand what scaling is. The basic idea of scaling is described in General Issues in Scaling, including the important distinction between a scale and a response format. Scales are generally divided into two broad categories: unidimensional and multidimensional. The unidimensional scaling methods were developed in the first half of the twentieth century and are generally named after their inventor. We’ll look at three types of unidimensional scaling methods here:
Thurstone or Equal-Appearing Interval Scaling
Likert or “Summative” Scaling
Guttman or “Cumulative” Scaling
In the late 1950s and early 1960s, measurement theorists developed more advanced techniques for creating multidimensional scales. Although these techniques are not considered here, you may want to look at the method of concept mapping that relies on that approach to see the power of these multivariate methods.
This document discusses multiple regression analysis and its use in predicting relationships between variables. Multiple regression allows prediction of a criterion variable from two or more predictor variables. Key aspects covered include the multiple correlation coefficient (R), squared correlation coefficient (R2), adjusted R2, regression coefficients, significance testing using t-tests and F-tests, and considerations for using multiple regression such as sample size and normality assumptions.
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 statistical inference, which involves drawing conclusions about an unknown population based on a sample. There are two main types of statistical inference: parameter estimation and hypothesis testing. Parameter estimation involves obtaining numerical values of population parameters from a sample, like estimating the percentage of people aware of a product. Hypothesis testing involves making judgments about assumptions regarding population parameters based on sample data. The document also discusses point estimation, interval estimation, standard error, and provides examples of calculating confidence intervals.
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
The document discusses various concepts related to measurement, scaling, and sampling in social science research. It defines measurement as assigning numbers or symbols to objects, events, or issues according to certain rules. Scaling is preparing a continuum to measure abstract concepts quantitatively. Common scales discussed include the Likert scale, Thurstone scale, and Guttman scale. The document also discusses the importance of validity and reliability in measurement. Finally, it covers various probability and non-probability sampling techniques used in research like simple random sampling, stratified sampling, and convenience sampling.
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
The document discusses stratified random sampling, which involves dividing a population into homogeneous subgroups called strata and randomly sampling from each stratum. It describes how to form strata based on common characteristics, how to select items from each stratum such as through systematic sampling, and how to allocate the sample size to each stratum proportionally according to the stratum's size within the overall population. An example is given of allocating a sample of 30 across 3 strata based on their relative population sizes.
Hypothesis testing involves stating a null hypothesis (H0) and an alternative hypothesis (H1). A test statistic is calculated from sample data and used to determine whether to reject or fail to reject H0. There are two types of errors: Type I rejects a true H0, Type II fails to reject a false H0. The significance level (α) limits Type I error, while power (1- β) measures the test's ability to reject H0 when it is false. Tests can be one-tailed if H1 specifies a direction, or two-tailed. The rejection region defines values where H0 will be rejected.
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 discriminant analysis, which is a statistical technique used to classify observations into predefined groups based on independent variables. It can be used to predict the likelihood an entity belongs to a particular class. The document outlines the objectives, purposes, assumptions, and steps of discriminant analysis. It provides examples of using it to classify individuals as basketball vs volleyball players or high vs low performers based on variables.
This document discusses primary and secondary data sources. It defines primary data as original data collected directly for the research project, while secondary data is data collected previously for another purpose. The document outlines advantages and disadvantages of both primary and secondary data. Primary data is more accurate but costly and time-consuming to collect, while secondary data is quicker and cheaper to obtain but may not be suitable or accurate for the research purpose. The document also categorizes different types of secondary data sources such as internal company data, published materials, computer databases, and syndicated services that collect standardized data from consumers or institutions.
This document provides an overview of sampling methods for research. It defines key terms like universe, sample, and population. It explains that sampling involves studying a subset of a larger population due to limitations of time, resources, and feasibility of studying every member. The document outlines different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It notes that sampling allows for time and cost savings while still providing accurate results. However, limitations include potential inaccuracies if not done scientifically and difficulty ensuring representativeness.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
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.
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 various techniques for scaling and measurement 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 and rank ordering are discussed as well as non-comparative techniques. Specific scaling approaches covered include Likert scales, semantic differentials, Stapel scales, and graphic rating scales. The document emphasizes that proper measurement and scaling are important aspects of the overall marketing research process.
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.
The document discusses the four scales of measurement as classified by Steven in 1946: nominal, ordinal, interval, and ratio scales. The nominal scale is the least precise and allows qualitative classification without rank order. The ordinal scale is more precise and allows rank ordering but with unequal units. The interval scale has equal units of measurement but no true zero point. The ratio scale is the most refined with an absolute zero point and is used in maintaining student cumulative records.
The document discusses different scales of measurement proposed by Stanley Smith Stevens, including nominal, ordinal, interval, and ratio scales. It then examines attitude measurement and different response types for measuring attitudes such as rating scales, ranking scales, categorization, and sorting. Key factors that influence selecting an appropriate measurement scale for attitudes include the research objectives, response types, number of dimensions, and whether responses involve forced or unforced choices.
caling is the branch of measurement that involves the construction of an instrument that associates qualitative constructs with quantitative metric units. Scaling evolved out of efforts in psychology and education to measure “unmeasurable” constructs like authoritarianism and self-esteem. In many ways, scaling remains one of the most arcane and misunderstood aspects of social research measurement. And, it attempts to do one of the most difficult of research tasks – measure abstract concepts.
Most people don’t even understand what scaling is. The basic idea of scaling is described in General Issues in Scaling, including the important distinction between a scale and a response format. Scales are generally divided into two broad categories: unidimensional and multidimensional. The unidimensional scaling methods were developed in the first half of the twentieth century and are generally named after their inventor. We’ll look at three types of unidimensional scaling methods here:
Thurstone or Equal-Appearing Interval Scaling
Likert or “Summative” Scaling
Guttman or “Cumulative” Scaling
In the late 1950s and early 1960s, measurement theorists developed more advanced techniques for creating multidimensional scales. Although these techniques are not considered here, you may want to look at the method of concept mapping that relies on that approach to see the power of these multivariate methods.
This document discusses multiple regression analysis and its use in predicting relationships between variables. Multiple regression allows prediction of a criterion variable from two or more predictor variables. Key aspects covered include the multiple correlation coefficient (R), squared correlation coefficient (R2), adjusted R2, regression coefficients, significance testing using t-tests and F-tests, and considerations for using multiple regression such as sample size and normality assumptions.
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 statistical inference, which involves drawing conclusions about an unknown population based on a sample. There are two main types of statistical inference: parameter estimation and hypothesis testing. Parameter estimation involves obtaining numerical values of population parameters from a sample, like estimating the percentage of people aware of a product. Hypothesis testing involves making judgments about assumptions regarding population parameters based on sample data. The document also discusses point estimation, interval estimation, standard error, and provides examples of calculating confidence intervals.
The document discusses the importance and benefits of sampling over a census for research purposes. It notes that sampling saves time and money compared to a census. Additionally, a sample may be more accurate than a census due to limitations in resources and risks of introducing unpopular actions to an entire market. The document also emphasizes that both sampling design and sample size are important considerations, as an inappropriate design will not allow findings to be generalized even with a large sample, and an inadequate sample size cannot meet study objectives. It provides some general guidelines for appropriate sample sizes.
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
The document discusses various concepts related to measurement, scaling, and sampling in social science research. It defines measurement as assigning numbers or symbols to objects, events, or issues according to certain rules. Scaling is preparing a continuum to measure abstract concepts quantitatively. Common scales discussed include the Likert scale, Thurstone scale, and Guttman scale. The document also discusses the importance of validity and reliability in measurement. Finally, it covers various probability and non-probability sampling techniques used in research like simple random sampling, stratified sampling, and convenience sampling.
Introductory Statistics discusses the definition and history of statistics. Statistics deals with quantitative or numerical data and is the scientific method of collecting, organizing, analyzing, and making decisions with quantitative data. Historically, Indian texts from the Mauryan period and Mughal period contained early forms of statistical analysis of topics like agriculture. The typical process of a statistical study involves defining objectives, identifying the population and characteristics, planning data collection, collecting and organizing data, performing statistical analysis, and drawing conclusions. Statistics is useful for simplifying complex data, quantifying uncertainty, discovering patterns to enable forecasting, and testing assumptions. Statistical techniques have various applications in fields like marketing, economics, finance, operations, human resources, information technology,
The document discusses stratified random sampling, which involves dividing a population into homogeneous subgroups called strata and randomly sampling from each stratum. It describes how to form strata based on common characteristics, how to select items from each stratum such as through systematic sampling, and how to allocate the sample size to each stratum proportionally according to the stratum's size within the overall population. An example is given of allocating a sample of 30 across 3 strata based on their relative population sizes.
Hypothesis testing involves stating a null hypothesis (H0) and an alternative hypothesis (H1). A test statistic is calculated from sample data and used to determine whether to reject or fail to reject H0. There are two types of errors: Type I rejects a true H0, Type II fails to reject a false H0. The significance level (α) limits Type I error, while power (1- β) measures the test's ability to reject H0 when it is false. Tests can be one-tailed if H1 specifies a direction, or two-tailed. The rejection region defines values where H0 will be rejected.
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 discriminant analysis, which is a statistical technique used to classify observations into predefined groups based on independent variables. It can be used to predict the likelihood an entity belongs to a particular class. The document outlines the objectives, purposes, assumptions, and steps of discriminant analysis. It provides examples of using it to classify individuals as basketball vs volleyball players or high vs low performers based on variables.
This document discusses primary and secondary data sources. It defines primary data as original data collected directly for the research project, while secondary data is data collected previously for another purpose. The document outlines advantages and disadvantages of both primary and secondary data. Primary data is more accurate but costly and time-consuming to collect, while secondary data is quicker and cheaper to obtain but may not be suitable or accurate for the research purpose. The document also categorizes different types of secondary data sources such as internal company data, published materials, computer databases, and syndicated services that collect standardized data from consumers or institutions.
This document provides an overview of sampling methods for research. It defines key terms like universe, sample, and population. It explains that sampling involves studying a subset of a larger population due to limitations of time, resources, and feasibility of studying every member. The document outlines different sampling methods like simple random sampling, stratified sampling, and cluster sampling. It notes that sampling allows for time and cost savings while still providing accurate results. However, limitations include potential inaccuracies if not done scientifically and difficulty ensuring representativeness.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
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.
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 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.
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.
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
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.
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.
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.
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.
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 document discusses measurement and scaling techniques used in research. It begins by defining measurement and scaling, and describes four levels of measurement scales: nominal, ordinal, interval, and ratio scales. It then explains different scaling techniques, including comparative techniques like paired comparison scales and rank order scales, as well as non-comparative techniques like Likert scales. The document provides examples to illustrate each scaling technique and discusses how to select the appropriate technique for a given research problem.
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 measurement and scaling techniques used in marketing research. It defines different types of measurement scales including nominal, ordinal, interval, and ratio scales. It also describes various scaling techniques such as paired comparison scaling, ranking scaling, constant sum scaling, Q-sort scaling, non-comparative scaling, continuous rating scales, Likert scales, semantic differential scales, and Stapel scales. The document emphasizes that reliability refers to a scale's ability to produce consistent results over multiple measurements, while validity is the extent to which a scale measures what it is intended to measure.
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.
This document discusses measurement and scaling techniques used in research. It defines measurement as observing and recording observations according to rules, while scaling is assigning objects to numbers or categories by rule. There are four levels of measurement: nominal, ordinal, interval, and ratio scales. Nominal scales use numbers as labels, ordinal scales show ranking, interval scales have equal distances between numbers, and ratio scales have a true zero point. Comparative scales like paired comparisons or ranking ask respondents to directly compare objects, while non-comparative scales like Likert or semantic differential scales rate single objects independently. The appropriate scale depends on the research problem and data type.
This document discusses measurement and scaling techniques used in research. It defines measurement as observing and recording observations according to rules, while scaling is assigning objects to numbers or semantics based on a rule. There are four levels of measurement scales: nominal, ordinal, interval, and ratio. Scaling techniques can be comparative, involving comparisons between objects, or non-comparative, involving single object evaluations. Common comparative techniques include paired comparisons, rank ordering, and constant sum, while common non-comparative techniques are continuous ratings and itemized ratings like Likert scales. The appropriate technique depends on the research problem and intended statistical analysis.
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.
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 document discusses measurement and scaling techniques used in survey research. It defines measurement as observing and recording characteristics according to rules, while scaling assigns objects numbers or categories according to a rule. There are four levels of measurement scales: nominal for categories, ordinal for ranking, interval for equal distances, and ratio for a true zero. Comparative scales ask respondents to directly compare objects, while non-comparative scales evaluate objects independently. Common scaling techniques include Likert scales, semantic differential scales, and constant sum scales. The document aims to help select an appropriate attitude measurement scale for research.
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.
Lecture 12 q uestion on leverage analysisKritika Jain
This document contains 22 questions from Amity Business School related to financial leverage, operating leverage, and combined leverage. Questions ask students to calculate various leverage ratios from income statements and balance sheets of companies. They are also asked to interpret the significance of leverage ratios and how they impact decision making. Students must calculate earnings per share, break-even point, changes in EPS from changes in sales or costs.
The document discusses financial leverage and how it is calculated. Financial leverage measures the relationship between earnings before interest and taxes (EBIT) and earnings per share (EPS). It reflects how a change in EBIT impacts EPS due to the presence of fixed financial charges like interest and dividends. The degree of financial leverage (DFL) is calculated as the percentage change in EPS divided by the percentage change in EBIT. Financial leverage exists when there are fixed financial costs, and is a measure of how much debt a firm uses.
1. Leverage reflects the responsiveness of one financial variable to changes in another variable. It is measured by the percentage change in the dependent variable divided by the percentage change in the independent variable.
2. Leverage refers to using fixed costs to magnify returns. There are operating fixed costs like rent and financial fixed costs like interest. Operating, financial, and total leverage can be measured.
3. Operating leverage measures the relationship between sales and earnings before interest and taxes (EBIT). It indicates how much EBIT changes with sales. Firms with high operating leverage face more risk from changes in sales.
The document contains questions related to time value of money concepts such as compound interest, present and future value of investments, annuities, and loans. It asks the reader to calculate future and present values under different interest rate scenarios, determine investment amounts needed to achieve future targets, and analyze loan and investment schemes. It also includes a mini case about determining retirement planning numbers.
The document discusses discounting techniques used to determine the present value of future cash flows. It provides formulas to calculate the present value of a single cash flow, an annuity, and an annuity due. Examples are given of calculating the present value of Rs. 50,000 received in 15 years, an ordinary annuity of Rs. 1,000 for 3 years, and an annuity due of Rs. 1,000 for 3 years. The document also provides an example of choosing between a lump sum payment and annual pension based on present value calculations.
The document discusses compounding techniques and calculating future values. It defines compound interest as interest earned on the initial principal sum becoming part of the principal. It then provides formulas and examples for calculating the future value of a single cash flow (lump sum) and the future value of a series of cash flows (annuity), including ordinary annuities where payments occur at the end of each period and annuities due where payments occur at the beginning of each period. Non-annual compounding and sinking funds are also briefly discussed.
This document discusses the time value of money concept from an Amity Business School financial management class. It explains that money has greater value when received today compared to in the future due to uncertainties and reinvestment opportunities. It also discusses how timelines can be used to visualize cash flows occurring at different points in time and how compounding and discounting techniques allow comparison of cash flows across time periods by converting them to a common point in time.
Lecture 2. introduction to financial managementKritika Jain
The document discusses financial management topics taught in an MBA program at Amity Business School. It defines the financial environment and system, including financial markets, instruments, intermediaries, and the regulatory framework. It then compares the objectives of profit maximization versus wealth maximization. Profit maximization is criticized for being vague, ignoring the time value of money and risk. Wealth maximization, also called value or net present worth maximization, is presented as a better objective as it focuses on maximizing shareholder value through appropriate financial decisions.
Lecture 1. introduction to financial managementKritika Jain
The document provides an overview of financial management. It defines financial management as planning and controlling a firm's financial resources, including procuring funds in an economic manner and employing funds optimally to maximize returns. It then outlines the evolution of financial management from the traditional to modern phase. Key aspects of financial management are investment, financing, and dividend decisions. Investment decisions involve selecting profitable investment avenues. Financing decisions relate to determining the optimal capital structure and sources of finance. Dividend decisions balance paying dividends to shareholders versus retaining profits for reinvestment.
Here are my analyses of the expected dividend payout ratios for each company described:
- A company with a large proportion of inside ownership, all of whom are high income individual: Medium/high payout ratio. High income insiders prefer current dividends.
- A growth company with an abundance of good investment opportunities: Low payout ratio. Growth companies retain more earnings to fund good investment opportunities.
- A company experiencing ordinary growth that has liquidity and much unused borrowing capacity: Medium/high payout ratio. With liquidity and borrowing capacity, it has flexibility to pay dividends while still investing for growth.
- A company that experiences an unexpected drop in earnings from a trend: Low payout ratio. It
This document discusses two theories on the impact of dividend declaration on firm valuation:
1) The irrelevance theory argues that dividend decisions do not impact shareholder wealth or market price since earnings can be retained or distributed as dividends through financing decisions.
2) The Modigliani-Miller approach also claims dividend policy does not affect market share price or firm value, which is determined by earnings capacity. Dividing earnings between retention and dividends does not change the firm's overall value.
The document provides an example to prove that under the MM hypothesis, paying a dividend of Rs. 6 per share for a firm with 5000 shares and expected earnings of Rs. 50,000 investing Rs. 100,000 does
The document discusses inventory management techniques. It describes the purposes of holding inventory as transaction motive, precaution motive, and speculative motive. It also discusses the risks and costs of holding inventory. The objectives of inventory management are to ensure continuous supply and avoid overstocking/understocking while maintaining optimal investment levels. Techniques discussed include determining minimum, maximum, reorder, and danger stock levels. It also discusses economic order quantity and receivables management concepts.
Loop Ltd requires net working capital to produce 100 units. It needs raw materials for 8 weeks of stock, work in progress for 6 weeks, and finished goods stock for 6 weeks. Accounts receivable are 2 weeks and accounts payable are 4 weeks. Estimated net working capital is the total working capital requirement plus a 20% contingency amount.
The document discusses various capital budgeting techniques used to evaluate investment projects, including payback period and net present value (NPV). It provides examples of how to calculate payback period for projects with both uniform and non-uniform cash flows. It also discusses the limitations of payback period as a capital budgeting method. The document then introduces NPV as a discounted cash flow technique and provides the formula for calculating NPV. It states that projects with positive NPV should be accepted while projects with negative NPV should be rejected.
The document discusses the steps involved in capital budgeting decisions and cash flow calculations for capital projects. It provides an example calculation for a company considering investing Rs. 100,000 in new machinery. The summary is:
1) Capital budgeting involves estimating cash flows, cost of capital, and selecting a decision criterion.
2) For a new machinery investment, the initial cash outflow is Rs. 120,000 and annual cash flows are calculated over 5 years based on revenues, expenses, depreciation, and taxes.
3) The example calculates the annual and terminal cash flows after tax for the machinery project.
For taxation purposes in India, depreciation is charged on a "block of assets" which is a group of assets in the same class (buildings, furniture, plant, etc.) that have the same prescribed depreciation rate, rather than on individual assets. There are 12 such blocks for 4 classes of assets specified in the Income Tax Act. Capital gains arise when the sale proceeds of a block of assets exceeds the written down value of that block.
The document outlines the capital budgeting process at Amity Business School, which includes generating investment ideas, estimating cash flows, evaluating cash flows, selecting projects, and monitoring execution. It emphasizes that capital budgeting should be based on cash flows rather than accrual accounting because cash flows are more certain amounts and avoid different interpretations. It provides principles for estimating cash flows, such as only including cash inflows and outflows, ignoring non-cash items like depreciation, calculating after-tax cash flows, and ignoring sunk costs already incurred.
- The document discusses capital budgeting and cash flow analysis for a proposed machinery investment of Rs. 1,20,000 with a 5 year life.
- It calculates the annual cash flows after tax over the 5 years, which range from Rs. 24,100 to Rs. 38,800, factoring in revenue, expenses, depreciation and taxes.
- The terminal cash flow at the end of 5 years is Rs. 48,800, which includes the final annual cash flow plus the expected scrap value of Rs. 10,000 for the machine.
The document discusses several theories of capital structure:
1) The traditional approach finds that a firm's value and cost of capital initially decrease with more debt but then increase after a certain point as debt rises.
2) Modigliani and Miller's approach suggests capital structure does not affect firm value in the absence of taxes.
3) Pecking order theory proposes firms prefer internal funds, then debt, and finally equity when raising capital, due to costs and information asymmetries.
4) Static trade-off theory finds an optimal capital structure where the marginal benefit of debt's tax shield equals the marginal costs of bankruptcy.
1. The net income approach suggests that a firm can minimize its weighted average cost of capital and maximize shareholder value by using as much debt financing as possible, as long as the cost of debt is less than the cost of equity and risk is unchanged.
2. The net operating income approach, proposed by Durand, argues that a company's total market value and overall cost of capital remain constant regardless of its debt-equity ratio, so every capital structure is optimal.
3. The document provides examples to illustrate how to calculate a firm's value and capitalization rates under each approach, assuming no corporate taxes.
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إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2. Measurement and Scaling
Measurement means assigning numbers or other
symbols to characteristics of objects according to certain
pre-specified 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.
3. 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.
4. Primary Scales of
Measurement
7 38
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
Third
place
Second
place
First
place
Finish
Finish
8.2 9.1 9.6
15.2 14.1 13.4
5. 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.
7. 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.
8. Primary Scales of
Measurement
Interval Scale• Numerically equal distances on the scale represent equal
values in the characteristic being measured.
• It 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.
• Any positive linear transformation of the form y = a + bx will
preserve the properties of the scale.
• It is not meaningful to take ratios of scale values.
• 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.
9. 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.
10. Primary Scales of Measurement
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
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 noncomparative 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.
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 of preference, 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 Toothpaste
Brands
Using Rank Order Scaling
Instructions: Rank the various brands of toothpaste 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. Preference for Toothpaste Brands
Using Rank Order Scaling
Brand Rank Order
1. Crest _________
2. Colgate _________
3. Aim _________
4. Gleem _________
5. Sensodyne _________
6. Ultra Brite _________
7. Close Up _________
8. Pepsodent _________
9. Plus White _________
10. Stripe _________
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 Scale
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. 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 PowerSum
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. Advantages-
- Allows for fine discrimination among
stimulus objects without requiring too
much time.
Disadvantages-
- Respondents may allocate more or few
points like 105 or 95.
- Use of too large number of units may be
too taxing on respondents.
25. 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.
26. 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
27. A relatively new research tool, the perception analyzer, provides
continuous measurement of “gut reaction.” A group of up to 400
respondents is presented with TV or radio spots or advertising copy.
The measuring device consists of a dial that contains a 100-point
range. Each participant is given a dial and instructed to continuously
record his or her reaction to the material being tested ..
As the respondents turn the dials, the
information is fed to a computer, which
tabulates second-by-second response
profiles. As the results are recorded by
the computer, they are superimposed on a
video screen, enabling the researcher to
view the respondents' scores
immediately. The responses are also
stored in a permanent data file for use in
further analysis. The response scores
can be broken down by categories, such
as age, income, sex, or product usage.
RATE: Rapid Analysis and
Testing Environment
28. 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.
29. Likert Scale
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.
30. 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.
31. Three dimensions of SD
• Evaluation is associated with the adjective like: nice-awful,
good-bad, sweet-sour, and helpful-unhelpful. Some
concepts which lie on the positive (good) side of this
dimension are: DOCTOR, FAMILY, GOD, CHURCH,
HAPPY, PEACE, SUCCESS, TRUTH, BEAUTY, and
MUSIC. Some concepts which lie toward the negative (bad)
pole are: DEVIL, DISCORDANT, DIVORCE, FRAUD,
HATE, DISEASE, SIN, WAR, ENEMY, and FAILURE.
32. • Potency: Some scales which define the Potency
dimension are big-little, powerful-powerless, strong-
weak, and deep-shallow. Concepts which lie toward the
positive (powerful) pole are: WAR, ARMY, BRAVE,
COP, MOUNTAIN, ENGINE, BUILDING, DUTY, LAW,
STEEL, POWER, and SCIENCE. Concepts which lie
toward the negative (powerless) pole are: BABY,
FEATHER, KITTEN,
33. • Activity: Activity scales are fast-slow, alive-dead,
noisy-quiet, and young-old. Some concepts high
in Activity are: DANGER, ANGER, ATTACK,,
ENGINE, FIRE, SWORD, TORNADO, WAR,
WIN, and PARTY. Among concepts which lie
toward the negative pole on the Activity
dimension are: CALM, SNAIL, REST, STONE,
and SLEEP.
35. 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.
+5 +5
+4 +4
+3 +3
+2 +2X
+1 +1
HIGH QUALITY POOR SERVICE
-1 -1
-2 -2
-3 -3
-4X -4
-5 -5
36. 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 for at least some 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
37. 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
Balanced and Unbalanced Scales
38. Rating Scale Configurations
-3 -1 0 +1 +2-2 +3
Cheer
Cheer detergent is: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
39. 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
Like very
much
Dislike
very much
100
75
50
25
0
Some Unique Rating Scale Configurations
40. Some Commonly Used Scales in
Marketing
CONSTRUCT SCALE DESCRIPTORS
Attitude
Importance
Satisfaction
Purchase Intent
Purchase Freq
Very Bad
Not all All Important
Very Dissatisfied
Definitely will Not Buy
Never
Bad
Not Important
Dissatisfied
Probably Will Not Buy
Rarely
Neither Bad Nor Good
Neutral
Neither Dissat Nor Satisfied
Might or Might Not Buy
Sometimes
Good
Important
Satisfied
Probably Will Buy
Often
Very Good
Very Important
Very Satisfied
Definitely Will Buy
Very Often
41. 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
Select a Reduced Set of Items Based on Qualitative Judgement
Evaluate Scale Reliability, Validity, and Generalizability
43. Measurement Accuracy
The true score model provides a framework for
understanding the accuracy of measurement.
XO = XT + XS + XR
where
XO = the observed score or measurement
XT = the true score of the characteristic
XS = systematic error
XR = random error
44. Potential Sources of Error on
Measurement
11) Other relatively stable characteristics of the individual that influence
the test score, such as intelligence, social desirability, and
education.
2) Short-term or transient personal factors, such as health, emotions,
and fatigue.
3) Situational factors, such as the presence of other people, noise, and
distractions.
4) Sampling of items included in the scale: addition, deletion, or
changes in the scale items.
5) Lack of clarity of the scale, including the instructions or the items
themselves.
6) Mechanical factors, such as poor printing, overcrowding items in the
questionnaire, and poor design.
7) Administration of the scale, such as differences among interviewers.
8) Analysis factors, such as differences in scoring and statistical
analysis..
45. Reliability
• Reliability can be defined as the extent to which
measures are free from random error, XR. If XR = 0,
the measure is perfectly reliable.
• In test-retest reliability, checks how similar the
results are if the research is repeated under similar
circumstances. Stability over repeated measures is
assessed with the Pearson coefficient.
• In alternative-forms reliability, two equivalent forms
of the scale are constructed and the same
respondents are measured at two different times, with
a different form being used each time.
46. Reliability
• Internal consistency reliability checks how well
the individual measures included in the research
are converted into a composite measure.
In split-half reliability, the items on the scale are
divided into two halves and the resulting half
scores are correlated. The coefficient alpha, or
Cronbach's alpha, is the average of all possible
split-half coefficients resulting from different ways
of splitting the scale items. This coefficient
varies from 0 to 1, and a value of 0.6 or less
generally indicates unsatisfactory internal
consistency reliability.
47. α =0 There is no consistency between the
various items.
α =1 Complete consistency between various
items
0.80≤
α≤0.95
There is very good reliability between
various items
0.70≤
α≤0.80
There is very reliability between various
items
0.60≤
α≤0.70
There is fair reliability between various
items
α≤0.60 There is poor reliability between various
items
48. Validity
• The validity of a scale may be defined as the extent to
which differences in observed scale scores reflect true
differences among objects on the characteristic being
measured, rather than systematic or random error. Perfect
validity requires that there be no measurement error (XO =
XT, XR = 0, XS = 0).
• Content validity checks how well the content of the
research are related to the variables to be studied; it seeks
to answer whether the research questions are
representative of the variables being researched. It is a
demonstration that the items of a test are drawn from the
domain being measured.
• Criterion validity reflects whether a scale performs as
expected in relation to other variables selected (criterion
variables) as meaningful criteria or checks how meaningful
the research criteria are relative to other possible criteria. .
49. Validity
• Construct validity addresses the question of what
construct or characteristic the scale is measuring.
Construct validity includes convergent, discriminant,
and nomological validity.
• Convergent validity is the extent to which the scale
correlates positively with other measures of the same
construct.
• Discriminant validity is the extent to which a
measure does not correlate (poor correlation) with
other constructs from which it is supposed to differ.
• Nomological validity how well the research relates to
other variables as required by theory
50. Relationship Between
Reliability and Validity
• If a measure is perfectly valid, it is also perfectly reliable.
In this case XO = XT, XR = 0, and XS = 0.
• If a measure is unreliable, it cannot be perfectly valid,
since at a minimum XO = XT + XR. Furthermore,
systematic error may also be present, i.e., XS≠0. Thus,
unreliability implies invalidity.
• If a measure is perfectly reliable, it may or may not be
perfectly valid, because systematic error may still be
present (XO = XT + XS).
• Reliability is a necessary, but not sufficient, condition for
validity.
52. Questionnaire Definition
• A questionnaire is a formalized set
of questions for obtaining information
from respondents.
53. Questionnaire Objectives
• It must translate the information needed into a set
of specific questions that the respondents can and
will answer.
• A questionnaire must uplift, motivate, and
encourage the respondent to become involved in
the interview, to cooperate, and to complete the
interview.
• A questionnaire should minimize response error.
54. Youth Research Achieves
Questionnaire Objectives
Youth research (YR) of Brookfield, Connecticut, conducts an omnibus survey
of children every quarter. Typically, YR interviews 150 boys and girls between
ages 6 and 8, along with 150 boys and girls between ages 9 and 12. YR uses
mall intercepts of mothers to recruit for its one-on-one interviews, which last
eight minutes. The study obtains children’s views on favorite snack foods,
television shows, commercials, radio, magazines, buzzwords, and movies.
55. Youth Research Achieves
Questionnaire Objectives
YR intentionally keeps its questionnaire to eight minutes because of attention span
limits of children. YR President Karen Forcade notes that some clients attempt to
meet all their research objectives with one study, instead of surveying, fine-tuning
objectives, and re-surveying. In doing so, these clients overlook attention limits of
young respondents when developing questionnaires.
“The questionnaires keep going through the
approval process and people keep adding
questions, ‘Well let’s ask this question, let’s add
that question, and why don’t we talk about this
also,’” Forcade said. “And so you end up keeping
children 25 minutes in a central location study and
they get kind of itchy.” The response error
increases and the quality of data suffers.
56. Forcade notes other lessons from interviewing children. When asking questions,
interviewers should define the context to which the question refers. “It involves
getting them to focus on things, putting them in a situation so that they can identify
with it,” Forcade said. “For example, when asking about their radio listening habits,
we said, ‘What about when you’re in Mom’s car, do you listen to the radio?’ rather
than, ‘How often do you listen to the radio? More than once a day, once a day,
more than once a week?’ Those are kind of big questions for little children.”
Questionnaires designed by
Youth Research to obtain
children’s views on favorite
snack foods, television shows,
commercials, radio,
magazines, buzzwords, and
movies attempt to minimize
response error.
Youth Research Achieves
Questionnaire Objectives
57. Specify the Information
Needed
Design the Question to Overcome the Respondent’s Inability and
Unwillingness to Answer
Determine the Content of Individual Questions
Decide the Question Structure
Determine the Question
Wording
Arrange the Questions in Proper Order
Reproduce the Questionnaire
Specify the Type of Interviewing Method
Identify the Form and Layout
Eliminate Bugs by Pre-testing
Questionnaire Design Process
58. Effect of Interviewing Method on
Questionnaire Design
Department Store Project
Mail Questionnaire
• Please rank order the following department stores in order of your
preference to shop at these stores. Begin by picking out the one store
that you like most and assign it a number 1. Then find the second
most preferred department store and assign it a number 2. Continue
this procedure until you have ranked all the stores in order of
preference. The least preferred store should be assigned a rank of
10. No two stores should receive the same rank number.
Store Rank Order
1.Parisian ____________
2.Macy's ____________
.
.
10. Wal-Mart ____________
59. Effect of Interviewing Method on
Questionnaire Design
Telephone Questionnaire
• I will read to you the names of some department stores. Please rate
them in terms of your preference to shop at these stores. Use a ten-
point scale, where 1 denotes not so preferred and 10 denotes greatly
preferred. Numbers between 1 and 10 reflect intermediate degrees of
preference. Again, please remember that the higher the number, the
greater the degree of preference. Now, please tell me your preference
to shop at .......(READ ONE STORE AT A TIME)
Store Not So Greatly
Preferred Preferred
1. GIP 1 2 3 4 5 6 7 8 9 10
2. CSM 1 2 3 4 5 6 7 8 9 10
.
.
.
10. Wal-Mart 1 2 3 4 5 6 7 8 9
10
60. Effect of Interviewing Method on
Questionnaire Design
Personal Questionnaire
• (HAND DEPARTMENT STORE CARDS TO THE
RESPONDENT). Here is a set of department store names,
each written on a separate card. Please examine these
cards carefully. (GIVE RESPONDENT TIME). Now, please
examine these cards again and pull out that card which has
the name of the store you like the most, i.e., your most
preferred store for shopping. (RECORD THE STORE NAME
AND KEEP THIS CARD WITH YOU). Now, please examine
the remaining nine cards. Of these remaining nine stores,
what is your most preferred store for shopping? (REPEAT
THIS PROCEDURE SEQUENTIALLY UNTIL THE
RESPONDENT HAS ONLY ONE CARD LEFT)
61. Effect of Interviewing Method
on
Questionnaire Design
Electronic Questionnaire
• This question for e-mail and Internet
questionnaires will be very similar to that
for the mail questionnaire.
• In all these methods, the questionnaire
is self-administered by the respondent.
62. Individual Question Content
Is the Question Necessary?
• If there is no satisfactory use for the data
resulting from a question, that question
should be eliminated.
63. Individual Question Content ─
Are Several Questions Needed Instead of
One?
• Sometimes, several questions are needed to obtain
the required information in an unambiguous manner.
Consider the question:
“Do you think Coca-Cola is a tasty and refreshing soft
drink?” (Incorrect)
• Such a question is called a double-barreled
question, because two or more questions are
combined into one.To obtain the required information,
two distinct questions should be asked:
“Do you think Coca-Cola is a tasty soft drink?” and
“Do you think Coca-Cola is a refreshing soft drink?”
(Correct)
64. Overcoming Inability To Answer –
Is the Respondent Informed?
• In situations where not all respondents are likely to
be informed about the topic of interest, filter
questions that measure familiarity and past
experience should be asked before questions about
the topics themselves.
• A “don't know” option appears to reduce uninformed
responses without reducing the response rate.
65. Overcoming Inability To Answer – Can the
Respondent Remember?
How many gallons of soft drinks did you
consume during the last four weeks? (Incorrect)
How often do you consume soft drinks in a
typical week? (Correct)
1. ___ Less than once a week
2. ___ 1 to 3 times per week
3. ___ 4 to 6 times per week
4. ___ 7 or more times per week
66. Overcoming Inability To Answer – Can the
Respondent Articulate?
• Respondents may be unable to articulate certain
types of responses, e.g., describe the atmosphere
of a department store.
• Respondents should be given aids, such as
pictures, maps, and descriptions to help them
articulate their responses.
67. Overcoming Unwillingness To
Answer – Effort Required of the
Respondents
• Most respondents are unwilling to
devote a lot of effort to provide
information.
68. Overcoming Unwillingness To Answer
Please list all the departments from which you purchased
merchandise on your most recent shopping trip to a
department store. (Incorrect)
In the list that follows, please check all the departments from
which you purchased merchandise on your most recent
shopping trip to a department store.
1. Women's dresses ____
2. Men's apparel ____
3. Children's apparel ____
4. Cosmetics ____
.
.
16. Jewelry ____
17. Other (please specify) ____ (Correct)
69. Overcoming Unwillingness To Answer
Context
• Respondents are unwilling to respond to questions which they
consider to be inappropriate for the given context.
• The researcher should manipulate the context so that the request
for information seems appropriate.
Legitimate Purpose
• Explaining why the data are needed can make the request for the
information seem legitimate and increase the respondents'
willingness to answer.
Sensitive Information
• Respondents are unwilling to disclose, at least accurately, sensitive
information because this may cause embarrassment or threaten the
respondent's prestige or self-image.
70. Overcoming Unwillingness To Answer
Increasing the Willingness of Respondents
• Place sensitive topics at the end of the questionnaire.
• Preface the question with a statement that the behavior of
interest is common.
• Ask the question using the third-person technique phrase
the question as if it referred to other people.
• Hide the question in a group of other questions which
respondents are willing to answer. The entire list of
questions can then be asked quickly.
• Provide response categories rather than asking for
specific figures.
• Use randomized techniques.
71. Choosing Question Structure –
Unstructured Questions
• Unstructured questions are open-ended
questions that respondents answer in their own
words.
What is your occupation?
Who is your favorite actor?
What do you think about people who shop at high-
end department stores?
72. Choosing Question Structure –
Structured Questions
• Structured questions specify the
set of response alternatives and the
response format. A structured
question may be multiple-choice,
dichotomous, or a scale.
73. Choosing Question Structure –
Multiple-Choice Questions
• In multiple-choice questions, the researcher provides a
choice of answers and respondents are asked to select
one or more of the alternatives given.
Do you intend to buy a new car within the next six
months?
____ Definitely will not buy
____ Probably will not buy
____ Undecided
____ Probably will buy
____ Definitely will buy
____ Other (please specify)
74. Choosing Question Structure –
Dichotomous Questions
• A dichotomous question has only two response
alternatives: yes or no, agree or disagree, and so on.
• Often, the two alternatives of interest are supplemented by
a neutral alternative, such as “no opinion,” “don't know,”
“both,” or “none.”
Do you intend to buy a new car within the next six months?
_____ Yes
_____ No
_____ Don't know
75. Choosing Question Structure –
Scales
Scales were discussed in detail-
Do you intend to buy a new car within the next six months?
Definitely Probably Undecided Probably Definitely
will not buy will not buy will buy will buy
1 2 3 4 5
76. Choosing Question Wording –
Define the Issue
• Define the issue in terms of who, what, when,
where, why, and way (the six Ws). Who, what,
when, and where are particularly important.
Which brand of shampoo do you use?
(Incorrect)
Which brand or brands of shampoo have you
personally used at home during the last month?
In case of more than one brand, please list all the
brands that apply. (Correct)
77. Choosing Question Wording
Defining the Question
The Respondent
It is not clear whether this question relates to
the individual respondent or the respondent's
total household.
The Brand of Shampoo
It is unclear how the respondent is to answer
this question if more than one brand is used.
Unclear
The time frame is not specified in this question.
The respondent could interpret it as meaning
the shampoo used this morning, this week, or
over the past year.
The W's
Who
What
When
Where At home, at the gym, on the road?
78. Choosing Question Wording –
Use Ordinary Words
“Do you think the distribution of soft drinks is
adequate?” (Incorrect)
“Do you think soft drinks are readily available when
you want to buy them?” ``
(Correct)
79. Choosing Question Wording –
Use Unambiguous Words
In a typical month, how often do you shop in
department stores?
_____ Never
_____ Occasionally
_____ Sometimes
_____ Often
_____ Regularly
(Incorrect)
In a typical month, how often do you shop in
department stores?
_____ Less than once
_____ 1 or 2 times
_____ 3 or 4 times
_____ More than 4 times (Correct)
80. Choosing Question Wording –
Avoid Leading or Biasing Questions
• A leading question is one that clues the respondent to what
the answer should be, as in the following:
Do you think that patriotic Americans should buy imported
automobiles when that would put American labor out of work?
_____ Yes
_____ No
_____ Don't know (Incorrect)
Do you think that Americans should buy imported
automobiles?
_____ Yes
_____ No
_____ Don't know (Correct)
81. Choosing Question Wording –
Avoid Implicit Alternatives
• An alternative that is not explicitly expressed in the
options is an implicit alternative.
1. Do you like to fly when traveling short distances?
(Incorrect)
2. Do you like to fly when traveling short distances,
or would you rather drive?
(Correct)
82. Choosing Question Wording –
Avoid Implicit Assumptions
• Questions should not be worded so that the answer is
dependent upon implicit assumptions about what
will happen as a consequence.
1. Are you in favor of a balanced budget?
(Incorrect)
2. Are you in favor of a balanced budget if it
would result in an increase in the personal income
tax?
(Correct)
83. Choosing Question Wording –
Avoid Generalizations and Estimates
“What is the annual per capita expenditure on groceries
in your household?” (Incorrect)
“What is the monthly (or weekly) expenditure on
groceries in your household?”
and
“How many members are there in your household?”
(Correct)
84. Choosing Question Wording
Dual Statements: Positive and
Negative
• Questions that are in the form of
statements should be worded both
positively and negatively.
85. Determining the Order of Questions
Opening Questions
• The opening questions should be interesting, simple, and
non-threatening.
Type of Information
• As a general guideline, basic information should be
obtained first, followed by classification, and, finally,
identification information.
Difficult Questions
• Difficult questions or questions which are sensitive,
embarrassing, complex, or dull, should be placed late in
the sequence.
86. Determining the Order of Questions
Effect on Subsequent Questions
• General questions should precede the specific
questions (funnel approach).
Q1: “What considerations are important to you in
selecting a department store?”
Q2: “In selecting a department store, how important is
convenience of location?” (Correct)
87. Determining the Order of Questions
Logical Order
The following guidelines should be followed for
branching questions:
• The question being branched (the one to which the
respondent is being directed) should be placed as
close as possible to the question causing the
branching.
• The branching questions should be ordered so that the
respondents cannot anticipate what additional
information will be required.
88. Ownership of Store, Bank,
and Other Charge Cards
Introduction
Store
Charge
Card
Purchased Products in a Specific Department
Store during the Last Two Months
How was Payment made? Ever Purchased in a
Department Store?
Bank
Charge
Card
Other
Charge
Card
Intentions to Use Store, Bank,
and other Charge Cards
Yes
Yes
No
No
CashCredit
Other
Flow Chart for Questionnaire
Design
89. Form and Layout
• Divide a questionnaire into several parts.
• The questions in each part should be numbered,
particularly when branching questions are used.
• The questionnaires should preferably be precoded.
• The questionnaires themselves should be numbered
serially.
90. 11/2
hours to 1 hour 59 minutes.........-4
2 hours to 2 hours 59 minutes...........-5
3 hours or more.................................-6
Less than 30 minutes.....................-1
30 to 59 minutes............................-2
1 hour to 1 hour 29 minutes..........-3
The American Lawyer
A Confidential Survey of Our Subscribers
(Please ignore the numbers alongside the answers. They are only to help
us in data processing.)
1. Considering all the times you pick it up, about how much time, in total, do
you spend reading or looking through a typical issue of THE AMERICAN
LAWYER?
Example of a Precoded
Questionnaire
91. Reproduction of the Questionnaire
• The questionnaire should be reproduced on good-quality paper
and have a professional appearance.
• Questionnaires should take the form of a booklet rather than a
number of sheets of paper clipped or stapled together.
• Each question should be reproduced on a single page (or
double-page spread).
• Vertical response columns should be used for individual
questions.
• Grids are useful when there are a number of related questions
they use the same set of response categories.
• The tendency to crowd questions together to make the
questionnaire look shorter should be avoided.
• Directions or instructions for individual questions should be
placed as close to the questions as possible.
92. Pretesting
Pretesting refers to the testing of the questionnaire on a small
sample of respondents to identify and eliminate potential
problems.
• A questionnaire should not be used in the field survey without
adequate pretesting.
• All aspects of the questionnaire should be tested, including
question content, wording, sequence, form and layout, question
difficulty, and instructions.
• The respondents for the pretest and for the actual survey
should be drawn from the same population.
• Pretests are best done by personal interviews, even if the actual
survey is to be conducted by mail, telephone, or electronic
means, because interviewers can observe respondents'
reactions and attitudes.
93. Pretesting
• After the necessary changes have been made, another pretest
could be conducted by mail, telephone, or electronic means if
those methods are to be used in the actual survey.
• A variety of interviewers should be used for pretests.
• The pretest sample size varies from 15 to 30 respondents for
each wave.
• Protocol analysis and debriefing are two commonly used
procedures in pretesting.
• Finally, the responses obtained from the pretest should be
coded and analyzed.
94. Observational Forms
Department Store Project
• Who: Purchasers, browsers, males, females, parents with
children, or children alone.
• What: Products/brands considered, products/brands
purchased, size, price of package inspected, or influence of
children or other family members.
• When: Day, hour, date of observation.
• Where: Inside the store, checkout counter, or type of
department within the store.
• Why: Influence of price, brand name, package size, promotion,
or family members on the purchase.
• Way: Personal observer disguised as sales clerk, undisguised
personal observer, hidden camera, or obtrusive mechanical
device.
95. Step 1. Specify The Information Needed
Step 2. Type of Interviewing Method
Step 3. Individual Question Content
Step 4. Overcome Inability and Unwillingness to Answer
Step 5. Choose Question Structure
Step 6. Choose Question Wording
Step 7. Determine the Order of Questions
Step 8. Form and Layout
Step 9. Reproduce the Questionnaire
Step 10. Pretest
Questionnaire Design Checklist
96. Step 1. Specify the Information Needed
1. Ensure that the information obtained fully addresses all
the components of the problem. Review components of
the problem and the approach, particularly the research
questions, hypotheses, and specification of information
needed.
2. Prepare a set of dummy tables.
3. Have a clear idea of the target population.
Step 2. Type of Interviewing Method
1. Review the type of interviewing method determined based
on considerations discussed in Chapter 6.
Questionnaire Design Checklist
97. Questionnaire Design Checklist
Step 3. Individual Question Content
1. Is the question necessary?
2. Are several questions needed instead of one to obtain
the required information in an unambiguous manner?
3. Do not use double-barreled questions.
98. Questionnaire Design Checklist
Step 4. Overcoming Inability and Unwillingness to Answer
1. Is the respondent informed?
2. If respondents are not likely to be informed, filter questions
that measure familiarity, product use, and past experience
should be asked before questions about the topics
themselves.
3. Can the respondent remember?
4. Avoid errors of omission, telescoping, and creation.
5. Questions which do not provide the respondent with cues can
underestimate the actual occurrence of an event.
6. Can the respondent articulate?
99. Questionnaire Design Checklist
Step 4. Overcoming Inability and Unwillingness to Answer
7. Minimize the effort required of the respondents.
8. Is the context in which the questions are asked appropriate?
9. Make the request for information seem legitimate.
10. If the information is sensitive:
a. Place sensitive topics at the end of the questionnaire.
b. Preface the question with a statement that the behavior of
interest is common.
c. Ask the question using the third-person technique.
d. Hide the question in a group of other questions which
respondents are willing to answer.
e. Provide response categories rather than asking for specific
figures.
f. Use randomized techniques, if appropriate.
100. Questionnaire Design Checklist
Step 5. Choosing Question Structure
1. Open-ended questions are useful in exploratory research and
as opening questions.
2. Use structured questions whenever possible.
3. In multiple-choice questions, the response alternatives should
include the set of all possible choices and should be mutually
exclusive.
4. In a dichotomous question, if a substantial proportion of the
respondents can be expected to be neutral, include a neutral
alternative.
5. Consider the use of the split ballot technique to reduce order
bias in dichotomous and multiple-choice questions.
6. If the response alternatives are numerous, consider using
more than one question to reduce the information processing
demands on the respondents.
101. Questionnaire Design Checklist
Step 6. Choosing Question Wording
1. Define the issue in terms of who, what, when, where, why, and way
(the six Ws).
2. Use ordinary words. Words should match the vocabulary level of the
respondents.
3. Avoid ambiguous words: usually, normally, frequently, often,
regularly, occasionally, sometimes, etc.
4. Avoid leading questions that clue the respondent to what the answer
should be.
5. Avoid implicit alternatives that are not explicitly expressed in the
options.
6. Avoid implicit assumptions.
7. Respondent should not have to make generalizations or compute
estimates.
8. Use positive and negative statements.
102. Questionnaire Design Checklist
Step 7. Determine the Order of Questions
1. The opening questions should be interesting, simple, and non-
threatening.
2. Qualifying questions should serve as the opening questions.
3. Basic information should be obtained first, followed by classification,
and, finally, identification information.
4. Difficult, sensitive, or complex questions should be placed late in the
sequence.
5. General questions should precede the specific questions.
6. Questions should be asked in a logical order.
7. Branching questions should be designed carefully to cover all
possible contingencies.
8. The question being branched should be placed as close as possible to
the question causing the branching, and (2) the branching questions
should be ordered so that the respondents cannot anticipate what
additional information will be required.
103. Questionnaire Design Checklist
Step 8. Form and Layout
1. Divide a questionnaire into several
parts.
2. Questions in each part should be
numbered.
3. The questionnaire should be pre-coded.
4. The questionnaires themselves should
be numbered serially.
104. Questionnaire Design Checklist
Step 9. Reproduction of the Questionnaire
1. The questionnaire should have a professional appearance.
2. Booklet format should be used for long questionnaires.
3. Each question should be reproduced on a single page (or
double-page spread).
4. Vertical response columns should be used.
5. Grids are useful when there are a number of related
questions which use the same set of response categories.
6. The tendency to crowd questions to make the questionnaire
look shorter should be avoided.
7. Directions or instructions for individual questions should
be placed as close to the questions as possible.
105. Questionnaire Design Checklist
Step 10. Pretesting
1. Pretesting should be done always.
2. All aspects of the questionnaire should be tested, including question content,
wording, sequence, form and layout, question difficulty, and instructions.
3. The respondents in the pretest should be similar to those who will be
included in the actual survey.
4. Begin the pretest by using personal interviews.
5. Pretest should also be conducted by mail or telephone if those methods are to
be used in the actual survey.
6. A variety of interviewers should be used for pretests.
7. The pretest sample size is small, varying from 15 to 30 respondents for the
initial testing.
8. Use protocol analysis and debriefing to identify problems.
9. After each significant revision of the questionnaire, another pretest should be
conducted, using a different sample of respondents.
10. The responses obtained from the pretest should be coded and analyzed.
108. SLIDE 8-1
The questionnaire method
• This is the simplest and most often used
method of primary data collection
• There is a pre-determined set of questions
in a sequential format
• Is designed to suit the respondent’s
understanding and language command
• Can be conducted to collect useful data
from a large population in a short duration
of time
109. SLIDE 8-2
Criteria for questionnaire design
• The spelt out research objectives need to be
converted into specific questions
• It must be designed to engage the respondent
and encourage meaningful response
• The questions should be designed in simple
language and be self-explanatory
110. SLIDE 8-3
Types of questionnaire
Formalized Non Formalized
Unconcealed Most researchstudies use
Standardized Questionnaires like
these.
The response categories have
more flexibility
Concealed Used for assessing psychographic and
subjective constructs
Questionnaires using
projective techniques or
sociometric analysis
111. SLIDE 8-4
Types of questionnaire
Formalized & unconcealed questionnaire: self-explanatory with
most response categories predefined
• Out of the following options, where do you invest
(tick all that apply)
Precious metals----------------, real estate------------, stocks---------,
Government instruments---------, mutual funds------any other-------
• Who carries out your investments?
Myself-----------, agent---------, relative-----------, friend------------, any
other----------
• What is your source of information for these decisions?
Newspaper------------, investment magazines-----------, company
records etc.----------, Trading portals------------, agent------------
112. SLIDE 8-5
Types of questionnaire
Formalized & concealed questionnaire: most response categories are
predefined, but latent cause of behaviour are derived from indirect
questions
Please indicate level of your agreement for the following statements.
SA - Strongly Agree; A-Agree; N-Neutral; SD- Strongly Disagree; D-Disagree
SA A N D SD
1 The individual of the present era is better informed about
everything than the individual before.
2 I believe that one must live for the day and worry about
tomorrow later.
3 An individual must at all times keep abreast of what is
happening in the world around him/her.
4 Books are best friends anyone can have.
5 I generally read and then decide what to buy.
113. SLIDE 8-6
Types of questionnaire
Non-formalized & concealed questionnaire: undisguised and most
response categories are not predefined
• Why do you think Maggi noodles are liked by young children?
---------------------------------------------------------------------------
• How do you generally decide on where you are going to invest
your money?-------------------------------------------------------------
• Give three reasons why you believe that the 2010 Commonwealth Games
in India are going to help the country?
-------------------------------------------------------------------------
114. SLIDE 8-7
Types of questionnaire
Non-formalized & concealed questionnaire:
disguised and most response categories are not
predefined,e.g.
Given below are two grocery lists –personify the
user
115. SLIDE 8-8
Types of questionnaire
method of administration
• Self-administered questionnaire: respondents
fills in the questionnaire him/her self
• Schedule: the investigator/researcher reads out
the questions and records the respondents’
answers.
116. SLIDE 8-9
Criteria for questionnaire selection
• Population characteristics
• Population spread
• Study area
117. SLIDE 8-10
The questionnaire design process
Convert the Research Objectives into the Information Needed
Content of the Questions
Method of Administering the Questionnaire
Motivating the Respondent to Answer
Determining Type of Questions
Pilot Testing the Questionnaire
Question Design Criteria
Determine the Questionnaire Structure
Physical presentation of the Questionnaire
Administering the Questionnaire
120. SLIDE 8-13
Mode of administration: Schedule
Now I am going to give you a set of cards. Each card will have the name of one television serial (Hand
over the cards to the respondent in a random order). I want you to examine them carefully (give her
some time to read all the names). I would request you to hand over the card which has the name of the
serial you like to watch the most. (Record the serial and keep this card with you). Now of the remaining
nine serials name your most favorite serial (continue the same process till the person is left with the last
card)
T.V. SERIAL RANK ORDER
1. 1 ___________________
2. 2 ___________________
3. 3 ___________________
4. 4 ___________________
5. 5 ___________________
6. 6 ___________________
7. 7 ___________________
8. 8 ___________________
9. 9 ___________________
10. 10 ___________________
121. SLIDE 8-14
Mode of administration:
telephone
Please listen very carefully; I am going to slowly read the name of ten popular T.V. serials. I want to know
how much you prefer watching them. You need to use a 1 to 10 scale, where 1 means I do not like watching
it and 10 means I really like watching it. For those in between you may choose any number between 1 and
10. However, please remember that the higher the number the more you like watching it. Now, I am going to
name the serials one by one. In case the name is not clear I will repeat the list again. So, the serial’s name
is-------------------. Please use a number between 1 and 10 as I had told you. O.k. thank you, the next name
is---------------------. And so on till all the 10 names have been read out and evaluated.
SERIAL
1. Balika Badhu 1 2 3 4 5 6 7 8 9 10
2. Sathiya 1 2 3 4 5 6 7 8 9 10
3. Sasural Genda Phool 1 2 3 4 5 6 7 8 9 10
4. Bidai 1 2 3 4 5 6 7 8 9 10
5. Pathshala 1 2 3 4 5 6 7 8 9 10
6. Bandini 1 2 3 4 5 6 7 8 9 10
7. Laptaganj 1 2 3 4 5 6 7 8 9 10
8. Sajan Ghar jaaana Hai 1 2 3 4 5 6 7 8 9 10
9. Tere liye 1 2 3 4 5 6 7 8 9 10
10. Uttaran 1 2 3 4 5 6 7 8 9 10
122. SLIDE 8-15
Mode of administration: mail
In the next question you will find the names of ten popular Hindi serials that are being aired on television
these days. You are requested to rank them in order of your preference to watch these programmes. Start
by identifying the serial which is your most favorite, to this you may give a rank of 1. Then from the rest of
the nine, pick the second most preferred serials and give it a rank number of 2.Please carry out this process
till you have ranked all 10. The one you prefer the least should have a score of 10. You are also requested
not to give two serials the same rank. The basis on which you decide to rank the serials is entirely
dependent upon you. Once again you are asked to rank all the 10 serials.
SERIAL RANK ORDER
1. Balika Badhu ___________________
2. Sathiya ___________________
3. Sasural Genda Phool ___________________
4. Bidai ___________________
5. Pathshala ___________________
6. Bandini ___________________
7. Laptaganj ___________________
8. Sajan Ghar Jaaana Hai ___________________
9. Tere Liye ___________________
10. Uttaran ___________________
123. SLIDE 8-16
Content of the questionnaire
Essential to ask the question
To gauge consumer’s shopping behaviour
Please indicate the level of your agreement for the following statements.
SA - Strongly Agree; A-Agree; N-Neutral; SD- Strongly Disagree; D-Disagree
Compared to the past (5-10 years)
SA A N D SD
1 The individual customer today shops more
2 The consumer is well informed about market offerings
3 The consumer knows what he/she wants to buy before he enters the
store
4 The consumer today has more money to spend
5 There are more shopping options available to the consumer today
124. SLIDE 8-17
Content of the questionnaire
Several questions or single question
“Why do you like the serial--------------(the one you ranked/prefer
watching most)?” (Incorrect)
"What do you like about-------------?"
“Who all in your household watches the serial?
and
"How did you first happen to hear about the serial?"
(Correct)
125. SLIDE 8-18
Motivating the respondent to answer
Assisting the respondent to provide the answer
Does he have the answer?
1. How do you evaluate the negotiation skills module with the
Communication and presentation skill module? (Incorrect)
1. Have you been through the following training modules?
Negotiation skills module
Yes/no
Communication & presentation skills
Yes/no
In case the answer to both is yes, please answer the following
question else move to the next question.
How do you evaluate the negotiation skills module with the
Communication and presentation skill module? (Correct)
126. SLIDE 8-19
Motivating the respondent to
answer
Assisting the respondent to provide the answer
Does he remember?
How much did you spend on eating out last month? (Incorrect)
1. When you go out to eat, on an average your bill amount is:
________ Less than Rs100
________ Rs 101-250
________ Rs 251-500
________ more than Rs 500
2. How often do you eat out in a week?
________ 1-2 times.
________ 3-4 times
________ 5-6 times
________ every day (correct)
127. SLIDE 8-20
Motivating the respondent to
answer
Assisting the respondent to provide the answer
Can he articulate?
Describe the river rafting experience.……...
(incorrect)
Describe the river rafting experience (Correct)
1 Unexciting exciting
2 Bad good
3 Boring interesting
4 Cheap expensive
5 Safe dangerous
128. SLIDE 8-21
Motivating the respondent to
answer
Assisting the respondent to answer
The perspective is not clear
“How many credit cards do you own?” or
“When did you last go on a holiday?” or
“How many movies do you watch in a fortnight?” (incorrect)
A spillover of a healthy quality of working life is also reflected in a
person’s way of living. Thus, we would like to know how you
live. (correct)
129. SLIDE 8-22
Motivating the respondent to
answer
Assisting the respondent to answer
Sensitive information/topic
Have you ever used fake receipts to claim your medical
allowance?
(Incorrect)
Have you ever spit tobacco on the road (to tobacco consumers)?
(Incorrect)
Do you associate with people who use fake receipts to claim their
medical allowance? (Correct)
Do you think tobacco consumers spit tobacco on the road?
130. SLIDE 8-23
Type of questions
Question Content
Open – ended Closed - ended
Dichotomous Multiple
Responses
Scales
131. SLIDE 8-24
Type of questions
Open ended questions:
• What is your age?
• How would you evaluate the work done by the present
government?
• How much orange juice does this bottle contain?
• What is your reaction to this new custard powder?
• Why do you smoke Gold Flake cigarettes?
• Which is your favorite TV serial?
• What training programme have you last attended?
• With whom in your work group do you interact with after office
132. SLIDE 8-25
Type of questions
Closed ended questions
1. Dichotomous questions
• Are you diabetic? Yes / No
• Have you read the new book by Dan Brown? Yes/no
• What kind of petrol do you use in your car? Normal/Premium
• What kind of cola do you drink? Normal/diet
• Your working hours in the organization are fixed/ flexible
133. SLIDE 8-26
Type of questions
Closed ended questions
2. Multiple choice questions
• How much do you spend on grocery products (average in one month)?
- Less than Rs. 2500/-
- Between Rs 2500-5000/-
- More than Rs 5000/-
• You do not currently sell organic food products because (Could be ≥ 1)
- You do not know about organic food products.
- You are not interested.
- You are interested but you do not know how to procure it.
- It is not profitable.
-The customer demand is too low
- any other--------------------
134. SLIDE 8-27
Questionnaire designing criteria
• Clearly specify the issue
• Use simple terminology
• Avoid ambiguity in questioning
• Avoid leading questions
• Avoid loaded questions
• Avoid implicit choices and assumptions
• Avoid double-barrelled questions
137. SLIDE 8-30Sequential order: branching
questions
Have you used any travel site for
your travel?
You have used it for
a. search
b. booking
c. both
Why have you not used it for booking,
listed below are a set of reasons. Please
tick the one(s) that are true
LIST OF REASONS
In case these problems are taken care of
will you use it?
Classification questions on gender; age;
education; profession; income; travel behavior
Tabulate and Terminate
Evaluate on the
attributes /features
under study
Evaluate on the
attributes /features
under study
Any other
recommendation
you have for MMT
5+5 questions related to attitude related
to travelling and internet security in
transactions
What site?
brand?
Not MMT
ANY OTHER
brand?
Prompt- MMT
Make my
trip(MMT)
MMT
Yes
Me- search only
No
No
Me-both
Yes
Yes
138. SLIDE 8-31
The questionnaire administration
• Physical characteristics of the questionnaire
• Pilot testing the questionnaire
• Preparing the final draft of the questionnaire
• Administering the questionnaire
139. SLIDE 8-32
The questionnaire method
Advantages Disadvantages
• Adaptability
• Assurance of anonymity
• Cost- & time-effective
• Scope of coverage
• Limited applicability
• Skewed sample
• Return ratio
• Clarification
• Spontaneity of response